Kamronbek Yusupov1, Md Rezanur Islam1, Insu Oh2, Mahdi Sahlabadi2, and Kangbin Yim2, 1Software Convergence, Soonchunhyang University, Asan-si, South Korea, 2Department of Information Security Engineering, Soonchunhyang University, Asan-si, South Korea
This paper outlines a mobile application architecture designed to aid users in managing stress and anxiety effectively in their daily lives. The application encompasses a range of features, including meditation, breathing exercises, and stress monitoring, to offer a comprehensive stress management tool. Beyond the technical aspects, the paper delves into ethical considerations related to user privacy and data security. The primary objective is to develop a user-friendly and impactful mobile application that equips individuals with better coping mechanisms for stress and anxiety.
Stress management; Anxiety; Mobile application; Meditation; Breathing exercises.
Gray Cox, Department of Computer College of the Atlantic, Bar Harbor, Maine, USA
This paper explores shortcomings in the conceptual framing of LLMs that characterizes them as nothing more than “autocomplete on steroids” (AOS). It first sketches the view and some key reasons for its appeal. It then argues the view overlooks ways the Attention function in GPT systems introduces features of emergent intentionality in LLM behavior because it tacitly frames the description with the mechanistic metaphor of efficient causality. A conceptual analysis of the functions of variable Attention in GPT reinforcement learning suggests Aristotelian categories of formal and final causality provide a better understanding of the kinds of pattern recognition found in LLMs and the ways their behaviors seem to exhibit evidence of design and purpose. A conceptual illustration is used to explain the neo-Aristotelian theory proposed. Then descriptions and analyses of a series of experiments with Claude 3 are used to explore empirical evidence for the comparative merits of that theory. The experiments demonstrate the LLM’s ability to engage in the production of texts in ways that exhibit formal and final causality that would be difficult to explain using mechanical conceptions of efficient causality that are implied by the “autocomplete on steroids” theory. The paper concludes with a brief review of the key findings, the limits of this study, and directions for future research that it suggests.
Autocompletion, Formal and Final Causality, Emergent Intentionality, Aristotelian theory of AI, Attention.
Iftakhar Ali Khandokar, Tanvina Khondokar, Saiful Islam, Tasmia Ishrat, Alam Chadni, and Priya Deshpande, PhD Department of Electrical and Computer Engineering, Marquette University
Sentiment Analysis is one of the fascinating branches of text analysis, where a body of text or document is labeled according to the emotion it conveys. To elaborate the problem domain more specifically the target text document might be labeled according to the type of emotion like positive or negative and sometimes neutral. The assignment of positive or negative sentiment is solely based on the context of the problem domain and it is subjective to its own root. In this work, we have aimed to achieve a similar purpose, but the target data we are focusing on is financial data. In the finance domain there is no personal entity comment or review that sentiment should be analyzed rather than label the data tuple according to the inner events that might be positive or negative for the norm of the finance world. The target data is the regular Bangla textual news articles which are a great representation of the current financial situation. Therefore we have attempted to classify the sentiment of financial news data using several feature models and also used unlabelled news data to enhance the performance of these models. Finally using the most optimal model we have also conducted some temporal analysis on 5-year time series news data.
Machine Learning, NLP, Text Analysis, Semi-Supervised Learning.
Ayalew Belay Habtie, Brett Hudson Matthews, David Myhre and Aman Aalam, My Oral Village, inc, Toronto, Canada
The advent of mobile money has transformed how people manage and conduct their financial transactions. However, conventional mobile money systems predominantly rely on text-based menus, posing significant challenges for illiterate and low-literate individuals in effectively using these services. In response, this study introduces a human-centered solution designed to closely mirror the familiar practices of handling cash among these user groups. Our solution comprises an interface layer, database layer, and digitalized currencies, allowing users to tap on irtual currencies or coins to perform various financial activities. Implemented within the Android environment, the solution includes tutorial videos to guide users in navigating and utilizing the application effectively. Our human-centered design approach for this Androidbased mobile money solution represents a significant advancement in enhancing financial empowerment for illiterate and low-literate individuals in Pakistan. By prioritizing user-friendly design principles and addressing the specific needs of these users, our application promotes greater financial inclusion and economic participation. This innovative solution not only bridges the gap between technological advancement and accessibility but also contributes to the socio-economic development of the country, fostering a more inclusive and equitable financial ecosystem.
Human-Centered Design, Digital Currency, Mobile Money, Financial Inclusion, Android Environment.
Giulio Ramaccioni, Faculty of Law, University e-Campus, Novedrate, Italy
The topic addressed in the research concerns the right to erasure (or the right to be forgotten) and its practical application. This right has now taken on great relevance in the European landscape thanks to Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Therefore, the study will be characterized by: (i) the analysis of the legislative frame of reference, represented by art. 17 of Regulation; (ii) the verification of the law in action, conducted through the study of five cases resolved out of court, which will allow us to identify the concrete operational rules adopted for their resolution: 1) the case of the online article about the collapse of well-known Italian company; 2) the case of the online article about rigged tenders; 3) the case of the website of the political party; 4) the case of the Panama Papers ; 5) the case of the website of an Italian Region. In this way, it will be possible to identify the legal problem characterizing the three cases in order to identify the concrete operational rules adopted for their solution.
Right to erasure, Right to be forgotten, Privacy, Data protection, Human rights.
Kobra Khanmohammadi1,3, Zakeya Namrud1,3, François Labrèche2, and Raphaël Khoury1, 1Département d’informatique et d’ingénierie Université du Quebec en Outaouais, Gatineau, Quebec, 2Secureworks, Atlanta, GA, 3All authors contributed equally
In recent years, there has been a noticeable increase in the number of publicly reported vulnerabilities, posing significant challenges for organizations striving to update their systems promptly. This underscores the critical need for prioritizing certain vulnerability fixes over others to mitigate the risk of cyberattacks. Unfortunately, the current methods available for assessing the exploitability impact of vulnerabilities have substantial shortcomings. In particular, they often rely on predictive calculations based on data that may not be readily available at the time a vulnerability is first reported. In this paper, we introduce an innovative exploitability prediction method that exclusively utilizes information available at the time of a vulnerability’s initial disclosure. Our approach demonstrates superior performance compared to the most widely used vulnerability prioritization algorithms in scenarios where data is subject to the aforementioned limitations.
Vulnerability prioritization, exploitability prediction, vulnerability assessment, CVE report analysis.
Husam Lahza1 and Badr Alsamani2, 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia, 2Badr Alsamani, Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
In an era where digital authentication is paramount, the prevalence of weak passwords poses significant cybersecurity risks. This study explores the integration of persuasive technology, specifically the Fogg Behavioral Model (FBM), to enhance password creation practices. By examining users motivations, abilities, and triggers, we establish a design principle to influence stronger password choices. Our proposed solution addresses gaps in traditional password strength meters by incorporating fear-based motivational messages and clear, concise instructions. This research contributes to both the theoretical understanding and practical implementation of sustainable password practices, aiming to reduce the practice of selecting weak passwords.
Password Security, Persuasive Technology, Fogg Behavioral Model, Cybersecurity, Password Strength, Password Meters .
Divyam Sharma1 and Divya Santhanam2
Writing Stories is an Engaging Yet Challenging Endeavor. Often, Authors Encounter Moments of Creative Block, Where the Path Forward in Their Narrative Becomes Obscured. This Paper is Designed to Address Such Moments by Providing an Innovative Solution: a Tool That Completes Stories Based on Given Prompts. By Inputting a Short Story Prompt, Users Can Receive a Conclusion to Their Story, Articulated in One Sentence or More, Thereby Enhancing the Storytelling Process With Ai-driven Creativity.this Tool Aims Not Only to Assist Authors in Navigating Writer’s Block but Also to Offer a Fun and Interactive Way for Anyone to Expand on Story Ideas Spontaneously. Through This Paper, We Explore the Intersection of Artificial Intelligence and Creative Writing, Pushing the Boundaries of How Stories Can Be Crafted and Concluded. To Create Our Final Textgeneration Models, We Used a Pre-trained Gpt-3.5 Model and a Newly Created Finetuned Ssmmamba Model, Both of Which Perform Well on a Comprehensive List of Metrics Including Bert Score, Meteor, Bleu, Rouge, and Perplexity. The Ssm Model Has Also Been Made Public for the Nlp Community on Huggingface Models as an Open Source Contribution, Which for the Timebeing is a First of Its Kind State-space Model for Story-generation Task on Huggingface.
Story Ending Generation, Zero-Shot Learning, State Space Models, LoRa, PEFT, LLM, Creative Writing.
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