Reviewerly/exHarmony huggingface dataset:
Cleaver Behavior Styles as a post-covid strategy in mipymes / Estilos de comportamento do cutelo como uma estratégia pós-cobiçada em mipimas

This research was carried out in response to the crisis that we face at a global level in which health is vulnerable to covid-19, impacting on various organizational contexts, strongly driven to react using technology through digital tools in order to respond and functionally to work responsibilities, it is considered important that mipymes manage to strengthen the relationship between technology and employees and minimize the risk of being left behind. It is a descriptive research, in which the bibliographic review of the evaluation of talents, analysis of the cleaver test, specifying requirements to develop software to be implemented in mipymes through human capital management was considered, providing information on 16 styles of behavior of talents to join the organization or collaborators. In this way the organization has the opportunity to be its own generator of change, not only by measuring the competencies of its collaborators, but also by being designed and organization to enhance what people are capable of thinking, creating and doing while face and resolve labor situations in favor of the organization.
Deepfake Detection by Exposing AI-Generated Fake Face Video

Deepfake algorithms can make forged pictures and videos that people cannot differentiate them from true ones. The suggestion of technology that locates and proves the truth of virtual visual media is as a result essential. Deepfakes generate realistic forged images or videos of targeted persons by swapping their faces another person saying or doing things that are not really done by them and public start trusting in such forged videos, as it is not identifiable with the normal human eye. This paper offers a novel method to locate Deepfakes. We present discussions on challenges, studies, advances and strategies associated with Deepfake along with the method based on long-term recurrent convolution network (LRCN). By reviewing the history of Deepfakes and cutting-edge Deepfake detection strategies, this method will give a new approach towards the Deepfake technology and will help in the advancement of new and strong strategy to deal with an increasing number of Deepfakes.
DEVELOPMENT OF METHODOLOGICAL BASES FOR INFORMATION SECURITY ASSESSMENT AND DIAGNOSTICS OF AGRO-FOOD ENTERPRISES

Introduction. The purpose of the article was to develop methodological foundations for assessing and diagnosing the information security of agricultural enterprises. Methods of research. The tasks of the article were solved by means of general and special methods of research: analysis and synthesis, systematization and generalization, method of grouping, dialectical approach. Results. The ways and methods of the information security assessment of the enterprise were defined and characterized. Among them are: standard-based assessment, risk-oriented assessment and economic indicators. Much of the methodology for assessing the level of an enterprise information security, agrarian enterprise in particular, were based on the identification of information risks based on US and British methods CRAMM, FRAP, OCTAVE, NIST, MSAT, COBRA and Russian GRIF 2006 methodology. Originality. It was proved that for methodological bases development of estimation and diagnostics of enterprise information security level it is expedient to use advantages of estimation methods by standard, at risk of information system and using group and private indicators of economic component of information security estimation. Practical importance. The concept of the methodology development for economic assessment and information security diagnostics of agri-food enterprises was proposed, which takes into account the advantages of the considered diagnostics and assessment methods of the information security level of agricultural enterprises, offers a quantitative and qualitative assessment of its components, determines the impact of integrated indexes on the performance indicators and safety of subjects agricultural business, and, as a result, offer effective ways to optimize the management of information security companies in agri-food sector. The main scientific provisions of the article can be used in the practice of agricultural enterprises. Keywords: concept, economic indicators of assessment, enterprise of agro-food sphere, management of information security, methodology, methods of assessment of information security, risk-oriented approach, standard.
Performance comparison of video encoders in light field image compression

Efficient compression plays a significant role in Light Field imaging technology because of the huge amount of data needed for their representation. Video encoders using different strategies are commonly used for Light Field image compression. In this paper, different video encoder implementations including HM, VTM, x265, xvc, VP9, and AV1 are analysed and compared in terms of coding efficiency, and encoder/decoder time-complexity. Light field images are compressed as pseudo-videos.
Science and Art in the Digital Age: A Synergy Problem

Today artistic perception of the world and scientific and technical understanding of reality remain the main forms of creative self-realization. For many centuries, starting in Antiquity, art and science went hand in hand in cultural history. However, during the heyday of technogenic civilization, there occurred a split, and since the second half of the 20 th century these two parts of a previously common culture became poorly compatible. According to the authors, the era of digitalization is going to completely replace the algorithmic and instructive professions in technology and service personnel with robots and artificial intelligence, and a person will have to develop the spheres of right-brain practices, which undoubtedly include scientific and technical creativity and art. Like all previous information revolutions, the modern digital revolution is creating new network structures of fast communication and ultra-long-distance, global order. Distance does not matter any more. In this situation, the cognitive maps of a person change radically, new types of self-organization and socialization appear, and there occurs a deformation of value spaces and worldview guidelines. Life in the uncertainty of this new world can only become effective in interaction of man and artificial intelligence, and the only aspect of activity that cannot be replaced by artificial intelligence is creativity and aesthetic experiences as well as culture. Thus, the authors conclude that a new synthesis in culture is needed, the ways of which are discussed in detail in the article. Among them are promotion of new concepts of Science-Art, application of post-Turing methodology, and use of quantum-synergetic anthropology, which develops new ideas about theatrical and engineering creativity, – and these will remain unattainable to artificial intelligence in foreseeable future.
Virtual beginners. Can technology come to the rescue? [engineer training]

With lockdown meaning hands-on and face-to-face training for engineers is limited, is VR, augmented and mixed reality finally coming of age and what is the best way to deliver training when everything has shut down? Covid-19 has forced the engineering sector to face urgent challenges - from business continuity to lack of staff, to supply chain problems and travel restrictions. But the show must go on - with essential services depending upon it.
Random Machines: A Bagged-Weighted Support Vector Model with Free Kernel Choice

Improvement of statistical learning models to increase efficiency in solving classification or regression problems is a goal pursued by the scientific community. Particularly, the support vector machine model has become one of the most successful algorithms for this task. Despite the strong predictive capacity from the support vector approach, its performance relies on the selection of hyperparameters of the model, such as the kernel function that will be used. The traditional procedures to decide which kernel function will be used are computationally expensive, in general, becoming infeasible for certain datasets. In this paper, we proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time, evaluated over simulation scenarios, and real-data benchmarking.
Assessment of Community Internet Intensity (CII) in Sakhalin Island

&amp;lt;p&amp;gt;Sakhalin Island is a region with high rate of seismic activity. Tens of felt earthquakes occur within the studied area every year. Rapid macroseismic observation through the web questionaries, social networks etc. gives reliable information about ground shaking intensities and today is processed by major seismological agencies (Bossu et al., 2018; Quitoriano and Wald, 2020).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The recent development of the methodology began with the web-based macroseismic observations following Dengler and Deweey (1998) and Wald et al. (1999). Widespread global use of Community Internet Intensity (CII) was routinely applied by the U.S. Geological Survey (USGS) through the USGS DYFI questionaries. Over 5 millions felt reports were collected during last 15 years (Quitoriano and Wald, 2020).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During last 5 years the methodology was tested in Sakhalin Island (Konovalov et al., 2018). For the collection of felt reports we used regional internet resource (https://eqalert.ru/#/). The DYFI USGS questionnaires translated into Russian were used for processing the macroseismic information. The felt reports of the respondents from each settlement were transformed to the Community Weighted Sum (CWS) which takes into account various indicators of ground shaking: human sensations, position of objects, visible damages of the building. The CII was calculated using the equation (Wald et al., 1999):&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;CII = 3.4 ln (CWS) &amp;amp;#8211; 4.38.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The obtained values were rounded to the first number after the comma. In general CII should be similar to the MM intensity.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During the period from 2016 to 2020 we have got about 400 felt reports. Most of the responses came in the first minutes after the origin time of seismic event. Data with only one report or incorrectly submitted questionnaires were excluded in further calculations. The small number of the felt reports may be explained by low population density of the central and northern districts of Sakhalin Island. Finally we have found correlation between the CII and PGA (cm/s/s) which is given by the equation:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;CII = 2.5 log (PGA) + 2.32.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;It is suggested that given approach can be used as a robust tool for express analysis of ground shaking. It is also a good way to involve the population and bring them closer to understanding the scientific process in the era of the growth of computer technology and social networks.&amp;lt;/p&amp;gt;
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization

Back-door attack poses a severe threat to deep learning systems. It injects hidden malicious behaviors to a model such that any input stamped with a special pattern can trigger such behaviors. Detecting back-door is hence of pressing need. Many existing defense techniques use optimization to generate the smallest input pattern that forces the model to misclassify a set of benign inputs injected with the pattern to a target label. However, the complexity is quadratic to the number of class labels such that they can hardly handle models with many classes. Inspired by Multi-Arm Bandit in Reinforcement Learning, we propose a K-Arm optimization method for backdoor detection. By iteratively and stochastically selecting the most promising labels for optimization with the guidance of an objective function, we substantially reduce the complexity, allowing to handle models with many classes. Moreover, by iteratively refining the selection of labels to optimize, it substantially mitigates the uncertainty in choosing the right labels, improving detection accuracy. At the time of submission, the evaluation of our method on over 4000 models in the IARPA TrojAI competition from round 1 to the latest round 4 achieves top performance on the leaderboard. Our technique also supersedes three state-of-the-art techniques in terms of accuracy and the scanning time needed.
The Role of Instagram on Dental Education

Objective: Technological developments affect educational processes, learning methods, and learning environments. Dentistry education should be adapted parallel with technological developments for students whose learning habits change with the increasing using of social media and mobile phones. This study aims to compare the efficacy of the mobile-based education method (Instagram application) and the traditional classroom education method. Material and Methods: The study was conducted with 42 volunteer students who had not taken general and oral pathology courses yet. Students (n:21) in the control group took radiological features of the odontogenic and non-odontogenic cysts and odontogenic tumors course by the classroom learning method. The study group (n:21) followed the same lessons' images on the newly opened @beuradyoloji Instagram account. Both groups were trained for 1 hour a week for 4 weeks. The knowledge test was held 15 days after the end of the courses to all students. Paired sample t-test was performed to investigate differences between the mean knowledge test scores of the groups. Results: The examination average for the study group was 71.97±9.3 and the control group was 68.33±8.39. Paired sample t-test showed no significant difference between the study and control groups in knowledge scores (p>0.05). Conclusion: The current study is the first that investigates the effect of the mobile-based education method on radiological features of the specific cysts and tumors. The study carried out on a limited number of students, shows that the value of mobile-based education has almost the same effectiveness to classroom education.
Poster: Repair Cross Browser Layout Issues by Combining Learning and Search-based technique

With the increasing number of browsers and platforms on which the applications can be executed, cross-browser incompatibilities (XBIs) are becoming a serious problem for organizations to develop web-based software. In order to eliminate cross browser issues, existing work tries to repair layout XBIs using search-based technique. However, the designed fitness function is too strict, and may miss the chance to repair some layout XBIs. For each reported layout XBI, it will search all possible candidate fixes, and cannot reuse existing repair solutions for similar issues. This paper proposes to combine learning and search based technique to improve the state-of-the-art of automated repair of layout XBIs. By extracting the characteristics of successfully repaired layout XBIs and learn a decision tree, our approach will accelerate the search process by directly applying the recommended solution to fix similar XBIs. Moreover, by redesigning search-based repair process, our technique can improve the chance to repair layout XBIs. The initial evaluation shows that the proposed approach is effective.
Review And Analysis Of Computer Vision Algorithms

Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;
Estimating compositions of the deep continental crust

&amp;lt;p&amp;gt;The deep continental crust's chemical makeup is central to the debate of crustal formation, evolution, strength, and bulk composition. The impenetrable depths and pressures of the deep (roughly &amp;gt; 10 km) crust force geoscientists to rely on indirect sampling methods, studying medium- to high-grade metamorphic terrains and xenoliths to ascertain the composition of the middle and lower continental crust. Analyzing the deep crust in situ requires geophysical data, such as seismic velocities: Vp, Vs, and the Vp/Vs ratio. Each method provides a different perspective on deep crustal composition, but alone, neither is definitive.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To address the nonuniqueness in crust composition modeling, we use thermodynamic modeling software (i.e. Perple_X) to relate observed seismic velocities to bulk compositions and mineralogies. We present a multidisciplinary model for the composition of Earth's deep crust, using geochemical and geophysical data. Through a Monte Carlo modeling approach, we determine the best-fit geochemical model for bulk middle and lower crustal compositions. For 12 different tectonic regimes, we quantify uncertainties in crustal composition, temperature, and seismic velocity while recognizing our own scientific biases. We present a global model of deep crustal composition conclude that regional scale geological variations benefit from a higher resolution model. Overall, our model forecasts 77% of the deepest continental crust has 45 to 55 wt.% SiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;; 15% 55 to 65 wt.% SiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;; 8% may have &amp;gt; 65 wt.% SiO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;. Of perhaps equal or greater importance, however, we present a scalable, modular program that can be altered to incorporate additional petrological and geophysical constraints, allowing geoscientists to more easily compare different scenarios for the deep crust.&amp;lt;/p&amp;gt;
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation

In Natural Language Understanding (NLU), to facilitate Cross-Lingual Transfer Learning (CLTL), especially CLTL between distant languages, we integrate CLTL with Machine Translation (MT), and thereby propose a novel CLTL model named Translation Aided Language Learner (TALL).TALL is constructed as a standard transformer, where the encoder is a pre-trained multilingual language model.The training of TALL includes an MT-oriented pre-training and an NLU-oriented fine-tuning.To make use of unannotated data, we implement the recently proposed Unsupervised Machine Translation (UMT) technique in the MToriented pre-training of TALL.The experimental results show that the application of UMT enables TALL to consistently achieve better CLTL performance than our baseline model, which is the pre-trained multilingual language model serving as the encoder of TALL, without using more annotated data, and the performance gain is relatively prominent in the case of distant languages.
Visual Question Answering with External Knowledge

Visual Question Answering (VQA) system is an algorithm that takes an image, and a natural language question about it as input and produces an answer as the output. One of the most intriguing features of the VQA task is the uncertainty of the questions asked. To extract the knowledge required in answering them needs a spread of image manipulations. A significant part of the ongoing advancement in vision to language issues is accomplished by merging Recurrent Neural Networks and Convolutional Neural Networks. We propose a fully trainable neural network based on attribute extraction, established on the CNN and RNN design, which will apply to different vision to language issues. We do that by embedding a particular attribute representation of the scene interpretable by users. Each semantic feature corresponds to a word strip-mined from the training image description and signifies high-level information regarding the image context. We train an RCNN-based classifier for every attribute, and therefore the set of attribute likelihoods for a picture that forms a high-level representation of image content are learned. We then train an LSTM to get the captions, and answers, on the idea of these likelihoods. In light of the proposed feature-based vision to language model, the subsequent contribution is to present a means of incorporating information outside to the picture into the VQA task.
The role of the national values in the history of pedagogical education

Tradition is a material and spiritual value that is inherited from the past to the future, passed down from generation to generation, and manifested in various spheres of society. There are national, cultural, domestic, socio-political, religious and other traditions. This article deals with the overview of the national values, especially about morality in upbringing young people over centuries and based on the views in the history of pedagogical education.
Blockchain-Based Business Processes: A Solidity-to-CPN Formal Verification Approach

With its span of applications widening by the day, the technology of Blockchain has been gaining more interest in different domains. It has intrigued many investors, but also numerous malicious users who have put different Blockchain platforms under attack. It is therefore an inescapable necessity to guarantee the correctness of smart contracts as they are the core of Blockchain applications. Existing verification approaches, however, focus on targeting particular vulnerabilities, seldom supporting the verification of domain-specific properties.In this paper, we propose a translation of Solidity smart contracts into CPNs (Coloured Petri nets) and investigate the capability of CPN Tools to verify CTL (Computation Tree Logic) properties.
Educational Use of Smart Phone: A Study with Special Reference to the Research Scholars in Central Library, Visva-Bharati, Santiniketan

Smartphones are becoming increasingly popular in students‟ lives for variety of purposes. Advancement in mobile technologies has demonstrated that they provide great potentials for educational and academic use. In this paper an attempt has been made to investigate into the use of smart phones for academic purposes by the Research Scholars (RS) at Visva-Bharati Central Library. The present study also investigates the perception of Research Scholars on proposed Smartphone- based resources and services if they are offered by their university. The findings suggest that smartphone can be very useful as an educational tool. The findings also reveal that the respondents‟ perception on the application of smartphone in the context of Library and Information services is found to be very positive.
Individual differences in speech perception: Evidence for gradiency in the face of category-driven perceptual warping

In contrast to the long-running debates around universally categorical vs. gradient speech perception, we find individual differences in listeners' sensitivity to subtle acoustic differences between speech sounds (within and between phoneme categories). Here, we used an EEG measure of listeners' early perceptual encoding of a primary acoustic cue and found that categorical listeners showed stronger perceptual warping around the boundary, even as sensitivity to within-category differences was present across the board.