Reviewerly/exHarmony huggingface dataset:
An Intelligent and Automated WCMS Vulnerability-Discovery Tool: The Current State of the Web

The main focus of the cyber-security community has been to make operating systems and communication networks more secure and harder for attackers to penetrate. This also applies to all the applications on the Internet. The most frequently used web application and user pages are today developed with the Web Content Management System (WCMS), as it allows user-friendly access, easy development and operation. WCMSs are present all over the world in many different environments. Any malware that can penetrate the WCMS can significantly affect the system itself and can cause a misconfiguration. The security and stability of these systems are important for reducing the risks and consequences of attacks or disfunctions with currently known similar tools. This paper presents a newly developed tool for identifying the vulnerabilities of the majority of Internet sites with WCMS applications and the remedies to be applied. Its key feature is the ability to perform automated, fast and dynamic vulnerability scans of the WCMS and the attached plug-ins on a large scale with in-built ethical respect.
Diagnostic Classification of Cervical Cell Images from Pap Smear Slides

Analysis of pap smear images under a microscope by experts is a laborious and time consuming task. Computer-assisted diagnostic (CAD) systems can simplify this tedious process and allow experts to focus on more critical cases. Effective screening and early diagnosis can help to detect precancerous cells and allow early treatment. In this study, we have used a deep learning approach for classification of cervical cell images which obtained from pap smear slides. The proposed method automatically classifies cervical cell images into five categories without using any pre-processing on raw input images. We have obtained the promising results as compared to the previous studies in the literature. The proposed model can give a second opinion to clinicians in their daily routines and help them to focus on more complex cases.
Discussion issues of evaluation of the scientific activity’s effectiveness using citation indices (review of domestic and foreign publications)

The article is devoted to the debatable issues of the practice using scientific citation indices as tools of bibliographic search and citation tools. Limitations of the formal indicators’ use for bureaucratic control and evaluation of the scientific activity’s effectiveness are shown. The results of interviewing the teaching staff of the technical University in possible areas of scientific articles’ use for various communication purposes are presented.
Heuristics for Quantum Compiling with a Continuous Gate Set

We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a quantum processor. As accurate CNOT gates are hard for the foreseeable Noisy- Intermediate-Scale Quantum devices era, our A* inspired algorithm attempts to minimize their count, while accounting for connectivity. We discuss the search strategy together with metrics to expand the solution frontier. For a workload of circuits with complexity appropriate for the NISQ era, we produce solutions well within the best upper bounds published in literature and match or exceed hand tuned implementations, as well as other existing synthesis alternatives. In particular, when comparing against state-of-the-art available synthesis packages we show 2.4x average (up to 5.3x) reduction in CNOT count. We also show how to re-target the algorithm for a different chip topology and native gate set, while obtaining similar quality results. We believe that empirical tools like ours can facilitate algorithmic exploration, gate set discovery for quantum processor designers, as well as providing useful optimization blocks within the quantum compilation tool-chain.
Symbolic regression by uniform random global search

This work describes a novel algorithm for symbolic regression, namely symbolic regression by uniform random global search (SRURGS). SRURGS has only one tuning parameter and is very simple conceptually. The method produces random equations, which is useful for the generation of symbolic regression benchmark problems. We have released well documented and open-source python code which passed a formal peer-review, so that interested researchers can deploy the tool in practice. We conduct experiments comparing SRURGS with symbolic regression by genetic programming (SRGP) using 100 randomly generated equations. Our results suggest that SRGP is faster than SRURGS in producing equations with good $$R^2$$ for simple problems. However, our experiments suggest that SRURGS is more robust than SRGP, able to produce good output in more challenging problems.
DIGITAL EDUCATIONAL ENVIRONMENT AS A TOOL OF SYSTEM CHANGES IN THE TEACHER’S PROFESSIONAL ACTIVITY

Purpose: Social and cultural prerequisites of Russia’s transition to an open information society have predetermined new requirements for graduates of modern educational organizations, ready for communicative activities in a digital educational environment. Informatization of education is characterized by increasing virtualization of the educational process and, as a consequence, the predominance of fragmented thinking, and increase in the gap between traditional and innovative educational technologies.&#x0D; Methodology: The basis of the digital educational environment in the context of the use of knowledge management technologies can be specialized knowledge management systems that combine knowledge of related scientific disciplines, which should provide integration, accumulation, and support, as well as the organization of access to knowledge of the educational environment. A distinctive feature of the development of mankind at the present stage is the transition to information society in which information and information processes are becoming one of the most important components of human life and society.&#x0D; Result: The development of the global process of informatization of society leads to the formation of not only the new information environment for people but also new, information way of their life and professional activities. Many researchers as the most acute problem of modern education called the mismatch of software, including for educational purposes, to high technical characteristics of computers, resulting in extremely low efficiency of computer use in training. Critically assessing the effectiveness of the existing software of the educational process and the ways of its development, we see the prospects for a radical change in the situation in the orientation to the use of knowledge management technologies that use software products that can be changed, adapting to the needs of the user and, in particular, to the didactic tasks of training.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of Digital Educational Environment as a Tool of System Changes in the Teacher’s Professional Activity is presented in a comprehensive and complete manner.
The Effect of Intellectual Capital on Competitive Advantage in the Jordanian Telecommunication Sector

This paper sought to examine the effect of intellectual capital on competitive advantage in the Jordanian telecommunication sector. Indeed, intellectual capital is generally assumed to be an important aspect of the organization and one of the most conducive to innovative activity and unrestrained competition. The paper finds that the tripod of intellectual capital, namely, human capital, structural capital, and relational capital has a significant influence on achieving a competitive advantage. Among these three dimensions, relational capital is the most influential component in enhancing the competitive advantage. Considering these results, the research presented many recommendations for future research, the most important ones is implementing this study on other sectors, resort to multi-method of data collection, and the use of probability sampling techniques.
IoU-uniform R-CNN: Breaking Through the Limitations of RPN

Region Proposal Network (RPN) is the cornerstone of two-stage object detectors, it generates a sparse set of object proposals and alleviates the extrem foregroundbackground class imbalance problem during training. However, we find that the potential of the detector has not been fully exploited due to the IoU distribution imbalance and inadequate quantity of the training samples generated by RPN. With the increasing intersection over union (IoU), the exponentially smaller numbers of positive samples would lead to the distribution skewed towards lower IoUs, which hinders the optimization of detector at high IoU levels. In this paper, to break through the limitations of RPN, we propose IoU-Uniform R-CNN, a simple but effective method that directly generates training samples with uniform IoU distribution for the regression branch as well as the IoU prediction branch. Besides, we improve the performance of IoU prediction branch by eliminating the feature offsets of RoIs at inference, which helps the NMS procedure by preserving accurately localized bounding box. Extensive experiments on the PASCAL VOC and MS COCO dataset show the effectiveness of our method, as well as its compatibility and adaptivity to many object detection architectures. The code is made publicly available at https://github.com/zl1994/IoU-Uniform-R-CNN,
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C^3 is available at this https URL.
MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions

We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences.Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions.This corpus is useful for developing sentence splitting approaches that learn how to transform sentences with a complex linguistic structure into a fine-grained representation of short sentences that present a simple and more regular structure which is easier to process for downstream applications and thus facilitates and improves their performance.
Inference on the Edge: Performance Analysis of an Image Classification Task Using Off-The-Shelf CPUs and Open-Source ConvNets

The portability of Convolutional Neural Networks (ConvNets) on the mobile edge of the Internet has proven extremely challenging. Embedded CPUs commonly adopted on portable devices were designed and optimized for different kinds of applications, hence they suffer high latency when dealing with the parallel workload of ConvNets. Reduction techniques playing at the algorithmic level are viable options to improve performance, e.g. topology optimization using alternative forms of convolution and arithmetic relaxation via fixed-point quantization. However, their efficacy is hardware sensitive. This paper provides an overview of these issues using as a case study an image classification task implemented through open-source resources, namely different architectures of MobileNet (vl), scaled, trained and quantized for the ImageNet dataset. In this work, we quantify the accuracy-performance trade-off on a commercial board hosting an ARM Cortex-A big. LITTLE system-on-chip. Experimental results reveal mismatches which arise from the hardware.
Exploiting deep representations for natural language processing

Advanced neural network models generally implement systems as multiple layers to model complex functions and capture complicated linguistic structures at different levels [1]. However, only the top layers of deep networks are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to expose all of these embedded signals with two types of mechanisms, namely deep connections and iterative routings. While deep connections allow better information and gradient flow across layers, iterative routings directly combine the layer representations to form a final output with iterative routing-by-agreement mechanism. Experimental results on both machine translation and language representation tasks demonstrate the effectiveness and universality of the proposed approaches, which indicates the necessity of exploiting deep representations for natural language processing tasks. While the two strategies individually boost performance, combining them can further improve performance.
Applications of Relationship Defined Everything of Semantics on Existence Computation

Based on our proposed modeling approach of relationship defined everything of semantics (RDXS) which prioritizes relationship as comprising entity and equalized semantic and relationship, from existence value level investigation which we specialized with Existence Computation (EC), we present in this work a few conceptual application of semantic revelation of implicit modes such as implicit pornographic identification, and majority dominating challenge increasingly prevailing in AI applications implicitly. The work is expected to contribute to natural language form common sense reasoning in AI expression, modeling and reasoning for currently challenging expressions which don't have deterministic conceptual semantics available theoretically due to implicit inconsistency and incompleteness of existence assumptions of targeted content.
On the Equivalence between Node Embeddings and Structural Graph Representations

This work provides the first unifying theoretical framework for node embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that relationship between structural representations and node embeddings is analogous to that of a distribution and its samples. We prove that all tasks that can be performed by node embeddings can also be performed by structural representations and vice-versa. We also show that the concept of transductive and inductive learning is unrelated to node embeddings and graph representations, clearing another source of confusion in the literature. Finally, we introduce new practical guidelines to generating and using node embeddings, which further augments standard operating procedures used today.
On Approaches to Assessing Contribution of Russian Universities in the Implementation of National Projects

The article provides a brief review of the literature related to the analysis of approachesto assessing the effectiveness of the universities of the country. The authors propose to consider the universities as key performers of strategic decisions at various hierarchical levels. Each of the strategic planning documents includes indicator systems and their target values in dynamics over the years. Monitoring of these indicators allows us to determine the effectiveness of the implementation of the strategic planning documents as a whole. The methodology given in the article for assessing effectiveness of the universities is directly or indirectly related to target indicators laid down in the strategic planning documents. This enables to evaluate the contribution of the universities to the development of socio-economic systems at various levels: industries, regions and the country as a whole. As an example, the approach to the development of the system of indicators is considered, which makes it possible to evaluate the contribution of the universities to the implementation of national projects. In the initial (pilot) version the system included 30 indicators directly related to national projects “Education”, “Science”, “Digital Economics” and a number of others. The article presents the results of testing of the developed methodology and the system of indicators at a number of the flagship universities of the Volga Federal District.
Real-Time Investigation of an Adaptive Fuzzy Synergetic Controller for a DC-DC Buck Converter

In this paper, an adaptive fuzzy synergetic controller (AFSC) for a DC-DC converter is introduced. Two robust control techniques, synergetic control combined with fuzzy logic control, are utilized to produce a reliable DC-DC power supply. This is-realized by an indirect adaptive control of a DC-DC buck converter. To further enhance robustness, a nonlinear constraint, the equivalent of the sliding surface, is used to guarantee performance and stability for any operating condition. To ensure overall strength, closed-loop signals are bounded and the stability is guaranteed using the Lyapunov theory. Control parameters are optimized using a PSO algorithm to further enhance performances. The proposed controller (AFSC) is designed through a dSpace based experimental setup to provide robust DC-DC buck converter voltage control.
Key Problems of the Critical Information Infrastructure through Scada Systems Research

In the current age, cyber security is an essential element of any information system. A key aspect is in the critical information infrastructure, where information security has become a top priority for information and network security experts. The interoperability of an ICT infrastructure with other components of it is an important aspect of its life cycle. Because Supervisory Control and Data Acquisition (SCADA) systems form part of the critical infrastructure, their cyber protection is particularly important in strategically important industrial and infrastructure sites — power plants, refineries, oil pipelines, treatment plants, manufacturing facilities, communications and transportation infrastructures. Along with the advancement of technology, the increasing number of Scada devices available online, the vulnerability of the sectors controlled by them has also increased. In the world of Internet of things (everything), the end devices cause a new wave of possible vulnerabilities in SCADA. They become the new places for attacks and breaches trough wich the system may be accessed or even compromised. There are a number of critical infrastructures in the Community whose disruption or destruction would have significant cross-border implications for more than one sector as a result of the interdependence of interconnected infrastructures. Such European critical infrastructures have been established and launched under a common procedure developed by the European Commission, with security requirements assessed according to a common minimum approach.&#x0D; The present article exposes and examines the critical infrastructures of the European Union and Bulgaria. Trough presenting the structure of a Scada system the vulnerabilities and the various possibilities of attacking it were analised. As an example, a specific case based on trees has been considered, and the obtained results were summarized and visualized. The consequences were analized and respecctive conclusion was done.
DETERMINATION OF EARTHQUAKE FOCUS COORDINATES USING THE CASSINI OVAL METHOD WITH SECOND- AND FOURTH-ORDER HYPERBOLA FIGURES

Objectives. To study the dependencies obtained when determining the coordinates of an earthquake hypocentre using the figures of fourth and second orders. Method . A comparative analysis of determining the coordinates of the earthquake focus using the Cassini oval method, both taking errors in the readings of seismic sensors into account the and ignoring them, is presented. Result . A new method is proposed for determining the coordinates of the earthquake hypocentre, which uses the fourth-order figure, the Cassini oval, in the calculations. A graph is obtained for the distribution of errors in determining the coordinates of the earthquake focus (using the Cassini oval) depending on the relative position of two seismic sensors with different values of their errors in determining the difference in travel times of seismic waves. Conclusion . Since the calculation results are independent of the error sign in determining the difference in the arrival times of seismic waves, the method is suitable for the initial determination of the coordinates of the earthquake hypocentre as well as for comparison with the results of other methods for identifying the error sign.
Interval Type-2 Fuzzy Logic Washing Machine

By using fuzzy logic controller, one can control uncertain system where uncertainty exists on the input parameters.Generally, the delay in the system may cause time shift which disturbs the stability of the system.Therefore, the time delay systems with uncertainty can be represented by type-2 fuzzy logic controller.In this paper, the reference values are considered as intervals and automatic selection of detergent intake needed for good washing is proposed by using interval type-2 fuzzy controller for the washing machine.Also transportation delay has been analyzed with the support of four different defuzzification methods and it is observed that Wu-Mendal uncertainty bound method gives better result to keep the system stabilized than other methods.