Reviewerly/exHarmony huggingface dataset:
Blockchain for the environmentally sustainable enterprise

Business Strategy and the EnvironmentEarly View EDITORIAL Blockchain for the environmentally sustainable enterprise Chunguang April Bai, Corresponding Author Chunguang April Bai cbai@uestc.edu.cn School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China Correspondence Chunguang April Bai, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China. Email: cbai@uestc.edu.cnSearch for more papers by this authorJames Cordeiro, James Cordeiro School of Business and Management, State University of New York, Brockport, New York, USASearch for more papers by this authorJoseph Sarkis, Joseph Sarkis Business School, Worcester Polytechnic Institute, Worcester, Massachusetts, USASearch for more papers by this author Chunguang April Bai, Corresponding Author Chunguang April Bai cbai@uestc.edu.cn School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China Correspondence Chunguang April Bai, School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China. Email: cbai@uestc.edu.cnSearch for more papers by this authorJames Cordeiro, James Cordeiro School of Business and Management, State University of New York, Brockport, New York, USASearch for more papers by this authorJoseph Sarkis, Joseph Sarkis Business School, Worcester Polytechnic Institute, Worcester, Massachusetts, USASearch for more papers by this author First published: 30 March 2022 https://doi.org/10.1002/bse.3026Read 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 Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
A Novel Efficient Power Saving Method for Always-On Display

This paper proposes a method of Always-On Display (AOD) function, named H/W Efficient Standalone AOD that operates AOD function inside Display Driver IC (DDI) itself with Application Processor (AP) sleep mode for low-power operation. Moreover, this paper also proposes a novel AOD operating architecture that can remove the additional memory required to express AOD information. Through experimental results, the proposed method demonstrates that the power consumption is reduced by 17.6&#x0025; effectively without costing additional memory or causing cognitive image quality loss compared to the conventional AOD method.
The practice of publishing in the Saratov province in the 1920s literature for high school

Against the background of an acute shortage of educational literature in support of higher education in the 1920s, dozens of famous teachers of Saratov universities acted as authors of textbooks and manuals. The provincial department of the state publishing house was responsible for their publication, and the preparation of the manuscript for publication was carried out by such organizations as: the subdivision of social security for students at Saratov State University, the general student fund for mutual assistance, the provincial departments of health and education, etc. A lot of educational literature for students of humanities were published by V. Z. Yaksanov’s private publishing house. The article examines the dynamics of educational book publishing to meet the needs of higher education in the region, its characteristic features and characteristics.
Subgraph Matching on Multiple Graph Streams

Nowadays, graph data can be collected via more than one resource and in parallel. This involves not only changing the information but also immediately updating the outcomes of the data mining task. In this case, it is undesirable to recompute all the tasks once the changes occur to the original data. Therefore, we propose a streaming graph table (SGTable) to capture inserted/removed edges and an incremental graph matching algorithm to retrieve new results on only new data. Besides, we do the multi-streaming environment experiments using Spark that gather graphs from various sources.
Chaos-Based Image Encryption Based on Bit Level Cubic Shuffling

This work studies the problem of chaos-based image encryption. First, a generalization of the 1D chaotic map proposed by (Talhaoui et al. in The Visual Computer, pp 1–11, 2020) [8] is constructed and studied. The generalized map showcases regions of constant chaotic behaviour, similar to the original map. Based on the new map, a statistically secure pseudo-random bit generator is designed, which is utilised in the encryption process. An image encryption technique based on shuffling the bit levels of an image is introduced, by first arranging the bits in a three dimensional matrix, and performing a three level shuffling, on each individual row, column, and bit level of the 3D matrix. The shuffling is then followed by an exclusive OR operation between the shuffled bits and a bitstream from the proposed chaotic bit generator, which results in the encrypted image. The combination of shuffling and XOR yields a ciphertext image that is resistant to a collection of attacks, like histogram, correlation, and entropy analysis, NPCR and UACI measures, cropping attacks, and is also robust to transmission noise. This is verified by testing the encryption process to a collection of plaintext images. Finally, the encryption/decryption process is implemented in a Graphical User Interface for ease of use.
DocSemMap: Leveraging Textual Data Documentations for Mapping Structured Data Sets into Knowledge Graphs

Today, knowledge graphs have been proven to enable the efficient integration of heterogeneous data sets. An important step in creating such knowledge graphs is the mapping of the attributes of a data set to the knowledge graph's ontology. So far, numerous methods have been developed to support this mapping process by using both the schema information as well as the actual data values from a data set in conjunction with external knowledge bases or machine learning approaches. A third source of information, namely textual data documentations, has not yet been considered. In this paper, we present DocSemMap, a novel approach that utilizes textual data documentations of data sets as an additional source for the creation of semantic mappings. We train custom embeddings on the textual data documentations. Further, we utilize pre-trained embeddings that allow us to identify similarities between excerpts of the textual data documentations and descriptions of ontological concepts. Based on this, we build candidate sets of the best suitable concepts for mapping and finally use weighted similarity scores to identify the best fitting concept for each attribute of a data set. The evaluation of our approach outperforms existing approaches for semantic mapping but still has potential for improvement.
The Role of Information System Management For Educational Institutions During Pandemic

In the world of education technology is very important, because nowadays almost every activity uses technology. One of them is information technology in order to store student data such as student biodata, teacher biodata, new student admissions data, attendance lists, alumni bios, addresses, educational history, teacher attendance lists and others, which can be viewed through the management information system. With a management information system, it can support the learning process, especially during the covid 19 pandemic. The pandemic forces the learning process to use technology. The government together with the Ministry of Education and Culture work together to meet the needs of technology in education, especially in Indonesia. The management information system has an impact on improving the quality of the school towards a better direction, it is also useful in managerial decisions that will be taken by the school.
Space-Time Multilayer Model for Battlefields Recognition

In the army, military power continues to decline due to aging and declining population. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable battlefield recognition based on data science In this paper, we propose a space-time multi layer model for battlefields recognition. We design an architecture and generate dataset for training the neural network. Simulation results are provided to demonstrate the performance of battlefields recognition.
Constructing unextendible product bases from multiqubit ones

The construction of multipartite unextendible product bases (UPBs) is a basic problem in quantum information. We respectively construct two families of $2\times2\times4$ and $2\times2\times2\times4$ UPBs of size eight by using the existing four-qubit and five-qubit UPBs. As an application, we construct novel families of multipartite positive-partial-transpose entangled states, as well as their entanglement properties in terms of the geometric measure of entanglement.
A Sparse-Motif Ensemble Graph Convolutional Network against Over-smoothing

The over-smoothing issue is a well-known challenge for Graph Convolutional Networks (GCN). Specifically, it is often observed that increasing the depth of GCN ends up in a trivial embedding subspace where the difference among node embeddings belonging to the same cluster tends to vanish. This paper believes that the main cause lies in the limited diversity along the message passing pipeline. Inspired by this, we propose a Sparse-Motif Ensemble Graph Convolutional Network (SMEGCN). We argue that merely employing the original graph Laplacian as the spectrum of the graph cannot capture the diversified local structure of complex graphs. Hence, to improve the diversity of the graph spectrum, we introduce local topological structures of complex graphs into GCN by employing the so-called graph motifs or the small network subgraphs. Moreover, we find that the motif connections are much denser than the edge connections, which might converge to an all-one matrix within a few times of message-passing. To fix this, we first propose the notion of sparse motif to avoid spurious motif connections. Subsequently, we propose a hierarchical motif aggregation mechanism to integrate the graph spectral information from a series of different sparse-motif message passing paths. Finally, we conduct a series of theoretical and experimental analyses to demonstrate the superiority of the proposed method.
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation

In real-world scenarios, many large-scale datasets often contain inaccurate labels, i.e., noisy labels, which may confuse model training and lead to performance degradation. To overcome this issue, Label Noise Learning (LNL) has recently attracted much attention, and various methods have been proposed to design an unbiased risk estimator to the noise-free dataset to combat such label noise. Among them, a trend of works based on Loss Decomposition and Centroid Estimation (LDCE) has shown very promising performance. However, existing LNL methods based on LDCE are only designed for binary classification, and they are not directly extendable to multi-class situations. In this paper, we propose a novel multi-class robust learning method for LDCE, which is termed “MC-LDCE”. Specifically, we decompose the commonly adopted loss (e.g., mean squared loss) function into a label-dependent part and a label-independent part, in which only the former is influenced by label noise. Further, by defining a new form of data centroid, we transform the recovery problem of a label-dependent part to a centroid estimation problem. Finally, by critically examining the mathematical expectation of clean data centroid given the observed noisy set, the centroid can be estimated which helps to build an unbiased risk estimator for multi-class learning. The proposed MC-LDCE method is general and applicable to different types (i.e., linear and nonlinear) of classification models. The experimental results on five public datasets demonstrate the superiority of the proposed MC-LDCE against other representative LNL methods in tackling multi-class label noise problem.MSC codesMulti-class ClassificationLabel NoiseLoss DecompositionCentroid Estimation
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss

Deep neural networks suffer from the overconfidence issue in the open world, meaning that classifiers could yield confident, incorrect predictions for out-of-distribution (OOD) samples. It is an urgent and challenging task to detect these samples drawn far away from training distribution based on the security considerations of artificial intelligence. Many existing methods based on neural networks mainly rely on complex processing strategies, such as temperature scaling and input preprocessing, to obtain satisfactory results. In this paper, we propose an effective algorithm for detecting out-of-distribution examples utilizing PEDCC-Loss. We mathematically analyze the nature of the confidence score output by the PEDCC (Predefined Evenly-Distribution Class Centroids) classifier and then construct a more effective scoring function to distinguish in-distribution (ID) and out-of-distribution samples. In this method, there is no need to preprocess the input samples and the computational burden of the algorithm is reduced. Experiments demonstrate that our method can achieve better OOD detection performance than Softmax-based methods.
Banking System Using Blockchain

Blockchain is a distributed ledger technology which aids in recording transaction and tracking capital in any business network. The main feature of blockchain is transparency. Our project is going to help these banks because we will be implementing blockchain ledger for creating a banking system in Linux. Blockchain will boost trust, security, translucence, and the traceability of data shared across a banking system and will be cost saving with new efficiencies. Because it provides rapid, shareable, and entirely clear information recorded on an immutable ledger that can only be viewed by authorised network users, blockchain is the ideal option for distributing data. This network can locate orders, capital, users, creations, and much more, and because participants share only one view of transparency, you can see all the details of a capital transaction from beginning to end, providing you with a greater sense of trust as well as new efficiencies and opportunities. Banking systems are transferring from their older methods to more recent ledger methods. Banking sector has started experimenting with blockchain ledger by copying current asset transactions on the blockchain. This enables the banking sector to have some space for the effectiveness of the blockchain solution. In infrastructure terms, Blockchain is software that is open source designed to support the transfer of digital capital among market participants in real time. Using any chosen blockchain APIs, one may exhibit big decrease in asset transfer costs and timelines. Today's banks are developing and operating their business models with the help of a trusted third-party service provider. As a result, Blockchain technology will be able to reconstruct the banking model since it can handle transaction records via a peer-to-peer network between trading partners without the need for a trusted third party.
Twitter Sentiment Analysis Using Social-Spider Lex Feature-Based Syntactic-Senti Rule Recurrent Neural Network Classification

Social platforms have become one of the major sources of unstructured text. Investigating the unstructured text and interpreting the meaning is a complex job. Sentiment Analysis is an emerging approach as the social platforms have lot of opinionated data. 1 It uses language processing, classification of texts and linguistics to retrieve the opinions from the text. Twitter is a micro blogging site which is popular amongst the social users as it is a vast open data-platform and it witnesses lot of sentiments. Twitter Sentiment Analysis is a process of automatic mining of user tweets for opinions, emotions, attitude to derive useful insights into community opinions and classify the opinions as well. Due to the enormous increase in the number of collaborative tweets, it has become complex to identify the terms that carries sentiments. Also, the unstructured tweets may have non-relevant terms and reduce the classification accuracy. To address these issues, we propose a Social-Spider Lex Feature Ensemble Model-Based Syntactic-Senti Rule prediction Recurrent Neural Network Classifier (S 2 LFEM-S2RRNN) to obtain better classification accuracy. Twitter is used as source of data and we have extracted the tweets using Twitter API. Initially, data pre-processing is done to remove unwanted data, symbols and content terms are extracted to improvise the dataset. Then, the significant lexical content terms are extracted employing the proposed Social Spider Lex Feature Ensemble Model (S 2 LFEM) based on Syntactic-Senti Rule Prediction. The semantics 4 of the terms are analysed on the verbs, subjectivity of the tweet patterns to count the overall weightage of tweets. Based on tweet weightage Recurrent Neural Network is trained to classify the tweets int to positive, negative and neutral. The experiment results show that the proposed classifier outperforms the existing models for sentiment classification in terms of accuracy with a performance score 94.1%.
Automatic Evaluation of Learning Behaviors for Online Lectures by OpenPose

To prevent that learners do something unrelated to an online lecture, we proposed an automatic evaluation method of learning behavior. This method extracted the pose of learners and a teacher by OpenPose, constructed a model that estimate to a teacher&#x2019;s pose at j sec in online lecture from a learner&#x2019;s one, and evaluated by measuring the difference between estimated and actual values. The result of an experiment showed this could detect the unrelated actions about 90%.
Innovation during the COVID-19 crisis: Why it was more critical for official statistics than ever1

During the COVID-19 pandemic, quantitative evidence was desperately needed in order to understand and manage an unprecedented situation, and to make important national, European and international decisions. Eurostat and the national statistical institutes (NSIs) played a key role in managing the pandemic, providing society with a high quality statistical information service. In particular, the crisis accelerated innovation in statistical production, steered complex processes of change towards the use of new data sources and privately held data for official statistics, enhanced the adoption of new statistical methods, and consequently the production of experimental statistics and dashboards. While the new data ecosystem provided opportunities for the production of official statistics, the importance of privacy preservation, data security and the development of adequate data quality frameworks remained a priority. Important strands of work for the future would be: satisfying the increased needs of users, as well as supporting decision-making and the delivery of government services in emergencies. NSIs would also do well to invest in innovation, collaborate and establish partnerships with the data providers and research communities that have worked closely with them since the beginning of the pandemic. This article is based on the experiences of six NSIs in the Netherlands, Spain, France, Italy, Germany and Finland during the pandemic.
An objective inter-comparison of trash mark constellations generated by manual and automated detection methods

Trash marks are unintentional markings observed on printed, scanned, or photocopied documents that result from permanent defects or transient material in office machines and can be used for source attribution of questioned documents. Trash mark examinations have been in use in forensic laboratories for decades, yet the method remains relatively untested and relies on training, experience, and anecdotal information to support its validity. This study generated and harnessed objective data to empirically test one of the foundational theories for assessing the origin of photocopied documents: provided trash marks are present in sufficient quantity and/or quality, no two machines will exhibit a constellation of trash marks that is indistinguishable from another. In this project, objective trash mark location and size data was generated for 50 known photocopiers using both a traditional and a novel, automated method. Inter-machine comparisons were conducted using a novel variant of the Hausdorff distance algorithm to generate a quantitative assessment of how similar or different the 2450 pairs of trash mark constellations were from one another. This study found that each of the machines bearing one or more trash marks exhibited objective differences in their trash mark constellations, ultimately providing support for the tested hypothesis.
Study of Principal Component Analysis (PCA) as a Face Recognition Method

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.
HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databases

Abstract The big graph database provides strong modeling capabilities and efficient querying for complex applications. Subgraph isomorphism which finds exact matches of a query graph in the database efficiently, is a challenging problem. Current subgraph isomorphism approaches mostly are based on the pruning strategy proposed by Ullmann. These techniques have two significant drawbacks- first, they are unable to efficiently handle complex queries, and second, their implementations need the large indexes that require large memory resources. In this paper, we describe a new subgraph isomorphism approach, the HyGraph algorithm, that is efficient both in querying and with memory requirements for index creation. We compare the HyGraph algorithm with two popular existing approaches, GraphQL and Cypher using complexity measures and experimentally using three big graph data sets—(1) a country-level population database, (2) a simulated bank database, and (3) a publicly available World Cup big graph database. It is shown that the HyGraph solution performs significantly better (or equally) than competing algorithms for the query operations on these big databases, making it an excellent candidate for subgraph isomorphism queries in real scenarios.