AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using graph analysis methods. Then, the users’ behavioral patterns are analyzed by applying metadata analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.

Current Problems of Financial Law in Poland and in the Czech Republic Including Effects of the COVID-19 Pandemic

It should be clearly stated that current pandemic of the COVID-19 virus has significantly impacted the public finances of many countries and considerably influenced the functioning of world’s economy. Allocation of public resources to prevent, or counteract, negative effects of the pandemic has taken various forms. Regardless of the extraordinariness of this situation, the possibility to use aid instruments depends on legislative changes and, thus, on the prior passing of appropriate legal provisions, since they determine the rules based on which these instruments are implemented. Poland, and the Czech Republic, have taken proper actions to combat the COVID-19 pandemic. Referring to the experience of both of these countries, it should be noted that legal and financial solutions used to counteract the pandemic have not always been conducted in accordance with constitutional norms, established financial law rules, or principles of conducting financial economy in the public finance sector. The Authors of this article, while evaluating these solutions, have decided to indicate certain general trends happening in the current financial law, which, unfortunately, are not always positive.

نموذج استراتيجي للخصخصة الناجحة

The importance of this research arises from its attempt to correct the weaknesses encountered by most developing countries namely The Republic of Sudan due to the absence of strategies that set leading guidelines for successful privatization processes. This research provides a full answer to a crucial question] How can successful privatization be achievedin developing countries? [The objective of this research extends to help privatization decision makers to avoid problems arising from application, as well as enhancing privatization programs by offering strong support to achieve their targets. Themethodology adopted by this research is a combination of many research methodologies such as descriptive, analytical, comparative, inductive and deductive approaches. The research findings are many, namely privatization is a complicated phenomenon, andat the same time, represents a junction where interests of conflicting parties meet corruption & cronyism that may accompany the execution of privatization has aggravated more economic & social problems. The recommendations proposed by this research include, more attention should be paid to promote awareness (among the top executive authorities, senior government staff & privatization process executors) to the risks associated with improper designing & incorrect implementation of privatization. Support and follow-up from the top government authorities to privatization programs will help minimize the problems that might hinder application. Governments to provide their countries with thenecessary prerequisites for successful privatization should adopt proper actions

Hecke operators in Bredon (co)homology, K-(co)homology and Bianchi groups

In this paper, we provide a framework for the study of Hecke operators acting on the Bredon (co)homology of an arithmetic discrete group. Our main interest lies in the study of Hecke operators for Bianchi groups. Using the Baum–Connes conjecture, we can transfer computations in Bredon homology to obtain a Hecke action on the [Formula: see text]-theory of the reduced [Formula: see text]-algebra of the group. We show the power of this method giving explicit computations for the group [Formula: see text]. In order to carry out these computations we use an Atiyah–Segal type spectral sequence together with the Bredon homology of the classifying space for proper actions.

Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan

In recent years, air pollution and climate change issues have pushed people worldwide to switch to using electric vehicles (EVs) instead of gas-driven vehicles. Unfortunately, most distribution system facilities are neither designed nor well prepared to accommodate these new types of loads, which are characterized by random and uncertain behavior. Therefore, this paper provides a comprehensive investigation of EVs’ effect on a realistic distribution system. It provides a technical evaluation and analysis of a real distribution system’s load and voltage drop in the presence of EVs under different charging strategies. In addition, this investigation presents a new methodology for managing EV loads under a dynamic response strategy in response to the distribution system’s critical hours. The proposed methodology is applied to a real distribution network, using the Monte Carlo method and the CYME program. Random driver behavior is taken into account in addition to various factors that affect EV load parameters. Overall, the results show that the distribution system is significantly affected by the addition of EV charging loads, which create a severe risk to feeder limits and voltage drop. However, a significant reduction in the impact of EVs can be achieved if a proper dynamic demand response programme is implemented. We hope that the outcomes of this investigation will provide decision-makers and planners with prior knowledge about the expected impact of using EVs and, consequently, enable them to take the proper actions needed to manage such load.

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

Artificial Intelligence for Healthcare in India

The purpose of the paper is to provide an overview of the issues related to Artificial Intelligence (AI) applications in the Indian healthcare sector and provide input to policy makers. A qualitative approach has been used in this study to identify government initiatives, opportunities and challenges for applications of AI. , and suggests improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges and recommendations for improving healthcare system of India using different applications of AI.

Artificial Intelligence in the Context of Global Resource Mobility. What Can Be Expected from It?

The impact of artificial intelligence (AI) on business, government, and society is getting more attention. The leading AI sectors have higher productivity but a lower share of GDP than those lagging in digitization and AI. There is a technological gap, with still unknown consequences concerning the social contract, the expected new digital welfare profile, as well as the business strategy about globalization. The hypothesis is that while digitization was already in motion (2000–2005), capital outflow from the US to MHGEs (market high-growth economies) in Asia negatively affected its productivity outcome. Additionally, it is expected that AI will give more market power to multinationals, reshaping the social contract. Thus, the current western social contract will no longer be able to cope with the consequences of the weakness of the nation-state, its policymakers, or the powerful profit-driven multinationals to deal with the overall effect of AI. We aim to look at the impact of this new state of technology on the social contract, focusing on the proper actions of government and business to deal with it. We used a descriptive approach based on desk research concerning productivity data, European government policies, trade model analysis, and business approach to AI. We expect to demonstrate the dynamic interaction of the K/L ratio within the prevailing status of global resources mobility, and the dangers unregulated AI represents to labor. Policy actions are needed concerning the legal status of AI and how to avert the collapse of the social contract and the rise of oligarchic cyber‑autocracies. Our general conclusion is as follows: While capital investments, which would have contributed to improved total factor productivity (TFP) in the USA, went to MHGEs, increasing their GDP growth in less than a decade, the broad use of Artificial Intelligence (AI) will reverse massive offshoring, and new types of manufacturing processes will emerge in developed countries.

Women’s autonomy in refusing risky sex and associated factors in Ethiopia: evidence from 2011 to 2016 EDHS data

Background Risky sexual behavior is a major public health concern of Ethiopians. Although studying the autonomy of women in refusing risky sex is significant to take proper actions, the issue is not yet studied. Accordingly, this population-based nationwide study was aimed at assessing women’s autonomy in refusing risky sex and its associated factors in Ethiopia. Method The sample was limited to married women of 2011 (n = 8369) and 2016 (n = 8403) Ethiopian Demographic and Health Survey data. Women's autonomy in refusing risky sex was measured based on wives' response to 'not having sex because husbands have other women. To examine associated factors, socio-demographic variables were computed using binary logistic regression. Result The finding revealed that the trend of women’s autonomy in refusing risky sex had declined from 78.9% in 2011 to 69.5% in 2016. Women aged from 25 to 34 were less likely autonomous in refusing sex compared to those who aged less than 24 years old (AOR = .7064; 95% CI 0.605, 0.965). The autonomy of women with higher educational status was three times more likely higher than those who have no formal education (AOR = 3.221; 95% CI 1.647, 6.300 respectively. The autonomy of women who are from rich households was more likely higher in comparison to women from poor households (AOR = 1.523; 95% CI 1.28, 1.813). The autonomy of women those who live in Tigray 2.9 times (AOR = 2.938; 95% CI 2.025, 4.263), Amhara 4.8 times (AOR = 4.870; 95% CI 3.388, 7.000), SNNP 1.9 times (AOR = 1.900; 95% CI 1.355, 2.664), and Addis Ababa 3.8 times (AOR = 3.809; 95% CI 2.227, 6.516) more likely higher than those who reside in Dire Dawa. Conclusion The autonomy of women in refusing risky sex has declined from 2011 to 2016. This infers that currently, women are more victimized than previously. Hence, possible interventions like empowering women shall be taken to protect women from certain health problems of risky sexual behavior.