Reviewerly/exHarmony huggingface dataset:
Object-Oriented Situational Approach to Enterprise Information Security Management

The article substantiates a method for reducing the cost of an information security management system, developed using a semiotic approach to classifying information security objects, as well as a situational approach to choosing information security methods depending on the object of protection. The method is based on the following criteria for the situational choice of information protection measures: a clear differentiation of the information level, the object of protected information corresponding to this level, and the purpose of its protection. It is shown that each of the information levels (signs, logical structures, meanings, and messages) as an object of protection (material objects, information processes, information, presentation forms), depending on the purpose (confidentiality, integrity, availability, continuity), requires specific protection measures. The choice of these measures depending on the object-target situation of information protection in the organization will ensure the necessary and sufficient level of information security and avoid redundancy of measures and unjustified costs. According to the key principle of information security, economic feasibility, the use of this method (determining not only the need but also the sufficiency of measures) will significantly reduce the cost of information security systems. Based on the developed method, a prototype of a software application was justified, the novelty of which is due to its functionality to automatically determine the composition of information security measures depending on the object and purpose of protection using standards for information security management in the organization.
Imprecise abstract argumentation as a support for forensic engineering

Purpose Civil engineering structures are regularly confronted with failures that can lead to catastrophic consequences. It is important, after a failure, to be able to identify the origin and the sequence of factors that led to it. This failure analysis by experts, called forensic engineering investigation, generally leads to the drafting of an expert report. These reports do not inform on the processes that guided the experts to a conclusion and the uncertainties involved. This paper aims to propose a new methodological approach to formalize the opinions of experts in forensic engineering. Design/methodology/approach The research consists in combining abstract argumentation with the theory of imprecise probabilities to take into account epistemic and stochastic uncertainties to support forensic engineering investigation. Findings A model and a tool to support forensic analysis are presented. An application on the collapse of the Brumadinho dam highlights the interest of the chosen approach. Originality/value This work is the first use of the abstract argument framework in civil engineering, and so in forensic engineering. Furthermore, it provides an innovative model based on imprecise probability for AAF.
Human shadow: remote monitoring system

Geolocation is a technology used in several fields such as: transport, telephony and rescue. We present a solution-based geolocation for monitoring needy people and especially children. The security perimeter contributes to the monitoring of supervised persons, which triggers an alert as soon as they leave the security perimeter. In addition, the solution offers assistance by providing the itinerary when needed.
Design Strategies for a Hybrid Video Synthesizer

INTRODUCTION: Technological advances have led to newer instruments and forms of audiovisual performance.Simultaneously, there has been a resurgence of analogue equipment in sound synthesis.However, such instruments for video synthesis are yet to be well established.OBJECTIVES: The primary objective of this research is to propose design guidelines for designing hybrid video synthesizers and develop a working prototype.METHODS: Through a study of historical and contemporary practises, design guidelines for a hybrid video synthesizer are proposed.A prototype instrument is then developed and used in live performances to justify the guidelines. RESULTS:The hybrid approach towards audiovisual performance has some merit, however, it may only be relevant to a certain group of people already familiar with modular workflows.CONCLUSION: The merits and demerits of our design proposal using the prototype instrument has been noted in this research.Future plans have also been discussed.
Performance Analysis of Adaptive Variational Mode Decomposition Approach for Image Encryption

A mapping function relation is generated by a variational mode decomposition (VMD) transform, and an adaptive image encryption algorithm based on VMD is proposed. The image sequence is generated using VMD is divided into high frequency, middle frequency, and low frequency according to the entropy value of its mode transformation. The advantages of this algorithm are that the cross-mapping is generated according to VMD, the low-frequency part is the main energy concentration area of the image, the image processing encryption is carried out in the other generated images, the encryption does not affect the original image, and the robustness is improved. The simulation results showed that the algorithm had a good effect of encryption and restoration by measuring the root mean square error and the Euclidean distance. Experiment showed that the proposed method was more robust to the complex distribution of faces than the existing methods considered, yielding favourably comparable results to the state-of-the-art methods on data encryption. The results also showed that the proposed method was scalable in other areas of datasets. Furthermore, we showed that the proposed method did not need the number of features as a prior, was aware of noises and outliers, and could be extended to a multi-view version for more accurate clustering accuracy. These steps, which should require generation of the final output from the styled paper, are mentioned here in this paragraph. First, users have to run "Reference Numbering" from the "Reference Elements" menu; this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the "Cross Linking" menu.
Performance Analysis of Gaussian Normal Basis GF ($2^{m}$) Serial Multipliers and Inverters

Cryptography hardware processors enable fine-tuned designs supporting secure communications in extreme environments such as resource-constrained applications. Secure communications of resource-constrained wireless devices require efficient cryptographic arithmetic circuits such as finite field multipliers and inverters. Gaussian Normal Basis (GNB) provides cost-free squaring operation which is advantageous in repeated square-multiply cryptographic algorithms. In this paper, we realize the Itoh-Tsuji inverter based on the parallel-in-serial-out multiplier, for the first time to the best of our knowledge. We also report the design complexity of the GNB multipliers and inverters using the same technology and provide a comprehensive analysis of existing GNB multipliers and inverters in current literature. We conduct performance analysis of different multiplier architectures with varying digit sizes (d ≥ 1) and bit-serial (d=1) inverter architectures. We also analyze the power variation of multiplier/inverter architectures for controlled input variations. The area, time complexities, and power analysis are reported using the standard 65nm CMOS and the open-source 45nm NangateOpenCellLibrary technology. The results show that the new inverter design has a competitive area and power consumptions compared to IT inverter based on Parallel-In-Parallel-Out multiplier when implemented using 65nm CMOS Standard cell library.
Effects of Program Representation on Pointer Analyses — An Empirical Study

Abstract Static analysis frameworks, such as Soot and Wala , are used by researchers to prototype and compare program analyses. These frameworks vary on heap abstraction, modeling library classes, and underlying intermediate program representation (IR). Often, these variations pose a threat to the validity of the results as the implications of comparing the same analysis implementation in different frameworks are still unexplored. Earlier studies have focused on the precision, soundness, and recall of the algorithms implemented in these frameworks; however, little to no work has been done to evaluate the effects of program representation. In this work, we fill this gap and study the impact of program representation on pointer analysis. Unfortunately, existing metrics are insufficient for such a comparison due to their inability to isolate each aspect of the program representation. Therefore, we define two novel metrics that measure these analyses’ precision after isolating the influence of class-hierarchy and intermediate representation. Our results establish that the minor differences in the class hierarchy and IR do not impact program analysis significantly. Besides, they reveal the sources of unsoundness that aid researchers in developing program analysis.
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation

Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning. Performing such adaptation, however, requires informative data about the environment to be available during the adaptation. In this paper, we present domain curiosity—a method of training exploratory policies that are explicitly optimized to provide data that allows a model to learn about the unknown aspects of the environment. In contrast to most curiosity methods, our approach explicitly rewards learning, which makes it robust to environment noise without sacrificing its ability to learn. We evaluate the proposed method by comparing how much a model can learn about environment dynamics given data collected by the proposed approach, compared to standard curious and random policies. The evaluation is performed using a toy environment, two simulated robot setups, and on a real-world haptic exploration task. The results show that the proposed method allows data-efficient and accurate estimation of dynamics.
Liquid Liabilities and Growth Finance Nexus: Implications for Nigeria’s Energy Sector

Abstract Liquid liabilities are required for the development of key sectors that drive the Nigerian economy by ensuring that credits are made available for investment purposes. However, controversies concerning the effectiveness of investments in fostering economic growth in Nigeria exist. These studies focus on the relationship amid liquid liabilities and finance for growth in Nigeria, with specific insight and implications for Nigeria’s energy sector. In achieving its objective, the study utilizes data from secondary sources from the annual CBN (1980-2018). This study finds that gross domestic savings significantly drive finance for growth in the long-run compared to stock market development and Remittance inflows. The findings imply that to finance growth efficiently in the Nigerian economy, attention should be paid to liquid liability development policies such as driving gross domestic savings by all stakeholders and attention should be paid to the Nigerian energy sector as it possesses the potential to both be a source of liquid liability and a viable investment option.
Face Inpainting based on Improved WGAN-modified

Image Inpainting aims to use the technical methods to repair and reconstruct the corrupted region of the corrupted image, so that the reconstructed image looks more authentic. In this paper, the improved Wasserstein Generative Adversarial Network combined with the perceived loss and context loss function is used to repair the corrupted region of the image. By finding the nearest neighbor coding method from the defective image in the latent space and using the improved WGAN to add the spectral normalization constraint, the two main problems of the original GAN including the pattern collapse and gradient dispersion can be greatly reduced, in which can produce more clear as well as realistic images. Test experiments on the CelebA face data set indicate that the method has a good inpaint result.
How a humanities background provides real value in cyber security

When you think of the technology industry generally, and in particular the cyber security vertical market, it often conjures up the image of hoodie-wearing software engineers working through the night, making arcane references to code. Or perhaps it's the frantic sound of typing on keyboards echoing throughout dark smoky rooms illuminated only by pale green light, possibly something reminiscent of The Matrix. The idea that only students of the hard sciences can contribute to the technology industry may prove to be detrimental to companies seeking qualified and experienced talent. By encouraging people from a liberal arts and humanities background to join the cyber security field, not only will we be driving key skills towards the industry, we will also be doing more to promote inclusivity and equality. As Trevor Morgan of comforte AG explains, every facet of the humanities has the ability to imbue future cyber security experts with critically desired soft skills.
Handwritten Boolean Algebra Derivation Recognition and Error Identification

Boolean algebra is an important study for the analysis and simplification of logic circuit. There are many methods that can simplify Boolean algebra expression quickly. However, simplification Boolean algebra expression by applying Boolean algebra laws was commonly taught in the class. The teacher or student must spend a lot of time on checking the correctness of this method line by line with bare eyes. This paper proposed the software to helps them by automatically find error locations from image of handwritten Boolean expression derivation. The image was segmented into the lines before characters would be extracted from each line. After that, the characters were recognise and rewriting into the text form. The expression of each line was simplified by the system, then it will be compared to the next line. Finally, the system returns the result in format of input image overlayed with a result from finding error process.
A Device for Capturing Inward-Looking Spherical Light Fields

We propose a calibration methodology for a novel type of inward-looking spherical light field acquisition device comprised of a moving CMOS camera with two angular degrees of freedom around an object. We designed a calibration cube covered with ChArUco markers to resolve viewpoint ambiguity. Our calibration model includes 20 unknowns describing the spherical parameters and the camera's geometrical and internal properties. These parameters are jointly optimized to reduce the reprojection error of the calibration cube's markers from multiple viewpoints, resulting in a model that can predict the pose of the camera from any other viewpoint. We successfully tested this calibrated model with a photogrammetry experiment followed by view synthesis of novel views using the resulting depth maps. Results show that the reconstructed image is highly accurate when compared to a real-life capturing of the same view.
An empirical assessment of threshold techniques to discriminate the fault status of software

To determine the high risk classes in software system researchers often turn to statistical and computational intelligence models, in preference to more easily performed binary classification through threshold value. In the later case, the knowledge of only threshold values can help the developers and testers to recognize the risk prone classes. The current study aims to identify the dichotomy that proficiently discriminates the faulty and non-faulty classes of the software systems. For the purpose, the study examined seven threshold techniques, i.e., odds ratio, Cohen’s kappa, maximum sum of specificity and sensitivity, Concordance Probability, Alves Rankings, value of an acceptable risk level, and standard deviation plus mean, to identify which ones recognize an optimal threshold for the software metrics. The dichotomous power of any threshold technique depends on the software measures, the optimal values of which are to be identified. This study made use of widely adopted object oriented metrics, Chidamber and Kemerer metric suite. The discrimination results of the techniques were further compared and the observations elicited from the results of experiments revealed that concordance probability and maximum sum of sensitivity and specificity achieved the best performance, whereas the odds ratio performed significantly worse than the best performing methods.
Making Negation-word Entailment Judgment via Supplementing BERT with Aggregative Pattern

This paper proposes two novel approaches to supplement BERT for making negation-word (NW) entailment judgment on a Chinese Social Studies QA dataset. Recently, BERT has shown excellent performance (and even outperforms human) on several natural language inference tasks. However, BERT is found to achieve the remarkable results mainly via utilizing the surface features (such as lexical distribution bias) that are unnoticed by humans. In our test, BERT's performance would degrade significantly if it is tested on a NW involved dataset in which the influence of lexical distribution bias has been removed. Since a single unmatched NW could toggle the overall judgment, we propose a Negation-Word-Aggregative-Pattern (NWAP) for reflecting the NW matching status, and use it to supplement BERT. Our first approach then supplements BERT with a NW toggling module, which decides if BERT's answer should be toggled according to the NWAP. The second approach concatenates the embedding of above NWAP with BERT's output vectors, and then feed them into a feedforward (FF) neural network (NN) to make the final judgment. Experiments show our toggling module outperforms BERT by 59% accuracy on those lexical-bias removed NW data-set. With adversary training data added to both BERT and our FF NN, our model still outperforms BERT by 23% accuracy.
Instance Matching in Knowledge Graphs Through Dynamic, Distributed and Affinity-Preserving Random Walk

A key step in the integration of data stored across independent knowledge graphs is to match instances that refer to the same real-world object (e.g., the same person). In this paper, we propose DAP-Walk, a novel approach for instance matching that is based on a dynamic and affinity-preserving Markov random walk. Our approach takes into account the local and global information mutually calculated from an association graph of candidate pairs of co-referents. Precisely, we leverage this graph to rank each candidate pair through the stationary distribution computed from the random walk on the association graph. We provide a scalable Spark-based implementation for DAP-Walk where the ranking of nodes of candidate co-referents is obtained by aggregating the distributed ranks calculated across spark workers. Experimental results on benchmark datasets show the efficiency and scalability of DAP-Walk compared to several state-of-the-art instance matching approaches.
PROJECT WORK IN TECHNOLOGY PARK AS FORM OF COFIGURATIVE LEARNING

T. Shaposhnikova, O. Gordienko, A. Egorova, D. Romanov, A. Gerashchenko Kuban State Technological University (RUSSIAN FEDERATION)
Transformation of pedagogical conditions into factors in the development of initiative in the work collective

Introduction. The problem of determining the pedagogical conditions and factors, as well as their correlation for the formation of initiative among the stuff is one of the urgent directions in the modern approach to personnel management. This issue is relevant in the possibility of applying it to the development of the initiative among the staff members. Materials and Methods . The main methods were the analysis of literature on the problem under study from different areas of scientific research, scientific articles on the topic of research, as well as generalization and systematization of the data obtained. The analysis of the relationship and interconnections of these concepts is carried out. Results. A comparative analysis of the terms "condition" and "factor" in various fields of knowledge has been carried out. It was found that there is no clear division between the concepts; in many studies, these concepts are identical. At the same time, differences were identified in the definitions of these concepts in philosophy, pedagogy and psychology. In the context of the problem of the formation of initiative in the work collective, it was determined that conditions and factors can transform into each other. A visual scheme was proposed for transforming conditions into factors through the assessment procedure and conscious actions to change conditions. A visual scheme was proposed for transforming conditions into factors through the assessment procedure and conscious actions to change conditions. The question of the specifics of the relationships between the concepts of conditions and factors, as applied to a specific team, remains open. The latter result means that it is necessary to conduct research in a specific work collective working for specific purposes. Discussions and Conclusions. The paper presents conclusions about the differences between the terms "condition" and "factors", presents the relationship of these concepts, conclusions about the possible mechanisms for the transition from one concept to another in the study of the development of initiative in the work collective, the dynamics of transformation of conditions into factors of successful pedagogical activity, as well as , a consolidated form of control of the dynamics of changes in specific conditions, important for the formation of initiative in the work collective, and their transformation into factors is proposed. As a result, a variant of the classification of the conditions for the development of initiative in the work collective is presented.
Victim identification application with face recognition based on android

Abstract The increase in the population causes the problems faced are also increasing. Problems that arise include that, the number of accident victims who have recently often occurred on the highway, where the victims of the accident suffered minor injuries to severe and some even have to take lives due to accidents on the highway. And the second is crime, like a murder case that can take someone’s life. Often victims do not carry identities, making it difficult for the police or related parties to identify victims of accidents and killings. This system is made in the hope that it can help the police or related parties to be able to recognize the face of the victim without an identity just by using the android application. So that it can save more time in identifying the identity of the victim. Victim face recognition algorithm using Facial Landmark Detector and for its implementation using the Dlib library. To do facial recognition, it is done by taking Figures of victims using an application on android which is then sent to the server for the recognition process. After the image is sent to the server, the recognition process will be carried out, the results of the introduction process are sent back to android to display the results. The level of accuracy of the victim’s face recognition with a wound if using a tolerance of 0.35 is 100%, but if using tolerance 0.2 the level of accuracy is only 25%.