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From Text To Multimodal: a Survey of Adversarial Example Generation in Question Answering Systems
No full textYigit, Gulsum/0000-0001-7010-169XIntegrating adversarial machine learning with question answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.Scientific and Technological Research Council of Turkiye(TUB ITAK)Open access funding provided by the Scientific and Technological Research Council of Turkiye(TUB ITAK).Science Citation Index Expande
elektrikli Araç Sarj Istasyonlarının Verimli Konumlandırılması için Kümeleme ve Matematiksel Optimizasyon Yaklaşımı ile Yer Tespiti
No full textIEEE SMC; IEEE Turkiye SectionThe increasing effects of global warming have led to a shift to more environmentally friendly fuels. As electric vehicles become more popular in Türkiye, the demand for charging stations has also increased. However, charging stations are not able to meet demand, hence there is no strategically located charging network. In this study, a prototype for the optimal placement of electric vehicle charging stations is developed using analytical and mathematical approaches such as clustering and mathematical modeling, and Kocaeli province of Türkiye is selected as the prototype city. A preliminary survey was designed to better understand the needs and preferences of electric vehicle users. Supported by an extensive literature review, this research collected critical data on the most important criteria for the construction of EV charging stations and created a dataset by applying a systematic and iterative selection process. Various clustering methods were applied to this dataset and the K-Means algorithm achieved the highest score. With the K-Means algorithm, the data were divided into three clusters and classified as good, medium and poor according to the survey results and distribution. Using the developed clustering model, predictions were made for 50 coordinates where charging stations are planned to be installed. The 22 coordinates that were rated as good and medium by the estimation were selected for further mathematical analysis. The mathematical model with the most critical constraints aimed to maximize the number of users. The solution consists of three phases, with each phase allowing only one installation per region. At each stage, locations from previous stages were removed from the model and rerun with updated utilization scores. With the mathematical model, the most suitable charging station locations were determined within 22 coordinates. © 2024 IEEE
Understanding the Immediate and Longitudinal Effects of Emotion Reactivity and Deviation From the Balanced Time Perspective on Symptoms of Depression and Anxiety: Latent Growth Curve Modeling
No full textAbdollahpour Ranjbar, Hamed/0000-0002-2923-5829Emotion reactivity (ER) captures the depth, sensitivity, and endurance of our emotional reactions, while deviation from a balanced time perspective (DBTP) characterizes our inflexibility and rigidity in adhering to specific time frames. This study investigates how ER and DBTP might predict the symptoms of depression and anxiety and DBTP's mediating role between ER and the symptoms of anxiety and depression in a three-wave longitudinal investigation. Data from 148 university students (82 males, 55.4%) with the age range of 18-29 (Mage = 19.92, SDage = 1.36) were collected at three time intervals using Emotion Reactivity Scale, Zimbardo Time Perspective Inventory, Future Negative subscale, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9. The study utilized latent growth curve modeling (LGCM) within a structural equation modeling framework. Results showed that greater DBTP at baseline predicted increased anxiety and depression symptoms and longitudinally reduced anxiety symptoms. The mediation model clarified that, initially, DBTP mediated the relationship between ER and anxiety/depression symptoms; however, over time, DBTP functioned as a suppressor of anxiety symptoms. This study establishes DBTP's predictive and dynamic significance for anxiety and depression, unveiling its mediating role in the interplay with emotional reactivity. These findings can inform tailored therapies addressing ER and temporal biases in this population.Social Science Citation Inde
Kurumsal Karbon Emisyonları Mali Kısıtlamaları Etkiler Mi? Gelişmiş Piyasalardan Kanıtlar
No full textBu çalışma kapsamında, karbon emisyonları ile finansal kısıtlamalar arasındaki ilişki ve farklı sahiplik özelliklerinin bu ilişkiyi nasıl etkilediği incelenmektedir. Analizlerde kullanılan örneklem 24 gelişmiş ülkede bulunan 2632 şirketten 2002 ile 2022 yılları arası için toplanan 20,774 şirket-yıl gözlemden oluşmaktadır. Sabit etkiler içeren panel veri tahmin metodolojisini kullanarak yaptığımız analizler, bir firmanın karbon emisyonları ile finansal kısıtları arasında anlamlı ve pozitif ilişki olduğunu ortaya koymaktadır. Sonuçlar, daha yüksek karbon emisyonlarına sahip firmaların daha büyük finansal kısıtlar yaşama olasılığının daha yüksek olduğunu göstermektedir. Ayrıca, farklı mülkiyet özelliklerinin de aracı değişken etkisine dair yeni kanıtlar sunmaktayız: Karbon emisyonlarının finansal kısıtlar üzerindeki pozitif etkisinin, daha fazla kurumsal ve daha fazla yabancı mülkiyete sahip firmalar için daha yüksek olduğunu göstermekteyiz. Buna karşılık, karbon emisyonlarının finansal kısıtlar üzerindeki pozitif etkisinin, daha yüksek devlet mülkiyetine sahip şirketler için daha düşük olduğunu gözlemlenmiştir. Genel olarak, bulgularımız çevresel performansın firmaların finansal kısıtlarını etkileyen önemli bir faktör olduğunu ve firmanın mülkiyet yapısının bu etkide aracı değişken rolü oynadığını göstermektedir
The Applications of Nature-Inspired Algorithms in Internet of Things-Based Healthcare Service: a Systematic Literature Review
No full textHeidari, Arash/0000-0003-4279-8551; zavvar, mohammad/0000-0003-1351-2921Nature-inspired algorithms revolve around the intersection of nature-inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature-inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real-world implementation issues, and the efficacy of nature-inspired algorithms in IoT-based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature-inspired algorithms in IoT-based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature-inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature-inspired algorithms in the dynamic landscape of IoT-based healthcare services. Providing a complete overview of the current issues associated with nature-inspired algorithms in IoT-based healthcare services. Providing a thorough overview of present methodologies for IoT-based healthcare services in research studies; Evaluating each region that tailored nature-inspired algorithms with many perspectives such as advantages, restrictions, datasets, security involvement, and simulation stings; Outlining the critical aspects that motivate the cited approaches to enhance future research; Illustrating descriptions of certain IoT-based healthcare services used in various studies. imag
Sweatshops, Disrespect, and Interference: How To Interfere in Sweatshops Without Disrespecting the Workers
No full textSweatshop defenders argue that interference in sweatshop conditions through consumer activism or government regulations is morally wrong because, first, such acts harm sweatshop workers, and second, they disrespect these workers. Distinguishing the prohibitive aspects of sweatshop interference as harm on the one hand, and disrespect on the other, these sweatshop defenders build both a consequentialist and a deontological foundation for their argument, respectively. This article crafts a rejoinder to the second foundation of the defenders' argument. In particular, the article responds to the defenders against their argument that interference in sweatshop conditions might be morally impermissible because interferers disrespect workers with their activism. The ground of the defended argument is an ex ante interpretation of contractualist ethics.Emerging Sources Citation Inde
Cryptocurrencies as a Means of Payment in Online Shopping
No full textTosun, Petek/0000-0002-9228-8907; Alreshaid, Faisal/0000-0002-3120-7341PurposeCryptocurrencies are becoming increasingly attractive as alternatives to traditional currencies. Although many retailers accept cryptocurrencies as a means of payment in online shopping, consumers' cryptocurrency adoption intention in online shopping (CCAI) is still low. This study aims to investigate the influence of attitudes, subjective norms, consumer trust, financial literacy and fear of missing out (FOMO) on CCAI.Design/methodology/approachA quantitative research approach was followed using a consumer survey. Hypothesized relationships were tested through regression and mediation analyses.FindingsThe results revealed that consumers could accept cryptocurrencies as a means of payment in online shopping. Attitudes, subjective norms, consumer trust and financial literacy directly and positively influence CCAI, while they indirectly affect CCAI through the mediating impact of FOMO.Practical implicationsMarketing managers should improve consumers' knowledge about cryptocurrencies and trust in online shopping to increase CCAI. Social media marketing can be appropriate, while the advertising content can address keeping up with others and staying connected.Originality/valueThis study addresses a critical gap in the literature by empirically examining the antecedents of CCAI within an original conceptual model based on the theoretical framework provided by the theory of planned behavior. Attitudes, subjective norms, trust and financial literacy influence CCAI, where FOMO plays a significant role as a mediator
A Machine Learning Approach To Steel Sheet Production Surface Quality
No full textThis study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE
Social Media and Tax Law
No full textThe tax implications of social media are numerous and highly debated, spanning such issues as the taxation of influencers, digital barter, and digital services taxes. This book offers a detailed overall analysis of the tax implications of social media, taking into consideration the unique characteristics of social media platforms and companies. Offering a comprehensive overview of tax law as it relates to the specificities of social media, the book examines taxation of influencers, taxation of social media companies, value added tax implications of the digital barter, the role that can be played by Pigouvian taxes in the field of social media, as well as the employment of social media as a tool for tax compliance.Widespread use of social media along with the proliferation of new social media platforms demonstrate the importance of social media tax law, and this book will be an important resource for tax administrations, lawyers, and researchers. © 2024 Alara Efsun Yazıcıoğlu.All rights reserved
A Topology Detector Based Power Flow Approach for Radial and Weakly Meshed Distribution Networks
No full textPower distribution networks may need to be switched from one radial configuration to another radial structure, providing better technical and economic benefits. Or, they may also need to switch from a radial configuration to a meshed one and vice-versa due to operational purposes. Thus the detection of the structure of the grid is important as this detection will improve the operational efficiency, provide technical benefits, and optimize economic performance. Accurate detection of the grid structure is needed for effective load flow analysis, which becomes increasingly computationally expensive as the network size increases. To perform a proper load flow analysis, one has to build the distribution load flow (DLF) matrix from scratch cost of which is unavoidable with the growing size of the network. This will considerably increase the computation time when the system size increases, compromising applicability in online implementations. In this study we introduce a novel graph-based model designed to rapidly detect transitions between radial and weakly meshed systems. By leveraging the characteristic properties of Sparse Matrix-Vector product (SpMV) operations, we accelerate power flow calculations without necessitating the complete reconstruction of the DLF matrix. With this approach we aim to reduce the computational costs and to improve the feasibility of near-online implementations.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Department for Business, Energy and Industrial Strategy and Scientific and Technological Research Council of Türkiye; Newton Fund, NF, (623801791); Newton Fund, NF; British Council, (120N996); British CouncilNewton Fund Institutional Links under the Newton-Katip Celebi Fund Partnership [623801791]; U.K. Department for Business, Energy and Industrial Strategy and Scientific and Technological Research Council of Turkiye (TUBITAK) - British Council [120N996]This work was supported in part by the Newton Fund Institutional Links under the Newton-Katip Celebi Fund Partnership under Grant 623801791; and in part by the U.K. Department for Business, Energy and Industrial Strategy and Scientific and Technological Research Council of Turkiye (TUBITAK) funded by the British Council under Grant 120N996 titled as "Implementing digitalization to improve energy efficiency and renewable energy deployment in Turkish distribution networks".Conference Proceedings Citation Index - Scienc