Reviewerly/exHarmony huggingface dataset:
Research on Algorithm and Mechanism of New Energy Vehicle Battery Distribution Based on Clustering Algorithm

In the promotion process of new energy vehicles, the endurance of vehicles is an important link that can not be ignored. Due to the limited number of new energy charging stations, replacing batteries in transit will become a new service mode. According to the characteristics of dynamic changes in the demand of new energy vehicles and power stations, a dynamic vehicle scheduling model for battery distribution routing problem of new energy vehicles is established. The adaptive genetic algorithm is constructed by improving the genetic algorithm with adaptive criteria. At the same time, the genetic algorithm is used to cluster the charging and replacing stations in time and space, and the clustering results are applied to the path adjustment, so that the charging and replacing stations can be added to the path where the charging and replacing stations with short time and space are located as much as possible, which can effectively reduce the search range and reach a better solution faster. The algorithm is programmed by MATLAB, and the numerical simulation of the distribution system shows the advantages of new energy vehicles in terms of use cost, which verifies the effectiveness of the model.
When Multidisciplinarity Meets Mathematics Teaching

The great expectations put in reform-based mathematics education to solve the existing conflict between the teaching constructum and the learning agents empower “some kind” of different thinking, not vertical, not lateral, but multidimensional. This situation needs to be explored using appropriate tools that enable a clear vision of the complex educational environment where it takes place. The chapter presents in a systematic sequence the various dimensions identified through the use of complex system analysis and the fuzzy set approach. Examples of implementation of the multidisciplinary approach support the theoretical aspects of the phenomena studied and reinforce a didactic change in the normative teaching model. This change complies to the use of differentiation skills in the planning stage enabling a wider perspective of the participants in the teaching situation: mathematics, teacher, and students.
Asymmetric Encryption Schemes

Another name for asymmetric encryption is public-key cryptography (PKC). This name comes from the fact that the cryptosystems in this category are using a pair of keys in their algorithms. Specifically, a public key is used by the encryption algorithm, and a private (secret) key is used by the decryption algorithm. In classical cryptography, the purpose of each type of key is well established, but in modern cryptography, these types of keys can be used for additional purposes. For example, another type of secret key can be a master key, used to encrypt and decrypt the keys of a specific cryptosystem.
Computer Aided Design and Application of Internet-Based SPOC Blending Courses Using Big Data and Cloud Computing

With the rapid development of the Internet, big data and cloud computing technology, blending teaching comes into being. As an online education scheme, blending teaching makes use of online teaching methods such as MOOCs, micro-lecture and flipped classroom. Through setting interactive teaching situation, blending teaching deeply combines traditional teaching with online teaching, and makes reform and innovation on the original theoretical knowledge and practical teaching content of English major. It is of great significance to improve the English major teaching status quo and maximize the utilization of resources and classroom efficiency. This paper first accurately defines the concepts of blending teaching and SPOC, and then deeply analyzes the construction foundation, construction principles and overall framework of SPOC blending teaching in English major courses, and puts forward the operation process and matters needing attention in the process of application in order to solve the problems existing in the traditional teaching for English majors and provide a reference for the better reform of English major teaching.
Anonymous Bitcoin transaction: A solution using secure multiparty computation

Abstract Bitcoin is a popular form of cryptocurrencies. Bitcoin provides users' anonymity through cryptographic pseudonyms. Bitcoin operates on a peer‐to‐peer network that maintains a public ledger, called blockchain , to log all transactions from one pseudonym to other, thereby hides the identity of the users. A transaction graph from the blockchain may unveil certain users' identities. To sustain with users' anonymity, mixing is often applied. CoinJoin , MixCoin , CoinShuffle , CoinParty and SecureCoin are some of the Bitcoin protocols that apply mixnet . Mixnet has certain limitations. Firstly, protocols assume “escrow addressee” that collects the coins, and performs mixing . The “escrow addressee” must be trustworthy. Secondly, protocols which do not assume “escrow addressee,” often use mixnet . Mixnet requires every participant must sign all others transactions. This incurs large volume of multi‐signature. Presently, Bitcoin protocol can include at most 15 multi‐signatures. Therefore, the protocols are not scalable. Finally, mixnet is “all‐or‐nothing.” That is, if all mixnodes are active then only the output is guaranteed. We present a multiparty shuffling protocol to anonymize the Bitcoin transactions. Our protocol is free from “escrow addressee” and multi‐signature. The protocol executes in multiple rounds. The protocol is not “all‐or‐nothing.” That means, every round guarantees some degree of anonymity.
Introduction

Computer graphics contains many areas of specialism such as data visualisation, computer animation, film special effects, computer games and virtual reality. Fortunately, not everyone working in computer graphics requires a knowledge of mathematics, but those that do, often look for a book that introduces them to some basic ideas of mathematics, without turning them into mathematicians.
Research on Feature Optimization Scheme Based on Data Feature Enhancement

Based on the common knowledge in the field of feature engineering that “data and features determine the upper limit of the model, and models and algorithms just approach the upper limit”, this article believes that the knowledge of feature engineering can be used to extract features from the original data to the maximum. Used by algorithms and models to improve the accuracy of algorithms and models. Based on the above considerations, this paper proposes a feature optimization scheme based on data feature enhancement (DFET) to improve the performance of deep learning models in natural language processing. The DFET feature optimization scheme uses machine learning classification algorithms to weaken noise features and adopts a sliding window Data enhancement is performed in the form of a window, the ignored features are selected through the form of windows, and low-dimensional data with significant features are generated. This part of the data is added to the original model training to fine-tune the model training, and then optimize the model to meet the requirements of improving accuracy. The paper examines the security bug report prediction problem in the field of natural language processing to verify the impact of data feature optimization on the performance optimization of deep learning models. Experiments show that compared with the basic deep learning model, the model after the DFET feature optimization scheme can show more excellent feature learning ability in text classification and prediction.
Quantum Map Approach to Entanglement Transfer and Generation in Spin Chains

Quantum information processing protocols are efficiently implemented on spin- $$\frac {1}{2}$$ networks. A quantum communication protocol generally involves a certain number of parties having local access to a subset of a larger system, whose intrinsic dynamics are exploited in order to perform a specific task. In this chapter, we address such a scenario with the quantum dynamical map formalism, where the intended protocol is cast into the form of a map acting on the local subset of spins. We reformulate widely investigated protocols, such as one-qubit quantum state transfer and two-qubit entanglement distribution, with the quantum map formalism and demonstrate its usefulness in exploring less investigated protocols such as multi-qubit entanglement generation.
Random logic networks: From classical Boolean to quantum dynamics

We investigate dynamical properties of a quantum generalization of classical reversible Boolean networks. The state of each node is encoded as a single qubit, and classical Boolean logic operations are supplemented by controlled bit-flip and Hadamard operations. We consider synchronous updating schemes in which each qubit is updated at each step based on stored values of the qubits from the previous step. We investigate the periodic or quasiperiodic behavior of quantum networks, and we analyze the propagation of single site perturbations through the quantum networks with input degree one. A non-classical mechanism for perturbation propagation leads to substantially different evolution of the Hamming distance between the original and perturbed states.
MultiselfGAN: A Self-Guiding Neural Architecture Search Method for Generative Adversarial Networks With Multicontrollers

In recent years, reinforcement learning (RL) and gradient optimization were applied with neural architecture search algorithms in the generative adversarial network (GAN) to achieve their state-of-the-art (SOTA) performance. However, the existing RL-based methods utilized the calculation of inception score or Fréchet inception distance as the reward value to guide the controller, which actually wasted much of searching time. In order to improve the search efficiency without degradation of performance, this article proposes recycling the discriminator to evaluate the performance of architectures, in other words, we propose to self-guide the search process. In the mean time, we introduce new concept of multiple controllers and the method of reward shaping to independently and effectively search the cell architectures. The experiments demonstrate the effectiveness and efficiency of our multiself GAN and the ablation study also exhibits its robustness.
Design of an Accurate Recommendation System for Mathematical Resources Based on Big Data Technology

Mathematics has penetrated into various fields from natural sciences to social sciences, and is an important channel for cultivating logical thinking. In the era of big data, teaching resources are exploding. The degree of informatization of education continues to increase. Various new forms of teaching resources continue to appear. Accurate recommendation is based on students' learning behaviors and actively recommends learning resources of interest to students. In order to give full play to the role of mathematical teaching resources under the background of big data, this paper designs an accurate recommendation process, compares commonly used recommendation algorithms, and builds an accurate recommendation process for mathematical resources based on content recommendation. It does not require data from other users. There is the independence between the users, new resources can be recommended immediately, providing support for the precise recommendation system design of mathematical resources.
The use of reference legal systems in the educational activity of lawyers

The relevance of the research topic is characterized by a high level of informatization in the professional activities of lawyers and the need to develop practical skills in using reference legal systems of students in the educational process. The research problem is as follows: what are the main possibilities of using legal reference systems in the educational activities of lawyers? The purpose of the research is to study the possibilities of reference legal systems in the educational activities of students in the direction of training "Jurisprudence". The main research method is system analysis. The article considers the functionality of reference legal systems for the formation of professional skills of students in the field of Jurisprudence. The use of reference legal systems in the educational activities of lawyers is considered as a modern information technology aimed at solving a wide range of professional tasks, including the development of practical skills in collecting, systematizing, processing and interpreting legal information. It has been concluded that the use of reference legal systems will contribute to the effective formation of general professional competencies of future lawyers.
Tracelet: Contact Tracing and Physical Distancing Device with Block-chain Technology for Pamantasan ng Cabuyao

In the Philippines, contact-tracing is done in the conventional method where people fill-up health-declaration forms. Proper physical distancing is not followed, especially in public places. This study is focused on concerns involving the conventional contact tracing and physical distancing in Pamantasan ng Cabuyao. Developed using modified incremental-prototyping methodology. The objective is to develop module device with ranging position, triggered when one-meter distance is compromised and physical distancing notification. Included a web-based application interface for contact-tracing where the administrator can update the status and registration of the users. Utilized block-chain technology as storage for the users’ information. The device is developmentally-designed, implemented and evaluated. Based on the series of testing, the device provides notification through blinking light, and set-off to exchange identifications. Users’ information is secured through the Block-chain. When a user is reported to be COVID-19 positive, all the users who have been in contact, will be alerted via text message.
Change in Formalism (F)

Differential privacy uses indistinguishability to compare the distribution of outputs given two neighboring inputs. Other formalisms have been proposed, some of which model the attacker more explicitly. This chapter of the Brief provides an overview of the corresponding notions.
Design of Hybrid True Random Number Generator for Cryptographic Applications

In real-time applications, unpredictable random numbers play a major role in providing cryptographic and encryption processes.Most of the existing random number generators are embedded with the complex nature of an amplifier, ring oscillators, or comparators.Hence, this research focused more on implementing a Hybrid Nature of a New Random Number Generator.The key objective of the proposed methodology relies on the utilization of True random number generators.The randomness is unpredictable.The additions of programmable delay lines will reduce the processing time and maintain the quality of randomizing.The performance comparisons are carried out with power, delay, and lookup table.The proposed architecture was executed and verified using Xilinx.The Hybrid TRNG is evaluated under simulation and the obtained results outperform the results of the conventional random generators based on Slices, area and Lookup Tables.The experimental observations show that the proposed Hybrid True Random Number Generator (HTRNG) offers high operating speed and low power consumption.
Smart Contract Enabled Decentralized Reputation System for E-Commerce Reviews

E-commerce has seen a surge in growth in the wake of the ongoing pandemic and is gaining ground worldwide. Inherently, product reviews by customers have fueled purchasing decisions. Blockchain is a decentralized ledger technology where a history of transactions are managed in an immutable manner. This technology is gaining traction as a result of general distrust and fragmentation in centralized systems. This paper proposes a smart contract enabled decentralized reputation system on a permissioned blockchain for E-commerce reviews. We present an overview of the limitations of current reputation system. We show how our approach addresses the fragmentation and credibility issues of product reviews in current reputation systems.
Semi-supervised Subspace Learning Via Constrained Matrix Factorization

This paper adopts the matrix factorization approach by improving the NMF model to build a semi-supervised learning framework (DCNMF) that integrates the linear discriminate analysis (LDA) and base cone volume constraints. The proposed DCNMF is a subspace learning model in which minimizing the basic cone volume of the learned subspace and reducing the data dimensionality so that the distance within-class samples is minimized and the distance between-class samples is maximized. The proposed method is evaluated by experiments on two cases of face recognition tasks, namely, various numbers of training data and different subspace dimensionalities.
Arabic Auto-CON: Automated Arabic Concordancer Construction from Arabic Corpus

The use of tools and apps to understand more about a language has become popular in recent years. This technology assists the learner in gaining more knowledge about a specific language’s vocabulary, collocations, grammar, etc. An example is a concordancer, which is a kind of program that can offer valuable knowledge in multiple contexts regarding the frequency of terms or word. The production of such a tool for Arabic has been limited for several years due to the complexities of the language. The purpose of this paper is therefore to create an automatic Arabic concordancer (auto-CON) with a simple GUI. The proposed method extracts information from a corpus and then provide the needed information depending on a user query. The main aim of the proposed concordance is to provide users with more information on the search word, including the concordance of the word entered, the origin of the word, the domain of each concordance, and the terms extracted from the same root word. The auto-CON tool relies on the creation of two dictionaries: the first dictionary contains all the occurrences and concordances of the word in the corpus, while the second dictionary provides the roots of the words and the words derived from those roots. Therefore, the auto-CON tool is designed to be an educational tool that could support learners of Arabic and Arabic societies. In addition, it can serve as a convenient method for people of different ages to gain more comprehensive knowledge about Arabic words and their concordances. The contribution of this tool is to develop the domain of Arabic NLP. This proposed approach was examined by user testing, in which two Arabic speakers and Arabic learners (international students) were asked to test the approach and provide feedback. The result of the user testing presented the effectiveness of the proposed method.
How to Tackle Difficult to Understand Phenomena. Fuzziness, Cognitivity, Memory

In worlds that are compatible with the assumption that we have overall visibility of it and the m data we observe appear without any constriction, so that they have the same relevance 1/m, probabilistic models are the landlords. Rather, if we have a limited scope we must ask other people to add data within their scope, and the relevance of the overall observations is no longer uniform. In this way we enter the modern connected worlds, made up of social networks and fuzzy graduations. In this chapter we examine this new family of worlds, and finalize the above communication and graduation tools to the assessment of cognitive algorithms as a counterpart of the inferential tools discussed in the previous chapter. Here the distinction between the conceptual model and the implementing hardware goes to vanish so that we will refer for instance to neural networks or quantum bits which precisely implement a given cognitive algorithm. The common features of these algorithms have to be searched in the triadic architecture Computation-Communication-Memory and in general strategies aimed at optimizing the overall objective of this contrivance, having feedback as a precious communication facility. This chapter offers a reasoned summary of many advances in computer science that have taken place in the last four decades. As such, it’s a very streamlined summary, which once again is oriented toward delineating compatible worlds in the province of the golden kit. The reader will probably not find new insights; rather, I hope he will enjoy a systematic view of current cognitive challenges in the field.
In the use of artificial intelligence and hyperspectral imaging in digital pathology for breast cancer cell identification

Hyperspectral (HS) imaging (HSI) is a novel technique that allows a better understanding of materials, being an improvement respect to other imaging modalities in multiple applications. Specifically, HSI technology applied to breast cancer histology, could significantly reduce the time of tumor diagnosis at the histopathology department. First, histological samples from twelve different breast cancer patients have been prepared and examined. Second, they were digitally scanned, using RGB (Red-Green-Blue) whole-slide imaging, and further annotated at cell level. Then, the annotated regions were captured with an HS microscopic acquisition system at 20× magnification, covering the 400-1000 nm spectral range. The HS data was registered (through synthetic RGB images) to the whole-slide images, allowing the transfer of accurate annotations made by pathologists to the HS image and extract each annotated cell from such image. Then, both spectral and spatial-spectral classifications were carried out to automatically detect tumor cells from the rest of the coexisting cells in the breast tissue (fibroblasts and lymphocytes). In this work, different supervised classifiers have been employed, namely kNN (k-Nearest-Neighbors), Random Forest, DNN (Deep Neural Network), Support Vector Machines (SVM) and CNN (Convolutional Neural Network). Test results for tumor cells vs. fibroblast classification show that the kNN performed with the best sensitivity/specificity (64/52%) trade-off and the CNN achieved the best sensitivity and AUC results (96% and 0.91, respectively). Moreover, at the tumor cells vs. lymphocyte classification, kNN also provided the best sensitivity-specificity ratio (58.47/58.86%) and an F1-score of 74.12%. The SVM algorithm also provided a high F-score result (70.38%). In conclusion, several machine learning algorithms provide promising results for cell classification in breast cancer tissue and so, future work must include these discoveries for faster cancer diagnosis.
Reinforcement Learning for Developing an Intelligent Warehouse Environment

Nowadays, warehouse optimization is one of the core components of logistics. With the development of artificial intelligence (AI) technology and the advancement of automation technology, building a smart warehouse is an important task. This paper presents the machine learning techniques and technologies for developing an intelligent warehouse. A reinforcement learning method is proposed to train a basic warehouse environment for an efficient storage policy. The experimental results help comprehend the building of the model of a neural network of the reinforcement learning algorithm and the characteristics of this technique. This study further helps to understand the basic concepts of machine learning techniques to develop an algorithm for smart warehouses.