Reviewerly/exHarmony huggingface dataset:
Analysis of Technical Solutions for the Creation and Development of the Quantum Internet

Research in the field of quantum physics in order to design and build quantum computers and the quantum Internet is carried out by scientists from many countries. Recently, the interests of researchers have been directed both at the creation of wired networks (through the connection of quantum computers via fiber-optic cables) and through wireless devices (up to satellites located in Earth orbit). This study examines the history of the development of quantum devices and the quantum Internet both in foreign countries and on the territory of the Russian Federation, provides a communication scheme between two quantum processors via a router. The data concerning articles, reports and the results of workshops published in scientific journals in the bibliographic and abstract database of Scopus are analyzed; graphs are built on their basis. The data obtained clearly show that the interest of the scientific community in the subject under consideration has grown significantly over the past decade. The article also describes in detail the advantages, disadvantages and features of the quantum Internet, which can both accelerate the development of new generation networks and slow down research and obtaining concrete results in this area.
Israel and the Regional Arms Race in the Digital Era

This chapter examines how Israel contributes to the contemporary arms race in the Middle East. Facing security challenges posed by the Iran-Hamas-Hezbollah axis, the Israel Defense Forces (IDF) proposed a digitalization plan to empower itself with cutting-edge technologies. The vision was then materialized by Israel’s defense industry, which developed modern weaponry and equipment based on IDF’s needs. As the state-of-the-art systems enhanced Israel’s military superiority, they also disturbed the regional arms dynamic and balance of power. In response, Israel’s adversaries will attempt to narrow the technological gap and develop asymmetrical countermeasures, which again alters Israel’s security environment. The outcome is a vicious circle of a regional arms race. This mechanism is exemplified by Israel’s AI-powered arms development and its impact on the conventional and nuclear arms races in the region. What further distorts the arms dynamic is the military aid and arms exports of the USA to the region. In the foreseeable future, the Middle East is likely to witness an acceleration of arms races as emerging cutting-edge technologies are introduced by regional powers into the military domain as force multipliers.
Rules vs. Principles in POSI

In recent days, several signatories to the Principles on Open Scholarly Infrastructure have taken to performing self-audits of their compliance with the principles. Of course, holding oneself to account in this way is a welcome development. Without some form of self-appraisal it is not possible to know how close one is to fulfilling the goals of POSI.
Strategic information planning and performance of SMEs: A structural equation modelling approach

BACKGROUND: The business environment is getting unstable due to which execution of decision support systems has become significant to maintain competitive advantage. Small and medium enterprises in particular confront issues, such as insufficiency of devising strategic planning, strategic decision-making, and information exchange, as well as difficulty increasing performance. Because functional strategic management and decision-making are required, strategic information systems planning is employed to accumulate information and assist decision-makers in developing and implementing the best strategy for gaining superior performance. Executives in information systems focus on technical difficulties while ignoring decisions to support strategic plans. OBJECTIVE: The purpose of this article is to investigate how strategic information systems planning contributes to increased performance in SMEs. METHODS: Data was gathered through questionnaires distributed to information system executives in SMEs, primarily in Jordan. The data was collected with the help of questionnaires adopted from prior studies. For Analysis after applying the diagnostic tests, structural equation modelling has been applied to test the framework developed based on the literature. RESULTS: A structural equation modeling (SEM) was conducted, the individual loadings of the SEM items are investigated, reliability and discriminant validity is tested, and path coefficients of hypothesized relationships of the developed model are tested. CONCLUSIONS: The findings suggested that managers should prioritize implementing strategic information systems planning so that they can gain superior performance with better agility in the future. This research not only enhances the current understanding of the significance of strategic information systems planning but also assists supervisors to improve the procedure.
A combined neural network mechanism for categorizing the normal and cancer cells

The dangerous form of acute lymphocytic leukemia damages the bone marrow tissue and white blood cells. Unformed white blood cells multiply and exchange healthy cells in the bone marrow. Everything spreads fast and, if not noticed, can be dangerous in some months. As a result, computer assisted diagnosis of acute lymphocytic leukemia has the possible to protect several lives, but it needs a high-precision categorization of malignant cells, that is difficult due to the graphical similarity of malignant and normal cells. Though deep convolutional neural networks and the classic machine learning algorithms have exposed excellent results in classifying blood cell images, they have not been able to make effective use of the long-term correlation between some important image attributes and image labeling. This study introduced Long short-term memory (LSTM) to solve this difficulty. This study actually combines VGG16 neural networks and LSTM, which provide the VGG16-LSTM framework, which improves the recognizing of image content and learns the structural features of images, as well as initiate big data training in clinical image processing. The transfer learning approach was used to transfer pre-trained weight parameters to the VGG16 area in the ImageNet database, and the custom loss function was used to train and integrate the network quickly and accurately with the weight parameters. Experimental findings reveal that the suggested network approach is more relevant and effective in categorizing cancer cell images than other existing methods.
Predicting the success of transfer learning for genetic programming using DeepInsight feature space alignment

In Transfer Learning (TL) a model that is trained on one problem is used to simplify the learning process on a second problem. TL has achieved impressive results for Deep Learning, but has been scarcely studied in genetic programming (GP). Moreover, predicting when, or why, TL might succeed is an open question. This work presents an approach to determine when two problems might be compatible for TL. This question is studied for TL with GP for the first time, focusing on multiclass classification. Using a set of reference problems, each problem pair is categorized into one of two groups. TL compatible problems are problem pairs where TL was successful, while TL non-compatible problems are problem pairs where TL was unsuccessful, relative to baseline methods. DeepInsight is used to extract a 2D projection of the feature space of each problem, and a similarity measure is computed by registering the feature space representation of both problems. Results show that it is possible to distinguish between both groups with statistical significant results. The proposal does not require model training or inference, and can be applied to problems from different domains, with a different a number of samples, features and classes.
The Design and Development of Computerized Geogebra Mathematical Model for Painting Color Learning System

Mobile learning has seen widespread adoption in recent years, with schools, businesses, governments, and other organizations increasingly putting it to use. Its primary applications lie in the sectors of workplace training and campus network. The use of color goes back a very long way in human history, particularly in artistic expression. Every shade of color has a certain message and may be used in a variety of contexts. Color has a significant impact on people’s lives, which in turn has an impact on people’s feelings, which in turn has an impact on the caliber of our lives and our level of pleasure. Mobile learning is popular among users because of the many benefits it offers, including the absence of geographical constraints. Learners may learn in the few minutes they have available. Through the use of the Geogebra Mathematical Model, the purpose of the learning system is to instill in users an increased sensitivity to color and color matching, as well as a comprehension of color’s role in everyday life and throughout history. On the basis of mobile learning and color research, this paper combines the benefits of these two methods and creates the painting color learning system to assist learners in providing an information platform for color learning, color match training, and instances. The purpose of this system is to help learners provide a platform for information regarding color learning, color match training, and instances. According to the findings of the study, the system stimulates the interest of the students in learning and encourages the enhancement of the quality of instruction.
Implementation of Quantum Image Encryption via Reversible Quantum Logic Gates Computing

With the development of quantum communication technology, Shor’s algorithm is expected to crack RSA encryption. Quantum communication will change the method of classical encryption in the future. We propose a quantum image encryption method via reversible quantum logic gates computing as quantum computing is reversible. An entangled pair of qubits are used for data transmission. The plaintext image is encrypted into an encrypted image via the quantum gate by using superdense coding to encrypt and transmit the operating quantum gate. The encrypted image is decoded by reversible computing, which is expected to provide a way for quantum image encryption.
Meta-transfer Learning for Contextual Emotion Detection in Face Affirmation

Communication between people relies on human expression more than any other factor. Emotional identification is, right now, a hot study area. Typically, people will show seven distinct feelings (happy, angry, surprised, sad, disgusted, fearful, and neutral). Humans communicate their feelings via words, body language, and facial expressions. Humans' ability to reveal inner thought processes via facial expressions is crucial to studying human behavior. Many fields use facial expression analysis, including those concerned with increased security, automated criminal identification, the diagnosis of mental illness, and communication between people and computers. Most studies on emotion detection have only used straightforward CNN and RNN models. However, training such a big data set may take a long because the models need a vast data set. To this end, we suggested a model that combines the Mobile Net-V2 model with the transfer learning strategy to speed up the generation time and improve the accuracy of emotion detection. In order to improve upon the current method, we have compiled a large CIFE data collection from the relevant literature. One of the best outcomes for emotion detection is attained by the suggested model architecture when tested on the data mentioned earlier. Based on what we found, the new system should be able to identify more accurately than the old one.
Design of a Contextual IoT Framework for the Improved User Experience and Services

Since the advent of IoT-enabled smart devices, researchers have been engaged in research to extract contextual information about the user and manage devices based on their environmental characteristics. However, very few practical use cases to manage this growth of devices and control them based on features like forecasted temperature and sleep patterns are being explored in this fairly unexplored sub-domain. To resolve this issue, we propose this technique which is a contextual engine which utilizes various APIs to gather data about the user and uses this contextual awareness to assist the user in interacting with devices, creating a contextually aware environment. In this paper, we propose novel algorithms to deal with the influx of data, and we perform analyses based on its characteristics and pattern. We evaluated our mobile application with a sample population and retrieved an overall accuracy of 95.52% of working optimally.
Depth Edge and Structure Optimization-Based End-to-End Self-Supervised Stereo Matching

This paper addresses the challenge of poor cross-domain generalization performance in deep learning methods for stereo matching, particularly when dealing with unseen scenes or disparity maps lacking ground-truth information. To overcome this issue, we propose a self-supervised network called SANet. The network integrates a lightweight algorithm, AANet_Edge, which is based on depth edges optimization. In SANet, we combine AANet_Edge with a novel algorithm called SDCO, which efficiently extracts depth edges using a segment structure and employs a two-layer optimization framework to generate accurate dense disparity maps. These maps are then utilized for pixel-by-pixel supervised training. Furthermore, SANet incorporates multi-scale reconstructed left maps and multi-scale edges-aware modules to learn the structural features of the input image. To evaluate the effectiveness of SANet, comprehensive experiments are conducted on two standard benchmark datasets, namely KITTI 2012 and KITTI 2015. The experimental results demonstrate that SANet produces accurate disparity maps for unseen scenes or limited images and achieves high cross-domain generalization performance.
Anomaly detection for high-dimensional data using a novel autoencoder-support vector machine

Aiming at anomaly detection upon a high-dimensional space, this paper proposed a novel autoencoder-support vector machine. The key thought is that using the autoencoder extracts the features from high-dimensional data, and then the support vector machine achieves the separation of abnormal features and normal features. To increase the precision of identifying anomalies, Chebyshev’s theorem was used to estimate the upper of the number of abnormal features. Meanwhile, the dot product operation was implemented in order to strengthen the learning of the model for class labels. Experiment results show that the detected accuracy of the proposed method is 0.766 when the data dimensionality is 5408, and also wins over competitors in detected performance for the considered cases. We also demonstrate that the strengthened learning of class labels can improve the ability of the model to detect anomalies. In terms of noise resistance and overcoming the curse of dimensionality, the former can carry out more efforts than the latter.
It is not real until it feels real: Testing a new method for simulation of eyewitness experience with virtual reality technology and equipment

Laboratory research in the psychology of witness testimony is often criticized for its lack of ecological validity, including the use of unrealistic artificial stimuli to test memory performance. The purpose of our study is to present a method that can provide an intermediary between laboratory research and field studies or naturalistic experiments that are difficult to control and administer. It uses Video-360° technology and virtual reality (VR) equipment, which cuts subjects off from external stimuli and gives them control over the visual field. This can potentially increase the realism of the eyewitness's experience. To test the method, we conducted an experiment comparing the immersion effect, emotional response, and memory performance between subjects who watched a video presenting a mock crime on a head-mounted display (VR goggles; n = 57) and a screen (n = 50). The results suggest that, compared to those who watched the video on a screen, the VR group had a deeper sense of immersion, that is, of being part of the scene presented. At the same time, they were not distracted or cognitively overloaded by the more complex virtual environment, and remembered just as much detail about the crime as those viewing it on the screen. Additionally, we noted significant differences between subjects in ratings of emotions felt during the video. This may suggest that the two formats evoke different types of discrete emotions. Overall, the results confirm the usefulness of the proposed method in witness research.
Deep pyramidal residual networks with inception sub-structure in image classification

Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. In the network structure of DPRN, as the network depth increases, the number of convolutional kernels also increases linearly or nonlinearly. On the one hand, in the DPRN block, the size of the receptive field is only 3 × 3, which results in insufficient network ability to extract feature map information of different filter sizes. On the other hand, the number of convolution kernels in the second 1x1 convolution will be multiplied by a coefficient relative to the first convolution, which can cause overfitting to some extent. In order to overcome these weaknesses, we introduce the inception-like structure on the basis of the DPRN network which is called by pyramid inceptional residual networks (PIRN). In addition, we also discuss the performance of PIRN network with squeeze and excitation (SE) mechanism and regularization term. Furthermore, some results in network performance are discussed when adding a stochastic depth networkto the PIRN model. Compared to DPRN, PIRN achieved better results on the CIFAR10, CIFAR100, and Mini-ImageNet datasets. In the case of using zero-padding, the multiplicative PIRN with SE mechanism achieves the best result of 95.01% on the CIFAR10 dataset. Meanwhile, on the CIFAR100 and Mini-ImageNet datasets, the additive PIRN network with a network depth of 92 achieves the best results of 76.06% and 65.86%, respectively. According to the experimental results, our method has achieved better accuray than that of DPRN with same network settings which demonstrate its effectiveness in generalization ability.
Determination and categorization of Red Blood Cells by Computerized framework for diagnosing disorders in the blood

The determination and categorization of red blood cells (RBCs) from microscopic pictures is a critical step in the diagnosis of sickle cell disease (SCD). Traditionally, such procedures are performed manually by pathologists using a light microscope. Furthermore, manual visual evaluation is a time-consuming operation that relies on subjective judgment, resulting in variations in RBC recognition and counts. Mature If there is a blood problem, RBCs suffer morphological alterations. There are both automated and manual systems available on the market for counting the number of RBCs. Manual counting entails collecting blood cells with a Hemocytometer. The traditional procedure of exposing the smear below a microscope and physically measuring the cells yields inaccurate findings, putting clinical laboratory staff under stress. Automatic counters are incapable of detecting aberrant cell. The computer-aided method will assist in achieving accurate outcomes in minimum time. In this study presents an image processing method for separating red blood cells from several other blood products. Its goal is to analyze and interpret blood smear images to aid in the categorizing of red blood cells across 11 categories. The WBCs are extracted from the image using the K-Medoids technique, that is resistant to exterior disturbance. Granulometric assessment has been used to distinguish between red and WBCs. Feature extraction is used to obtain important features that aid in categorization. The categorization outcomes aid in a rapid diagnosis of disorders such as Normochromic, Iron Deficiency, Hypochromic, Sickle Cell, and Megaloblastic.
What’s the point of having open scholarly infrastructures and how do we test their resilience?

It is sometimes easy, when discussing openness, to get bogged down in the technical weeds. People often want detail and specifics: what open license should I use? Precisely how much revenue do I need to keep in reserve safely to wind-down an organization? When does advocacy become lobbying?
Human pose completion in partial body camera shots

Many actual images contain partial body camera shots, in which a significant part of the body is not visible. This issue is especially prevalent in film images, where less than 10% are full-body shots. Most 2D human pose estimation methods return incomplete poses when applied to partial body images. This lack of completeness becomes a problem in some situations, for example, when the 2D pose is converted to a 3D pose by a two-stage 3D human pose estimation method since most of these methods require a complete pose to work. This article proposes a new technique, called CompletePose, consisting of completing the missing keypoints when 2D human pose estimation methods are applied to images with partial body camera shots. A Conditional Generative Adversarial Network is used to obtain a complete and plausible pose, realistic enough to predict a 3D pose with a two-stage 3D human pose estimation method. A complete empirical validation has been carried out with the Human3.6 M dataset and a new dataset, called CHARADE, specially built and made public for reproducibility and benchmarking for this research. Quantitative evaluation employing the Fréchet distance shows that the approach manages to approximate the actual data distribution. The qualitative evaluation shows that the completed poses enable obtaining plausible 3D poses from images previously intractable.
OCR for laser marking quality assessment

Since 2020 in the USA1 and 2021 in Europe, all medical devices have to be marked with a Unique Device Identification (UDI) code to ensure their traceability. UDI codes are laser marked but the engraving process is error-prone due laser-related or external conditions. Defects may be assessed visually but this process is costly and gives rise to human errors. Using machine vision to perform this task for large batches of UDI codes may be challenging due to alterations in readability caused by marking defects or image quality. Therefore, we have tested several learned methods to achieve two goals: correctly recognize characters and identifying marking defects on UDI codes. As the codes were engraved on cylindrical metallic surfaces with a metallic paint effect, we had to address the problem of specular and stray reflections through the development of a tailor-made lighting engine. Our image grabbing and processing pipeline comprises of an imaging device designed to prevent reflections onto engraved codes; an Optical Character Recognition (OCR) algorithm (multilayer perceptron, support vector machine, classical image segmentation), and a probabilistic model to detect faulty characters that need to be further qualified by a human operator. Our results show that multilayer perceptron (MLP) and support vector machine (SVM) recognition performances are very close together and above classical image segmentation.