Search results for: legal judgment prediction

Authors: Michael Sunday Afolayan

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This research work examined the challenges bedevilling the development of legal framework for electronic commercial transactions (e-commerce) in Nigeria. Nigeria does not have a clear-cut legislation regulating electronic commerce in its jurisdiction despite the geometrical rate of growth and adoption of this method of trade. It specifically posed a great challenge looking at taxation, evidential and jurisdictional issues in e-commerce in Nigeria. The author in a broader research work which is abridged here, traced the origin and development of e-commerce and the attendant laws applicable in Nigeria, examining their sufficiency or otherwise. In carrying out the research work, doctrinal mode of legal research was adopted, examining both primary and secondary sources of legal research materials within their contextual meanings. It was found that the failure to enact a law which has direct regulatory bearing on e-commerce in Nigeria has led to adoption and application of circumstantial laws, rules and common law principles to tackle the problems arising out of electronic commercial transactions, especially in the areas of taxation, evidential and jurisdictional challenges. It was ultimately suggested that there is urgent need to sign into law, the Electronic Transaction Bill which had already been passed by the National Assembly since 2017.

Keywords: e-commerce, legislation, taxation, evidential, jurisdiction

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Tecla Rita Karendi, Andy Cons Matata

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Although before and at the advent of colonial administration, several women such as Mekatilili wa Menza and Muthoni Nyanjiru took up leadership positions in resisting the colonial administration. Kenya is generally considered a patriarchal society. Many women who tried to take up positions of leadership in postcolonial Kenya, such as the Nobel Prize winner Wangari Maathai, were branded as prostitutes or generally immoral women. However, the Constitution of Kenya, 2010, has since made a huge impact not only in the area of affirmative action but also in various aspects of the feminist legal theory such as the constitutional requirement that no more than two-thirds of the members of the elective or appointive bodies should be of the same gender. This favours women who are often sidelined in elective posts such as parliament or county assemblies and state-appointed posts in the parastatals and commissions. The constitution also recognizes the right to abortion, which was outrightly outlawed in the independence constitution. Certain practices adverse to women’s health, such as wife inheritance, female genital mutilation, and property rights, are either outlawed or framed to recognized women’s rights. The education of the girl-child is also now considered a priority, unlike in the past. Despite these developments, a lot remains to be done.

Keywords: feminist legal theory, constitution of Kenya, 2010, affirmative action, leadership

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Authors: Pouira Askary

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Indeed, in our globalized world which is facing with various international crises, the transnational corporations and other business enterprises have the capacity to foster economic well-being, development, technological improvement and wealth, as well as causing adverse impacts on human rights. The UN Human Rights Council declared that although the primary responsibility to protect human rights lie with the State but the transnational corporations and other business enterprises have also a responsibility to respect and protect human rights in the framework of corporate social responsibility. In 2011, the Human Rights Council endorsed the Guiding Principles on Business and Human Rights, a set of guidelines that define the key duties and responsibilities of States and business enterprises with regard to business-related human rights abuses. In UN’s view, the Guiding Principles do not create new legal obligations but constitute a clarification of the implications of existing standards, including under international human rights law. In 2014 the UN Human Rights Council decided to establish a working group on transnational corporations and other business enterprises whose mandate shall be to elaborate an international legally binding instrument to regulate, in international human rights law, the activities of transnational corporations and other business enterprises. Extremely difficult task for the working group to codify a legally binding document to regulate the behavior of corporations on the basis of the norms of international law! Concentration of this paper is on the origins of those human rights applicable on business enterprises. The research will discuss that the social and ethical roots of the CSR are much more institutionalized and elaborated than the legal roots. Therefore, the first step is to determine whether and to what extent corporations, do have an ethical responsibility to respect human rights and if so, by which means this ethical and social responsibility is convertible to legal commitments.

Keywords: CSR, ethics, international law, human rights, development, sustainable business

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: Harriet Kebirungi, Majaliwa Jackson-Gilbert Mwanjalolo, S. Livingstone Luboobi, Richard Joseph Kimwaga, Consolata Kabonesa

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This paper assessed gender responsiveness of water and sanitation policies and legal frameworks at Makerere University, Uganda. The objectives of the study were to i) examine the gender responsiveness of water and sanitation related policies and frameworks implemented at Makerere University; and ii) assess the challenges faced by the University in customizing national water and sanitation policies and legal frameworks into University policies. A cross-sectional gender-focused study design was adopted. A checklist was developed to analyze national water and sanitation policies and legal frameworks and University based policies. In addition, primary data was obtained from Key informants at the Ministry of Water and Environment and Makerere University. A gender responsive five-step analytical framework was used to analyze the collected data. Key findings indicated that the policies did not adequately address issues of gender, water and sanitation and the policies were gender neutral consistently. The national policy formulation process was found to be gender blind and not backed by situation analysis of different stakeholders including higher education institutions like Universities. At Makerere University, due to lack of customized and gender responsive water and sanitation policy and implementation framework, there were gender differences and deficiencies in access to and utilization of water and sanitation facilities. The University should take advantage of existing expertise within them to customize existing national water policies and gender, and water and sanitation sub-sector strategy. This will help the University to design gender responsive, culturally acceptable and environmental friendly water and sanitation systems that provide adequate water and sanitation facilities that address the needs and interests of male and female students.

Keywords: gender, Makerere University, policies, water, sanitation

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Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Rehan Waheed, Abdul Shakoor

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Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Abdelkader Hocine, Abdelhakim Maizia

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The exponential growth of reinforced fibers composite materials use has prompted researchers to step up their work on the prediction of their reliability. Owing to differences between the properties of the materials used for the composite, the manufacturing processes, the load combinations and types of environment, the prediction of the reliability of composite materials has become a primary task. Through failure criteria, TSAI-WU and the maximum stress, the reliability of multilayer tubular structures under pressure is the subject of this paper, where the failure probability of is estimated by the method of Monte Carlo.

Keywords: composite, design, monte carlo, tubular structure, reliability

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Authors: Yelba Maria Carrillo

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The focus of this research study was to explore barriers, if any, faced by parents or legal guardians who are of Latinx background and speak Spanish as a primary language or are bilingual speakers of Spanish and English; barriers that limit their understanding of and involvement in their gifted child’s academic life. This study was guided by a qualitative case study design. The primary investigator hosted focus group interviews at a Magnet Middle School in Southern California. The groups consisted of 25 parents, or legal guardians of bilingual (English/Spanish) or former English learner students enrolled in a school serving 6th-8th grades. The primary investigator interviewed Latinx Spanish-speaking parents or legal guardians of gifted students regarding their perception of their child’s giftedness, parental involvement in schools, and fostering their child’s exceptional abilities. Parents and legal guardians described children as creative, intellectual, and highly intelligent. Key themes such as student performance, language proficiency, socio-emotional, and general intellectual ability were strong indicators of giftedness. Barriers such as language and education inhibited parent and legal guardian ability to understand their child’s giftedness, which resulted in their inability to adequately contribute to the development of their children’s talents and advocate for the appropriate services for their children. However, they recognized the importance of being involved in their child’s academic life and the importance of nurturing their ‘dón’ or ‘gift.’ La Familia is the foundation and core of Latinx culture; and, without a strong foundation, children lack guidance, confidence, and awareness to tap into their gifted abilities. Providing Latinx parents with the proper tools and resources to appropriately identify gifted characteristics and traits could lead to early identification and intervention for students in schools and at home.

Keywords: gifted education, gifted Latino students, Latino parent involvement, high ability students

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Authors: Renan De Caldas Honorato, Ana Augusta Ferreira De Freitas

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Homosexuality is still criminalized in a large number of countries. In some of them, being gay or lesbian can even be punished by death. Added to this context, the experiences of social discrimination faced by the LGBT population, including homophobia, machismo and misogyny, cause numerous restrictions throughout their lives. The possibility of confronting these challenges in moments that should be pleasant, such as on a trip or on vacation, is unpleasant, to say the least. In the current scenario of intensifying the use of Social network sites (SNSs) to search for information, including in the tourist area, this work aims to analyze the sharing of tourist experiences with situations of confrontation and perceptions of homophobia, machismo and misogyny, and restrictions suffered in tourist destinations. The fieldwork is a community of LGBT backpackers based on Facebook. Netnography was the core method adopted. A qualitative approach was conducted and 463 publications posted from January to December 2020 were assessed through the computer-mediated discourse analysis (CMDA). The results suggest that these publications exist to identify the potential exposure to these offensive behaviors while traveling. Individuals affirm that the laws, positive or not, in relation to the LGBT public are not the only factors for a place to be defined as safe or not for gay travelers. The social situation of a country and its laws are quite different and this is the main target of these publications. The perception of others about the chosen destination is more important than knowing your rights and the legal status of each country and it also lessens uncertainty, even when they are never totally confident when choosing a travel destination. In certain circumstances, sexual orientation also needs to be protected from the judgment of hosts and residents. The systemic treatment of homophobic behavior and the construction of a more inclusive society are urgent.

Keywords: homophobia, hospitality, machismo, misogyny

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Authors: Yuliya Leonidovna Kiva-Khamzina

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The legal nature of the contract for educational services causes a lot of controversies. In particular, it raises the question about industry sector relationships, which require making a contract for educational services. The article describes the different types of contracts classifications for services provision from the perspective of civil law, deals with the specifics of the contract on rendering educational services; the author makes the conclusion that the contract for the provision of educational services is a complex institution that includes elements of the civil and administrative law. The following methods were used to conduct the study: dialectical method of cognition, the historical method, systemic analysis, classification.

Keywords: administrative aspect, civil aspect, educational service, industry, legal nature, services provision

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Authors: Ejeh Benjamin Ijuo

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The purpose of this study was to investigate the influence of social, economic, political, and legal environment of sport organizations on sport development in zone ten (10) national zonal sport offices in Nigeria (Plateau, Nasarawa, Benue and F.C.T Abuja). To achieve this purpose, a structured 26 item questionnaire (ISEPLESOQ) designed by the researcher was used for this study. Related literature to this study was reviewed. 311 copies of questionnaire were administered to randomly selected respondents. Out of this number, 306 was dully completed and returned representing 98.4%. The respondents included: Athletes, games masters/ mistresses, coaches in state sport councils, zonal sport coordinators, team managers, directors of state sports council. Four research questions were answered using the mean and standard deviation, while the inferential statistics of chi-square(x2) test of goodness of fit was used to test the four hypotheses at 0.05 alpha levels. The findings of this study revealed that the social, economic, political and legal environment of sport organizations significantly influenced sport development in zone ten (10) national zonal sport offices in Nigeria. It was also established that the general environment of sport organizations influences people’s participation in sport, funding and sponsorship of sports, sitting of equipment and facilities at different locations, selection of athletes. It was therefore, recommended among other things that government should privatize and commercialized sport programmes to enable corporate organizations and individuals participation. Lt was further suggested that the federal government should harness her social, economic, political and legal environment to improve sport development in Nigeria.

Keywords: sport organization, sport development, sport environment, zonal sport offices

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Kamalendu Pal

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This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a combination of linguistic variables, fuzzy numbers, and by using defuzzification process. The results show that the designed evaluation method creates suitable mechanism in order to improve the performance of the knowledge-based system.

Keywords: case-based reasoning, fuzzy number, legal decision-support system, linguistic variable, rule-based reasoning, system evaluation

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Authors: Monika Zalewska

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Hans Kelsen was a versatile legal thinker whose achievements in the fields of legal theory, international law, and the theory of democracy are remarkable. All of the fields tackled by Kelsen are regarded as part of his “pure theory of law.” While the link between international law and Kelsen’s pure theory of law is apparent, the same cannot be said about the link between the theory of democracy and his pure theory of law. On the contrary, the general thinking concerning Kelsen’s thought is that it can be used to legitimize authoritarian regimes. The aim of this presentation is to address this concern by identifying the common ground between Kelsen’s pure theory of law and his theory of democracy and to show that they are compatible in a way that his pure theory of law and authoritarianism cannot be. The conceptual analysis of the purity of Kelsen’s theory and his goal of creating ideology-free legal science hints at how Kelsen’s pure theory of law and the theory of democracy are brought together. The presentation will first demonstrate that these two conceptions have common underlying values and meta-ethical convictions. Both are founded on relativism and a rational worldview, and the aim of both is peaceful co-existence. Second, it will be demonstrated that the separation of law and morality provides the maximum space for deliberation within democratic processes. The conclusion of this analysis is that striking similarities exist between Kelsen’s legal theory and his theory of democracy. These similarities are grounded in the Enlightenment tradition and its values, including rationality, a scientific worldview, tolerance, and equality. This observation supports the claim that, for Kelsen, legal positivism and the theory of democracy are not two separate theories but rather stem from the same set of values and from Kelsen’s relativistic worldview. Furthermore, three main issues determine Kelsen’s orientation toward a positivistic and democratic outlook. The first, which is associated with personality type, is the distinction between absolutism and relativism. The second, which is associated with the values that Kelsen favors in the social order, is peace. The third is legality, which creates the necessary condition for democracy to thrive and reveals that democracy is capable of fulfilling Kelsen’s ideal of law at its fullest. The first two categories exist in the background of Kelsen’s pure theory of law, while the latter is an inherent part of Kelsen’s concept of law. The analysis of the text concerning natural law doctrine and democracy indicates that behind the technical language of Kelsen’s pure theory of law is a strong concern with the trends that appeared after World War I. Despite his rigorous scientific mind, Kelsen was deeply humanistic. He tried to create a powerful intellectual weapon to provide strong arguments for peaceful coexistence and a rational outlook in Europe. The analysis provided by this presentation facilitates a broad theoretical, philosophical, and political understanding of Kelsen’s perspectives and, consequently, urges a strong endorsement of Kelsen’s approach to constitutional democracy.

Keywords: hans kelsen, democracy, legal positivism, pure theory of law

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Authors: L. Edirisinghe, Z. Jin, A. W. Wijeratne, R. Mudunkotuwa

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Empty container repositioning is a fundamental problem faced by the shipping industry. The virtual container yard is a novel strategy underpinning the container interchange between carriers that could substantially reduce this ever-increasing shipping cost. This paper evaluates the shipping industry perception of the virtual container yard using chi-square tests. It examines if the carriers perceive that the selected independent variables, namely culture, organization, decision, marketing, attitudes, legal, independent, complexity, and stakeholders of carriers, impact the efficiency and benefits of the virtual container yard. There are two major findings of the research. Firstly, carriers view that complexity, attitudes, and stakeholders may impact the effectiveness of container interchange and may influence the perceived benefits of the virtual container yard. Secondly, the three factors of legal, organization, and decision influence only the perceived benefits of the virtual container yard. Accordingly, the implementation of the virtual container yard will be influenced by six key factors, namely complexity, attitudes, stakeholders, legal, organization and decision. Since the virtual container yard could reduce overall shipping costs, it is vital to examine the carriers’ perception of this concept.

Keywords: virtual container yard, imbalance, management, inventory

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Pujiyono, Reda Manthovani, Deny Tri Ardianto, Rabani Halawa, Isharyanto

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This research has background that the taxpayer (defendant) who has paid off the tax payable and the tax penalty payable after the tax case file has been transferred to the court, while the legality of stopping the prosecution of tax cases on the grounds that in the interest of state revenue is not regulated in the provisions of Law Number 8 of 1981 concerning The Criminal Procedure Code and Law Number 28 of 2007 concerning the Third Amendment to Law Number 6 of 1983 concerning General Provisions and Tax Procedures as amended several times, most recently by Law Number 16 of 2009 concerning Stipulation of Government Regulation in Lieu of Law Number 5 of 2008 concerning Fourth Amendment to Law Number 6 0f 1983 concerning General Provisions and Tax Procedures to become Law, even though at the investigation stage it regulates the mechanism for stopping the investigation for the sake of the interest of acceptance ne this is because before the case file is transferred to the court where at the request of the Minister of Finance of The Republic of Indonesia can stop the investigation in the interest of state revenue so that based on this phenomenon a legal vacuum is found. Therefore, a non-penal policy is needed from the public prosecutor to resolve tax crime cases without going through litigation in court through the penal mediation method using the Plea Bargaining System which adheres to the principles of restorative justice and obligative justice based on the ultimum remedium principle and the principle of opportunity in order to realize the principle of fast, simple and low cost justice (content principle). This research is a normative legal research, using a statutory approach, conceptual approach, and comparative law approach. Regulations that is used in many countries, include America, The Netherlands and Singapore. The results of this study indicate that there is a reformulation of the tax criminal justice system which regulates the mechanism, qualifications and authority to terminate the prosecution of tax cases in the interest of state revenues in order to achieve legal goals which are not only for legal certainty but more that, namely providing benefits and legal justice for people seeking justice.

Keywords: obligative justice, regulation, state reveneus, tax criminal

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: T. Holecki, J. Wozniak-Holecka, K. Sobczyk

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Development, implementation, and evaluation of the effects of health policy programs, resulting from the identified health needs and health status of residents, is the own task of all local government units in Poland. This is due to the obligation to provide access to healthcare services to all residents and the implementation of tasks in the field of health promotion based on specific legal acts. Until the end of 2016 local governments financed health policy programs only with their own funds. Currently, there are additional resources available from the public health insurance subsidising up to 80% of health policy programs costs in cities with a population under 5 thousand people and up to 40% in bigger cities. Changes in legal provisions do not translate automatically to increased involvement of local government units in the implementation of public health tasks. The main objective of the study was to assess the actual impact of the new legal regulation on financing local health policy programs on the engagement of local administration in this area of public health activity. To achieve this aim, we analyzed difference in the number of local governments developing and implementing health policy programs before and after the new law came into force. The aim of the study was also to estimate the level of expenditures incurred by self-government units and the National Health Fund to cover the costs of health policy programs. In the first stage of the project, legal acts concerning the subject of research and financial data published by the National Health Fund were analyzed. The material for the second, main stage of the study was the detailed financial data obtained from the National Health Fund and data obtained from local government units. The results present the situation in Poland in territorial terms, divided into 16 voivodships.

Keywords: health care system, health policy programs, local self-governments, public health

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Authors: Cristian Păuna

Abstract:

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: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Rahmi Kopar

Abstract:

Legitimate expectations principle is considered one of the most dominant elements of the Fair and Equitable Treatment Standard which is today’s most relied upon treaty standard. Since its utilization by arbitral tribunals is relatively new, the contours of the legitimate expectations concept under investment treaty law have not been precisely defined yet. There are various fragmented views arising both from arbitral tribunals and scholarly writings with respect to its limits and use even though the principle is ‘firmly rooted in arbitral practice.’ International energy investments, due to their characteristics, are more prone to certain types of risks, especially the political risks. Thus, there are several mechanisms to protect an energy investment against those risks. Stabilisation is one of these investment protection methods. Stability provisions can be found under domestic legislations, as a contractual clause, or as a separate legal stability agreement. This paper will start by examining the roots of the contentious concept of legitimate expectations with reference to its application in domestic legal systems from where the doctrine under investment treaty law context was transplanted. Then the paper will turn to the investment treaty law and analyse the main contours of the doctrine as understood and applied by arbitral tribunals. 'What gives rise to the investor’s legitimate expectations?' question is answered mainly by three categories of sources: the general legal framework prevalent in a host state, the representations made by the officials or organs of a host state, and the contractual commitments. However, there is no unanimity among the arbitral tribunals and the scholars with respect to the form these sources should take. At this point, the study will discuss the sources of a stability provision and the effect of these stability provisions found in various legal sources in creating a legitimate expectation for the investor. The main questions to be discussed in this paper are as follows: a) Do the stability provisions found under different legal sources create a legitimate expectation on the investor side? b) If yes, what levels of legitimate expectations do they create? These questions will be answered mainly by reference to investment treaty jurisprudence.

Keywords: fair and equitable treatment standard, international energy investments, investment protection, legitimate expectations, stabilization

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Authors: Nisar Alungal Chungath

Abstract:

“No human being is illegal" has become a dictum strongly upheld in the context of global immigration and migration, highlighting the ethical and moral dimensions of how societies and governments treat individuals and communities who have crossed political borders or are living in a country without legal authorization. It seeks to shift the focus from categorizing human beings as illegal immigrants to recognizing their inherent human rights and the complexities of their circumstances. As a complex social phenomenon, law has been a crucial instrument in shaping, regulating and governing human societies and vice versa. The law has now become a humongous political project of the modern majoritarian regimes to democratically illegitimize and illegalize the unpopular sections and minorities. Drawing from the theoretical frameworks of dialectics, the paper explores the philosophical underpinnings of the historical evolution and dynamic nature of modern law. The paper employs a phenomenological approach to analyze the dialectical relations between individuals, societies, and legal systems, aiming to shed light on the ethical and political implications of these interactions. By examining the historical essence of law, its relationship with social and cultural norms, and the role of power dynamics, this article argues for constantly maintaining the dialectics of law—the dynamic interplay between legal norms, social practices, cultural values, and historical contexts through a philosophical and phenomenological lens, in order to bridge the gap between universal principles and particular contexts. The paper will shed light to the dialectics of the law in the context of instances of the legal persecutions of the modern secular democracies such as Citizenship Amendment Act-2019, India.

Keywords: phenomenology, dialectic, modern law, politics, resistance, margins

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

Abstract:

This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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