Reviewerly/exHarmony huggingface dataset:
Support Vector Machines with Quantum State Discrimination

We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification.
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality).Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible.In this work, we investigate this through the lens of human language ability.We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example.Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions.We analyze how human-generated adversarial examples compare to the recently proposed TEXTFOOLER, GENETIC, BAE and SEMEMEPSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate.Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentimentpreserving examples, though they do so by being much more computationally efficient.
THE NEEDS ANALYSIS OF FOUR PRIMARY LANGUAGE SKILLS IN DEVELOPING JAPANESE TEACHING MATERIALS FOR TOURISM PURPOSES

The aim of this study was to analyse the needs of four primary language skills in developing Japanese teaching materials for tourism purposes. Research data was collected by applying questionnaire and interview method. In Tourism and Marketing Management Study Program - FPIPS UPI, the needs analysis result of Japanese teaching materials for tourism showed the average level of percentage ranging from 4 to 5 by applying Likert scale measurement. Based on the questionnaire conducted to 40 participants, speaking skills showed the highest response rate, both from students and lecturer’s perception. Listening Skills from student’s perception yielded approximately 86% or an average of 4.3, while from lecturer’s perception yielded 90% or an average of 4.5. Meanwhile, speaking skills from student’s perception reach 95% response rate or an average of 4.7, and from lecturer’s perception yielded 100% response rate or an average of 5.0. For the need of reading skills, student’s perception showed 86% response rate or an average of 4.3, and 80% or an average of 4.0 for lecturer’s perception. Further, the need of writing skills yielded 88% or an average 4.4 response rate from the students’ perception, and 80% or an average of 4.0 response rate of the lecturer’s perception.
Lessons Learnt from Engineering Science Projects Participating in the Horizon 2020 Open Research Data Pilot

Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation.
Forgery detection algorithm based on texture features

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.
Blockchain Adoption: Applications and Challenges

In recent times, more scholastic and social attention have been paid to blockchain and its distributed ledger system mechanism. The reasons for this ever-increasing attention cannot be far-fetched: blockchain now occupies a copious position in the present-day ways of doing things economically, digitally and ‘digital-socially’. Blockchain could be described as a distributed ledger system that allows secure transactions without a central management system. In this distributed ledger system, transactions are coded into blocks, which are linked to each other in the form of a chain. The first application of blockchain is in the bitcoin cryptocurrency. Though not limited to bitcoin, blockchain finds usefulness in security and trusts for instance, digital assets could be coded into blocks to ensure and enforce quality of trust. Consequent upon the quality of trust the blockchain confers on a digital asset, transparency among participating nodes is guaranteed. This is because, any change made to any record in a given block automatically initiates and enforces a corresponding change in all other blocks in the chain hence tampering or breach is almost impossible. Owing to its impressive prospects in the socioeconomic and political ecosystem, this paper was conceived to examine the current developments around this novel technology with particular emphasis on its benefits and proposed challenges and needs to fill the gap created in the vital socioeconomic domains. The paper concludes that the blockchain technology is a plausible approach to restoring the trust, confidentiality, availability and integrity in transactions in the cyberspace and the world at large as majority of the global economy thrives in the cloud.
Finitely Materialisable Datalog Programs with Metric Temporal Operators

DatalogMTL is an extension of Datalog with metric temporal operators that has recently received significant attention. In contrast to plain Datalog, where scalable implementations are often based on materialisation (a.k.a. forward chaining), reasoning algorithms for recursive fragments of DatalogMTL are automata-based and not well suited for practice. In this paper we propose the class of finitely materialisable DatalogMTL programs, for which forward chaining reasoning terminates after finitely many rounds of rule application. We show that, for bounded programs (a large fragment of DatalogMTL where temporal intervals are restricted to not mention infinity), checking whether a program is finitely materialisable is feasible in exponential time, and propose sufficient conditions for finite materialisability that can be checked more efficiently. We finally show that fact entailment over finitely materialisable bounded programs is ExpTime-complete, and hence no harder than Datalog reasoning.
Frustration-induced anomalous transport and strong photon decay in waveguide QED

We study the propagation of photons in a one-dimensional environment consisting of two noninteracting species of photons frustratingly coupled to a single spin 1/2. The ultrastrong frustrated coupling leads to an extreme mixing of the light and matter degrees of freedom, resulting in the disintegration of the spin and a breakdown of the “dressed-spin,” or polaron, description. Using a combination of numerical and analytical methods, we show that the elastic response becomes increasingly weak at the effective spin frequency, showing instead an increasingly strong and broadband response at higher energies. We also show that the photons can decay into multiple photons of smaller energies. The total probability of these inelastic processes can be as large as the total elastic scattering rate, or half of the total scattering rate, which is as large as it can be. The frustrated spin induces strong anisotropic photon-photon interactions that are dominated by interspecies interactions. Our results are relevant to state-of-the-art circuit and cavity quantum electrodynamics experiments.Received 24 July 2020Accepted 16 August 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L032058Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLight-matter interactionOpen quantum systemsQuantum transportScattering theoryTechniquesMatrix product statesRenormalization groupSpin-boson modelCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & OpticalQuantum Information
Factors Affecting Corporate Security Policy Effectiveness in Telecommuting

COVID-19 has prompted a rise in telecommuting practices in most companies worldwide. Meanwhile, companies are struggling to cope with the new and evolving security threats in telecommuting using old control methods. Specifically, there is an increased danger of hacking attacks in telecommuting environments. Furthermore, corporate concerns regarding telecommuting security have led to a questioning of existing control methods that no longer seem adequate. Significant research has been conducted on the factors that improve the effectiveness of corporate security policies, such as formal control, informal control, and extrarole behaviors. However, these studies did not consider telecommuting environments, which surged after the COVID-19 outbreak. Telecommuting loosens the physical control over employees and eliminates the collegial environment in which employees encourage each other to protect system information. This study determined how the factors that influence the effectiveness of existing information security policies behave in a telecommuting environment. Our study shows that specification and mandatoriness are the most important factors for an effective telecommuting security policy. We conclude that this sudden change in the working environment has rendered existing security controls obsolete, and specification and mandatoriness are likely to receive increasingly more attention in the growing field of telecommuting security policy.
Comparing Concepts of Quantum and Classical Neural Network Models for Image Classification Task

While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is necessary to simulate and study quantum systems that will transfer the numerical input data to a quantum form and enable quantum computers to use the available methods of machine learning. This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network developed for the problem of classification of handwritten digits from the MNIST data set. The comparative results of two models: classical and quantum neural networks of a similar number of training parameters, indicate that the quantum network, although its simulation is time-consuming, overcomes the classical network (it has better convergence and achieves higher training and testing accuracy).
Effects of Mobile Learning Science Course on Students’ Academic Achievement and Their Opinions about the Course

The aim of this research was to investigate the effect of mobile learning on students’ academic achievements and their opinions about the course. This research was a mixed study where both quantitative and qualitative methods were used together. The quantitative part of the study used quasi-experimental models in a pre-test/post-test design according to the patterned paired control group. In the qualitative part, a semi-structured interview method was used from descriptive models. The study group consisted of 28 students studying in the 8th grade of a state secondary school in the Pasinler district of Erzurum province of Turkey in the 2018-2019 academic year. The study lasted approximately six weeks. As a result of the study, it was observed that there was a positive increase in academic achievement in favor of the experimental group of students. Moreover, although there were differences of opinion among the students, a large majority declared that the mobile supported learning increased their interest towards the course. Recommendations are made based on this research.
Prior‐guided multiscale network for single‐image dehazing

Single-image dehazing is an important problem because it is a key prerequisite for most high-level computer vision tasks. Traditional prior-based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then recover haze-free images. However, these prior-based methods always encounter over-enhancement, such as halos and colour distortion. To solve this problem, many works use a convolutional neural network to retrieve original images. However, without priors as guidance, these learning-based methods dehaze effectively in synthetic datasets but perform poorly in real scenes. Hence, in this paper, we propose a prior-guided multiscale network for single-image dehazing named PGMNet. Specifically, prior-based methods are adopted to acquire dehazed images of the training dataset in advance and then send these dehazed images to a parameter-shared encoder to form multiscale features. During the decoding process, these multiscale features are adopted to guide the prior-guided multiscale network to recover more image details. Moreover, considering that these prior-based dehazed images usually contain some over-enhanced regions, a spatial attention guided feature aggregation module and squeeze-and-excitation module are adopted to alleviate colour distortion. The proposed PGMNet takes the advantage of prior-based methods in real haze removal and provides superior performance compared with the state-of-the-art methods on both synthetic and real-world datasets.
TEACHING DIGITAL CULTURAL HERITAGE AND DIGITAL HUMANITIES THE CURRENT STATE AND PROSPECTS

Abstract. Digital literacy and technology education has gained much relevance in humanities and heritage related disciplines during the recent decades. Against this background, the purpose of this article is to examine the current state of educational programs in digital cultural heritage and related disciplines primarily in Europe with supplemental information from the US. A further aim is to highlight core topics, challenges, and demands, and to show innovative formats and prospects.
Introducing Depth Information Into Generative Target Tracking

Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.
An Improved Single Step Non-Autoregressive Transformer for Automatic Speech Recognition

Non-autoregressive mechanisms can significantly decrease inference time for speech transformers, especially when the single step variant is applied. Previous work on CTC alignment-based single step non-autoregressive transformer (CASS-NAT) has shown a large real time factor (RTF) improvement over autoregressive transformers (AT). In this work, we propose several methods to improve the accuracy of the end-to-end CASS-NAT, followed by performance analyses. First, convolution augmented self-attention blocks are applied to both the encoder and decoder modules. Second, we propose to expand the trigger mask (acoustic boundary) for each token to increase the robustness of CTC alignments. In addition, iterated loss functions are used to enhance the gradient update of low-layer parameters. Without using an external language model, the WERs of the improved CASS-NAT, when using the three methods, are 3.1%/7.2% on Librispeech test clean/other sets and the CER is 5.4% on the Aishell1 test set, achieving a 7%~21% relative WER/CER improvement. For the analyses, we plot attention weight distributions in the decoders to visualize the relationships between token-level acoustic embeddings. When the acoustic embeddings are visualized, we find that they have a similar behavior to word embeddings, which explains why the improved CASS-NAT performs similarly to AT.
Consortium Capabilities for Enterprise Blockchain Success

Enterprise blockchain projects have great promise.They can cut costs and promote efficiency through disintermediation, increase transparency for tracking intercompany transactions, expand knowledge through consortia databases, and improve workflows through shared business processes.Despite its potential, blockchain technology has failed to produce promised benefits for enterprise networks.While the underlying technology has advanced rapidly, managerial capabilities needed to form and manage blockchain consortia have lagged, and as a result, few consortia have succeeded.This chapter reviews the extant literature on blockchain consortia and provides a framework that identifies (1) foundational conditions that precede effective consortium formation, (2) capabilities required for effective consortium functioning and evolution, and (3) partner and ecosystem-level outcomes associated with successful blockchain projects.
The Status Quo and Prospects of the Information Construction of Teaching Management in Colleges and Universities

With the continuous expansion of colleges and universities in our country, the teaching management of colleges and universities has become more difficult, leading to such a situation: because the number of students is increasing, schools need to increase the management of teacher assignment and student performance. Schools need to invest more manpower for efficient teaching management. In the current information age, advanced information technology is widely used in various fields, which can not only help people reduce working hours, but also effectively improve work efficiency. The use of information technology in university teaching management can achieve good management results. Therefore, based on his own teaching management experience, the author integrates information technology into the current situation of teaching management in colleges and universities, carries out related investigations and proposes reasonable solutions.