Reviewerly/exHarmony huggingface dataset:
The Internet of Things, AI Analytics and the System of Justice: needed integration of technology and law

“Artificial Intelligence” and the Internet of Things define an evolving universe of sensing, storage and analysis of ever more information on people's lives. It permeates life as no other technology has. Yet the many opportunities for great benefits from these technologies are also opportunities for injury and oppression across multiple dimensions. We must devise and implement legal/social/technical mechanisms to assure the proper administration of justice. This is critical in respect to privacy protections, as a way to best secure people's privacy and personal autonomy.
Multilevel Fusion of Deep Features for Face Morphing Attack Detection

Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.
Challenges of shopping cart content categorization with LSTM neural networks

This case study investigates the performance of different neural network architectures on shopping cart content categorization. Two Bag-of-Words and Four Long Short-Term Memory unit (LSTM) models were tested on the corpus. The noisy corpus consists of brand names, nouns, and adjectives, where the word order in some cases might be cardinal. Although bigrams can effectively capture the existence of word sequences, the models using LSTM units turn out to outperform the other models as they result in higher accuracy. This paper describes how an effective text categorization model can be developed on a corpus with peculiarities and focuses on the differences between the tested models.
Maldivian Sign Language Recognition Application

Maldivian Sign Language (MVSL) is the national sign language for the deaf or the hearing-impaired people in Maldives. These people with disabilities could easy communicate with other deaf people but face great difficulty conversing with ordinary people. There are many recognition applications and models made for many sign languages with different approaches. Sadly, no such technology has been made to recognize MVSL signs. Therefore, this research aims to recognize three dynamic MVSL signs (“water”, “promise” and “read”) in real-time and later expand to recognize more signs to create a translation application for MVSL. We propose an approach with MediaPipe holistic to extract key point landmarks from the face, two hands and the pose from the videos and build the model for recognition using LSTM. The model was able to obtain an accuracy of 93%.
A Pilot Study for Measuring the Usability and User-Experience of Immersive Virtual Reality Navigation Methods

Virtual reality is a sub-category of extended reality and is a fast-growing technology which enables users to immerse themselves in a digitally generated world, known as a virtual environment. However, virtual reality is not without its flaws, of which the current biggest flaw is that of cybersickness. cybersickness is a type of motion sickness causing feelings of dizziness, nausea and other symptoms which are induced by the use of technology. To test the effects of an omnidirectional treadmill compared to virtual reality touch controllers on the onset of cybersickness, as part of a usability and user experience evaluation of the two navigation methods, a pilot study was conducted to inform future research. The pilot study not only served its initial purpose but gave an indication that an omnidirectional treadmill might assist some, be it not all, users to mitigate the effects of cybersickness when compared to using touch controllers, while also being usable and providing a satisfying user experience.
A Smart Automatic Agent Operating System Using Screen Analysis And Remote Control

In this century, more automated production has been driven by high demand. As a result, more automated production machines are required to meet market demands. Unfortunately, most machine screen activities still need humans to control manually, and most of the operations are simple and repeated. The human eye must constantly assess the production situation and control the keyboard and mouse to operate machinery to do excessively repetitive tasks. We proposed a smart automated agent operating system called “iScript” to upgrade the old machine to a more automated machine. “iScript” is a software solution that uses image recognition techniques to automate agent operation by automatically controlling the keyboard and mouse to perform agent operations using preset scripts. The agent operation system is also integrated with a modular software design. The essential modules include the Steps Control Module (SCM), Function Plug-in Module (FPM), Variable Module (VM), Condition Control Module (CCM), and Operator Control Module (OCM). The modular design makes the script setting highly expandable. It can set customized scripts for varied purposes and applications dynamically, making production process management more accessible and efficient and accomplishing many tiresome tasks necessary for industrial automation upgrades. The proposed smart automatic agent operating system has been applied to several industrial applications, and the cases of application are shared in this research.
A Blockchain-based Incentive Management System for Educational Organizations

Educational organizations have long used student incentive management systems either manually or digitally. Research has proven that providing incentives to students based on academic performance motivates them, which in turn helps further improve their performance. However, most of the existing digital incentive management systems are managed centrally, and hence lack transparency and integrity. Such centrally managed systems are less effective, and may even gradually demotivate students, leading to poor academic performance. Therefore, a decentralized and distributed incentive management system is necessary. To this end, by applying blockchain technology, we have developed a novel decentralized incentive management system called Token-ED. This system minimizes trust imbalance by allowing students to view their incentives via a web platform. Once an incentive has been recorded, it cannot be illegally modified. We believe such transparency will help motivate learners to improve their academic performance. Token-ED works based on a token-based incentive system. Teachers can award tokens to students based on their performance in curricular and co-curricular activities. Students can redeem their earned tokens later to get benefits - such as scholarships, gift cards, and badges - from the school. Token-ED also provides the ability to educational institutions to freely customize and deploy their tokens, making them unique and appealing. We have implemented the system and conducted a preliminary user study. The result shows that majority of the users found the system useful, easy to use, and wanted it to be implemented in their educational institutions. We believe using blockchain for incentive systems would be the norm in education sector in near future.
Preliminary Findings: Use of CNN Powered Criminal Identification System

Face is the principal distinguishing attribute of the human body in recognizing an individual. Face recognition is vital for the law enforcement authorities in primary detection and identification of the criminals. However, it becomes very difficult and time-consuming to manually identify every individual in highly populous cities and public places. In this paper, we propose an automatic criminal identification technique face recognition of the criminals using history of their criminal activities. The proposed system will detect the detailed features of human face and recognize the suspect entering a public location. For this purpose, we have built a facial recognition system to apply in Convolutional Neural Networks (CNN). In the simulation, we use the Labeled Faces in the Wild (LFW) dataset to train and test the CNN-powered face recognition system. Preliminary findings prove the feasibility of implementing this system in effective detection and identification of individuals. Such an automatic system will be operative to identify and catch the criminals in public places in a more efficient way.
Data-driven Predictive Model of Windows 10's Vulnerabilities

A threat source that might exploit or create a hole in an information system, system security procedures, internal controls, or implementation is a computer operating system vulnerability. Since information security is a problem for everyone, predicting it is crucial. The typical method of vulnerability prediction involves manually identifying traits that might be related to unsafe code. An open framework for defining the characteristics and seriousness of software problems is called the Common Vulnerability Scoring System (CVSS). Base, Temporal, and Environmental are the three metric categories in CVSS. In this research, neural networks are utilized to build a predictive model of Windows 10 vulnerabilities using the published vulnerability data in the National Vulnerability Database. Different variants of neural networks are used which implements the back propagation for training the operating system vulnerabilities scores ranging from 0 to 10. Additionally, the research identifies the influential factors using Loess variable importance in neural networks, which shows that access complexity and polarity are only marginally important for predicting operating system vulnerabilities, while confidentiality impact, integrity impact, and availability impact are highly important.
Analysis of Single Image Super Resolution Models

Image Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image super-resolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.
Autoencoder-based Node Embedding Regeneration and Prediction in Dynamic Graphs

In this paper, we introduce a neural network model capable of generating embeddings for graph nodes using just their initial features and without any prior knowledge about their neighbors. To this end, we start with generating embeddings from a model trained on the entire graph knowledge (node features, node neighbors, and random walks). We then build a deep autoencoder model that learns to map node features to embeddings similar to those generated by the model trained on the entire graph knowledge. By using a tuned loss function that acts as a strong regularization for the autoencoder, our model is designed to generate embeddings with minimum error to those generated from the first model. It learns how to generate embeddings that reflect the positioning of the nodes in the graph even though it only uses nodes' initial features. We evaluate the model on the amazon and Alibaba graph datasets by computing the mean squared error, cosine similarity, and mean average percentage error between the embeddings generated by our model and the ones we used as reference. We also evaluate the ability to reconstruct the initial graph by repredicting the neighbors of each node using cosine similarity between nodes generated embedding and its neighbors' reference embeddings. Results have shown an efficiency higher than 80 % in graph generation and neighborhood detection.
Corporate Information Security Policies Targeting Ransomw are Attack

The ransomware attacks have created challenges for the entire world today and industries are getting affected from such sophisticated attacks, whether they are healthcare, educational, financials or any other service sectors, they are not safe from these malware attack. In these types of attacks, user data is encrypted or inaccessible to the victim, the hacker then demands money from the victim to give them access to their data after payment is done. This study guides how to mitigate ransomware attacks by adopting corporate information security policies in the organization with timely complete compliance. Ransomware is often designed to spread across networks and target information asset of organization, in healthcare Electronic Medical Record, HIMS, Database Server, File Servers, Application Server, Web Server, Domain Controllers and all associated & connected devices including IOT device, SCADA (Supervisory Control and Data Acquisition), once they are targeted the entire organization operations can be halted and paralyzed. Ransomware frequently changes its techniques to exploit the vulnerability, this research is based on technical & administrative controls, security standards, procedures, guidelines, best practices by following security frameworks i.e. (ISO 27001, HIPPA, and NIST) and the objective is to mitigate the attacks.
Node authentication using the voting scheme for clustered network

To keep the network secure, malicious nodes must be prevented from accessing the network. Only the authorized node would be allowed to join the network. To address this problem a node authentication scheme is proposed. The proposed scheme is based on the principle of voting. Further, each new node, which wants to join to network, should get acceptance from other member nodes. The member nodes are voting to accept or reject a new node. The vote of each member node is based on its weight. When the sum of votes from member nodes exceeded 50%. Then the node would join a network. Otherwise, the new node would be rejected. In this work, security data is stored as a decentralized database Blockchain. The proposed scheme comprises four stages to achieve node authentication of the new node that wants to join with cluster nodes. This scheme relies on calculating the weighted proportion for each node. It takes into account two factors to calculate the trusted degree: age (which represents the period existing in the network from the start of joining the network to the current date) and contributions (which represent the number of building blocks in the Blockchain).
An Automated Question Answering(Q/A)System For Academic Environment

Almost every Internet user has daily many generals enquires. Users can solve these queries from the Internet Search Engines (ISE). These search engines provide the articles or web pages as result. Users need to expend time on the Internet to get the exact result. Sometimes users may also physically visit the location to get resolve their queries. The academic domain has applicants and students who have more queries about academics. These both have lots of queries and ISEs can't solve these queries. Ontology is used to represent domain-specific data. In this study, Ontology has the format of functional-style syntax. Using Ontology with Semantic Web Rule Language (SWRL) technology. We introduce a domain-specific general Academic Question Answering (Q/A) System for academics. This Q/A System can cover most of the important departments of academics. A user can ask from the system through the submission of data values for a query. Academia Ontology is generated through the Protégé tool. The answer data is retrieved from data properties for applying the relevant rules. Rules are generated with Semantic Web Rule Language (SWRL). This is open to getting focused on Academics Q/A Systems and further applying to other domains with the Semantic Web and Ontology-based approaches. The experimental results explain the feasibility of the Q/A System. This system can respond with more accurate and exact results with an F-Measure of 48%. This Academic Q/A System has the potential to solve the general queries of most of the stakeholders in academia. This Q/A System has been produced to answer a variety of questions in academia. This system can further be extended to embed a natural language interface by employing Natural Language Processing. Further work includes the extension of systems to other domains as well e.g. financial institutions, and government bodies.
Emotional States Discovery under Multiple Disguising Effects for Speaker Identification

Today“s technology is rapidly evolving towards the discovery of abnormal voices since voices are not neutrally spoken most of the time. Introducing disguising effects to abnormal voices has become a crucial threat in various activities. As a result, this would have a considerable impact on the Speaker Identification (SI) performance. Therefore, identifying the unknown speaker in emotional talking conditions under multiple disguising effects is a fundamental concern to be exploited in this research. Six distinct emotional talking conditions along with three effects, i.e.., low-pitched, high-pitched, and Electronic Voice Conversion (EVC) are used to evaluate the performance of speaker identification. Furthermore, this study employs two different speech databases, which are a global English dataset called Speech Under Simulated and Actual Stress (SUSAS) and an Arabic Emirati-accented database. Results reveal that the average speaker identification performance in neutral talking condition is the highest under low-pitched effect with a 96% accuracy. On the other hand, the average reported performance for all of the emotional states under the same effect reaches 81.7%.
An Intelligent Anti-cheating Model in Education Exams

With the emergence of new communication and entertainment technologies, the educational system suffers from the ever-increasing number of cheating cases in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sup> xams. Today, most students have become lazy and want to pass the exam without making a substantial effort in their exam preparation. Consequently, several cheating techniques have emerged, and classical surveillance in the exam has become obsolete. There-fore, the necessity to leverage edge technologies for automating cheating case detection becomes a must. This paper proposes an anti-cheating model focusing on student behavior analysis by analyzing the student's posture in real-time using deep learning and computer vision techniques. We first extract high-level domain features from video frames to realize this goal using CNN-Facial-landmark and media pipe models. Then, we use an LSTM classification model for cheating case prediction. The implementation outcomes show that the proposed model accuracy on the train set reached 94%, and the test set achieved 75%.
WebUAV-3 M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking

Unmanned aerial vehicle (UAV) tracking is of great significance for a wide range of applications, such as delivery and agriculture. Previous benchmarks in this area mainly focused on small-scale tracking problems while ignoring the amounts of data, types of data modalities, diversities of target categories and scenarios, and evaluation protocols involved, greatly hiding the massive power of deep UAV tracking. In this article, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers. WebUAV-3M contains over 3.3 million frames across 4,500 videos and offers 223 highly diverse target categories. Each video is densely annotated with bounding boxes by an efficient and scalable semi-automatic target annotation (SATA) pipeline. Importantly, to take advantage of the complementary superiority of language and audio, we enrich WebUAV-3M by innovatively providing both natural language specifications and audio descriptions. We believe that such additions will greatly boost future research in terms of exploring language features and audio cues for multi-modal UAV tracking. In addition, a fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers. We provide extensive evaluations and detailed analyses of 43 representative trackers and envision future research directions in the field of deep UAV tracking and beyond. The dataset, toolkits, and baseline results are available at https://github.com/983632847/WebUAV-3M.
Movie Recommendation System with Sentiment Analysis

A recommendation system is a system that, depending on certain data, makes suggestions to users for specific resources like books, movies, songs, etc. The characteristics of previously loved movies are typically used by movie recommendation systems to anticipate what movies a user would like. Such recommendation systems are advantageous for businesses that gather data from a lot of clients and want to successfully offer the finest recommendations. When creating a movie recommendation system, several variables may be taken into account, including the movie's genre, cast, and even director. The algorithms are capable of recommending movies based on a single attribute or a combination of two or more. The recommendation algorithm in this study is based on the kinds of genres that the user would want to watch. The method used to do this is content-based filtering with genre relevance. Movie Lens set of data is the one processed by the system. R is the data analysis programmed utilized.
On Implicit and Inverse Function Theorems on Euclidean Spaces

Summary Previous Mizar articles [7, 6, 5] formalized the implicit and inverse function theorems for Frechet continuously differentiable maps on Banach spaces. In this paper, using the Mizar system [1], [2], we formalize these theorems on Euclidean spaces by specializing them. We referred to [4], [12], [10], [11] in this formalization.