Reviewerly/exHarmony huggingface dataset:
A Block Cipher Algorithm Based on Magic Square for Secure E-bank Systems

Nowadays the E-bank systems witnessed huge growth due to the huge developments in the internet and technologies. The transmitted information represents crucial information that is exposed to various kinds of attacks. This paper presents a new block cipher technique to provide security to the transmitted information between the customers and the e-bank systems. The proposed algorithm consists of 10 rounds, each round involves 5 operations. The operations involve Add round key, Sub bytes, Zigzag method, convert to vector, and Magic Square of order 11. The purpose of this algorithm is to make use of the complexity of the Magic Square algorithm, the speed of addition operation, the confusion provided by the zigzag, using these operations with Galois field 28 GF(28), and repeating these operations for several rounds to build fast high secure encryption algorithm. This algorithm is designed to provide fast with high complexity and security which is suitable to encrypt the data that is transmitted over the internet. Speed, complexity, and The National Institute of Standards and Technology Framework NIST suite tests were done. The complexity of the proposed algorithm is = ((256)32)r+1*((256)89)r+1 + (256)121. The proposed technique gives higher speed and security in the encryption and decryption phases, according to the results of the experiments. The degree of randomness has grown by 31.8 percent. Due to a decrease in the time of encrypting and decrypting, as well as the usage of the central processing unit (CPU), efficiency is improved. The encryption process throughput is enhanced by 13%, while the decryption process throughput is increased by 11.6 percent with the recommended approach.
The Use of Emojis in Online Human Sex Trafficking Ads

Technology has dramatically changed the way criminals conduct their illicit activities. Specifically, the Internet has become a major facilitator of online human sex trafficking. Traffickers are using these technologies to market their victims which presents new challenges for efforts to combat sex trafficking. This study used knowledge management principles and natural language processing methods to develop an improved ontology of online sex trafficking ads. The language of these ads is constantly evolving; therefore, this study explored the role of a new type of indicator, emoticons, to the ontology of human trafficking indicators.
Bribery in Rating Systems: A Game-Theoretic Perspective

Rating systems play a vital role in the exponential growth of service-oriented markets. As highly rated online services usually receive substantial revenue in the markets, malicious sellers seek to boost their service evaluation by manipulating the rating system with fake ratings. One effective way to improve the service evaluation is to hire fake rating providers by bribery. The fake ratings given by the bribed buyers influence the evaluation of the service, which further impacts the decision-making of potential buyers. In this paper, we study the bribery of a rating system with multiple sellers and buyers via a game-theoretic perspective. In detail, we examine whether there exists an equilibrium state in the market in which the rating system is expected to be bribery-proof: no bribery strategy yields a strictly positive gain. We first collect real-world data for modeling the bribery problem in rating systems. On top of that, we analyze the problem of bribery in a rating system as a static game. From our analysis, we conclude that at least a Nash equilibrium can be reached in the bribery game of rating systems.
METHODICAL FEATURES OF IMPLEMENTATION OF THE RELATIONSHIP BETWEEN SYMMETRY AND ASYMMETRY BASED ON STEM EDUCATION

The authors analyze the concept of symmetry and asymmetry in the process of teaching physics in technical institutions of higher education. The relationship between symmetry and asymmetry in the process of teaching physics based on STEM technologies is revealed. The use of fundamental ideas of physics (for example, symmetry and asymmetry) taking into account STEM technologies is highlighted. The interrelation of symmetry and conservation laws about their fundamentality is clarified; the technique of the integrated approach of physics and disciplines of the professionally-oriented profile, on an example of studying cosmetic loadings, is presented. It was found that the formation of independent cognitive-exploratory activities of students should use physics problems with consideration of fundamental concepts, such as symmetry caused by the development of motives, cognitive interest and scientific thinking and the acquisition of professional competencies. The demonstration of engineering and technical component of STEM education and development of methods of studying disciplines taught to students of technical institutions of higher education, taking into account the integrated approach and interdisciplinary links, is relevant. The transition to STEM training requires improving the methodology of teaching physics in terms of integrated, systematic, professionally-oriented approaches, which includes: the use of new methods, techniques, teaching aids that would help solve several methodological problems; application and introduction in the educational process of physics of interesting and important scientific achievements, as well as strengthening those aspects that stimulate and intensify the independent cognitive activity of students of technical institutions of higher education. It is established that to improve the quality of teaching physics in future technical specialists it is necessary to systematically improve the methodology of educational and cognitive activities, and more widely apply STEM-learning technologies, which leads to productive mental and practical activities of students in the process of mastering educational material. In the future, work on the study of this problem can be carried out in the following areas: development of a new approach to changing the structure and content of working curricula, improving the content and system of teaching physics taking into account STEM technologies, strengthening the connection between the teaching of physics course and the professional orientation of students of non-physical specialities of technical universities in the context of STEM education.
DeepVID: A Self-supervised Deep Learning Framework for Two-photon Voltage Imaging Denoising

High-speed population-level voltage imaging is suffered from the shot noise limit. We developed a self-supervised deep learning framework for voltage imaging denoising (DeepVID) without the need for any ground-truth high-SNR data.
Exploring Embodied Schemas-Based Interaction: Designing with Doors

Based on the theories of embodied schema, this short paper introduces the theoretical foundations, ideation process, and user research on three design concepts. People's conceptual understanding is structured by a range of recurrent patterns originating from long-term bodily experiences with environments. The term "embodied schema" emphasizes that the schematic structures are grounded in the bodily interaction with physical spaces and objects. This study developed three conceptual designs to explore how the embodied schema of Containment can inform designing meaningful interactions in public spaces. The design results indicate that embodied schemas-based design thinking provides useful guidance for designing meaningful interactions with public spaces. In future studies, we will continue to explore more possibilities of embodied schemas-based design in consideration with other types of schemas.
Feedback of PC Security Measures Using Microsoft Defender for Endpoint

We build a security measure feedback system, where Microsoft Defender and Microsoft Defender for Endpoint extract insufficient security measures based on users’ PC state. Then the system feeds back the security measures to the users. This process hopefully facilitates the users to be aware of vulnerabilities of their PCs and improve the settings for better security.
Chinese Predicate Chunk Knowledge Base Construction and Internal Boundary Recognition

Under current researches on Chinese language, sentences are usually chunked into the components of the same level. However, in actual Chinese environment, the predicate block in a sentence and the subject and object component blocks before and after it constitute the skeleton of the event representation, and the subblocks inside the predicate block modify the core predicates from different perspectives and serve as the related components of the event. Therefore, it is necessary to treat the predicate block and the component inside the predicate block as different levels of block components. Dividing into primary and secondary components plays an important role in understanding the main semantics of a sentence by abstracting the outline and facilitating the event reasoning and calculation. Therefore, this paper first defines predicate as the core of the predicate block and further defines the sub-block inside the predicate block. The predicate-centered predicate blocks are annotated with encyclopedia corpus with relatively high sentence complexity. At the same time, the components inside the predicate block are divided based on the subblock types defined in this paper. As of now, the knowledge base includes 36,360 predicate blocks. Based on this knowledge base, this paper creates an internal boundary recognition task for predicate blocks, and tests it with a sequential labeling model, which provides a baseline for the subsequent researches in the future.
ENDASh: Embedding Neighbourhood Dissimilarity with Attribute Shuffling for Graph Anomaly Detection

Recent unsupervised GNN based graph anomaly detection (GAD) methods adopt specific mechanisms designed for anomaly detection. This is in contrast to earlier methods that utilise components such as graph autoencoders that were designed for more general use-cases. However, these newer methods only lead to a modest increase in detection accuracy at the cost of complicated optimisation schemes and higher runtimes. To overcome these issues, we propose Embedding Neighbourhood Dissimilarity (END) with Attribute Shuffling (ENDASh), a simple but scalable and effective GAD framework. ENDASh utilises our proposed END measure to quantify the degree of abnormality of nodes using GraphSAGE embeddings that were optimised with Attribute Shuffling, a data augmentation method designed to project anomalies away from inliers in the latent space. Extensive experiments on real-world benchmarks demonstrate the competitive GAD performance of our ENDASh while being computationally efficient and capable of operating in an inductive environment.
Operators Skill Evaluation with Virtual Reality Technology

Mining application requires specialized education for operators in their explicit field to advance focussed awareness and accurate skills to do their jobs excellently. The objective of teaching is to empower all operatives to achieve their job in a method that is acceptable to the company and the staff. The study does not include all procedures and forms in place yet, but it embraces a system comparable to the one used in our Air Force sometime before. Also, the study relies on On Job Training (OJT) model training for the operators on a particular machine or process type. A training card for each process/machine type have been made up as well. The specific tasks related to the machine/process that the operator will be doing has been listed. The card is given to the operator and the operator is paired up with a more skilled operator from that machine. Also, listing the specific functions of the machine/process typically required different products to be processed through that sometimes have their own unique things about them. There are several older, more experienced operators to train and evaluate freshers. Therefore, the intends to analyse the various factors contributing to the human efficiency of Dumper Operators in Quarries, effectively creating an Operator Skills Matrix (OSM). The main objective is to create a blueprint for automated modelling of the operator efficiencies and to generate required data. Operator Skills Matrix could be effective in building an assessment metric. Operator Skill Index (OSI) centred on which the complete work of any operator would be standardized. Once such a benchmark is created, person in-charge can take sufficient methods to re-train whoever lacks skill. Operators are very important to improve safety and productivity which reduces the cost of mining. Thus, latest technique available is Virtual Reality Training (VR).
An Asymmetric Parallel Residual Convolutional Neural Network for Pen-Holding Gesture Recognition

Based on deep residual structure, attention mechanism, and CNN, we propose an asymmetric parallel residual convolutional neural network for pen-holding gesture recognition in this paper. To verify the effectiveness of the network, we build a pen-holding gestures dataset containing 923 images of 7 classes, and quadruple the training set by using data augmentation technology. The experimental results show that the accuracy of the propose network reaches $$76.22\%$$ on basic pen-holding gestures dataset, and $$82.16\%$$ on the augmentation pen-holding gestures dataset. The network and the dataset construction method proposed in this paper can be used to build a pen-holding gesture recognition system, and have reference value for similar recognition tasks without high-quality public datasets.
Users’ Acceptance and Sense of Presence towards VR Application with Stimulus Effectors on a Stationary Bicycle for Physical Training

This research's objective is to identify lacking elements in various effectors utilized in current physical training for cyclists.This encompasses both virtual reality-based system and indoor conventional training.Another objective is to identify user's acceptance from the use of vProCycle; which acts as the primary instrument of this study.Virtual Reality (VR) technology is a computer-generated simulation experience where immersive surroundings replicate lifelike environments -and is used for cyclists' physical training.Distinctive combinations of stimulus effectors (such as altitude, wind-effect, visuals, audio etcetera) have been applied to simulate actual world training environment.This is in order to increase the fidelity of presence for the participants involved, with emphasis on the five human senses.However, in this research the focus is only on hearing, sight, and interaction.The methodology of this mixed-mode pilot study is inclusive of 2 cyclists as participants and a 30 minute training session inside the hypoxic chamber room, whereby they have experienced a VR visual route replica of L'Étape du Tour, France.Variables composed of distinctive stimulus effectors are employed during the training, and survey interviews are utilized to gain users' insight.Results from this pilot study on the presence level indicate that the cyclists' have given high scores.This high score means that the cyclists were immersed while using the vProCycle system.In addition, the cyclists' also gave a high score on the level of technology acceptance towards using vProCycle.The main contribution from this study is to understand how various combinations of stimulus effectors can be applied in a VR-based training system.
The Effect of the Discipline of Learning, Family Environment, and Learning Motivation toward Learning Achievement

This study was examined the effect of the discipline of learning, family environment, and learning motivation toward learning achievement. Subjects in this study were students at Faculty of Economic State University of Jakarta Class 2019-2020 totaling 119 students. This research is the study population using the method of data collection in the form of questionnaire. Processed using the Smart PLS 3.3.2 application program. Using a Structural Equation Modeling (SEM). Based on the result influences of Discipline learning, family environment, and motivation toward learning achievement is simultaneous research can be concluded that some variables affect to students learning achievement.
An Overview of Adversarial Attacks and Defenses

In recent years, machine learning has become more and more popular, especially the continuous development of deep learning technology, which has brought great revolutions to many fields. In tasks such as image classification, natural language processing, information hiding, multimedia synthesis, and so on, the performance of deep learning has far exceeded the traditional algorithms. However, researchers found that although deep learning can train an accurate model through a large amount of data to complete various tasks, the model is vulnerable to the example which is modified artificially. This technology is called adversarial attacks, while the examples are called adversarial examples. The existence of adversarial attacks poses a great threat to the security of the neural network. Based on the brief introduction of the concept and causes of adversarial example, this paper analyzes the main ideas of adversarial attacks, studies the representative classical adversarial attack methods and the detection and defense methods.
A Construction of Codebooks Asymptotically Meeting the Levenshtein Bound

Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q2+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.
An Improved Low-Light Image Enhancement Algorithm Based on Deep Learning

At present, most low-light image enhancement algorithms rely on artificial design with a priori information and constraints, but it cannot accurately capture deep structural features of the images. In this paper, we propose an improved algorithm with EnlightenGAN as the base framework and consider the phenomenon that the distance reflected by Mean Square Error (MSE) is quite different from human intuition. By introducing Structural Similarity (SSIM) in the loss function, we try to improve the structural similarity of the enhanced images to make them more consistent with natural and human intuition. For the instability problem during Generative Adversarial Networks (GAN) training, the relatively loose and easy-to-compute Energy-Based GAN (EBGAN) is used instead of Wasserstein GAN (WGAN). The improved algorithm is finally tested by using Low-light Image Enhancement (LIME) dataset as well as the test dataset of EnlightenGAN. The Peak Signal to Noise Ratio (PSNR) and SSIM values of the images processed by using the modified algorithm are calculated and compared with other algorithms, and the results show the proposed method is effective.
Post-Quantum Cryptographic Accelerators

The impending realization of scalable Quantum computers has led to active research in Post-Quantum Cryptography. Amongst various classes of Quantum-resistant cryptographic schemes, Lattice-based cryptography is emerging as one of the most viable replacements; five out of seven 3rd round finalists in the NIST Post-Quantum Cryptography competition are Lattice-based in their construction. This chapter surveys the practicality of deployment of some of these schemes as coprocessors for high-speed applications. The study of Post-Quantum Cryptography schemes as coprocessors on custom hardware platforms is imperative since they are much more computationally intensive, compared to their classical counterparts used today. Additionally they generally have much larger key sizes, requiring higher storage budgets as well. In this context the performance requirements of a cryptosystem, area, power and memory footprints, are bench-marked, along with side channel attack resistance (if provided). This chapter discusses Lattice-based cryptosystems implemented on configurable hardware (primarily Field Programmable Gate Arrays (FPGAs) and some Application-Specific ICs results), embedded processors for light-weight IoT end-node devices, Application-Specific Instruction Set Processors (ASIPs) and a few hardware software coprocessing designs. Most of these reported results are manually optimised with a few high level synthesis optimisations. The chapter concludes by identifying some research gaps and an outlook for the future.
Technosphere – Alchemy of Technicque

Technology has reshaped the world. In the digital age, technology creates a new reality, a technosphere that brings radical changes to the world and life, at all levels of human experience. There is no end in sight to the technological modification of the world, nor are the consequences known - we are talking only about the possibilities of technology. The hyperreality of the technosphere is fascinating precisely because of its seductiveness. Changing our experiences in technoculture invites us to rethink new social rationality.
Unsupervised Deep Image Set Hashing for Efficient Multi-label Image Retrieval

Hashing has been widely used in approximate nearest neighbor searching for large-scale image retrieval. Recently, many deep hashing methods have been proposed and significantly shown improved performance. However, most of these methods encode each image separately, ignoring that the images in the same set represent the same object or person. When used in large-scale scenes, they need to be compared one by one, which leads to poor retrieval performance. To solve this problem, we propose an unsupervised deep image set hashing method for multi-label image retrieval, which takes the image set as input, extracts image features and calculates set features, then constructs semantic structure through statistical information of features, and finally preserves this semantic structure through pair-wise loss function. Experiments show that our method has certain advantages on benchmark datasets.
Accurate Solar Pv Power Prediction Interval Method Based on Frequency-Domain Decomposition and Lstm-Svr Model

The stability operation and real-time control of the integrated energy system with distributed energy resources determines the higher and higher requirements for the accuracy of solar photovoltaic (PV) output power prediction. This paper proposes an accurate PV power prediction interval approach based on frequency-domain decomposition and hybrid deep learning (DL) model. First, ensemble empirical mode decomposition (EEMD) is used to decompose and reconstruct the original PV energy time-series data into high and low frequency sub-series followed by the statistical feature extraction process. Second, an improved long-short-term-memory network (LSTM) model with the designed hyperparameters based on Bayesian optimization (BO) is proposed to predict the sub-series with the different minute-hour-day intervals. Third, support vector regression (SVR) is utilized to analyze the initial time node and reduce the fluctuation error of the prediction value near zero. Finally, a comparative study with SVR, KNN, BPNN, LSTM, GRU and Stacked-LSTM models is constructed by using an actual dataset collected from Arizona, US. The results fully show that the proposed hybrid model can improve both forecasting accuracy and stability significantly.