Reviewerly/exHarmony huggingface dataset:
Based on a Calibration Machine and Process to Develop a Six-Axis Force/Torque Sensor

This study developed a six-axis force/torque sensor and calibration system. To improve the stability of the calibration system, voice coil motors (VCMs) were used as the system’s output force device. A simply and directly designed structure lent reliability to the six-axis calibration force/torque sensor. Furthermore, the system combined two precise one-axis load cells to receive the output reactionary force from the VCMs, and then used a proportional-integral-derivative controller to reduce the steady-state time. Finally, a mechanism was designed to adjust the orientation of the calibration force. The six-axis force/torque sensor is based on the principle of mechanical deformation of the structure. Resistance strain gauges were used as measuring devices because of their wide measurement range and high level of accuracy. According to the results of a strain analysis using ANSYS, we designed a crab-type force sensor as the main structure of our sensor. Through measuring the strain gauges on the elastic body surfaces, we obtained the corresponding voltages of each applied calibration force. In this study, we conducted least squares estimation (LSE) and maximum likelihood estimation (MLE) to determine the transfer function between the calibration force/torque and the voltages of the sensor. Overall, we integrated the entire system with LabVIEW and simplified its usage to minimize operation errors. Furthermore, the six-axis calibration process was conducted to verify the proposed method.
Subject Recognition in Chinese Sentences for Chatbots

Subject (In this paper, subject means “ /zhu ti” in Chinese, while we use “grammatical subject” to denote traditional “ /zhu yu” in Chinese.) recognition plays a significant role in the conversation with a Chatbot. The misclassification of the subject of a sentence leads to the misjudgment of the intention recognition. In this paper, we build a new dataset for subject recognition and propose several systems based on pre-trained language models. We first design annotation guidelines for human-chatbot conversational data, and hire annotators to build a new dataset according to the guidelines. Then, classification methods based on deep neural network are proposed. Finally, extensive experiments are conducted to testify the performance of different algorithms. The results show that our method achieves 88.5% $$F_1$$ in the task of subject recognition. We also compare our systems with three other Chatbot systems and find ours perform the best.
Improving accuracy of FIR filters for computing convolution transforms for smooth non-bandlimited signals

We propose to improve the accuracy of FIR filters for computing convolution transforms for smooth non-bandlimited (NBL) signals by designing filters by the identification method with using a pair of bandlimited portions of the chosen NBL input and output signals related with each other by the given transform. A design example of type IV linear phase differentiator is presented, where filter coefficients are calculated from the bandlimited portions of the Cauchy pulse and its derivative. The performance of the designed differentiator is evaluated by comparing the accuracy of computed derivatives for several smooth NBL test signals, such as the Cauchy pulse, the Hilbert transform of Cauchy pulse, the Gaussian function, as well as for a bandlimited sinc-function. Evaluation results demonstrate that the proposed design method generates more accurate differentiators than the Parks-McClellan algorithm, the impulse response truncation method and the identification with using full-band Cauchy pulse and its derivative.
AGGRESSIVE BEHAVIOUR AND BULLYING AS MOST FREQUENT EDUCATIONAL PROBLEMS AT SCHOOLS AND POSSIBILITIES OF PREVENTION

At primary and also secondary schools we are facing more and more often psychological and physical attacks on pupils and teachers.It includes various insults, fights among pupils, ridicule, destruction of school property or personal objects of pupils and teachers.The behaviour of such individuals is influenced by several factors: negative family environment, inadequate spending of leisure time, influence of peer groups and influence of media -television, Internet.Children often witness aggressive behaviour in their families and consider it a natural part of life and they often adopt the incorrect behavioural patterns of their parents.The behaviour of individuals is also influenced by changes that have taken place in our society over the last decades (unemployment, rising divorce rate, criminality, drug addiction and other socio-pathological phenomena).The rising brutality and lower age of attackers and aggressors is an alarming fact for our society.That is the reason why various disciplines such as pedagogy, psychology, sociology, criminology and others, deal with these issues.Our goal is to point out the aggressive behaviour, bullying and prevention of these socio-pathological phenomena by pupils at schools.
Sports Data Analytics: A Case Study of off-Field Behavior of Players

The field of sports science is highly emerging and has made the headlines in the research and development activities, with new challenges and trends in the recent past. Sports analytics is the analysis of historical big data mainly available in the form of statistics to provide a proper insight into the entire team, an individual, or even the coach. It also facilitates decision-making for both on-field and off-field performances of all the concerned. The term “sports analytics” was popularized in 2011. It has also created a platform under the confluence of many disciplines such as data mining, machine learning, big data analytics, artificial intelligence, and predictive analytics. The paper aims at analyzing the off-field behavior of players using a statistical approach. The focus of the study is to comprehend the nature of data set, explore the relation between the attributes of the data set, and create a model to understand how the data relates to the underlying population using a real world data set. It comprises motion sensor data of 19 activities among both male and female categories. The sports data set is referred from the UCI repository. It consists of motor sensor data collected for both male and female players. The data set also clearly displays the three dimensions of big data namely, volume, variety, and veracity.
Clarification of Topic Differences Between Shops and Consumers for Personalization of Stories

In recent years, story marketing has been used in order to achieve differentiation of products that are becoming homogeneous. However, it cannot be always said that the information provided by a store matches what the individual consumer demands. In this research, we propose a method to create stories reflecting the interests and preferences of individual customers and consumers. In the proposed method, a topic group expressing the customer's interests and preferences are obtained through the topic analysis of the texts of store interview and the customers 'past experiences. The interest of an individual customer for each topic is estimated based on the obtained topic group. The estimated interests help stores to determine what stories they should provide for each customer. In order to verify the validity of the proposed method, an experiment was conducted to reveal differences in interests among consumers as well as differences in the information provided by the store and the interests of each customer. From the experiment, it was found that when there are multiple topics in the information by the store, the interest in the topic is different by each subject. Additionally, it was demonstrated that the customer may be interested in topics that the store has not included.
Hardware-based approach to guarantee trusted system boot in embedded systems

In recent years, computer systems spread in our daily lives have been experiencing an increasing wave of attacks that disrupt their normal operation or leak sensitive data. Some of them, so-called I/O attacks, are performed by malicious peripherals that perform read or write accesses to DRAM memory through unauthorized DMA (Direct Memory Access) requests during the system boot. During this period the system is particularly vulnerable, which allows hackers to make use of peripherals to perform malicious accesses to DRAM memory. As consequence, it degrades system security. In this context, this paper presents a hardware-based secure-system boot sniffer (SBS) Watchdog to prevent I/O attacks during system boot. The SBS Watchdog is tightly connected to the PCIe communication bus to prevent peripherals from accessing DRAM memory during the BIOS execution and operating system loading, i.e., during the system boot. Compared to existing approaches, the proposed technique is fully transparent to the user, can be applied to any operating system, induces no performance degradation since the SBS Watchdog is turned-off after system boot, and cannot be unintentionally disabled by users during system configuration procedure. The proposed technique was implemented in a commercial FPGA. Practical experiments have demonstrated the high effectiveness of the proposed technique to prevent I/O attacks during system boot.
An Overview of Visualization Techniques: A Survey of Food-Related Research

The variety of food including food choice and preference, food production, dietary intake and other have been done by previous researchers. Other than that, the implementation of visualization techniques has been used in visualizing the result and its findings regarding on food-related research. However, there is lack of understanding towards food-related research in Malaysia. Furthermore, the graphical view that had been illustrated is very limited which lead into the difficulty of understanding the pattern of food-related studies. Therefore, this paper purposely is to do an overview regarding on the visualization techniques that have been used in displaying the results and its finding. The methods that have been used in this study is by constructing the comparison table of visualization techniques such as tree map, heat map, canonical map, word cloud and semantic tree. The classification of visualization techniques also has been differentiated into four type of structures of visualization which are hierarchical, relational, textual and spatial. The aim of this study is to analyze the variety of visualization techniques. This study also is to identify the criteria of each visualization techniques such as size, color, latitude and longitude, vertices, edges and others. Lastly, this paper produces the conceptual framework of visualization techniques as expected outcome due to assist people in making decision in order to get the suitable of visualization technique based on the criteria that matches with its requirement.
Signed Distance Based Reconstruction for Exploration and Change Detection in Underground Mining Disaster Prevention

This publication describes an application of a Truncated Signed Distance Mapping approach for disaster intervention in underground mine shafts through geometrical change detection of the shaft walls. The paper describes two main problems of such an approach (aligning two potentially huge point clouds and automatic change detection by comparing the reconstructed volumes) and explains in detail the proposed solution.
Fundamentals of Ethical Hacking and Penetration Testing

This workshop provides the fundamental concepts of Ethical Hacking with hand on practice on many Penetration Testing tools. The workshop is organized mainly in two sessions; Theoretical concepts and Practical working on Ethical Hacking tools (Hands on Labs). The hands-on practical labs involve Reconnaissance, Scanning, Vulnerability Assessment, Web Application attack (SQL injection) and malware injection concepts that eventually give the participants a real time exposure of a complete cyber-attack process and recommendations for avoidance. The participants will be provided with all Ethical Hacking tools kit, which they can use for, further learning and improve their security analysis skills.
Model Selection for Multi-directional Ensemble of Regression and Classification Trees

Multi-directional ensembles of Classification and Regression treeS (MERCS) extend random forests towards multi-directional prediction. The current work discusses different strategies of induction of such a model, which comes down to selecting sets of input and output attributes for each tree in the ensemble. It has been previously shown that employing multi-targets trees as MERCS component models helps reduce both model induction and inference time. In the current work, we present a novel output selection strategy for MERCS component model that takes relatedness between the attributes into account and compare it to the random output selection. We observe that accounting for relatedness between targets has a limited effect on performance and discuss the reasons why it is inherently difficult to improve the overall performance of a multi-directional model by altering target selection strategy for its component models.
A Framework for Web Archiving and Guaranteed Retrieval

As of today, ‘web.archive.org’ has more than 338 billion web pages archived. How many of those pages are 100% retrieval. How many of the pages were left out or ignored just because the page doesn’t have some compatibility issue? How many of them were vernacular language and encoded in different formats (before UNICODE is standardized)? If we are talking about the content-type text. Consider other mime types which were encoded and decoded with different algorithms. The fundamental reason for this lies with the fundamental representation of digital data. We all know a sequence of 0 s and 1 s doesn’t make proper sense unless it is decoded properly. At the time of archiving, the browsers which could have rendered properly might have gone obsolete or upgraded way beyond to recognize old formats or the browser platforms could have been upgraded to recognize old formats. We studied various data preservation, web archiving related works and proposed a new framework that could store the exact client browser details (user-agent) in the WARC record and use it to load corresponding browser @ client side and render the archived content.
PsyRTS: a Web Platform for Experiments in Human Decision-Making in RTS Environments

This paper presents PsyRTS: an open-source web-platform designed to create psychological experiments using a dynamic environment based on real-time strategy games. This platform has characteristics present in Real-Time Strategy (RTS) games and allows the researcher to manipulate variables regarding visibility, resource availability and presence of other agents while at the same time enabling human participation through existing online platforms.
The Terminology of Borrowing

This article specifically addresses electroacoustic music compositions that borrow from existing musical and sound resources. Investigating works that borrow and thrive upon existing sound sources presents an array of issues regarding terminology, authorship and creativity. Embedding borrowed elements into new electroacoustic music goes beyond the simplicity of ‘cut and paste’ as composers approach this practice with new and novel techniques. Musical borrowings have been widely studied in fields of popular and classical music, from cover songs to quotations and from pastiches to theme and variations; however, borrowings that take place within the field of electroacoustic music can be less clear or defined, and demand a closer look. Because the components and building blocks of electroacoustic music are often recorded sound, the categories of borrowing become vast; thus incidences of borrowing, in some shape or form, can appear inevitable or unavoidable when composing. The author takes on this issue and proposes a new framework for categorising borrowings as a helpful aid for others looking to sample in new compositional work, as well as for further musicological study. The article will consider the compositional process of integration and reworking of borrowed material, using a repertoire study to showcase the variety of techniques in play when sound materials change hands, composer to composer. Terminology already in use by others to describe sound borrowing in electroacoustic music will be investigated in an effort to show the multitude of considerations and components in action when borrowing takes place. Motivations for borrowing, borrowing types, borrowing durations, copying as imitation, and composers’ reflections upon borrowing will all be considered within the article, along with discussions on programmatic development and embedding techniques. At the heart of this article, the author aims to show how widespread and pervasive borrowing is within the electroacoustic repertoire by drawing attention to varieties of sound transplants, all considered as acts of borrowing.
TumbleBit++: A Comprehensive Privacy Protocol Providing Anonymity and Amount-Invisibility

Since the advent of bitcoin, the privacy of bitcoin has become a hot issue. Many coin mixing protocols guarantee the anonymity and unlinkability of the payer and payee of a transaction. However, due to the publicity of blockchain, the confidentiality of transaction amounts has not been provided. Everyone has the chance to get the amount of a transaction, which poses a challenge to the privacy of users. To overcome the problem, we propose an improved mixing protocol based on TumbleBit, which is named TumbleBit++. TumbleBit++ combines confidential transactions with centralized untrusted anonymous payment hub, and achieves the protection of transaction amounts without undermining the anonymity of TumbleBit. TumbleBit++ allows multiple payers to trade in different transaction amounts, and Tumbler, as an untrusted third party, does not know the exact amount of each transaction and the flow of funds between the payer and payee of one transaction.
Leadership: Key Challenges and Methods of Development

The Department of "Social processes, media and advertising technologies" conducts active research in the field of leadership technologies in such areas as politics, advertising and public relations, pedagogy. The round table highlighted the following points: first, the modern interpretation of the leading theories of leadership, the phenomenon of leadership in the minds of civil society, the disclosure of the nature of leadership in the new social and economic conditions; secondly, the role of leadership in politics, new forms of political PR campaigns and a number of other topics. Thirdly, in our opinion, the area of advertising and public relations is interesting, in which leadership is actively developed and implemented in the communication belt of opinion leaders in social media, influencers, bloggers, columnists – the new leaders of the new generation of Russian society. And, finally, fourth, the actual direction of implementation of leadership tools in the field of education and specifically in higher education was discussed.
The linear complexity of new q-ary generalized cyclotomic sequences of period pn

In this paper, we study the linear complexity of new q-ary generalized cyclotomic sequences of length p n over the finite field of order q. We show that these sequences have the high linear complexity when n ≥ 2. These sequences are constructed by new generalized cyclotomic classed prepared by X. Zeng at el.
Hindi and Punjabi Continuous Speech Recognition Using CNSVM

CNNs are playing a vital role in the field of automatic speech recognition. Most CNNs employ a softmax activation layer to minimize cross-entropy loss. This layer generates the posterior probability in object classification tasks. SVMs are also offering promising results in the field of ASR. In this article, two different approaches: CNNs and SVMs, are combined together to propose a new hybrid architecture. This model replaces the softmax layer, i.e. the last layer of CNN by SVMs to effectively deal with high dimensional features. This model should be interpreted as a special form of structured SVM and named the Convolutional Neural SVM. (CNSVM). CNSVMs incorporate the characteristics of both models which CNNs learn features from the speech signal and SVMs classify these features into corresponding text. The parameters of CNNs and SVMs are trained jointly using a sequence level max-margin and sMBR criterion. The performance achieved by CNSVM on Hindi and Punjabi speech corpus for word error rate is 13.43% and 15.86%, respectively, which is a significant improvement on CNNs.
Security Analysis of Compressed Encryption With Sparse Matrices Against Chosen Plaintext Attacks

In this paper, we study the security of a compressed sensing (CS) based cryptosystem that encrypts a plaintext with a sparse measurement matrix. The secret matrix is constructed by a bipolar keystream and a random permutation, and renewed at each encryption. The CS-based cryptosystem performs efficient encryption with a small number of nonzero entries in the matrix, and guarantees reliable decryption for a legitimate recipient. Through a quantitative analysis, we demonstrate that the CS-based cryptosystem achieves the security against a chosen plaintext attack (CPA) with overwhelmingly high probability, by showing that an adversary needs to distinguish a prohibitively large number of candidate keystreams.
CAT Tool: A Technical Wonder or A Vase?---An Empirical Study of CAT Tools

Based on an empirical study of computer assisted translation (CAT) tools, this paper proves that CAT tools are feasible, efficient and convenient, though they have limitations in usage and problems for beginners. In a word, CAT tools are technical wonders which can help translators do numerous things to reduce their work load.
Efficiency of the organization of personal choice in the development by the students of the methods of creative activity

The reasons for introducing opportunity of a choice in lesson structure as an intrinsic feature of teacher personality has been represented. And it demands relevant organization in the process of professional training. The correlation between the readiness of a teacher to act under conditions of uncertainty and the readiness of students to choose ways of creative activities has been substantiated. The basic aspects of organization of students' personal choice of ways of creative activities, for example, heuristic tasks, have been examined. The factors, which raise or lower the effectiveness of organization of that choice, have been highlighted.
Optimal Multiple Assignment Schemes Using Ideal Multipartite Secret Sharing Schemes

A multiple assignment scheme (MAS) is a method to construct secret sharing schemes (SSSs) for general access structures. There are MASs using threshold and ramp SSSs. The paper proposes new MASs using ideal SSSs realizing compartmented access structures and those using SSSs realizing multi-level access structures. Since the ideal SSSs realizing compartmented access structures and SSSs realizing multi-level access structures are natural generalizations of threshold and ramp SSSs, respectively, the new MASs cannot be less efficient than those using threshold or ramp SSSs.