Search results for: Collective Intelligence

Authors: Alexandros Lioulemes, Michail Theofanidis, Varun Kanal, Konstantinos Tsiakas, Maher Abujelala, Chris Collander, William B. Townsend, Angie Boisselle, Fillia Makedon

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This paper presents a home-based robot-rehabilitation instrument, called ”MAGNI Dynamics”, that utilized a vision-based kinematic/dynamic module and an adaptive haptic feedback controller. The system is expected to provide personalized rehabilitation by adjusting its resistive and supportive behavior according to a fuzzy intelligence controller that acts as an inference system, which correlates the user’s performance to different stiffness factors. The vision module uses the Kinect’s skeletal tracking to monitor the user’s effort in an unobtrusive and safe way, by estimating the torque that affects the user’s arm. The system’s torque estimations are justified by capturing electromyographic data from primitive hand motions (Shoulder Abduction and Shoulder Forward Flexion). Moreover, we present and analyze how the Barrett WAM generates a force-field with a haptic controller to support or challenge the users. Experiments show that by shifting the proportional value, that corresponds to different stiffness factors of the haptic path, can potentially help the user to improve his/her motor skills. Finally, potential areas for future research are discussed, that address how a rehabilitation robotic framework may include multisensing data, to improve the user’s recovery process.

Keywords: human-robot interaction, kinect, kinematics, dynamics, haptic control, rehabilitation robotics, artificial intelligence

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Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

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Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Megan E. Davies

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The effects of climate change impact the environment and our physical health but also demonstrate a growing risk factor for Canadians’ individual and collective mental health. Past research and expert predictions are discussed while exploring the connection between mental health concerns and climate change consequences, resulting in a call to action for psychological sciences to be integrated into solution planning. With the direct and indirect effects of climate change steadily increasing, political and legal aspects of sustainability, as well as the repercussions for mental health being seen in Canada regarding climate change, are investigated. An interdisciplinary perspective for reviewing the challenges of climate change is applied in order to propose a realistic plan for how policymakers and mental health professionals can work together moving forward in applying interventions that mediate against the effects of climate change on Canadians’ mental health.

Keywords: climate change, mental health, policy change, solution planning, sustainability

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Authors: Gordon James Knowles

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Historically the Chinatown district in Sandwich Islands has been plagued with the traditional vice crimes of illegal drugs, gambling, and prostitution since the early 1800s. However, a new form of psychologically addictive arcade style table gambling machines has become the dominant form of illegal revenue made in Honolulu, Hawaii. This study attempts to document the drive, desire, or will to play and wager with arcade style video gaming and understand the role of illegal game rooms in facilitating pathological gambling addiction. Indicators of police corruption by Chinese organized crime syndicates related to protection rackets, bribery, and pay-offs were revealed. Information fusion from a police science and sociological intelligence perspective indicates insurgent warfare is being waged on the streets of Honolulu by the People’s Republic of China. This state-sponsored communist terrorism in the Hawaiian Islands used “contactless” irregular warfare entailing: (1) the deployment of psychologically addictive gambling machines, (2) the distribution of the physically addictive fentanyl drug as a lethal chemical weapon, and (3) psychological warfare by circulating pro-China anti-American propaganda newspapers targeted at the small island populace.

Keywords: Chinese and Polynesian organized crime, china daily newspaper, electronic arcade style table games, gaming technology addiction, illegal compulsive gambling, and police intelligence

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Authors: Katerina Antoniou

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When examining conflicts around the world, it is evident that the majority of intractable conflicts are steeped in identity. Identity seems to be not only a causal variable for conflict, but also a catalytic parameter for the process of reconciliation that follows ceasefire. This paper focuses on the process of identity securitization that occurs between rival groups of heterogeneous collective identities – ethnic, national or religious – as well as on the relationship between identity securitization and the ability of the groups involved to reconcile. Are securitized identities obstacles to the process of reconciliation, able to hinder any prospects of peace? If the level to which an identity is securitized is catalytic to a conflict’s discourse and settlement, then which factors act as indicators of identity de-securitization? The level of an in-group’s identity securitization can be estimated through a number of indicators, one of which is narrative. The stories, views and stances each in-group adopts in relation to its history of conflict and relation with their rival out-group can clarify whether that specific in-group feels victimized and threatened or safe and ready to reconcile. Accordingly, this study discusses identity securitization through narrative in relation to intractable conflicts. Are there conflicts around the world that, despite having been identified as intractable, stagnated or insoluble, show signs of identity de-securitization through narrative? This inquiry uses the case of the Cyprus conflict and its partitioned societies to present official narratives from the two communities and assess whether these narratives have transformed, indicating a less securitized in-group identity for the Greek and Turkish Cypriots. Specifically, the study compares the official historical overviews presented by each community’s Ministry of Foreign Affairs website and discusses the extent to which the two official narratives present a securitized collective identity. In addition, the study will observe whether official stances by the two communities – as adopted by community leaders – have transformed to depict less securitization over time. Additionally, the leaders’ reflection of popular opinion is evaluated through recent opinion polls from each community. Cyprus is currently experiencing renewed optimism for reunification, with the leaders of its two communities engaging in rigorous negotiations, and with rumors calling for a potential referendum for reunification to be taking place even as early as within 2016. Although leaders’ have shown a shift in their rhetoric and have moved away from narratives of victimization, this is not the case for the official narratives used by their respective ministries of foreign affairs. The study’s findings explore whether this narrative inconsistency proves that Cyprus is transitioning towards reunification, or whether the leaders are risking sending a securitized population to the polls to reject a potential reunification. More broadly, this study suggests that in the event that intractable conflicts might be moving towards viable peace, in-group narratives--official narratives in particular--can act as indicators of the extent to which rival entities have managed to reconcile.

Keywords: conflict, identity, narrative, reconciliation

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Laiba Amin, Rashna H. Sukhia, Mubassar Fida

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Introduction: This study evaluates the accuracy of responses from ChatGPT, Google Bard, and Microsoft Copilot regarding dentofacial orthopedic appliances. As artificial intelligence (AI) increasingly enhances various fields, including healthcare, understanding its reliability in specialized domains like orthodontics becomes crucial. By comparing the accuracy of different AI models, this study aims to shed light on their effectiveness and potential limitations in providing technical insights. Materials and Methods: A total of 110 questions focused on dentofacial orthopedic appliances were posed to each AI model. The responses were then evaluated by five experienced orthodontists using a modified 5-point Likert scale to ensure a thorough assessment of accuracy. This structured approach allowed for consistent and objective rating, facilitating a meaningful comparison between the AI systems. Results: The results revealed that Google Bard demonstrated the highest accuracy at 74%, followed by Microsoft Copilot, with an accuracy of 72.2%. In contrast, ChatGPT was found to be the least accurate, achieving only 52.2%. These results highlight significant differences in the performance of the AI models when addressing orthodontic queries. Conclusions: Our study highlights the need for caution in relying on AI for orthodontic insights. The overall accuracy of the three chatbots was 66%, with Google Bard performing best for removable Class II appliances. Microsoft Copilot was more accurate than ChatGPT, which, despite its popularity, was the least accurate. This variability emphasizes the importance of human expertise in interpreting AI-generated information. Further research is necessary to improve the reliability of AI models in specialized healthcare settings.

Keywords: artificial intelligence, large language models, orthodontics, dentofacial orthopaedic appliances, accuracy assessment.

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Authors: Younis R. Elhaddad, Mohamed A. Alshaari

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Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time.

Keywords: genetic algorithm, magic square, pan-magic square, computational intelligence

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Authors: Yuna Lee

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In the current policy process, there has been a growing interest in more open approaches that incorporate creativity and innovation based on the forecasting groups composed by the public and experts together into scientific data-driven foresight methods to implement more effective policymaking. Especially, citizen participation as collective intelligence in policymaking with design and deep scale of innovation at the global level has been developed and human-centred design thinking is considered as one of the most promising methods for strategic foresight. Yet, there is a lack of a common theoretical foundation for a comprehensive approach for the current situation of and post-COVID-19 era, and substantial changes in policymaking practice are insignificant and ongoing with trial and error. This project hypothesized that rigorously developed policy systems and tools that support strategic foresight by considering the public understanding could maximize ways to create new possibilities for a preferable future, however, it must involve a better understating of Behavioural Insights, including individual and cultural values, profit motives and needs, and psychological motivations, for implementing holistic and multilateral foresight and creating more positive possibilities. To what extent is the policymaking system theoretically possible that incorporates the holistic and comprehensive foresight and policy process implementation, assuming that theory and practice, in reality, are different and not connected? What components and environmental conditions should be included in the strategic foresight system to enhance the capacity of decision from policymakers to predict alternative futures, or detect uncertainties of the future more accurately? And, compared to the required environmental condition, what are the environmental vulnerabilities of the current policymaking system? In this light, this research contemplates the question of how effectively policymaking practices have been implemented through the synthesis of scientific, technology-oriented innovation with the strategic design for tackling complex societal challenges and devising more significant insights to make society greener and more liveable. Here, this study conceptualizes the notions of a new collaborative way of strategic foresight that aims to maximize mutual benefits between policy actors and citizens through the cooperation stemming from evolutionary game theory. This study applies mixed methodology, including interviews of policy experts, with the case in which digital transformation and strategic design provided future-oriented solutions or directions to cities’ sustainable development goals and society-wide urgent challenges such as COVID-19. As a result, artistic and sensual interpreting capabilities through strategic design promote a concrete form of ideas toward a stable connection from the present to the future and enhance the understanding and active cooperation among decision-makers, stakeholders, and citizens. Ultimately, an improved theoretical foundation proposed in this study is expected to help strategically respond to the highly interconnected future changes of the post-COVID-19 world.

Keywords: policymaking, strategic design, sustainable innovation, evolution of cooperation

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Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

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Authors: Jacquelyn Burkell, Jane Bailey

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Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

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Authors: Namrata Mishra

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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

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Authors: Sanelisiwe Ndlovu

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Cities have become testbeds for automation and experimenting with artificial intelligence (AI) in managing urban services and public spaces. Smart Cities and AI systems are changing most human experiences from health and education to personal relations. For instance, in healthcare, social robots are being implemented as tools to assist patients. Similarly, in education, social robots are being used as tutors or co-learners to promote cognitive and affective outcomes. With that general picture in mind, one can now ask a further question about Smart Cities and artificial agents and their moral standing in the African context of personhood. There has been a wealth of literature on the topic of personhood; however, there is an absence of literature on African personhood in highly automated environments. Personhood in African philosophy is defined by the role one can and should play in the community. However, in today’s technologically advanced world, a risk is that machines become more capable of accomplishing tasks that humans would otherwise do. Further, on many African communitarian accounts, personhood and moral standing are associated with active relationality with the community. However, in the Smart City, human closeness is gradually diminishing. For instance, humans already do engage and identify with robotic entities, sometimes even romantically. The primary aim of this study is to investigate how African conceptions of personhood and community interact in a highly automated environment such as Smart Cities. Accordingly, this study lies in presenting a rarely discussed African perspective that emphasizes the necessity and the importance of relationality in handling Smart Cities and AI ethically. Thus, the proposed approach can be seen as the sub-Saharan African contribution to personhood and the growing AI debates, which takes the reality of the interconnectedness of society seriously. And it will also open up new opportunities to tackle old problems and use existing resources to confront new problems in the Fourth Industrial Revolution.

Keywords: smart city, artificial intelligence, personhood, community

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Authors: Jenny Kwon

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Following the outbreak of the COVID-19 pandemic globally, another type of pandemic, ageism, appeared on the surface. Ageism, the stereotypes, prejudice, and discrimination directed towards others or oneself based on chronological age, has adversely impacted older adults' lives during the pandemic. In the short term, older adults struggled with health issues (e.g., high rate of infection and mortality) and experienced social disconnection (e.g., loneliness and depression). Ultimately, older adults' self-perceptions of aging, self-esteem and intergenerational relationships were negatively influenced. To closely look into the impact of ageism during the pandemic on U.S. older adults' aging process, the current study has three specific purposes. First, the study introduces a theoretical foundation (i.e., stereotype embodiment theory) in the development of ageism research. Second, the study reports on examples of ageism toward U.S. older adults manifested in the context of COVID-19. Finally, collective responsibilities and future research directions are proposed to fight against ageism.

Keywords: ageism, COVID-19, older adults, pandemic, stereotype embodiment

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Authors: Ashir Ahmed

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Peacekeeping is a collective responsibility of governments, law enforcement agencies, communities, families, and individuals. Moreover, the complex nature of peacekeeping activities requires a holistic and collaborative approach where various community sectors work together to form collective strategies that are likely to be more effective than strategies designed and delivered in isolation. Similarly, it is important to learn from past programs to evaluate the initiatives that have worked well and the areas that need further improvement. Review of recent peacekeeping initiatives suggests that there have been tremendous efforts and resources put in place to deal with the emerging threat of terrorism, radicalization and violent extremism through number of de-radicalization programs. Despite various attempts in designing and delivering successful programs for deradicalization, the threat of people being radicalized is growing more than ever before. This research reviews the prominent de-radicalization programs to draw an understanding of their strengths and weaknesses. Some of the weaknesses in the existing programs include. Inaccessibility: Limited resources, geographical location of potential participants (for offline programs), inaccessibility or inability to use various technologies (for online programs) makes it difficult for people to participate in de-radicalization programs. Timeliness: People might need to wait for a program on a set date/time to get the required information and to get their questions answered. This is particularly true for offline programs. Lack of trust: The privacy issues and lack of trust between participants and program organizers are another hurdle in the success of de-radicalization programs. The fear of sharing participants information with organizations (such as law enforcement agencies) without their consent led them not to participate in these programs. Generalizability: Majority of these programs are very generic in nature and do not cater the specific needs of an individual. Participants in these programs may feel that the contents are irrelevant to their individual situations and hence feel disconnected with purpose of the programs. To address the above-mentioned weaknesses, this research developed a framework that recommends some improvements in de-radicalization programs. One of the recommendations is to offer 24/7, secure, private and online hotline (also referred as helpline) for the people who have any question, concern or situation to discuss with someone who is qualified (a counsellor) to deal with people who are vulnerable to be radicalized. To make these hotline services viable and sustainable, the existing organizations offering support for depression, anxiety or suicidal ideation could additionally host these services. These helplines should be available via phone, the internet, social media and in-person. Since these services will be embedded within existing and well-known services, they would likely to get more visibility and promotion. The anonymous and secure conversation between a person and a counsellor would ensure that a person can discuss the issues without being afraid of information sharing with any third party – without his/her consent. The next stage of this project would include the operationalization of the framework by collaborating with other organizations to host de-radicalization hotlines and would assess the effectiveness of such initiatives.

Keywords: de-radicalization, framework, hotlines, peacekeeping

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Authors: Stephen M. Magu

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The 9/11 and Christmas bombing attacks in the United States are mostly associated with the inability of intelligence agencies to connect dots based on intelligence that was already available. The 1998, 2002, 2013 and several 2014 terrorist attacks in Kenya, on the other hand, are probably driven by a completely different dynamic: the invisible hand of corruption. The World Bank and Transparency International annually compute the Worldwide Governance Indicators and the Corruption Perception Index respectively. What perhaps is not adequately captured in the corruption metrics is the impact of corruption on terrorism. The World Bank data includes variables such as the control of corruption, (estimates of) government effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law and voice and accountability. TI's CPI does not include measures related to terrorism, but it is plausible that there is an expectation of some terrorism impact arising from corruption. This paper, by examining the incidence, frequency and total number of terrorist attacks that have occurred especially since 1990, and further examining the specific cases of Kenya and Nigeria, argues that in addition to having major effects on governance, corruption has an even more frightening impact: that of facilitating and/or violating security mechanisms to the extent that foreign nationals can easily obtain identification that enables them to perpetuate major events, targeting powerful countries' interests in countries with weak corruption-fighting mechanisms. The paper aims to model interactions that demonstrate the cost/benefit analysis and agents' rational calculations as being non-rational calculations, given the ultimate impact. It argues that eradication of corruption is not just a matter of a better business environment, but that it is implicit in national security, and that for anti-corruption crusaders, this is an argument more potent than the economic cost / cost of doing business argument.

Keywords: corruption, global south, identification, passports, terrorism

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Authors: Dina Sabirova, Madina Moldagali

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The rapid development of new media, especially the Internet, has led to major changes in folk culture. The net space is increasingly becoming a creation of the ‘folk’ imagination, saturated with multimedia stories with collective authorship, like traditional folklore. Moreover, the Internet picks up and changes old folklore traditions, such as the form of publication, the way of storytelling, or gave a new morality to the ‘old tales’. In this article, the similarities and differences between Internet folklore/ cyber-folklore/ digital folklore and oral folk art were examined by using the material of modern Kazakh authors. The relationship between tradition and innovation was studied in order to interpret the sequence of the authors' research taking into account the realities. The material of the article was the prose texts of Kazakh writers published in internet magazines and social networks. An immanent and intertextual analysis of the text was carried out. Thus, the new forms of Internet folklore lead to new forms of expression and social morality in society

Keywords: internet literature, modern Kazakhstani authors, net folklore, oral folk art

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Authors: Vanja Skoric

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The paper examines whether the impacts of artificial intelligence (AI) can be meaningfully addressed through the rights-based approach to AI design, investigating in particular how the inclusive, participatory process of assessing the AI impact would make this viable. There is a significant gap between envisioning rights-based AI systems and their practical application. Plausibly, internalizing human rights approach within AI design process might be achieved through identifying and assessing implications of AI features human rights, especially considering the case of vulnerable individuals and communities. However, there is no clarity or consensus on how such an instrument should be operationalised to usefully identify the impact, mitigate harms and meaningfully ensure relevant stakeholders’ participation. In practice, ensuring the meaningful inclusion of those individuals, groups, or entire communities who are affected by the use of the AI system is a prerequisite for a process seeking to assess human rights impacts and risks. Engagement in the entire process of the impact assessment should enable those affected and interested to access information and better understand the technology, product, or service and resulting impacts, but also to learn about their rights and the respective obligations and responsibilities of developers and deployers to protect and/or respect these rights. This paper will provide an overview of the study and practice of the participatory design process for AI, including inclusive impact assessment, its main elements, propose a framework, and discuss the lessons learned from the existing theory. In addition, it will explore pathways for enhancing and promoting individual and group rights through such engagement by discussing when, how, and whom to include, at which stage of the process, and what are the pre-requisites for meaningful and engaging. The overall aim is to ensure using the technology that works for the benefit of society, individuals, and particular (historically marginalised) groups.

Keywords: rights-based design, AI impact assessment, inclusion, harm mitigation

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Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

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Authors: Magdi Elmessiry, Adel Elmessiry

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The fashion industry represents a significant portion of the global gross domestic product, however, it is plagued by cheap imitators that infringe on the trademarks which destroys the fashion industry's hard work and investment. While eventually the copycats would be found and stopped, the damage has already been done, sales are missed and direct and indirect jobs are lost. The infringer thrives on two main facts: the time it takes to discover them and the lack of tracking technologies that can help the consumer distinguish them. Blockchain technology is a new emerging technology that provides a distributed encrypted immutable and fault resistant ledger. Blockchain presents a ripe technology to resolve the infringement epidemic facing the fashion industry. The significance of the study is that a new approach leveraging the state of the art blockchain technology coupled with artificial intelligence is used to create a framework addressing the fashion infringement problem. It transforms the current focus on legal enforcement, which is difficult at best, to consumer awareness that is far more effective. The framework, Crypto CopyCat, creates an immutable digital asset representing the actual product to empower the customer with a near real time query system. This combination emphasizes the consumer's awareness and appreciation of the product's authenticity, while provides real time feedback to the producer regarding the fake replicas. The main findings of this study are that implementing this approach can delay the fake product penetration of the original product market, thus allowing the original product the time to take advantage of the market. The shift in the fake adoption results in reduced returns, which impedes the copycat market and moves the emphasis to the original product innovation.

Keywords: fashion, infringement, blockchain, artificial intelligence, textiles supply chain

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Andrew D. Henshaw, James M. Austin

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Introduction: Human trafficking is an increasingly serious transnational criminal enterprise and social security issue. Despite ongoing efforts to mitigate the phenomenon and a significant expansion of security scrutiny over past decades, it is not receding. This is true for many nations in Southeast Asia, widely recognized as the global hub for trafficked persons, including men, women, and children. Clearly, human trafficking is difficult to address because there are numerous drivers, causes, and motivators for it to persist, such as non-military and non-traditional security challenges, i.e., climate change, global warming displacement, and natural disasters. These make displaced persons and refugees particularly vulnerable. The issue is so large conservative estimates put a dollar value at around $150 billion-plus per year (Niethammer, 2020) spanning sexual slavery and exploitation, forced labor, construction, mining and in conflict roles, and forced marriages of girls and women. Coupled with corruption throughout military, police, and civil authorities around the world, and the active hands of powerful transnational criminal organizations, it is likely that such figures are grossly underestimated as human trafficking is misreported, under-detected, and deliberately obfuscated to protect those profiting from it. For example, the 2022 UN report on human trafficking shows a 56% reduction in convictions in that year alone (UNODC, 2022). Our Approach: To better understand this, our research utilizes a bespoke methodology. Applying a JAM (Juxtaposition Assessment Matrix), which we previously developed to detect flows of dark money around the globe (Henshaw, A & Austin, J, 2021), we now focus on the human trafficking paradigm. Indeed, utilizing a JAM methodology has identified key indicators of human trafficking not previously explored in depth. Being a set of structured analytical techniques that provide panoramic interpretations of the subject matter, this iteration of the JAM further incorporates behavioral and driver indicators, including the employment of Open-Source Artificial Intelligence (OS-AI) across multiple collection points. The extracted behavioral data was then applied to identify non-traditional indicators as they contribute to human trafficking. Furthermore, as the JAM OS-AI analyses data from the inverted position, i.e., the viewpoint of the traffickers, it examines the behavioral and physical traits required to succeed. This transposed examination of the requirements of success delivers potential leverage points for exploitation in the fight against human trafficking in a new and novel way. Findings: Our approach identified new innovative datasets that have previously been overlooked or, at best, undervalued. For example, the JAM OS-AI approach identified critical 'dark agent' lynchpins within human trafficking that are difficult to detect and harder to connect to actors and agents within a network. Our preliminary data suggests this is in part due to the fact that ‘dark agents’ in extant research have been difficult to detect and potentially much harder to directly connect to the actors and organizations in human trafficking networks. Our research demonstrates that using new investigative techniques such as OS-AI-aided JAM introduces a powerful toolset to increase understanding of human trafficking and transnational crime and illuminate networks that, to date, avoid global law enforcement scrutiny.

Keywords: human trafficking, open-source intelligence, transnational crime, human security, international human rights, intelligence analysis, JAM OS-AI, Dark Money

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Authors: Mohammadhosein Hasanbeig, Lacra Pavel

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In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability.

Keywords: distributed control, game theory, multi-agent learning, reinforcement learning

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Authors: Nourah Almulhim

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This paper takes a critical discourse analysis (CDA) approach to investigate discourse and ideology in political blogs, focusing in particular on the Conservative Home blog from the UK’s current governing party. The Conservative party member’s discourse strategies as the blogger, alongside the discourse used by members of the public who reply to the blog in the below-the-lines comments, will be examined. The blog discourse reflects the writer's political identity and authorial voice. The analysis of the below-the-lines comments enables members of the public to engage in creating adversative positions, introducing different language users who bring their own individual and collective identities. These language users can play the role of news reporters, political analysts, protesters or supporters of a specific agenda and current socio-political topics or events. This study takes a qualitative approach to analyze the discriminatory context towards Islam/Muslims in ' The Conservative Home' blog. A cognitive approach is adopted and an analysis of dominant discourses in the blog text and the below-the-line comments is used. The focus of the study is, firstly, on the construction of self/ collective national identity in comparison to Muslim identity, highlighting the in-group and out-group construction. Second, the type of attitudes, whether feelings or judgments, related to these social actors as they are explicated to draw on the social values. Third, the role of discursive strategies in justifying and legitimizing those Islamophobic discriminatory practices. Therefore, the analysis is based on the systematic analysis of social actors drawing on actors, actions, and arguments to explicate identity construction and its development in the different discourses. A socio-semantic categorization of social actors is implemented to draw on the discursive strategies in addition to using literature to understand these strategies. An appraisal analysis is further used to classify attitudes and elaborate on core values in both genres. Finally, the grammar of othering is applied to explain how discriminatory dichotomies of 'Us' Vs. ''Them' actions are carried in discourse. Some of the key findings of the analysis can be summarized in two main points. First, the discursive practice used to represent Muslims/Islam as different from ‘Us’ are different in both genres as the blogger uses a covert voice while the commenters generally use an overt voice. This is to say that the blogger uses a mitigated strategy to represent the Muslim identity, for example, using the noun phrase ‘British Muslim’ but then representing them as ‘radical’ and ‘terrorists'. Contrary to this is in below the lines comments, where a direct strategy with an active declarative voice is used to negatively represent the Muslim identity as ‘oppressors’ and ‘terrorists’ with no inclusion of the noun phrase ‘British Muslims’. Second, the negotiation of the ‘British’ identity and values, such as culture and democracy, are prominent in the comment section as being unique and under threat by Muslims, while in the article, these standpoints are not represented.

Keywords: xenophobia, blogs, identity, critical discourse analysis

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Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

Procedia PDF Downloads 299