Reviewerly/exHarmony huggingface dataset:
ALGORITHM AS A PROBLEM SOLVING TECHNIQUE FOR TEACHING AND LEARNING OF THE MALAY LANGUAGE

Computational thinking or CT refers to the thought processes involved in expressing solutions as computational steps or algorithms that can be carried out by a computer. CT is not merely using software or computer programs alone but is also interlinked with information and communication technology (ICT) and computer science based on problems; tools; action (praxis); solutions; concepts; and social needs. Hence, the aim of the study was to determine the effectiveness of using scratch program to teach Malay language subject. As part of the methodology, 32, 4th year students in a primary school of Seremban district participated in the study. A case study was used. The teachers teach Malay language subject using scratch programs with the theme of agriculture and title of food product from agriculture. The students were to arrange the basic words into grammatical sentences based on the scratch program and write a cake recipe using an algorithmic technique, a series of instructions contained in the scratch program. This conceptual teaching and learning algorithm was conducted in five steps namely the induction set; step 1; step 2; step 3; and enrichment and recovery. The result of this study shows that students could understand the concepts of algorithms taught and did not require recovery activities. This implies that the algorithm concept of how to prepare a cake through a scratch program has been fully understood by the students. Students are also excited about the algorithmic techniques and the scratch program generated. As a conclusion, the student's reaction as they remain focused throughout the session shows that the use of algorithms and scratch programs is better than conventional methods in teaching the Malay Language.
Dot-Com Design: How do we tell a Web History?

"Dot-Com Design: How do we tell a Web History?." Information, Communication & Society, 22(13), pp. 2042–2043
A Tale of Two Trees: One Writes, and Other Reads. Optimized Oblivious Accesses to Large-Scale Blockchains.

The Bitcoin network has offered a new way of securely performing financial transactions over the insecure network. Nevertheless, this ability comes with the cost of storing a large (distributed) ledger, which has become unsuitable for personal devices of any kind. Although the simplified payment verification (SPV) clients can address this storage issue, a Bitcoin SPV client has to rely on other Bitcoin nodes to obtain its transaction history and the current approaches offer no privacy guarantees to the SPV clients. This work presents $T^3$, a trusted hardware-secured Bitcoin full client that supports efficient oblivious search/update for Bitcoin SPV clients without sacrificing the privacy of the clients. In this design, we leverage the trusted execution and attestation capabilities of a trusted execution environment (TEE) and the ability to hide access patterns of oblivious random access memory (ORAM) to protect SPV clients' requests from a potentially malicious server. The key novelty of $T^3$ lies in the optimizations introduced to conventional ORAM, tailored for expected SPV client usages. In particular, by making a natural assumption about the access patterns of SPV clients, we are able to propose a two-tree ORAM construction that overcomes the concurrency limitation associated with traditional ORAMs. We have implemented and tested our system using the current Bitcoin Unspent Transaction Output database. Our experiment shows that the system is feasible to be deployed in practice while providing strong privacy and security guarantees to Bitcoin SPV clients.
An Improved Invasive Weed Algorithm with RBFNN for Optimizing Classification Problems

Classification or decision-making task is very common in every field of endeavor. Many algorithms have been developed to help to make this kind of task successful. Invasive Weed Optimization (IWO) has been commonly used to help perform this task. However, its classification accuracy still leaves something to be desired. This study attempted to develop an improved Invasive Weed Optimization (IIWO) that would perform this task better. Our objectives were as follows: 1) to use IWO to optimize the parameters of Radial Basis Function Neural Network classifier and 2) to improve the dispersal of offspring solutions in the search space by using 3 spatial distributions: Normal, Cauchy, and Levy distributions, instead of only one as in IWO. We evaluated the performance of IIWO against two conventional classification algorithms: Genetic Algorithm (GA) and Gradient Descent (GD) algorithm on 5 benchmark datasets and found that IIWO made more accurate predictions than these two algorithms on 3 out of the 5 datasets and nearly as accurate as them on the other 2 datasets. The reasons that IIWO performed as well as this was that the inner working of RBFNN allowed the algorithm to estimate the objective value more accurately and the use of 3 spatial distributions to disperse offspring solutions instead of one in IWO helped make the offspring solutions spread to cover the whole search space better.
Overnight large-scale subjective video quality assessment using automatic test generation and crowdsourcing Internet marketplaces

Subjective quality feedback by actual human viewers is crucial for reliable evaluation of various solutions and configurations for video processing or encoding. However, it is generally a time consuming and expensive process. Therefore, in many cases evaluation of video quality is done using objective measures, which may be poorly correlated with actual subjective results. In order to address this issue, we have developed VISTA (VIsual Subjective Testing Application), an easy to use application for visually comparing pairs of video sequences played synchronously side by side, and a user interface for indicating the relative subjective quality of the two video sequences. In addition, we developed a system for automating the evaluation process called Auto-VISTA. The system receives as input guidelines for the required testing session, prepares the content to be compared, launches the app in a crowdsourcing Internet marketplace (such as Amazon Mechanical Turk), and performs collection and analysis of the results. Thus, obtaining large scale subjective feedback becomes cheap and accessible, which in turn allows for fast and reliable evaluation cycles of different video encoding and processing solutions, or tuning various configurations and settings for a given solution.
Development of OCD (Offensive, Counter Attack, Devensive) Learning Strategies to Improve Understanding Reading Skills of 4th Grade Students

This research (R & D) uses a Four-D Model approach with the aim of developing OCD (Offensive, Counter Attack, Defensive) learning strategies to improve reading comprehension skills with exposition text material.
Drowsy-DC: Data Center Power Management System

In a modern data center (DC), a large majority of costs arise from energy consumption. The most popular technique used to mitigate this issue is virtualization and more precisely virtual machine (VM) consolidation. Although consolidation may increase server usage by about 5-10%, it is difficult to actually witness server loads greater than 50%. By analyzing the traces from our cloud provider partner, confirmed by previous research work, we have identified that some VMs have sporadic moments of data computation followed by large periods of idleness. These VMs often hinder the consolidation system which cannot further increase the energy efficiency of the DC. In this paper we propose a novel DC power management system called Drowsy-DC, which is able to identify the aforementioned VMs which have matching patterns of idleness. These VMs can thus be colocated on the same server so that their idle periods are exploited to put the server to a low power mode (suspend to RAM) until some data computation is required. While introducing a negligible overhead, our system is able to significantly improve any VM consolidation system; evaluations showed improvements up to 81% and more when compared to OpenStack Neat.
Information Fusion Algorithm Based on Internet of Things Technology and Its Application

With the development of the Internet of Things in the social development, information fusion technology is widely used in social life. This paper studies the information fusion algorithm of the Internet of Things, and discusses the effect of the application information fusion algorithm with examples to propose the Internet of Things technology. Prospects for information fusion algorithms.
Assembly of the Caledonian Orogenic Wedge, Jämtland, Sweden

Collisional orogeny creates the largest mountain belts on Earth. The Caledonides of Scandinavia are a deeply eroded, ancient mountain belt, which today exposes a deep section through the former oro ...
NEAR: Neighborhood Edge AggregatoR for Graph Classification

Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis, and data mining. Recent GNN algorithms are based on neural message passing, which enables GNNs to integrate local structures and node features recursively. However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships. In this article, we propose Neighborhood Edge AggregatoR (NEAR), a framework that aggregates relations between the nodes in the neighborhood via edges. NEAR, which can be orthogonally combined with Graph Isomorphism Network (GIN), gives integrated information that describes which nodes in the neighborhood are connected. Therefore, NEAR can reflect additional information of a local structure of each node beyond the nodes themselves in 1-hop neighborhood. Experimental results on multiple graph classification tasks show that our algorithm makes a good improvement over other existing 1-hop based GNN-based algorithms.
MOBILE INFORMATION SECURITY MANAGEMENT FOR SMALL ORGANISATION TECHNOLOGY UPGRADES: THE POLICY-DRIVEN APPROACH AND THE EVOLVING IMPLEMENTATION APPROACH

Information security management researchers are often focused on the information security policy, its implementation and evaluation as the primary means of ensuring that organisations protect their valuable data. However, information security is usually nested with a variety of other concerns (for instance technology upgrades, information access, efficiency and sustainability issues, employee satisfaction), so this policy-driven approach is seldom operated in isolation. We investigate the approach as implied in the mobile information security literature, provide a literature-inspired characterisation and use it to analyse an iPad implementation for politicians in a Swedish municipality. The analysis provides only a partial explanation for security work in this kind of small organisation technology upgrade, so we develop a complementary approach: the evolving implementation approach. A suggestion is made for how the two approaches can be reconciled, and implications for both practitioners and researchers derived.
Analysing revisions in online writing

New technology has given new insights into the writing process of professional text producers. Keystroke logging can give the researcher knowledge about how text producers write and revise their texts, both at the level of form and content. With a point of departure in the results of Haugaard’s (2016) study of journalistic writing, this article suggests a reassessment of the online revision taxonomy developed by Lindgren/Sullivan (2006b), with a focus on external revisions. In order to be able to interpret the effect of online revisions on the text by means of keystroke logging, it is proposed that revisions be analysed with a point of departure in the semantic content involved, rather than according to location, i.e. in the text already transcribed (contextual revision) or in the text currently being transcribed (pre-contextual revision), as suggested by Lindgren/Sullivan (2006b). It is argued that contextual and pre-contextual revisions should not be conceptualised as dichotomous entities, but as open categories on a continuum of semantically meaningful context, on the basis of which revisions can be interpreted depending on the degree of completeness of the context in which they are made.
Quantum Multi-User Detection Based on Coherent State Signals

Multi-user detection is one of the important technical problems for modern communications. In the field of quantum communication, the multi-access channel on which we apply the technology of quantum information processing is still an open question. In this work, we investigate the multi-user detection problem based on the binary coherent-state signals whose communication way is supposed to be seen as a quantum channel. A binary phase shift keying model of this multi-access channel is studied and a novel method of quantum detection proposed according to the conclusion of the quantum measurement theory. As a result, the average interference between deferent users is presented and the average error probability of the quantum detection is derived theoretically. Finally, we show the maximum channel capacity of this effective detection for a two-access quantum channel
Customer Sentiment towards Freebies in Telecom Sector: A Social Media Mining Approach

Organisations rely heavily on social media for inferring customer's feedback and sentiments associated with their products or services. This study, using social media data and aims to find the sentiments associated with 'JIO', a new entrant among Indian cellular service providers. They initially offered all the services for free (freebies) to attract subscribers. Tweets were analysed using text mining techniques and 'R' statistical tool. The findings reveal that, despite 23% users of 'JIO' having a negative sentiment, the customer-base of service provider is continuously increasing, suggesting high inclination towards freebies. Further, some service parameters were negatively mentioned, pointing towards the need for improvement. The presence of a system bug was identified, of which the service provider was unaware otherwise. Future prospects include sentiment analysis of customers from other platforms. This is an independent work, conducted utilising text mining and results could be implemented to monitor and upgrade service quality.
Ferramentas da cosmetologia e da estética para o tratamento do fibro edema gelóide: revisão atual da literatura

The Fibro Edema Geloid (FEG), known as cellulitis, is a multifactorial disease that affects the dermis and subcutaneous tissue causing changes in the appearance and architecture of the skin, which may affect self-esteem. The objective of this study was to evidence from a bibliographic review of integrative type (RI) the current techniques (2012-2018) for the treatment of FEG. The databases were LILACS, BVS and SciELO (N=11; 100%). The results (N=3, 27.27%) indicated that electrolyphysis, electrolyphoresis, endermology, microagulation, growth factors, vitamin C and imaging are effective alternatives for its therapeutics. Our findings point to the need for further studies on the treatment of FEG, because scientific publications can greatly contribute to the safe reproduction and use of the techniques.
Nuts &amp; Bolts: Essentials for Becoming Conversant in eResource Management

This article is based on the same-named presentation given on April 15, 2019 at the Texas Library Association Annual Conference. Exploring eResource Management in this hour-long presentation allowe...
Direct Study of the Particle Differential Mobility Analyzer (DMA) Systems: a review article

Background & Aim: One of the unknown equipment inside our country, especially for researchers, is the DMA system, which used to particle size distribution, especially for nanoparticles. The purpose of this paper was to review the studies carried out in this field, assessment of the evolution and importance of the application of this system and introduce the true position of this system among researchers in the field. Methods: In this study, by searching in the databases including, Google Scholar، ProQuest، Science Direct and PubMed, with selecting keywords such as DMA, Electrical Mobility and Particle Counters, 16 studies were examined, from 1950 to 2018. Results: According to the results, the main advantages of DMA systems include their high ability to measure and classify a wide range of particles with a diameter of 1 to 1000 nanometer, quick response time, high-resolution particle size measurement, minimum sample loss and low cost of design and development. Conclusion: Differential mobility analyzer system is a powerful tool in the measurement and determine the particle size distribution based on their electrical mobility.So this system can be widely used in industrial and research areas.
CYBER SECURITY AND DIGITALIZED FINANCIAL SYSTEM: A PARADIGM SHIFT IN GLOBAL MARKET

This paper conducts a synthesis analysis of ‘Cybersecurity and financial stability’, compile the thematic areas and finally unearth the future research issues based on the researches done to date. However, the literature on this research agenda is yet at infancy stage, although the economic aspects of cybersecurity are gaining widespread momentum and visibility in the growing body of literature. Hence the issues of cybersecurity puzzle the industry practitioners to find the possible solutions. Given this background, we make fundamental efforts to critically review the past and current studies across several thematic areas regarding cybersecurity, such as: (a) Cost issue, (b) Firm performance issue (c) Risk framework issue (d) Disclosure issue, and (e) IT development issue. The review shows that investment in cyber technology is mushrooming, nonetheless a resistant security system is yet to be achieved. Finally, with a thoughtful analysis, we compile three unaddressed research areas, such as (a) Technology and overhead cost, (b) Technology and signaling effect, and (c) Technology and risk framework. Overall, beyond the academic realm, the findings of this review could be useful to the risk analysts, bankers, and policymakers.
ADOPTION OF NEW MEDIA TECHNOLOGIES IN UNIVERSITY LIBRARIES: PATHWAY TO IMPROVING STANDARD OF EDUCATION FOR NATIONAL DEVELOPMENT IN NIGERIA

Nigerian standard of education has been reported to be at its lowest ebb among the comity of nations due to the myriad of challenges bedeviling the nation in recent times. Libraries are the heart of tertiary institutions and the standard of education in these institutions and the nation at large depend to a large extent on the quality and effectiveness of the libraries. New media technologies adopted in libraries in developed climes enable users such as lecturers, students and researchers to access and use current, relevant and reliable information for teaching, learning and research activities, as well as facilitate connectivity, interactivity, communication, collaboration and sharing of information between libraries and the tech-savvy users. Effective teaching and learning as well as implementing the research conducted in tertiary institutions could result to national development of key sectors in the country. This paper review discussed the concepts of new media technologies adoption in university libraries; the standard of university education in Nigeria; research and national development and the role of new media technologies in improving standard of education. The paper also highlighted the challenges of adopting new media technologies in Nigerian university libraries and proffered workable solutions.
Fast and Furious, PFAS

Journal AWWAVolume 111, Issue 9 p. 11-11 DC Beat Fast and Furious, PFAS Chris Moody, Chris MoodySearch for more papers by this author Chris Moody, Chris MoodySearch for more papers by this author First published: 03 September 2019 https://doi.org/10.1002/awwa.1357Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume111, Issue9September 2019Pages 11-11 RelatedInformation
PENERAPAN JARINGAN SYARAF TIRUAN UNTUK PENENTUAN SALAK UNGGUL DENGAN MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION

Rokan Hulu is one of district in Riau Province that cultivated Snake Fruit plant. Recently, the cultivation of this plant doesn’t conducted maximally because of the lack of farmer’s knowledge. The varieties of high quality Snake Fruit takes effect toward the productivity of Snake Fruit. Because of that, it needs evaluation in determining high quality Snake Fruit. Artificial Neural Network with Learning Vector Quantization in this research is used to train high quality Snake Fruit plant based on it’s class, namely High Quality I and High Quality II. The testing of this method is conducted by using Matlab in order to obtain classification accuracy level. This research uses 60 training dataset and 40 testing dataset of Snake Fruit plant. These data are obtained by using observation at Snake Fruit Plantation located in Desa Rambah Muda Kecamatan Rambah Hilir Kabupaten Rokan Hulu. The result of this research shows that the highest accuracy percentage of training and testing data are respectively 91.66% and 92.50%.