Reviewerly/exHarmony huggingface dataset:
A Virtual Confucius Chatbot

In this chapter, we describe a virtual Confucius chatbot that reacquaints users with the teachings of Confucius through conversations. The chatbot can analyse the philosophical intent of the user and generate deep and meaningful answers based on Confucianism. Confucianism is commonly defined as “...a system of philosophical, ethical and political thought based on the teachings of Confucius”, which originated through the teachings of Confucius during the 6th Century BCE. It is a way of life or a philosophy of human nature that considers human relationships as the foundation of the society. Confucius teachings has highly influenced the development of several cultures in Asia, making Confucianism an intangible cultural heritage. In this research, we explore ways to apply Natural Language Processing to model the knowledge and teachings of Confucius, through natural conversations between human and computer. Our study revealed that users find their experiences of interacting with the virtual Confucius very encouraging and enjoyable. We also found that this chatbot has the benefit of improving intergenerational relationships between parents and children through shared play. It can also act as a virtual companion or mentor to users by providing them with personalised advice and encouragement through cultural and philosophical conversations.
WHAT THE PROFESSION OF CYBERSECURITY NEEDS TO KNOW AND DO

Presently, 71% of annual losses are due to failures in the physical and human attack domains, while electronic breaches account for roughly 29%. While the lowest percentage of losses (29%) falls into the area of the classic technology-based attacks, unfortunately these are often the only kind of attacks factored into an organization’s cybersecurity planning. Surprisingly, in most organizations, human or physical types of threats are simply not part of traditional cyberdefense thinking. Most active cyberdefense solutions do not consider embodying integrated and well-defined behavioral controls into the cybersecurity process. And as a result, well executed attacks against the non-electronic attack surface are almost certain to succeed. We argue that the profession must find ways to ensure that the real-world practice of cybersecurity involves the creation and adoption of a complete, correct, and highly effective set of well-defined and commonly accepted controls; ones that are capable of closing off every feasible type of adversarial action. To be completely effective, the solution must amalgamate all of the essential concepts of cyberdefense into a single unifying practice model, one that has real-world currency. Professional societies help to serve as the developers and sanctioners of the fundamental ideas in their respective fields and the creation of the CSEC2017 document provides an authoritative statement of the elements of the field of cybersecurity for a broad array of practitioners. This paper discusses the CSEC2017 thought model and outlines the eight knowledge areas specified for the discipline to represent the complete body of knowledge within the field.
Morphological comparison of signals and its application for estimating signal lag time

Mathematical methods are proposed for comparing the shape of signals with amplitudes distorted by unknown monotonic transformations. These methods are based on the solution of the problems of the best approximation of the signal under analysis to signals of a specified class, and used in estimating the lag time of one fragment of signal with respect to another. Such problems arise, for example, in determining the direction of propagation of a sound wave in the atmosphere. The solution to this problem is based on the assumption that the conditions of the signal detection are different at different spatial points; as a result, the measured signals differ, not only in their lag time but also in nonlinear distortions, such that only the general sape of the signal is preserved: if the amplitude of one signal is the result of a strictly monotonic transformation of the amplitude of another signal, then their shapes are equivalent. In addition, the measurements are accompanied by an additive noise with an unknown dispersion.
An Evaluation of OCR Systems Against Adversarial Machine Learning

Optical Character Recognition (OCR), while representing a significant progress in the field of computer vision can also contribute to malicious acts that imply automation. As an example, copycats of whole books use OCR technologies to eliminate the effort of typing by hand whenever a clear text version is not available; the same OCR process is also used by various bots in order to bypass CAPTCHA filters and gain access to certain functionalities. In this paper, we propose an approach for automatically converting text into unrecognizable characters for the OCR systems. This approach uses adversarial machine learning techniques, based on crafting inputs in an evolutionary manner, in order to adapt documents by performing a relatively small number of changes which should, in turn, make the text unrecognizable. We show that our mechanism can preserve the readability of text, while achieving great results against OCR services.
Comparison Of Reversible Image Watermarking Methods Based On Prediction-Errors

This study compares two reversible image watermarking algorithms applied to a digital image. The first algorithm is a method based on adaptive watermarking of prediction-errors. With this method, as in the existing reversible image watermarking methods, pixels are embedded in two different ways according to the values of prediction-errors instead of applying the same watermarking algorithm to all pixels in the imageAccordingly, the image pixels are divided into two blocks as uniform and non-uniform. The watermarking capacity has been increased by embedding 2-bit to specific pixels of uniform blocks and 1-bit to specific pixels of non-uniform blocks. The second algorithm is a method based on interpolation errors. In this method, interpolation error is utilized. The two methods compared with each other were compared with the existing methods in the literature by using computer simulations, and superior and weak aspects of the methods were determined.
RGAM: An Architecture-Based Approach to Self-management

Software systems must self-adapt to changes in their execution environment and in the user requirements and usages. An architectural solution enables tackling more effectively problems related to self-adaptation. This paper presents an architecture-based approach to self-adaptation that relies on architectural reflection and on closed-loop principles. The approach has been developed and tested on a real scenario related to the management of emergencies in case of floods.
Not Only Slurs. A Pragma-Rhetorical Approach to Verbal Abuse

The topic of this paper is the complex phenomenon of verbal abuse. The aim is to show the productivity of a pragma-rhetorical approach in addressing this issue. Indeed, integrating modern pragmatics with classical rhetoric, this approach does not isolate words from social practices and therefore is not exclusively focused on slurs (as is the case in the current debate on this topic in the field of the philosophy of language) but it concerns verbal abuse in general. I argue that this wider approach, especially thanks to its focus on the intertwining of linguistic and social components, is more adapted to shedding light on the role that words play in performing violence. Particularly, I take into account three (interrelated) controversial issues: (1). the question of whether intrinsically offensive words exist; (2). the problem concerning the existence of the so-called “neutral-counterpart” of a slurring word; (3). the reclamation or rehabilitation of a slur. Finally, I briefly focus on the case of the N-word as a paradigmatic example able to show the advantages of the pragma-rhetorical approach.
A Multicriteria Group Decision Support System

In the present study, the authors propose a group decision support system (Web-GDSS), which allows multi-agents systems and multicriteria analysis systems to help decision-makers in order to obtain a collective decision, using web services. The proposed system operates on two main stages. First, decision-makers are in a different location away from each other. They must store their location in databases and invoke the appropriate web service. Second, in the case of negotiation between decision-makers, monotonic concession protocol will lead to an agreement using CONDORCET and BORDA voting methods.
Multiple Support Vector Machines for Binary Text Classification Based on Sliding Window Technique

Supervised machine learning algorithms, such as support vector machines (SVMs), are widely used for solving classification tasks. In binary text classification, linear SVM has shown remarkable efficiency for classifying documents due to its superior performance. It tries to create the best decision boundary that enables the separation of positive and negative documents with the largest margin hyperplane. However, in most cases there are regions in which positive and negative documents are mixed due to the uncertain boundary. With an uncertain boundary, the learning classifier is more complex, and it often becomes difficult for a single classifier to accurately classify all unknown testing samples into classes. Therefore, more innovative methods and techniques are needed to solve the uncertain boundary problem that was traditionally solved by non-linear SVM. In this paper, multiple support vector machines are proposed that can effectively deal with the uncertain boundary and improve predictive accuracy in linear SVM for data having uncertainties. This is achieved by dividing the training documents into three distinct regions (positive, boundary, and negative regions) based on a sliding window technique to ensure the certainty of extracted knowledge to describe relevant information. The model then derives new training samples to build a multiple SVMs based classifier. The experimental results on the TREC topics and standard dataset Reuters Corpus Volume 1 (RCV1), indicated that the proposed model significantly outperforms six state-of-the-art baseline models in binary text classification.
CONSENSORS: A Neural Network Framework for Sensor Data Analysis

Machine breakdowns in industrial plants cause production delays and financial damage. In the era of cyber-physical systems, machines are equipped with a variety of sensors to monitor their status. For example, changes to sensor values might indicate an abnormal behavior and, in some cases, detected anomalies can be even used to predict machine breakdowns. This procedure is called predictive maintenance, which pursues the goal to increase machine productivity by reducing down times. Thereby, anomalies can be either detected by training data models based on historic data or by implementing a self-learning approach. In this work, the use of neural networks for detecting anomalies is evaluated. In the considered scenarios, anomaly detection is based on temperature data from a press of a machine manufacturer. Based on this, a framework was developed for different types of neural networks as well as a high-order linear regression approach. We use the proposed neural networks for restoring missing sensor values and to improve overall anomaly detection. An evaluation of the used techniques revealed that the high-order linear regression and an autoencoder constitute best practices for data recovery. Moreover, deep neural networks, especially convolutional neural networks, provide the best results with respect to overall anomaly detection.
Quality Assurance Framework in the Field of Education Including Objective Method for Assessing Learning Outcomes Based on Three Policies and JABEE Criteria

A framework for quality assurance of engineering education is proposed including an objective method for assessing learning outcomes. The framework is based on the three policies for the quality assurance of higher education in Japan, and follows the criteria of JABEE. The proposed framework consists of, (1) quality assurance by the JABEE accreditation, (2) information on the educational system of the program, (3) information on the measuring methods of each student’ s score and achievement level of the leaning outcomes, and (4) showcase-portfolio depicting the evidence for the achievement level of the leaning outcomes.
Learning IP network representations

We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a meanbased approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security.
Analyzing Pipe Production Fault Rates by Association Rules and Classification According to Working Conditions and Employee Characteristics

In order to survive in a competitive environment, companies are required to increase the productivity by identifying the factors that affect the failure rates. In this study, fault rates of a pipe manufacturing company are investigated. Therefore, based on data attributes such as demographic characteristics of employees, employee training, physical working conditions, social facilities etc. were gathered. Briefly, data were analyzed by association rules, correlation, and various classification algorithms. The association rules, measurements, and correlations of attributes are examined to understand the current situation. Then, based on this information, various classification algorithms have been used for estimation. The higher accuracy of the prediction, the greater results will occur. Therefore, the accuracy of the classification algorithms is compared and the algorithm with the highest performance is achieved.
Pauli-based fermionic teleportation with free massive particles by electron-exchange collisions

Fundamental non-locality in quantum mechanical scattering processes is investigated by means of Fermionic teleportation. Our scenario employs free massive particles, electrons and atoms, within a twofold electron-exchange collision approach whereby, in the first collision, also generating the necessary spin–spin entanglement between initially unpolarized electrons and atoms. It is shown that in a second collision an arbitrary spin polarization state of a free electron can be teleported onto the other electron which has never been in contact with the first one. The underlying scheme relies on and is a direct consequence of the Pauli-principle and makes use of a high symmetry in spin space thereby avoiding restoration of the teleported states. The scattering process (teleportation) is highly symmetric allowing for interchanging the electronic and atomic constituents without loss of generality. Re-interpretation of Li and Na data reveals the feasibility and capability of Fermionic teleportation demonstrated by numerical and experimental data for the teleportation fidelity.
A NLP Approach to Software Quality Models Evaluation

This paper aims to analyze and identify the variations and similarities between models/standards in the software quality domain. The approach combines analysis at several levels, starting with a naive comparison done by the software quality expert, going through several NLP specific similarities measures. The final goal is to be able to rapidly identify solutions to make a software compliant with new standard. The focus of the current study is on the lexical analysis of software quality models based on natural language processing.
Pedagogical Preconditions for the Development of Productive Communication Skills within the Special Course “Computers and Banking”

Undergraduate students seeking a degree in Management Studies at non-linguistic universities reveal insufficient level of the development of oral and written communication skills in professional and business communication in English. This underpins the need for more intensive training in productive types of speech by referring to a special textbook (a special course) that takes into account the specifics of professionally oriented training for students. Computers and Banking is one of the possible foundations for building professionally oriented teaching of a foreign language in a non-linguistic university. In this regard, it seems necessary to determine the role and place of our “Computers and Banking” discipline both in the system of professionally oriented teaching of foreign languages in a non-linguistic university in general, and in a number of similar special courses in particular. In this article, a concept of a “speech skill” and the pedagogical preconditions for the development of productive speech skills within the framework of a special course “Computers and Banking” are analyzed, the mechanisms for producing oral and written statements in a foreign language for pedagogical purposes are studied. Based on the analysis of domestic and foreign literature on foreign language teaching methodology and psycholinguistics, textbooks and manuals on economics (banking) and the basics of computer science, special manuals for teaching professional-business communication in English, we established that the discipline “Computers and Banking” possesses a methodological potential for developing productive speech skills, and determined the structure of professional and business foreign language communication of undergraduate students in various fields of their activity.
Fuzzy Simulation of Human Behaviour in the Health-e-Living System

This chapter shows an application of fuzzy set theory to preventive health support systems where adherence to medical treatment is an important measure to promote health and reduce health care costs. Preventive health care information technology systems design include ensuring adherence to treatment through Just-In-Time Adaptive Interventions (JITAI). Determining the timing of the intervention and the appropriate intervention strategy are two of the main difficulties facing current systems. In this work, a JITAI system called Health-e-living (Heli) was developed for a group of patients with type-2 diabetes. During the development stages of Heli it was verified that the state of each user is fuzzy and it is difficult to get the right moment to send motivational message without being annoying. A fuzzy formula is proposed to measure the adherence of patients to their goals. As the adherence measurement needed more data, it was introduce the DisCo software toolset for formal specifications, the modelling of human behaviour and health action process approach (HAPA) to simulate the interactions between users of the Heli system. The effectiveness of interventions is essential in any JITAI system and the proposed formula allows Heli to send motivational messages in correspondence with the status of each user as to evaluate the efficiency of any intervention strategy.
Quality of Service in Dynamic Resource Provisioning: Literature Review

Resource provisioning is major problem in cloud computing because of the rapid growth in demand of resources and these resources are allocated according to dynamic nature of application. Unconstraint use of these resources can lead to two major problems namely under provisioning and over provisioning. Therefore, to implement provisioning is major concern in cloud computing. This paper has discussed and analyzed the methods incorporated in different research papers to understand objectives, performance on various QoS attributes and issues related to current cloud computing environment. This research work also presents details about prior research work, popular factors and future direction in resource provisioning.
Synthesis of Classification Models and Review in the Field of Machine Learning

Classification method is an important technique used in machine learning for predictive analytics. Classification enables business to predict future trends and behaviors of an enterprise with the help of their past data. Classification is a supervised learning model, which is built in twostep process, first building the classification model and second predicting the outcome for unknown data. This chapter describes various classification models by learning mechanisms and categorizes them into different statistical, probabilistic, and heuristic methods, and explains them with example dataset. It also compares these models and their efficiencies with model evaluation techniques and briefs some blended classification models. The goal of this chapter is to provide a comprehensive review of different classification techniques and give a quick refresher on classification models in big data analytics. The comparison of various classification models helps the readers to quickly decide which classification model to choose for the given business scenario.
Building a collaborative communication ethics manifesto

Courses: This activity could be used in courses where persuasion, dialogue, and communication ethics are primary. Thus, a communication ethics course, or course units where students discuss ethics, would be appropriate. For instance, even a human communication course could use this activity to describe ethical treatment of “the other” or a mass communication class can use this activity to discuss ethical power (i.e. the power of technology or news media). This activity could also be modified for use in a public speaking, rhetoric, organizational, or small group communication.Objective: This activity should be used in tandem with the National Communication Association (NCA) ethical credos on communication. In 2014, to mark the 100th anniversary of the NCA, NCA reaffirmed three ethical credos: The Credo for Free and Responsible Communication in a Democratic Society (National Communication Association, 2013); The Credo for Free and Responsible Use of Electronic Communication Network (National Communication Association, 1963); and the Credo for Ethical Communication (National Communication Association, 1999). When training students to think ethically, it is important to help them develop categories and determine declarative statements. Thus, the NCA documents are effective examples of communication manifestos. Ultimately, through collaboration with partners, small groups of students will develop a communication ethics manifesto in a format similar to the NCA examples. Specifically, they will identify ethical dilemmas, in the activity these are called “Urgent 21st Century Communication Ethics Issues,” and determine declarative statements that provide an ethical framework to address the issue. The “big question”: can student groups create a declarative and collaborative manifesto that addresses urgent 21t century communication ethics issues?
Extraction of Frequent Sub-Sequences from Long Sequence Using Neural Network

本稿は与えられた系列データから頻出な部分列を抽出することを目的とした．特に，抽出に際して(1)オンライン学習，(2)複数部分列の抽出, (3)さまざまな長さの部分列の抽出, (4)頻出とするしきい値の調節の4つすべて可能にすることをめざした．提案手法は，スパイキングニューロンを用いた2ブロックからなるニューラルネットワークである．LIFモデルに基づくユニットにより構成し，STDP学習則に基づいた結合荷重の更新を行うことで，自己組織的に部分列を抽出するネットワークを構築する．この結果，1つのSTDPのパラメータを調整するだけで，同じ系列から頻出として抽出する部分列を変化させることができた．具体的には，3,000シンボル長の系列から3シンボル長の部分列（出現頻度は0.4%, 3%, 5%）を抽出した際，3%以上出現する部分列の抽出と5%以上出現する部分列の抽出の切り替えに成功した．