May 20 ~ 21, 2023, Zurich, Switzerland
Taraf alshalan and Ghala alamri, Dr. Maali Alabullhafith, College of Computer Sciences and Information at Princess Norah University, Riyadh, Saudi Arabia.
Autism Spectrum Disorder is a Developmental Disability That Can Cause Significant Social, Communication and Behavioural Challenges. Parents of Children on the Spectrum Find It Difficult for Their Kids to Communicate With Them and Other People Which Makes It Challenging for Social Interactions. Researchers Have Introduced Different Solutions Such as Therapy Robot That Teaches Social Skills to Children With Autism. Using Virtual Reality to Train Emotional and Social Skills to Children With Autism Spectrum Disorder to Solve This Problem. However, These Solutions Focus Only on the Person on the Spectrum Which Might Take Years to See Their Impact. In This Study, We Introduced a Solution That Focuses on the Other Perspective, an Advanced and Interactive Intelligent Technology That Can Educate Neurotypical People on How to Communicate With People on the Spectrum in Different Scenarios and Environments While Seeing the Consequences of That Interaction From the Point of View of a Person on the Spectrum and Be Aware of Their Actions and Fully Engaged Using Virtual Reality (Vr). Virtual Reality is a Technology That Simulates Experiences That Can Be Similar to the Real World. We Achieved This Work&s Objective by Implementing a Storyline Game That is Vr-based.
Neruodivergent, Neurotypical, Virtual Reality, Communicating on the spectrum.
Annie Wu1, Yu Sun2, 1Barrington High School, 616 W Main St, Barrington, IL 60010, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
Road Signs Provide Essential Information and Precautions to Drivers, and Are Crucial to the Safety of Both Drivers and Pedestrians . Red Signs, Such as Stop Signs, Yield Signs, and Do Not Enter Signs, Are Regulatory Signs That Organize Traffic . Yellow Signs Serve as Precautions to Prevent Accidents. Traffic Lights Dictate Whether to Go or Stop in an Intersection . Although Road Signs Are Intended to Attract Drivers’ Attention and Help Them Operate Their Vehicles Safely, Drivers Can Still Misread Road Signs, Resulting in Car Accidents and Serious Injuries.
Machine Learning, Image classification, Autonomous vehicle.
Taehoon Kim and Im-Yeong Lee, Department of Software Convergence, Soonchunhyang Univ., Asan-si 31538, Republic of Korea
In existing smart vehicles, vehicle owners must secure self-sovereignty for authentication. To this end, Holders in DID (Decentralized Identifier) do not rely on traditional IdM (Identity Management), but control their identity data and authenticate their credentials with Verifiers. However, for the Verifier to authenticate the Holder, there is a situation where additional data other than the VP is required, and this is because the DID based on data access control is transmitted in a general encryption scheme, causing detailed access control problems and inefficiency problems. Study on CP-ABE (Ciphertext Policy Attribute-Based Encryption)-based data access control schemes in the DID is being actively conducted to solve this problem. However, existing schemes on DID-based CP-ABE generate various security threats. This paper proposes a study on user authentication based on DID and CP-ABE for self-sovereign identity of smart vehicles in smart vehicles.
Smart Vehicle, Decentralized Identifier, Self-Sovereign Identity, Ciphertext Policy Attribute-Based Encryption.
Adwita Arora1, Krish Chopra1, Divya Chaudhary2, Ian Gorton2 and Bijendra Kumar1, 1Netaji Subhas University of Technology, India, 2Northeastern University, USA
The COVID-19 pandemic has forced people to resort to social media to express their thoughts and opinions, some of which are indicative of their thoughts and feelings, which could be analysed further. In this paper, we aim to analyse the impact of the COVID-19 pandemic on social media users by Sentiment analysis of data collected from popular social media platforms, Twitter and Reddit. The textual data is preprocessed and are made fit for proper sentiment analysis using two unsupervised methods, VADER and TextBlob. Special care is taken to translate tweets or comments not in the English language to ensure their proper classification. We perform a comprehensive analysis of the emotions of the users specific to the COVID pandemic along with a time-based analysis of the trends, and a comparison of the performance of both the tools used. Geographical distribution of the sentiments is also done to see how they vary across regional boundaries.
Sentiment Analysis, Social Media Analysis, Natural Language Processing, COVID-19.
T.D.H.Jayathma, Ministry of Education, Isurupaya, Sri Lanka.
Government ICT projects play a major role in the economic development of a country, strategically empowering the digitally capable citizens. Digital infrastructure implementation is necessary for a country and the implementation of ICT projects are aimed at providing convenient, efficient and effective service delivery by creating a digitally inclusive community. Throughout the world, many countries have initiated the setup of ICT infrastructure and installation of ICT projects. Although many government organizations have started to implement ICT projects, more than 60% of the government ICT projects have failed. According to the research findings, numerous factors affect the failure of government ICT projects, and out of the reasons the ineffective project management is the prominent reason for most of the failures. Successful ICT Projects have shown a good percentage of public participation, for better delivery of e-services to citizens through ICT applications. Countries which have initiated E-Government & m-government, deliver services to the public as one-stop-service, with the usage of successful ICT projects by delivering services with a dynamic ecosystem. Some notable successes are shown in countries like Denmark, Korea, Estonia, Malaysia and Singapore, where the E-Government Development Index(EGDI) is shown a better value. But in developing countries, although they have taken e-government initiatives by implementing various ICT projects, some have shown success while most of the projects have failed. A successful government ICT project has followed the phases of the project management lifecycle such as, project initiation, project planning, project execution, monitoring and controlling, and closure of the project. Adoption of best practices, usage of agile methodologies, continuous follow-ups, proper documentation and planning for deliverables with proper vision and strategy are significant features of successful government ICT Projects.
Government, ICT Projects, IT Project Management, E-government.
Yuan-Cheng Lai, Yi-An Chen, Chuan-Kai Yang, Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
As Internet of Things (IoT) continues to flourish, integrating IoT applications across networks becomes an important research topic. In this topic, the previous work focused on achieving resource management and quality assurance with proposed job assignment and resource allocation algorithms. In this paper, we propose a mechanism, called Smart-Contract Integration (SCI), which integrates IoT heterogeneous networks with smart contracts in blockchain. SCI records every IoT process that is generated by humans, machines, and data in blockchain, with the intent to tackle the challenges of resource management, trust, and security issues. In addition, with designed smart contracts and applications, we conquer the issues of extensive involvement, privacy, and incentive frameworks through encryption, signatures, transactions, and data sharing. Finally, to prove the concept of our proposal, we implemented a control and management application that can adapt to the existing IoT infrastructure in integrating IoT heterogeneous networks. The experimental results show that our developed smart contracts can handle 64 requests of management and transactions within 200 milliseconds in heterogeneous networks.
Internet of Things, Heterogeneous Network, Smart Contract, Blockchain.
Rashmi Dhakad1 and Dr. Luigi Benedicenti2, 1Faculty of Computer Science, University of New Brunswick, Fredericton, Canada, 2 Dean, Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
For More Than a Decade Scientist Have Focused on the Emotions of Software Developers in Order to Understand Emotion’s Impact on Their Productivity, Creativity, and Quality of Work. In Recent Times, There is a Sharp Increase in Open-source Software Collaborations and Software Development Models That Are Globally Distributed. A Crucial Aspect of These Collaborations is the Affect of Emotional Contagion. Emotional Contagion is a Phenomenon of Transfer of One’s Affective State to Another. In This Research Study, We Follow Through Previously Established Research and Build on It How Emotional Contagion Happens in Large Open-source Software Development. We Further Establish How Emotional Contagion Happens During Different Time and How It Affects the Overall Development Process.
Emotional Contagion, Software Development Process, Open-source Repositories, Oss, Sentiment Analysis, Commits
Keshav Kapur1 and Rajitha Harikrishnan2, 1, 2 Department of Information and Communication Technology, Manipal Institute of Technology, Manipal, India
There is a Vast Amount of Data Generated Every Second Due to the Rapidly Growing Technology in the Current World. This Area of Research Attempts to Determine the Feelings or Opinions of People on Social Media Posts. The Dataset We Used Was a Multi-source Dataset From the Comment Section of Various Social Networking Sites Like Twitter, Reddit, Etc. Natural Language Processing Techniques Were Employed to Perform Sentiment Analysis on the Obtained Dataset. In This Paper, We Provide a Comparative Analysis Using Techniques of Lexicon-based, Machine Learning and Deep Learning Approaches. The Machine Learning Algorithm Used in This Work is Naive Bayes, the Lexicon-based Approach Used in This Work is Textblob, and the Deep-learning Algorithm Used in This Work is Lstm.
Natural Language Processing, Naive Bayes, Textblob, Lstm , Deep Learning.
George Manias1, María Angeles Sanguino2, Sergio Salmeron2, Argyro Mavrogiorgou1, Athanasios Kiourtis1, Dimosthenis Kyriazis1, 1Department of Digital Systems, University of Piraeus, Piraeus, Greece, 2ATOS Research and Innovation, Madrid, Spain
The tremendous growth and usage of social media in modern societies have led to the production of an enormous real-time volume of social texts and posts, including tweets, that are being produced by users. These collections of social data can be potentially useful, but the extent of meaningful data in these collections is still of high research and business interest. One of the main elements in several application domains, such as policy making, addresses the scope of public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing (NLP), for identifying and extracting subjective information from raw texts. An additional challenge refers to the exploitation and correlation of the sentiment that can be derived for different entities into the same text or even a sentence to analyze the different sentiments that can be expressed for specific products, services, and topics by considering all available information that can be depicted within a text in a holistic way. To this end, this paper investigates the importance of the utilization of an Entity-Level Sentiment Analysis (ELSA) mechanism to enhance the knowledge and the task of sentiment analysis on tweets with main objective the overall enhancement of the policy making procedures of modern organizations and businesses.
Twitter Sentiment Analysis, Entity-Level Sentiment Analysis, Named Entity Recognition, Policy Making.
Poorva Sawant, Parag Rane, Mumbai, India
Unreliable and deceiving information is spreading at a great speed these days across the world through social media sources. Fake news is a growing problem in our modern society, and it has become increasingly difficult to distinguish between real and fake news due to the advancement of technology. Fake or misinformation about the latest CORONA pandemic wreaked havoc. Studies conducted during the epidemic COVID that false news might have menaced public health broadly. Another set of probes ,in association with WHO, discovered that nearly,6,000 people worldwide were hospitalized due to Covid19- related false news. It also redounded in the deaths of at least 800 people. All of this, within the initial three months of the pandemic. There have been trails of fake news transferring fake preventative measures or symptoms across the media and stoner world. numerous countries have put strict measures to contain the similar spread of viral fake news or deceiving communications which can risk mortal life. Identifying and securing against propaganda has been an ongoing exercise since before the arrival of the Internet. Detecting and averting the spread of unreliable media content is a delicate problem, especially given the rate at which news can spread online. With the increase in the use of social media platforms; the leading cause for spreading such news can be that fake news can be published and propagated online faster and is also cheaper when compared to traditional news media such as newspapers and television. Online fake news or information which is deliberately designed to deceive readers is mostly commonly manually written; but with the recent progress in natural language generation techniques, models have been built to generate realistic looking ‘Fake news’. This creates a greater need to handle the fake news identification problem in a different way to not just classify the fake and real news, but also to mark the human-generated and machine-generated (neural) fake news. The new advances in identifying false information and detecting machine-generated text using AI will help to curb the spread of false information at the source if we can prepare those in a position of influence to fight against it. Governments and News agencies are now looking at Artificial intelligence as the means to separate the good from bad in the news field. That is because artificial intelligence makes it easy to understand behaviours by the use of techniques like pattern recognition. This study looks at the problem of machine-generated fake news classification as more of a comparative analysis of Human Vs Machine Generated fake news and identify the differences or similarities of the patterns. With the explosion of large language models fake news can be easily created and with proper grammar and sentences.
NLP, Fake News , Generative AI.
LingyunLi1,2,3*, XianhuiLu1,3, and KunpengWang1,3, 1State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, China, 2School of Computer Science, Liaocheng University, China, 3School of Cyber Security, University of Chinese Academy of Sciences, China
Protecting the transaction from address-based tracking is one of the core issues in the blockchain privacy preservation. In this paper, we propose a transaction framework through which the trader of a transaction organization transacts on the public chain of blockchain with privacy-enhancing whereas the manager gets access to the transaction of the trader with access control based on cryptography. In the proposed framework, the hash-based one-time address is utilized to protect transactions from unauthorized tracking; furthermore, the hash-based one-time signature is creatively being used twice to verify and track the transactions safely in the semi-honest model;through access control, the authorized managers can obtain transaction information within their authorities.Compared with the standard Bitcoin transaction system, the proposed system achieves privacy-enhancing and post-quantum security.
Post-quantum, Privacy-enhancing, Blockchain, Security, Hash-based Signature, Security.
Yohanes Satria Nugroho1 Chuan-Kai Yang2 and Yuan-Cheng Lai3, 1, 2, 3Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan
Sign Language Recognition is a variant of Action Recognition that consists of more detailed features, such as hand shapes and movements. Researchers have been trying to apply computer-based methods to tackle this task throughout the years. However, the methods proposed are constrained by hardware limitations, thus limiting them from being applied in real-life situations. In this research, we explore the possibilities of creating a lightweight Sign Language Recognition model so that it can be applied in real-life situations. We explore two different approaches. First, we extract keypoints and use a simple LSTM model to do the recognition and get 75% of Top-1 Validation Accuracy. For the second one, we used the lightweight MoViNet A0 model and achieved 71% of Top-1 Test accuracy. Although these models achieved worse results than the state-of-the-art I3D, their complexity in terms of FLOPs are far better.
Sign Language Recognition, Lightweight Model, Keypoints Estimation
Nilam and Babita Choudhary, Department of Computer Engineering, SKIT Jaipur, Rajasthan, India
Agriculture is a critical sector in India that plays a major role in the country s economy and sustainability. India is recognized as a significant producer of various agricultural products, and the cultivation of crops heavily relies on the quality of soil. Traditionally, farmers who had practical experience would cultivate crops, but sometimes they may not be able to correctly choose the most appropriate crop based on characteristics of soil and other climatic conditions. To address this issue, a recommendation system has been proposed which utilizes ML classification algorithms to suggest the most suitable crop for a particular type of soil. The recommendation system employs various ML classification algorithms, like Gaussian Naive Bayes, KNN, Random Forest, , Decision Tree, LDA and many others to make crop recommendations. Comparison has been made among various ML algorithms based on efficiency and execution time.
Crop Recommendation, Machine Learning, KNN, Random Forest, Gaussian Naive Bayes, Decision Tree