Search results for: law enforcement intelligence

Authors: Adesoji Tunbosun Jaiyeola, Josiah Adeyemo

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Sediment yield can be considered to be the total sediment load that leaves a drainage basin. The knowledge of the quantity of sediments present in a river at a particular time can lead to better flood capacity in reservoirs and consequently help to control over-bane flooding. Furthermore, as sediment accumulates in the reservoir, it gradually loses its ability to store water for the purposes for which it was built. The development of hydrological models to forecast the quantity of sediment present in a reservoir helps planners and managers of water resources systems, to understand the system better in terms of its problems and alternative ways to address them. The application of artificial intelligence models and technique to such real-life situations have proven to be an effective approach of solving complex problems. This paper makes an extensive review of literature relevant to the theories and applications of evolutionary algorithms, and most especially genetic programming. The successful applications of genetic programming as a soft computing technique were reviewed in sediment modelling and other branches of knowledge. Some fundamental issues such as benchmark, generalization ability, bloat and over-fitting and other open issues relating to the working principles of GP, which needs to be addressed by the GP community were also highlighted. This review aim to give GP theoreticians, researchers and the general community of GP enough research direction, valuable guide and also keep all stakeholders abreast of the issues which need attention during the next decade for the advancement of GP.

Keywords: benchmark, bloat, generalization, genetic programming, over-fitting, sediment yield

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Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Abdulmajeed Abdullah Alqarni

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This paper is devoted a study of the current frameworks applicable to foreign investments at the levels of domestic and international law, with a particular focus on the legitimate balance to be achieved between the rights of the host state and the legal protections owed to foreign investors. At the wider level of analysis, the paper attempts to map and critically examine the relationship between foreign investment and economic development. In doing so, the paper offers a study in how current discourses and practices on investment law can reconcile the competing interests of developing and developed countries. The study draws on the growing economic imperative for developing nations to create a favorable investment climate capable of attracting private foreign investment. It notes that that over the past decades, an abundance of legal standards that establish substantive and procedural protections for legal forms of foreign investments in the host countries have evolved and crystalized. The study then goes on to offer a substantive analysis of legal reforms at the domestic level in countries such as Saudi Arabia before going on to provide an in- depth and substantive examination of the most important instruments developed at the levels of international law: bilateral investment agreements and double taxation agreements. As to its methods, the study draws on case studies and from data assessing the link between double taxation and economic development. Drawing from the extant literature and doctrinal research, and international and comparative jurisprudence, the paper excavates and critically examines contemporary definitions and norms of international investment law, many of which have been given concrete form and specificity in an ever-expanding number of bilateral and multilateral investment treaties. By reconsidering the wider challenges of conflicts of law and jurisdiction, and the competing aims of the modern investment law regime, the study reflects on how bilateral investment treaties might succeed in achieving the dual aims of rights protection and economic sovereignty. Through its examination of the double taxation phenomena, the study goes on to identify key practical challenges raised by the implementation of bilateral treaties whilst also assessing the sufficiency of the domestic and international legal solutions that are proposed in response. In its final analysis, the study aims to contribute to existing scholarship by assessing contemporary legal and economic barriers to the free flow of investment with due regard for the legitimate concerns and diversity of developing nations. It does by situating its analysis of the domestic enforcement of international investment instrument in its wider historical and normative context. By focusing on the economic and legal dimensions of foreign investment, the paper also aims to offer an interdisciplinary and holistic perspective on contemporary issues and developments in investment law while offering practical reform proposals that can be used to be achieve a more equitable balance between the rights and interests of states and private entities in an increasingly trans nationalized sphere of investment regulation and treaty arbitration.

Keywords: foreign investment, bilateral investment treaties, international tax law, double taxation treaties

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Authors: Amy Williamson, Kerry Armstrong, Jason Edwards, Patricia Obst

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Australian national policy endorses a responsive approach to work health and safety (WHS) regulation, combining positive motivators (education and guidance), with compliance monitoring and enforcement to encourage and secure compliance with legislation. Despite theoretical support for responsive regulation, there is limited evidence regarding how to achieve best results in practice. Using positive psychology as a novel paradigm, this study aims to investigate how non-punitive regulatory interactions can be improved to further encourage regulatory compliance in the construction industry. As part of a larger project, semi-structured interviews were conducted with 35 inspectorate staff and 11 managers in the Australian (Queensland) construction industry. Using an inductive, grounded approach, an in-depth qualitative investigation was conducted to identify the positive psychological principles which underpin effective use of the non-punitive aspects of responsive regulation. Results highlighted the importance of effective engagement between inspectors and industry managers. This involved the need to interact cooperatively and encourage compliance with WHS legislation. Several strategies were identified that assisted regulatory interactions and the ability of inspectors to engage. The importance of communication and interpersonal skills was reported to be critical to any interaction, regardless of the nature of the visit and regulatory tools used. In particular, the use of clear and open communication fostered trust and rapport which facilitated more positive interactions. The importance of respect and empathy was also highlighted. The need for provision of guidance and direction on how to achieve compliance was also reported. This related to ensuring companies understand their WHS obligations, providing specific advice regarding how to rectify a breach and meet compliance requirements, and ensuring sufficient follow up to confirm that compliance is successfully achieved. In the absence of imminent risk, allowing companies the opportunity to comply before further action is taken was also highlighted. Increased proactive engagement with industry to educate and promote the vision of safety at work was also reported. Finally, provision of praise and positive feedback was reported to assist interactions and encourage the continuation of good practices. Evidence from positive psychology and organisational psychology was obtained to support the use of each strategy in practice. In particular, the area of positive leadership provided a useful framework to consider the factors and conditions that drive positive interactions within the context of work health and safety and the specific relationship between inspectors and industry managers. This study provides fresh insight into key psychological principles which support non-punitive regulatory interactions in the area of workplace health and safety. The findings of this research contribute to a better understanding of how inspectors can enhance the efficacy of their regulatory interactions to improve compliance with legislation. Encouraging and assisting compliance through effective non-punitive activity offers a sustainable pathway for promoting safety and preventing fatalities and injuries in the construction industry.

Keywords: engagement, non-punitive approaches to compliance, positive interactions in the workplace, work health and safety compliance

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Authors: Kelvin Lo

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Nowadays, numerical modelling in geotechnical engineering is very common but sophisticated. Many advanced input settings and considerable computational efforts are required to optimize the design to reduce the construction cost. To optimize a design, it usually requires huge numerical models. If the optimization is conducted manually, there is a potentially dangerous consequence from human errors, and the time spent on the input and data extraction from output is significant. This paper presents an automation process introduced to numerical modelling (Plaxis 2D) of a trench excavation supported by a secant-pile retaining structure for a top-down tunnel project. Python code is adopted to control the process, and numerical modelling is conducted automatically in every 20m chainage along the 200m tunnel, with maximum retained height occurring in the middle chainage. Python code continuously changes the geological stratum and excavation depth under groundwater flow conditions in each 20m section. It automatically conducts trial and error to determine the required pile length and the use of props to achieve the required factor of safety and target displacement. Once the bending moment of the pile exceeds its capacity, it will increase in size. When the pile embedment reaches the default maximum length, it will turn on the prop system. Results showed that it saves time, increases efficiency, lowers design costs, and replaces human labor to minimize error.

Keywords: automation, numerical modelling, Python, retaining structures

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Hang Ju, Changmin Zhu

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The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

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Authors: Kapil Khanna, Swee Kuik, Joowon Ban

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The current linear economic system is unsustainable due to its dependence on the uncontrolled exploitation of diminishing natural resources. The integration of business innovation and supply chain management has brought about the redesign of business processes through the implementation of a closed-loop approach. The circular economy (CE) offers a sustainable solution to improve business opportunities in the near future by following the principles of rejuvenation and reuse inspired by nature. Those business owners start to rethink and consider using waste as raw material to make new products for consumers. The implementation of CE helps organisations to incorporate new strategic plans for decreasing the use of virgin materials and nature resources. Supply chain partners that are geographically dispersed rely heavily on innovative approaches to support supply chain management. Presently, numerous studies have attempted to establish the concept of supply chain management (SCM) by integrating CE principles, which are commonly denoted as circular SCM. While many scholars have recognised the challenges of transitioning to CE, there is still a lack of consensus on business best practices that can facilitate companies in embracing CE across the supply chain. Hence, this paper strives to scrutinize the SCM practices utilised for CE, identify the obstacles, and recommend best practices that can enhance a company's ability to incorporate CE principles toward business innovation and supply chain performance. Further, the paper proposes future research in the field of using specific technologies such as artificial intelligence, Internet of Things, and blockchain as business innovation tools for supply chain management and CE adoption.

Keywords: business innovation, challenges, circular supply chain, supply chain management, technology

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Authors: Poulomi Bhadra, Manjushree Palit, Sanjeev P. Sahni

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Forensic science uses all branches of science for criminal investigation and trial and has increasingly emerged as an important tool in the administration of justice. However, the growth and development of this field in India has not been as rapid or widespread as compared to the more developed Western countries. For successful administration of justice, it is important that all agencies involved in law enforcement adopt an inter-professional approach towards forensic science, which is presently lacking. In light of the alarmingly high average acquittal rate in India, this study aims to examine the lack of understanding and appreciation of the importance and scope of forensic evidence and expert opinions amongst law professionals such as lawyers and judges. Based on a study of trial court cases from Delhi and surrounding areas, the study underline the areas in forensics where the criminal justice system has noticeably erred. Using this information, the authors examine the extent of forensic understanding amongst legal professionals and attempt to conclusively identify the areas in which they need further appraisal. A cross-sectional study done using a structured questionnaire was conducted amongst law professionals across age, gender, type and years of experience in court, to determine their understanding of DNA, fingerprints and other interdisciplinary scientific materials used as forensic evidence. In our study, we understand the levels of understanding amongst lawyers with regards to DNA and fingerprint evidence, and how it affects trial outcomes. We also aim to understand the factors that prevent credible and advanced awareness amongst legal personnel, amongst others. The survey identified the areas in modern and advanced forensics, such as forensic entomology, anthropology, cybercrime etc., in which Indian legal professionals are yet to attain a functional understanding. It also brings to light, what is commonly termed as the ‘CSI-effect’ in the Western courtrooms, and provides scope to study the existence of this phenomenon and its effects on the Indian courts and their judgements. This study highlighted the prevalence of unchallenged expert testimony presented by the prosecution in criminal trials and impressed upon the judicial system the need for independent analysis and evaluation of the scientist’s data and/or testimony by the defense. Overall, this study aims to define a clearer and rigid understanding of why legal professionals should have basic understanding of the interdisciplinary nature of forensic sciences. Based on the aforementioned findings, the author suggests various measures by which judges and lawyers might obtain an extensive knowledge of the advances and promising potentialities of forensic science. This includes promoting a forensic curriculum in legal studies at Bachelor’s and Master’s level as well as in mid-career professional courses. Formation of forensic-legal consultancies, in consultation with the Department of Justice, will not only assist in training police, military and law personnel but will also encourage legal research in this field. These suggestions also aim to bridge the communication gap that presently exists between law practitioners, forensic scientists and the general community’s awareness of the criminal justice system.

Keywords: forensic science, Indian legal professionals, interdisciplinary awareness, legal education

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Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

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Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Shivaram P. Arunachalam, Mustafa Y. Sir, Andy Boggust, David M. Nestler, Thomas R. Hellmich, Kalyan S. Pasupathy

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Emergency departments (EDs) provide urgent care to patients throughout the day in a complex and chaotic environment. Real-time location systems (RTLS) are increasingly being utilized in healthcare settings, and have shown to improve safety, reduce cost, and increase patient satisfaction. Radio Frequency Identification Device (RFID) data in an ED has been shown to compute variables such as patient-provider contact time, which is associated with patient outcomes such as 30-day hospitalization. These variables can provide avenues for improving ED operational efficiency. A major challenge with ED financial operations is under-coding of critical care services due to physicians’ difficulty reporting accurate times for critical care provided under Current Procedural Terminology (CPT) codes 99291 and 99292. In this work, the authors propose a framework to optimize ED critical care billing using RFID data. RFID estimated physician-patient contact times could accurately quantify direct critical care services which will help model a data-driven approach for ED critical care billing. This paper will describe the framework and provide insights into opportunities to prevent under coding as well as over coding to avoid insurance audits. Future work will focus on data analytics to demonstrate the feasibility of the framework described.

Keywords: critical care billing, CPT codes, emergency department, RFID

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Authors: Sharmila Parmanand

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This paper examines dominant media representations of prostitution in the Philippines and interrogates sex workers’ interactions with the media establishment. This analysis of how sex workers are constituted in media, often as both innocent victims and immoral actors, contributes to an understanding of public discourse on sex work in the Philippines, where decriminalisation has recently been proposed and sex workers are currently classified as potential victims under anti-trafficking laws but also as criminals under the penal code. The first part is an analysis of media coverage of two prominent themes on prostitution: first, raid and rescue operations conducted by law enforcement; and second, prostitution on military bases and tourism hotspots. As a result of pressure from activists and international donors, these two themes often define the policy conversations on sex work in the Philippines. The discourses in written and televised news reports and documentaries from established local and international media sources that address these themes are explored through content analysis. Conclusions are drawn based on specific terms commonly used to refer to sex workers, how sex workers are seen as performing their cultural roles as mothers and wives, how sex work is depicted, associations made between sex work and public health, representations of clients and managers and ‘rescuers’ such as the police, anti-trafficking organisations, and faith-based groups, and which actors are presumed to be issue experts. Images of how prostitution is used as a metaphor for relations between the Philippines and foreign nations are also deconstructed, along with common tropes about developing world female subjects. In general, sex workers are simultaneously portrayed as bad mothers who endanger their family’s morality but also as long-suffering victims who endure exploitation for the sake of their children. They are also depicted as unclean, drug-addicted threats to public health. Their managers and clients are portrayed as cold, abusive, and sometimes violent, and their rescuers as moral and altruistic agents who are essential for sex workers’ rehabilitation and restoration as virtuous citizens. The second part explores sex workers’ own perceptions of their interactions with media, through interviews with members of the Philippine Sex Workers Collective, a loose organisation of sex workers around the Philippines. They reveal that they are often excluded by media practitioners and that they do not feel that they have space for meaningful self-revelation about their work when they do engage with journalists, who seem to have an overt agenda of depicting them as either victims or women of loose morals. In their assessment, media narratives do not necessarily reflect their lived experiences, and in some cases, coverage of rescues and raid operations endangers their privacy and instrumentalises their suffering. Media representations of sex workers may produce subject positions such as ‘victims’ or ‘criminals’ and legitimize specific interventions while foreclosing other ways of thinking. Further, in light of media’s power to reflect and shape public consciousness, it is a valuable academic and political project to examine whether sex workers are able to assert agency in determining how they are represented.

Keywords: discourse analysis, news media, sex work, trafficking

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Prakash Singh, Piet Maphodisa Kgohlo

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Knowledge-worker productivity is one of the biggest leadership challenges facing all organizations in the twenty-first century. It cannot be denied that knowledge-worker productivity affects all organizations. The work and the workforce are both undergoing greater changes currently than at any time, since the beginning of the industrial revolution two centuries ago. Employees welcome collegial teaming (CT) as an innovative way to develop their work-integrated learning competencies. Human resource development policies must evoke the symbiotic relationship between CT and work-integrated learning, seeing that employees need to be endowed with the competence to move from one skill to another, as each one becomes obsolete, and to simultaneously develop their cognitive and emotional intelligence. The outcome of this relationship must culminate in the development of highly productive knowledge-workers. While this study focuses on teachers, the conceptual framework and the findings of this research can be beneficial for any organization, public or private sector, business or non-business. Therefore, in this quantitative study, the benefits of CT are considered in developing human resources to sustain knowledge-worker productivity. The ANOVA p-values reveal that the majority of teachers agree that CT can empower them to overcome the challenges of managing curriculum change. CT can equip them with continuous and sustained learning, growth and improvement, necessary for knowledge-worker productivity. This study, therefore, confirms that CT benefits all workers, immaterial of their age, gender or experience. Hence, this exploratory research provides a new perspective of CT in addressing knowledge-worker productivity when organizational change alters the vision of the organization.

Keywords: collegial teaming, human resource development, knowledge-worker productivity, work-integrated learning

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Authors: Ali Riza Perçin, Eser Bingül

Abstract:

Internet technologies have created opportunities with which people share their ideologies, thoughts and products. This virtual world, named social media has given the chance of gathering individual users and people from the world's remote locations and establishing an interaction between them. However, to an increasingly higher degree terrorist organizations today use the internet and most notably social-network media to create the effects they desire through a series of on-line activities. These activities, designed to support their activities, include information collection (intelligence), target selection, propaganda, fundraising and recruitment to name a few. Meanwhile, these have been used as the most important tool for recruitment especially from the different region of the world, especially disenfranchised youth, in the West in order to mobilize support and recruit “foreign fighters.” The recruits have obtained the statue, which is not accessible in their society and have preferred the style of life that is offered by the terrorist organizations instead of their current life. Like other terrorist groups, for a while now the terrorist organization Islamic State (IS) in Iraq and Syria has employed a social-media strategy in order to advance their strategic objectives. At the moment, however, IS seems to be more successful in their on-line activities than other similar organizations. IS uses social media strategically as part of its armed activities and for the sustainability of their military presence in Syria and Iraq. In this context, “Generation Y”, which could exist at the critical position and undertake active role, has been examined. Additionally, the explained characteristics of “Generation Y” have been put forward and the duties of families and society have been stated as well.

Keywords: social media, "generation Y", terrorist organization, islamic state IS

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

Abstract:

As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

Abstract:

This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem

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Authors: Bilge B. Calik, Ummuhan B. Aslan, Suat Erel, Sehmus Aslan

Abstract:

Objective: Intellectual disability (ID) is characterized by limitations in both intellectual functioning and adaptive behavior, which covers many everyday social and practical skills. The aim of this study was to evaluate the balance and coordination performance determined between mild and moderate ID adolescents who regularly play sport. Methods: The study comprised a total of 179 participants, of which 135 were male adolescents with mild and moderate-level ID who regularly play sports (16.52 ± 2.17 years) and 44 age-matched male adolescents with typical development without ID who do not do any sports (16.52 ± 0.99 years). The participants with ID were students of Special Education Schools for the mentally disabled and had been diagnosed with ID at a Ministry of Health Hospital. The adolescents with mild and moderate ID had been playing football in their school teams at least 2 days a week, for at least one year. Balance and coordination of adolescents were assessed by Bilateral coordination and balance subtests of Short Form Bruininks-Oseretsky Test of Motor Proficiency (BOT-2 SF). Results: As a result of the evaluations comparing coordination and balance scores significant differences were determined between all three groups in favor of the peers without ID (p<0.05). Conclusions: It was observed that balance and coordination levels of adolescents with mild ID were better than those of adolescents with moderate-level ID but lower than those of peers without ID. These results indicate a relationship between IQ level and motor performance. Further comparative studies are needed on individuals with ID who play and do not play sports in order to examine the impact of participation in sports on the motor skills of individuals with ID.

Keywords: balance, coordination, intellectual disability, motor skills, sport

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Authors: Christodoulou Christos, Politis Anastasios

Abstract:

The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

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Authors: R. S. Quintal, J. L. Tesch Santos, M. D. Davis, E. C. de Santana, M. de F. Bandeira dos Santos

Abstract:

The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government.

Keywords: process management, management control, business intelligence, Brazilian Navy

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

Abstract:

During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Nathaniel Carpenter

Abstract:

Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

Abstract:

Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

Abstract:

The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

Procedia PDF Downloads 148