Reviewerly/exHarmony huggingface dataset:
Group Signature Based Federated Learning in Smart Grids

Smart Grids are the need of today&#x0027;s energy distribution system, which maintains a systematic communication between suppliers and consumers. Often these grids need to communicate to the Human Machine Interface (HMI) server regarding their findings of the customer needs and availability. However, some external entities might compromise the HMI server, which tends to misuse smart grids&#x0027; personal information. Hence, the grids should not reveal their or their customer&#x0027;s identity to the server. Federated Learning (FL) can solve this situation where the data from various smart grids can be collected without disclosing the grid&#x0027;s identity. We have proposed a group signature-based federated signature-based in which grid components sign with the group signature instead of their personal signatures. We have verified the security of our algorithm with the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator.
Parallel Pivoted Subgraph Filtering with Partial Coding Trees on GPU

The pivoted subgraph isomorphism problem is a special subgraph isomorphism problem that focuses on the pivoted nodes rather than the entire subgraphs. The key challenge in adapting existing techniques to the pivoted problem is eliminating their redundant intermediate results. In this paper, we propose a GPU-based pivoted subgraph isomorphism filtering technique, where information of each node is encoded into a series of codes. When performing a pivot subgraph search, the candidate nodes satisfying the coding requirements are collected parallelly on GPU while others are filtered away. Then the final result can be effectively retrieved by a verification process on the filtered nodes. As demonstrated by the experimental results, our method dramatically reduces the processing time of the pivoted subgraph isomorphism problem. Compared to the state-of-the-art GPU-friendly subgraph matching method GpSM which also focuses on filtering effect, the algorithm’s execution time is halved, confirming that our approach can effectively process pivoted subgraph isomorphism queries.
ORGANIZATION OF THE EDUCATIONAL PROCESS USING ELECTRONIC TEXTBOOKS

This scientific article assesses the process of studying educational materials in subjects in secondary schools of the Kyrgyz Republic using electronic textbooks. Currently, there are very few electronic textbook options for school subjects. In fact, electronic versions of school subjects would give impetus to the self-development of students with the help of electronic versions of textbooks. In this case, the students would get time for independent study of incomprehensible learning materials. The article focuses on the educational activities in which the student works without the participation of a teacher. In this case, the student undergoes self-analysis and planning of educational activities, or rather, evaluates whch materials are not fully known, for this the student independently analyzes the educational materials and repeats. This, of course, ensures the quality of education for itself and develops independently (at the same time, the student uses audio, video materials, textual content of the textbook, presentations and computer test in an electronic textbook).
A Survey on Privacy-Preserving Data Publishing Methods and Models in Relational Electronic Health Records

The collections of various digital information from corporations, public sectors, private sectors, and individuals have formed remarkable opportunities for analysis and decision-making. The electronic health record is highly constructive for various study purposes; however, it should be carried in a way that preserves the identity and privacy of the patients. Due to the digital transformation, the flow of data increases that lead to the need for big data. Protecting privacy cannot be straightforward as there are several privacy threats such as membership disclosure, identity disclosure, and attribute disclosure. The privacy methods such as generalization, suppression, pseudonymization, bucketization, slicing are illustrated with examples. The study focused on various privacy models such as k-anonymity, l-diversity, t-closeness, differential privacy, and cryptographic technique. The models are discussed with the sample dataset and workflow. The paper has discussed the security and privacy concerns on data publishing, the need for big data scope, an overview of threats, samples, privacy methods, models, and challenges in privacy-preserving data publishing.
Offer Your Supporters Virtual Field Trips

Special Events GaloreVolume 22, Issue 5 p. 4-4 Events That Engage Your Participants Offer Your Supporters Virtual Field Trips First published: 07 April 2022 https://doi.org/10.1002/speg.31772Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume22, Issue5May 2022Pages 4-4 RelatedInformation
An Explainable Recommendation Based on Acyclic Paths in an Edge-Colored Graph

We propose a novel recommendation algorithm based on acyclic paths in an edge-colored graph. In our method, all the objects including users, items to recommend, and other things usable to recommendation are represented as vertices in an edge-colored directed graph, in which edge color represents relation between the objects of its both ends. By setting each edge weight appropriately so as to reflect how much the object corresponding to its one end is preferred by people who prefer the object corresponding to its other end, the probability of an s-t path, which is defined as the product of its component edges’ weights, can be regarded as preference degree of item t (item corresponding to vertex t) by user s (user corresponding to vertex s) in the context represented by the path. Given probability threshold $$\theta $$ , the proposed algorithm recommends user s to item t that has high sum of the probabilities of all the acyclic s-t paths whose probability is at least $$\theta $$ . For item t recommended to user s, the algorithm also shows high probability color sequences of those s-t paths, from which we can know main contexts of the recommendation of item t for user s. According to our experiments using real-world datasets, the recommendation performance of our method is comparable to the non-explainable state-of-the-art recommendation methods.
Integrating Safety Guarantees into the Learning Classifier System XCS

On-line learning mechanisms are frequently employed to implement self-adaptivity in modern systems. With more widespread use in technical systems that interact with their physical environment, e.g. cyber-physical systems, the fulfillment of safety requirements is increasingly gaining attention. We focus on the learning classifier system XCS with its human-interpretable rules and propose an approach to integrate safety guarantees into its rule base. We leverage the interpretability of XCS' rules to internalize the safety-critical knowledge, as opposed to related work, which relies on an external safety monitor. The experimental evaluation shows that such manually injected knowledge not only gives safety guarantees but aids the learning mechanism of XCS. Especially in complex environments where XCS is struggling to find the optimal solution, the use of hand-crafted forbidden classifiers leads to a performance that is up to 41.7 % better than with an external safety monitor.
Efficient, Geometry-Based Convolution

Several computationally intensive applications in machine learning, signal processing, and computer vision call for convolution between a fixed vector and each of the incoming vectors. Often, the convolution need not be exact because a subsequent processing unit, such as an activation function in a neuron network or a visual unit in image processing, can tolerate a computational error, hence allowing the optimization of the convolution algorithm. This paper develops a method of approximate convolution and quantifies its performance in software and hardware. The key idea is to take advantage of the known fixed vector, view a convolution as a dot product, and approximate the angles between the fixed vector and an incoming vector geometrically. We evaluate the proposed method in terms of the accuracy, running time complexity, and hardware power consumption on the field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware platforms. In a benchmark test, the accuracy of the approximate convolution is 3.7% lower than that of the exact convolution, a tolerable loss for machine learning and signal processing. The proposed method reduces the number of operations in the hardware, and reduces the power consumption of conventional convolution by approximately 20% and the existing approximate convolution by approximately 10%, while maintaining the same throughput and latency. We also test the proposed method on 2D convolution and convolutional neural network (CNN). The proposed method reduces complexity, power consumption for 2D convolution, and power consumption for CNN of the conventional method by approximately 22%, 25%, and 13%, respectively. The proposed method of approximate convolution trades off accuracy with running time complexity and hardware power consumption, and it has practical utility in computationally intensive tasks that tolerate a margin of convolutional error.
Principles of Data Privacy and Security in a Cyber World

Today, due to the rapid development of technology and communication, personal rights can be damaged. In particular, the electronic processing of personal data, which is a part of personal rights, may cause problems in terms of data security. Therefore, as a result of the development of technology and communication, all kinds of information should be able to move freely, and at the same time, fundamental rights dependent on the person should be protected. By clarifying the concepts of data, personal data, and data processing, the study examines the reliability of the data processed in the light of these concepts at the point of data security.
Texture Dataset Construction and Texture Image Retrieval based on Deep Learning

In the deep texture image retrieval, to address the problem that the retrieval performance is affected by the lack of sufficiently large texture image dataset used for the effective training of deep neural network, a deep learning based texture dataset construction and texture image retrieval method is proposed in this paper. First, a large-scale texture image dataset containing rich texture information is constructed based on the DTD texture image dataset, and used as the source dataset for pre-training deep neural networks. To effectively characterize the information of the source texture dataset, a revised version of the VGG16 model, called ReV-VGG16, is adaptively designed. Then, the pre-trained ReV-VGG16 model is combined with the target texture image datasets for the transfer learning, and the probability values of the output from the classification layer of the model are used for the computation of the similarity measurement to achieve the retrieval of the target texture image dataset. Finally, the retrieval experiments are conducted on four typical texture image datasets, namely, VisTex, Brodatz, STex and ALOT. The experimental results show that our method outperforms the existing state-of-the-art texture image retrieval approaches in terms of the retrieval performance.
Using Python to Study Reader Reception of English Translations of Mencius

This study collects and analyzes reader comments of English translations of Mencius from Amazon and Goodreads based on Python data processing tool. A comment database is built with the purpose of analyzing readers' emotional attitudes, recognition of the value of the work and focus dimensions. Translation quality, translation gains and losses, and the reflection of China's image is examined through both a quantitative and qualitative research. The study finds that readers' acceptance of the English translations of Mencius is generally good. Chinese philosophy and culture are attractive and charming, which also stimulates readers' interest and enthusiasm for understanding Chinese culture and related elements. In view of the fact that there are few studies approaching English translations of Mencius and the image of China based on reader reviews, this research can provide insight for related research on the positioning and influence of foreign translation of Chinese classics.
Research on Attention Analysis Based on Vision

Attention analysis technology refers to the analysis of people's attention during work or study through monitoring various data. This paper proposed a visual-based attention analysis method, in which a dataset for attention analysis was built and the neural network was adopted to analyze attention. Good results had been achieved on the self-built dataset with a 93% recognition rate.
Lattice-Based Cryptography and NTRU

This chapter presents an important topic in cryptography, namely, the encryption schemes based on lattices. These structures are important because it has been proven they can be included in post-quantum cryptography. Lattices are one of the few techniques that can resist quantum attacks and are good candidates for encryption schemes for quantum computers.
LCR-GAN: Learning Crucial Representation for Anomaly Detection

Anomaly detection is pivotal and challenging in artificial intelligence, which aims to determine whether a query sample comes from the same class, given a set of normal samples from a particular class. There are a plethora of anomaly detection methods based on generative models; however, these methods aim to make the reconstruction error of the training samples smaller or extract more information from the training samples. We believe that it is more important for anomaly detection to extract crucial representation from normal samples rather than more information, so we propose a semi-supervised method named LCR-GAN. We conducted extensive experiments on four image datasets and 15 tabular datasets to demonstrate the effectiveness of the proposed method. Meanwhile, we also carried out an anti-noise study to demonstrate the robustness of the proposed method.
On the enumeration and classification of σ-LCD codes over finite commutative chain rings

Let Re denote a finite commutative chain ring with the maximal ideal 〈u〉 and the nilpotency index e, and let R‾e=Re/〈u〉 be the residue field of Re. Let σ0 be an automorphism of Re, and let σ‾0 be the corresponding automorphism of the residue field R‾e of Re, defined as σ‾0(a+〈u〉)=σ0(a)+〈u〉 for all a+〈u〉∈R‾e. Let σ be an automorphism of Ren corresponding to the automorphism σ0 of Re, defined as σ(v1,v2,…,vn)=(σ0(v1),σ0(v2),…,σ0(vn)) for all (v1,v2,…,vn)∈Ren. In this paper, we obtain explicit enumeration formulae for all σ-LCD codes of an arbitrary length over the chain ring Re when σ‾02 is the identity automorphism of R‾e. With the help of these enumeration formulae and by applying the classification algorithm, we classify all Euclidean LCD codes of lengths 2, 3, 4 and 5 over the chain ring F2[u]/〈u2〉 and of lengths 2, 3 and 4 over the chain ring F3[u]/〈u2〉, and all σ-LCD codes of lengths 2, 3 and 4 over the chain ring F4[u]/〈u2〉, where σ0 is an automorphism of F4[u]/〈u2〉 such that the corresponding automorphism σ‾0 of the residue field F4 has order 2. Besides this, we show that the class of σ-LCD codes over Re is asymptotically good, and that every free linear [n,k,d]-code over Re is equivalent to a σ-LCD [n,k,d]-code over Re when |R‾e|>4. We also explicitly determine all inequivalent σ-LCD [n,1,d]-codes and [n,n−1,d]-codes over Re for 1≤d≤n.
The Quantum Multiple-Access Channel With Cribbing Encoders

Communication over a quantum multiple-access channel (MAC) with cribbing encoders is considered, whereby Transmitter 2 performs a measurement on a system that is entangled with Transmitter 1. Based on the no-cloning theorem, perfect cribbing is impossible. This leads to the introduction of a MAC model with noisy cribbing. In the causal and non-causal cribbing scenarios, Transmitter 2 performs the measurement before the input of Transmitter 1 is sent through the channel. Hence, Transmitter 2's cribbing may inflict a "state collapse" for Transmitter 1. Achievable regions are derived for each setting. Furthermore, a regularized capacity characterization is established for robust cribbing, i.e. when the cribbing system contains all the information of the channel input. Building on the analogy between the noisy cribbing model and the relay channel, a partial decode-forward region is derived for a quantum MAC with non-robust cribbing. For the classical-quantum MAC with cribbing encoders, the capacity region is determined with perfect cribbing of the classical input, and a cutset region is derived for noisy cribbing. In the special case of a classical-quantum MAC with a deterministic cribbing channel, the inner and outer bounds coincide.
Analyzing the Sentiments by Classifying the Tweets Based on COVID-19 Using Machine Learning Classifiers

In the current scenario, almost all the countries face one of the biggest disasters in COVID-19. This paper has to analyze the tweets related to COVID 19 and discuss the various machine learning algorithms and their performance analysis on the tweets associated with COVID-19. The implemented classification algorithms are applied to classify the sentiments to predict whether they relate to COVID-19 or non-COVID-19. Ten most popular classification algorithms implemented. The Linear Support Vector Machine (LSVM) achieved the highest test accuracy in these algorithms with 90.3%. Logistic regression has performed better in recall with 96.06%, F1 score of 90.46%, ROC_AUC with 90.48%. Random forest classifier has achieved the better specificity and precision of 99.16% and 96.3%, respectively. Out of all, stochastic gradient descent (SGD) has attained better results in all the computational parameters.
Cardiovascular Disease Prediction Via Literature Embedding Model Using Risk Factors, Symptoms, and Genotype Information

Accurate prediction of cardiovascular disease (CVD) requires multifaceted information consisting of not only a patient’s medical history, but genomic data, symptoms, lifestyle, and risk factors which are often not incorporated into a decision making process as the data are vast, difficult to obtain, and require complex algorithms. However, given the vast amount of publicly available information and recent advances in machine and deep learning techniques, acquiring multifaceted data for accurate diagnosis and medical decision making via artificial intelligence has become more attainable. In this work we illustrate a literature embedding model, which identifies various risk factors, symptoms, mechanisms, and genes associated with CVD using a query word (i.e., “stroke”), and then based on the collected information the model predicts whether or not a person will be susceptible to CVD. For this purpose, we collected published literature from PubMed using search keywords consisting of a word such as “heart” and 19,264 human gene names, then trained our literature embedding model using the collected abstracts. For the intrinsic evaluation, we analyzed whether or not the captured words and genes were correctly identified as risk factors and associated symptoms for the input query words. For the extrinsic evaluation, we used our embedding model as feature selection and dimensionality reduction tasks on cohort data for CVD prediction. Our model accurately (average accuracy of >96%) captured associated risk factors, symptoms, and genes for a given input query word (intrinsic evaluation). Using the selected features and reduced dimensions, our method provided better performance for CVD prediction with less computational time when compared with other popular methods (extrinsic evaluation). Our model provided outstanding results for both evaluation tasks, which enables accurate risk factor identification for CVD prediction. Our model has the potential to facilitate easier collation of multifaceted information for better data mining of vast publicly available data so that efficient and accurate risk factors and symptoms can be identified, which enables better-informed decisions for CVD prediction and treatment.
Text-Dependent and Text-Independent Writer Identification Approaches

Writer identification is a wide-spreading biometric which can be used as a legitimate mean to identify an individual. It facilitates the experts to automatically identify the person in many security concerns applications such as forensic science. Due to this, much attention has been drawn in this field from the last few decades. On the basis of input text, it can have various forms like online, offline, text-dependent or text-independent writer identification. The paper will present a systematic study on text-dependent and text-independent writer identification of handwritten text images for various Indic and non-Indic scripts. The various segmentation techniques used to segment handwritten text are also presented in detail. The various datasets available for researchers are given for various scripts such as English, Arabic, Chinese, Japanese, Dutch, Farsi, Devanagari, Bangla, and Kannada discussed by doing exhaustive analysis of various studies. We hope that our research will be helpful in giving better understanding of the area and provides various directions for further research.
Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network

Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in the real world, especially for industrial graphs at a billion-level, the storage of graphs can easily consume Terabytes so that the process of GNNs has to be processed in a distributed manner. As a result, the execution could be inefficient due to the expensive cross-node communication and irregular memory access. Various GNN accelerators have been proposed for efficient GNN processing. They, however, mainly focused on small and medium-size graphs, which is not applicable to large-scale distributed graphs. In this paper, we present a practical Near-Data-Processing architecture based on a memory-pool system for large-scale distributed GNNs. We propose a customized memory fabric interface to construct the memory pool for low-latency and high throughput cross-node communication, which can provide flexible memory allocation and strong scalability. A practical Near-Data-Processing design is proposed for efficient work offloading and bandwidth utilization improvement. Moreover, we also introduce a partition and scheduling scheme to further improve performance and achieve workload balance. Comprehensive evaluations demonstrate that the proposed architecture can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> higher training speed compared to two state-of-the-art distributed GNN frameworks: Deep Graph Library and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P^{3}$ </tex-math></inline-formula> , respectively.