Reviewerly/exHarmony huggingface dataset:
Screen reading: Electromagnetic information leakage from the computer monitor

Introduction/purpose: The security of systems can be jeopardized by compromising emanations. This paper provides an overview of computer screen attacks. New technologies can be used to exfiltrate sensitive data from computer screens. Emission security is the prevention of electromagnetic signal attacks that are conducted or radiated. Methods: This paper examines the impact of a side-channel attack that intercepts compromised information from a computer screen. The leakage of electromagnetic data is also explained. Software-defined radios are described to explain malicious attacks on computer monitors. Results: The source of the electromagnetic signal determines the nature of the side-channel information they carry. The most well-known issue associated with revealing emissions is the possibility of intercepting visual information displayed on computer monitors. Conclusion: Visual data displayed on computer monitors could be intercepted by a software-defined radio which can digitize the desired frequency spectrum directly from an antenna, present it to a digital signal processor, and output it to an application for revealing sensitive data. A Grdović, M. et al, Screen reading: electromagnetic information leakage from the computer monitor, pp.836-855 variety of countermeasures, such as shielding, zoning, soft TEMPEST, and similar techniques, can be used to prevent data leakage.
Towards a Privacy, Secured and Distributed Clinical Data Warehouse Architecture

Reputed organisations are always prompting Data Warehouses (DWs), which are essential for storing and mining their historical datasets. When it comes to the healthcare industry, DWs are becoming ever so imperative, as efficient storage for medical data is vital for one’s health while mining it and seeking new insights. While clinical datasets are very complex, their timely integration and analysis are crucial to providing excellent care for patients. This research aims to provide an efficient data warehousing solution with multiple privacy and security measures integrated by design. Securing the data at all stages: during data input, exploration, pre-processing, selection, analysis, and presentation, is very challenging. This research explores data security from a holistic perspective and possible distributed analysis mechanisms while streamlining data sharing between healthcare centres to increase efficiency and better patient treatments. This study also considers security and privacy issues at all stages of the data warehousing process (data lifecycle) to ensure its correct handling and use. We focus on distributed clinical data warehouse architectures. We also describe the main requirements of a clinical data warehouse for the whole data lifecycle, Data Capture, Acquisition Management, Archiving, Sharing, Reporting, Analysis, and Privacy and Security. The proposed architecture is evaluated considering existing state-of-the-art concerning data analysis and sharing capabilities while ensuring data security and privacy.
Exploration of the Major Upgrading and Talent Training Mode in Local Universities under the Background of New Engineering

In order to respond to the needs of the development of the new era, the country has put forward the strategy of new engineering construction.Local universities must actively respond to the strategy of new engineering construction in engineering education.Through constructing new engineering majors, upgrading existing majors and exploring talent cultivation mode, so as to form a new engineering talent cultivation program with its own characteristics.This paper discusses the ideas of construction of new engineering majors in local colleges and universities from the incompatibility and outstanding problems faced by the current status quo of engineering majors education in local colleges and universities with the requirements of new engineering construction.
Research and Application of Topology Analysis Method for Large-scale Distribution Grids

With the increasing number of distribution grid equipment, the scale of distribution grids becomes larger and larger, and the structure becomes more and more complex. Efficiently storing, managing and analyzing massive distribution grid topological data is the key to improving the digitization and intelligence level of grids. Aiming at constructing a large-scale distribution grid topology analysis platform, firstly, the graph model representation and construction method of grid data is designed, and a grid topology data storage method based on the distributed graph database is proposed. Then, optimizing the storage structure and using a hot cache policy accelerates the topology analysis speed of large-scale grid graph data; The unified graph model representation for grid historical data improves topology analysis capability and data utilization rate. Finally, a topology analysis platform for large-scale data is built to support applications in distribution grids. The experiment results show that compared with the traditional method, the proposed grid topology analysis platform can significantly improve the performance of grid data query and topology analysis, and provide technical support for the intelligent operation of distribution grids.
A Framework of Student's Learning Motivation in Virtual Reality in the History of Animation

Virtual reality (VR) is a virtual environment that might be identical to or opposed to the actual world that has a variety of applications, including entertainment (video games), business (virtual meetings) and education (including medical and military training). In an educational context, VR has been used in various subjects but VR has not been applied in learning the “history of animation”. Besides the student's motivation to learn this subject has yet to be explored. This paper presents the preliminary model of VR application in learning the history of animation incorporating the Self-Determination Theory that focuses on two main components, autonomy and relatedness, to achieve the satisfaction of basic learning needs. To verify the preliminary framework, a pilot test that focuses on a qualitative approach included eight students who had learned the history of animation participated in a focus group discussion. They were asked about their motivation for the satisfaction of basic learning needs according to the context of autonomy and relatedness. The discussion session has been transcribed and analysed into four themes, i) excitement ii) self-confidence iii) socially immersive, and iv) practical access. The qualitative findings have given the credibility to be further studied for its reliability and validity through a quantitative approach to implementing VR in learning the “history of animation” in the next phase of this study.
Perception-and-Regulation Network for Salient Object Detection

Effective fusion of different types of features is the key to salient object detection (SOD). The majority of the existing network structure designs are based on the subjective experience of scholars, and the process of feature fusion does not consider the relationship between the fused features and the highest-level features. In this paper, we focus on the feature relationship and propose a novel global attention unit, which we term the “perception-and-regulation” (PR) block, that adaptively regulates the feature fusion process by explicitly modelling the interdependencies between features. The perception part uses the structure of the fully connected layers in the classification networks to learn the size and shape of the objects. The regulation part selectively strengthens and weakens the features to be fused. An imitating eye observation module (IEO) is further employed to improve the global perception capabilities of the network. The imitation of foveal vision and peripheral vision enables the IEO to scrutinize highly detailed objects and to organize a broad spatial scene to better segment objects. Sufficient experiments conducted on the SOD datasets demonstrate that the proposed method performs favourably against the 29 state-of-the-art methods.
Evolution of Legal Regulation of State Accreditation of Educational Activities in Russia

Education provides opportunities for self-realization and development of talents, that is, ensures the achievement of the corresponding national development goal. Therefore, control over the quality of education should be brought to a new level, taking into account the concept of the ongoing reform of control and supervisory activities and information technology development. In this paper, the author, based on the historical-legal and formal-legal method, offers the author's approach to the periodization of the legal regulation of accreditation as a form of control over the quality of education. The author notes that the modern model of quality control of education is based on the assessment of the performance of accreditation indicators and is the result of the evolution of quality control of higher education in Russia. In general, the development of legal regulation of accreditation in the field of education includes, according to the author, 4 stages: "The formation of certification and accreditation as interrelated forms of control" (1987-2007); "Unification of certification and accreditation procedures" (2007-2010); "Improvement of state accreditation procedures. Introduction of accreditation expertise" (2010-2021); "Addition of state accreditation with accreditation monitoring tools" (2021–present). It is revealed that accreditation monitoring is becoming a new form of control activity, which is systematic. Additionally, within the framework of the conducted research, the author has developed some proposals for improving the legal regulation of accreditation monitoring.
Recognition of Dynamic Filipino Sign Language using MediaPipe and Long Short-Term Memory

Filipino Sign Language (FSL) is the official sign language of the Philippines. FSL helped develop the communication gap between impaired individuals, however, only few are knowledgeable regarding the FSL. FSL recognition system can aid the communication gap among Deaf, Mute, and others that serves as a tool to convey message. Previous research includes three types of sign language recognition categorized as data gloves-based, vision-based, and hybrid. Existing wearable devices for sign language recognition which are mostly glove-based, and some hybrid approaches is intrusive as there is still a need for the user to wear something. Existing video-based approaches only recognize static sign language, mostly letters and numbers, only focus on the hand shape, and others require specific background setup. Therefore, there is a need for a dynamic FSL recognition system using MediaPipe and Long-Short-Term Memory (LSTM) capable of detecting pose and hand landmarks and recognize Filipino Sign Language. Using the MediaPipe and LSTM algorithm, the system achieved an accuracy of 95% for the actual testing and has 98% model accuracy. Thus, the recognition of dynamic Filipino sign language using MediaPipe and Long Short-Term Memory is concluded to be highly accurate in recognizing dynamic FSL signs.
Comparison of Novel Optimized Random Forest Technique and Gradient Boosting for Credit Card Fraud Detection with Improved Precision

Aim:The purpose of this research study is to detect credit card fraud using Novel Optimized Random Forest Technique (NORFT) and Gradient Boosting (GB).Materials and Methods: The Novel Optimized Random Forest Technique Algorithm uses parallel Decision Tree technique in addition with Random Forest Technique to improve the prediction of Credit Card Fadulants.Total sample size of 40 is used for testing and analysis, based on Gpower statistical analysis tool by considering gpower 0.8.In NORFT used N=20 and in GB used N=20 to measuring the performance of both algorithms.Result: Novel Optimized Random Forest Technique provides mean precision of 92.52%, and compared with Gradient Boosting algorithm of mean precision is 88.56%.Statistical significance value was fixed as (p>0.05) and obtained 0.477, this shows that NORFT is not statistically significant with alternative hypotheses.Conclusion: Based on the result, improved precision comparison results show that the efficiency of the Novel Optimized Random Forest Technique is better than Gradient Boosting.
Multi-modal brain MRI images enhancement based on framelet and local weights super-resolution

Magnetic resonance (MR) image enhancement technology can reconstruct high-resolution image from a low-resolution image, which is of great significance for clinical application and scientific research. T1 weighting and T2 weighting are the two common magnetic resonance imaging modes, each of which has its own advantages, but the imaging time of T2 is much longer than that of T1. Related studies have shown that they have very similar anatomical structures in brain images, which can be utilized to enhance the resolution of low-resolution T2 images by using the edge information of high-resolution T1 images that can be rapidly imaged, so as to shorten the imaging time needed for T2 images. In order to overcome the inflexibility of traditional methods using fixed weights for interpolation and the inaccuracy of using gradient threshold to determine edge regions, we propose a new model based on previous studies on multi-contrast MR image enhancement. Our model uses framelet decomposition to finely separate the edge structure of the T2 brain image, and uses the local regression weights calculated from T1 image to construct a global interpolation matrix, so that our model can not only guide the edge reconstruction more accurately where the weights are shared, but also carry out collaborative global optimization for the remaining pixels and their interpolated weights. Experimental results on a set of simulated MR data and two sets of real MR images show that the enhanced images obtained by the proposed method are superior to the compared methods in terms of visual sharpness or qualitative indicators.
Neural architecture search based on dual attention mechanism for image classification

&lt;abstract&gt; &lt;p&gt;Deep learning neural networks based on the manual design for image classification tasks usually require a large amount of a priori knowledge and experience from experts; thus, research on designing neural network architectures automatically has been widely performed. The neural architecture search (NAS) method based on the differentiable architecture search (DARTS) ignores the interrelationships within the searched network architecture cells. The optional operations in the architecture search space lack diversity, and the large parametric and non-parametric operations in the search space make the search process inefficient. We propose a NAS method based on a dual attention mechanism (DAM-DARTS). An improved attention mechanism module is introduced to the cell of the network architecture to deepen the interrelationships between the important layers within the architecture by enhancing the attention between them, which improves the accuracy of the architecture and reduces the architecture search time. We also propose a more efficient architecture search space by adding attention operations to increase the complex diversity of the searched network architectures and reduce the computational cost consumed in the search process by reducing non-parametric operations. Based on this, we further analyze the impact of changing some operations in the architecture search space on the accuracy of the architectures. Through extensive experiments on several open datasets, we demonstrate the effectiveness of the proposed search strategy, which is highly competitive with other existing neural network architecture search methods.&lt;/p&gt; &lt;/abstract&gt;
HADC: A Hybrid Compression Approach for DNA Sequences

In the blossoming age of Next Generation Sequencing (NGS) technologies, genome sequencing has become much easier and more affordable. The large number of enormous genomic sequences obtained demand the availability of huge storage space in order to be kept for analysis. Since the storage cost has become an impediment facing biologists, there is a constant need of software that provides efficient compression of genomic sequences. Most general-purpose compression algorithms do not exploit the inherent redundancies that exist in genomic sequences which is the reason for the success and popularity of reference-based compression algorithms. In this research, a new reference-based lossless compression technique is proposed for deoxyribonucleic acid (DNA) sequences stored in FASTA format which can act as a layer above gzip compression. Several experiments were performed to evaluate this technique and the experimental results show that it is able to obtain promising compression ratios saving up to 99.9% space and reaching a gain of 80% for some plant genomes. The proposed technique also succeeds in performing the compression at acceptable time; even saving more than 50% of the time taken by ERGC in most experiments.
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks

We study protecting a user’s data (images in this work) against a learner’s unauthorized use in training neural networks. It is especially challenging when the user’s data is only a tiny percentage of the learner’s complete training set. We revisit the traditional watermarking under modern deep learning settings to tackle the challenge. We show that when a user watermarks images using a specialized linear color transformation, a neural network classifier will be imprinted with the signature so that a third-party arbitrator can verify the potentially unauthorized usage of the user data by inferring the watermark signature from the neural network. We also discuss what watermarking properties and signature spaces make the arbitrator’s verification convincing. To our best knowledge, this work is the first to protect an individual user’s data ownership from unauthorized use in training neural networks.
Software Quality Evaluation Model based on Multiple Linear Regression and Fuzzy Comprehensive Evaluation Method

At present the scale and complexity of software is increasing exponentially. It is very essential to establish software quality measurement model and method in order to measure software quality. By analyzing software quality characteristics, this paper put forward a software quality model. Then combine with multiple linear regression and fuzzy comprehensive evaluation method to build a quality evaluation algorithm. Finally we select some software projects to perform research verification on the software quality evaluation model and algorithm. And result shows the feasibility and effectiveness of the model and method.
Seeing Far in the Dark with Patterned Flash

Flash illumination is widely used in imaging under low-light environments. However, illumination intensity falls off with propagation distance quadratically, which poses significant challenges for flash imaging at a long distance. We propose a new flash technique, named “patterned flash”, for flash imaging at a long distance. Patterned flash concentrates optical power into a dot array. Compared with the conventional uniform flash where the signal is overwhelmed by the noise everywhere, patterned flash provides stronger signals at sparsely distributed points across the field of view to ensure the signals at those points stand out from the sensor noise. This enables post-processing to resolve important objects and details. Additionally, the patterned flash projects texture onto the scene, which can be treated as a structured light system for depth perception. Given the novel system, we develop a joint image reconstruction and depth estimation algorithm with a convolutional neural network. We build a hardware prototype and test the proposed flash technique on various scenes. The experimental results demonstrate that our patterned flash has significantly better performance at long distances in low-light environments. Our code and data are publicly available. ( https://github.com/zhsun0357/Seeing-Far-in-the-Dark-with-Patterned-Flash ).
Sign Language Recognition Using Deep Learning

Sign language is the primary language of the people with speech and hearing impairment.Hearing impaired people use sign language to express themselves, participate in conversations, learn, and live as normal a life as possible.When deaf and dumb persons try to converse in sign language with those who aren't familiar with it, a problem occurs.This is where modern technology can step in.Although many existing projects have proposed methods to alleviate the problem but most of these have used external sensor or certain algorithms that do not work well under certain conditions like variation in skin color, inclusion of facial data and similarity between few signs and gestures.In our project we have used MediaPipe to solve the problem of variation in skin color as it is a very accurate hand tracking framework by Google.We have also created 3 sets of customs datasets to train 3 different deep learning models -2 of these models are used specifically used for predicting particular groups of letters which are similar to each other.This fixes the issue arising when similar signs are encountered.The propose prototype can be used to help educate the elementary school children using which they can learn to fingerspell the American sign language alphabets and string them into basic words and learn them in a picturized manner.At the end our prototype should be able to detect the sign alphabets from the live video, print them on screen and display the image of the words formed using the sign alphabets.
Interoperation Analysis of Reconfigurable Networked Microgrids through Quantum Approximate Optimization Algorithm

The maximum power exchange section is playing an important role in analyzing the interoperation of network microgrids. Mathematically, the problem is formulated into a weighted Max-Cut problem, which is non-deterministic polynomial-time hard (NP-hard). A Quantum Approximate Optimization Algorithm (QAOA) is introduced in this paper to search the approximate maximum power exchange sections in networked microgrids. QAOA is a hybrid quantum-classical algorithm, which uses a classical optimizer to train a parametrized quantum circuit. Layer number and angle parameters of the quantum circuit are discussed, which highly impact the performance of QAOA. Numerical examples on a typical reconfigurable networked microgrid system test and verify the effectiveness of QAOA in getting the maximum power exchange sections. This quantum computing implementation sheds light on the development of quantum algorithms to resolve the challenges in power systems that are hard to solve by classical computers.
Tibetan Jiu Chess Game Algorithm based on Expert Knowledge

Tibetan Jiu Chess is a unique chess game with complex rules, which is suitable to be used as the object of computer games. The expert knowledge database is established through the field investigation in Lhasa, Tibet. Tibetan Jiu Chess game algorithm based on expert knowledge is proposed in this paper. The algorithm is generally divided into formation recognition module, game strategy module in layout stage and game strategy module in Battle Stage. Especially for the most important battle stage, the chess power of the algorithm has been greatly improved by combining the distilled expert knowledge and better strategies. The experiments were made between the human players and the proposed algorithm in the "Minshan Cup" Tibetan chess National Open held in Lhasa City, Tibet Province in 2021. The results show that the designed algorithm based on expert knowledge is with a higher winning rate than a one-duan grading human chess player.