Reviewerly/exHarmony huggingface dataset:
Novel Approach for Person Detection Based on Image Segmentation Neural Network

With the rise of the modern possibilities in computer science and device engineering, as well as with growing population in big cities among the world, a lot of new approaches for person detection have become a very interesting topic. In this paper, two different approaches for person detection are tested and compared. As the first and standard approach, the YOLO architectures, which are very effective for image classification, are adapted to the detection problem. The second and novel approach is based on the encoder-decoder scheme causing the image segmentations, in combination with the locator. The locator part is supposed to find local maxima in segmented image and should return the specific coordinates representing the head centers in the original image. Results clearly report this approach with U-Net used as encoder-decoder scheme with the locator based on local peaks as the more accurately performing detection technique, in comparison to YOLO architectures.
Case management of the state of new york child protection system in the united states: Implications for the improvement of case management of the child protection system in south korea

연구목적: 본 연구는 미국 뉴욕의 아동학대예방을 위한 사례관리 실천체계를 분석하여한국 아동보호체계에서의 사례관리 전문성 발전 방안을 모색하고자 하였다. 방법: 미국 뉴욕의 아동학대예방체계 사례관리 실무자 4명을 대상으로 심층면담을 실시하여 분석하였다. 결과: 뉴욕의 아동학대예방 사례관리체계는 전문성을 유지하기 위해 첫째, 엄격한 사례관리 실무자 자격, 둘째, 수준 높은 수퍼비전체계, 셋째, 강점중심의 평가체계, 넷째, 전문성향상을 위한 교육체계, 다섯째, 실무자들의 스트레스 관리를 돕는 Self-Care체계, 여섯째,사례관리 실천에 대한 명확한 원칙과 가치, 일곱째, 질적 개입이 가능한 사례 수, 여덟째,가족의 역량강화를 돕는 해결기반사례관리모델과 가족의 참여를 격려하는 가족팀회의, 마지막으로, 정부와 민간의 유기적 협력체계를 구축하고 있음을 제시하고 있다. 결론: 미국 뉴욕의 아동학대예방을 위한 사례관리체계를 살펴본 결과 아동학대예방을 위한 전문적 사례관리체계가 구축되어 있고, 정부 주도의 적극적 개입과 지원체계를 확인할 수 있었다. 이를 기반으로 우리나라 아동학대예방 사례관리체계의 전문성 향상을 위한 실천적·정책적 함의를 결론에서 제안하였다.Objectives: The purpose of this study is to find ways to develop and improve case managementexpertise in the child protection system of South Korea by analogizing the casemanagement system in child abuse prevention of the State of New York in the UnitedStates. Methods: The in-depth interviews were conducted with four case management practitionersof the State of New York child abuse prevention system. Results: The results of the analysis show that the following nine components are present;high standards of case management practitioner qualification; a high-quality supervisionsystem; a strength-oriented evaluation system; an educational system for professional improvement;a self-care system to help case management practitioners manage their stressfrom dealing with child abuse; clear principles and values for the practice of case management;the appropriate number of cases where case management practitioners are able to intervenequalitatively; a solution-based model to improve family’s empowerment and familyteam conferences to encourage family involvement in case management; and a strong cooperationsystem between the Administration for Children’s services(ACS) and the case managementagency. Conclusions: The analytic results show that the US government at all levels actively engagesand supports the case management system in child abuse prevention. Based on thisanalysis, practical implications for improving the professionalism of the case managementsystem for child abuse prevention in South Korea were proposed.
Multi-model Software Process Improvement based on C2M and MR-MPS-SW Models

The pursuit of competitive advantage has boosted organizations to improve their development processes. With this, a range of maturity models for software development processes has emerged on the market. In this sense, research efforts have been increasingly necessary to understand how organizations should deal with the various maturity models, given the heterogeneity of the characteristics of these models. In this context, this work aims to propose a mapping between MR-MPS-SW and C2M models. The proposed mapping was reviewed and evaluated by five experts with experience in software process improvement. This mapping allowed us to identify that 82.2% of C2M practices with some type of coverage for the expected MR-MPS-SW results. Finally, it is possible to see that the models here mapped are not conflicting, but rather complementary, with C2M being partially adherent to MR-MPS-SW.
An Analysis of Power Characteristics of Oil-Free Scroll Vacuum Pumps

This paper presents an analysis of the indicator power of an oil-free scroll vacuum pump based on the indicator diagrams obtained through high-speed pressure sensors. These values are compared with the results of calculations using a mathematical model of the pump working process. It is shown that the divergence of the calculated results and experimental values does not exceed 4%, which confirms the adequacy of the developed mathematical model. The total power of the scroll pump exceeds the indicator power by more than 2 times due to the friction losses between the face seals and disks of the reciprocal scroll elements, friction losses in the stuffing box seals and rolling bearings, as well as due to the coefficient of efficiency of the motor. The influence of the radial clearance between the scroll elements on the power consumption is considered. It is shown that at low pressures nearing the ultimate pressure, the power increases with the increased clearance, while at inlet pressures exceeding 40 kPa it decreases. The performed analysis can be used for selecting the optimal geometrical parameters of the scroll elements and increasing power efficiency of the pump depending on specific operating conditions.
News Articles Evaluation Analysis in Automotive Industry Using GPT-2 and Co-occurrence Network

News articles have great impacts on asset prices in the financial markets. Many attempts have been reported to ascertain how news influences stock prices. Stock price fluctuations of highly influential companies can have a major impact on the economy as a whole. In particular, the automobile industry is a colossal industry that leads the Japanese industry. However, the limitations in the number of available data sets usually become the hurdle for the model accuracy. In this study, we constructed a news evaluation model utilizing GPT-2. A news evaluation model is a model that evaluates news articles distributed to financial markets based on price fluctuation rates and predicts fluctuations in stock prices. We have added news articles generated by GPT-2 as data for analysis. Besides, we used a co-occurrence network analysis to review the overview of the news articles. News articles were classified through Long Short-Term Memory (LSTM). The results showed that the accuracy of the news evaluation model improved by generating news articles using a language generation model through GPT-2. More detailed analyses are planned for the future.
A window-based sequence-to-one approach with dynamic voting for nurse care activity recognition using acceleration-based wearable sensor

This paper introduces a window-based sequence-to-one approach with dynamic voting for nurse care activity recognition using acceleration-based wearable sensors. Nurse care activity recognition is an essential part of ensuring high quality patient care and providing constructive and concrete feedback to the care team. Some of the current sensing approaches for activity recognition include vision-based sensing and non-wearable RF sensing. However, their application is limited in real-life scenarios due to restrictive factors such as perceived privacy and sensitivity to specific occupant paths. To overcome these limitations, acceleration-based wearable sensing have been introduced in recent works. However, the duration distribution of nursing activity instances are biased and skewed. This skewness leads to imbalanced datasets which will result in low performance for the common predictive models. Further, uncertainties such as ambient noise and environmental factors affect the signals and thus can potentially reduce the activity recognition performance. To overcome the first challenge, we separate the signals into short windows with adaptive overlapping ratios for activity instances having different lengths, which balances the label distribution due to event length variations. Further, we use a multi-layer Long Short-Term Memory (LSTM) model to predict nursing activities of each sliding window and introduce a voting-based scheme for complementing the predictions across the signal windows and addressing the uncertainty challenge. We validate our approach through participation in "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data" as team HealthyVibes. On the challenge dataset our model achieves 97.4% and 43.9% accuracy for training and validation, respectively.
A Blockchain-Based Decentralized Public Auditing Scheme for Cloud Storage

Cloud computing has attracted wide attention because that it can provide the data storage and computation to us. However, when the users outsource the data to the cloud server, the users lost the control over the data, so the data auditing is very important to protect users’ data. To reduce computation and communication costs of the users, the existing schemes utilized a trusted third party (TPA) to conduct verification on behalf of users. However, TPA is a centralized party, which is vulnerable to external attacks and internal faults. In this paper, we present a decentralized public auditing scheme for cloud storage based on blockchain, which improves the reliability and stability of auditing result. In the proposed scheme, the cloud service providers works together to perform data verification without TPA. Finally, the security analysis shows that the scheme can resist a variety of attacks, and the experimental results demonstrate that proposed scheme has enhanced security and reliability.
A Unified Model for Skewed Circular Data

The well-known circular distributions such as von Mises, cardioid and wrapped Cauchy distributions are symmetric despite the fact that the circular data sets are not always symmetric. Here, in order to fit suitable models to asymmetric data sets on the circle, a new unified skew model is introduced which includes some well-known existing asymmetric distributions as special cases. To illustrate the practical usefulness of the proposal, a real data set related to aerology is analysed.
DFDM - A DeepFakes Detection Model Based on Steganography Forensic Network

AI image tampering technology - DeepFakes has a huge impact on the authenticity of video information in the network, making “seeing is believing” no longer credible. Finding effective methods for detecting DeepFakes is very important. The existing detection methods mostly rely on artificially designed features. This paper first proposes an adaptive deep learning detection model - DFDM for DeepFakes. DFDM uses deep learning networks to adaptively extract features of forgery images, which is efficient when facing a large number of samples and avoid the limitations of traditional detection methods. DFDM refers to the structure of steganalysis and forensic networks and pays attention to the anomaly of pixel distribution in images. DFDM can identify the anomaly of image texture features better than the general classification network. DFDM abandons the residual processing in the steganographic detection network, and designs a custom convolution kernel suitable for identifying image texture features to improve the accuracy of model detection. In the model network structure, DFDM replaces the mean pooling layer in steganalysis networks with the max-pooling layer, maximally retaining the image texture features. The simulation results show that the detection accuracy of DFDM for full-face replacement reaches 0.99, and the detection accuracy for partial-face replacement reaches 0.98.
VR-Participation &amp; Dialogue: Towards Integrated Framework for Virtual Reality-Mediated Consensus and Community Building*

Successful e-Participation requires a thriving community of users-citizens who engage and collaborate with governments and decision makers on key democratic and social maters. Effective community building and meaningful social interactions are contingent on strong, organic consensus achieved through engaging dialogue rather than discussions or argumentation.The emerging social Virtual Reality platforms offer new means of immersive communication that brings an opportunity to overcome some of the challenges identified to be hindering state-of-the-art e-Participation from supporting constructive citizen-to-government dialogue. In this paper we investigate the key concepts and explore the principles of dialogue and consensus building in the context of e-Participation. We match those principles with specific VR affordances and propose an Integrative Framework for Virtual-Reality-Mediated Consensus and Community Building. Finally, we discuss the application of the framework to e-Participation.
Server and Storage Performance Testing for Data Center Construction

The increase in data triggered by the smartphone revolution has led to a growing interest in cloud-based data centers. However, while much research has been conducted on virtualization technologies for cloud data centers, there has not been enough research on the performance of physical systems. As manufacturers only provide performance figures measured in their own system’s favor, objective and consistent performance testing metrics and methods are needed for performance measurement. This paper suggests some performance testing metrics and methods for cloud-based physical systems using TPC, SPECjbb and SPC.
Extravagant “fake” morphemes in Dutch. Morphological productivity, semantic profiles and categorical flexibility

Abstract Dutch features several morphemes with “privative” semantics that occur as left-hand members in compounds (e.g., imitatieleer ‘imitation leather’, kunstgras ‘artificial grass’, nepjuwelen ‘fake jewels’). Some of these “fake” morphemes display great categorical flexibility and innovative adjectival uses. Nep , for instance, is synchronically attested as an inflected adjective (e.g., neppe cupcake ‘fake cupcake’). In this paper, we combine an extensive corpus study of eight Dutch “fake” morphemes with statistical methods in distributional semantics and collexeme analysis in order to compare their semantic and morphological properties and to find out which factors are the driving forces behind their exceptional “extravagant” morphological behavior. Our analyses show that debonding and adjectival reanalysis are triggered by an interplay of two factors, i.e., type frequency and semantic coherence, which allow us to range the eight morphemes on a cline from more schematic to more substantive “fake” constructions.
Tips for Graduate Students Attending Virtual Conferences

CSA NewsVolume 65, Issue 10 p. 40-41 STUDENTS Tips for Graduate Students Attending Virtual Conferences Lauren E. Schwarck, Lauren E. Schwarck M.S. student, Agronomy Department, Purdue UniversitySearch for more papers by this authorClayton J. Nevins, Clayton J. Nevins Ph.D. candidate, Soil and Water Sciences Department, University of FloridaSearch for more papers by this author Lauren E. Schwarck, Lauren E. Schwarck M.S. student, Agronomy Department, Purdue UniversitySearch for more papers by this authorClayton J. Nevins, Clayton J. Nevins Ph.D. candidate, Soil and Water Sciences Department, University of FloridaSearch for more papers by this author First published: 21 September 2020 https://doi.org/10.1002/csan.20286Read 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 onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume65, Issue10October 2020Pages 40-41 RelatedInformation
Examining the Extent of Internet Access in Public Libraries in South Africa

The provision of free internet access to users, anchors public libraries as the beacon to be heavily relied on for information access. This article reports on a study undertaken to determine the extent of internet access in South African public libraries. The paper was guided by research questions on the level of internet connectivity in South African public libraries and the challenges experienced in providing internet access to public libraries in South Africa. The study employed a survey design in which a questionnaire was used to collect data from 322 respondents, while interviews were used to collect data from nine participants. The study used the non-probability purposive sampling technique to select heads of provincial public libraries (directors) and further used stratified, probability proportional to size, and systematic techniques to select heads of public libraries. No sampling was done on the nine heads (directors) of provincial public libraries, as the nature of this population did not warrant further dissection due to its small size. The study achieved a response rate of 63% which was adequate to proceed with the analysis. It emerged that 97% of public libraries were connected to the internet, 80% of them were connected to the internet through fixed lines, and that each of the nine provinces had its own internet service provider. The study recommended, among others, that fixed lines for internet access be laid to all public libraries in South Africa, and that a government agency be used as a dedicated internet service provider for public libraries across all nine provinces.
The Influence of Different Methods on the Quality of the Russian-Tatar Neural Machine Translation

This article presents the results of experiments on the use of various methods and algorithms in creating the Russian-Tatar machine translation system. As a basic algorithm, we used a neural network approach based on the Transformer architecture as well as various algorithms to increase the amount of parallel data using monolingual corpora (back-translation). For the first time experiments were conducted for the Russian-Tatar language pair on the use of transfer learning (based on Kazakh-Russian parallel corpus). As the main training data, we created and used the parallel corpus with a total volume of about 1 million Russian-Tatar sentence pairs. Experiments show that the created system is superior in quality to the currently existing Russian-Tatar translators. The best quality for the Russian-Tatar translation direction was achieved by our basic model (BLEU 35.4), and for the Tatar-Russian direction – by the model for which the back-translation algorithm was used (BLEU 39.2).
KryptonEyed: Playing with Gaze Without Looking

As eye-tracking technologies become more affordable, the number of mainstream gaze-enabled games increases. These allow triggering in-game actions when the eyes focus on objects and locations of interest. Such gaze interactions follow the interaction paradigm ”what you look at is what you get”. We challenge this use of gaze interaction and propose to play without looking - with the eyes closed. We designed the game prototype KryptonEyed to introduce closing the eyes for eyes-only game control. Players are required to close their eyes and perform eye movements behind the eyelids before opening them to aim the teleportation of the main character. The game contains three levels integrating the proposed gaze mechanic in distinct game scenarios. These explore different challenges in their game dynamics and interaction metaphors to use the technique in various contexts of play.
Smart HealthCare Model: An End-to-end Framework for Disease Prediction and Recommendation of Drugs and Hospitals

The need for an intelligent healthcare system is indispensable. The limited knowledge and experience of doctors lead to inaccurate predictions and diagnosis of the health-related issue. Patients also have very little knowledge about the drugs that have been prescribed to them. This kind of knowledge is crucial for the patients to judge their diagnosis done by the doctor. To make people familiar with this, we have proposed a model. This model consists of three phases. (1) The first phase deals with the disease prediction on the basis of symptoms entered by the patient. (2) The second phase will suggest the patient the best drug suitable for his condition. (3) The last phase is recommending the best hospital for his/her treatment. Various machine learning algorithms have been applied to the dataset. Results show that the model is able to give medical guidance precisely and effectively.
Interrelation of Pattern Recognition, Machine Thinking and Learning Problems

The paper reviews the state of the art in structural recognition − one of the areas of modern pattern recognition. It is shown that the main problems of structural recognition go beyond the problems of machine learning, and thereby slightly moderates the revived idea that the pattern recognition problem is completely exhausted by the machine learning problem. At the same time, it is shown that the basic concepts and problems of structural recognition form the base for formalizing a certain class of thought processes that differ from the learning processes and are called imaginative thinking. The mathematical base of the works included in the review is the classical theory of Constraint Satisfaction Problem − one of the acknowledged paradigms of machine thinking. It is shown how the application of this theory to real recognition problems necessitates its further generalizations, which in turn expand the concept of machine thinking, the formalization of which this theory was originally oriented to. The generalized problem of structural recognition and imaginative thinking is formulated, a particular case of which is the classical Constraint Satisfaction Problem. For the Gibbs statistical model of recognizable objects, it is shown that the recognition of these objects is reduced to one or another particular case of a generalized structural recognition problem. For more general statistical models, not necessarily Gibbs ones, the analysis of the known learning procedures is carried out when they are used with learning information of a limited volume. The flaw of these procedures, known as the effect of short samples, is studied, its causes and ways of overcoming are shown.