Reviewerly/exHarmony huggingface dataset:
Integrating Corporate Digital PR and Marketing Materials in LSP Courses

The last decades of rapid development of technologies, growth of competitiveness and active globalization processes led to wide spread of new means of communication and communicative practices, digital genres development in professional, academic and corporate communication, and a new role of promotional discourse. The promotional discourse affecting all types of discourse leads to emergence of hybrid genres, discourse forms, and new dimensions of any professional, academic or corporate performance. New role of digital genres and promotional discourse in both national and transnational context need to be studied not only by PR, marketing and corporate communication specialists but also by specialists of any professional and academic areas.The paper presents the project on integration of authentic corporate digital PR and marketing materials into LSP courses for engineering and economics students. The approaches to selection of corporate resources and core digital genres relevant to students’ professional and academic needs, learning materials and tasks development as well as components of blended course design are discussed. Based on the project results, suggestions are made for incorporation of corporate digital PR and marketing materials in LSP courses in different foreign languages for different students’ audiences.
Speech Impact of the Educational and Scientific Text of Engineering and Humanities Profiles

The paper considers peculiarities of speech impact of educational and scientific texts of engineering and humanities majors as a factor that ensures implementation of the pedagogical principle of textbook content accessibility for students. The goal of the study is to examine the concept of competence in terms of the theory of psychological attitude, to identify its components and to find out, based on the analysis of educational texts, what elements of the language form and the content of these texts contribute to developing the competence. The research material is university textbooks on engineering and humanities subjects. Methods of the study are discourse analysis, methods of communicative and pragmatic linguistics, functional and semantic analysis.The analysis has shown that the language resources, which describe the intellectual activity aimed at acquiring new knowledge, especially joint intellectual activity of the author and the reader, are used much more often in the textbooks on engineering subjects distinguished by their abstract content. This makes the content of the educational text closer to the reader and involves the reader in the process of active thinking. Besides, the textbooks which are recognized by students and teachers as effective, i.e. explaining complex engineering topics in a clear and simple manner, use various speech strategies which motivate the reader to study for a profession and create an optimistic environment.
A Traffic Monitoring and Policy Enforcement Framework for HTTP

Due to the accessibility and popularity of Internet, web based applications are commonly used for providing different services to the users. At the same time, the simplicity to conduct attacks and the availability of several attack tools have made web applications the most common target for attackers. Hence monitoring and analysis of web applications require special attention. In this paper, we describe a policy enforcement and web attack detection framework for HTTP protocol. The proposed framework can monitor and analyze HTTP traffic to detect injection, misconfiguration and directory traversal attacks. Moreover, this framework can be used to enforce web application access policies involving content type, URL and device level access.
Information Extraction with Negative Examples for Author Biographies in Scientific Literatures

Information extraction (IE) is a textual information processing task concerned with the automatic extraction of entity mentions and relational structures from documents, which has been widely studied and applied in various fields such as biomedicine and news. In this paper, we extend this technique to biographies and propose a new BioIE3 dataset to extract the authors' experiences (including birth, education, work and awards) from their profiles in the scientific literatures. Named entity recognition (NER) and relation extraction (RE) tasks are performed simultaneously in a neural architecture based on the Skip-Gram Word2vec, Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) models. On the basis of the above work, negative examples are especially introduced into the training stage to effectively improve and increase the robustness of the model.
Comment of Secure File Storage and Retrieval in Cloud Based on MRSA Cryptographic Algorithm

Data confidentiality is a major concern for data outsourcing. It is especially relevant for data storage and retrieval in cloud where the client has no control over it. Therefore, solutions have been proposed to offer a secured environment to ensure data confidentiality. Thangavel et al. attempt to improve data confidentiality by proposing the MRSA encryption scheme in 2015. However, their attempt failed to deliver the purpose of confidentiality due to the misuse of the RSA algorithm. The authors would like to comment the secure file storage and retrieval in cloud based on cryptographic algorithm by illustrating their error and also showing the way of how to correct it. In this case, the goal of data confidentiality in cloud storage and retrieval can be facilitated.
A Quantitative Comparator of Heuristic Methods for Optimal Route in Hilly Terrain

The computation of optimal route for various kinds of automatic or manned vehicles on hilly terrain is an important task in route planning applications. In the absence of a road network, the topography factors of the terrain (slope, elevation, etc.) and the climbing angle of the vehicle play an important role in the computation of the optimal route between two points. This kind of problem has been addressed in the artificial intelligence domain, and the graph search algorithms can be applied to find a solution. The A* algorithm and various versions of A* have been reported in the literature to achieve the faster results while maintaining the optimality criteria of the solution. The speed-up in all the versions of A* algorithms is achieved through the use of heuristic knowledge available in the problem domain. There may exist various heuristic functions that fulfil the admissibility criteria but produce different results as far as the speed and the optimality are concerned. This paper presents a performance measure for quantitative comparison of the various heuristics functions that can be used for optimal route selection in hilly terrain.
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Intelligent Cloud Training System based on Edge Computing and Cloud Computing

Equipment simulation training based on cloud computing is emerging. However, the latency between cloud center and client is long, and the energy consumption management is difficult, which are influencing the development of cloud training. Intelligent cloud training system based on edge computing and cloud computing is introduced in this paper. Intelligent gateway is introduced, through which the task and resources are scheduled and managed together. The popularity of training resources is analyzed. The management of servers in cloud center and edge is intelligently switched between timing sleep and task-activation. Intelligent training service provisioning is achieved through above measures. The simulation results show that the system and management methods are effective on improving training service quality and lower the energy consumption.
Research on the Influence of Gamma Correction Method on Local Feature Descriptors

Local feature descriptors are widely used in image processing, but their image matching failures in nonlinear illumination environments are very obvious, especially for daytime and nighttime illumination changes. The Sift algorithm is not effective in matching experiments, So illumination is a very difficult problem for local feature descriptors. In order to solve the problem of image matching under the illumination changing environment, this paper uses a gamma correction method to preprocess the illumination image. This method can improve the matching accuracy of sift descriptors in the illumination changing environment, and the experimental results show that the performance is effective
Sentimental Content Analysis and Prediction of Text

In the advancement of technology, the web era revolutionized mankind life; huge amounts of data are available on the internet in the form of articles and blogs. From this huge volume of data opinion mining is an important for extracting the raw data to become useful information. Sentiment analysis provides categorization in opinion mining as positive or negative class for content analysis. English language is considered as a universal language and used almost every part of the word, so classification of opinion is important to get the end meaning of the word phrase and comments. No literature is available for classification of sub opinion in the text mining. SAP of Text through Machine Learning algorithm (KNN) is a three-step technique of opinion mining. In this study, authors have put articles at first removing stop-words, tokenizing the sentence and revamping the tokens, it will calculate the polarity of the sentence, paragraph and text through contributing weighted words by keeping sentiment shifters and intensity clauses in consideration. Secondly, over polarization of sentence is adjusted. Finally, overall trend of the input text on the basis of tokenization and polarization of sentence is predicted with proposed algorithm and compared with KNN. Furthermore, domain specific analysis is a distinct feature of the proposed model where data can be updated according to the required domain to ensure the optimal level of efficiency.
Window Functions in Rhythm Based Biometric Authentication

Emerging new technologies and new threads entail additional security; therefore, biometric authentication is the key to protecting the passwords by classifying unique biometric features. One of the protocols recently proposed is the rhythm-based authentication dealing with the dominant frequency components of the keystroke signals. However, frequency component itself is not enough to understand the whole keystroke sequence; therefore, the characteristic of a password could only be analyzed by transformations providing the information of when which dominant frequency arises. In this paper, the biometric signal generated from keystroke data is divided into windows by various window functions and sizes for frequency and time localization. As a guide for signal processing in biometrics and biomedicine, we compared Hamming and Blackman widow functions with various sizes in short time Fourier transformations of the signals and found out that Blackman is more appropriate for biometric signal processing.
Mandarin-Tibetan Bilingual Cross-language Voice Conversion Based on Semi-hidden Markov Model

In recent years, deep learning based voice conversion (VC) has significantly improved the performance of the conversion system. However, such systems generally require a large amount of parallel corpus from the source speakers and the target speaker, and the parallel corpus of Mandarin-Tibetan bilingual is difficult to obtain. In order to solve this problem, we propose a method based on the semi-hidden Markov model (HSMM) using Mandarin-Tibetan bilingual average voice model and speaker adaptive technology for VC. This method does not require parallel corpus. Firstly, it obtains the Mandarin-Tibetan bilingual average voice model using mixed language multi-speaker corpus training, then uses a small number of source speaker corpora to adaptively convert the average voice model to obtain the speaker-related acoustic model. Finally, the text corresponding to the source speaker's speech is translated, and the context-dependent labels of the translated text is input into the speaker-related acoustic model, the converted speech is output to realize cross-language VC. The experimental results show the effectiveness of this method, the converted speech MOS score: 3.37 points; DMOS score: 3.00 points.
Effective Trace Acquirement during Product Information Diffusion and Application

Information dissemination has become part of people's daily communication and there is great interest for both academic and industrial communities.Most previous studies have focused on the strategy and mechanisms.The methods controlling the process of information diffusion have rarely been studied.Thus, previous studies have failed to effectively mine the value of product information diffusion on social networks.In this study, based on the information diffusion product in consumer self-organized social networks, the control of the product information diffusion process was explored.The node identification principle of the QR code sender designed in this study and the linked list that associated information with specific nodes allowed the acquisition of effective traces in long-chain transmission from the information source to the value nodes, and solved user information disclosure during the transmission process.This method was applied to the tracing system of defective vehicles, achieving accurate recall of defective vehicles.
ATP-OIE

This paper describes an innovative Open Information Extraction method known as ATP-OIE1. It utilizes extraction patterns to find semantic relations. These patterns are generated automatically from examples, so it has greater autonomy than methods based on fixed rules. ATP-OIE can also summon other methods, ReVerb and ClausIE, if it is unable to find valid semantic relations in a sentence, thus improving its recall. In these cases, it is capable of generating new extraction patterns online, which improves its autonomy. It also implements different mechanisms to prevent common errors in the extraction of semantic relations. Lastly, ATP-OIE was compared with other state-of-the-art methods in a well known texts database: Reuters-21578, obtaining a higher precision than with other methods.
Deer in The Headlights: Short Term Planning via Reinforcement Learning Algorithms for Autonomous Vehicles

Reinforcement learning has seen a resurgence recently in tandem with the resurgence of deep neural networks. Recent work done by DeepMind and others has shown us the enormous potential of reinforcement learning when applied to certain problem domains. Of these domains, one of them is of particular contemporary relevance; Autonomous Vehicles. Several multinational corporations are pouring enormous resources into developing cars which can autonomously drive themselves. Autonomous Vehicles are expected to bring about the largest revolution in the automobile industry, since the invention of the internal combustion engine. One of the key components of autonomous vehicles is reinforcement learning. In this paper, we explored the performance of a Reinforcement Learning agent, known as Dueling DQN, to a simulated driving environment, presenting a short term planner to be used as a lane following and obstacle avoidance system as part of a larger, integrated system.
Using off-the-shelf data-human interface platforms: traps and tricks

With the development of learning algorithms, the constantly increasing computing power and the available amount of multimedia data, the adoption rate of data science techniques is steadily growing. Machine and deep learning algorithms are already used in a wide variety of ways to solve domain-specific problems. However, the potential of such methodologies will be fulfilled when also non specialized data scientists will be empowered with their use. Focusing on such perspective, this work does not deal with a classical data science problem, but instead exploits existing and available easy to use data-human interfaces. To this aim, we picked an exemplar scenario, amounting to an existing qualitative activity recognition data set that was in the past analyzed utilizing feature selection techniques and custom machine learning paradigms. We here verify how it is today possible, without changing the default settings and/or performing any type of feature selection, to employ the machine and deep learning algorithms provided by different publicly accessible tools (namely, Weka, Orange, Ludwig and KNIME) to address the same problem. Nevertheless, not all of the utilized platforms and algorithms provided satisfactorily results: we here finally discuss the possible issues and opportunities posed by such approach.
Tutorial on Benchmarking Big Data Analytics Systems

The proliferation of big data technology and faster computing systems led to pervasions of AI based solutions in our life. There is need to understand how to benchmark systems used to build AI based solutions that have a complex pipeline of pre-processing, statistical analysis, machine learning and deep learning on data to build prediction models. Solution architects, engineers and researchers may use open-source technology or proprietary systems based on desired performance requirements. The performance metrics may be data pre-processing time, model training time and model inference time. We do not see a single benchmark answering all questions of solution architects and researchers. This tutorial covers both practical and research questions on relevant Big Data and Analytics benchmarks.
THE MANAGEMENT OF THE PROFESSIONAL DEVELOPMENT OF TEACHERS OF THE INSTITUTION OF GENERAL SECONDARY EDUCATION BY MEANS OF PROJECT TECHNOLOGIES

&#x0D; &#x0D; &#x0D; The article determines the relevance and practical direction of the management of professional development of teachers of the institution of general secondary education by means of project technologies. The concept of “management”, "professional development", "project technologies", "portfolio of projects", defined tasks of project technologies and management conditions for professional development of teachers of institutions of general secondary education by means of project technologies were determined, the expediency of using project technologies in the management of professional development of teachers of the institution of general secondary education. It is pointed out the importance of ensuring the effective management of the professional development of teachers of the institution of general secondary education by means of project technologies in accordance with the formation of a new approach to education aimed at solving certain pedagogical tasks combining goals, methods, content and forms of work.&#x0D; &#x0D; &#x0D;
Comment on MRSAC Scheme

Although cloud storage features for its strengths of energy-saving and immediate access, potential concerns of data security, longevity and accessibility may limit its scale, development, and acceptance for users. Innovative methods are required to employ a cloud storage model securely while being efficient. Therefore, Anbuchelian et al. proposed a modified RSA cryptosystem algorithm in their paper of “Efficient and secure auditing scheme for privacy-preserving data storage in cloud” in 2019 to meet this goal. In their scheme, a multilevel hash tree algorithm combined with RSA cryptosystem algorithm (MRSAC) is combined to ensure both security and efficiency can be met in cloud storage. The authors offer comments on the MRSAC scheme through examining its validity based on the review of MRSAC algorithm and examples of experimental tests. Consequently, drawbacks and theoretical defects of this scheme are identified, and this scheme is proven to be invalidated. This study contributes to the knowledge by validating the current research of MRSAC scheme based on fundamental understanding of RSA encryption and digital signature.
A Novel High Capacity Data Hiding Algorithm using Salt and Pepper Noise

In recent years, data hiding using images has become a hot topic for research. Steganography is the art of hiding data in a seemingly innocuous cover medium. In this paper, a novel algorithm is proposed by merging two concepts of data hiding and salt-pepper noise removing. Two procedures are involved in the new algorithm, embedding by adding noise, and extracting by removing noise. The input for the first procedure is a cover image and secret data, while the output is a stego image. In the second procedure, the input is the received stego image, and the output is the cover image and the secret data. The experimental results show that the proposed approach has a better performance in terms of the embedding rate compared to that of the other approaches.
Evaluating Elders' Bio-Psycho-Social Health Dimensions Using Their Smartphones

The percentage of elder people in developed countries is increasing rapidly. A high percentage of them usually present multiple and chronic diseases. A patient with several diseases requires specific and coordinated care that is difficult to configure. Different frameworks can evaluate their functional status and identify the required care, together with the associated cost to the health system. Nevertheless, these frameworks are usually questionnaires that have to be periodically performed by the patients with the assistance of already overloaded professionals. In this chapter, the authors make use of mobile technologies to build a system capable of monitoring the activities of the elderly and analysing these data to assess their bio-psycho-social status. The experiments carried out show us that it correctly evaluates these patients and reduces the effort required by health professionals.
MSMPX: Microarchitectural Extensions for Meltdown Safe Memory Protection

In recent years several hardware enforced pointer protection schemes have been proposed. The most notable amongst them is the Intel MPX, which can identify spatial violations at run time. Recently, it is shown that Intel MPX is vulnerable to a potent attack called Meltdown, which exploits the processor’s transient behavior during speculative execution.In this paper, we show that there is a fundamental design flaw in Intel MPX and all other hardware enforced pointer protection schemes that we surveyed, making all of them vulnerable to Meltdown. We then suggest a design strategy called MSMPX, that provides hardware enforced pointer protection, while at the same time being immune to Meltdown. We compare the hardware overheads on an OpenRISC processor and the performance overheads with respect to Intel MPX.
Machine Learning Methods for Assessing Economic Security Indicators in the Life Cycle of On-Board Automation Systems on a Given Platform

A mathematical approach is presented using a unified indicator of assessing economic security in the field of creating on-board complexes of technical equipment as a payload for installation on a given technical platform, based on an assessment of the probability of various kinds of threats and the probability of their successful reflection. Various methods for estimating probabilities are presented, their capabilities and applications are revealed. It is shown that machine learning methods in combination with probabilistic programming are most suitable for the requirements of the digital economy. The analysis of the possibilities of using machine learning technologies and assessing the probability of programming in assessing and predicting economic security in the formation of onboard automation systems based on the rear platform is carried out.