Reviewerly/exHarmony huggingface dataset:
IMPLEMENTING COMPUTER ASSISTED LANGUAGE LEARNING FOR SPEAKING CLASS

Computer Assisted Language Learning (CALL) is a program derived from CAL (Computer-Assisted Learning) which is implemented to language and the prime focus of CALL is on the usage of computers in language learning. Much of the research has been aimed at improving written communication skills and little has been done to address the issue of increasing oral proficiency. In second/foreign language classrooms, the CALL environment creates situations that stimulate interest, allows dialogue and communication, enhances creativity, fosters a sense of personal worth, facilitates collaboration among students working together, permits rich learning experiences for all students, and strengthens communication skills. By using CALL, many resources of material can be easily developed in teaching learning process.
Working Memory Load Performance Based on Collocation of Virtual and Physical Hands

The use of real-like hands in virtual reality simulators is common; however, research into understanding how the human brain perceives hands in virtual environments is limited. Self avatars are a great way to improve the users presence and perception of space [6], but the precise implementation of avatars is arduous, and including only hands is an attractive alternative. Earlier psychology research reported that the closer the hands to the studied object, the lower the working memory load. We hypothesized that in virtual environments, virtual hands that are collocated with the users hands should improve the users working memory load, and we tested our hypothesis with a between-participant study (n=30) measuring working memory load with the Stroop Interference Task.
Kernel-based scale-invariant feature transform and spherical SVM classifier for face recognition

Face recognition system is a computer application which is capable of measuring and matching the distinctive features intended for the purpose of identifying or verifying a person from a digital image. In digital or connected cameras, face recognizing system detects the faces in the image and measure their characteristics. It is then matched with the templates that are stored in a database. Nowadays, automated face recognition system is a relatively new concept. In this paper, we propose kernel based Scale Invariant Feature Transform and spherical SVM classifier for face recognition. Furthermore, a novel weightage function for feature extraction and classification, which is termed as Multi Kernel Function (MKF) is also proposed. To extract facial features, we adopt SIFT technique, which is modified in the descriptor stage by the proposed MKF weightage function, thereby evolving a new technique which we termed as KSIFT. Multi-kernel Spherical SVM classifier is used for the classification purpose. The experimental results of our proposed system are evaluated and we analysed the recognition performance by the metrics such as FAR, FRR and Accuracy. Then, the performance is also compared with the existing systems like HOG, SIFT and WHOG and our proposed method attains the higher accuracy of 99% for the face recognition system.
Lonely together? Identifying the determinants of collective corrective action against uncivil comments:

Journalists, scholars, and citizens have raised concerns regarding the high share of incivility in comment sections of news outlets. The current study surveyed members of the social movement #ichbi...
일본 기계설계분야 어휘분석

This article aims to help learners wishing to work in Japanese machinery and automobile design and manufacturing industries learn vocabulary...
LibGuides: Student Success Week 2019: Introduction to Library Services and Resources Micro-credential

SSW is three days of workshop-based programming that will be held here at the library on September 3rd, 4th, and 5th. It will run from approximately 10-4 each day, with a mixture of 30- minute, 60- minute, and 90- minute workshops and trainings that are g
A brief history of software and software engineering

The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practiceindependent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and microservices can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
Can AI improve accuracy and efficiency of mammography

A multicase study of artificial intelligence (AI) shows that adding the technology to assessment of digital mammograms may increase accuracy and efficiency of the readings.
Out With the Old, in With the … ArchivesSpace

What do you do when your cataloging infrastructure is outdated, unusable by your patrons, and creating excess work for your staff? Get new infrastructure! At Special Collections and Archives in the Reed College Library, we are in the process of implementing ArchivesSpace, a collection management tool for archives. As we go, we are professionalizing our description practices, preparing to contribute to regional aggregations (Archives West), and, most importantly, increasing access to our collections. The software is also easier to use and reduces the time we spend doing research for our patrons.
JAVASCRIPT ASYNCHRONOUS PROGRAMMING

JavaScript has become more and more popular in recent years because its wealthy features as being dynamic, interpreted and object-oriented with first-class functions. Furthermore, JavaScript is designed with event-driven and I/O non-blocking model that boosts the performance of overall application especially in the case of Node.js. To take advantage of these characteristics, many design patterns that implement asynchronous programming for JavaScript were proposed. However, choosing a right pattern and implementing a good asynchronous source code is a challenge and thus easily lead into less robust application and low quality source code. Extended from our previous works on exception handling code smells in JavaScript and exception handling code smells in JavaScript asynchronous programming with promise, this research aims at studying the impact of three JavaScript asynchronous programming patterns on quality of source code and application.
In defense of OSVOS.

As a milestone for video object segmentation, one-shot video object segmentation (OSVOS) has achieved a large margin compared to the conventional optical-flow based methods regarding to the segmentation accuracy. Its excellent performance mainly benefit from the three-step training mechanism, that are: (1) acquiring object features on the base dataset (i.e. ImageNet), (2) training the parent network on the training set of the target dataset (i.e. DAVIS-2016) to be capable of differentiating the object of interest from the background. (3) online fine-tuning the interested object on the first frame of the target test set to overfit its appearance, then the model can be utilized to segment the same object in the rest frames of that video. In this paper, we argue that for the step (2), OSVOS has the limitation to 'overemphasize' the generic semantic object information while 'dilute' the instance cues of the object(s), which largely block the whole training process. Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i.e. online fine-tuning on the target instance. Through extensive experiments using different network structures as the backbone, we show that the proposed video loss module can improve the segmentation performance significantly, compared to that of OSVOS. Meanwhile, since video loss is a common module, it can be generalized to other fine-tuning based methods and similar vision tasks such as depth estimation and saliency detection.
Exploration of another Sol Lewitt puzzle from Barry Cipra

At MOVES 2019, Barry Cipra casually introduced a new Sol Lewitt puzzle to fellow conference goers. Several brainstorming sessions ensued with Barry, Peter Winkler , Donna Dietz, and other attendees. This paper is to document the puzzle and some insights so others can enjoy and build on this lovely puzzle. (Look for it in an upcoming book by Peter!)
Image Encryption Algorithm Based on Chaotic Sequence

With the rapid development of communication technology and the Internet, the security of image information has also received more attention. The information characteristics of an image can be described by pixel position and pixel value. Therefore, the encryption algorithm for the image is generally based on these two aspects (Commun Nonlinear Sci Number Simulat 17(7):2969–2977, [1]; Int J Bi-furcation Chaos 7(7):1579–1997, [2]). Using a method alone can easily be cracked by an attacker, so image encryption often combines pixel position scrambling with gray value diffusion. In this paper, chaotic sequences are applied to image data encryption, and pixel position scrambling and gray value diffusion are combined to obtain ciphertext images. And the feedback mechanism is introduced in the diffusion link, so that the ciphertext of each pixel is not only related to the current plaintext, but also relevant to the ciphertext of all the previous pixels, thereby increasing the complexity of the ciphertext.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions may be hard to specify/model and/or to compute. Moreover, the transfer of policies learned in a simulator to the real-world has limitations (reality gap). On the other hand, machine learning methods that rely on the transfer of human knowledge to an agent have shown to be time efficient for obtaining well performing policies and do not require a reward function. In this context, we analyze the use of human corrective feedback during task execution to learn policies with high-dimensional state spaces, by using the D-COACH framework, and we propose new variants of this framework. D-COACH is a Deep Learning based extension of COACH (COrrective Advice Communicated by Humans), where humans are able to shape policies through corrective advice. The enhanced version of D-COACH, which is proposed in this paper, largely reduces the time and effort of a human for training a policy. Experimental results validate the efficiency of the D-COACH framework in three different problems (simulated and with real robots), and show that its enhanced version reduces the human training effort considerably, and makes it feasible to learn policies within periods of time in which a DRL agent do not reach any improvement.
The Effect of Hanger Reflex on Virtual Reality Redirected Walking

One of the major challenges in virtual reality (VR) is to create a perception of a large virtual space within a limited physical space. Here, we explore the effect of haptic-based navigation by Hanger Reflex (HR) on the perception in redirected walking (RDW) with visual manipulation. Seven individuals walked along a straight path in VR while, unbeknown to them, the visual scene was rotated with a curvature gain of π/36, forcing them to walk in a circular path in real space. HR rotation (in the left, right, and neutral direction) was induced by a wearable haptic device during each of the walking trials, and they reported their perceived walking direction and effort to walk along the path on visual analog scale. Experiment results showed that HR can influence the perception in RDW, but the effects may be complex and therefore require further investigation.
Proceedings of the Mathematics for Industry NZ Study Group 2017

This Special Section of the ANZIAM Journal (Electronic Supplement) contains the refereed papers from the 2017 Mathematics for Industry NZ Study Group (MINZ 2017) held at Victoria University, Wellington, New Zealand from 26--30 June, 2017. The MINZ is a special interest meeting of ANZIAM, the Australia and New Zealand Industrial and Applied Mathematics division of the Australian Mathematics Society. MINZ was formed to provide a national entity to host workshops where companies pitch their problems to New Zealand’s mathematical scientists. They then work collaboratively to generate practical solutions through modelling, statistical analysis or computation.
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks

A new framework for a secure and robust consensus in blockchain-based IoT networks is proposed using machine learning. Hyperledger fabric, which is a blockchain platform developed as part of the Hyperledger project, though looks very apt for IoT applications, has comparatively low tolerance for malicious activities in an untrustworthy environment. To that end, we propose AI-enabled blockchain (AIBC) with a 2-step consensus protocol that uses an outlier detection algorithm for consensus in an IoT network implemented on hyperledger fabric platform. The outlier-aware consensus protocol exploits a supervised machine learning algorithm which detects anomaly activities via a learned detector in the first step. Then, the data goes through the inherent Practical Byzantine Fault Tolerance (PBFT) consensus protocol in the hyperledger fabric for ledger update. We measure and report the performance of our framework with respect to the various delay components. Results reveal that our implemented AIBC network (2-step consensus protocol) improves hyperledger fabric performance in terms of fault tolerance by marginally compromising the delay performance.
Pitjantjatjara language change: some observations and recommendations

Pitjantjatjara is often regarded as a robust language with more than 3000 speakers, including children, across a range of communities. Nevertheless, the language has been affected by colonialism and many community members are concerned about language change. In this paper, A??angu educators from Pukatja/ Ernabella work together with a non-Indigenous linguist to survey changes we have noticed in the language and to make recommendations for the future. We report changes in pronunciation, grammar and the ways the language is used. In some cases, these changes result directly from contact between languages or other changes in the cultural setting of people speaking Pitjantjatjara today. We see these as winds of change that are sweeping across the language and call for the construction of a windbreak to protect Pitjantjatjara language and culture to keep it strong for future generations.
SOBRE A SONICIDADE

Through the concept of sonicity, developed in the present text, Wolfgang Ernst seeks to reconstruct sound both in its aesthetic character, as a manifest temporal phenomenon inscribed in a certain technocultural environment, and in its epistemological dimension, as object of information and knowledge. In addition to presenting the theoretical unfolding of his research on the sound expressions of media, Ernst demonstrates how his particular approach on the prominent field of Media Archeology operates. Through a technomathematical drive towards such objects, Ernst highlights one of the main methodological implications of his research: deriving insights from the interior of the technological agents. This article presents a partial translation of the first chapter of his book Sonic Time Machines: Explicit Sound, Sirenic Voices, and Implicit Sonicity, where such questions are addressed.
Transparency foes work to cloud Trump’s executive order

Real transparency is the single, first, and most important step toward bringing down healthcare costs and creating a competitive marketplace.
From programming to software engineering

The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practiceindependent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and microservices can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
PENGARUH MEDIA GAMBAR FOTOGRAFI TERHADAP KEMAMPUAN MENULIS TEKS EKSPLANASI SISWA KELAS XI SMK PAB 3 MEDAN ESTATE

This research aims to describe text writing ability explanatory text before and after using the photographic picture media and description is there the effect of photographic image media on writing ability explanatory text class students XI SMK PAB 3 Medan Estate Year Of Learning 2018/2019. Research population is a class student XI SMK PAB 3 Medan Estate Year Of Learning 2018/2019. that number 66 students. and who become research samples as much 34 students, the class XI Accounting for pre-test and post-test classes, appearance of this sample is done by using class random sampling. This research method is experimental method. Research instruments used are esay tests formed written tested before and after participating in learning using photographic image media. The conclusions obtained from this study are ability to write explanatory text before using media photographic images got pre-test 74,12 in category B (good), the ability to write explanatory text after using photographic image media got A post-test score of 84.56 in category A (very good). The use of photographic media images has a significant influence on students' ability to write explanatory text. The price of = 8.093 is greater than at = 0.05 = = 66 which is 8.093 1,668 so that the better use of photographic image media then bushkin improves the ability of students to write explanatory texts.
Network Service Header (NSH) and Segment Routing Integration for Service Function Chaining (SFC)

This document describes two application scenarios where NetworkService Header (NSH) and Segment Routing (SR) techniques can bedeployed together to support Service Function Chaining (SFC) in anefficient manner while maintaining separation of the service andtransport planes as originally intended by the SFC architecture. Inthe first scenario, an NSH-based SFC is created using SR as thetransport between Service Function Forwarders (SFFs). SR in this caseis just one of many encapsulations that could be used to maintain thetransport-independent nature of NSH-based service chains. In thesecond scenario, SR is used to represent each service hop of the NSH-based SFC as a segment within the segment-list. SR and NSH in thiscase are integrated. In both scenarios SR is responsible for steeringpackets between SFFs along a given Service Function Path (SFP) whileNSH is responsible for maintaining the integrity of the service plane,the SFC instance context, and any associated metadata. Theseapplication scenarios demonstrate that NSH and SR can work jointly andcomplement each other leaving the network operator with theflexibility to use whichever transport technology makes sense inspecific areas of their network infrastructure, and still maintain anend-to-end service plane using NSH.