Reviewerly/exHarmony huggingface dataset:
Mutual Consistency-Ensured Bi-Directional GAN for Anomaly Detection

Anomaly detection is a challenging and fundamental issue in computer vision tasks. In recent years, Generative Adversarial Networks (GAN) and Bi-directional GAN based anomaly detection methods have achieved remarkable results. However, the performance of these approaches for anomaly detection depends on the ability to reconstruct a given normal image and to predict its latent variables. Therefore, we design a novel Bi-directional GAN-based anomaly detection model to improve these abilities. Especially, in order to reconstruct the image, we introduce the consistency loss for ensuring mutual mappings in both image and latent space. Moreover, we propose introducing the projection discriminator as an alternative of concatenating discriminator in order to perform efficient conditioning in the Bi-directional GAN model. In experiments, we evaluate the effectiveness of our model in MNIST and MVTec Metal Nut. Our experiment showed that our model allows us to detect various real anomalies such as bent, scratch, color, and flip, and outperforms the conventional ones.
Conjunctive coding of color and shape in convolutional neural networks

Understanding how the visual system conjunctively codes color and shape has long fascinated cognitive psychologists, cognitive neuroscientists, and neurophysiologists. Recent developments in convolutional neural networks (CNNs) provide us with an excellent opportunity to examine how color and shape conjunctions may be encoded in artificial systems only trained to perform object recognition. To determine whether CNNs encode color and shape independently or in an interactive manner, we used representational similarity analysis to characterize the responses of Alexnet, VGG19, Cornet, Resnet, and Googlenet to different objects, each presented in several different colors. Regardless of the CNN examined, we found that whereas lower layers of the CNNs encode colors in a similar manner across different objects, in higher layers the color spaces associated with different objects are more distinct. The converse is also true: early layers encode shape in a more similar manner across colors than later layers. Interestingly, the similarity between the color spaces of different objects was only weakly (though significantly) associated with the objects’ shape similarity. These results held when color and shape similarity were equated, and when uniformly colored “silhouette” images were used instead of naturally textured images. These results demonstrate that rather than being encoded in an orthogonal manner, color and shape processing becomes increasingly interactive in higher layers of a CNN, suggesting that neural networks optimized for object recognition will naturally develop conjunctive coding of color and shape. These results will be compared with those from responses from visual regions in the human brain to test whether a similar conjunctive coding scheme exists in natural visual systems.
PPMLP 2020: Workshop on Privacy-Preserving Machine Learning In Practice

With the rapid development of technology, data is becoming ubiquitous. User privacy and data security are drawing much attention over the recent years, especially with the European Union's General Data Protection Regulation (GDPR) and other laws coming into force. On one hand, from the customers' perspective, how to protect user privacy while making use of customers? data is a challenging task. On the other hand, data silos are becoming one of the most prominent issues for the society. From the business? perspective, how to bridge these isolated data islands to build better AI systems while meeting the data privacy and regulatory compliance requirements has imposed great challenges to the traditional machine learning paradigm. PPMLP will provide an opportunity to connect researchers from both CCS community and machine learning community to tackle these challenges.
Quantum algorithms for the Goldreich–Levin learning problem

The Goldreich–Levin algorithm was originally proposed for a cryptographic purpose and then applied to learning. The algorithm is to find some larger Walsh coefficients of an n variable Boolean function. Roughly speaking, it takes a $$poly(n,\frac{1}{\epsilon }\log \frac{1}{\delta })$$ time to output the vectors w with Walsh coefficients $$S(w)\ge \epsilon $$ with probability at least $$1-\delta $$ . However, in this paper, a quantum algorithm for this problem is given with query complexity $$O(\frac{\log \frac{1}{\delta }}{\epsilon ^4})$$ , which is independent of n. Furthermore, the quantum algorithm is generalized to apply for an n variable m output Boolean function F with query complexity $$O(2^m\frac{\log \frac{1}{\delta }}{\epsilon ^4})$$ .
RETRACTED: Effective Ways to Cultivate the Autonomous Learning Ability of Minority College Students under the Network Big Data Environment

Abstract The era of big data provides convenience for college students to study independently. College teachers can make use of the relevant characteristics of big data to improve teaching methods, enrich teaching content and guide the independent learning of college students with scientific teaching evaluation, and explore the cultivation mode of college students’ independent learning ability. Big data in order to better research network environment of ethnic minorities is effective way to cultivate college students’ autonomous learning ability, this study in a university of ethnic minority college students as the research object, through a combination of big data technology to improve the way of the network teaching, through questionnaire as tools, supplemented by semistructured interview, investigation of ethnic minority college students’ autonomous learning ability, from the autonomous learning ability, independent learning behavior, autonomous learning psychology discusses three dimensions of ethnic minority college students’ autonomous learning situation, and discusses how to develop ethnic minority college students’ autonomous learning ability, according to the results, After combining big data teaching, the attendance rate increased by 21.7%.
RETRACTED: Research on the Training Model of Business English Professionals Based on Big Data Analysis—Employment-oriented

Abstract Employment-oriented and competency-based personnel training mode is one of the main tasks of education reform and development. Through investigation and research based on big data technology, this paper analyses the problems existing in the training of business English professionals, and provides a basis for the construction of a training program for business English professionals adapted to the development of regional economy and society.
Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset. Several studies use the Euclidean and Gower distance as measurement methods in numerical data clustering. This study aims to compare the performance of the k-medoids clustering on a numerical dataset using the Euclidean and Gower distance. This study used seven numerical datasets and Silhouette, Dunn, and Connectivity indexes in the clustering evaluation. The Euclidean distance is superior in two values of Silhouette and Connectivity indexes so that Euclidean has a good data grouping structure, while the Gower is superior in Dunn index showing that the Gower has better cluster separation compared to Euclidean. This study shows that the Euclidean distance is superior to the Gower in applying the k-medoids algorithm with a numeric dataset.
RETRACTED: Research on the Cultivation of Cross-cultural Awareness in English Translation Teaching Based on Computer Multimedia Technology

Abstract With the continuous development of the times, the importance of education is gradually highlighted. English is the second language most Chinese students learn. In the process of learning, students can not only grasp some basic theoretical knowledge, but also constantly cultivate students’ cross-cultural awareness. Through the cultivation of cross-cultural awareness, students can better understand the content of foreign culture and language. At present, many colleges and universities have carried out college English translation teaching based on computer multimedia technology. By comparing and interpreting the cultures of the two countries, the students deepen their impression of foreign cultures and constantly improve their English level, which promotes their better development.
CONSTRUCTION OF NEURAL NETWORKS IN ONLINE ENVIRONMENTS WHEN STUDYING THE CONTENT LINE "MODELING AND FORMALIZATION" IN THE LESSONS OF INFORMATICS AT SCHOOL

Modeling as a universal approach to the study of the structure and behavior of complex objects is used in many academic disciplines, as it is a general didactic means of acquiring knowledge. A promising direction in the development of mathematical modeling is modeling the behavior of intelligent systems, which is being improved due to the introduction of new information technologies, telecommunication systems and the massive use of computer technology. Modeling takes on the functionality of a powerful and effective tool for researching a variety of intelligent systems from various fields of human activity. One of the most popular methods for studying the functioning of intelligent systems are machine learning methods for processing large amounts of data, in particular, the apparatus of neural networks. The article discusses the features of constructing the simplest neural networks by using the Teachable Machine online system and online compilers of programming languages in the classroom on computer modeling at school. Detailed examples of building neural network models in Python and C ++ programming languages are given.
MaSRChain: A Trusted Manuscript Submission and Review System Based on Blockchain

Manuscript submission and review (MaSR) systems play an important role in scholarly publishing. However, there are some problems to be solved. Authors cannot gain an authoritative copyright certificate of manuscripts. Journals and conferences cannot achieve effective detection of multiple contributions with one manuscript. Reviewers may intentionally submit malicious evaluations due to competition. In this paper, we propose a trusted decentralized manuscript submission and review system based on blockchain (MaSRChain) to solve problems above. At first, we use blockchain and Attribute-Based Encryption (ABE) to protect manuscript copyright and realize access control of manuscripts for authors. Secondly, we utilize blockchain to realize manuscript sharing that encrypted by Locality Sensitive Hash (LSH), which can achieve multiple contributions detection among different institutions. Thirdly, we apply Ring Signature to realize authentication of review evaluations, while providing some anonymity to reviewers. Finally, we conduct experiments based on Hyperledger Fabric and experimental results demonstrate the effectiveness and efficiency of the system.
Construction of a Cloud Scenario Knowledge Graph for Cloud Service Market

Fast-growing cloud computing technology makes cloud application scenarios more diversified and difficult to choose. A knowledge recommending system is an effective way to reach automatic recommendation services. In this paper, we constructed a cloud scenario knowledge graph (CSKG) for cloud service market to assist cloud knowledge management and provided a web service interface for recommended applications. First a cloud scenario domain ontology was defined based on product knowledge of cloud suppliers and experience of domain experts. Then the BiLSTM-CRF model and Rule-based method were combined to automatically extract structured knowledge from multi-source and heterogeneous texts, where we proposed a tagging schema combined with entity extraction and relation classifier. Finally, a cloud scenario knowledge graph was constructed and visualized, several application examples based on the CSKG service were suggested.
An Empirical Evaluation of Requirements Prioritization Techniques

In today's time and budget intensive software development market, quick delivery is the basic motive of teams.Software development teams strive to gain customer satisfaction by all possible means.Requirements prioritization is the most challenging customer input dependent task in the software development life cycle that decides the fate of a project.Selection of a well-suited requirements prioritization technique may result in customer satisfaction and ontime delivery time.Literature reports on many requirements prioritization techniques in practice.However, each has its own features that can outperform the rest for a certain case.Therefore, this research is conducted to empirically evaluate the existing techniques in terms of certain quality measures (i.e., accuracy, efficiency, and scalability).The selected techniques are evaluated for the small, medium and large scale of requirements sets.For that, we selected five existing techniques that are multi-criteria-decision-making techniques and have user involvement (i.e., Analytical Hieratical Process (AHP), Analytical Network Process (ANP), FuzzyAHP, FuzzyANP and Interactive Genetic Algorithm (IGA)).The experimental results showed that among the five selected techniques, FuzzyAHP is the most efficient and accurate technique for the large dataset of requirements.
Parameters Selection of Twin Support Vector Regression Based on Cloud Particle Swarm Optimization

Twin Support Vector Regression (TSVR), a novel regressor, obtaining faster learning speed than classical support vector regression (SVR), has attracted the attention of many scholars. Similar to SVR, TSVR is also sensitive to its parameters. Therefore, how to select the suitable parameters has become an urgent problem for TSVR. In this paper, a parameters selection version for TSVR, termed parameters selections of twin support vector regression based on cloud particle swarm optimization (TSVR-CPSO), is proposed. Using the characteristics of randomness and stable tendency of normal cloud model, the inertia weight of PSO can be generated by the basic cloud generator of cloud model. To do so, we can improve the diversity of population for PSO, thus greatly improve the ability of diagnosis to avoid falling into local optimal. Based on the above idea, the cloud particle swarm optimization (CPSO) model is constructed. At last, CPSO is used to search the optimal combination of TSVR parameters. Simulations show that the proposed algorithm is an effective way to search the TSVR parameters and has good performance in nonlinear function estimation.
“Through the looking glass: envisioning new library technologies” pandemic response technologies: tracking technologies and artificial intelligence

Abstract Purpose The purpose of this paper is to focus on tracking technologies and artificial intelligence, two technologies many of us carry as part of our smartphones every day in the context of the 2020 pandemic. Design/methodology/approach This column is part of a series using examples and headlines generated by this unique pandemic to explore larger technological trends and to provide a glimpse into the future of these underlying technologies. Findings Looking ahead, it is clear that the current pandemic has heightened the value and importance of these technologies, accelerating existing trends and heightening the importance for information professionals to understand their strengths and weaknesses. Originality/value This column will help information professionals have the context they need to understand these technologies as they evolve and enable them to prepare their organizations and communities to effectively use, promote and develop policies to deal with them. Once a technology has been integrated into our lives, its impact can feel invisible, regardless of its impact. Over time, as the technology continues to improve and change, those evolutions may remain unseen. Yet, extraordinary circumstances, such as a global pandemic, can provide a new lens for understanding these underlying technologically evolutionary trends. This is particularly true if the technology interacts with our lives in novel ways.
High Fidelity Embedding of Electronic Patient Record in Medical Images

In support of easier access and effective management of the increasing electronic patient record (EPR) and medical images, an improved Pixel Repetition (PR) based embedding scheme is investigated in this paper for hiding EPR in the medical images to ensure higher-rate embedding without any noticeable visual degradation. Particularly, a medical image is up-sampled using PR that creates a block of four pixels from each original pixel called the seed. Each block embeds 4 bits of EPR-data using PR-embedding conditions followed by the embedding of k-bits of the data in the LSBs (Least Significant Bits) of the seed pixels. Furthermore, a pixel correction technique (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -bit correction) has been applied to enhance the quality of the embedded medical image. The proposed scheme demonstrates significantly higher embedding capacity in comparison to other state-of-the-art techniques while maintaining good visual quality of the embedded medical image.
Service composition based on pre-joined service network in graph database

To perform an arbitrary service composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service composition approach in a graph database named pre-joined service network. Firstly, the proposed approach constructs and stores a composition network with all services and compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the pre-joined semantic indexing graph approach.
3D face modeling and rapid casting based on reverse design

Abstract This paper first obtains human body data based on a 3D scanner, combines with Geomagic Studio to obtain a 3D model; and explores three 3D printing technologies for rapid prototyping of the model. The 3D prototype is turned out of the wax mold using the silicone rubber reworking process, and finally the melting is used. Mold casting process to complete portrait casting. Practice has proved that this method realizes the entire workflow of 3D face data acquisition, rapid prototyping, and rapid casting, and has high application value and market prospects in the production of 3D portrait artwork.
RETRACTED: Research on the Construction of Practical Teaching Evaluation System of Mechanical and Electrical Specialty in Colleges and Universities Based on Computer Multimedia Technology

Abstract As a talent training base, mechatronics majors in colleges and universities are facing teaching reform under the background of new curriculum reform in China. With the increasing demand for practical talents, mechatronics majors should actively build a practical teaching system, focusing on practical teaching. Only practice teaching can improve students’ practice ability quickly. The practical teaching system mainly includes training base, teaching content, course implementation and other links. This paper analysis the principles of constructing the mechatronics major practical teaching system based on computer multimedia technology, and actively formulates the practical teaching evaluation index system framework and the practical teaching system approach.
Strategic Human Resource Management Practices and Employee Retention: A Study of the Moderating Role of Job Engagement

The purpose of the present paper was to investigate the moderating role of job engagement in the impact of SHRM practices on the employee retention of Guilan Technical & Vocational Training Organization, and therefore, to provide an empirical and practical contribution in order to enhance employee retention through job engagement. The statistical population consisted of 372 managers and employees, and the optimal sample was determined by Cochran's formula to be 189. The results revealed that SHRM practices and dimensions have a significant impact on employee retention, and job engagement has a moderating role in the impact of SHRM and all its dimensions except performance appraisal on employee retention. The findings also indicate the moderating effect of job engagement in the relationship between SHRM practices and employee retention for three types of job engagement, namely vigor, dedication, and absorption. The association between the demographic variables and the factors also investigated. This study provides an empirical and practical contribution to improving employee retention through job engagement. The theoretical implications, future directions, and limitations are also discussed.
RETRACTED: Analysis of Teaching Dilemma and Path Choice of Comprehensive Accounting Training Course Based on Computer-aided Technology

Abstract With the development of computer and the continuous development of science and technology, people are now in the era of rapid development of information. The arrival of the "Internet" era has a certain impact on the current education model. Traditional teaching concepts and modes can no longer meet the needs of current social development. Under the background of "Internet", the comprehensive training courses of accounting in higher vocational colleges should be continuously optimized and improved. In the course of the implementation of the comprehensive accounting training course, the main role of students is brought into full play, students’ interest in learning in the teaching process is stimulated, students’ practical operation ability is greatly improved, and problems in future work can be solved better based on computer-aided technology by using existing knowledge.