Reviewerly/exHarmony huggingface dataset:
Foreword to the Special Issue on Quantum Radar—Part 2

The articles in this special issue focus on the applications supported by quantum radar. QUANTUM physics has had a large impact on our everyday life. Relevant examples range from transistors and Monolithic microwave-integrated circuit, or MMIC,1 that make most of our technological society, as well as to nuclear energy, and to the science of materials. Some of these technologies have met with indisputable success while others have not proven to be useful to date. It should be remembered that the idea of a clean energy coming from nuclear fusion is an unfulfilled promise: such technologies have always involved a change of paradigm that made them quite different from known expectations. Note that Von Neumann machines, the foundations of our computers, are based on hardware made from quantum mechanics effects but they are otherwise easy to understand. A quantum computer is a rather different object and not so easy to comprehend unless an extensive knowledge of quantum physics is achieved. The same applies to the quantum radar (QR), the main topic of this special issue, which is quite different from a classical radar, both in terms of the working principles and the kind of hardware to realize it. The today’s radars are based on the propagation of electromagnetic waves of the classical (nonquantum) physics. The QR promises to be a radical change. It should be said that we are yet at a pioneering stage and we cannot be certain yet if a goal will be achieved nor in what form we will see it, though, R&D is progressing.
Quantum key distribution system against the probabilistic faint after-gate attack

In practical quantum key distribution (QKD) systems, a single photon-detector (SPD) is one of the most vulnerable components. Faint after-gate attack is a universal attack against the detector. However, the original faint after-gate attack can be discovered by monitoring the photocurrent. This paper presents a probabilistic generalization of the attack, which we refer to as probabilistic faint after-gate attack, by introducing probability control modules. Previous countermeasures for photocurrent monitoring may fail in detecting the eavesdropper under some specific probabilities. To mitigate this threat, we provide a method to determine the detectable boundary in the limitation of precision of photocurrent monitoring, and investigate the security of QKD systems under such boundaries using the weak randomness model.
Neural Networks for Image Recognition in Mobile Applications

The image recognition and pattern recognition on a mobile platform are getting more and more popular. Many of libraries and tools were created to make implementing new solutions easier. The goal of the paper was to present the analysis of selected machine learning algorithms for image recognition in mobile applications on the Android platform on the example of application that recognises the chosen images – tree leafs. The MLP (multilayer perceptron) artificial neural network was used as a tool to classify the objects. The program was implemented and trained in Python. The important part of the research was the image pre-processing: many of popular algorithms were tested, for example a mean filter or a Canny’s edges detector. The final result was the mobile solution that uses the learned model of MLP neural network to recognize a leaf on the photo.
Digital environment for the formation of design thinking of students in technology lessons

This article analyzes and systematizes the main trends in the formation and development of design thinking in technology education. On the basis of fundamental research, the author designed an innovative complex to determine the level of formation of design thinking, offered tools to improve the level of design thinking in school students in the lessons "Technology", arranged design sites for the formation of design thinking. We have experimentally determined the effectiveness of using distance technologies in the formation of design thinking in the technological education of schoolchildren.
Transfer Learning on Fine-Grained Categorization

During the last decade, the relevance of transfer learning has strongly emerged in the domains of machine learning and data mining. Transfer learning relies on knowledge transfer to a target domain by using prior knowledge developed in a related domain. Applications of transfer learning include classification, regression, and clustering. If done successfully, transfer learning may significantly improve training efficiency, yielding a simple and an effective approach in creating new classifiers, based on the available datasets and existing “related” classifiers. In this paper, we apply transfer learning to the problem of classifying car types using a large-scale set of 196 car types. We demonstrate the usefulness of transfer learning and illustrate how it can help create accurate classifiers that outperform the current state-of-the-art classifiers.
Part-of-Speech Tagging Using Multiview Learning

In natural language processing, character-level representations are vector representations of the particular character. Character-level representations have recently focused on enriching subword information by stacking deep neural models. Ideally, applications of several character-level representations can help capture different aspects of the subword information. However, this approach has often failed in the past, mainly because of the nature of traditionally used simple concatenation models. In this study, we explore different character-level modeling techniques. During the learning process, long short-term memory-based character representations can introduce different views for a part-of-speech tagger. After investigating two previously reported techniques, we propose two additional extended methods: (1) a multihead-attention character-level representation for capturing several aspects of subword information, and (2) an optimal structure for training two different character-level embeddings based on joint learning. We evaluate our results on the part-of-speech (POS) tagging dataset of the Conference on Natural Language Learning (CoNLL) 2018 shared task in universal dependencies. We show that our method substantially improves POS tagging results for many morphologically rich languages where the character information should be considered more substantially. Moreover, we compare the performance of our model with recent state-of-the-art POS taggers, which are trained with language models such as Bidirectional Encoder Representations from Transformers (BERT) and Deep Contextualized Word Representations (ELMo); our multiview tagger shows better results for nine languages. The proposed character model shows significant improvements in Ancient Greek, with average gains of 8.89 points in accuracy compared to the previous word representation model. Therefore, our empirical experiments indicate that character-level representations are more important than word representations for morphologically rich languages in terms of performance.
Analysis of Blind Reconstruction of BCH Codes

In this paper, the theoretical lower-bound on the success probability of blind reconstruction of Bose–Chaudhuri–Hocquenghem (BCH) codes is derived. In particular, the blind reconstruction method of BCH codes based on the consecutive roots of generator polynomials is mainly analyzed because this method shows the best blind reconstruction performance. In order to derive a performance lower-bound, the theoretical analysis of BCH codes on the aspects of blind reconstruction is performed. Furthermore, the analysis results can be applied not only to the binary BCH codes but also to the non-binary BCH codes including Reed–Solomon (RS) codes. By comparing the derived lower-bound with the simulation results, it is confirmed that the success probability of the blind reconstruction of BCH codes based on the consecutive roots of generator polynomials is well bounded by the proposed lower-bound.
Research on the Construction of College English Course Assessment System Based on Computer Technology

Abstract With the development of computer technology, English courses in colleges not only bring a rare opportunity, but also make English courses and courses face severe challenges. Therefore, colleges should be on the computer such a backdrop, speed up the study of college students English course examination the pace of work, provide abundant information on using the computer, breakthrough the limitation of the teaching material content, are helpful to the teaching of all kinds of resources to enrich the teaching content, to further establish English course examination evaluation system.
Binary Text Representation for Feature Selection

In many real-world applications, a high number of words could result in noisy and redundant information, which could degrade the general performance of text classification tasks. Feature selection techniques with the purpose of eliminating uninformative words have been actively studied. In several information-theoretic approaches, such features are conventionally obtained by maximizing relevance to the class while the redundancy among the features used is minimized. This is an NP-hard problem and still remains to be a challenge. In this work, we propose an alternative feature selection strategy on binary representation data, with the purpose of providing a theoretical lower bound for finding a near optimal solution based on the Maximum Relevance-Minimum Redundancy criterion. In doing so, the proposed strategy can achieve a theoretical approximation ratio of \(\frac{1}{2}\) by a naive greedy search. The proposed strategy is validated by empirical experiments on five publicly available datasets, namely, Cora, Citeseer, WebKB, SMS Spam and Spambase. Their effectiveness is shown for binary text classification tasks when compared with well-known filter feature selection methods and mutual information-based methods.
Performance analysis of heterogeneous database management system (DBMS) synchronization using message digest 5

Currently, the Database Management System (DBMS) keeps growing and is available on various platforms. The use of various types of DBMS in a system can cause problems with limitations in the synchronization process because each DBMS only provides synchronization methods for similar DMBS. Previous research has examined heterogeneous database synchronization algorithms on mobile devices using the Message Digest 5 (MD5) method. The process of synchronization with this method is done by comparing the hash values of each record data in the source table with the data hash values in the destination table. If a different hash value is obtained, the synchronization process is based on the type of transaction that has been determined. Based on the principle of work, this algorithm requires a long synchronization time for large-scale databases because it has to check the hash values of each data record. This study examined the performance analysis of the DBMS synchronization method using MD5. The testing was performed by observing the parameters of execution time of synchronization ptocess for the MySQL DBMS and PostgreSQL types. The results showed that the execution time of the MD5 synchronization process was proportional to the number of data records that need to be synchronized. This is because the hashing process that must be done on every data record and synchronization mechanism requires all data to be sent to check and compare the hash values of each data record in the destination synchronization database even though the data has not changed.
Extracting Handwritten Regions In Japanese Document Images

Extracting handwritten regions in document images is an important task that has received a lot of interest in the image processing community because of its wide application in digitizing documents for recovery, storing, and searching purposes. Typically, the handwritten regions are found using different shapes features in document images such as the vertical and horizontal lines. Those approaches are time-consuming because the processing time depends largely on the complexity of the form (i.e., the number of information boxes). Moreover, we cannot use these features to gain more knowledge about the document. This paper presents a new approach to the problem that includes company's logo and form's title. First, each document is classified into different categories based on its logo and form's name and then the handwritten regions are extracted based on the type of each document. The classification stage is performed using a local feature descriptor. For performance comparison, four local descriptors (SIFT, SURF, BRIEF and ORB) were used to evaluate their performance in detecting and classifying logos in document image. A dataset of more than 2000 Japanese document images was built from 100 unpublished images with varying orientation and scaling to evaluate the accuracy of the proposed method.
PCA-based 3D Facial Reenactment From Single Image

Facial re-enactment from videos and images is one of the essential tasks in character animation applied in the creation of video games and movies. In this paper, we propose an efficient method for 3D facial re-enactment for online facial expression transferring. By approximating the pose, the approximation of expression parameters is reduced to a linear regression problem of which the closed-form solution is readily available, resulting in the improvement in inference speed. We also test the proposed method with multiple variants of initial blendshapes estimation and compare the results with the prior in term of both quality and speed.
The implementation of a digital curriculum on the local level

This study explores the implementation of a digital curriculum on the local level in Switzerland. It thus contributes to the developing field of digital education research. The implementation of digital curricula that reflect on the importance of teaching digital skills has become a topical matter in many countries around the world. However, the practical realization of these efforts has not always been straightforward. The Swiss local experience in the framework of the EDUNUM project shows promising results after the first year of project's deployment and constitutes a successful effort at introducing a digital curriculum on the local level of government. The success factors include the impeccable organization and rich content of courses for teachers, inclusion of experts from different fields and possibility to co-create the project with the involved actors. The concerns are predominantly related to the speed of project's introduction and the related lack of clarification of its different aspects that were overlooked at the time of project's introduction. Furthermore, even though the involvement of experts is overall beneficial, the cooperation between different partners has been challenging. The main contribution of this study consists in clarifying certain questions that have been raised in previous studies and thus encouraging similar initiatives in other contexts.
Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories

An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.
A $$\varPi _1^0$$-Bounded Fragment of Infinitary Action Logic with Exponential

Infinitary action logic is an extension of the multiplicative-additive Lambek calculus with Kleene iteration, axiomatized by an $$\omega $$ -rule. Buszkowski and Palka (2007) show that this logic is $$\varPi _1^0$$ -complete. As shown recently by Kuznetsov and Speranski, the extension of infinitary action logic with the exponential modality is much harder: $$\varPi _1^1$$ -complete. The raise of complexity is of course due to the contraction rule. We investigate fragments of infinitary action logic with exponential, which still include contraction, but have lower (e.g., arithmetically bounded) complexity. In this paper, we show an upper $$\varPi _1^0$$ bound for the fragment of infinitary action logic, in which the exponential can be applied only to formulae of implication depth 0 or 1.
Object of Study of Literacy: a learning object based on mobile learning to aid in the process of child literacy

The basic literacy is a complex phase, composed of several stages that require dedication to the conclusion of the process. Nowadays, with the evolution of electronic devices and with the advent of data mining technology, respectively, the teaching and monitoring of the stages of literacy can be facilitated by the introduction of its principles into the educational process. Based on this, this paper proposes the presentation of a learning object, consisting mainly of a mobile application and a follow-up system, named Object of study of Literacy, Objeto de Estudo de Letramento (OEL), for teaching and monitoring the initial stages of the process of basic literacy.
Strength of Composition Attacks in Multiple Independent Data Publication

Each organization either government or other collects person-specific data and releases them with a guarantee that sensitive information of an individual remains to conceal. The situation arises when one patient goes two hospitals and the two hospitals publish their collected data independently, an adversary who knows about an individual's visits may infer his/her private information by composition attack. Therefore, privacy preservation for multiple independent data publishing has concerned considerable research interest nowadays. Following this line, the strength of composition attacks requires an in-depth analysis. To meet this gap, in this work we analyze the strength of composition attack concerning the size of the dataset as well as the overlapping ratio of individuals. In addition to these, different values of the anonymization parameter (k of k-anonymization model) have also been analyzed. Our results show that the success of the composition attack has a significant relation with the size of the data set and individual overlapping ratio which assists to seek a new approach to mitigating composition attack in multiple independent data releases.
The Application of Cloud Computing in the Practice Teaching of Business English Major in Higher Vocational Colleges

Abstract With the rapid development of information technology, cloud computing is also gradually applied to business English teaching practice in higher vocational education. Cloud computing can build a broad training platform for students, extend students’ English knowledge, and also meet the practical training needs of business English students, greatly improving the efficiency of business English teaching in vocational colleges. The effective connection between cloud computing and business English practice teaching meets the requirements of quality education, so higher vocational colleges should pay attention to it. This article analyzes the overview of cloud computing, explores the current status of business English teaching, and proposes strategies for cloud computing applications.
Comparing Physical and Immersive VR Prototypes for Evaluation of an Industrial System User Interface

Since immersive VR devices have become commodities, immersive environments appear as a new tool in the development of high-fidelity prototypes of systems in which the user interaction relies on expensive or unusual hardware, e.g., industrial systems. However, there is not enough evidence that the interface of a complex system and its VR counterpart have equal usability and user experience qualities. Our main objective is to assess the feasibility of carrying out studies on user-based evaluation in industrial interactive systems through immersive VR simulation. To achieve this, we compared user assessment with a conventional prototype of an industrial system with its immersive VR simulation. We performed within-subjects user testing in both the physical and the VR setups, and collected (i) experimenters’ observations on usability issues and (ii) subjective and objective measures of 16 participants. Subjective measures were taken using standardized questionnaires and objective measures by logging the elapsed time to fulfill task scenarios. Our results indicate that the perceived quality of the immersive VR system is indistinguishable from the physical counterpart regarding User Experience, usability, and cybersickness. On the other hand, the users’ performance on VR simulation was significantly slower in immersive VR. Finally, the same usability issues could be detected with either of the conditions.
Web Security Education in A Multidisciplinary Learning Context

Web security research and education are attracting much more attentions from industry and academia. In this paper, we report our teaching and learning experiences of a course entitled Web Security and Countermeasures (WebSC) in a multidisciplinary learning context. To achieve the learning outcome of practical skills improvement, we design a rounded teaching contents by jointly considering basic concepts, high-risk threats in OWASP TOP 10-2017 and emerging confrontation tools like PowerShell. Then, we coordinately adopt apprenticeship, interactive, problem-based and collaborative learning methods to improve the performance of all students. Finally, we integrate formative and summative assessment methods to conduct quantitative evaluation on students' learning performance and the instructor's teaching performance. Statistical results demonstrate the effectiveness of our course design and reveal some unforeseen findings that are very valuable for continuous improvement in near future.
Data Center Networks: The Evolving Scenario

Data centers (DCs) have emerged as an irreplaceable and essential infrastructure to support this ever-growing trend as a result of the exponential growth of Internet services. A DC typically houses a large number of computing and storage nodes, interconnected by a network that has been specially designed, namely DC. The DCN serves as a foundation for interaction and plays a key role in the optimization of DC operations. In this paper, a brief survey of various architectures of data center network (DCN) is presented based on their switching and transmission technology. This paper also depicts a comparison of different topologies of representative data center networks with respect to several properties to highlight their advantages and disadvantages.