Reviewerly/exHarmony huggingface dataset:
Distributed Decision Trees

In a budding tree, every node is part internal node and part leaf. This allows representing the tree in a continuous parameter space and training it with backpropagation, like a neural network. Unlike a traditional tree whose construction is composed of two distinct stages of growing and pruning, “bud” nodes grow into subtrees or are pruned back dynamically during learning. In this work, we extend the budding tree and propose the distributed tree where the children use different and independent splits; hence, multiple paths in a tree can be traversed at the same time. In a traditional tree, the learned representations are local, that is, activation makes a soft selection among all the root-to-leaf paths in a tree, but the ability to combine multiple paths of the distributed tree gives it the power of a distributed representation, as in a traditional perceptron layer. Our experimental results show that distributed trees perform comparably or better than budding and traditional hard trees.
How to Avoid Repetitions in Lattice-Based Deniable Zero-Knowledge Proofs

Interactive zero-knowledge systems are a very important cryptographic primitive, used in many applications, especially when deniability (also known as non-transferability) is desired. In the lattice-based setting, the currently most efficient interactive zero-knowledge systems employ the technique of rejection sampling, which implies that the interaction does not always finish correctly in the first execution; the whole interaction must be re-run until abort does not happen. While repetitions due to aborts are acceptable in theory, in some practical applications it is desirable to avoid re-runs for usability reasons. In this work we present a generic technique that departs from an interactive zero-knowledge system (that might require multiple re-runs to complete the protocol) and obtains a 3-moves zero-knowledge system (without re-runs). The transformation combines the well-known Fiat-Shamir technique with a couple of initially exchanged messages. The resulting 3-moves system enjoys honest-verifier zero-knowledge and can be easily turned into a fully deniable proof using standard techniques. We show some practical scenarios where our transformation can be beneficial and we also discuss the results of an implementation of our transformation.
Simplex: Repurposing Intel Memory Protection Extensions for Secure Storage

The last few decades have seen several hardware-level features to enhance security, but due to security, performance, and/or usability issues these features have attracted steady criticism. One such feature is the Intel Memory Protection Extensions (MPX), an instruction set architecture extension promising spatial memory safety at a lower performance cost due to hardware-accelerated bounds checking. However, recent investigations into MPX have found that is neither as performant, accurate, nor precise as software-based spatial memory safety. Given its ubiquity, we argue that it provides an under-utilized hardware resource that can be salvaged for security purposes. We propose Simplex, an open-sourced library that re-purposes MPX registers as general purpose registers. Using Simplex, we demonstrate securely storing sensitive information directly on the hardware (e.g. encryption keys). We evaluate for performance, and find that deployment is feasible in all but the most performance-intensive code, with amortized performance overhead as low as about 1%.
Named Entity Recognition on CORD-19 Bio-Medical Dataset with Tolerance Rough Sets

Biomedical named entity recognition is becoming increasingly important to biomedical research due to a proliferation of articles and also due to the current pandemic disease. This paper addresses the task of automatically finding and recognizing biomedical entity types related to COVID (e.g., virus, cell, therapeutic) with tolerance rough sets. The task includes i) extracting nouns and their co-occurring contextual patterns from a large BioNER dataset related to COVID-19 and, ii) annotating unlabelled data with a semi-supervised learning algorithm using co-occurence statistics. 465,250 noun phrases and 6,222,196 contextual patterns were extracted from 29,500 articles using natural language text processing methods. Three categories were successfully classified at this time: virus, cell and therapeutic. Early precision@N results demonstrate that our proposed tolerant pattern learner (TPL) is able to constrain concept drift in all 3 categories during the iterative learning process.
Augmented Reality for Naval Domains

Since the pioneering work of Ivan Sutherland and the success of Tom Furness’ “Super Cockpit,” augmented reality (AR) has been an interest of the US Department of Defense (DoD) for some time. As hardware advances enabled more ambitious applications, the US Navy and US Marine Corps (USMC) have carried this research forward into new application areas and into development programs to explore the practical potential of the technology to assist in accomplishing tasks required in Naval applications (including land-based operations of USMC). We review a selection of AR research and development conducted by the US Navy, ranging from basic research to prototype development. We draw lessons from the progression of projects through the R&D spectrum and explore common threads through varied applications. Challenges that currently limit the application of AR technology to naval domains are construction of environmental models that enable integration of the virtual and real elements, ability to operate in mobile scenarios, and usability concerns such as information overload.
An Unsupervised DNN Embedding System for Image Clustering

Recently, AutoEmbedder has been introduced that implements DNN classifiers as an embedding system. Although the AutoEmbedder generates clusterable embeddings, it is trained in a supervised approach. Therefore, in this paper, we introduce an unsupervised AutoEmbedder (UAutoEmbedder) that is trained in an unsupervised fashion. Through rigorous research effort, we discovered that AutoEmbedder is an ideal architecture for data augmentation dependent unsupervised learning. Hence, we propose a random sampling scheme and perform image data augmentation to train the AutoEmbedder system in an unsupervised manner. Furthermore, we reform the training procedure of the AutoEmbedder that properly utilizes the unsupervised learning strategy. We evaluate the model by applying different datasets and conduct an in-depth study of the generated results. From the comprehensive evaluations, we perceive that UAutoEmbedder has enormous opportunities in unsupervised learning and image data augmentation.
Vision Transformers-based Hand Gesture Classification

Hand gesture recognition (HGR) is a hot topic in machine learning and image processing communities. HGR is also vital for some Human-Computer Interaction (HCI) applications. Up to now, traditional machine learning approaches and deep convolutional neural networks (CNN) have been applied to HGR. Although these methods perform well enough on HGR, in this paper, we used a recent model namely vision transformer (ViT) on HGR. ViT is developed for improving the performance of CNN. ViT has a similar architecture to CNN but it has also different layers for the classification task. We used the ViT in a transfer learning fashion and applied it to the NTU hand gesture dataset. A holdout cross-validation test approach is considered in experiments and classification accuracy is used as a performance measure. The experimental works produce a 96.4% accuracy score and a comparative study with CNN models is carried out. Results show that the proposed model has potential in HGR.
Proposing an augmented reality-based system for fashion and beauty makeover

With the large-scale virtualization of essential and non-essential services in today’s world, it has become imperative to develop an efficient and all-round online shopping system which stimulates the shopping experience while avoiding physical contact. Especially given the recent pandemic situation, such an online shopping system which incorporates the experience of physical shopping would turn out to become extremely useful. This paper thus proposes various methodologies, techniques and technologies that can be used to effectively implement the said system in the fashion industry, by using augmented reality and machine learning for a holistic user experience.
Super-resolution of document images using transfer deep learning of an ESRGAN model

This paper presents a novel super-resolution approach of document images. It is based on transfer deep learning of an ESRGAN model. This model, which showed good robustness on natural images, has been adapted to document images by using better levels of fine-tuning and a post-processing to enhance contrast. The experiments were carried out on our document image dataset that we built from document images presenting different challenges. Documents of different categories with different complexity levels and degradation kinds. The results obtained are better compared to ten existing approaches, which we have developed and tested on the same dataset with the same evaluation protocol.
Blockchain Based Project Management

With the latest advancements in blockchain technology, it is now easier than ever to create secure and transparent trust systems with the infrastructure of peer-topeer networks. However, adopting this technology requires a fundamental shift in how traditional businesses operate. To assist in this transition, Hyperledger Fabric allows businesses and organizations that require confidential transactions to coexist on the same network with features such as channels and identity services. Thus, applications such as project management which requires accountability and transparency in many levels are made possible with a better perspective for all parties.This paper proposes and implements a solution based on a private permissioned blockchain which is essential for assuring access control policies and non-deniability purposes where a participant’s anonymity is not required in a blockchain. By using the non-deniability and manageability of the blockchain technology, we implemented a solution to show an account-able role-based access control mechanism architecture for medium to large businesses. (Abstract)
Computational Identification of Author Style on Electronic Libraries - Case of Lexical Features

In the present work, we intend to present a thorough study developed on a digital library, called HAT corpus, for a purpose of authorship attribution. Thus, a dataset of 300 documents that are written by 100 different authors, was extracted from the web digital library and processed for a task of author style analysis. All the documents are related to the travel topic and written in Arabic. Basically, three important rules in stylometry should be respected: the minimum document size, the same topic for all documents and the same genre too. In this work, we made a particular effort to respect those conditions seriously during the corpus preparation. That is, three lexical features: Fixed-length words, Rare words and Suffixes are used and evaluated by using a centroid based Manhattan distance. The used identification approach shows interesting results with an accuracy of about 0.94.
The Use of Immersive Technologies in Industry 4.0: A State-of-Art Review

This research reviews the main immersive technologies used in the scientific field of Industry 4.0. In the investigation, an account of the type of Industry 4.0 technology used in implementing these solutions has been made, as well as the same country where the investigations were carried out. Finally, immersive technologies require devices, platforms, and specific software to construct a program that compacts the 3D models and their programming to realize an entire immersive system.
Towards Studying the Relationship between Job Satisfaction and Organizations’ Information Security

Recently, all organizations have held a variety of data, including product and customer insights. These data frequently contain sensitive information that, if revealed, destroyed, or deleted, would result in significant financial loss. Information security is the concept that focuses on securing the information of organizations. It prevents unauthorized access or alteration by malicious attackers or organizations' employees. Job satisfaction is a key factor that organizations seek to achieve for their employees. Hence, employees become more committed and are in a state of loyalty and attachment to the organization and its goals. Although many studies have been conducted on job satisfaction and the information security; however, there are still gaps that need to be filled in the literature. Therefore, this paper aims to explicate the related studies in order to find the relationship between job satisfaction and organizations’ information security, and to examine their impacts on each other. To do so, this study employed the exploratory research design because it concentrates on gaining insights and well-grounded information for the investigation. Three phases, which are (1) identifying the problem, (2) reviewing the literature, and (3) synthesizing and reporting the findings, were followed to achieve the aim of this study. The results indicate a vital relationship exists between job satisfaction and the organizations' information security leveraging the business organizations. Further research would focus on examining this relationship through interviews with experts in business organizations and surveying the impact of employee job satisfaction on the organizations' information security.
Efficient Arabic Text Classification Using Feature Selection Techniques and Genetic Algorithm

The rapid growth of the Internet in recent years has been accompanied by a huge increase in the number of data being shared at every moment, especially text data. This huge amount of text data requires finding new techniques to be able to deal with the data. One form of these texts is the Arabic texts which poses a big challenge to researchers due to the highly derivative nature of Arabic texts. In this study, we first aim to create an effective classification model for Arabic texts, where we will start by creating a huge high-quality dataset consisting of different versions collected by web scraping techniques. Then, we apply term weighting (TW) and feature selection (FS) procedures to be able to classify our collected Arabic dataset and propose an approach that is based on genetic algorithm (GA) to ensure that the best subset of features is selected with high quality. The results prove that our proposed approach can select up to 1% of the original feature size with high efficiency. The results provide detailed information about the result of each classification technique and TW schemes, and runtime needed to obtain the best results to aid in future research aimed at developing Arabic text classification techniques.
TACS: A Calibrated Highway Surveillance Dataset for Traffic Analysis

Access to live streams of traffic surveillance cameras is becoming more common across the world. Despite this, many current traffic analysis algorithms do not utilize such cameras; rather, they rely on other sources of data. This can be attributed to the arduous process of extracting the necessary traffic information for such algorithms. The TACS (Traffic Analysis on Calibrated Streams) dataset aims to remove this barrier by annotating sig-nificant traffic information on realistic traffic surveillance video. We collect live video from the Virginia Department of Transportation and perform accurate camera calibration. After calibrating the camera, we obtain a top-down view of the video stream and extract the scene scale. From this, we calculate the real world position and speed of each vehicle. On top of such information, we calculate vehicle lane positions and two types of road congestion metrics. We collect between 10,000 and 15,000 frames from each of its 10 traffic video sources. Thus, the TACS dataset contains a wide variety of data and provides researchers with rich annotations for traffic-related algorithms.
Design and Enhancement of a CNN Model to Augment the Face Recognition Accuracy

In the last decade, smart home security applications have relied more on human biometrics in their functions, due to the reliability and the high-precision results these technologies provide. Face recognition is one of the popular biometrics in the field of image processing technologies. Human face recognition processing is a complicated operation that involves different factors and circumstances such as the illumination degree and the position of the face that affects the final recognition rate. In this research, the Convolution Neural Network (CNN) architecture is used in the extracting phase of significant features of the face shape, and the SoftMax classification layer was used to identify faces in the fully connected CNN layer. This paper provides an update of CNN architecture by applying a three-batch normalization layer to the CNN design. By applying this modification, the system network speed increased with a better recognition rate. The recognition rate also increased by applying two DWT levels with a bio5.5 filter to the training group of the database images and the tested image before applying the PCA dimensional reduction algorithm instead of using the PCA algorithm alone. The obtained face recognition rates have been improved to 99.75% by applying the proposed CNN scheme. While applying the proposed hybrid approach (using the PCA next to applying DWT-2 levels with bior5.5 filter) has registered a 99.25% recognition rate compared to a 96.75% recognition rate when obtained by applying the PCA method alone. The research has adopted using a set of 360 training images and 40 test images set of the standard ORL Database in its work.
Feature Selection Approach for Phishing Detection

This study offers a novel approach to the feature selection. We propose a method to reduce the number of features while maintaining high detection accuracy by combining two popular feature importance methods and the GRiS mean method. The proposed model also improves the training time, test time, and usage of memory. Among the Ill features of the latest published phishing website detection dataset, the efficient features were selected with this approach. Using the Random Forest algorithm and the proposed feature selection approach with only nineteen features, the accuracy of phishing detection can reach up to 97.12%.
Radar 2023 – Autonom Semantic Scholar

In year 2023 of information era, computers also need to read. In this paper, computer processing methodology of RST made X-Band radar systems is explained in terms of “See” the environment and “Read”, “Understand”, and “Show” the environment to users. Seeing requires computer system to own or use data from a sensor such as radar or camera. Reading requires the radar to process using an alphabet such as wavelets. Understanding is performed by formal concept analysis autonomously by semantic learning, similar to deep learning. Output is shown in 3D graphic engines and the radar displays of 2023 now require 4K TV.
A Generative Approach to Mitigate Bias in Face Matching using Learned Latent Structure

This work tackles the problem of bias in face matching algorithms. Face matching refers to the task of matching a low-resolution face image of a person with a high-resolution face image of the same person and has applications in security and personalization. Algorithmic bias is the difference in performance of an algorithm based on demographic descriptors of various users. Such bias can significantly reify and amplify societal biases and make certain advancements in technology benefit one section of the society while hurting the other. This work proposes a generative AI framework that can counter multiple kinds bias (e.g., gender bias and age bias) at the same time. The framework consists of two major components: a variational auto-encoder (VAE) that converts the images into their more generic underlying representation, and second, a neural network architecture that uses the above representations to undertake multi-label classification. A generative approach is useful in ensuring that the system learns to deal with the underlying (latent) structure of the data for better generalizability and bias reduction. The approach is tested over a public image dataset and found to be effective at reducing bias while maintaining high accuracy.
FLUID: Formulated LiqUid Instability Dataset

We present FLUID, a new dataset of more than 47,000 pictures of softener samples developing typical instabilities of formulated liquids of industrial interest. The full-color pictures, labelled by a pool of experts, are organized in 5 categories (stable, phase separation, creaming, cracking, and flocculation) and can be accessed either as single frames or as an ordered series of 8 to 10 frames, equally spaced in time, belonging to the same sample. FLUID is publicly available on-line at the link https://github.com/mauriziodemiccounina/FLUID.git and is intended to become a benchmark dataset. It can be used to perform instability detection analysis and potentially forecast of failures in formulated liquids. Furthermore, we show that it represents a precious tool to train learning-based methods and boost their performance by means of deep architectures.
Automated Honduran Banknote Image Classification using Machine Learning

A new L200 banknote arrived to commemorate the bicentennial of Honduras. This made necessary to update automated methods for banknote image classification. The goal of this work was to develop algorithms that take centered images of banknotes and determine their denominations and visible sides. Two classification methods are presented. The first one uses local descriptors such as ORB or SIFT to match keypoints between the input image and templates to create feature vectors, and the images are then classified using Support Vector Machines or Random Forests. The second method is a Convolutional Neural Network (CNN) called LempiraNet, where transfer learning was used to deal with the limited data available. In both methods, image preprocessing can be used to locate the banknote to make its classification easier. To evaluate the effectiveness of these methods, two sets of 412 and 265 images were used for training and testing respectively. Multiple configurations were considered per method, and each one was evaluated in terms of recall, precision, F1 and run time. It was found that both methods reached at least 98% F1 score when using image preprocessing for locating the banknote in the input image. Also, it is observed that SIFT has better performance than ORB. In terms of run time, LempiraNet was at least 20 times faster than the other method, making it usable in real applications.
An Approach to Constructing Formal Model for Product Families

Software product family engineering is mainly used to analyze and manage product family feature models by using variability modeling. The formal specification approach is beneficial to ensure the quality of feature models. However, existing formal methods use an informal approach in the domain analysis phase and separate the variability modeling from the feature specification, which leads to consistency problems in the final product family feature models obtained. To solve this problem, this paper proposes a method to check the consistency of its results with the variability model and guide the user to solve the inconsistencies during the process of formalizing the feature, with the guidance of rule or based on it. We will give the format of the specification and the algorithm for consistency checking, and the consistency checking rule library will focus on checking the consistency of variables and functional scenarios. We give a case study to show that our approach is effective.