Reviewerly/exHarmony huggingface dataset:
A Novel Quantum Color Image Encryption Scheme Based on Controlled Alternate Quantum Walks

With the development of network era, how to ensure the security of important information has become increasingly prominent. However, modern data security tools based on computational complexity may be cracked by quantum computing. Quantum walk (QW), which is a universal computing model, plays an important role in designing quantum algorithms. In this paper, we use controlled alternate quantum walk (CAQW) as a pseudorandom number generator (PRNG) to control the encryption of quantum color images, and provide a quantum circuit for the quantum color image encryption scheme. Simulation and analysis results show that our scheme can resist attacks such as differential attack, statistical analysis attack, etc., and the processing efficiency can be improved exponentially compared with the classical image encryption scheme, which has good encryption effect and high security.
LSDNN: Local-Salient Deep Neural Network for Baggage Re-Identification with Material Discerning

Baggage re-identification(re-ID) aims to identify the same baggage across multiple non-overlapping cameras between the pre-check and post-check area, which is preferably challenging. Similar to the person re-ID, the task is also disturbed by the reasons for variations of lighting, occlusion, and motion blurring. Compared to the person re-ID, salient inter-class similarity and and intra-class dissimilarity, which is harder to distinguish. In this paper, we propose a Local-Salient Deep Neural Network (LSDNN), which is composed of a global branch, feature clip branch, and feature split branch supervised by multi-loss. The global branch extracts main appearance features about color and shape. Meanwhile, the other two branches pay more attention to baggage textures and patterns that are the critical characteristics to accomplish accurate classification. In addition, we creatively take material attributes discerning into consideration to alleviate the enormous impact of inter-class similarity and intra-class dissimilarity. Experiments show that the proposed LSDNN achieves 84.7% mAP and 82.4% Rank-1 accuracy on the MVB dataset.
Research and Application on the Relay Protection Setting Comparison System Based on Fuzzy Matching

Correct relay protection setting is essential to the relay protection correct operation and the safe operation of power grid. Currently, protection setting check still adopts the traditional manual checking mode, which depends on individual technical level and working experience. This paper proposes a relay protection setting comparison system based on fuzzy matching method. A typical relay protection setting database and comparison process is established. The fuzzy matching method is used to realize item matching, different types of relay protection setting lists are automatically compared. The comparison system proposed in this paper effectively improves the accuracy and efficiency of relay protection verifying in Shandong electric power grid and avoids protection setting error caused by human reasons.
Learning to Truncate Ranked Lists for Information Retrieval

Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically determine the number of returned documents according to some user-defined objectives, in order to reach a balance between the overall utility of the results and user efforts. Existing methods formulate this task as a sequential decision problem and take some pre-defined loss as a proxy objective, which suffers from the limitation of local decision and non-direct optimization. In this work, we propose a global decision based truncation model named AttnCut, which directly optimizes user-defined objectives for the ranked list truncation. Specifically, we take the successful transformer architecture to capture the global dependency within the ranked list for truncation decision, and employ the reward augmented maximum likelihood (RAML) for direct optimization. We consider two types of user-defined objectives which are of practical usage. One is the widely adopted metric such as F1 which acts as a balanced objective, and the other is the best F1 under some minimal recall constraint which represents a typical objective in professional search. Empirical results over the Robust04 and MQ2007 datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines.
Cloud computing based big data platform construction of university informatization teaching service in Yunnan Province under the background of community of destiny

This paper summarizes the current situation of the university education informatization, cloud computing and the application of big data in education, and preliminarily constructs the big data platform model of university informatization intelligent teaching service of Yunnan University Teacher Education Alliance Based on cloud computing Part of the content of the model is described to provide reference for regional alliance education related research.
Design Choices for Central Bank Digital Currency: Policy and Technical Considerations

Central banks around the world are exploring and in some cases even piloting Central Bank Digital Currencies (CBDCs). CBDCs promise to realize a broad range of new capabilities, including direct government disbursements to citizens, frictionless consumer payment and money-transfer systems, and a range of new financial instruments and monetary policy levers. CBDCs also give rise, however, to a host of challenging technical goals and design questions that are qualitatively and quantitatively different from those in existing government and consumer payment systems. A well-functioning CBDC will require an extremely resilient, secure, and performant new infrastructure, with the ability to onboard, authenticate, and support users on a massive scale. It will necessitate an architecture simple enough to support modular design and rigorous security analysis, but flexible enough to accommodate current and future functional requirements and use cases. A CBDC will also in some way need to address an innate tension between privacy and transparency, protecting user data from abuse while selectively permitting data mining for end-user services, policymakers, and law enforcement investigations and interventions. In this paper, we enumerate the fundamental technical design challenges facing CBDC designers, with a particular focus on performance, privacy, and security. Through a survey of relevant academic and industry research and deployed systems, we discuss the state of the art in technologies that can address the challenges involved in successful CBDC deployment. We also present a vision of the rich range of functionalities and use cases that a well-designed CBDC platform could ultimately offer users.
Adaptive federated filter for multi-sensor nonlinear system with cross-correlated noises

This paper presents an adaptive approach to the federated filter for multi-sensor nonlinear systems with cross-correlations between process noise and local measurement noise. The adaptive Gaussian filter is used as the local filter of the federated filter for the first time, which overcomes the performance degradation caused by the cross-correlated noises. Two kinds of adaptive federated filters are proposed, one uses a de-correlation framework as local filter, and the subfilter of the other one is defined as a Gaussian filter with correlated noises at the same-epoch, and much effort is made to verify the theoretical equivalence of the two algorithms in the nonlinear fusion system. Simulation results show that the proposed algorithms are superior to the traditional federated filter and Gaussian filter with same-paced correlated noises, and the equivalence between the proposed algorithms and high degree cubature federated filter is also demonstrated.
Implementing Data Management Workflows in Research Groups Through Integrated Library Consultancy

Comprehensive research data management is fundamental to ensuring reproducible, open scientific research. However, sufficient research data assistance is often not offered at universities, and when offered, typically only provides basic services that are viewed as optional. Integrating information specialists into research groups provides a potentially promising means of improving data management by providing personalized data management workflows. Workflows are comprehensive, executable guides that require planning, implementation, feedback, and adaptation. Comprehensive data management workflows should include a file organization scheme, the creation of data management roles for members, a data storage/sharing guide, and training and evaluation. Librarians, who regularly interact with faculty and students and are familiar with data management tools, are uniquely situated to assist with the creation and assessment of these workflows.
Dependability Analysis of Bitcoin subject to Eclipse Attacks

The immense potential of the blockchain technology in diverse and critical applications (e.g., financial services, cryptocurrencies, supply chains, smart contracts, and automotive industry) has led to a new challenge: the dependability modeling and analysis of the blockchain-based systems. In this paper, we model the Bitcoin, a peer-to-peer cryptocurrency system built on the blockchain technology that allows individuals to trade freely without involving banks or other intermediate agents. We analyze the dependability of the Bitcoin system subject to the Eclipse attack. A continuous-time Markov chain-based method is suggested to model the system behavior under the Eclipse attack and further quantify the dependability of the Bitcoin system. The effects of several model parameters (related to the miner’s habits in system protection, restart, and mining frequency) on the system dependability are demonstrated through numerical examples. Findings from this work may provide effective guidelines in designing a resilient and robust Bitcoin system.
A New Dataset for Smartphone Gesture-based Authentication

In this paper, we consider the problem of authentication on a smartphone, based on gestures.Specifically, the gestures consist of users holding a smartphone while writing their initials in the air.Accelerometer data from 80 subjects was collected and we provide a preliminary analysis of this data using machine learning techniques.The machine learning techniques considered include principal component analysis (PCA) and support vector machines (SVM).The results presented here are intended to provide a baseline for additional research based on our dataset.
Towards Large-Scale Empirical Assessment of Web APIs Evolution

Web Application Programming Interfaces (APIs) decouple the internal implementation of a service from its consumers which can reuse and compose them to rapidly build new applications. Many Web APIs are described with the OpenAPI Specification (OAS). The goal of our research is to check the feasibility of using API descriptions found in public open source repositories to study how APIs evolve over time. To do so, we collected a large dataset of OAS documents by crawling open source repositories, we parsed the corresponding metadata and measured the API size in order to extract a simple model to track the lifecycle of API artifacts and observe common evolution behaviors. Our preliminary results indicate that only a subset of the APIs changes, but as opposed to the expectation that APIs should only grow to maintain backward compatibility we also detected a number of APIs with a more variable history. We also study the stability of API artifacts over time and whether APIs are more or less likely to change as they age.
MODERN EUROPEAN EXPERIENCE OF PRACTICAL TEACHER TRAINING

The article considers the issue of studying modern European experience of practical teacher training. It was found out that efficiency of preparing future teachers to their practical activity directly depends on all the levels – from top to bottom – state educational policies and support, regulations in the field of teacher training, modern initial teacher training programmes, inner motivation to become a teacher. It is pointed out the need to apply an integrated approach in mastering the readiness to follow the profession. As we are talking about the European experience, a special attention is paid to study and analysis of regulatory European framework. It helps to define new strategies of developing practical teacher training system, offers recommendations on efficiency of implementing innovative methods and technologies in teacher training process. According to some reports of European Commission, the effectiveness of teacher training is determined by comprehending the teaching profession and the professional development of teachers as a coherent continuum with several, interconnected perspectives, which include teachers’ learning needs, support structures, job and career structures, competence levels and local school culture. Within practical teacher training there is a special role of partner schools where a special position belongs to a school-based teacher/ educator, who does his/ her duties on the basis of a partner school. That educator teaches pupils at school and has responsibilities to support partnership with a teacher training university or college in providing the practical training of students – future teachers. It means to be a mentor/ facilitator/ cooperating teacher. It is found out that basis of training is the practical formation of qualities which are essential for the future teacher`s practical training. Students are educated to develop such pedagogical characteristics as flexibility, reflexivity, awareness of the internal ambiguity of positions and points of view, ability to take alternative decisions and to form the following basic personality traits as sociability, creativity, mobility, independence, responsibility for the personal choice, decision and the results of the teachers` activities.
Putting People in the Picture

Students entering the corporate sphere, particularly in client-facing positions, find themselves in a world of ambiguity that needs to be analyzed, clarified, and effectively described and communicated in order to transform real-world problems into real-world computing solutions. Navigating this world involves skills ranging from requirements gathering and creative thinking through high-level design and communication. Academic programming experience is frequently based on assignments that are pre-defined in specific, unambiguous language. This approach leaves a skills gap in translating amorphous, ambiguous problem scenarios into specifications. Video scenarios are a flexible, ready-to-use tool to aid in developing the skills needed to fill this gap. The short videos show individuals discussing or experiencing situations for which they need computing assistance. However, the situations are presented in ways that are ambiguous, open-ended, and sometimes contradictory. Students must utilize critical listening, observation, and thinking skills to define the problem the characters are experiencing and design potential solutions. Video scenarios can be easily used to develop requirements gathering, design specification, and communication skills in classes ranging from general education through capstone seminars. They are particularly well suited for software engineering and design courses, including those in certificate programs. By putting a face on a problem, video scenarios highlight the role of the user and provide a setting for developing user stories, use cases, and interaction skills integral to agile development. Student feedback from using video scenarios in a software engineering course is also presented. Videos and materials are available at http://youtube.com/virt-university.
Criterions of Supersinguliarity and Groups of Montgomery and Edwards Curves in Cryptography

We consider the algebraic affine and projective curves of Edwards over the finite field Fpn. It is well known that many modern cryptosystems can be naturally transformed into elliptic curves. The criterions of the supersingularity of Montgomery and Edwards curves are found. In this paper, we extend our previous research into those Edwards algebraic curves over a finite field and we construct birational isomorphism of them with cubic in Weierstrass normal form. One class of twisted Edwards is researched too. We propose a novel effective method of point counting for both Edwards and elliptic curves. In addition to finding a specific set of coefficients with corresponding field characteristics for which these curves are supersingular, we also find a general formula by which one can determine whether or not a curve Ed[Fp] is supersingular over this field. The method proposed has complexity O( p log2 2 p ) . This is an improvement over both Schoof’s basic algorithm and the variant which makes use of fast arithmetic (suitable for only the Elkis or Atkin primes numbers) with complexities O(log8 2 pn) and O(log4 2 pn) respectively. The embedding degree of the supersingular curve of Edwards over Fpn in a finite field is additionally investigated. Singular points of twisted Edwards curve are completely described. Due existing the birational isomorphism between twisted Edwards curve and elliptic curve in Weierstrass normal form the result about order of this curve over finite field is extended on cubic in Weierstrass normal form. Also it is considered minimum degree of an isogeny (distance) between curves of this two classes when such isogeny exists. We extend the existing isogenous of elliptic curves.
Academic Outcomes of Students in University Digital Environment at Different Levels of Higher Education: Who Is More Successful?

The problem of empirical assessment of various aspects of learning in the digital educational environment seems to be of particular relevance.At the same time, it is emphasized that there is a lack of comparative studies of students’ educational outcomes at different levels of higher education who have completed e-courses.The study was conducted at the Moscow State University of Psychology and Education (MSUPE), the sample size is N = 424 students.Subject of the study is immediate and long-term academic achievements of students in two levels of higher education who completed e-courses.Purpose of the study is to evaluate the differences in the academic achievements and knowledge retention of graduate and undergraduate students.Key findings of students’ academic outcomes comparative analysis are the following: (1) No differences were found between graduate and undergraduate students in the pretest, final test and overall e-course grade indicators.(2) The same tendency was revealed in students of both groups: pretest scores are low, posttest scores significantly and strongly increase, and then after 1.5—4 months they significantly decrease, while remaining significantly higher than the input indicators.The knowledge retention scores are very scattered in comparison with the direct ones.(3) The gain score effect size and the improvement index are significant for the final test only without adjusting for clustering, i.e.ignoring the fact that the sample consists of several student groups.A median graduate student would have a higher score than a median undergraduate student.Cluster-level effect size is not statistically significant.Cluster-level effect size for overall e-course grade indicators with difference-in-differences adjustment is also not reliable.(4) The knowledge retention scores in both students’ categories do not differ.The gain score effect sizes for knowledge retention, taking into account both the final test and the pretest, are not significant.(5) The psychometric characteristics of the academic achievement test in the field of empirical data quantitative analysis can be considered satisfactory.
Construction of Chinese Sports Knowledge Graph Based on Neo4j

With the development of the times, the new generation of technology represented by sports combined with big data and artificial intelligence has been widely used in the field of sports. Although technological innovation and popularization are driving the development of sports and moving forward steadily, currently the Internet has many sports websites and sports news websites, but how to conduct unified management and accurate query of sports knowledge data information that is widely distributed and complex in the sports field, Satisfying more people's search queries to obtain corresponding sports knowledge, which is still a difficult problem to solve. Therefore, the knowledge graph technology is innovatively introduced into the sports field, and various types of data information and their relationships are intuitively described in the form of graphs to optimize the accuracy and efficiency of information query. Constructing Chinese sports knowledge graphs based on knowledge of sports disciplines, using graph database Neo4j to improve the storage of knowledge graphs, and presenting an accurate and fast intelligent query method, has made a beneficial exploration for the construction and application of knowledge graphs in the sports field.
Unsupervised Multi-modal Hashing for Cross-Modal Retrieval

The explosive growth of multimedia data on the Internet has magnified the challenge of information retrieval. Multimedia data usually emerges in different modalities, such as image, text, video, and audio. Unsupervised cross-modal hashing techniques that support searching among multi-modal data have gained importance in large-scale retrieval tasks because of the advantage of low storage cost and high efficiency. Current methods learn the hash functions by transforming high-dimensional data into discrete hash codes. However, the original manifold structure and semantic correlation are not preserved well in compact hash codes. We propose a novel unsupervised cross-modal hashing method to cope with this problem from two perspectives. On the one hand, the semantic correlation in textual space and the locally geometric structure in the visual space are reconstructed by unified hashing features seamlessly and simultaneously. On the other hand, the $$\ell _{2,1}$$ -norm penalties are imposed on the projection matrices separately to learn the relevant and discriminative hash codes. The experimental results indicate that our proposed method achieves an improvement of 1%, 6%, 9%, and 2% over the best comparison method on the four publicly available datasets (WiKi, PASCAL-VOC, UCI Handwritten Digit, and NUS-WIDE), respectively. In conclusion, the proposed framework which combines hash functions learning and multimodal graph embedding is effective in learning hash codes and achieves superior retrieval performance compared to state-of-the-art methods.