Reviewerly/exHarmony huggingface dataset:
Real-Time Eye Blinking for Password Authentication

For user authentication and protection, personal identification numbers (PIN) are commonly used. Users join a physical PIN, which can be broken or hacked using overhead surfing or thermal tracking. PIN verification with hands-off eye blinks, on the other hand, leaving no physical fingerprints and thus provides a more secure alternative to password entry. Eye blinks-based authentication is the process of detecting eye blinks through multiple picture frames and creating a PIN. This project represents the real-time applications that avoids shoulder surfing and thermal tracking spasms by combining eye blink-based PIN entry, face detection, and OTP (One Time Password). To prevent shoulder surfing and thermal tracking attacks, we'll suggest a three-layer protection scheme. Our system contains three layers which are 1. Face reorganization, 2. Eye blink verification, and 3. OTP by combining all these layers we are going to implement our secure framework to avoid thermal tracking and shoulder surfing attacks. In our frameworks, there is no physical entry of passwords so we are completely avoiding the shoulder surfing and thermal tracking attacks. For the first layer of security, we are using the Deep Learning algorithm, for the second layer we are using OpenCV.
Study on Cyber Attack Damage Assessment Framework

Cyberspace has expanded as a result of the rapid spread of the Internet.This expansion has led to a change from conventional warfare to a form that includes cyber warfare.Cyber warfare, which occurs in cyberspace, consists of numerous cyberattacks using the vulnerabilities of cyber assets.Cyberattacks have increased with increases in the number of assets connected to the network and the information held by these assets; information can control the outcome of a war.Therefore, it is very important to assess the damage from cyberattacks between physical operations because such cyberattacks may be directly linked to the failure of an operation.This paper presents a cyber damage assessment framework for assessing the damage from cyberattacks between physical operations.
A New Benchmark Data Set for Chemical Laboratory Apparatus Detection

In chemistry laboratory, the safety of the experimenter is essential. The chemical laboratory apparatus is an important part of the chemical laboratory, and apparatus safety cannot be ignored. An auxiliary device that takes advantage of artificial intelligence and computer vision for the automatic detection of chemical laboratory apparatus can adapt to the changing realities to improve personnel safety. The most important task is to accurately identify and locate these apparatuses from the image. In this work, we captured 2246 images (data augmentation to 13,476) in the real chemical laboratory environment, containing 21 types of chemical laboratory apparatus, and produced an open Chemical Laboratory Apparatus Dataset (CLAD) by annotating the images. To implement feature extraction, classification and localization in one algorithm and automate the whole, we conducted experiments on this dataset using the deep learning models Faster R-CNN (3 backbones), Single Shot MultiBox Detector (SSD), YOLOv3-SPP and YOLOv5, to fully analyze the performance of this dataset. Experiments demonstrate Faster R-CNN (resnet50-fpn) performs the best in terms of average precision (99.0%), YOLOv5 performs the best in terms of average recall (94.5%), and in terms of inference speed YOLOv5 can process the most images per second (62) on our device, and it has satisfied real-time applications.
Extractive Text Summarization Using Feature-Based Unsupervised RBM Method

A methodology for creating shorter and meaningful summaries for single documents is provided. With a lot of content to be had on the web, it’s far simply no longer possible to go through each information source in complete detail. Consequently, a great mechanism is needed to extract relevant information. To overcome these challenges, information in the form of text is summarized with the objective to get relevant knowledge without loss of any information. A methodology for extractive text summarization for single-document summary is devised and developed in this work. It uses a restricted Boltzmann machine to choose essential phrases from the text. The text documents used for summarization are in the English language. Various aspects are used to generate meaningful phrases, and the restricted Boltzmann machine is being utilized to enrich and abstract those features to improve the consequent accuracy without sacrificing any significant information. The sentences are scored, and an extracted summary is created based on those enhanced features. The result indicates that the presented methodology tackles the problem of text overload by producing an appropriate summary. The result of RBM has been compared with the Text Rank, Lex Rank, LSA, and Luhn algorithm. The experimentation is carried out, and the summary is generated for eight different document sets and the result is evaluated using the ROUGE-1 score.
Practical Solving of Discrete Logarithm Problem over Prime Fields Using Quantum Annealing

This paper investigates how to reduce discrete logarithm problem over prime fields to the QUBO problem to obtain as few logical qubits as possible. We show different methods of reduction of discrete logarithm problem over prime fields to the QUBO problem. In the best case, if n is the bitlength of a characteristic of the prime field $$\mathbb F_p$$ , there are required approximately $$2n^2$$ logical qubits for such reduction. We present practical attacks on discrete logarithm problem over the 4-bit prime field $$\mathbb F_{11}$$ , over 5-bit prime field $$\mathbb F_{23}$$ and over 6-bit prime field $$\mathbb F_{59}$$ . We solved these problems using D-Wave Advantage QPU. It is worth noting that, according to our knowledge, until now, no one has made a practical attack on discrete logarithm over the prime field using quantum methods.
Integration of digital technologies in educational model for transportation professionals

The relevance of transportation specialists training for knowledge-intensive production is connected with the reform of Russian Doctoral Program (from 2011) and epidemiological situation. The purpose of the study is to design Doctoral Program model for Transportation Professionals based on the doctoral student‘s assessment of training in the period from March 2020 to December 2021. Empirical methods (interview, conversation), the semantic analysis method, the methods of analysis, synthesis and modeling were used to obtain and prosses the results. The reference group consisted of 59 students (transportation specialists) (1st and 4th grades) of different transportation specializations. The positive responses of 1st grade students (69.70 %) and 4th grade students (69.23 %) on hybrid educational model demonstrate the possibility of implementation a hybrid educational model based on actualization of on-line lectures + on-line practical classes (if it is necessary) + off-line experiment + off-line individual consultations depending on the grade and specialization. The flexible hybrid model will allow to train highly qualified transportation professionals in accordance with modern educational and technological trends and optimize the process of research work itself.
Regressing Word and Sentence Embeddings for Low-Resource Neural Machine Translation

In recent years, neural machine translation (NMT) has achieved unprecedented performance in the automated translation of resource-rich languages. However, it has not yet managed to achieve a comparable performance over the many low-resource languages and specialized translation domains, mainly due to its tendency to overfit small training sets and consequently strive for new data. For this reason, in this article, we propose a novel approach to regularize the training of NMT models to improve their performance over low-resource language pairs. In the proposed approach, the model is trained to copredict the target training sentences both as the usual categorical outputs (i.e., sequences of words) and as word and sentence embeddings. The fact that word and sentence embeddings are pretrained over large corpora of monolingual data helps the model generalize beyond the available translation training set. Extensive experiments over three low-resource language pairs have shown that the proposed approach has been able to outperform strong state-of-the-art baseline models, with more marked improvements over the smaller training sets (e.g., up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+6.57$</tex-math></inline-formula> BLEU points in Basque–English translation). A further experiment on unsupervised NMT has also shown that the proposed approach has been able to improve the quality of machine translation even with no parallel data at all.
Design of a Secure Blockchain-Based Toll-Tax Collection System

The most advanced application of the Electronic Toll Collection (ETC) system is to collect the Toll-Tax Amount (TA) without slowing down a vehicle’s speed at the toll plazas of the national highways. A few existing ETC systems suffer from various unexpected activities such as data security, transparency of the stored data, the privacy of users, and data immutability as these systems perform in a centralized platform. Blockchain is a secure technology for its nascent features such as decentralization, transparency, and data security. In this paper, a Blockchain-based Automated Toll-Tax Collection System (BATCS) has been proposed. The proposed system can collect an appropriate TA without stopping the vehicle while it passes the toll plaza. While vehicles cross the toll plaza, the predefined amount of tax will be deducted automatically from the bank account. The smart contract can authenticate the vehicle data and collect TA automatically at toll plazas. This research work provides Security, Trust, Transparency, and Privacy (STTP) in the field of the ETC system. The significant benefits of the BATCS concerning the RFID-based system are less fuel consumption and more time-saving for a vehicle. It also provides the zero-waiting time in the queuing line of the toll plazas.
Paired Image to Image Translation for Strikethrough Removal from Handwritten Words

Transcribing struck-through, handwritten words, for example for the purpose of genetic criticism, can pose a challenge to both humans and machines, due to the obstructive properties of the superimposed strokes. This paper investigates the use of paired image to image translation approaches to remove strikethrough strokes from handwritten words. Four different neural network architectures are examined, ranging from a few simple convolutional layers to deeper ones, employing Dense blocks. Experimental results, obtained from one synthetic and one genuine paired strikethrough dataset, confirm that the proposed paired models outperform the CycleGAN-based state of the art, while using less than a sixth of the trainable parameters.
Non-government organisations as a basis for sustainable development of education

The article examines the experience of team work of scientists-members of a non-government organisation (NGO) “Smart Math”. The analysis of the advantages of scientists’ collaboration and communication are offered in the current research. The paper presents the findings of scientific collaboration and cooperation of researchers, whose activity is represented on the open educational platform “Higher School Mathematics Teachers”. The areas of activity of a team of researchers, which brings together teaching staffof Ukrainian universities, and tackling the issue of developing on-line courses are described in the present article. The outcome of such collaboration of the members of “Smart Math” in 2020 is the increase by 4,7 in the average citation index in scientometrical databases publications.
Reshaping Local Path Planner

This letter proposes a path planner that reshapes a global path locally in response to sensor-based observations of obstacles in the environment. Two fundamental concepts enable the resultant algorithm (a) a path-following <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synthetic vehicle</i> whose steering actions are non-myopically optimized to result in a smooth traversible path that meets path curvature constraints, and (b) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-aware</i> turning moment-field that enables obstacle avoidance while eluding the typical local-minimum-induced stagnation associated with potential field methods. The use of the combination of the two concepts results in a reduced action space over which optimization needs to be performed towards minimizing the path deviation subject to obstacle avoidance, and thus results in an efficient algorithm that can be implemented online. We demonstrate the algorithm in simulations as well as in field experiments, performing real time local path planning and obstacle avoidance on two different vehicle platforms (an ackerman steering vehicle two-axled vehicle, and a differential-steering 4-axled vehicle) in an unstructured off-road terrain.
Unsupervised abnormality detection in medical images with deep generative methods

Detecting and identifying pathological structures is one of the key tasks in the research of medical image computing. In clinical practice, the task is one step of diagnosis involving doctors and radiologists, and can be tedious and time-consuming. With the rise of machine learning and deep learning, researchers have formulated the task mathematically and proposed numerous methods to automate and accelerate this process. Current research has been actively developed around several topics, such as semantic segmentation, active learning and continual learning, where one of each can present years of research works. In spite of the great variety of research, we would like to dedicate this chapter to one of these interesting tasks, unsupervised abnormality detection, where pathological structures are detected as abnormalities according to prior knowledge representation in the form of features or distribution for healthy anatomy. Unsupervised abnormality detection, though more challenging than supervised detection, has the advantage of data efficiency and capability of detecting pathologies without specification. Approaches such as probabilistic models and generative models are learned on a dataset consisting of clinically confirmed healthy images, and then new images are evaluated by the learned model to decide whether they have high probability in the estimated distribution, e.g., healthy images, or low probability, e.g., abnormal images. Typical deep-learning-based approaches are built with generative models such as generative adversarial networks and variational autoencoders, and have been evaluated on large public datasets. The detection accuracy of unsupervised abnormality detection has seen significant improvement in recent years and gradually narrows the gap with supervised methods.
Low Latency Streaming for Path-Walking VR Systems

Highly immersive content in the form of extended reality (XR) is attracting attention as an alternative to conventional video services such as YouTube and Facebook. Many galleries and museums already offer online virtual reality (VR) tours where users are free to choose the spot they want to move to, beyond merely looking around. Although the ease of implementation, this key-spot hopping is still far from giving the real feeling of walking. Meanwhile, in recent volumetric or light-field-based studies, view rendering that supports free and continuous viewpoint movements has been attempted. With online services in mind, however, the high data volume and computational complexity are a big obstacle to practical applications. Path-walking VR, the target system of this paper, can be a good compromise, where the viewer can enjoy the virtual space while walking along the route. The interactive path-walking VR service is entry-level immersive video, but streaming over the network is still challenging. One of the main problems to be tackled is that the movement patterns of viewers need to be reflected in the streaming strategy to improve the quality of experience. Unlike unidirectional video, the movement of the viewer determines which images and how many images should be transmitted. This paper proposes schemes to reduce streaming delays by reflecting the viewer’s movement characteristics. It is differentiated from existing studies for omnidirectional video in that the proposed schemes control not only image quality but also view update rate. The first is a caching strategy which takes advantage of the geometrical locality of the virtual space that the viewer will soon reach a position close to the current position. This not only reduces the communication delay from the server, but also decreases the burden of server-side request handling. The second scheme uses the relationship between the viewer’s speed and the field of vision. The image quality is adjusted according to the viewer’s speed and head direction. Experimental results show that the proposed schemes achieve stable viewer’s experience by considering walking characteristics in virtual space. It is expected that the results of this paper will provide insight to those who design interactive streaming systems for immersive media applications.
Landmark-Aware and Part-Based Ensemble Transfer Learning Network for Static Facial Expression Recognition from Images

Facial expression recognition from images is a challenging problem in computer vision applications. Convolutional neural network (CNN), the state-of-the-art method for various computer vision tasks, has had limited success in predicting expressions from faces having extreme poses, illumination, and occlusion conditions. To mitigate this issue, CNNs are often accompanied by techniques like transfer, multitask, or ensemble learning that provide high accuracy at the cost of increased computational complexity. In this article, the authors propose a part-based ensemble transfer learning network that models how humans recognize facial expressions by correlating visual patterns emanating from facial muscles’ motor movements with a specific expression. The proposed network performs transfer learning from facial landmark localization to facial expression recognition. It consists of five subnetworks, and each subnetwork performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification. The network’s performance is evaluated using the Cohn-Kanade (CK+), Japanese female facial expression (JAFFE), and static facial expressions in the wild datasets, and it outperforms the benchmark for CK+ and JAFFE datasets by 0.51% and 5.34%, respectively. Additionally, the proposed ensemble network consists of only 1.65 M model parameters, ensuring computational efficiency during training and real-time deployment. Gradient-weighted class activation mapping visualizations of the network reveal the complementary nature of its subnetworks, a key design parameter of an effective ensemble network. Lastly, cross-dataset evaluation results show that the the proposed ensemble has a high generalization capacity, making it suitable for real-world usage.
A Feasible Algorithm for Solubility of Transitive Graphs

Transitive, weighted graphs, with the weights coming from an Abelian group and satisfying a transitivity property, were studied in [1] and were referred to as the transitive systems. Of central importance is the question of completing a given system to a transitive system where every ordered pair of the vertices can be assigned a weight. It was proven in [1] that, in the finite case, the question can be reduced to the situation when the Abelian group is a finite cyclic group. Nevertheless, in practical applications, the most common assignments of the weights are coming from the groups of numbers. For this reason, we consider the case when the underlying Abelian group G is torsion-free. We show that within the class of all torsion-free Abelian groups, the underlying transitive and unweighted graph predetermines the possibility of completion of a given transitive system. Such a graph is G-soluble. The question of G-solubility of a graph is equivalent to its ℝ-solubility. This, in turn, is equivalent to solvability of a certain system of linear equations in ℝ. As a result, G-solubility of a transitive graph can be determined by means of a feasible algorithm.
DF E-Commerce: A Deep Learning Integrated Ontology Driven Model for E-Commerce Product Recommendation for Improved Machine Intelligence

A product recommendation system is the critical component of any functioning E-commerce industry. It is crucial for these corporations to present their application with a recommendation system capable of furnishing products according to the desires of their customers by considering an array of factors. This paper proposes a unique approach that integrates textual data and data comprised of images by hybridizing both Machine Learning and Deep Learning strategies, thereby emphasizing user search queries and product images impartially. Distinct stages are presented for intermediate image set generation prior to user search preferences and term set generation related to the user query. A customized web-crawler along with search queries and click rates are used respectively. The architecture utilizes a nature-inspired Dolphin’s echolocation metaheuristic algorithm, with objective functions scilicet SemantoSim, Normalized Google Distance (NGD), and Shannon’s entropy to compute semantic similarity and semantic deviants. Experimentations indicated when compared with baseline models that the proposed DF E-commerce outperformed the baseline models with an accuracy of 97.3% with an FDR of 0.04.
Unveiling the Process of Phishing Precautions Taking: The Moderating Role of Optimism Bias

Phishing has emerged as a critical security threat to individuals and organizations. Academics and practitioners have developed various methods to convince individuals to take anti-phishing precautions. However, people are not rational decision makers. One important factor influencing one’s risk decision making is optimism bias, which refers to people’s tendency to believe that they are less likely to experience negative events and more likely to experience positive events than others. It is well indicated in the literature that optimism bias may hinder one’s efforts to develop and implement risk-reducing behaviors. To the best of our knowledge, the important role played by optimism bias in people’s decision-making process with respect to phishing has not been fully explored in previous studies. Drawing on the theory of reasoned action, this research explored how optimism bias moderates the relationship between phishing risk and attitude toward the adoption of anti-phishing precautions. The results indicate that phishing risk determines the attitude toward anti-phishing precautions, and that optimism bias weakens this relationship.