Reviewerly/exHarmony huggingface dataset:
Invited Commentary—Exploring patterns of personal alarm system use and impacts on outcomes

Australasian Journal on AgeingVolume 40, Issue 3 p. 241-242 INVITED COMMENTARY Invited Commentary—Exploring patterns of personal alarm system use and impacts on outcomes Susan Brandis, Corresponding Author Susan Brandis sbrandis@bond.edu.au orcid.org/0000-0003-2613-4114 Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Qld, Australia Correspondence Susan Brandis, Faculty of Health Sciences & Medicine, Bond University, 2 Promethean Way, Robina, 4226, Gold Coast, Qld, Australia. Email: sbrandis@bond.edu.auSearch for more papers by this author Susan Brandis, Corresponding Author Susan Brandis sbrandis@bond.edu.au orcid.org/0000-0003-2613-4114 Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Qld, Australia Correspondence Susan Brandis, Faculty of Health Sciences & Medicine, Bond University, 2 Promethean Way, Robina, 4226, Gold Coast, Qld, Australia. Email: sbrandis@bond.edu.auSearch for more papers by this author First published: 05 August 2021 https://doi.org/10.1111/ajag.12989Read 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. Volume40, Issue3September 2021Pages 241-242 RelatedInformation
Text Cohesion and Prediction of General Proficiency in Reading-Writing Integrated Tasks

This paper considers the degree to which performance data can help predict Japanese EFL learners' general receptive proficiency in an integrated reading-writing task. In the previous studies, there have been a great amount of discussions as to the use of Automatic Essay Scoring for independent tasks such as writing test. However, there is little study which deals with reading-writing integrated tasks in order to show the predictability for general linguistic proficiency. Eighty-four Japanese EFL learners participated in this study. Our finding is that the 26 cohesive indices in the TAACO processing tool account for 38% of their proficiency in random forest and XGBoost. This paper concludes that this approach for the use of performance data and cohesive indices is promising for future research. In addition, in the case of Japanese EFL learners, the result of the occurrence of determiners has implications for second language acquisition theory in terms of linguistic factors.
Inference of time series components by online co-evolution

Abstract Time series data is often composed of a multitude of individual, superimposed dynamics. We propose a novel algorithm for inferring time series compositions through evolutionary synchronization of modular networks (ESMoN). ESMoN orchestrates a set of trained dynamic modules, assuming that some of those modules’ dynamics, suitably parameterized, will be present in the targeted time series. With the help of iterative co-evolution techniques, ESMoN optimizes the activities of its modules dynamically, which effectively synchronizes the system with the unfolding time series signal and distributes the dynamic subcomponents present in the time series over the respective modules. We show that ESMoN can adapt modules of different types. Moreover, it is able to precisely identify the signal components of various time series dynamics. We thus expect that ESMoN will be useful also in other domains—including, for example, medical, physical, and behavioral data domains—where the data is composed of known signal sources.
Research on Deep Neural Network Model Compression Based on Quantification Pruning and Huffmann Encoding

With the rapid development of hardware GPU and the advent of the era of big data, neural networks have developed rapidly and greatly improved the recognition performance in various fields. The application of nerual networks to intelligent mobile embedded military equipment will become the wave of the development of a new generation of deep learning. However, neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we propose a new method called "deep compression", which including three stages: pruning, quantification and Huffmann encoding, to reduce the storage requirement of neural networks without impacting original accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, weapply Huffmann encoding. We evaluated our method on both MNIST and ImageNet. On the ImageNet dataset, our method reduced the storage of AlexNet by 35× without loss of accuracy and compressed VGG-16 model by 49×, also with no loss of accuracy. Our method is an efficient solution for real-time multi-objective recognition based on lightweight deep neural networks.
WWT 빅데이터를 활용한 중학교 STEAM 프로그램 개발 및 적용

This study developed a big data-based STEAM (Science, Technology, Engineering, Art & Mathematics) program using WWT (World Wide Telescope), focusing on content elements of ‘solar system’, ‘star and universe’ in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to one middle school 176 students simple random sampled. The results of this study are as follows. First, we developed a program to encourage students to actively and voluntarily participating, utilizing the astronomical data platform WWT. Second, in the paired t-test based on the difference between the pre- and post-scores of the creative problem solving measurement test, significant statistical test results were shown in ‘idea adaptation’, ‘imaging’, ‘analogy’, ‘idea production’ and ‘elaboration’ sub-factors except ‘attention task’ sub-factor (p < .05). Third, in the paired t-test based on the difference between the pre- and post-scores of the STEAM attitude test, significant statistical test results were shown in ‘interest’, ‘communication’, ‘self-concept’, ‘self-efficacy’ and ‘science and engineering career choice’ sub-factors except ‘consideration’ and ‘usefulness / value recognition’ sub-factors (p < .05). Fourth, in the STEAM satisfaction test conducted after class application, the average values of sub-factors were 3.16∼3.90. The results indicated that students’ understanding and interest in the science subject improved significantly through the big data-based STEAM program using the WWT.
WAYS OF FORMATION OF VALUES AND VALUE PROFESSIONAL ORIENTATIONS OF STUDENTS OF HIGHER EDUCATION INSTITUTIONS OF UKRAINE

Aspects of formation of values and value professional orientations of student youth in higher institutions of Ukraine have been revealed. Approaches to the interpretation of the essence of "values", "professional values", "moral values", "value professional orientations" in the scientific program have been described; the current state of formation of professional values of student youth in professional training of higher educational institution has been researched; the pedagogical conditions of formation of professional values of future specialists in professional training have been theoretically substantiated
Choosing a Consensus Mechanism for a Blockchain Based P2P Instant Transaction System Integrated with IoT

The Internet of Things (IoT) will play a significant role in the future market of services. Services require payments. Payments must be confirmed as fast as possible to protect customers and merchants. Integration of IoT to peer-to-peer networks provides a need for choosing an appropriate consensus mechanism and other ways to make transactions faster without losing the required level of security. As security of modern blockchain projects is controlled by decentralized applications like smart contracts, reviewed protocols are supporting this technology.
Crypto-Concerns: A Cyberskeptic Looks for Weak Links in the Blockchain

In her article, “Beyond Bitcoin: Leveraging Blockchain to Benefit Business and Society,” Professor Weldon explores the potential of blockchain technology to transform corporate governance and risk management and to promote the principles of transparency that animate various mandatorydisclosure regimes.I too am very excited by blockchain’s potential to revolutionize and make more transparent many business practices, but I also have some, pun intended, crypto-concerns. I admit that these concerns are based upon Blockchain technology interacts with the law in a number ofcontexts, corporate governance being one (as well as copyright and other IP, tax, antitrust, securities regulation, banking, criminal, corporate, maritime, insurance, and on and on), and it raises questions about the very nature of what blockchain technology can represent, such as Bitcoin, zencash, Ether, or other cryptocurrencies or currency-related products. Are these currencies “property” as traditionally conceptualized? Are they legally-defined securities, as another article in this symposium explores? Scholars, lawyers, and policy makers are grappling very publicly with these questions alongside IT professionals. This intense interest is obvious; ifyou search Google with the term “Blockchain” returns “About 219,000,000 results (0.39 seconds)[.]” There also are hundreds of scholarly articles that discuss blockchain questions on research networks.I will focus on just a few concerns. The first is on the “smart contracting” aspect of blockchain technology, and the second pertains to its current negative environmental impact. My concluding remarks will touch upon the concept of “transparency” in the promotion of blockchain technology in the legal context.
Binary and Rotational Coded-Aperture Imaging for Dynamic Light Fields

Coded-aperture imaging has been utilized for compressive light field acquisition; several images are captured using different aperture patterns, and from those images, an entire light field is computationally reconstructed. This method has been extended to dynamic light fields (moving scenes). However, this method assumed that the patterns were gray-valued and of arbitrary shapes. Implementation of such patterns required a special device such as a liquid crystal on silicon (LCoS) display, which made the imaging system costly and prone to noise. To address this problem, we propose the use of a binary aperture pattern rotating along time, which can be implemented with a rotating plate with a hole. We demonstrate that although using such a pattern limits the design space, our method can still achieve a high reconstruction quality comparable to the original method.
A Multi-Level Feature Fusion Network For Scene Text Detection with Text Attention Mechanism

To solve the problems of missed text detection and inaccurate text region location in natural scene text detection, a multi-scale and multi-level feature fusion network with attention mechanism is proposed. This method uses the Mask RCNN as the basic framework and improve the backbone ResNet with deformable convolution and rectangular pooling, which extracts multi-level features including global-level, word-level, and character-level, which can extract more comprehensive and richer text feature information. Besides, this paper proposes a region proposal network with text attention mechanism. Experimental results show that the algorithm model effectively extract more useful feature information, significantly improves the recall and accuracy of text detection compared with the current existing methods, and can be applied to actual text detection tasks.
An Adaptive Automated Arabic Essay Scoring Model Using the Semantic of Arabic WordNet

An Automated Essay Grading (AEG) system is designed to be used in public schools, universities and companies, which is used to saving time and cost and to reduction in errors and unfairness due to human bias. The AEG widespread use and applied for multi-language such as English and French and others, but the researches in automated Arabic essay is limited. Therefore, this research introduces an Arabic Automated Essay Grading (AAEG). The aim of this research is to build a new model that is able to evaluate students' answers to Arabic essay questions with a score that is close to that provided by the teacher manually. The model relies on the semantic of Arabic WordNet model to achieve the accuracy and to avoid the weakness in stemming, after using a hybrid method in stemming with Arabic WordNet tables and using Arabic WordNet to search for all synonyms for all reference answer words so that students' marks don't oppress because they didn't write the same words reference answer.
Colors - One: Perceptually Based Color Photo Editing

We introduce ‘Colors - One’, a perceptually based color photo editing app built for the Apple ecosystem. The app’s core algorithm augments standard Poisson image editing methods to allow the prediction and editing of perceived image color, rather than pixel color. Users can isolate 16 unique hues and edit the contrast color of each hue individually. The resulting photo edits are striking and provide new insights into the nature of perceptual color representations.
An Approach for Offline Handwritten Character Shape Reconstruction Using Active Contour and Morphological Techniques

Optical Character Recognition (OCR) models have increased their popularity in the past decade. The accuracy of OCR models has been increased with the new research models, which have improved the recognition rate of the OCR algorithms. In this work, the improved model for character shape restoration practices of handwritten documents is proposed. The proposed model involves the active contour selection (ACS) model to detect object locations, where the shape restoration method can be applied. The contour selection model utilizes the snake model for marking and localization of the characters in all possible shapes using polygonal point marking for the extraction of the object boundary. Then the distantia factor is applied over the detected contour regions to analyze the distance between two contour regions, which decides upon the selection of the target regions, where the opening needs to be closed. The combination of adaptive dilation and adaptive erosion has been utilized for filling or closing of the target region, which verifies the angle of the contour objects and thickness for removal of the smudge regions. The proposed model has undergone various experiments to assess the proposed model quality against the existing models. The proposed model has been found efficient and improved in terms of obtained performance parameters of peak signal-to-noise ratio (PSNR), mean squared error (MSE) and relative error. The proposed model has been recorded with 0.0006 percent relative error compared to 0.03, 0.26 and 0.03 in the case of the ring radius method, restoration through the medial axis and iterative midpoint method, respectively, which shows the robustness of the proposed model.
STL Performance Experiments and Analysis in undergraduate Data Structure Course

When having undergraduate data structure courses, we discovered that after learning STL containers, students still don't fully understand how does STL works, the characteristic of different containers, and how computer architecture can influence the performance of STL containers. In order to help students to have a better understanding of STL containers, and learn more about how the memory and cache will influence the performance of containers, so that storing, searching and sorting can be done more efficiently when facing different kinds of restrictions such as limited time and memory, this paper gives an introduction to the implementation of the seven popular STL containers, which are vector, list, deque, set, map, unordered_set, and unordered_map. The performance of insertion, searching, and sorting of them is tested with different sizes of data. After the test, we analyze the result from the perspective of the memory hierarchy. According to the experiments, vector has the fastest insertion speed; Among the three sortable containers, the sorting performance of vector and deque are very close, while list is much slower. Among set, map, unordered_set, unordered_map and unordered_set possess the best searching performance. Based on the test results, we offer some suggestions on how to choose a container for different requirements.
The Role of Effective Inventory Management System on Customers’ Satisfaction; The Case Study of Ethiopia Electric Utility, Northeast Region Store Dessie

Various continuous utility management activities have been pursued by the Ethiopian Electric Power Company (EEPCo), the state-owned monopoly that produces, transmits, distributes and sells electric power nationally. However, based on an organizational review of the vertically integrated state-owned Ethiopian electricity company undertaken by IPA Energy and Water Economics, a major organizational reform could bring acceleration in business operations and achievement of corporate goals. In general, the main outcome of this research will be examining the role of an effective inventory management system on customers’ satisfaction. Inventory management is an important factor in successful customers’ satisfaction. The researcher uses a case-study style with some practical modifications to match the contextual requirements. In this research, data mining, machine learning and data science methods were applied to the data detected on Twitter. Also in this study, text mining methods were applied to the data collected on Twitter. In total, 142,000 tweets were collected. It was observed that the homogeneity of the data set increased the success rate. Besides, different machine learning algorithms were tried within the scope of the study and results with high success rates were tried to be obtained. The highest success rate was obtained with the naive Bayes (kernel) algorithm with 52.09%. As a result of applied machine learning algorithms, it has been observed that the effective stock management system is associated with customer satisfaction from sentence structures and words used, concerning the thesis in question.
FPRC5: Improving the Data Security using Format PreservedRC5 Block Cipher Algorithm in Credit Card Application

The implementation and current presentation of statistical analysis methods for encoding a message /converting code into plain text is proposed in this paper. Data using Format-Preserving Encryption(FPE) is discussed in this article. FPE converts plaintext of a specific format into a ciphertext of the same format- e.g., converting valid credit card number (CCN) into valid CCN. To defend confidentiality or own data, cryptography is commonly used in routine apps like internet transfers, VPN systems, file encryption, or complete hard drives. When introducing an FPE scheme, the data structure is maintained, database structures are improved to secure, and the database integrity is ensured. It is suitable for masking data. Data masking hides or replaces a unique data form with random data Ensuring Data Security in Databases Using Format Preserving Encryption. In this project, the FPE was designed based on a block cipher and used as the basis for data encryption the RC5 bit encryption because RC5 has the variable word size, round numbers, and secret variable key. Also, active or protected format protection encryption/decryption systems and quantitative input data must be implemented to improve data security or ensure accurate and confidential messages. This approach's implementation helps all forms of data formats, but this work analyzes numerical information methodology, like CCN. This is tested by RC5 on multiple CCN, symmetric key encryption algorithm. The execution time of FPE is 4934470 nanoseconds. The execution time of the proposed format preserved RC5 algorithm is very less in comparison to the existing AES+FPE approach.
Towards an alternative to lockdown: Pandemic management leveraging digital technologies and artificial intelligence

Practitioners of pandemic management seek a clear understanding on how to manage leverage technology, regulation, and other mechanisms to fight the pandemic. Two key levers are prevention of infections, closely followed by mitigation, the ability to break chains of existing infections. We review approaches toward these key levers, illustrating the catalytic potential of information and communication technology and artificial intelligence. Pandemics have so far been mitigated by “one-size-fits-all” massive lockdowns with big social and economic impacts. Towards improved effectiveness balanced with less negative side effects, one could take a more informed personalized as well as collective risk management approach, which we term Pandemic Cohort Management. It can drastically improve prevention and mitigation. It relies on real-time data, to enable informed personalized decisions, instead of “one-size-fits-all.” Furthermore, it scales down all the way to the level of meetings of small groups of people and optimizes individual as well as collective risk and cost models.
VG3 Cipher for Secure Image Transmission

Abstract The rampant growth of Internet and communication has revolutionized the way people communicate and it has led to rapid enhancement in modern information warfare. The world around us requires the information security more than ever before due to copious reasons primarily being the importance of financial and personal data which can be utilized by the malicious hackers to gain undue advantage. Out of innumerable methodologies available for data safeguarding, cryptography has always been an indispensable tool. However, the changing ways of cyber-attacks demand an upgraded cryptographic version that can handle potential attacks on media of myriad data types. This paper proposes one such encrypting technique which is based on biological methods and used principally for the encoding of digital images based on amalgamation of DNA encryption and Rubik’s cube.
Surface slope tolerances: the transition from geometric raytracing to scalar wave theory

In a previous presentation, the author presented a method of tolerancing mid-frequency surface ripple, based on a sensitivity parameter that related the slope error on the surface to the ray deviation at the image plane. The method established an upper allowable limit for the slope error on the surface. Because the analysis method was based on geometric raytracing, it included no consideration of scale. (Snell’s law is Snell’s law, regardless of the size of the feature.) Although the intention was to apply this design method only to macroscopic surface features, the notion of a slope limit without a scale limitation raises the question as to the region of validity, i.e., when do we need to take diffraction or scatter into account? In this presentation, we examine the transition between geometric raytracing and scalar diffraction theory. This is entirely analogous to the transition between the geometric model of a Ronchi test and that of a diffraction-based, lateral shear interferometer.