Reviewerly/exHarmony huggingface dataset:
Design a Secure Communication Technique using Concepts of Image Steganography and Cryptography

With increase in the data transmission and data exchange in daily life, along with the increasing data thefts and other adversary snooping, the security and privacy of data has become an important thing to be concerned about. Throughout the years many different approach, ideas, algorithms and protocols have been developed to protect the data from snoopers and adversaries and ensure secure communication. Every approach has its own pros and cons. This project is an attempt to combine two different approaches of data protection- (Steganography and Cryptography) to develop a powerful and secured method for data exchange.
Invisible Boundary in Design, Emotional Safety Net

As our society gets complicated and faster than ever, we have much relied on technologies in order to keep our lives intact. Moreover, new technologies are introduced every day and every month, and it is hard for the users to keep up with the technologies. This phenomenon contradicts between the users and trends, the users seek out better technologies, simultaneously, and the users tend to keep their old habits and are harder to be swayed to get into a new system than times with fewer technologies involved in because the users tend to stay in their comfort zones in scope of artifact. Nowadays, interconnected and interrelated technologies make even harder for the users to move to the new system. The comfort zone from the users plays a big role in not only design, but also marketing field, brand loyalty. Once the users create their comfort zone in scope of artifact, the users establish “emotional safety net” in scope of artifact. This paper will define, “emotional safety net in design” and explore how to incorporate to design process.
Design Algorithm for Empathy

Emotional design is a design theory that takes the emotional values/effects of users into consideration in the design process. This theory has been devoted to the appearance of the final product with integration of an emotion and/or interactivity. Emotional design is closely related to user behavior research as it often incorporates physical interactions with the users. However, the study of the user behavior is often limited to the instant interaction with the users without consideration of cultural and social behavior. The article, Design Algorithm for Empathy, emphasizes a more systemic approach within the emotional design process, which should involve strategies created from service design research and that of sociology. The paper argues that the execution of emotional design should involve a significantly more comprehensive research on cultural and social sources. The article is also concerned with the focus merely on the appearance of the design, which may result in superficial and temporary problem solving tactics rather than a more sustainable strategy.
Supervised Re-identification: Optimizing the Annotation Effort

Supervised distance metric learning-based methods for person re-id are specifically popular because of their robustness toward large color variations and fast training speed. However, like other supervised methods, metric learning algorithms have their own burden of human labeling effort especially for large camera networks. The total number of training pairs assumed to be available by these algorithms increases tremendously with network size and number of persons in each camera. Manual labeling of such huge number of pairs is a tedious and expensive process. So, naturally a question arises: given a camera network, can we come up with a strategy of choosing a minimal subset of image pairs for labeling without compromising on recognition performance? This is a problem of considerable significance in the context of person re-id in multi-camera networks, especially in larger ones.
Information supervisory model for financial risk prevention and control based on twin-SVM

Aiming at the problems of poor effect and low accuracy of traditional financial market risk prevention and control methods, a financial risk prevention and control information monitoring model based on double support vector machine is proposed. The improved support vector machine and asymmetric COvAR data were combined with covariance operation to reduce the actual tail risk overflow. Through financial aggregation and covariance tail data of current characteristic financial system, the spillover effect of financial risk is obtained. According to the extreme value statistical analysis theorem, it is determined that the current financial risk gradually obeys the extreme value. In order to verify the effectiveness of the method, the financial data from 2012 to 2018 provided by a bank were used as experimental samples to conduct simulation experiments. Experimental data show that the proposed method has lower boundary cost and higher market arbitrage, and has a strong market applicability.
Commit-reveal strategy to increase the transaction confidentiality in order to counter the issue of front running in blockchain

Blockchain technology has certainly revolutionized the world. A decentralized approach devoid of a centralized server/third party access provides complete anonymity, security, and integrity. But still, there is some vulnerability in Blockchain, one of them we discussed in this paper is Front-Running. Front running is mostly exercised in the stock market sector where the traders try to front-run to gain more profit. Now as blockchain technology is flourishing at an immense rate, new forms of front running techniques have been encountered. Thus creating transaction vulnerabilities. In this paper, we explained the front-running issues on MCS-Dapps deployed on Ethereum Blockchain. Furthermore, we will study its working on the system and how it affects clients and miners on their gas prices. Then we implemented a commit-reveal strategy to increase the transaction confidentiality to counter the issue of front running.
Reducing Video Transmission Cost of the Cloud Service Provider with QoS-Guaranteed

With the advancement of cloud computing technology, many service providers are combining with cloud service providers to build a highly available streaming video-on-demand cloud platform and provide video services to end users. Generally, cloud service providers deploy many edge cloud CDN nodes in different geographic areas and provide video services to end users. However, when an end-user wants to watch certain videos and request video resources from surrounding edge cloud CDN nodes, the edge cloud CDN node will request missing video clips from other cloud nodes. Therefore, this will generate a large amount of additional video transmission costs and reduce the quality of service of the cloud service provider. To reduce or even minimize the video transmission cost of edge cloud CDN nodes while ensuring the quality of service (QoS). We designed a video transmission algorithm called Netdmc to ensure transmission quality. The algorithm can be divided into two parts. The first part is a low-latency video request algorithm based on ensuring service quality, and the second part is a video request algorithm based on minimizing video transmission costs. The simulation results demonstrate that the Netdmc algorithm can effectively reduce the cost of cloud service providers and ensure the quality of video services.
Development of methods formalisation subject technology design of multimedia edition

This article discusses a step-by-step methodology that allows, based on the holarchic model, to formalise the initial stages of subject technology for creating multimedia products. For this, the method of systems analysis and a systems approach were used. A procedure is proposed for searching for dominant (critical) connections between adjacent criteria for assessing the quality of multimedia publications, which makes it possible to optimise connections in the representation of the initial graph. A procedure for quantitative assessment of the relationships between related criteria for assessing the quality of multimedia publications is presented. The results of the assessment allow the multimedia developer to identify the most important ones among the group of dominant connections, which should be paid attention first of all. The result of the research is a mixed model for quantifying alternative versions of multimedia publications.
Manufacturing Cost-reduction Algorithm Through Correlation analysis Between Process-specific Unit Cost Data of FOM Data File Set and Machine-cutting Tool Life

It have been composed only introducing automatic equipment without analyzing manufacturing process in many smart factories; hence, productivity does not improve significantly. In this study, production data obtained from a computerized NC machining center were analyzed based on the operation and tool costs for each process to reduce the tool management cost of the cutting process for the die manufacturing, which is weak in cost analysis. Tool cost of the minimum cost required for each process was then modeled using linear programming. We derived a modeling logic that could manage the life of the medium cutting process using the programming method and achieved a cost reduction of up to 12% compared to the operation cost by linking tool cost to the manufacturing per process cost. The proposed algorithm can be applied to field equipment as a manual cost item one of the items of the FOM which manufacturing optimization system.
Deep Learning Network-Based End-to-End Scene Text Spotter for Korean Characters

Scene text spotting detects text boxes and recognizes the words from scene images in an end-to-end way. Existing studies proposed scene text spotters for English and showed their promising performance. However, studies for non-English text spotter are rarely conducted because of a lack of fine-quality labeled training datasets. In particular, Korean text spotting is considered to be harder than English because of a large number of characters in Korean. In this study, we propose an end-to-end scene text spotting network specialized in Korean texts that can read more than 2,300 characters. To overcome the lack-of-dataset problem, we propose using a transfer learning method. By pretraining both modules (detector and recognizer) of the network with multi-language datasets and fine-tuning only the recognizer with the Korean dataset, we could construct the robust scene text spotter. We expect that our work can offer useful learning guidelines to future scene text models for non-English language that does not have sufficient training datasets.
FOMs-Based Smart Manufacturing Innovation Methodology: Based on OKR Techniques

Changes in manufacturing sites triggered by the fourth Industrial Revolution are inadequate from the perspective of SMEs. The construction of 30,000 smart factories can be achieved quantitatively, but it is necessary to upgrade data-based smart factories. However, SMEs lack manpower and are managed manually by Excel. Therefore, difficulties in systematic goal establishment and manufacturing innovation activities can be solved through smart-factory operation management (FOMs)-based smart manufacturing innovation methodology. Three differences in above technology can be ascribed from the existing innovation methodology. Firstly, there is systematization of goal establishment by objective & key results (OKR). Secondly, change in management of 4M (man, machine, material, method) occurs through a multidimensional analysis by FOMs system. Thirdly, it is improving its level with continuous execution and feedback. The objective is to align with the goals of the manufacturing company to focus on them and quickly track them to challenge them.
Exploring the Perception and Experience of Middle School Teachers on Teacher Competency after COVID-19 Pandemic

The purpose of this study is to identify the perceptions of middle school teachers" teacher competency after COVID-19, and reveal the activating and hindering factors necessary for strengthening teacher competency. In order to achieve the purpose, a focus group interview was conducted with 18 middle school teachers in B city, and the interviews were analyzed using the NVivo12 program. The result is as follows: Firts, the essential competencies for teachers after COVID-19 can be classified into teaching competency, student guidance competency, community competency, and ICT competency. The second theme is opinions on "response for the required teacher competence building", including communication and collaboration with fellow teachers, professional learning community, and utilization of various contents. The third theme is a plan for future competency building, and based on the results of this study, implications were presented so that the design and operation of teacher competency reinforcement policies could be implemented practically and effectively.
Designing Service System Prototype Workshops

The design focus is shifting from industrial projects to service systems due to the service economy. The research on designing a service system becomes more important. The difficulty of designing a new service system is how to share values among many stakeholders involved. The scope of design becomes wider, more complex and including more interactions among various stakeholders. Because of these shifts, the new research on service system design is emerging. In this paper, a service system design framework is proposed, which is based on two viewpoints, such as systems with value sharing condition and scopes of system layers. Using the framework, a service system prototype workshop is designed and executed. The paper shares the evaluation result and findings.
Discourse Analysis of Voice-Based Computer-Mediated Communication in Distributed Work

Computer-mediated remote voice communication among care providers is analyzed by a newly proposed discourse model. Nursing and care are central to healthcare services, and improvements in the quality and efficiency of healthcare processes are important issues. Effective communication support that does not interfere with normal operations is an urgent need. Here we examined a “smart voice tweet system” intended to overcome this problem by supporting collaboration across different sites. We field-tested this system at an elderly care facility in Japan for 5 days in 2013. We observed the activities of groups of care workers during residents’ lunch and dinner and collected data on use of the system. By discourse analysis based on the proposed model, we confirm the validity of the voice tweet system as a support for remote voice communication among staff collaborating to achieve tasks. The findings can be used to design rules for estimating the ongoing status of collaborations and classifying voice tweets by purpose to decide whether they should be sent or just recorded. This research is based on joint research conducted by the Toshiba Corporation, the Shimizu Corporation, and the Japan Institute of Science and Technology with support from the Japan Science and Technology Agency.
Correction to: Continuous–discrete filtering techniques for estimating states of nonlinear differential–algebraic equations (DAEs) systems

A Correction to this paper has been published: 10.1007/s40435-022-00955-z
Term-Level Taxonomy Generation

We proceed to discuss term-level taxonomy generation in this chapter. Different from topic-level taxonomies, each node in a term-level taxonomy is a single term (or a set of synonyms) representing a specific concept. Term-level taxonomies are important to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most term-level taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the “is-a” relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this chapter, we present a term-level taxonomy generation method named HiExpan. It constructs a task-guided term-level taxonomy from a domain-specific corpus, and allows users to input a “seed” taxonomy as the task guidance.
Finding Claims

Unlike many of the standard tasks in NLP, argumentation mining is not a single unified process, but a constellation of subtasks, which are of different prominence depending on the goals of the underlying target application. For a (hypothetical) example, in order to obtain the gist of a Twitter conversation, it can be sufficient to extract claims and purported evidence material. By contrast, in order to run a deep analysis of argumentation strategies in left-wing vs. right-wing newspapers, the editorials ideally need to be mapped to a complete constellation of statements, their argumentative relationships, and the reasoning patterns underlying those relationships.
Introduction

In this first chapter, we begin (Section 1.1) by studying one of the common definitions of argument and explaining it point by point. This is supplemented by outlining further central aspects of argumentation, viz. the dimensions of logos, ethos, and pathos, and the concept of the ‘critical discussion’ as a fundamental idealized context for activities of arguing. We then move to the applied side of computational linguistics, and provide a definition of argumentation mining in Section 1.2, followed by an outline of reasons for studying automatic argumentation mining in Section 1.3. We finish the chapter by explaining the structure of the book in Section 1.4.
Assessing Argumentation

While finding argument components and possibly arranging them in a tree or graph structure constitute the core tasks of argumentation mining, there is a small body of research aiming at going further and computing ‘deeper’ information about arguments. The first to be considered here is the problem of detecting enthymemes, which amounts to construing the (logically) missing premises for an argument to make sense. Similarly, one can try to identify the underlying reasoning pattern or argumentation scheme (cf. Section 3.3), which explains why (or not) a particular constellation of inferences works. Finally, we discuss work on the quality of arguments, which encompasses both measuring how ‘good’ an argument is, as well as a set of various other attributes that researchers have drawn attention to.
Usability Study of Auditory CAPTCHA

CAPTCHA is a security system to distinguish whether a user is a human being or an automated program by asking questions that are hard for artificial intelligence yet relatively easy for human to answer. Two most popular forms of CAPTCHAs are text and audio; this study attempts to explore the latter one, which is common in situation where visual interaction is not applicable, such as in voice-based interaction or for visually challenged users. Auditory CAPTCHAs can be breached by content analysis and guessing through Automatic Speech Recognition (ASR), it is then necessary to blend certain level of interference to counterattack. However, by doing so, auditory CAPTCHAs have became too hard to human being to solve. Solving auditory CAPTCHAs is akin to Cocktail Party Effect, which refers to our ability to process main audio signals preferentially and ignore other irrelevant ones in noisy environments. This study explores the current designs of auditory CAPTCHAs to see how well our “cocktail party ability” performs when interacting with different CAPTCHA designs. An experiment with repeated measurement factorial design is conducted; thirty-six participants take part. The main signals, or the signals to be processed, are pronounced either by random male speaker (RMS), random female speaker (RFS), or mixed speaker (MS); while the interference signals, or the signals to be ignored, are pronounced either by random male (RMN), random female (RFN), or mixed noise (MN). Fifty percent of the interference contents sound similar to the main contents, while the other fifty percent are normal conversation noises. Error rates and subjective preferences are collected during the experiments. Results show that sound similarity is problematic; the error rates are significantly higher than its counterpart. The combination of RMS and RFN has significantly lower error rate due to greatest pitch difference; our participants also prefer this one for its relative easiness. On the other hand, for combination of RMS and RMN, the error rates are significantly higher and the preference scores lower. The results have important implications for auditory CAPTCHA design.
Digital Human Model Applied to Training and Education in Sports

The purpose of the study is to present a 3D digital interactive environment that is being developed in a game engine software to work with 3D DHM applied to training and education on Sports. This platform is being developed considering the need to analyze data from the same athletes' movements being repeated in different time or even to compare athletes' movements with different skill levels. The main 3D digital platform advantage is its flexibility to handle motion capture data from different MOCAP systems in order to facilitate kinematic analysis by users of low cost motion capture systems. Another important advantage is its portability that allows it to be used in different hardware platforms, as tablets and cell phones. The 3D platform development followed some specific steps, which make it possible not only to visualize the performed motion but also make the interaction between the user and the 3D character. The first step consists on the automatic reconstruction of the 3D character body segments based on motion capture data. The visual representation has as benefit that reduces noise that may be generated in the process of retargeting the motion capture data to a specific rig and character that differs from the actual bone structure original data. The visual representation is generated based on laser scanning data. This makes the representation to be a precise copy of the original bone position and structure of the athlete' specific movement that is being captured. The second step is to link each bone segment by generating a 3D model with a collision area that is necessary for future interaction with the user. After those steps, the user can select to track and generate data of a specific body segment; to play/pause the athlete movement and to draw graphs of segmental angles, joint angles and angular velocity. This functionality is still under development and test. The first application of the 3D digital platform was the movement analysis of high and low skill level Jiu-Jitsu athletes. This analysis allowed an improvement on the athletes’ performance and skills. In the future the integration between the 3D scanned athlete’s model and a virtual environment will allow to develop a virtual simulator that can be applied to education, training and entertainment.
Decision-making Scheme Using Prisoner’s Dilemma: Fraud Detection in Financial Transactions

A digital Instrument and Control (I&C) system has been developed and installed in nuclear power plants, and due to installation of the digital I&C systems, Cyber security concerns are increasing in nuclear industry. In this paper, cyber threats of I&C system were analyzed in aspect of system confidentiality, integrity and availability (CIA). Since Cyber security for all digital assets is not […]
A Game Theoretical Approach for Cyber Security Awareness

This research presents the design and evaluation of cyber attackers as a game simulated approach that teaches and alerts users about the activities of attacker-defender in cyberspace. Game theory provides a foundational approach for network security investigation and simulation. What motivated this research was, various techniques have been developed for modelling the interactions between internet […]