Reviewerly/exHarmony huggingface dataset:
A study on process-based assessment using Google classroom

This study aimed at investigating the effects of Google classroom for Korean EFL high school students. The study was designed to review Google classroom in relation to Process-based assessment. For the study, 100 high school students enrolled in the regular English class for a year. The overall opinion towards educational digital apps including learning management system (LMS) were collected before and after the study. The study concluded with three notable findings. First, Google classroom, a free LMS, offered students more chances to get individual feedback from a teacher and peers. Second, all kinds of learners’ projects were automatically piled up in their ePortfolio so that both the instructor and the learners were able to keep track of their learning process. Third, it encouraged interactions in a classroom by allowing them to work together on a project in a real time. This paper illustrates how Google classroom helped a teacher assess students’ performance in a classroom and how students felt about Google classroom after joining the paperless classroom for a year.
Recognition of Pedestrian Clothing Attributes from Far View Images using Convolutional Neural Network

Attributes of pedestrians is a very important factor when it comes to the task of re-identifying pedestrians or distinguishing one pedestrian from another. Different types of clothing on a pedestrian are more recognizable than other attributes. Recognizing multiple attributes together explores the relationship among them and their contribution towards each other's representation. We address the pedestrian attribute recognition from far view images task as a multi label single class classification problem and approach it with a specialized convolutional neural network model that achieves an overall accuracy of 90.16% for 34 pedestrian clothing attributes including hair as defined in the PETA dataset which consists of far-view images. We compare the recognition accuracy of the attributes that were previously worked with in some of the previous CNN models. Our model outperforms these previous models in the overall accuracy and for more than half of the attributes.
The Storage of Data from TXML document into Temporal Object Relational Database

Extensible markup language (XML) is nowadays one of the most important standards for web information management and complex data representation. Massive Data is still treated, tagged and stored in XML Document. Yet, the most methods for processing XML Data have its difficulties due to the construction of schema-centric and semi-structured information resources of the major portion of existing XML data. Therefore, a huge amount of data treated, transferred and stored using Temporal Database system. There is a need for integrated methods that deal with for the storage of data from XML document into a temporal database. In this paper, we present the results of a research study concerning the migration of structured XML document including temporal data features into Object-Relational database according to the management varying time Data. First, we propose a data Model based on tree graph for tracking historical data in an XML Document for summarizing and indexing temporal XML documents. To achieve this, we constructing an effective index using Dewey Key with additional information structure. In our approach, we develop our own XML storage document and Build the temporal Database system according To object Relational Database concepts. In the next, we will develop our TORDB design scheme in order to simplify the migration of data from XML files into Object relational database implemented with varying time features. In addition, an algorithm is implemented for validating our solution and show that our mapping strategy is feasible and efficient.
Systematic Review on Employee Engagement and its Impact on Employee Retention

The core idea of this paper is to escalate the readers’ speculation in the domain of employee engagement and employee retention. The focus lies on the influence of the former on the latter, where both act as important tool in human resource development. This paper also strives to probe and provide an in depth understanding of the domain of employee engagement specifically as a tool for retention of employees in today's organizations. The paper is grounded on secondary research in the areas of employee engagement and retention. This review is based on papers of last 10 years only, that is, 2009–2019. This body of work is based upon the existing literatures reviewed by the authors in order to develop deeper understanding on the topic and its impact on today's organizational framework. This paper seeks to bring clarity and explore trends in employee engagement. The authors have tried to reveal the drivers of employee engagement and retention by making a methodical search in online database of Emerald insight and Google scholar of the keywords “Employee Engagement and Retention”. Out of a massive result, papers pertaining to last 10 years and owing the key interest areas of authors were selected and scrutinised. The paper deals with the driving factors of employee engagement and retention in today's organization. Effect of employee engagement on organizational performance. Drivers of employee engagement and retention. The effect of employee engagement on employee retention. Enhancing strategies for employee engagement and retention. The outcomes of this study are based on 13 scholarly articles, and are relevant to the area of study only. Thus, articles have been examined in accordance to the selected domain of the study only. Thirteen scholarly articles on drivers of employee engagement and retention of organizations thus indicate further scope of research opportunities in these selected domains.
A Distributed and Trusted Web of Formal Proofs

Most computer checked proofs are tied to the particular technology of a prover’s software. While sharing results between proof assistants is a recognized and desirable goal, the current organization of theorem proving tools makes such sharing an exception instead of the rule. In this talk, I argue that we need to turn the current architecture of proof assistants and formal proofs inside-out. That is, instead of having a few mature theorem provers include within them their formally checked theorems and proofs, I propose that proof assistants should sit on the edge of a web of formal proofs and that proof assistant should be exporting their proofs so that they can exist independently of any theorem prover. While it is necessary to maintain the dependencies between definitions, theories, and theorems, no explicit library structure should be imposed on this web of formal proofs. Thus a theorem and its proofs should not necessarily be located at a particular URL or within a particular prover’s library. While the world of symbolic logic and proof theory certainly allows for proofs to be seen as global and permanent objects, there is a lot of research and engineering work that is needed to make this possible. I describe some of the required research and development that must be done to achieve this goal.
Improving PKI, BGP, and DNS Using Blockchain: A Systematic Review.

The Internet has many backbone components on top of which the whole world is connected. It is important to make these components, like Border Gateway Protocol (BGP), Domain Name System (DNS), and Public Key Infrastructure (PKI), secure and work without any interruption. All of the aforementioned components have vulnerabilities, mainly because of their dependence on the centralized parties, that should be resolved. Blockchain is revolutionizing the concept of today's Internet, primarily because of its degree of decentralization and security properties. In this paper, we discuss how blockchain provides nearly complete solutions to the open challenges for these network backbone components.
Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that more available memory bandwidth corresponds to better graph processing performance. However, in this work we show that this is not necessarily the case. We demonstrate that the key factor in the utilization of the memory system for graph algorithms is not the raw bandwidth, or even latency of memory requests, but instead is the number of memory channels available to handle small data transfers with low locality. Using several widely used graph frameworks, including Gunrock (on the GPU) and GAPBS & Ligra (for CPUs), we characterize two very distinct memory hierarchies with respect to key graph analytics kernels. Our results show that the differences in peak bandwidths of several of the latest Pascal-generation GPU memory subsystems aren't reflected in the performance of various analytics. Furthermore, our experiments on CPU and Xeon Phi systems show that the number of memory channels utilized can be a decisive factor in performance across several different applications. For CPU systems with smaller thread counts, the memory channels can be underutilized while systems with high thread counts can oversaturate the memory subsystem, which leads to limited performance. Lastly, we model the performance of including more channels with narrower access widths than those found in existing memory subsystems, and we analyze the trade-offs in terms of the two most prominent types of memory accesses found in graph algorithms, streaming and random accesses.
The IMU and Bend Sensor as a Pointing Device and Click Method

A pointing device is one of the important interfaces between human and computer. A mouse is usually used as a pointing device; however, people with physical impairments who can not use a mouse due to its operation should use a table. One candidate for solving this problem is using an upper-arm movement combined with an inertial measurement unit sensor. The upper-arm gestures would be mapped and used to manipulate the mouse cursor on the monitor display. The `clicking' action will be accomplished using a bending sensor attached on the opposite upper-arm. This study evaluates the combination of the IMU and bend sensor (IMU+Bend) as a substitute for the mouse. The evaluation is based on ISO/TS 9241-411: Ergonomics of human-system standard. The results mentioned that the use of IMU is a promising way to emulate the movement of cursor. However, the usage of bend sensor is uncomfortable when it is used as a clicking method. The throughput of the proposed is 1.75 bps, and qualitative results show that the mean of comfort is 4.85 on a Likert scale ranged from 1 to 7.
Real-Time Gazed Object Identification with a Variable Point of View Using a Mobile Service Robot

As sensing and image recognition technologies advance, the environments where service robots operate expand into human-centered environments. Since the roles of service robots depend on the user situations, it is important for the robots to understand human intentions. Gaze information, such as gazed objects (i. e., the objects humans are looking at) can help to understand the users’ intentions. In this paper, we propose a real-time gazed object identification method from RGBD images captured by a camera mounted on a mobile service robot. First, we search for the candidate gazed objects using state-of-the-art, real-time object detection. Second, we estimate the human face direction using facial landmarks extracted by a real-time face detection tool. Then, by searching for an object along the estimated face direction, we identify the gazed object. If the gazed object identification fails even though a user is looking at an object, i. e., has a fixed gaze direction, the robot can determine whether the object is inside or outside the robot’s view based on the face direction, and, then, change its point of view to improve the identification. Finally, through multiple evaluation experiments with the mobile service robot Pepper, we verified the effectiveness of the proposed identification and the improvement of the identification accuracy by changing the robot’s point of view.
Implementation of a Personal Authentication System T-Time

Currently, the most commonly used personal authentication system for digital deveces use text-based passwords. However, long passwords which increse the security are hard to remember and decrease the usability. Nowadays, alternative systems are used such as graphical password systems and biometrics systems. Each of them has its own problem also. Lost password cannot be replaced in case of biometric system and graphical password systems are weak at shoulder-surfing attacks. This paper describes implementation of a new personal authentication scheme called T-TIME. T-TIME uses touch signal generation time information which is cumbersome to identify by watching.
Unsupervised Learning of the Set of Local Maxima.

This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1. Therefore, c and h provide training signals to each other: a point x' in the vicinity of x satisfies c(x)=-1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.
A Case Study of Class Bulletin Board Using a Virtual Machine on Google Cloud Platform

4차산업혁명시대에 IT 분야의 클라우드 기술이 각광 받고 있다. 클라우드는 개발자가 프로젝트에 필요한 인프라 보다, 프로젝트 자체에 집중하도록 도와주는 서비스 모음으로서, 고객이 API를 사용해 가상 서버를 프로비저닝하고 시간당 비용을 지불하는 것이라 할 수 있다. 전형적인 클라우드 애플리케이션은 어떤 서버가, 어떤 것을 처리하는지를 생각하기보다, 가능한 호스팅을 사용하며, 대부분의 경우 전통적인 애플리케이션에서 서버를 사용하는 것처럼 컨테이너를 사용한다. 본 연구에서는 클라우드 기술을 살펴보고, 기술 확장성 면에서 앞서가는 구글 클라우드 플랫폼의 가상 머신 인스턴스를 구성하여, 대학 정규 강좌 수업 게시판을 만들어 운영한 사례를 소개하고자 한다. 연구의 목적은 구글 클라우드 플랫폼과 가상 인스턴스에 의한 대학 정규 수업 게시판을 만들어 적용하고 운영해 봄으로써, 구글 클라우드 기술 사용을 확산하고자 한다. 아울러 보편화에 근접한 클라우드 기반의 가상 인스턴스의 다양한 활용 가능성을 확인하기 위한 클라우드 컴퓨팅의 기술 기반을 제공하고자 한다.In the era of the Fourth Industrial Revolution, cloud technology in the IT field is in the spotlight. The cloud is a collection of services that help developers focus on the project itself rather than on the infrastructure required for the project, which allows customers to use APIs to provide virtual servers and pay on an hourly basis. A typical cloud application uses hosting as much as possible rather than thinking about which server handles which data and information. In most cases, the cloud application uses containers just like traditional applications. In this study, we will look into cloud technologies, introduce VM instances of the Google Cloud Platform, which are advanced in terms of technology scalability, and introduce the case of creating and operating class bulletin boards for a regular university course. The purpose of this study is to spread the use of Google cloud technology by creating, applying and operating a virtual class bulletin board on a Google cloud platform at a regular university class. In addition, it aims to provide a technology foundation for cloud computing to identify various applications of cloud-based virtual instances that are close to universalization.
Almost Optimal Cheating-Detectable (2,2,n) Ramp Secret Sharing Scheme

In this research, we consider a strong ramp secret sharing scheme that can detect cheating. A cheating-detectable (k,L,n) ramp secret sharing scheme has been studied so far, and a strong ramp secret sharing scheme which achieves lower bounds on the size of shares and random number used in encoding (i. e., share generation), and the successful cheating probability of impersonation attack has been presented. Now a challenging task is to achieve the lower bound on the successful cheating probability of substitution attack. In this paper, we present a strong (2,2,n) ramp secret sharing scheme that almost achieves the lower bound on the successful cheating probability of substitution attack. Although the parameter is limited to (2,2,n) type ramp scheme, the proposed scheme is the first to almost achieve the lower bound. Moreover the proposed scheme also achieves other lower bounds such as those on the size of shares and random number used in encoding, and the successful cheating probability of impersonation attack. To achieve the lower bound on the successful cheating probability of substitution attack, we take a unique strategy to construct the scheme. Most existing works present generic type verification functions that can be applied to arbitrary linear and strong (k,L,n) ramp secret sharing schemes. On the other hand, our proposed verification function (one of those which we call limited type verification functions) cannot be applied to arbitrary strong (k,L,n) ramp schemes but applied to a special case of strong (2,2,n) ramp schemes with certain property, which make it possible to (almost) achieve the lower bound on the successful cheating probability of substitution attack. Fortunately, we can prove that, for any secret (S_1,S_2) ∊ GF(p^m)^2, there exist sufficiently many strong (2,2,n) ramp schemes with which the proposed scheme is proven to be secure. Further, we suggest that it is difficult to generalize our strategy to more generalized parameter because of the difficulty to find conditions of strong (k,L,n) ramp schemes with which any limited type verification functions guarantee security against substitution attacks.
Automatic Attendance Taking: A Proof of Concept on Privacy Concerns in 802.11 MAC Address Probing

Modern data communication paradigm involves many unsolicited data transmissions that poses privacy issues given the proliferation of big data and artificial intelligence (A.I.). In 802.x protocol which dominate wireless communication; Wi-Fi enabled devices voluntarily embed devices MAC address during SSID discovery when connecting to access point (AP). Such vulnerability has been massively exploited for unauthorized devices tracking without user consents. This paper proposed an opportunistic attendance taking system (OATA) using MAC address probing as a proof of concepts to demonstrate the significance of this exploit. The intuition is that student’s attendance can be implied based on the MAC address of their mobile devices when approaching lecture hall installed with AP(s). The body of this work focuses on comparing the OATA to some prominent attendance taking methods in pervasiveness and accuracy. For the operational hypothesis, a non-synthetic dataset is used for experimental evaluation to simulate realness and to minimize hawthorn effect. OATA is designed to circumvent MAC address randomization that is used on modern IOS and Android OS for accurate tracking. The experimental results showed that OATA is highly accurate at capturing device’s presence; achieving true positive rate (TPr) of 0.938 and false positive rate (FPr) of 0.063 with fast convergence time. The significance of this study highlight the concerns that big data coupled with increasingly intelligent A.I. can divulge more information than originally intended. Consequently, user’s privacy is compromised as their personal communication devices are demonstrated to be potentially exploited for unsolicited location tracking.
Towards automatic visual fault detection in highly expressive human-like animatronic faces with soft skin

Designing reliable, humanoid social robots with highly expressive human-like faces is a challenging problem. Their construction requires complex mechanical assemblies, large numbers of actuators and involves soft material. When deploying these robots in the field they face problems of wear and tear and mechanical abuse. Mechanical defects of such faces can be hard to analyze automatically or by manual visual inspection. We propose a method of automatic visual calibration and actuator fault detection for complex animatronic faces. We use our approach to scan three expressive animatronic faces, and analyze the data. Our findings indicate that our approach is able to detect faulty actuators even when they contribute to the overall expression of the face only marginally, and are hard to spot visually.
Construction of the Computer Network Courses in College Physical Education Distance Video Teaching

With the development of the computer technology, the multimedia technology and the communication technology, a new educational model, represented by the educational technology, has emerged, in which the distance education is a prominent part. Physical education is a bilateral activity of the teaching and learning with strong practicality and participation. At present, the resources on the network are fewer than those of other disciplines, and there is a lack of systematicness and integrity. For the physical education and teaching, the demonstration and the imitation of actions are very suitable to be presented in the form of the multimedia contents. Therefore, in this context, based on the mature campus network of colleges and universities, and based on the Internet technology, the computer technology, the multimedia technology and the modern communication technology, it is very necessary to design and develop a long-distance network educational system and platform for physical education.
Strong-Separation Logic

Most automated verifiers for separation logic target the symbolic-heap fragment, disallowing both the magic-wand operator and the application of classical Boolean operators to spatial formulas. This is not surprising, as support for the magic wand quickly leads to undecidability, especially when combined with inductive predicates for reasoning about data structures. To circumvent these undecidability results, we propose to assign a more restrictive semantics to the separating conjunction. We argue that the resulting logic, strong-separation logic, can be used for compositional program verification and bi-abductive static analysis just like separation logic, while remaining decidable even in the presence of both the magic wand and the list-segment predicate -- a combination of features that leads to undecidability assuming the standard semantics.
On Repairing Web Services Workflows

When a composite web service—i.e., a composition of individual web services—is executed and fails, it is desirable to reuse as much as possible the results that have been obtained thus far. For example, a travel agent, after receiving an order to arrange for a trip from LA to NY from a customer, would typically identify the flights and the hotels, obtain the confirmation from the customer, and place the reservations using the credit card information provided by the user; if something is wrong (e.g., at the last step, the credit card information was wrong), the travel agent would prefer to place the reservations using another means (e.g., a different card) instead of starting from the beginning. This paper introduces an approach for dealing with service failures in the context of workflow execution. The paper defines the notion of a web service composition (WSC) problem and the notion of a solution workflow for a WSC problem. The paper describes two approaches to repair a partially executed workflow, with the goal of effectively reusing parts of the workflow that have been successfully executed. The usefulness of these approaches are demonstrated in an implementation using Answer Set Programming (ASP) in the well-known shopping domain.