Reviewerly/exHarmony huggingface dataset:
A Family of Probabilistic Triple-bit Encryptions for Secure Cloud Applications

In order to strengthen secure cloud storage and retrieval, we propose a new family of triple-bit encryption switching protocols which can encrypt any plaintext m ∈ {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> and generate the ciphertexts of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2n/3·log N</sup> size where k=log N, N is the RSA composite, n ∈ ℕ and is a multiple of an integer 3. The proposed protocols support encryption switching (i.e., switching from homomorphic setting to non-malleability). The semantic security of all the proposed protocols is guaranteed under standard quadratic residuosity assumption and the non-malleability is guaranteed under resilient to adaptive chosen ciphertext attack. The proposed scheme can encrypt a binary string of any length with any probability distribution and also provide the optional tamper evidence. Finally, a tamper-proof secure information retrieval method over the untrusted cloud has been presented.
Electromagnetic Field Quantization

Following a brief review of the quantization of the simple harmonic oscillator starting from the Lagrangian formalism, we use a formal analogy to quantize the single-mode electromagnetic field along the same lines. We then extend the analysis to multimode fields and consider a number of states of the field of importance for quantum optics before discussing two important ways to characterize them, photocounting and homodyne detection. The chapter concludes with a discussion of quasiprobability distribution function descriptions of the field.
Ultra-short-term Radiation Prediction based on FY-4 Satellite Cloud Images and Artificial Neural Network

Surface solar radiation is affected by many random mutation factors, which makes the ultra-short-term prediction face great challenges. In this paper, the surface radiation observation station in the northwest (Dunhuang) desert area with broad PV prospects is selected as the research object. The input parameters of the test sample are: cloud forecast value, reflectivity and brightness temperature value of a satellite cloud image closest to the forecast time. The MATLAB software is used to model the prediction program and to predict the surface solar radiation in the next 10 minutes. A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation. The model is a lightweight calculation model, it satisfies the calculation precision of engineering requirements. The results show that the diurnal variation trend of measured and predicted radiation values is basically the same. Among them, the prediction accuracy of the model for cloudy days is higher, while for snowy days with more abrupt changes, the prediction error of abrupt points is larger. The model can provide reference for ultra-short-term prediction of surface radiation.
Thoughts on Criminal Laws and Regulations for Data Security Crimes in the Era of Artificial Intelligence

The accumulation of big data has continuously promoted the development of artificial intelligence. Data has become the core element of the Internet and computer fields. The behavior of using data as the object of crime and the use of data to infringe other legal interests continue to appear, and the importance of data security it is also increasingly recognized by the theoretical and practical circles. “Data is fundamentally different from information and computer information systems, and the main basis for data protection in current Chinese legislation and judicial practice is information security and computer information system security.” Therefore, the provisions of China’s criminal law have shown limitations and hysteresis. Learn from the provisions of the “European Cybercrime Convention” to discuss how to build China’s data security crime sanctions system, and provide a feasible reference for China to effectively combat and control data security crimes.
Conclusion and Research Direction

In this concluding chapter, we briefly revisit what we have studied in this book and then outline the recent state-of-the-art results related to quantum cryptanalysis of symmetric ciphers.
An Analysis of Effectiveness of StegoAppDB and Data Hiding Efficiency of StegHide Image Steganography Tools

Steganography is a method and technique of concealing existence of communication by embedding secret messages in digital media such as images, videos, electronic books and audio files. Steganographic techniques can be applied in several different ways. On one hand, people can benefit from their use. For example, the steganographic techniques can be used to protect copyright. On the other hand, the techniques can be used for malicious reasons. The steganography can be used to conceal parts of a ransomware attack or for delivering a malicious JavaScript. Steganalysis is a practice and approach to detect coded messages using visual perception, mathematical analysis, or other methods. Detection methods of steganalysis can be divided into two categories: specific steganalysis and universal steganalysis. The specific detection methods deal with the planned steganographic systems (algorithms), while the general detection methods provide recognition irrespective of what the steganographic systems are. This paper primarily focuses on calculating the data hiding efficiency of the steganographic tool Steghide 0.5.1 and StegoAppDB dataset images. This study also analyzes the performance of different embedding techniques and well-known encryption algorithms (AES, DES, 3DES, RC2) with several modes of operation (CBC, CFB, CTR, ECB, OFB) and how they impact the PSNR.
Adaptive Variable Parameter ZNN for Solving Time-varying Linear Matrix Equation

An adaptive fuzzy variable parametric zeroing neural network (AFV-ZNN) model is proposed to solve time-varying linear matrix equation. Distinguishing from varying-gain recurrent neural network models and other zeroing neural network models, the fuzzy design parameter of AFV-ZNN is adaptive as based on the residual matrix. The model adopts three different activation functions to achieve a faster convergence rate. The theoretical analysis and examples illustrate the faster convergence of the ATV-ZNN model, and the better performance of activation functions, compared with existing neural networks in simulation experiments.
Research on agricultural machinery operation path optimization based on traveling salesman problem (TSP)

Abstract Based on the characteristics and properties of TSP, a new heuristic algorithm is designed. Firstly, the nodes on the graph are divided into regions, and then the minimum spanning tree is found in the region. Then, the nodes larger than 2 are broken and reconnected into trees with 2 degrees except the start and end points. Finally, the start and end points of adjacent regions are connected to get the solution of the problem. Two examples show that this heuristic algorithm can effectively avoid the round trip of several points between different regions. The algorithm is applied to the optimization of agricultural machinery operation path, which provides a reference for the daily operation of agricultural machinery households.
Multiple Network and Double System Orthogonal Low-Ranking Training Melanoma Image Grouping

Abstract A significant method to identify and track early cancer of the breast in clinical practise is histopathological image analysis. The diagnosis of breast cancer is still facing issues with an open in healthcare sector, however, with a limited quality. We are creating a classification system based on histological picture pictures, integrating deep learning with mechanical methodologies of learning, in sequence to enhance the prediction of early recognized breast cancer and to minimize the work pressure of physicians. In particular, through pre-trained Deep Convection Neural Networks, we build a multi-network extraction model, create an efficient method of reducing features and train an ecosystem supporting vector machine (E-SVM). Next, we use scale transformation and colour improvement approaches to prepare histological pictures. Second, four pre-trained DCNNs extract the multi-network functionality. Thirdly, the Dual Network Orthogonal Low-Rank Learning (DOLL) role selection approach is further introduced to increase efficiency and to reduce unnecessary efficiencies. An E-SVM is at last instructed by melded usefulness and casting a voting procedure for characterization, what isolates the pictures into four gatherings considerate, in situ carcinomas, obtrusive carcinomas, and ordinary. The suggested procedure is tested by us for the public ICIAR 2018 Challenges Data Set on histology photographs of breast cancer. Our approach can offer very promising productivity and underperform province approaches through analytical outcomes.
Anomaly Detection in Videos Using Deep Learning Techniques

People’s concern for safety in public places has been increasing nowadays and anomaly detection in crowded places has become very important due to this aspect. This paper provides an approach towards identifying suspicious behaviors automatically in a crowded environment. In light of this, we have validated two powerful deep learning based models namely CNN and VGG16. So, for this purpose, we have collected a number of CCTV videos for detecting and differentiating between both normal and anomalous activities. Now based on videos collected, they have been trained using VGG16 and CNN model towards achieving the best accuracy
Blockchain Based Transaction Management System for IC Industry

With the development of Internet technology, all walks of life are undergoing industrial revolution and upgrading. Nowadays, many transactions in the integrated circuit (IC) industry chain have moved from offline to online, most of which are completed through trusted third-party platforms as the transaction medium. This transaction model, which relies on centralized institutions, is vulnerable to malicious cyberattacks, poses a risk of core technology theft for technology-intensive industries, and lacks the ability to trace the trajectory of transactions. To solve the above problems, this paper proposes a consortium chain-based transaction management system for the IC industry chain based on the transaction characteristics of commodities in the IC industry chain, designs an improved Raft consensus mechanism based on stability, credibility and contribution factors, a Prometheus-based data monitoring mechanism, and a set of smart contracts for on-chain transactions, implements a prototype system and tests its performance. The results show that the system can solve the above problems well and has high credibility and stability.
ProGS: Property Graph Shapes Language

Property graphs constitute data models for representing knowledge graphs. They allow for the convenient representation of facts, including facts about facts, represented by triples in subject or object position of other triples. Knowledge graphs such as Wikidata are created by a diversity of contributors and a range of sources leaving them prone to two types of errors. The first type of error, falsity of facts, is addressed by property graphs through the representation of provenance and validity, making triples occur as first-order objects in subject position of metadata triples. The second type of error, violation of domain constraints, has not been addressed with regard to property graphs so far. In RDF representations, this error can be addressed by shape languages such as SHACL or ShEx, which allow for checking whether graphs are valid with respect to a set of domain constraints. Borrowing ideas from the syntax and semantics definitions of SHACL, we design a shape language for property graphs, ProGS, which allows for formulating shape constraints on property graphs including their specific constructs, such as edges with identities and key-value annotations to both nodes and edges. We define a formal semantics of ProGS, investigate the resulting complexity of validating property graphs against sets of ProGS shapes, compare with corresponding results for SHACL, and implement a prototypical validator that utilizes answer set programming.
Improving Stateful Premise Selection with Transformers

Premise selection is a fundamental task for automated reasoning in large theories. A recently proposed approach formulates premise selection as a sequence-to-sequence problem, called stateful premise selection. Given a theorem statement, the goal of a stateful premise selection method is to predict the set of premises that would be useful in proving it. In this work we use the Transformer architecture for learning the stateful premise selection method. We outperform the existing recurrent neural network baseline and improve upon the state of the art on a recently proposed dataset.
Network Virtualization Incorporation and Projection System Technologies Using Block Chaining

Abstract With the proposed time-lock encryption method, mutually untrustworthy users will create a shared public key for the implementation in a completely distributed and asyncratic setting like internet or blockchain networks with time-critical privacy conservation applications such as e-voting or online auction. A “putting puzzles inside another puzzle” construction mechanism was proposed to resolve the probabilistic characteristics of the predicted key crack time. Experimental findings indicate that the solution proposed will deliver a more precise main break time, which would make it faster and more exact to reach the expected time limit. As described above, we can further estimate computing power by combining our scheme and the blockchain, to decode time-sensitive message. Miners are ready to supply under the updated PoW scheme.
Improving Face Recognition Using Pre-trained Models for Mask Wearer Images

Wearing a mask hides half of the face, making it difficult to recognize it as a face in computer vision. There is a problem that it becomes impossible to identify the whereabouts of a person or an individual because it is not recognized as a face. In this paper, we aim to improve the recognition rate by using learning-based method and combining both NVIDIA's pre-trained model and face images with and without masks.
SASSCAL WebSAPI: A Web Scraping Application Programming Interface to Support Access to SASSCAL’s Weather Data

The Southern African Science Service Centre for Climate and Land Management (SASSCAL) was initiated to support regional weather monitoring and climate research in Southern Africa. As a result, several Automatic Weather Stations (AWSs) were implemented to provide numerical weather data within the collaborating countries. Meanwhile, access to the SASSCAL weather data is limited to a number of records that are achieved via a series of clicks. Currently, end users can not efficaciously extract the desired weather values. Thus, the data is not fully utilised by end users. This work contributes with an open source Web Scraping Application Programming Interface (WebSAPI) through an interactive dashboard. The objective is to extend functionalities of the SASSCAL Weathernet for: data extraction, statistical data analysis and visualisation. The SASSCAL WebSAPI was developed using the R statistical environment. It deploys web scraping and data wrangling techniques to support access to SASSCAL weather data. This WebSAPI reduces the risk of human error, and the researcher’s effort of generating desired data sets. The proposed framework for the SASSCAL WebSAPI can be modified for other weather data banks while taking into consideration the legality and ethics of the toolkit.
Penerapan Media Gambar dalam Pembelajaran Berbahasa Anak di Taman Kanak Kanak Cinta Ananda Kecamatan Syiah Kuala Banda Aceh

This research entitled Application of Image Media in Children's Language Learning in Cinta Ananda Kindergarten in Syiah Kuala Banda is aimed to find out how a picture media can influence the children's language ability which is applied in children's language learning in Cinta Ananda Kindergarten Syiah Kuala Banda Aceh. The problem statements is how the application of media images in children's language learning in Cinta Ananda Kindergarten Syiah Kuala Banda Aceh. The method used in this research is descriptive qualitative method. Data collection methods used in this study are the record and note techniques. The results of this study are data in the form of types of image media used by teachers in children's language learning. Based on the results of the analysis, children's language skills in the Cinta Ananda Kindergarten  Syiah Kuala Banda Aceh were improved through un-projected image media such as still images in the form of paintings, series drawing and flash card .
Training projects on robotics as a way of organization of practical activities and interpersonal interaction of schoolchildren in the pandemic period

The article discusses an approach to teaching robotics in high school in the context of distance learning. It is proposed to organize work within the framework of an educational project under the conditions of dividing schoolchildren into teams, building a tree of project goals, determining a specific result at each stage, constant interaction of participants using virtual tools to regulate the workflow, using a service for simulating electronic devices in real time. The indicated conditions were formulated on the basis of the experience of organizing the project activities of tenth graders in the framework of teaching the elective course "Robotics" at the Lyceum 39, Nizhny Tagil. It is concluded that the joint work of schoolchildren on an educational project is a solution to compensate for the lack of interpersonal communication, gaining practical experience in a situation of forced self-isolation.
Students ‘ Research Work in Russian Language and Literature: a Meta-Subject Approach

The article presents the experience of implementing the metasubject approach in the research activities of students. The authors believe that it is necessary to pay attention to the organization of research activities in the lessons of the Russian language and literature in accordance with the requirements of the Federal State Educational Standard. The research method is aimed at developing the thinking of students, and research work in the field of philology has its own specifics. The article describes the features of different types of research activities of students in the field of philology, gives recommendations for organizing the educational process and involving students in research work.
Game-theoretic Approach to a Decision-making Problem for Blockchain Mining

It is an important decision-making problem for a miner in the blockchain networks if he/she participates in the mining so that he/she earns a reward by creating a new block earlier than other miners. We formulate this decision-making problem as a noncooperative game, because the probability of creating a block depends not only on one's own available computational resources, but also those of other miners. Through theoretical and numerical analyses, we show a hysteresis phenomenon of Nash equilibria depending on the reward and a jump phenomenon of miner decisions by a slight change in reward. We also show that the reward for which miners decide not to participate in the mining becomes smaller as the number of miners increases.
Single Image Haze Removal Using Dark Channel Saturation Priori Model and Non-linear Diffusion Patch Method

In this article, a simple and effective restoration-based haze-removal approach is proposed. This approach is based on refining the course transmission map further by a novel non-linear diffusion patch method. The robustness of the proposed method is validated using quantitative analysis and is compared with other approaches with standard performance metrics. This technique can handle illumination, preserve edges better and ensures the original color of the image is retained. It can be used in many systems for example in object detection and tracking in order to recognize active traffic participants clearly on the road. Other applications include remote sensing for weather prediction, smart cars for smooth navigation and consumer electronics for fault identification.
Leukemia Prediction Using SVNN with a Nature-Inspired Optimization Technique

Blood smear examination is a basic test that helps us to diagnose various diseases. Presently, this is done manually, though automated and semiautomated blood cell counters are also in vogue. Automated counters are very costly, require specialized and proper maintenance to work, and trained manpower. Thus, in small laboratories and in periphery blood smears are mostly being done manually, followed by microscopic evaluation by trained medicos. Though this method is easily available and cost-effective, but there are always chances of variation in the result due to differences in the methods of preparation of slides and experience of the pathologist. To overcome the manual methods of blood smear examination, various studies are being undertaken which are targeted not only to identify different blood cells but also to specifically identify blast cells which are the cornerstone of diagnosis of acute leukemia by automated methods. This study proposes a leukemia detection method using salp swarm optimized support vector neural network (SSA-SVNN) classifier to identify leukemia in initial stages. Adaptive thresholding on LUV transformed image was used to perform segmentation of the preprocessed smear. From the segments, the features (shape, area, texture, and empirical mode decomposition) are extracted. Blast cells are detected using the proposed method based on the extracted features. The accuracy, specificity, sensitivity, and MSE of the proposed method are found to be 0.96, 1, 1, and 0.1707, respectively, implies that compared to other methods—KNN, ELM, Naive Bayes, SVM, there is improvement in leukemia detection.
A Distributed Approach for Autonomous Cooperative Transportation

Autonomous mobile robots have now emerged as a means of transportation in several applications, such as warehouse, factory, space, and deep-sea where direct human intervention is impossible or impractical. Since explicit communication provides a better and reliable way of multi-robot coordination compared to implicit communication, so it is preferred in critical missions, such as search and rescue, where efficient and continuous coordination between robots is required. Cooperative object transportation is needed when the object is either heavy or too large or needs extra care to handle (e.g., shifting a glass table) or has a complex shape, which makes it difficult for a single robot to transport. All group members need no participation in the physical act of transport; cooperation can still be achieved when some robots transport the object, and others are involved in, say, coordination and navigation along the desired trajectory and/or clear obstacles along the path. A distributed approach for autonomous cooperative transportation in a dynamic multi-robot environment is discussed.
Tea Marketing Strategy Based on Big Data

With the continuous changes in the economic environment and consumer demand, the traditional tea market is also undergoing transformation and upgrading. This research mainly discusses tea marketing strategies based on big data. It can grasp the needs of consumers more accurately, and monitor the situation of competitors and monitor the market. The future development is predicted to a certain extent. Competitor products are clearly differentiated. 37% of the respondents like to buy tea in tea specialty stores, and 33% choose supermarkets or specialty stores to buy tea. This research is helpful to provide reference and reference for other enterprises dedicated to the marketing.