Reviewerly/exHarmony huggingface dataset:
Machine learning methods for medical diagnostics based on a multimodal approach: a brief review

The brief review shows the potential of machine learning to improve the accuracy of multimodal medical diagnostic methods to a new level. Various machine learning algorithms, modalities, and cases demonstrating this approach’s significance are considered.
Blockchain-based solutions for Cloud Computing Security: A Survey

Cloud computing is a technology that provides infrastructure and data services at an affordable cost, with minimal effort, and with a high level of scalability. And it is continuously increasing demands daily. Furthermore, it raised many security challenges that had to be addressed. Blockchain has become a critical technology for providing security, particularly regarding integrity, authenticity, and confidentiality. It also can help cloud computing growth by overcoming security challenges. Many blockchain-based solutions were proposed for security challenges in cloud computing. This survey gives a background about cloud computing and blockchain. Also, it briefly studies how blockchain can affect cloud computing, especially the security improvements it can provide. The main contribution of this paper is to explore and summarizes existing blockchain-based solutions for cloud computing security challenges.
Structural Model of Consumer Heuristic Information Processing for Fashion Products in the Virtual Reality Store Environment

Background/Objectives: This study aims to estimate the structural model of consumer heuristic information processing in the context of virtual reality(VR) fashion store. Specific objectives are to identify underlying dimensions of mental imagery, to examine the effect of mental imagery on telepresence, and to predict consumer choice heuristic for purchasing fashion products in the VR store environment&#x0D; Methods: The VR store of fashion brand X was selected as a stimulus in the VR experiment. A self-administered questionnaire was developed to measure mental imagery, telepresence and choice heuristics. By online survey, a total of 319 usable responses was obtained from consumers (female=169, males=150) who were aged from 20 to 39 years old.&#x0D; Results: The mental imagery was consisted of three factors, such as elaboration, haptic imagery, and spatial imagery. Estimated structural model showed that the elaboration and spatial imagery had positive effects on telepresence. Also, the telepresence influenced choice heuristics(e.g., intuition based heuristic and experience based heuristic), suggesting the mediating effect of telepresence between mental imagery and choice heuristics for fashion products at the VR store.&#x0D; Conclusion/Implications: This study provides insights into understanding mechanism of information processing in the VR experience. Also, managerial implications were discussed for applications of VR technology into strategic retail management in the fashion markets.
NOT A BOOK: Goodreads and the Risks of Social Cataloging with Insufficient Direction

Social cataloging websites, such as Goodreads, LibraryThing, and StoryGraph are widely popular with individuals who want to track their reading and read reviews. Goodreads is one of the most popular sites with 90 million registered users as of 2019. This paper studies a Goodreads cataloging rule, NOT A BOOK (NAB), through which users designate items as invalid to the site’s scope while preserving some of their metadata. By reviewing NAB, we identify thirteen types of invalid items. We go on to discuss how these item types unevenly reflect the rule itself and the emergence of a “non-book” sense through social cataloging.
6 Persian Named Entity Recognition with Structural Prediction Methods

Named-entity recognition (NER) is still a challenging task for a number of languages due to the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. The ArmanPersoNERCorpus, the first manually-annotated Persian NER corpus that we have released in 2018, has enabled supervised prediction of named entities in the Persian language. Tackling NER as a sequential labelling task, in this chapter we compare the performance of a range of sequential classifiers on the aforementioned annotated corpus. The applied classifiers include a conventional CRF, the SVM-HMM, the Jordan recurrent neural network, the BiLSTM-CRF (a state-of-the-art deep learning architecture for the NER task), and the Flair neural language model. Moreover, the performance of each classifier has been assessed using various word embeddings as pre-trained feature vectors, including Hellinger PCA, CBOW, skip-gram, GloVe and fastText. The combination of the Flair model and the fastText word embeddings has achieved the highest CoNLL-F1 score of 78.59 % which outperforms the previous state-of-the-art on Persian NER by 1.14 percentage points.
Method of depth simulation imaging and depth image super-resolution reconstruction for a 2D/3D compatible CMOS image sensor

This paper presents a depth simulation imaging and depth image super-resolution (SR) method for two-dimensional/three-dimensional compatible CMOS image sensors. A depth perception model is established to analyze the effects of depth imaging parameters and evaluate the real imaging effects. We verify its validity by analyzing the depth error, imaging simulation, and auxiliary physical verification. By means of the depth simulation images, we then propose a depth SR reconstruction algorithm to recover the low-resolution depth maps to the high-resolution depth maps in two types of datasets. With the best situation in depth accuracy kept, the root mean square error (RMSE) of Middlebury dataset images are 0.0156, 0.0179, and 0.0183 m. The RMSE of RGB-D dataset images are 0.0223 and 0.0229 m. Compared with other listed conventional algorithms, our algorithm reduces the RMSE by more than 16.35%, 17.19%, and 23.90% in the Middlebury dataset images. Besides, our algorithm reduces the RMSE by more than 9.71% and 8.76% in the RGB-D dataset images. The recovery effects achieve optimized results.
2 Challenges of Persian NLP: The Importance of Text Normalization

Data is said to be the new oil. This applies to textual data as well. The amount of plain textual data on the Internet is abundant, and it is still growing. However, similar to oil, raw data is not valuable by itself. Its value and potential are availed through further accurate processing and analysis. And this is where Natural Language Processing (NLP) and its procedures come into the picture. Since NLP tasks require standardized and high-quality inputs for high efficiency and performance, raw textual data requires at least a basic level of cleaning and standardization. Therefore, text normalization - a process of transforming noisy data into improved, i. e., a standard representation - is often a prerequisite for a variety of NLP tasks, including information extraction, machine translation, or sentiment analysis. The Persian language, being the 5th content language of the Web, can be a great source of diverse information. However, several language-specific characteristics hinder the potential application of Persian data. The focus of this chapter lies in describing challenges that the Persian language poses for NLP and evaluating the impact of normalizing a large Persian corpus for one of the downstream NLP tasks - the discovery of multiword expressions (MWEs).
Real-world abnormal event detection via super-resolution and few-shot learning

The performance of abnormal event detection has been greatly improved in recent years. However, due of the problems of low resolution and few labeled training samples, it is difficult to apply in practice. To address these issues, we propose a few-shot scene-adaptive abnormal event detection method. First, to restore high-quality video from low-quality video, we propose a weighted recurrent backprojection network (WRBPN). WRBPN selectively extracts supplementary information from adjacent frames based on their similarity, which is beneficial to the reconstruction of the target frame and suppresses the interference of irrelevant frames. Second, we propose an abnormal event detection network to detect abnormal events in high-quality surveillance videos. The network extracts multiscale features to obtain more discriminative representations and uses variance attention to suppress the interference of background noise. On this basis, the video frame is predicted, and abnormal event detection is performed according to the prediction error. Finally, in order to quickly adapt the model to new scenarios, we propose a metalearning-based approach. During the training process, the distribution of model parameters is learned, and the specific parameters of the model are obtained via distribution sampling. Experiments on benchmark datasets demonstrate the effectiveness of our method.
Expert System For Diagnosing Pests And Diseases Of Ginger Plants Using Case-Based Reasoning Method

Ginger (Zingiber officinale Rosc.) is a medicinal plant that has many benefits and properties that makes ginger one of the export commodities of Indonesian spices. One of the obstacles in exporting fresh ginger is the low quality of ginger caused by pest and disease attacks. Generally in preventing and controlling the spread of pests and diseases namely consult an expert, but the availability of experts is not always there in every area. Therefore this research builds a website based expert system to diagnose pests and diseases of ginger plants with the Case-Based Reasoning method. This method is a problem-solving that uses knowledge from previous experience to solve new problems. The expert system can diagnose five diseases and two pests with 39 symptoms. Based on the simulation test data as many as 50 done randomly, obtained a percentage value of 96% for expert diagnosis results with a system for the Case-Based Reasoning method. This Expert System can help farmers identify and control pests and diseases that attack the ginger plant.
A Study on the Effectiveness of a Liberal Arts Class about Cultural Diversity for Foreign Students

본 연구는 한성대학교의 교양 필수 과목인 〈문화다양성과 문화간 대화〉 외국인 분반 수업의 효과성을 검증하기 위한 연구이다. 연구 질문은 다음 두 가지이다. 첫째, 이 수업은 학생들의 글로벌시민의식 함양에 영향을 미치는가? 둘째, 이 수업은 학생들의 대학생활적응에 영향을 미치는가? 연구를 위해 2022년 2학기에 해당 수업을 수강한 외국인 유학생 59명을 대상으로 수강 전과 수강 후에 설문조사를 시행하였으며, 그 결과에 대해 대응표본 t검정을 실시하였다. 검증 결과, 해당 수업을 통해 수강 학생들의 글로벌시민의식이 향상되었으며, 대학생활적응에도 긍정적인 영향을 미쳤음을 확인할 수 있었다. This study is to verify the effectiveness of a college liberal arts subject for foreign students called Cultural Diversity and Intercultural Dialogue. The first study question is whether taking the class affects the cultivation of students" global citizenship, and the second question asks participating in the class affects students" adaptation to college life. To this end, surveys were conducted on 59 foreign students who participated in the class before and after taking the class, and a paired t-test was conducted on the results. In conclusion, it was confirmed that the global citizenship of the students was improved through the class and taking the class also had a positive effect on their adaptation college life.
Real-time Detection of Specific Events: A Case Study of Detecting Falls

Detecting people who have fallen is a crucial problem since they may have severe injuries. In this study, we combined existing datasets to create a new dataset for generalized fall detection performance in a wild environment with diverse domains. Furthermore, we propose simple yet powerful rule-based methods for fall detection and real-time operation: the bounding box ratio and bounding box overlap. Our method was evaluated with YOLOv5 as a backbone network and achieved performance improvements by 0.126 in precision, 0.08 in recall, 0.156 in 50 mAP, and 0.11 in 95 mAP compared to our baseline, the VFP290K dataset [7]. In addition, compared to the baseline, the performance of our method improved by 0.349 in precision and 0.104 in the F1 score.
A Study on the Policy Analysis and Implications of Age Policy for Law-intruding Juveniles - Focusing on the Model of Kingdon’s Policy Streams -

현재 만 10세이상 14세 미만의 청소년은 범죄를 저질러도 형사처벌 대신 보호처분을 받도록 하고 있다. 최근에는 이를 악용한 청소년이 일부러 범법행위를 하면서 도발하던 사례가 언론에 보도가 되어 사회적 공분을 사는 등 촉법소년의 상한 연령을 낮춰야 한다는 주장은 꾸준히 제기되어 왔다. 그러나, 학계 전문가 대부분은 처벌 목적의 연령 조정은 근본적인 해법이 될 수 없다는 주장이 우위를 차지하고 있으며, 시민단체 등에서도 촉법소년 연령 조정에는 반대의 입장을 고수하고 있다. 하여 역대 정부는 촉법소년 연령을 조정하기 위한 정책 결정을 하였어도 시행까지 가지 못하는 일들이 계속 반복되어 왔다. 따라서, 본 연구에서는 과거 역대 정부에서 촉법소년 연령 조정정책 결정 과정을 분석하여 올바른 정책의 결정과 시행에 필요한 시사점을 찾아보고자 하였다. 이러한 정책의 결정은 일정한 흐름을 가진다는 Kingdon의 정책 흐름 이론을 활용하여 정책의 문제인식, 대안의 흐름, 정치의 흐름 등의 요소를 시간의 흐름에 따라 분석하여 시사점을 찾는데 연구의 목적을 두었다. 촉법소년에 대한 연구는 오랜 시간 지속적으로 다뤄진 주제로 다소 식상할 수 있다는 연구에 한계는 있다. 그러나 본 연구는 그간 반복되어온 촉법소년 연령 조정정책에 대하여 정책학적 이론에 맞춰 분석 함으로써, 오랜 기간 정책의 대안을 제시하지 못하는 원인을 찾고 정책 수립의 방향성을 제시하였다는데 연구의 의의가 있다. Currently, teenagers aged 10 to 14 are required to receive protective measures instead of criminal punishment even if they commit a crime. Recently, cases in which teenagers who abused it intentionally provoked by committing illegal acts have been reported in the media, causing social resentment, etc it has been consistently argued that have to lower age group of Law-intruding juveniles. However, most academic experts argue that age adjustment for punishment cannot be a fundamental solution, and civic groups are also opposed to age adjustment for juvenile offenders. Therefore, even if previous governments made policy decisions to adjust the age of young people, things that could not be implemented have been repeated. Therefore, in this study, we tried to find the implications necessary for the decision and implementation of the right policy by analyzing the decision process of Law-intruding juveniles age group adjustment policy in the past governments. The purpose of this study was to find implications by analyzing factors such as policy problem recognition, alternative flow, and political flow over time by using Kingdon"s policy stream theory that policy decisions have a certain flow. There is a limit to the study that research on Law-intruding juveniles can be somewhat boring as a subject that has been continuously dealt with for a long time. However, this study is meaningful in that it analyzed the repeated a Law-intruding juveniles policy for young people in accordance with policy theory to find the cause of not being able to present an alternative to policy for a long time and to suggest the direction of policy establishment.
Calculating the future <b>Quantum Supremacy: How the Quantum Computer Revolution Will Change Everything</b> <i>Michio Kaku</i> Doubleday, 2023. 352 pp.

A quantum computing primer offers insights into the technology’s most promising potential applications
The Relationship among Principal’s Transformational Leadership, Teacher Efficacy, and Teacher Job Satisfaction: The Conditional Process Analysis of Democratic School Climate

본 연구는 학교장의 변혁적 리더십, 교사 효능감, 교사 직무만족도 간 관계에서 민주적 학교풍토의 조건부 과정(조절된 매개연구)을 분석하는 데 목적이 있다. 분석 자료는 경기교육종단연구 9차년도(2020년) 자료이다. 해당자료는 SPSS 21.0과 PROCESS macro v4.0을 사용하여 분석되었다. 연구결과, 첫째, 학교장의 변혁적 리더십은 교사 직무만족도에 정적인 영향을 미쳤다. 즉 학교장이 변혁적 리더십을 효과적으로 발휘할수록 교사 직무만족도가 올라간다. 둘째, 교사 효능감은 학교장의 변혁적 리더십과 교사 직무만족도를 매개하였다. 이는 학교장의 변혁적 리더십이 교사 직무만족도를 증진시키는 과정에서 교사 효능감을 통해 그 과정이 이루어짐을 의미한다. 셋째, 민주적 학교풍토는 학교장의 변혁적 리더십→교사 효능감의 관계와 학교장의 변혁적 리더십→교사 직무만족도의 관계에 대해 유의한 조건부 직접효과를 가졌다. 즉 민주적 학교풍토는 학교장의 변혁적 리더십이 교사 효능감에 미치는 영향과 교사 직무만족도에 미치는 영향을 강화하였다. 넷째, 민주적 학교풍토는 학교장의 변혁적 리더십→교사 효능감→교사 직무만족도의 관계에 대해 유의한 조건부 간접효과를 가졌다. 즉 학교장의 변혁적 리더십이 교사 직무만족도에 영향을 미치는 과정에서 교사 효능감을 매개하는 경로는 민주적 학교풍토의 수준에 따라 그 효과가 달라졌다. 본 연구결과에 터해 교사 직무만족도 증진을 위한 정책적 제언을 제시하였다. The purpose of this study is to analyze the conditional process of the democratic school climate in the relationship among the principal’s transformational leadership, teacher efficacy, and teacher job satisfaction. As a result, first, principal’s transformational leadership had a positive effect on teacher job satisfaction. The more the principal demonstrates transformational leadership effectively, the higher the teacher job satisfaction. Second, teacher efficacy mediated the principal’s transformational leadership and teacher job satisfaction. This means the principal’s transformational leadership enhances teacher job satisfaction through teacher efficacy. Third, the democratic school climate had a significant conditional direct effect on the relationship between principal’s transformational leadership and teacher efficacy, between principal’s transformational leadership and teacher job satisfaction. The democratic school climate strengthened the effect of the principal’s transformational leadership on teacher efficacy and job satisfaction. Fourth, the democratic school climate had a significant conditional indirect effect on the relationship among the principal’s transformational leadership, teacher efficacy, and teacher job satisfaction. In the process in which the principal’s transformational leadership affects teacher job satisfaction, the path that mediates teacher efficacy differs depending on the level of the democratic school climate. Based on the results, policy suggestions were presented.
Spatial and Temporal Solar Potential Variation Analysis in Uganda Using Measured Data

The paper presents an analysis of spatial and temporal solar potential variations in Uganda. The solar radiation potential distribution was investigated based on measured data from 56 ground meteorological stations across Uganda from January 2015 to February 2022. The data were recorded after every 5-minute interval. The monthly average daily Global Horizontal Irradiance (GHI) was analysed using the spatial inverse distance weighting (IDW) interpolation technique in ArcGIS 10.7.1. The GHI distribution was found to have maxima at equinox and minimum between June and July. The average global horizontal irradiation is found to be 1680 KWh per year, with a daily variability below 10%. This is above the threshold for solar energy applications and with such low variability, large scale solar PV plants can be installed with minimum backup requirements. Northern Uganda receives the highest average daily global horizontal irradiation of 5.38 Whm–2 day–1, while Western Uganda receives 4.16 Whm–2 day–1, the lowest. The average AM/PM ratio depicted a value above 2 for all regions in Uganda and therefore, optimal solar energy exploitation for fixed systems favours eastern orientation. The central, eastern and northern Uganda which is 69.3% of the country has relief favourable for large scale solar PV power plants and CSP facilities.&#x0D; Keywords: GHI, IDW, Global solar radiation, Solar insolation, PV power plants, Uganda
Edge‐featured graph attention network with dependency features for causality detection of events

Abstract Causality detection, as a more fine‐grained task than causality extraction, which aims to detect the components that represent the cause and effect in sentence‐level texts with causality, is a significant task in the field of Natural Language Processing (NLP). Previous research on causality detection has concentrated on text token features whilst ignoring the dependency attributes between tokens. In this paper, we propose a model that uses the Edge‐featured Graph Attention Network based on dependency‐directed graphs for the causality detection task. To begin, we convert the texts with causality into the representation of dependency‐directed graphs (DDG), which regard the dependency attributes between tokens as edge features. Then we use Edge‐featured Graph Attention Network to aggregate the node and edge features of DDG. Finally, we put the graph embedding into Bi‐directional Long Short‐Term Memory (BiLSTM) layer to learn the dependencies between forward and backward long‐distance nodes in DDG. Experiments on three datasets prove that this method achieves better performance in precision, recall, and other evaluation metrics compared with other methods.
Managing Data For Patron Privacy: Comprehensive Strategies For Libraries,

"Managing Data For Patron Privacy: Comprehensive Strategies For Libraries,." Technical Services Quarterly, 40(2), pp. 120–121
Fidelity-Based Smooth Min-Relative Entropy: Properties and Applications

The fidelity-based smooth min-relative entropy is a distinguishability measure that has appeared in a variety of contexts in prior work on quantum information, including resource theories like thermodynamics and coherence. Here we provide a comprehensive study of this quantity. First we prove that it satisfies several basic properties, including the data-processing inequality. We also establish connections between the fidelity-based smooth min-relative entropy and other widely used information-theoretic quantities, including smooth min-relative entropy and smooth sandwiched R\'enyi relative entropy, of which the sandwiched R\'enyi relative entropy and smooth max-relative entropy are special cases. After that, we use these connections to establish the second-order asymptotics of the fidelity-based smooth min-relative entropy and all smooth sandwiched R\'enyi relative entropies, finding that the first-order term is the quantum relative entropy and the second-order term involves the quantum relative entropy variance. Utilizing the properties derived, we also show how the fidelity-based smooth min-relative entropy provides one-shot bounds for operational tasks in general resource theories in which the target state is mixed, with a particular example being randomness distillation. The above observations then lead to second-order expansions of the upper bounds on distillable randomness, as well as the precise second-order asymptotics of the distillable randomness of particular classical-quantum states. Finally, we establish semi-definite programs for smooth max-relative entropy and smooth conditional min-entropy, as well as a bilinear program for the fidelity-based smooth min-relative entropy, which we subsequently use to explore the tightness of a bound relating the last to the first.