Search results for: vulnerability intelligence

Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate and even exceed some benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. A common facet of many ITS is their use of Bayesian Knowledge Tracing (BKT) to estimate parameters necessary for the implementation of the artificial intelligence component, and for the probability of mastery of a knowledge component relevant to the ITS. While various techniques exist to estimate these parameters and probability of mastery, none directly and reliably ask the user to self-assess these. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course for which detailed transaction-level observations were recorded, and users were also routinely asked direct questions that would lead to such a self-assessment. Comparisons were made between these self-assessed values and those obtained using commonly used estimation techniques. Our findings show that such self-assessments are particularly relevant at the early stages of ITS usage while transaction level data are scant. Once a user’s transaction level data become available after sufficient ITS usage, these can replace the self-assessments in order to eliminate the identifiability problem in BKT. We discuss how these findings are relevant to the number of exercises necessary to lead to mastery of a knowledge component, the associated implications on learning curves, and its relevance to instruction time.

Keywords: Bayesian Knowledge Tracing, Intelligent Tutoring System, in vivo study, parameter estimation

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Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

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Authors: Yating Yang, Xue Zhang, Chengli Zhao

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In the increasingly interconnected global environment brought about by globalization, it is crucial for countries to timely grasp the development motivations in relevant research fields of other countries and seize development opportunities. Motivation, as the intrinsic driving force behind actions, is abstract in nature, making it difficult to directly measure and evaluate. Drawing on the ideas of social influence network theory, the research motivations of a country can be understood as the driving force behind the development of its science and technology sector, which is simultaneously influenced by both the country itself and other countries/regions. In response to this issue, this paper improves upon Friedkin's social influence network theory and applies it to motivation description, constructing a dynamic alliance network and hostile network centered around the United States and China, as well as a sensitivity matrix, to remotely assess the changes in national research motivations under the influence of international relations. Taking artificial intelligence as a case study, the research reveals that the motivations of most countries/regions are declining, gradually shifting from a neutral attitude to a negative one. The motivation of the United States is hardly influenced by other countries/regions and remains at a high level, while the motivation of China has been consistently increasing in recent years. By comparing the results with real data, it is found that this model can reflect, to some extent, the trends in national motivations.

Keywords: influence network theory, remote assessment, relation matrix, dynamic sensitivity matrix

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Hiba El Assibi

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This study aims to weakly solve Kalah, a two-player board game, by developing a start-to-finish winning strategy using an optimized Minimax algorithm with Alpha-Beta Pruning. In weakly solving Kalah, our focus is on creating an optimal strategy from the game's beginning rather than analyzing every possible position. The project will explore additional enhancements like symmetry checking and code optimizations to speed up the decision-making process. This approach is expected to give insights into efficient strategy formulation in board games and potentially help create games with a fair distribution of outcomes. Furthermore, this research provides a unique perspective on human versus Artificial Intelligence decision-making in strategic games. By comparing the AI-generated optimal moves with human choices, we can explore how seemingly advantageous moves can, in the long run, be harmful, thereby offering a deeper understanding of strategic thinking and foresight in games. Moreover, this paper discusses the evaluation of our strategy against existing methods, providing insights on performance and computational efficiency. We also discuss the scalability of our approach to the game, considering different board sizes (number of pits and stones) and rules (different variations) and studying how that affects performance and complexity. The findings have potential implications for the development of AI applications in strategic game planning, enhancing our understanding of human cognitive processes in game settings, and offer insights into creating balanced and engaging game experiences.

Keywords: minimax, alpha beta pruning, transposition tables, weakly solving, game theory

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Mathula Thangarajh

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Duchenne muscular dystrophy (DMD) is a severe form of X-linked muscular dystrophy caused by mutations in the dystrophin gene resulting in progressive skeletal muscle weakness. Boys with DMD also have significant cognitive disabilities. The intelligence quotient of boys with DMD, compared to peers, is approximately one standard deviation below average. Detailed neuropsychological testing has demonstrated that boys with DMD have a global developmental impairment, with verbal memory and visuospatial skills most significantly affected. Furthermore, the total brain volume and gray matter volume are lower in children with DMD compared to age-matched controls. These results are suggestive of a significant structural and functional compromise to the developing brain as a result of absent dystrophin protein expression. There is also some genetic evidence to suggest that mutations in the 3’ end of the DMD gene are associated with more severe neurocognitive problems. Our working hypothesis is that (i) boys with DMD do not make gains in neurodevelopmental skills compared to typically developing children and (ii) women carriers of DMD mutations may have subclinical cognitive deficits. We also hypothesize that there may be an intergenerational vulnerability of cognition, with boys of DMD-carrier mothers being more affected cognitively than boys of non-DMD-carrier mothers. The objectives of this study are: 1. Assess the neurodevelopment in boys with DMD at 4-time points and perform baseline neuroradiological assessment, 2. Assess cognition in biological mothers of DMD participants at baseline, 3. Assess possible correlation between DMD mutation and cognitive measures. This study also explores functional brain abnormalities in people with DMD by exploring how regional and global connectivity of the brain underlies executive function deficits in DMD. Such research can contribute to a better holistic understanding of the cognition alterations due to DMD and could potentially allow clinicians to create better-tailored treatment plans for the DMD population. There are four study visits for each participant (baseline, 2-4 weeks, 1 year, 18 months). At each visit, the participant completes the NIH Toolbox Cognition Battery, a validated psychometric measure that is recommended by NIH Common Data Elements for use in DMD. Visits 1, 3, and 4 also involve the administration of the BRIEF-2, ABAS-3, PROMIS/NeuroQoL, PedsQL Neuromuscular module 3.0, Draw a Clock Test, and an optional fMRI scan with the N-back matching task. We expect to enroll 52 children with DMD, 52 mothers of children with DMD, and 30 healthy control boys. This study began in 2020 during the height of the COVID-19 pandemic. Due to this, there were subsequent delays in recruitment because of travel restrictions. However, we have persevered and continued to recruit new participants for the study. We partnered with the Muscular Dystrophy Association (MDA) and helped advertise the study to interested families. Since then, we have had families from across the country contact us about their interest in the study. We plan to continue to enroll a diverse population of DMD participants to contribute toward a better understanding of Duchenne Muscular Dystrophy.

Keywords: neurology, Duchenne muscular dystrophy, muscular dystrophy, cognition, neurodevelopment, x-linked disorder, DMD, DMD gene

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Sandro Bartolini, Alessandro Mecocci, Alessio Medaglini

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The growing popularity of systems based on unmanned aerial vehicles (UAVs) is highlighting their vulnerability, particularly in relation to the positioning system used. Typically, UAV architectures use the civilian GPS, which is exposed to a number of different attacks, such as jamming or spoofing. This is why it is important to develop alternative methodologies to accurately estimate the actual UAV position without relying on GPS measurements only. In this paper, we propose a position estimate method for UAVs based on monocular visual odometry. We have developed a flight control system capable of keeping track of the entire trajectory travelled, with a reduced dependency on the availability of GPS signals. Moreover, the simplicity of the developed solution makes it applicable to a wide range of commercial drones. The final goal is to allow for safer flights in all conditions, even under cyber-attacks trying to deceive the drone.

Keywords: visual odometry, autonomous uav, position measurement, autonomous outdoor flight

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Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Arrian Cornwell, Larisa Yarovaya, Mary Thomson

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This paper seeks to examine the transboundary nature of foreign subsidiary exit vs. stay decisions when threatened by conflict in a host country. Using the concepts of nested vulnerability and teleconnections, we show that the threat of conflict can transcend bounded territories and have non-linear outcomes for actors, institutions and systems at broader scales of analysis. To the best of our knowledge, this has not been done before. By introducing the concepts of ‘leapfrogging upwards’ and ‘cascading downwards’, we develop a two-stage model which characterises the impacts of conflict as transboundary phenomena. We apply our model to a dataset of 266 foreign subsidiaries in six conflict-afflicted host countries over 2011-2015. Our results indicate that information is transmitted upwards and subsequent pressure flows cascade downwards, which, in turn, influence exit decisions.

Keywords: subsidiary exit, conflict, information transmission, pressure flows, transboundary

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

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Authors: Anette Alén

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Intellectual property (IP) regimes have traditionally been designed to integrate various conflicting elements stemming from private entitlement and the public good. In IP laws and regulations, this design takes the form of specific uses of protected subject-matter without the right-holder’s consent, or exhaustion of exclusive rights upon market release, and the like. More recently, the pursuit of ‘balance’ has gained ground in the conceptualization of these conflicting elements both in terms of IP law and related policy. This can be seen, for example, in European Union (EU) copyright regime, where ‘balance’ has become a key element in argumentation, backed up by fundamental rights reasoning. This development also entails an ever-expanding dialogue between the IP regime and the constitutional safeguards for property, free speech, and privacy, among others. This study analyses the concept of ‘balance’ in EU copyright law: the research task is to examine the contents of the concept of ‘balance’ and the way it is operationalized and pursued, thereby producing new knowledge on the role and manifestations of ‘balance’ in recent copyright case law and regulatory instruments in the EU. The study discusses two particular pieces of legislation, the EU Digital Single Market (DSM) Copyright Directive (EU) 2019/790 and the finalized EU Artificial Intelligence (AI) Act, including some of the key preparatory materials, as well as EU Court of Justice (CJEU) case law pertaining to copyright in the digital era. The material is examined by means of document analysis, mapping the ways ‘balance’ is approached and conceptualized in the documents. Similarly, the interaction of fundamental rights as part of the balancing act is also analyzed. Doctrinal study of law is also employed in the analysis of legal sources. This study suggests that the pursuit of balance is, for its part, conducive to new battles, largely due to the advancement of digitalization and more recent developments in artificial intelligence. Indeed, the ‘balancing act’ rather presents itself as a way to bypass or even solidify some of the conflicting interests in a complex global digital economy. Indeed, such a conceptualization, especially when accompanied by non-critical or strategically driven fundamental rights argumentation, runs counter to the genuine acknowledgment of new types of conflicting interests in the copyright regime. Therefore, a more radical approach, including critical analysis of the normative basis and fundamental rights implications of the concept of ‘balance’, is required to readjust copyright law and regulations for the digital era. Notwithstanding the focus on executing the study in the context of the EU copyright regime, the results bear wider significance for the digital economy, especially due to the platform liability regime in the DSM Directive and with the AI Act including objectives of a ‘level playing field’ whereby compliance with EU copyright rules seems to be expected among system providers.

Keywords: balance, copyright, fundamental rights, platform liability, artificial intelligence

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: R. Lekaviciene, D. Antiniene

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Attempts to split the construct of emotional intelligence (EI) into separate components – ability to understand own and others’ emotions and ability to control own and others’ emotions may be meaningful more theoretically than practically. In real life, a personality encounters various emotional situations that require exhibition of complex EI to solve them. Emotional situation solution tests enable measurement of such undivided EI. The object of the present study is to determine sociodemographic and other factors that are important for emotional situation solutions. The study involved 1,430 participants from various regions of Lithuania. The age of participants varied from 17 years to 27 years. Emotional social and interpersonal situation scale EI-DARL-V2 was used. Each situation had two mandatory answering formats: The first format contained assignments associated with hypothetical theoretical knowledge of how the situation should be solved, while the second format included the question of how the participant would personally resolve the given situation in reality. A questionnaire that contained various sociodemographic data of subjects was also presented. Factors, statistically significant for emotional situation solution, have been determined: gender, family structure, the subject’s relation with his or her mother, mother’s occupation, subjectively assessed financial situation of the family, level of education of the subjects and his or her parents, academic achievement, etc. The best solvers of emotional situations are women with high academic achievements. According to their chosen study profile/acquired profession, they are related to the fields in social sciences and humanities. The worst solvers of emotional situations are men raised in foster homes. They are/were bad students and mostly choose blue-collar professions.

Keywords: emotional intelligence, emotional situations, solution of situation, young people

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Authors: Jessica Kate Simonds

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The last successful hijacking perpetrated by Somali pirates in 2012 represented a critical turning point for the identity and brand of Indian Ocean (IO) insecurity, coined in this paper as the era of the post-piratical. This paper explores the broadening of the PMSC business model to account and contribute to the design of a new IO security environment that prioritises foreign and insurgency drone activity and Houthi rebel operations as the main threat to merchant shipping in the post-2012 era. This study is situated within a longer history of analysing maritime insecurity and also contributes a bespoke conceptual framework that understands the sea as a space that is produced and reproduced relative to existing and emerging threats to merchant shipping based on bespoke models of information sharing and intelligence acquisition. This paper also makes a prominent empirical contribution by drawing on a post-positivist methodology, data drawn from original semi-structured interviews with senior maritime insurers and active merchant seafarers that is triangulated with industry-produced guidance such as the BMP series as primary data sources. Each set is analysed through qualitative discourse and content analysis and supported by the quantitative data sets provided by the IMB Piracy Reporting center and intelligence networks. This analysis reveals that mechanisms such as the IGP&I Maritime Security Committee and intelligence divisions of PMSC’s have driven the exchanges of knowledge between land and sea and thus the reproduction of the maritime security environment through new regulations and guidance to account dones, rebels and bombs as the key challenges in the IO, beyond piracy. A contribution of this paper is the argument that experts who may not be in the highest-profile jobs are the architects of maritime insecurity based on their detailed knowledge and connections to vessels in transit. This paper shares the original insights of those who have served in critical decision making spaces to demonstrate that the development and refinement of industry produced deterrence guidance that has been accredited to the mitigation of piracy, have shaped new editions such as BMP 5 that now serve to frame a new security environment that prioritises the mitigation of risks from drones and WBEID’s from both state and insurgency risk groups. By highlighting the experiences and perspectives of key players on both land and at sea, the key finding of this paper is outlining that as pirates experienced a financial boom by profiteering from their bespoke business model during the peak of successful hijackings, the private security market encountered a similar level of financial success and guaranteed risk environment in which to prospect business. Thus, the reproduction of the Indian Ocean as a maritime security environment reflects a new found purpose for PMSC’s as part of the broader conglomerate of maritime insurers, regulators, shipowners and managers who continue to redirect the security consciousness and IO brand of insecurity.

Keywords: maritime security, private security, risk intelligence, political geography, international relations, political economy, maritime law, security studies

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Authors: Weeam Abdulrahman, Gawaher Al-Madwary, Fatima Al-Ammari, Razan Mohammad

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This research discusses the AI features in Replika which is a dialog with a customized characters application, interaction and communication with AI in different ways that is provided for the user. spreading a survey with questions on how the AI worked is one approach of exposing the app to others to utilize and also we made an analysis that provides us with the conclusion of our research as a result, individuals will be able to try out the app. In the methodology we explain each page that pops up in the screen while using replika and Specify each part and icon.

Keywords: Replika, AI, artificial intelligence, dialog app

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Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Barna Arnold Keserű

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In the last few years (what is called by many scholars the big data era) artificial intelligence (hereinafter AI) get more and more attention from the public and from the different branches of sciences as well. What previously was a mere science-fiction, now starts to become reality. AI and robotics often walk hand in hand, what changes not only the business and industrial life, but also has a serious impact on the legal system. The main research of the author focuses on these impacts in the field of private law, with special regards to liability and intellectual property issues. Many questions arise in these areas connecting to AI and robotics, where the boundaries are not sufficiently clear, and different needs are articulated by the different stakeholders. Recognizing the urgent need of thinking the Committee on Legal Affairs of the European Parliament adopted a Motion for a European Parliament Resolution A8-0005/2017 (of January 27th, 2017) in order to take some recommendations to the Commission on civil law rules on robotics and AI. This document defines some crucial usage of AI and/or robotics, e.g. the field of autonomous vehicles, the human job replacement in the industry or smart applications and machines. It aims to give recommendations to the safe and beneficial use of AI and robotics. However – as the document says – there are no legal provisions that specifically apply to robotics or AI in IP law, but that existing legal regimes and doctrines can be readily applied to robotics, although some aspects appear to call for specific consideration, calls on the Commission to support a horizontal and technologically neutral approach to intellectual property applicable to the various sectors in which robotics could be employed. AI can generate some content what worth copyright protection, but the question came up: who is the author, and the owner of copyright? The AI itself can’t be deemed author because it would mean that it is legally equal with the human persons. But there is the programmer who created the basic code of the AI, or the undertaking who sells the AI as a product, or the user who gives the inputs to the AI in order to create something new. Or AI generated contents are so far from humans, that there isn’t any human author, so these contents belong to public domain. The same questions could be asked connecting to patents. The research aims to answer these questions within the current legal framework and tries to enlighten future possibilities to adapt these frames to the socio-economical needs. In this part, the proper license agreements in the multilevel-chain from the programmer to the end-user become very important, because AI is an intellectual property in itself what creates further intellectual property. This could collide with data-protection and property rules as well. The problems are similar in the field of liability. We can use different existing forms of liability in the case when AI or AI led robotics cause damages, but it is unsure that the result complies with economical and developmental interests.

Keywords: artificial intelligence, intellectual property, liability, robotics

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Authors: Swati Bajpai, S. P. K Jena

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Background: There is dramatic increase in the prevalence and severity of overweight in adolescents, raising concerns about their psychosocial and cognitive consequences, thereby indicating the immediate need to understand the effects of increased weight on scholastic performance. Although the body of research is currently limited, available results have identified an inverse relationship between obesity and cognition in adolescents. Aim: to examine the association between increased Body Mass Index in adolescents and their neurocognitive functioning. Methods: A case –control study of 28 subjects in the age group of 11-17 years (14 Males and 14 females) was taken on the basis of main inclusion criteria (Body Mass Index). All of them were randomized to (experimental group: overweight) and (control group: normal weighted). A complete neurocognitive assessment was carried out using validated psychological scales namely, Color Progressive Matrices (to assess intelligence); Bender Visual Motor Gestalt Test (Perceptual motor functioning); PGI-Memory Scale for Children (memory functioning) and Malin’s Intelligence Scale Indian Children (verbal and performance ability). Results: statistical analysis of the results depicted that 57% of the experimental group lack in cognitive abilities, especially in general knowledge (99.1±12.0 vs. 102.8±6.7), working memory (91.5±8.4 vs. 93.1±8.7), concrete ability (82.3±11.5 vs. 92.6±1.7) and perceptual motor functioning (1.5±1.0 vs. 0.3±0.9) as compared to control group. Conclusion: Our investigations suggest that weight gain results, at least in part, from a neurological predisposition characterized by reduced executive function, and in turn obesity itself has a compounding negative impact on the brain. Though, larger sample is needed to make more affirmative claims.

Keywords: adolescents, body mass index, neurocognition, obesity

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Authors: Rocio Murad, Juan Carlos Rivillas, Nury Alejandra Rodriguez, Daniela Roldán

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In Colombia, around 5 million women have suffered forced displacement and all forms of gender-based violence, mostly adolescents and young women, single mothers, or widows with children affected by the war. After the signing of the peace agreements, the department of Antioquia has been one of the most affected by the armed conflict, from a territorial and gender perspective in the period. The objective of the research was to analyze the situation of sexual and reproductive rights in the department of Antioquia from a territorial and gender perspective in the period after the signing of the Peace Agreement. A mixed methodology was developed. The quantitative component conducted a cross-sectional descriptive study of barriers to access to contraceptive methods, safe abortion and gender-based violence based on microdata from the 2015 National Demographic and Health Survey. In the qualitative component, a case study was developed in Dabeiba, a municipality of Antioquia prioritized in order to deepen the experiences before, during and after the armed conflict in sexual and reproductive rights; using three research techniques: Focused observation, Semi-structured interviews, and Documentary review. The results showed that there is a gradient of greater vulnerability to greater effects of the conflict and that the subregion of Urabá Antioqueño, to which Dabeiba belongs, has the highest levels of vulnerability in relation to departmental data. In this subregion, the percentage of women with an unmet need for contraceptive methods (9%), women with unintended pregnancies (31%), of women between 15 and 19 years of age who are already mothers or are pregnant with their first child (32%) and the percentage of women victims of physical violence (42%) and sexual violence (13%) by their partners are significantly higher. Women, particularly rural and indigenous women, were doubly affected due to the existence of violence that is specifically directed at them or that has a greater impact on their life projects. There was evidence of insufficient, fragmented and disjointed social and institutional action in relation to women's rights and the existence of androcentric and patriarchal social imaginaries through which women and the feminine are undervalued. These results provide evidence of violations of sexual and reproductive rights in contexts of armed conflict and make it possible to identify mechanisms to guarantee the re-establishment of the rights of the victims, particularly women and girls. Among the mechanisms evidenced are: working for the elimination of gender stereotypes; supporting the formation and strengthening of women's social organizations; working for the concerted definition and articulated implementation of actions necessary to respond to sexual and reproductive health needs; and working for the recognition of reproductive violence as specific and different from sexual violence in the context of armed conflict. Also, it was evidenced that it is necessary to implement prevention, attention and reparation actions.

Keywords: sexual and reproductive rights, Colombia, armed conflict, violence against women

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Authors: Marta Maria Aguilar Carceles, Ginesa Torrente Hernandez

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The goal of the current study is focused on giving an interdisciplinary comprehension of an increased phenomenon in recent years: violence against parents. Violence can take different forms depending on the context and the vulnerability of the victims, but in this kind of situations, the relationship between parents and young people can become abusive and uncontrollable. Taking a sample from the Spanish Criminal Courts, this study explores those psychological and sociological factors that can contribute to the appearance and continuity of this kind of behaviors in minors. It is considered factors like the type of offence, presence or absence of psychopathology in the subjects, family aspects, or sociodemographic factors, getting a criminal profile of the minor and evaluating which measures are more efficient or adequate in each particular case. Finally, it will be discussed on how getting effective interventions and restorative responses to address teen violence against their parents within the Spanish Legal System.

Keywords: criminality, legal system, parents, tyrant sons, violence

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Authors: Rebekah Granger Ellis, Richard B. Speaker, Pat Austin

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This study used two psychological scales to examine the level of social and emotional intelligence and moral judgment of over 500 gifted and talented high school students in various academic and creative arts programs in a large metropolitan area in the southeastern United States. For decades, numerous models and programs purporting to encourage socio-affective characteristics of adolescent development have been explored in curriculum theory and design. Socio-affective merges social, emotional, and moral domains. It encompasses interpersonal relations and social behaviors; development and regulation of emotions; personal and gender identity construction; empathy development; moral development, thinking, and judgment. Examining development in these socio-affective domains can provide insight into why some gifted and talented adolescents are not successful in adulthood despite advanced IQ scores. Particularly whether nonintellectual characteristics of gifted and talented individuals, such as emotional, social and moral capabilities, are as advanced as their intellectual abilities and how these are related to each other. Unique characteristics distinguish gifted and talented individuals; these may appear as strengths, but there is the potential for problems to accompany them. Although many thrive in their school environments, some gifted students struggle rather than flourish. In the socio-affective domain, these adolescents face special intrapersonal, interpersonal, and environmental problems. Gifted individuals’ cognitive, psychological, and emotional development occurs asynchronously, in multidimensional layers at different rates and unevenly across ability levels. Therefore, it is important to examine the long-term effects of participation in various gifted and talented programs on the socio-affective development of gifted and talented adolescents. This quasi-experimental longitudinal study examined students in several gifted and talented education programs (creative arts school, urban charter schools, and suburban public schools) for (1) socio-affective development level and (2) whether a particular gifted and talented program encourages developmental growth. The following research questions guided the study: (1) How do academically and artistically talented gifted 10th and 11th grade students perform on psychometric scales of social and emotional intelligence and moral judgment? Do they differ from their age or grade normative sample? Are their gender differences among gifted students? (2) Does school environment impact 10th and 11th grade gifted and talented students’ socio-affective development? Do gifted adolescents who participate in a particular school gifted program differ in their developmental profiles of social and emotional intelligence and moral judgment? Students’ performances on psychometric instruments were compared over time and by type of program. Participants took pre-, mid-, and post-tests over the course of an academic school year with Defining Issues Test (DIT-2) assessing moral judgment and BarOn EQ-I: YV assessing social and emotional intelligence. Based on these assessments, quantitative differences in growth on psychological scales (individual and school) were examined. Change scores between schools were also compared. If a school showed change, artifacts (culture, curricula, instructional methodology) provided insight as to environmental qualities that produced this difference.

Keywords: gifted and talented education, moral development, socio-affective development, socio-affective education

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Authors: Loris Franchi, Daniele Calvi, Sabrina Corpino

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This paper presents a design methodology in which stakeholders are assisted with the exploration of a so-called negotiation space, aiming to the maximization of both group social welfare and single stakeholder’s perceived utility. The outcome results in less design iterations needed for design convergence while obtaining a higher solution effectiveness. During the early stage of a space project, not only the knowledge about the system but also the decision outcomes often are unknown. The scenario is exacerbated by the fact that decisions taken in this stage imply delayed costs associated with them. Hence, it is necessary to have a clear definition of the problem under analysis, especially in the initial definition. This can be obtained thanks to a robust generation and exploration of design alternatives. This process must consider that design usually involves various individuals, who take decisions affecting one another. An effective coordination among these decision-makers is critical. Finding mutual agreement solution will reduce the iterations involved in the design process. To handle this scenario, the paper proposes a design methodology which, aims to speed-up the process of pushing the mission’s concept maturity level. This push up is obtained thanks to a guided negotiation space exploration, which involves autonomously exploration and optimization of trade opportunities among stakeholders via Artificial Intelligence algorithms. The negotiation space is generated via a multidisciplinary collaborative optimization method, infused by game theory and multi-attribute utility theory. In particular, game theory is able to model the negotiation process to reach the equilibria among stakeholder needs. Because of the huge dimension of the negotiation space, a collaborative optimization framework with evolutionary algorithm has been integrated in order to guide the game process to efficiently and rapidly searching for the Pareto equilibria among stakeholders. At last, the concept of utility constituted the mechanism to bridge the language barrier between experts of different backgrounds and differing needs, using the elicited and modeled needs to evaluate a multitude of alternatives. To highlight the benefits of the proposed methodology, the paper presents the design of a CubeSat mission for the observation of lunar radiation environment. The derived solution results able to balance all stakeholders needs and guaranteeing the effectiveness of the selection mission concept thanks to its robustness in valuable changeability. The benefits provided by the proposed design methodology are highlighted, and further development proposed.

Keywords: concurrent engineering, artificial intelligence, negotiation in engineering design, multidisciplinary optimization

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