Reviewerly/exHarmony huggingface dataset:
Synthesizing differentially private location traces including co-locations

Abstract Privacy-preserving location synthesizers have been widely studied to perform private geo-data analysis. They have also been used for generating datasets for research or competitions. However, existing location synthesizers do not take into account the friendship information of users. Because friends tend to visit the same place at the same time in practice, a location synthesizer should consider such co-locations of friends to generate a more realistic dataset. In this paper, we propose a novel location synthesizer that generates location traces including co-locations of friends. Our location synthesizer models the information about the co-locations with two parameters: friendship probability and co-location count matrix . Our synthesizer generates a synthetic graph based on the friendship probability and then generates synthetic co-locations using the synthetic graph and the co-location count matrix. The two parameters in our synthesizer provide strong privacy guarantees—the friendship probability provides node differential privacy (DP) and the co-location count matrix provides user-level DP. We evaluate our synthesizer using two real datasets. Our experimental results show that our synthesizer preserves co-locations and other statistical features while providing DP with reasonable privacy budgets, e.g., 0.2-node DP and 2-user-level DP.
End-to-End Depth-Guided Relighting Using Lightweight Deep Learning-Based Method

Image relighting, which involves modifying the lighting conditions while preserving the visual content, is fundamental to computer vision. This study introduced a bi-modal lightweight deep learning model for depth-guided relighting. The model utilizes the Res2Net Squeezed block's ability to capture long-range dependencies and to enhance feature representation for both the input image and its corresponding depth map. The proposed model adopts an encoder-decoder structure with Res2Net Squeezed blocks integrated at each stage of encoding and decoding. The model was trained and evaluated on the VIDIT dataset, which consists of 300 triplets of images. Each triplet contains the input image, its corresponding depth map, and the relit image under diverse lighting conditions, such as different illuminant angles and color temperatures. The enhanced feature representation and improved information flow within the Res2Net Squeezed blocks enable the model to handle complex lighting variations and generate realistic relit images. The experimental results demonstrated the proposed approach's effectiveness in relighting accuracy, measured by metrics such as the PSNR, SSIM, and visual quality.
Using of Blockchain in the Context of Automotive Industry: A Survey

Blockchain technology (BC) is being implemented in many areas of our life including drug and food safety, logistics, supply chain, etc. to increase security, traceability, trust of data shared across any business network. The automotive industry is one of the most sophisticated advanced industries that needs to implement a blockchain-based systems where different parties and users can communicate with each other in trust way. Therefore, BC technology can benefit the automotive industry by enhancing security, privacy, integrity, traceability, etc. This survey paper discusses the possibility of using blockchain technology in the automotive industry to improve the process of vehicle registration, tracking used vehicle history, etc. In addition, the paper surveys the status of research studies until 2022 in the field of using blockchain technology in the automotive industry.
A Blockchain-Based Solution for Defining and Enforcing Personal Data Access Policies

Driven by the increase in the availability of private information in digital format combined with the ever increasing interest in the use of this information for data mining and knowledge discovery, new regulations and standards are being created to protect private user information and achieve meaningful consent in the digital realm. Classical privacy enforcing solutions such as access-control-lists and non-standardized data sharing agreements are not capable of effectively covering the requirements set out by these new privacy regulations. At this same time, blockchain smart contracts have shown the possibility of representing various object types with user defined interaction rules, all of which can be unambiguously interpreted by machine. In this paper, we represent the private records of an individual as blockchain assets and demonstrate how this allows for a user to control access to their private records thus clarifying the user consent process and providing better regulatory compliance. We also present the design of a hardware wallet protocol which stores asset data and only reveals this data if blockchain transaction approval can be shown.
Towards a Conceptual Model for Assessing User Satisfaction in Metaverse-Based Learning

this paper proposes a conceptual model for assessing user satisfaction in metaverse-based learning, a novel field that utilizes virtual reality technology to create an immersive and interactive learning experience. Despite the growing popularity of metaverse-based learning, there is a lack of research on how to evaluate user satisfaction in this type of learning environment. The proposed model includes four factors: Perceived Trialability, Perceived Observability, User Compatibility, and Perceived Complexity, each of which can be measured using statistical methods. The study aims to address the research gap in evaluating user satisfaction in metaverse-based learning and provides a model for educators and researchers to assess and improve user satisfaction in this innovative learning environment. The findings of this study contribute to the growing body of literature on metaverse-based learning and provide insights into the unique challenges associated with implementing this technology in educational settings.
A Proposed Cloud Quality Model (IaaSQual) for “Infrastructure as a Service (IaaS)” from User's Perspective

Infrastructure as a Service (IaaS) is a service provided by cloud computing systems vendors. IaaS provides virtual computing resources to the users such as hardware, servers, data center space and storage, and network components all over the internet. Just like any other growing technology, cloud computing and specifically IaaS faces some challenges that affect the quality of service and must be conquered to achieve users' satisfaction and the most reliable service. Therefore, the service must be dependable to satisfy the business. In this study, a quality model will be proposed from the users' perspective to help the IaaS vendor to understand the IaaS users' nonfunctional requirements.
Towards Leveraging Explainable Artificial Intelligent (XAI) in Requirements Engineering (RE) to Identify Aspect (Crosscutting Concern): A Systematic Literature Review (SLR) and Bibliometric Analysis

The software development life cycle contains many phases, among the most notable ones is requirements engineering (RE), it is considered a very important activity because poorly implemented RE steps can result in poor software quality which might lead to expensive maintenance costs and so many other undesirable issues. Thus, owing to the importance of RE in general and aspect (crosscutting concerns) identification in particular, researchers have proposed many approaches that can help to produce a clear and complete set of requirements document. On the other hand, artificial intelligence methods have a proven record of handling complicated text. Yet there is insufficient understanding of how AI and explainable AI can be employed and incorporated to be utilized in RE. Nevertheless, join forces to produce research for beneficial outcomes in this theme. The present study is a comprehensive SLR that reviewed the current state-of-the-art of AI/XAL in RE and aspect orientation. We reviewed the research from 2007 till 2022, PRISMA methodology is utilized to obtain a final set of (n =23) articles from four major scientific databases (Scopus, and Web of Science). Clear and new taxonomies were invented during the review discussion. Additionally, to enhance the contribution of this research, a bibliometric comprehensive analysis was conducted on the basis of annual scientific production, country scientific production, and co-occurrence. As a result, it is deemed a noteworthy topic of extensive exploration especially after we identify critical research gaps with their corresponding solutions and generic thoughts to serve as a guide for future directions.
Unmasking the Truth: A Deep Learning Approach to Detecting Deepfake Audio Through MFCC Features

Deepfake content is artificially created or altered using artificial intelligence (AI) methods to appear real. Synthesis can include audio, video, images, and text. Deepfakes may now produce content that looks normal, making it more difficult to identify. Significant progress has been made in identifying video deep fakes in recent years; However, most of the investigations into voice deep fake detection have used the ASVSpoof-2019 dataset and several machine learning and deep learning algorithms. This research uses machine-based and deep-learning approaches to identify fake audio. Melted frequency cepstral coefficients (MFCCs) are used to extract the most useful information from the sound. We choose the 2019 ASVSpoof dataset, which is the latest reference dataset. Experimental results show that Convolutional Neural Networks (CNN): (CNN-LSTM) outperformed other machine learning (ML) models in terms of accuracy, achieving an accuracy of up to 88%.
An Image Encryption Algorithm Based on XOR, Flipping, Expansion Permutation and Shifting

In this paper, we propose a new algorithm to encrypt color and grayscale images by creating a strong private key and using XOR, flipping, expansion permutation, and variable circular shifting. The implementation results of the proposed algorithm showed that it is resistant to statistical, differential, and brute force attacks, as verified through the measures of entropy, histograms, correlation, key Sensitivity, keyspace, PSNR, MSE, SSIM, NPCR, and UACI. The overall results demonstrated the efficacy and robustness of the new algorithm.
A Systematic Review of the use of Virtual Reality in Education

The emergence of affordable virtual reality technology has opened up new possibilities for immersive and engaging education and training. In the past, technical challenges and high costs have hindered the progress of virtual reality in education, but now educational institutions and industries are quickly adopting it as a key training tool. Virtual learning environments have proven to be successful in various domains due to their ability to create a powerful sense of presence and effective immersion. This study examined a virtual reality application that offers safe and controlled on-site training experiences that can revolutionize education and training by providing learners with interactive, engaging, and effective learning experiences. The study involved analyzing the benefits and limitations of using this application, as well as providing recommendations for how it could be improved or used more effectively in educational settings. The goal of the study is to determine whether virtual reality has the potential to revolutionize education and training by providing learners with interactive, engaging, and effective learning experiences that are tailored to their individual needs.
An XAI Approach to Predictive Analytics of Pancreatic Cancer

Despite intensive research, survival rate for pancreatic cancer, a fatal and incurable illness, has not dramatically improved in recent years. Deep learning systems have shown superhuman ability in a considerable number of activities, and recent developments in Artificial Intelligence (AI) have led to its widespread use in predictive analytics of pancreatic cancer. However, the improvement in performance is the result of model complexity being raised, which transforms these systems into “black box” methods and creates uncertainty about how they function and, ultimately, how they make judgements. This ambiguity has made it difficult for deep learning algorithms to be accepted in important field like healthcare, where their benefit may be enormous. As a result, there has been a significant resurgence in recent years of scholarly interest in the topic of Explainable Artificial Intelligence (XAI), which is concerned with the creation of novel techniques for interpreting and explaining deep learning models. In this study, we utilize Computed Tomography (CT) images and Clinical data to predict and analyze pancreatic cancer and survival rate respectively. Since pancreatic tumors are small to identify, the region marking through XAI will assist medical professionals to identify the appropriate region and determine the presence of cancer. Various features are taken into consideration for survival prediction. The most prominent features can be identified with the help of XAI, which in turn aids medical professionals in making better decisions. This study mainly focuses on the XAI strategy for deep and machine learning models rather than prediction and survival methodology.
Image Splicing Localization Using Superpixel and Wavelet Mean Squared Error

Image splicing is a form of image forgery where one image is seamlessly pasted onto another. While image splicing itself is not necessarily illegal and is often used for aesthetic purposes, entertainment, or humor, it can also be misused for criminal activities. To aid digital forensics in detecting the specific location of the spliced image area, an image splicing localization algorithm is required. Previous research has proposed a method for detecting the splicing area by employing superpixel segmentation and noise level estimation. This method involves dividing the photo into smaller parts, estimating the noise level in each part, and clustering these estimated noise levels. By clustering the noise levels, the different clusters represent distinct levels of noise and help identify the splicing area. In an effort to enhance the previous research, this study introduces a new method for detecting the splicing area using superpixel segmentation and wavelet mean squared error. The research demonstrates that the wavelet denoising mean squared error value can be effectively employed to detect the image splicing area, outperforming the noise level estimation method under certain conditions.
Mobile Object Detection and Tracking Using DRONES

The great development in the fields of artificial intelligence and robotics in recent times has led to the emergence of many useful uses in these technologies in various areas of life. Several leading technology companies are racing to develop applications that exploit artificial intelligence, machine learning, and other technologies. In this paper, we would like to direct the educational sectors to the possibility of applying artificial intelligence and computer vision technologies in the areas of detection and recognition of objects during movement, using one of the educational drone products; DJI Tello. This effort can be used as an entry point or introduction to other means that benefit the security or intelligence systems. The paper included various tasks in the educational fields to achieve full vision of some targets such as object detection, face detection, object tracking, track follow in addition to the utilize of the programmable drones. The results of the work came in the form of software that has full control over the used drone (DJI Tello), with wide security and intelligence applications. The results showed that this drone can be used in the educational sectors as a start to begin programming drones, their uses, and applying artificial intelligence techniques using them.
A Roadmap for Artificial Intelligence Augmented Software Development Life Cycle: Aspects of Knowledge Vaporization

Artificial Intelligence Augmented Software Development Life Cycle is a process for developing software that incorporates AI techniques and technologies. One aspect of AIASDLC is knowledge vaporization, which refers to the process of extracting and encapsulating knowledge from the AI model during development and making it available for use in other parts of the software development life cycle. The advantages of AIASDLC include; improved performance, increased efficiency, better decision-making, better user experience, better scalability, better security and compliance, and knowledge vaporization. Requirement engineering is the process of gathering, analyzing, and specifying the requirements for a software system. The context boundary, domain knowledge, application domain, and system boundary are all important concepts in requirement engineering that help define the scope and constraints of the system being developed. Understanding the context boundary is important because it helps identify the external factors that will affect the requirements of the system. Domain knowledge is the understanding and expertise in a specific field or area that is necessary for effective requirement engineering. The application domain is the specific area or field where the system will be used, and it is important to understand the application domain to gather and document the correct requirements for the system
Correcting Wide-Range of Arabic Spelling Mistakes Using Machine Learning and Transformers

Different languages are subject to several types of spelling mistakes. In this paper, we use a deep neural network machine learning model called transformer to correct wide range of Arabic soft spelling mistakes including: lexical and semantic errors (due to the sound of the letter), keyboard errors (due to the character position on the keyboard), common typing errors (like deleting space, preposition errors, and third pronoun errors), and some random typing errors when the user replaces a letter with some other letter. Our approach has two stages: first, we use huge Wiki-40B dataset of Arabic texts which is error free. We inject this dataset with synthetic targeted errors, then we perform training and testing on this dataset. In the second stage, we use the training weights of the first stage to perform training and testing on a dataset that we have collected for this purpose, which contains real Arabic spelling mistakes. We call this dataset REALMS, which stands for REal Arabic Language Mistakes in Spelling. The proposed model can correct 99.12% of the artificial errors that were injected into the Wiki-40B dataset. Additionally, it achieves a character error rate of 1.14% and accuracy of 98.85% on the REALMS dataset.
FastText Embedding and LSTM for Sentiment Analysis: An Empirical Study on Algerian Tweets

Complex deep learning models have been developed for classification tasks, but their complexity often leads to longer training times. This study investigates the impact of FastText embedding on the performance of a sentiment deep learning model for Algerian Twitter. As this method becomes an increasingly important source for understanding public opinion and sentiments, the results of the proposed model combining FastText and Long short-term memory (LSTM) show its effectiveness with an accuracy of 88,95 %.
Digital Forensic Analysis of Hologram Projection Fans

The increasing popularity of hologram projection fans has brought about new opportunities for advertising, entertainment, and communication. However, as with any new technology, there is a potential for misuse and criminal activity. The use of digital forensic analysis techniques to investigate hologram projection fan incidents is an important area of research that can aid law enforcement and security agencies in identifying and prosecuting individuals involved in illegal activities. Therefore, this study aims to perform a digital forensic analysis of hologram projection fans, in order to provide a better understanding of how these devices can be used in criminal activity and to develop effective techniques for investigating such incidents. To this end, novel experiments were conducted to simulate the illegal usage intent of the holographic fans, then we collect and analyze experimental data to retrieve digital forensics artifacts that have not been discovered before. The results show that: first, the removable SD card of the hologram fan contains all multimedia that were run on the fan. Second, it's essential to perform a physical image when acquiring data, because individuals may take actions when using the fan to display illicit content such as attempting to hide their tracks by deleting evidence from the Secure Digital (SD) card or other devices. In future work, other devices with different operating systems may be involved to highlight the obstacles that face the process of successful forensic analysis.
Detection of Fabricated Survey Data using Clustering Analysis

Data quality is the key of research successful. As more area of social sciences relay on the survey data, the quality of these data become more important. The data comes from the Questionnaires are used in the analysis of hypothesis that depend on the qualitative data. But, in a lots of situation, the answers are fraud according to different reasons. As a result, the analysis of these data leads to unrealistic hypotheses. Detection of this type of data can prevent the using of them and avoiding the incorrect results. Traditionally, statistics methods have been exploited to fraud data. But with machine learning evolving, more opportunity is available to provide solutions for problems with automated techniques. In this paper, clustering analysis would be suggested to detect the fake data based on learning process, clustering is central to many data-driven application domains. The goal of a clustering analysis is usually to obtain a symbolic description of the problem. Therefore, it would be used to describe the fraud data. The paper research work was based on training data, were given from two aspects. One dataset that is real data, were produced from real survey using more than 50 parameters. While another dataset is not real, it was produced fraudulently. Two clustering methods were used; k-means and k-mode according to their popularity and good results. The results uncover that fraud data has different behavior from real data. So it can be detected easily from this strange behavior.
Abstractive Multi-Document Summarization Using Sentence Fusion

Multi-document abstractive text summarization aims to generate a gist focusing on salient concepts in the set of related documents. Abstracting is such a human ability that it is difficult to model. Thus automatic abstracting is tough and it is considered one of the most difficult tasks in natural language processing (NLP). Over the past few years with the advancement of deep learning technology in the natural language processing (NLP) domain, abstractive text summarization also has made significant progress using a text-to-text generative model. One of the drawbacks of the text-to-text generative model is its input length restriction, which limits the amount of text that can be processed at once. This can pose challenges when summarizing multiple text documents as a whole. To address this issue, In this paper, we present an approach that uses an unsupervised extractive summarization model for creating an extract which is passed to a deep learning-based sentence fusion model to fuse extracted sentences. Finally, it uses a KL divergence-based method to select a subset of fused sentences to create a summary that maximizes the information content. We have trained the proposed model on a benchmark multi-document text summarization dataset called multinews dataset and tested the trained model on the DUC2004 dataset. Experimental results show that the proposed approach outperforms the state-of-the-art systems.