Reviewerly/exHarmony huggingface dataset:
New challenges of the system of secondary specialized education in Russia

Discusses the problems of the vocational secondary education system in Russia. It is shown that in the information age, changes in all areas of society also relate to education, which should correspond to the requirements of the time. Coronavirus pandemic, known as COVID-19, is a serious problem faced by the modern world. In a situation of self-isolation education changes its format to remote learning. In connection with these, a problem arises: how to organize the educational process so as not to lose face-to-face interaction, which is an indicator of the high quality of education? A comparative analysis of the pedagogical, philosophical and sociological works on the problems of the development of modern society as a whole, and relevant issues of secondary specialized education and distance education in particular, has been performed. It is shown that for secondary specialized education this seems to be the most important, because, the age characteristics of students imply difficulties in organizing self-education, vocational education, in addition to theoretical knowledge, involves the development of practical skills, the implementation of which in a remote format causes a number of difficulties. In the current crisis situation, masters of industrial training and teachers of special disciplines need to continue the education process without reducing its quality. To achieve this goal, the development of virtual courses, simulators, as well as models of network interaction “college — production” are proposed. It was revealed that the teacher-tutor, using effective teaching methods that provide information technology, will help learners to orient themselves towards self-education. Secondary special education is faced with challenges that must be answered adequately: the transition to distance learning without losing the quality of education, preparing young people to independently respond to emerging challenges of the present, preparing teachers (especially of professional disciplines) for the specifics of work in a remote format.
Design and Implementation of Highly Secure Residents Management System Using Blockchain

This article presents the design and implementation of highly secure and reliable database system for resident records management system using blockchain technology.Blockchain provides highly secure and reliable data access environment.In blockchain, several data fragments are packed into one block and all blocks are connected to form the chain of blocks.In our prototype, each event of resident such as birth, moving, employment and so on, is assigned to data fragment and certain amount of data fragment, says 20 fragments are packed into block.We also developed the web application interface to avoid installing any applications in users' PC or smartphone.Prototype development proved the possibility to use the blockchain technology to large amount of data management system with highly secure and reliable features.
Using Data Mining Tools in Wall-Following Robot Navigation Data Set

Discovering knowledge in databases is a process for searching clear patterns, not previously known, but with high potential in decision support and characterization of the data. The purpose of data mining is to use the appropriate algorithm for finding dependencies and schemas in the prepared data set, and then representing them in a formal form understandable by the user.In this work, we want to show the possibilities of using data mining and statistical methods to analyze data obtained from mobile robot SCITOSG5 during the simple wall-following task.
Radioactivity and heat production characterization by natural gamma ray well logging and multi-fractal techniques in banting district, Malaysia

Natural gamma ray well logging technique is used to characterize the radioactivity (GR) laterally and vertically in Banting district, SW of Malaysia. Seven drilled boreholes, along N–S profile with their natural gamma ray records are utilized to compute the heat production (HP) parameter, based on the Bucker and Rybach relationship.The analysis of 3467 measured points in those boreholes indicates that GR varies between 6.24 API and 358.4 API with an average of 79.95 API, while HP varies between 0.086 and 5.65 μw/m3 with an average of 1.25 μw/m3.The multi-fractal Concentration-Number (C–N) is used to characterize the radioactivity and heat production variations and to isolate different GR and HP populations in the study region. The high radioactivity and heat production ranges are mainly related to the silty clay layers, accompanied by uranium and thorium.
Cross-Lingual Public Opinion Tracing Based on Blockchain Technology

One of the important issues of public opinion governance in the internet age is to study the characteristics and laws of the generation, development and spread of public opinion on complex social networks and to set up an effective tracing model based on blockchain technology. As a decentralized shared database, blockchain technology is tamper-resistant and traceable. Because of this, it is an important tool for containing the spread of fabricated public opinion, maintaining national security and stability, and protecting corporate image and personal interests. Given the current cross-lingual tracing technology and the fact that blockchain is decentralized and immutable, this project aims for establishing a blockchain-based cross-lingual public opinion tracing system targeting at the characteristics and laws of the spread of public opinion in social network. This study focuses on four aspects: the architecture that is based on complex chain structure; the consensus-based safety and liveness of the system; the management of the system based on smart contracts; standards and rules of this blockchain-based tracing technology. Through the study, a commonly used multilingual tracing system is expected to be built by using the sophisticated blockchain technology.
Conditional Anonymity enabled Blockchain-based Ad Dissemination in Vehicular Ad-hoc Network

Advertisement sharing in vehicular network through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is a fascinating in-vehicle service for advertisers and the users due to multiple reasons. It enable advertisers to promote their product or services in the region of their interest. Also the users get to receive more relevant ads. Usually, users tend to contribute in dissemination of ads if their privacy is preserved and if some incentive is provided. Recent researches have focused on enabling both of the parameters for the users by developing fair incentive mechanism which preserves privacy by using Zero-Knowledge Proof of Knowledge (ZKPoK) (Ming et al., 2019). However, the anonymity provided by ZKPoK can introduce internal attacker scenarios in the network due to which authenticated users can disseminate fake ads in the network without payment. As the existing scheme uses certificate-less cryptography, due to which malicious users cannot be removed from the network. In order to resolve these challenges, we employed conditional anonymity and introduced Monitoring Authority (MA) in the system. In our proposed scheme, the pseudonyms are assigned to the vehicles while their real identities are stored in Certification Authority (CA) in encrypted form. The pseudonyms are updated after a pre-defined time threshold to prevent behavioural privacy leakage. We performed security and performance analysis to show the efficiency of our proposed system.
Automatic library categorization

Software ecosystems contain several types of artefacts such as libraries, documentation and source code files. Recent studies show that the Maven software ecosystem alone already contains over 2.8 million artefacts and over 70, 000 libraries. Given the size of the ecosystem, selecting a library represents a challenge to its users.
Evaluating Explanations of Convolutional Neural Network Image Classifications

In this paper, we seek to automate the evaluation of explanations of image classification decisions made by complex convolutional neural networks (CNN). Explanation frameworks like Local Interpretable Model-agnostic Explanations (LIME) treat complex machine learning models, such as deep neural networks, as black boxes and generate human-interpretable explanations of their decisions using linear proxy models. We propose a pair of experiments to quantitatively evaluate the quality of generated explanations by measuring their sufficiency and salience. To test if a generated explanation contains sufficient information for classification, we test the ability of a trained CNN to classify that explanation properly. We test explanations for salience by training two new CNNs, one using raw image data and the other using explanations as training data, and comparing their classification precision and recall on a common set of test data. We use our new evaluation framework to test our hypothesis that LIME is able to generate explanations that are both sufficient and salient. Our results show that the generated explanations have the potential to be sufficient and salient, provided that the complexity of the explanations is enough to describe the underlying classes.
Knowledge Graph Generation with Deep Active Learning

Creating a knowledge graph automatically from raw unstructured text has always been a job of domain expert which takes months to curate and refine. In this paper, we propose a domain-independent semi-automatic knowledge graph learning system that can be trained with less amount of data, to identify entities and relations from a large text corpus. The system performs the following tasks to extract knowledge graph from the text: (i) Named Entity Recognition (NER), and (ii) Relation Identification (Open Relation Extraction (OpenRE) and Classification). The system uses deep active learning to calculate confidence scores using maximum normalized log-probability on each prediction for both NER, and relation identification. We experimented with both LSTM and transformer based models for NER and relation identification tasks. We achieved around 88% F1 score for the NER task on OntoNotes-5.0 English data set with 40% training data set and above 83% F1 score for relation identification on TACRED dataset. The OpenRE and relation classification systems were trained on domain-specific datasets. To the best of our knowledge, we are the first to introduce a knowledge graph generation learning system with deep active learning.
Adaptive volumetric texture segmentation based on Gaussian Markov random fields features

An adaptive method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is extracted for each voxel in a given volumetric texture image using an estimation cube. However, the selection of the size for this estimation cube causes some fundamental issues related to the uncertainty principle and the inability of the model to capture different texture patterns. These issues are tackled here by employing an adaptive method where the size of the estimation cube is adaptively varying to capture different patterns and also minimize the number of voxels that are related to different texture classes inside the estimation cube. The feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are hence employed to segment volumetric textures. These features are smoothed by applying an averaging filter using an adaptive averaging technique. Such an averaging filter improves the segmentation results considerably. Our method proposed here is evaluated on a synthetic volumetric texture dataset and compared with other methods to demonstrate the superiority of our segmentation method.
Approach to the Search for Software Projects Similar in Structure and Semantics Based on the Knowledge Extracted from Existed Projects

This article presents a new effective model, algorithms, and methods for representing the subject area of an information system. The subject area is presented in the form of fragments of the knowledge base of the design support system. The knowledge base is formed in the process of analyzing class diagrams in UML notation and project source code. The proposed approaches can reduce the time of the design process and increase the quality of the obtained information system through the use of successful information system design solutions used in other projects. Search for successful design solutions is carried out using the developed metrics for determining the similarity of software systems projects. The metrics allow calculating the match of pattern in OWL ontology format with the source code of the project.
A forensic examination of four popular cross‐platform file‐sharing apps with Wi‐Fi P2P

Abstract File‐sharing apps with Wi‐Fi hotspot or Wi‐Fi Direct functions become more popular. They can work on multiple platforms and allow users to transfer files in a concealed manner. However, when criminals use these apps in illegal activities, it becomes an important issue for investigators to find digital evidence on multiple platforms. At present, there are few studies on this topic, and most of them are limited to the single platform problem. In this paper, we propose a forensic examination method for four popular cross‐platform file‐sharing apps with Wi‐Fi hotspot and Wi‐Fi Direct functions: Zapya, SHAREit, Xender, and Feem. We use 22 static and live forensic tools for 11 platforms to acquire, analyze, and classify the forensic artifacts. In our experiments, we find many useful forensic artifacts and classify them into six categories. The experimental results can support law enforcement investigations of digital evidence and provide information for future studies on other cross‐platform file‐sharing apps.
Learning causal dependencies in large-variate time series

A major challenge in causal inference from observational data is to discriminate between associative dependencies and effective causal relationships. This is particularly challenging in large-variate and temporal settings (e.g. in spatio-temporal time series) where the multivariate nature of interactions induces a significant correlation between most of the variables. In recent years, a number of data-driven approaches have been proposed to learn the mapping between some features of the data distribution and the probability of a causal connection between a pair of variables. Most state-of-the-art approaches, however, deal with bivariate cases neglecting the role of the context determined by the other variables. This is a strong limitation in large-variate and temporal settings which are the object of this study. In order to address the context issue, this paper introduces a new set of descriptors based on interaction information to featurize the context and justifies its introduction by using a graphical modeling formalism. The resulting causal inference method is assessed on a number of large-variate synthetic stationary time series. The assessment shows that the proposed method outperforms several state-of-the-art causal inference techniques.
Influence of Emotional Speech on Continuous Speech Recognition

Emotions are an important part of human communication, but they can present harsh conditions for an automatic continuous speech recognition system. This paper presents an analysis of to which level the emotional speech degrades speech recognition accuracy, when dealing with a highly inflected Slovenian language. Namely, the language characteristics are those that also influence the speech recognition performance, and inflection is one of the most challenging ones. Moreover, Slovenian belongs to the group of under-resourced languages, like other Slavic languages. The speech recognition system was developed with the Slovenian BNSI Broadcast News speech database. The Interface speech database was used for the experiments with the emotional speech. The analysis was carried out with HMM and DNN acoustic models, combined with a 3-gram statistical language model. The results show that emotional speech degrades speech recognition accuracy in the range between 5% and 7% absolutely.
An Approach to Architect using Spaces and Flows

Abstract Today, architecture as a discipline is gaining prominence for addressing the increasing complexity associated with enterprises, systems, software, software intensive systems and other entities. This is evident by the increasing architecture related activities that are performed as part of many of these endeavors. Many architecture frameworks, that have been found useful by communities of practice, are increasingly found applicable in different situations requiring architectural interventions. However, it is often the case that there is no foundational basis for many of these practices other than the fact that they are found to be useful. In this paper, spaces and flows are introduced as the foundational concepts for architecting any entity. An approach that utilizes these concepts is proposed. The underlying rationale for the approach and the foundational concepts is also discussed. Further, the usefulness of these concepts and approach is illustrated by means of an exemplar.
Different Approaches in Cross-Language Similar Documents Retrieval in the Legal Domain

The problem of cross-lingual information retrieval in the legal domain is up-to-date, because of the need of studying the best international practices to improve legislation. One of the possible solutions is thematically similar document retrieval. However, there is an important task to transfer between languages. The paper describes different approaches to solve this problem: from classical mediator-based methods to modern procedures of distributive semantics. As a test collection, we have used the UN digital library. The combination of the extended translation model and BM25 ranking function demonstrates the best results.
Modular networks of spiking neurons for applications in time-series information processing

Spiking neural networks with complex spatiotemporal dynamics support efficient information processing of time-series signals. Here, we investigate the relationship between complexity of network dynamics and modular topology of networks using numerical simulations and discuss their effect on the classification performance of spoken-digit recognition tasks. The results show that modular networks generate spatially complex dynamics in which partially and globally synchronous bursts coexist. The classification rate of the modular reservoir network was approximately 75%, a value of which was comparable to that of a random network. This was caused by the randomly-connection structure between the input-reservoir and reservoir-readout layers, thus appropriate inference methods and asymmetry of connections should be introduced to take advantage of the complex dynamics in modular networks.
A Self-Adaptive Blockchain Framework to Balance Performance, Security, and Energy Consumption in IoT applications

The Internet of Things (IoT) is “the network of physical objects, devices, vehicles, buildings and other items embedded with electronics, software, sensors, and network connectivity which allows them to gather and exchange data” [1]. The main feature of IoT is its notable impact on various aspects of the daily life of potential users. Some examples of the possible applications where IoT will play a leading role in the next years include Smart Buildings, healthcare and enhanced learning [2]. There are several challenges that IoT poses to organizations and developers such as security, big-data, heterogeneity, variety and variability, real-time deployment and logging, integration, and many others. The privacy risks of IoT are exacerbated by a lack of fundamental security safeguards in many of the first generation IoT products on the market, for instance, in video surveillance and the cameras which can be considered as “things” [3], [4].
Design of energy efficient datacenter with optimized virtual infrastructure

The Cloud computing concepts provide everything as a service to global users without any limitation on regions and Technology. The individual user requirement, medium or large scale industry needs will be fulfill virtual infrastructure bycloud service provider with less cost. The existing most of data center has major issue on energy management, pollution and carbon emission. The power conception would be monitor in all the levels in data center helps to implementing optimizationtechnique will be to reduce energy usage. The server consolidation, schedule, optimization, automation technique will reduce power and carbon emission. The effective hardware and software usage and selection of right power source also reducepollution. This paper has been used virtualization technology for the green cloud data center to provide resources such asapplication, desktop VDI, hosted servers and server virtualization. Those resources have been hosted on hypervisors. It was monitoring and based on resource usage category, load, priority, threshold and implemented possible optimization, to reduceenergy. The experimental setup based on monitoring data and implemented referred optimization techniques helps to achievereduce energy and carbon emission.
Assessment of vocabulary knowledge through a mobile application

Abstract Educational assessment refers to the variety of methods and tools that help educators measure knowledge, skills, attitudes, beliefs, or educational needs of their students. It is the systematic process of data collection and further analysis that is done in order to understand students´ learning outcomes. The measured variable can help educators understand the complex cognitive development of the students and help them improve their learning. Assessment feedback plays an important role in the process of learning improvement. Students need to know the established learning outcomes and understand how well their learning matches with the expectations. The aim of this paper is to provide information on the formative assessment of vocabulary knowledge through a vocabulary mobile learning application. Thanks to the ubiquitous character of the mobile devices, students can easily access the learning materials, practice the vocabulary, and get the immediate assessment feedback which can help them improve their learning performance.
Measurement of Visual Attention to Advertising Using Eye-Tracking Techniques

Eye-tracking techniques can determine where within an image a respondent is looking by measuring fixation time within an area of interest (AOI). This chapter details the results of four recent eye-tracking experiments conducted by the authors. The first presented participants with an image of a composite advertisement containing eight different day trip package and found a significant relationship between visual attention to a particular package (as measured by fixation time) and subsequent intention to purchase. The second study demonstrates that visual attention to an image can be manipulated through the use of textual information. The third experiment analyses the effectiveness of two versions of a tourism print advertising which confirms that advertisements with less text attracts more attention. The fourth experiment investigates the effectiveness of a promotion on a hotel website which did not attract the attention of participants. Together these studies confirm the efficacy of eye-tracking for tourism marketing effectiveness studies.