Search results for: algorithmic fairness

Authors: Zubier Arfan, Paul Johnson

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The electronification of financial markets and the rise of algorithmic trading has sparked a lot of interest from the mathematical community, for the market making-problem in particular. The research presented in this short paper solves the classic stochastic control problem in order to derive the strategy for a market-maker. It also shows how to calibrate and simulate the strategy with real limit order book data for back-testing. The ambiguity of limit-order priority in back-testing is dealt with by considering optimistic and pessimistic priority scenarios. The model, although it does outperform a naive strategy, assumes constant volatility, therefore, is not best suited to the LOB data. The Heston model is introduced to describe the price and variance process of the asset. The Trader's constant absolute risk aversion utility function is optimised by numerically solving a 3-dimensional Hamilton-Jacobi-Bellman partial differential equation to find the optimal limit order quotes. The results show that the stochastic volatility market-making model is more suitable for a risk-averse trader and is also less sensitive to calibration error than the constant volatility model.

Keywords: market-making, market-microsctrucure, stochastic volatility, quantitative trading

Procedia PDF Downloads 115

Authors: Magid Maatallah

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The problem is, investment management in modern conditions boils down to risk management which is very underdeveloped in Islamic financial theory and practice. Add to this the fact that, in Islamic perception, this is one of the areas of conventional finance in need of drastic reforms. This need was recently underlined by the story of Long Term Capital Management (LTCM ), ( told by Roger Lowenstein in his book, When Genius Failed, Random House, 2000 ). So we face a double challenge, to develop Islamic techniques of risk management and to see that these new techniques are free from the ills with which conventional methods are suffering. This is different from the challenge faced in the middle of twentieth century, to develop a method of financial intermediation free of interest.Risk was always there, especially in business. But industrialization brought risks unknown in trade and agriculture. Industrial production often involves long periods of time .The longer the period of production the more the uncertainty. The scope of the market has expanded to cover the whole world, introducing new kinds of risk. More than a thousand years ago, when Islamic laws were being written, the nature and scope of risk and uncertainty was different. However, something can still be learnt which, in combination with the modern experience, should enable us to realize the Shariah objectives of justice, fairness and efficiency.

Keywords: financial markets, Islamic framework, risk management, investment

Procedia PDF Downloads 519

Authors: Nipawan Tharasak, Ladda Hirunyava

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The objective of the research was to study factors affecting working efficiency and the relationship between working environment, satisfaction to human resources management and operation employees’ working efficiency of Faculty of Management Science, Suan Sunandha Rajabhat University. The sample size of the research was based on 33 employees of Faculty of Management Science. The researcher had classified the support employees into 4 divisions by using Stratified Random Sampling. Individual sample was randomized by using Simple Random Sampling. Data was collected through the instrument. The Statistical Package for the Windows was utilized for data processing. Percentage, mean, standard deviation, the t-test, One-way ANOVA, and Pearson product moment correlation coefficient were applied. The result found the support employees’ satisfaction in human resources management of Faculty of Management Science in following areas: remuneration; employee recruitment & selection; manpower planning; performance evaluation; staff training & developing; and spirit & fairness were overall in good level.

Keywords: faculty of management science, operational factors, practice performance, staff working

Procedia PDF Downloads 208

Authors: Vesna Lukovic

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Online platforms as new business models play an important role in today’s economy and the functioning of the EU’s internal market. In the travel industry, algorithms used by online platforms for short-stay accommodation provide suggestions and price information to travelers. Those suggestions and recommendations are displayed in search results via recommendation (ranking) systems. There has been a growing consensus that the current legal framework was not sufficient to resolve problems arising from platform practices. In order to enhance the potential of the EU’s Single Market, smaller businesses should be protected, and their rights strengthened vis-à-vis large online platforms. The Regulation (EU) 2019/1150 of the European Parliament and of the Council on promoting fairness and transparency for business users of online intermediation services aims to level the playing field in that respect. This research looks at Airbnb through the lenses of this regulation. The research explores key determinants and finds that although regulation is an important step in the right direction, it is not enough. It does not entail sufficient clarity obligations that would make online platforms an intermediary service which both accommodation providers and travelers could use with ease.

Keywords: algorithm, online platforms, ranking, consumers, EU regulation

Procedia PDF Downloads 105

Authors: Marcus Smith

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DNA profiling has made a valuable contribution to criminal investigations over the past thirty years. Direct matching DNA profiles from a crime scene and suspect, or between a suspect and a database remain of great importance to crimes such as murder, assault, and property theft. As scientific and technological advancement continues, a wide range of new DNA profiling applications has been developed. The application of new techniques involves an interesting balancing act between admitting probative evidence in a criminal trial, evaluating its degree of relevance and validity, and limiting its prejudicial impact. The impact of new DNA profiling applications that have significant implications for law enforcement and the legal system can be evaluated through a review of relevant case law, legislation and the latest empirical evidence from jurisdictions around the world including the United States, United Kingdom, and Australia. There are benefits in further examining the implications of these new developments, including how the criminal law can best be adapted to ensure that new technology is used to enhance criminal investigation and prosecution while ensuring it is applied in a measured way that respects individual rights and maintains principles of fairness enshrined in the legal system.

Keywords: criminal procedure, forensic evidence, DNA profiling, familial searching, phenotyping

Procedia PDF Downloads 114

Authors: Eftah Bte Moh, Hj Abdullah Izazol Binti Idris, Abd. Aziz Bin Abd. Shukor

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This study aimed to identify the alignment of understanding and assessment practices among secondary school teachers. The study was carried out using quantitative descriptive study. The sample consisted of 164 teachers who taught Form 1 and 2 from 11 secondary schools in the district of North Kinta, Perak, Malaysia. Data were obtained from 164 respondents who answered Expectation Alignment Understanding and Practices of School Assessment (PEKDAPS) questionnaire. The data were analysed using SPSS 17.0 +. The Cronbach alpha value obtained through PEKDAPS questionnaire pilot study was 0.86. The results showed that teachers' performance in PEKDAPS based on the mean value was less than 3, which means that perfect alignment does not occur between the understanding and practices of school assessment. Two major PEKDAPS sub-constructs of articulation across grade and age and usability of the system were higher than the moderate alignment of the understanding and practices of school assessment (Min=2.0). The content was focused on PEKDAPs sub-constructs which showed lower than the moderate alignment of the understanding and practices of school assessment (Min=2.0). Another two PEKDAPS sub-constructs of transparency and fairness and the pedagogical implications showed moderate alignment (2.0). The implications of the study is that teachers need to fully understand the importance of alignment among components of assessment, learning and teaching and learning objectives as strategies to achieve quality assessment process.

Keywords: school based assessment, alignment, understanding, assessment practices

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: K. Kalaiarasan, B. Pandeeswari, A. Arockia John Francis

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Wireless, Multi-hop networks consist of one or more intermediate nodes along the path that receive and forward packets via wireless links. The backpressure algorithm provides throughput optimal routing and scheduling decisions for multi-hop networks with dynamic traffic. Xpress, a cross-layer backpressure architecture was designed to reach the capacity of wireless multi-hop networks and it provides well coordination between layers of network by turning a mesh network into a wireless switch. Transmission over the network is scheduled using a throughput-optimal backpressure algorithm. But this architecture operates much below their capacity due to out-of-order packet delivery and variable packet size. In this paper, we present Xpress-T, a throughput optimal backpressure architecture with TCP support designed to reach maximum throughput of wireless multi-hop networks. Xpress-T operates at the IP layer, and therefore any transport protocol, including TCP, can run on top of Xpress-T. The proposed design not only avoids bottlenecks but also handles out-of-order packet delivery and variable packet size, optimally load-balances traffic across them when needed, improving fairness among competing flows. Our simulation results shows that Xpress-T gives 65% more throughput than Xpress.

Keywords: backpressure scheduling and routing, TCP, congestion control, wireless multihop network

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Authors: Dmitri Terzi

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The article presents the results of an algorithmic study of a special optimization problem of the transport type (traveling salesman problem): 1) To solve the problem, a new natural algorithm has been developed based on the decomposition of the initial data into convex hulls, which has a number of advantages; it is applicable for a fairly large dimension, does not require a large amount of memory, and has fairly good performance. The relevance of the algorithm lies in the fact that, in practice, programs for problems with the number of traversal points of no more than twenty are widely used. For large-scale problems, the availability of algorithms and programs of this kind is difficult. The proposed algorithm is natural because the optimal solution found by the exact algorithm is not always feasible due to the presence of many other factors that may require some additional restrictions. 2) Another inverse problem solved here is to describe a class of traveling salesman problems that have a predetermined optimal solution. The constructed algorithm 2 allows us to characterize the structure of traveling salesman problems, as well as construct test problems to evaluate the effectiveness of algorithms and other purposes. 3) The appendix presents a software implementation of Algorithm 1 (in MATLAB), which can be used to solve practical problems, as well as in the educational process on operations research and optimization methods.

Keywords: traveling salesman problem, solution construction algorithm, convex hulls, optimality verification

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Authors: Mounir Kehal

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The post-globalization epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behavior has come to provide the competitive and comparative edge. Enterprises have turned to explicit - and even conceptualizing on tacit - knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualized. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper, we present an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain.

Keywords: knowledge management, knowledge maps, knowledge diffusion, organizational cartography

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Authors: Herbert W. Corley

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A scalar equilibrium (SE) is an alternative type of equilibrium in pure strategies for an n-person normal-form game G. It is defined using optimization techniques to obtain a pure strategy for each player of G by maximizing an appropriate utility function over the acceptable joint actions. The players’ actions are determined by the choice of the utility function. Such a utility function could be agreed upon by the players or chosen by an arbitrator. An SE is an equilibrium since no players of G can increase the value of this utility function by changing their strategies. SEs are formally defined, and examples are given. In a greedy SE, the goal is to assign actions to the players giving them the largest individual payoffs jointly possible. In a weighted SE, each player is assigned weights modeling the degree to which he helps every player, including himself, achieve as large a payoff as jointly possible. In a compromise SE, each player wants a fair payoff for a reasonable interpretation of fairness. In a parity SE, the players want their payoffs to be as nearly equal as jointly possible. Finally, a satisficing SE achieves a personal target payoff value for each player. The vector payoffs associated with each of these SEs are shown to be Pareto optimal among all such acceptable vectors, as well as computationally tractable.

Keywords: compromise equilibrium, greedy equilibrium, normal-form game, parity equilibrium, pure strategies, satisficing equilibrium, scalar equilibria, utility function, weighted equilibrium

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Authors: Zahra Abdolkarimi, Naser Zouri

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Nowadays, there is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Additionally, robotics system recommended RoboCup factor as a provider of some standardization and testing method in case of computer discussion widely. The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. In addition, decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidently. Consequences, shows method of our discussion is the best way for Particle Swarm Optimization and Fuzzy system compare to other decision of robotics algorithmic.

Keywords: PSO algorithm, inference fuzzy system, chaos theory, soccer robot league

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Authors: Mounir Kehal, Adel Al Araifi

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The post-globalisation epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behaviour has come to provide the competitive and comparative edge. Enterprises have turned to explicit- and even conceptualising on tacit- Knowledge Management to elaborate a systematic approach to develop and sustain the Intellectual Capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualised. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper we present likewise an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain.

Keywords: knowledge management, knowledge maps, knowledge diffusion, organizational cartography

Procedia PDF Downloads 392

Authors: N. S. Yen, T. H. Yang, W. H. Huang, Y. F. Fang, H. W. Cho

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Ultimatum game is an experimental paradigm to study human decision making. There are two players, a proposer and a responder, to split a fixed amount of money. According to the traditional economic theory on ultimatum game, proposer should propose the selfish offers to responder as much as possible to maximize proposer’s own outcomes. However, most evidences had showed that people chose more fair offers, hence two hypotheses – fairness favoring and strategic concern were proposed. In current study, we induced the motivation in participants to be either selfish or altruistic, and manipulated the task variables, the stake sizes (NT$100, 1000, 10000) and the share sizes (the 40%, 30%, 20%, 10% of the sum as selfish offers, and the 60%, 70%, 80%, 90% of the sum as altruistic offers), to examine the two hypotheses. The results showed that most proposers chose more fair offers with longer reaction times (RTs) no matter in choosing between the fair and selfish offers, or between the fair and altruistic offers. However, the proposers received explicit self-interest instruction chose more selfish offers accompanied with longer RTs in choosing between the fair and selfish offers. Therefore, the results supported the strategic concern hypothesis that previous proposers choosing the fair offers might be resulted from the fear of rejection by responders. Proposers would become more self-interest if the fear of being rejected is eliminated.

Keywords: ultimatum game, proposer, self-interest, fear of rejection

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Remus Titiriga

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The paper focuses on Pascal's indivisibles evolving to Leibniz's infinitesimals. It starts with parallel developments by the two savants in Combinatorics (triangular numbers for Pascal and harmonic triangles for Leibniz) and their implication in determining the sum of mathematical series. It follows with a focus on the geometrical contributions of Pascal. He considered the cycloid and other mechanical curves the epitome of geometric comprehensibility in a series of challenging problems he posed to the mathematical world. Pascal provided the solutions in 1658, in a volume published under the pseudonym of Dettonville, using indivisibles and ratios between curved and straight lines. In the third part, the research follows the impact of this volume on Leibniz as the initial impetus for the elaboration of modern calculus as an algorithmic method disjoint of geometrical intuition. Then paper analyses the further steps and proves that Leibniz's developments relate to his philosophical frame (the search for a characteristic Universalis, the consideration of principle of continuity or the rule of sufficient reason) different from Pascal's and impacting mathematical problems and their solutions. At this stage in Leibniz's evolution, the infinitesimals replaced the indivisibles proper. The last part of the paper starts with speculation around "What if?". Could Pascal, if he lived more, accomplish the same feat? The document uses Pascal's reconstructed philosophical frame to formulate a positive answer. It also proposes to teach calculus with indivisibles and infinitesimals mimicking Pascal and Leibniz's achievements.

Keywords: indivisibles, infinitesimals, characteristic triangle, the principle of continuity

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Authors: Muhammad Rifqi Hafizhudin Arif, Mukhamad Zulfal Faradis, Ahmad Hidayatullah

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Indonesia is the country that uses conventional financial system adopted from European countries as a form of finance in the national banking system. Many of the derivative products of conventional banks either investment, buy and sell, saving and loan, which is not in accordance with Islamic Ethics. While the majority population in Indonesia are belief in Islam, which Islam has had financial management guide is written in the Quran, the Hadith, as well as the opinions of experts who strongly prohibits the use of interest in each transaction activities. Many different expert opinions on the application of the Islamic financial system in Indonesia. However, as the majority of the population of Indonesia, Islamic community have not been able to get the opportunities to choose the Islamic financial system that has mutual benefit between consumers and banks, particularly fairness in transactions, ethical investment, uphold the values of solidarity and brotherhood in every transaction activities, and avoid speculation. In this paper, we will discuss the reasons for the importance of providing an option for Islamic community as the majority of the population of Indonesia to use the banking system which adopted the Islamic ethical values that have been much discussed by other researchers in various countries. The existence of this research is expected to Government, academia and the general public aware of the urgency of Islamic economic system implementation in Indonesian banking as the solution and justice especially for the Islamic community to use the values which they held.

Keywords: Islamic economic system, conventional system, Islamic value, banking

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Authors: Ali Muhammad

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The study examines the consequences of decision making in Kuwait work organization. The framework used in this study proposes that participation in decision making improves organizational ethical climate, which in turn increases employee’s trust in supervisor and trust in the organization. Furthermore, the model suggests that allowing employees to voice their opinions positively effects their perceptions of organizational justice. Providing employees with the opportunity to participate in decision making (voice), enhances their perceptions of the fairness of those decisions. Allowing employees to express their opinions and feeling about decisions being made show that the organization respect appreciates their views. This feeling of respect and appreciation reflects positively on employee’s perception of justice. Survey data were collected from a sample of 292 employees working in Kuwaiti work organizations. Pearson correlation, non-parametric tests, and structural equation models were used to analyze the data. Results of the analysis show that participation in decision making enhances employee perception of ethical climate, which in turn increases perception organizational justice and organizational trust. Implications of the findings and directions for future research are discussed.

Keywords: participation in decision making, organizational trust, trust in supervisor, organizational justice, ethical climate

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Authors: Omotolani Victoria Somoye

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The paper examines modern approaches to the insurable interest, which is a fundamental principle of insurance law that affects the enforceability of insurance contracts. The study starts by examining the competing definitions of the nature of the insurable interest doctrine. It finds that while legal interest theory is seen to be sufficient as the test of insurable interest, the paper argues on how this approach deprives the insured of a full indemnity of losses suffered. The problem with the Nigerian and English current legislative framework is that it defines insurable interest as a legally recognized interest of the insured in the subject matter of insurance. However, other countries like Australia, the United States, South Africa, and more recently, Canada, have rejected the English test and trodden their own path along the factual expectancy line. The study justifies the rationale behind the departure of similar common law jurisdictions and argues that the English and Nigerian position, which appears to be too rigid, harsh on the insured, and no longer fit for purpose in the 21st century, should be revised. The paper concludes that the common law doctrine does not represent better interests of certainty, justice, and fairness, as well as not meeting the policy behind the requirement of insurable interest. This paper adopts a doctrinal comparative research methodology to examine complex areas of insurable interest in selected countries and work out some suggestions for reforming the Nigerian and English laws by referring to the approaches of other jurisdictions.

Keywords: Australia, common law, English law, insurable interest, insurance, Nigeria

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Authors: Rabiatu Bonku, Faisal Alkaabneh

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Inspired by a food banks distribution operation for non-profit organization, we study a variant synchronized vehicle routing problem for equitable resource allocation. This research paper introduces a Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of efficiently distributing vital resources, particularly for food banks serving vulnerable populations in urban areas. Our optimization approach places a strong emphasis on social equity, ensuring a fair allocation of food to partner agencies while minimizing wastage. The primary objective is to enhance operational efficiency while guaranteeing fair distribution and timely deliveries to prevent food spoilage. Furthermore, we assess four distinct models that consider various aspects of sustainability, including social and economic factors. We conduct a comprehensive numerical analysis using real-world data to gain insights into the trade-offs that arise, while also demonstrating the models’ performance in terms of fairness, effectiveness, and the percentage of food waste. This provides valuable managerial insights for food bank managers. We show that our proposed approach makes a significant contribution to the field of logistics optimization and social responsibility, offering valuable insights for improving the operations of food banks.

Keywords: food banks, humanitarian logistics, equitable resource allocation, synchronized vehicle routing

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Authors: M. F. Amado, A. Amado, F. Poggi, J. Correia de Freitas

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Today it is widely understood that global energy consumption patterns are directly related to the ongoing urban expansion and development process. This expansion is based on the natural growth of human activities and has left most urban areas totally dependent on fossil fuel derived external energy inputs. This status-quo of production, transportation, storage and consumption of energy has become inefficient and is set to become even more so when the continuous increases in energy demand are factored in. The territorial management of land use and related activities is a central component in the search for more efficient models of energy use, models that can meet current and future regional, national and European goals. In this paper, a methodology is developed and discussed with the aim of improving energy efficiency at the municipal level. The development of this methodology is based on the monitoring of energy consumption and its use patterns resulting from the natural dynamism of human activities in the territory and can be utilized to assess sustainability at the local scale. A set of parameters and indicators are defined with the objective of constructing a systemic model based on the optimization, adaptation and innovation of the current energy framework and the associated energy consumption patterns. The use of the model will enable local governments to strike the necessary balance between human activities, economic development, and the local and global environment while safeguarding fairness in the energy sector.

Keywords: solar urban planning, solar smart city, urban development, energy efficiency

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Ma Chaoyang

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This article starts from the perspective of environmental justice theory and analyzes the accessibility and regional equity of park green spaces in the central urban area of Chengdu in 2022 based on the improved Gaussian 2SFCA analysis method and Gini coefficient method. Then, according to the relevant analysis model, it further explores the correlation between the spatial distribution of park green spaces and the socio-economic conditions of residents in order to provide a reference for the construction and research of Chengdu's park city under the guidance of fairness and justice. The results show that: (1) Overall, the spatial distribution of parks and green spaces in Chengdu shows a significantly uneven distribution of extreme core edge, with a certain degree of unfairness; that is, there is an environmental injustice pattern. (2) The spatial layout of urban parks and green spaces is subject to strong guiding interference from the socio-economic level; that is, there is a high correlation between housing prices and the tendency of parks. (3) Green space resources Gini coefficient analysis shows that residents of the three modes of transportation in the study area have unequal opportunities to enjoy park and green space services, and the degree of unfairness in walking is much greater than that in cycling and cycling.

Keywords: parks and green spaces, environmental justice, two step mobile search method, Gini coefficient, spatial distribution

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Authors: A. Ashraff

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The increasing importance of the web as a medium for electronic and business transactions has served as a catalyst or rather a driving force for the introduction and implementation of recommender systems. Recommender Systems play a major role in processing and analyzing thousands of data rows or reviews and help humans make a purchase decision of a product or service. It also has the ability to predict whether a particular user would rate a product or service based on the user’s profile behavioral pattern. At present, Recommender Systems are being used extensively in every domain known to us. They are said to be ubiquitous. However, in the field of recruitment, it’s not being utilized exclusively. Recent statistics show an increase in staff turnover, which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements, and pay scale. Further investigations revealed that there are lacking guidance or support, which helps a job seeker find the company that will suit him best, and though there’s information available about companies, job seekers can’t read all the reviews by themselves and get an analytical decision. In this paper, we propose an approach to study the available review data on IT companies (score their reviews based on user review sentiments) and gather information on job seekers, which includes their Psychometric evaluations. Then presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker. The theoretical approach, Algorithmic approach and the importance of such a system will be discussed in this paper.

Keywords: psychometric tests, recommender systems, sentiment analysis, hybrid recommender systems

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Zahid Shafait, Jiayu Huang

Abstract:

Purpose: This study is based on a trait-based theory of emotional intelligence. This study intends to explore the enabling effect of organizational climate, i.e., affiliation, innovation, and fairness, on the emotional intelligence of teachers in Chinese higher education institutes. This study, additionally, intends to investigate the direct impact of teachers’ emotional intelligence on their learning outcomes, i.e., cognitive, social, self-growth outcomes and satisfaction with the university experience. Design/methodology/approach: This study utilized quantitative research techniques to scrutinize the data. Moreover, partial least squares structural equation modeling, i.e., PLS-SEM, was used to assess the hypothetical relationships to conclude their statistical significance. Findings: Results confirmed the supposed associations, i.e., the organizational climate has an enabling effect on emotional intelligence. Likewise, emotional intelligence was concluded to have a direct and positive association with learning outcomes in higher education. Practical implications: This study has investigated abandoned research that is enabling the effects of organizational climate on teachers’ emotional intelligence in Chinese higher education. Organizational climate enables emotionally intelligent teachers to learn efficiently and, at the same time, augments their satisfaction and productivity within an institution. Originality/value: This study investigated the enabling effects of organizational climate on teachers’ emotional intelligence in Chinese higher education that is original in investigated country and sector.

Keywords: organizational climate, emotional intelligence, learning outcomes, higher education

Procedia PDF Downloads 44

Authors: Luay Alsalmi

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Background: Facial filler is the most common type of cosmetic surgery next to botox. Permanent filler is preferred nowadays due to the low cost brought about by non-recurring injection appointments. However, such fillers pose a higher risk for complications, with even greater adverse effects when the procedure is done using unknown dermal filler injections. AIM: This study aimed to establish an algorithm to categorize and manage patients that receive permanent fillers. Materials and Methods: Twelve participants were presented to the service through emergency or as outpatient from November 2015 to May 2021. Demographics such as age, sex, date of injection, time of onset, and types of complications were collected. After examination, all cases were managed based on an algorithm established. FACE-Q was used to measure overall satisfaction and psychological well-being. Results: The algorithm to diagnose and manage these patients effectively with a high satisfaction rate was established in this study. All participants were non-smoker females with no known medical comorbidities. The algorithm presented determined the treatment plan when faced with complications. Results revealed high appearance-related psychosocial distress was observed prior to surgery, while it significantly dropped after surgery. FACE-Q was able to establish evidence of satisfactory ratings among patients prior to and after surgery. Conclusion: This treatment algorithm can guide the surgeon in formulating a suitable plan with fewer complications and a high satisfaction rate.

Keywords: facial filler, FACE-Q, psycho-social stress, botox, treatment algorithm

Procedia PDF Downloads 57

Authors: Ali Momeni

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Visual media has had a significant impact on the mental structure and behaviors of humanity. One interactive platform that has played a major role in this is Instagram. In Islamic countries, particularly Iran, many Muslims have embraced this interactive media platform for various reasons. Instagram has also provided an opportunity for individuals to become famous and gain micro-celebrity status through its semi-algorithmic features. A notable group of Iranian women who have gained fame through Instagram are religious Muslim women who have transitioned into bloggers. These Iranian religious women bloggers (IRWB) have garnered a large following by showcasing different models of hijab and their private lives. This research aims to qualitatively study the representation of femininity and religiosity of these women. The main question addressed in this study is the acceptance of Instagram activity by IRWB among religious women. Drawing on concepts such as 'The Society of the Spectacle' and 'Celebrity Online', this study utilized the netnography method to analyze 14 pages of IRWB. Data was collected in two phases, with the first phase involving the analysis of religious women's comments on posts related to these themes. The second phase included interviews with religious women students who view or follow these pages. A total of 120 comments and 14 interviews were thematically analyzed. The results revealed that the reception of these pages by religious women fell into four main themes: the spectacle of femininity, the commercialization of religiosity, the distortion of Islam, and the construction of religiosity and femininity. Ultimately, religious women did not find these pages to be reflective of their own experiences of female and religious life.

Keywords: women, bloggers, instagram, IRWB, reception.

Procedia PDF Downloads 37

Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

Procedia PDF Downloads 107

Authors: Todd Zhou, Mikhail Yurochkin

Abstract:

Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

Procedia PDF Downloads 101