Reviewerly/exHarmony huggingface dataset:
Gelato: Feedback-driven and Guided Security Analysis of Client-side Web Applications

Modern web applications are getting more sophisticated by using frameworks that make development easy, but pose challenges for security analysis tools. New analysis techniques are needed to handle such frameworks that grow in number and popularity. In this paper, we describe Gelato that addresses the most crucial challenges for a security-aware client-side analysis of highly dynamic web applications. In particular, we use a feedback-driven and state-aware crawler that is able to analyze complex framework-based applications automatically, and is guided to maximize coverage of security-sensitive parts of the program. Moreover, we propose a new lightweight client-side taint analysis that outperforms the state-of-the-art tools, requires no modification to browsers, and reports non-trivial taint flows on modern JavaScript applications. Gelato reports vulnerabilities with higher accuracy than existing tools and achieves significantly better coverage on 12 applications of which three are used in production.
A deep neural architecture for sentence semantic matching

Sentence semantic matching (SSM) is a fundamental research task in natural language processing. Most existing SSM methods take the advantage of sentence representation learning to generate a single or multi-granularity semantic representation for sentence matching. However, sentence interactions and loss function which are the two key factors for SSM still have not been fully considered. Accordingly, we propose a deep neural network architecture for SSM task with a sentence interactive matching layer and an optimised loss function. Given two input sentences, our model first encodes them to embeddings with an ordinary long short-term memory (LSTM) encoder. Then, the encoded embeddings are handled by an attention layer to find the key and important words in the sentences. Next, sentence interactions are captured with a matching layer to output a matching vector. Finally, based on the matching vector, a fully connected multi-layer perceptron outputs the similarity score. The model also distinguishes the equivocation training instances with an improved optimised loss function. We also systematically evaluate our model on a public Chinese semantic matching corpus, BQ corpus. The results demonstrate that our model outperforms the state-of-the-art methods, i.e., BiMPM, DIIN.
Continuous quantum error detection and suppression with pairwise local interactions

The authors study a continuous measurement scheme for the four qubit Bacon-Shot code, which can store a single qubit in four physical qubits, and show that it can both detect and suppress quantum errors.
Study on the Application of Robot-Assisted Education in Industrial Design

This paper summarizes the background of robot research and application in China and abroad, concludes the national policies regarding robots in recent years, and categorizes the application of robots in education. Subsequently, after a literature review on the robot research at home and abroad, we have found that applying robots in educational activities can help improve teaching effects and unlock students' enthusiasm for learning. Therefore, based on the educational features of industrial design, humanoid robots are used for program and curriculum design to establish a robot-assisted educational design model. Classroom teaching is conducted based on three robot-assisted models, providing reference for the theory and practice study on robot-assisted teaching in the future.
PREDICTION OF CREDIT CARD PAYMENT NEXT MONTH THROUGH TREE NET DATA MINING TECHNIQUES

A number of research initiatives have recently been launched around the world regarding the conceptualization, specification, design and development principles of the future use of credit cards, storing secret information on them, while most time we use them for online payment. In addition, if it has enough money, we can pay for what we need at any time. Therefore, the goal of this proposed research is to use data mining techniques to predict credit card payment next month. Our proposed system contains five steps: (a) find the suitable database from the internet because this database is not available in Iraq, (b) pre-process the credit card database based on person correlation matrix to determine which feature is less correlated with other to remove it and reduce the time of prediction, (c) split pre-processing database into two parts training and testing dataset, (d) apply TreeNet prediction data mining techniques (TPDMT) on training dataset to test if we need payment next month or do not, find the optimal tree. TreeNet based on Boosting Machine usually makes the predictor to use Decision Trees (DTs). (e ) Finally, pass the testing dataset on the optimal tree results from TPDMT, then using the five measures related to confusion matrix to evaluate the results including “Accuracy (AC), recall or true positive rate (TP), precision (P), F-measure (considers both precision and recall) and Fb”.
A design-driven sonification process for supporting expert users in real-time anomaly detection: Towards applied guidelines

INTRODUCTION: We present early results of an ongoing investigation into the use of sound for real-time monitoring of anomalous behaviour in digital and digital/physical systems.OBJECTIVES: We aim to define design guidelines to both support authors in the process of creating sonifications that are both efficient and engaging and the transition of sonification into a mass medium for the representation of data in everyday life.METHODS: Through two Design Actions, we apply Design Research to the definition of the use case, the interaction paradigm and the experimental protocol for real-world evaluation of sonification tools.RESULTS: Two Design Actions are described.Methodologies and results of the first experimental phase are presented in detail along with their influence on the second phase, currently ongoing.CONCLUSION: We sketch a tentative design-driven process for sonifications for the real-time monitoring of anomalous behaviour in digital and digital/physical systems.
Agile-management 3.0 concept as a factor of technological progress development in the digital society

The relevance of research into the philosophy of Agile Management 3.0 as a factor of the advancement of technological development in a digital society is of great novelty and great practical importance as it promotes technological advancement in a digital society.The purpose of the research is to formulate the concept of Agile Management 3.0 as a factor of the development of technological progress in a digital society.Formulation of research objectives: to identify the value of Agile management for training coaches; track the evolution of on-demand service platforms; expose the activities of next-generation technology companies; explore the evolution of countries into an information or digital society; to substantiate the directions of value creation of industries and innovations on the basis of digital technologies.Research Methods: Theory and methodology of complexity, based on synergistic principles of self-organization, were used to solve new problems.Today there is a modern evolution from crowdsourcing to crimesorsing.Crowdsourcing started out as a legitimate tool for increasing complex production and research challenges Analysis of the latest research and publications on the subject.We are based on O'Reilly Tim's "Who knows what the future will be" (K.,
A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

In the literature, the multiplicative neuron model artificial neural networks are trained by gradient-based or some artificial intelligence optimization algorithms. It is well known that the hybrid algorithms give successful results than classical algorithms in the literature and the use of hybrid systems increase day by day. From this point of view, different from other studies contribute to multiplicative neuron model artificial neural networks, the properties of an artificial intelligence optimization technique, artificial bat algorithm, and a gradient-based algorithm, backpropagation learning algorithm, is used together firstly by using the proposed method in this study. Thus, both a derivative and a heuristic algorithm were used together firstly for multiplicative neuron model artificial neural networks. The proposed method is applied to three well-known different real-world time series data. The performance of the proposed method is both compared with gradient-based optimization algorithms, some artificial optimization algorithms used for the training of artificial neural networks and some popular analyze methods. The analysis results show that the proposed hybrid method has superior performance than other methods.
APPROACH FOR MINIMIZATION OF PHONEME GROUPS IN AUTHORSHIP ATTRIBUTION

The developed mathematical support for authorship attribution software includes a combination of statistical methods (Student’s t-test, Kolmogorov-Smirnov’s test) and a statistical model for determining significant differences between styles. The combination of statistical methods allows us to enhance test validity of authorship attribution by obtaining the same results by the two methods applied. The model developed makes it possible to identify a consonant phoneme group with high style identification capability. The phoneme position in a word is taken into account. The greater number of significant differences is, the higher authorship identification capability of the phoneme group is. The developed system software is based on the algorithms of the used combination of methods and statistical model. The Java programming language provides platform independence. The minimized number of consonant phoneme groups makes the process of style and authorship attribution more automated. The obtained results of comparisons of the scientific, belles-lettres, conversational and newspaper styles are presented. The data obtained allows us to assert that the used combination of methods and the developed statistical model improve test validity of style and authorship attribution.
Testing of large-aperture aspheric mirrors using a single coated lens

The measurement of aspheric surfaces is very difficult and challenging. An optical system with a single coated lens is proposed for measuring large-aperture aspheric convex/concave surfaces. In this system, the first surface of the compensator is used as an auto-collimation surface, which can realize high-precision null compensation. Based on the third-order aberration theory, the initial design parameters of the system are obtained by analytical calculation, and the parameters are then optimized numerically. An oblate spheroid with an equivalent aperture of 426 mm is taken as an example, and the measuring accuracy of the proposed method can achieve 0.0063λ rms.
An Improved Software Project Monitoring Task Model of Agile Kanban Method: A Practitioners’ Perspective

Monitoring software project development is essential to ensure that the project progress is according to budget, schedule, and quality expectations. Currently, Agile Methods (AMs) have received wide recognition within the software engineering (SE) field due to their flexibility and effectiveness. One of the AMs methods used in managing software project development is Kanban method. This method is gaining attention due to its ability to enhance understanding, visibility, and controlling the project workflow. Thus, this paper aims to discuss the initial result of the proposed model for improving the software project monitoring task of the Agile Kanban method (i-KAM). To achieve this aim, the expert review method was used to ensure that suitable components and associated criteria have been included in i-KAM. In this study, six domain experts, which are software practitioners, have been identified based on predefined characteristics. The proposed model was verified based on five dimensions, which are understandability, relevance, feasibility, organization, and comprehensiveness. The experts’ opinions and comments were obtained and subsequently quantified by using descriptive analysis. Findings revealed that this study has fulfilled its objective and has acquired constructive suggestions from the practitioners’ perspective. Future work will continue to enhance i-KAM according to the recommendations and remarks from the experts. A focus group and case study methods could be conducted in order to validate the revised i-KAM. Besides, a prototype will be developed and then implemented within a real software development setting.
Vouw: Geometric Pattern Mining Using the MDL Principle

We introduce geometric pattern mining, the problem of finding recurring local structure in discrete, geometric matrices. It differs from existing pattern mining problems by identifying complex spatial relations between elements, resulting in arbitrarily shaped patterns. After we formalise this new type of pattern mining, we propose an approach to selecting a set of patterns using the Minimum Description Length principle. We demonstrate the potential of our approach by introducing Vouw, a heuristic algorithm for mining exact geometric patterns. We show that Vouw delivers high-quality results with a synthetic benchmark.
ANALYSIS OF TYPICAL NETWORK ATTACKS ON AUTOMATED SYSTEMS OF INTERNAL AFFAIRS DEPARTMENTS

Abstract. Aim Important contemporary trends in the theory and functional practice of secure automated systems at informatisation facilities of internal affairs bodies include an increase in the number of threats realised through remote unauthorised access (network attacks) on confidential in-formation resources, as well as the increasing complexity of implementing mechanisms aimed at providing protection from such attacks. In order to increase the effectiveness of existing and prospective automated security systems at the informatisation facilities of internal affairs bodies, it is necessary to identify and analyse typical network attacks aimed at components and software comprising these systems. Method. The method for solving this problem consists in a comprehensive analysis of the process of implementing network attacks on automated systems when they are used in secure mode in the informatisation facilities of internal affairs bodies. Results. Based on the analysis of information held in the security threat database developed by the Russian Federal Service for Technical and Export Control (FSTEC), modern features and operations of secure automated systems on the informatisation facilities of bodies of internal affairs and the results of a survey of experts in the field of information protection of allocated in accordance with the classification typology, eight types of dan-gerous attacks on automated systems of the internal affairs bodies are described taking into account their sources, objects, effects and possible consequences of implementation. Conclusion. The presented results are of use in further studies to conduct a quantitative assessment of the danger of typical attacks and for developing a private model of actual attacks for a specific automated system, taking into account their functional features in secure mode operations at the informatisation facility of the internal affairs body.
Analysis of Edge Detection on Compressed Images with Different Complexities

This paper provides edge detection analysis on images, which consist of different numbers of details (small, medium and high number of details) and which are compressed by different compression algorithms -JPEG, JPEG2000 and SPIHT.Images from the BSD (Berkeley Segmentation Database) database were used and compressed with different number of bits per pixel.The analysis was performed for five edge detectors: Canny, LoG, Sobel, Prewitt, and Roberts.The fidelity of the detected edges was determined using the objective measures Figure of Merit (FOM), F measure and Performance Ratio (PR), where the reference value was taken from the GroundTruth image.Based on the results presented in the tables, it can be concluded that edge detection behaves differently depending on the number of bits per pixel and applied compression algorithm, as well as, the number of details in the image.Roberts operator has been proven to be the best solution, when it is necessary to perform better edge detection over compressed images with small a number of details, but Canny shows better results for images with a high number of details.
Designing a Transformational Model for Decentralization of Electronic Health Record Using Blockchain

Electronic Health Records management in healthcare system is one of the perplexing systems which should be secured, decentralized, and authentic, and any minute error in these systems can be all around expensive both monetarily and socially. The data residing in existing EHR systems is centralized in healthcare institutions or hospitals in databases on servers inside the institutions or hospitals. These systems may suffer from different issues like no distributed access of EHRs, interoperability issues like handling different formats of data and their exchange, security and recovery issues like any loophole in the system can lead to cyber-attacks. The blockchain innovation empowers the usage of profoundly secure and protection saving decentralized frameworks where exchanges are not under the influence of any outsider associations. Utilizing the blockchain innovation information put in a fixed compartment called block conveyed over the system in an undeniable and mutable way. Data security and protection are improved by the blockchain innovation in which information are scrambled and disseminated over the whole system. This paper provides an insight into the existing health record management systems and their challenges. Also this paper introduces blockchain technology, its architecture, and impacts in the healthcare industry. In addition, a model for the generation of the unique identity of the patient as Unique Patient Identifier (UPID) is proposed to have the single patient identification record for universal access using blockchain technology.
General Model for Tracking Manufacturing Products Using Graph Databases

One of the major problems in the manufacturing industry consists of the fact that, when manufacturing a product, many parts from different lots are supplied and mixed to a certain degree during an indeterminate number of stages, what makes it very difficult to trace each of these parts from its origin to its presence in a final product. In order to overcome this limitation, we have worked towards the design of a general solution aiming to improve the traceability of the products from several manufacturers. This solution is based on the exploitation of graph databases which allows us to significantly reduce response times compared to traditional relational systems.
A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Nowadays, ontologies have been exploited in many current applications due to the abilities in representing knowledge and inferring new knowledge. However, the manual construction of ontologies is tedious and time-consuming. Therefore, the automated ontology construction from text has been investigated. The extraction of taxonomic relations between concepts is a crucial step in constructing domain ontologies. To obtain taxonomic relations from a text corpus, especially when the data is deficient, the approach of using the web as a source of collective knowledge (a.k.a web-based approach) is usually applied. The important challenge of this approach is how to collect relevant knowledge from a large amount of web pages. To overcome this issue, we propose a framework that combines Word Sense Disambiguation (WSD) and web approach to extract taxonomic relations from a domain-text corpus. This framework consists of two main stages: concept extraction and taxonomic-relation extraction. Concepts acquired from the concept-extraction stage are disambiguated through WSD module and passed to stage of extraction taxonomic relations afterward. To evaluate the efficiency of the proposed framework, we conduct experiments on datasets about two domains of tourism and sport. The obtained results show that the proposed method is efficient in corpora which are insufficient or have no training data. Besides, the proposed method outperforms the state of the art method in corpora having high WSD results.
AN ANALYSIS OF RECOUNT TEXT IN ENGLISH TEXTBOOKS USED BY TENTH GRADE STUDENTS

This study was aimed at investigating the extent to which recount texts in two selected English textbooks meet the recount text criteria and to find out the similarities and differences of the recount texts between the two selected English textbooks. This study employed a descriptive qualitative method. The data in this study were four forms of recount texts taken from the two selected textbooks. Data analysis was done through analyzing, displaying, reducing, and drawing a conclusion. The data of this study include the generic structure, social objectives, and linguistic elements. The results of the analysis showed findings as follows: (1) four forms of the recount texts in the English textbooks have met the generic structure criteria, namely orientation, event, and reorientation; (2) one of the four recount texts fulfilled social goals; and (3) the four recount texts fulfilled the linguistic element. In short, the recount texts in English textbooks were similar to in terms of linguistic elements and type of process, while differences were found in terms of social goals and generic structure.
Reasoning About Typicality and Probabilities in Preferential Description Logics

In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) ⊑ D, whose intuitive meaning is that “normally/typically Cs are also Ds”. This extension is based on a minimal model semantics corresponding to a notion of rational closure, built upon preferential models. We recall the basic concepts underlying preferential Description Logics. We also present two extensions of the preferential semantics: on the one hand, we consider probabilistic extensions, based on a distributed semantics that is suitable for tackling the problem of commonsense concept combination, on the other hand, we consider other strengthening of the rational closure semantics and construction to avoid the so called “blocking of property inheritance problem”.