Reviewerly/exHarmony huggingface dataset:
Academic freedom, resisting intellectuals, and the idea of university in South Asia

Based on the personal experiences of the author who has long-term experience of working as a teacher in South Asian universities, as well as his conversations with fellow academics, students, and teachers alike, this chapter can be regarded as a philosophical attempt to describe the nature of resistance in universities in general and South Asian universities in particular. On one hand, the author regards it very important that universities retain their peculiar epistemological position, but, on the other hand, he is partially pessimistic about whether this is actually possible, as the establishment and authorities are directing universities into different directions and at times, suppressing the dissent by force. The chapter connects with several other chapters in the book through its emphasis on artistic resistance. This dimension highlights the “poetics” of resistance – poetics is a “rule and method” behind dissent – something that resembles a grammar. However, by using the word poetics, it is implied that there is aesthetics in the events of resistance: something that appeals to emotions and not only to reason.
Not Just Streaks: Towards Ground Truth for Single Image Deraining

We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real domain gap; moreover, rigorous evaluation remains a challenge due to the absence of a real paired dataset. We fill this gap by collecting a real paired deraining dataset through meticulous control of non-rain variations. Our dataset enables paired training and quantitative evaluation for diverse real-world rain phenomena (e.g. rain streaks and rain accumulation). To learn a representation robust to rain phenomena, we propose a deep neural network that reconstructs the underlying scene by minimizing a rain-robust loss between rainy and clean images. Extensive experiments demonstrate that our model outperforms the state-of-the-art deraining methods on real rainy images under various conditions. Project website: https://visual.ee.ucla.edu/gt_rain.htm/.
Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Visually impaired people use braille letters to write and read, which not all people with normal vision can read.It causes parents of children with impaired vision to have difficulties in assisting them in learning at home.At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress.In this research, braille letters are identified through images taken using a scanner as a tool to input the images.Then, the Canny edge detection method is used obtain all the edges of each braille dot.Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet.The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 0 0 , 30 0 , 45 0 , 60 0 , 120 0 , 135 0 , 180 0 and wavelengths of 3, 6, 13, 28, and 58.These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms.Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers.Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall.Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.
Software Traceability in Agile Development Using Topic Modeling

Tracing the implementation of requirements for making better software identifies whether the application fulfils users' desires; progress of development; problematic areas in the testing process, and how far those apply to the source code.In this paper, the software development method we studied was the agile method, Extreme Programming (XP).The artifacts in the agile approach considered vital include the requirement documents, test documents, and source codes.We used Topic Modelling to map the content similarities from those documents to make trace links.The three topic modelling methods we compared consist of Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF).The NMF method proved itself the most stable, with an accuracy value of 67% for the requirement, 59% for testing, and 48% for defect lists.The second application results proved more accurate with 70%, 79%, and 54%.Although NMF lost to LSA in the second application (LSA achieved an accuracy of 79%, 84%, and 56%), the precision and recall values showed almost similar results.We successfully found the link in the source code based on keywords extracted from each topic.This research provides a way of explaining the requirement in detail, simplifying it for tracing purposes such as the consistent use of terms, technical details inclusion, and mentioning all the variables involved.In the future, sentence structure and synonyms need recognition as part of pre-processing to build better trace links.
TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

Due to the decentralized and public nature of the Blockchain ecosystem, the malicious activities on the Ethereum platform impose immeasurable losses for the users. Existing phishing scam detection methods mostly rely only on the analysis of original transaction networks, which is difficult to dig deeply into the transaction patterns hidden in the network structure of transaction interaction. In this paper, we propose a \underline{T}ransaction \underline{S}ub\underline{G}raph \underline{N}etwork (TSGN) based phishing accounts identification framework for Ethereum. We first extract transaction subgraphs for target accounts and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. In order to make our model incorporate more important information about real transactions, we encode the transaction attributes into the modeling process of TSGNs, yielding two variants of TSGN, i.e., Directed-TSGN and Temporal-TSGN, which can be applied to the different attributed networks. Especially, by introducing TSGN into multi-edge transaction networks, the Multiple-TSGN model proposed is able to preserve the temporal transaction flow information and capture the significant topological pattern of phishing scams, while reducing the time complexity of modeling large-scale networks. Extensive experimental results show that TSGN models can provide more potential information to improve the performance of phishing detection by incorporating graph representation learning.
An Agile and Efficient Neural Network Based on Knowledge Distillation for Scene Text Detection

Text detection is increasingly in demand recently and poses significant challenges for the tradeoff among the detection accuracy, memory resources, and inference speed in the case of applying to the portable device such as mobile phones. Current methods mainly focus on the detection accuracy but neglect either the running speed or the memory consumption. To this end, an agile and efficient neural network for scene text detection that balances the detection performance, running speed, and the model size is hereby proposed. In order to reduce the network parameters and speed up, the neural network for text detection is firstly pruned; and then, the pruned neural network is trained with the structured knowledge distillation for improving the detection performance. The method is implemented on three benchmark text datasets, i.e., ICDAR2015, Total-Text, and MSRA-TD500. The experimental results demonstrate that the hereby proposed method achieves the best comprehensive performance with a faster running speed and much less memory resources while the text detection accuracy is comparable to that acquired using the excellent text detection methods.
Assessment of heavy metals pollution in the sediments of Haditha reservior and Eupheates River, within Anbar Governorate/Iraq

10 sediment samples were obtained from Haditha reservoir and Anah city in Al-Anbar governorate for this study during (October, 2021). to assess sediment contamination in trace metals like (Cd, Fe, Pb, Zn, Cu, and Ni), from applying geochemical contamination coefficients such as Geo - Accumulation variable I-Geo, Contamination Factor CF, as well as Pollution loud Index PLI. The research area is found in the northwestern region of Al-Anbar governorate, between the longitudes (42º23'.13'' - 42º01'.10'' E). and latitudes (34º10'.22'' - 34º22'.7'' N), The area which is located between Haditha Dam and Anah city. The results of geochemical analyzes showed that it soil sediment has a high value of the elements (Ni, Fe and Cd), which exceeds the limits of local studies, this can be affected by Anthropogenic activities like urbanization, industrialization, agricultural runoff, fertilizers, and also the Haditha dam's electrical station, but the elements (Zn, Pb and Cu), Within the permissible limits of local studies.The results of the I-geo shows that the elements (Ni, Zn, Pb, Fe and Cu), has unpolluted (class- 0). While Cd is (class- 1), slightly polluted.
BOBA: Byzantine-Robust Federated Learning with Label Skewness

In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically distributed. In this paper, we address label skewness, a more realistic and challenging non-IID setting, where each client only has access to a few classes of data. In this setting, state-of-the-art AGRs suffer from selection bias, leading to significant performance drop for particular classes; they are also more vulnerable to Byzantine attacks due to the increased variation among gradients of honest clients. To address these limitations, we propose an efficient two-stage method named BOBA. Theoretically, we prove the convergence of BOBA with an error of the optimal order. Our empirical evaluations demonstrate BOBA's superior unbiasedness and robustness across diverse models and datasets when compared to various baselines. Our code is available at https://github.com/baowenxuan/BOBA .
Pengaruh Model Pembelajaran E-Learning Terhadap Hasil Belajar IPA Siswa Kelas IV SDN Se-Kecamatan Alian Tahun Ajaran 2021/2022

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The study aimed to prove the effect of e-learning on natural science learning outcomes to fourth grade students of public elementary schools in Alian Sub-district in academic year of 2021/2022 and to measure the contribution of e-learning on natural science learning outcomes to fourth grade students of public elementary schools in Alian Sub-district in academic year of 2021/2022.The population were 762 fourth grade students and the samples were 264 fourth grade students. Data collection techniques used questionnaires and tests. The validity method included question analysis using Pearson correlation analysis. Reliability used the Alpha (Cronbach’s) formula. The prerequisite test used linearity and normality tests. Data Analysis used simple regression analysis, simple correlation, and adjusted R square with a significance level of 5%.The results showed that the correlation value of e-learning was 0.342 on science learning outcomes. The significance value was 0,000less than 0.05. The e-learning contributed 69.9% on natural science learning outcomes and the remaining 30.1% was influenced by other factors.&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
UPAYA MENINGKATKAN KARAKTER SISWA GEMAR MEMBACA MELALUI PEMBERDAYAAN PERPUSTAKAAN BERTARAF NASIONAL DI SD MUHAMMADIYAH 1 ALTERNATIF KOTA MAGELANG TAHUN AJARAN 2021/2022

&lt;p&gt;&lt;em&gt;National school library standard is one the efforts to develop a love of reading. The study aimed to describe the efforts to develop a love of reading in students through national school library standard, to describe the impact of efforts to develop a love of reading in students through national school library standard, and to analyze the obstacles and solutions faced in SD Muhammadiyah 1 Alternatif Kota Magelang. It was a qualitative research with case study method. The collection techniques were observation, interviews, document studies, and questionnaires. The results indicated that six components to develop a love of reading in students through national school library standard were the collection components, facilities and infrastructure, library services, librarian, library administration, management, and performance strengthening. &lt;em&gt;The positive impacts of school library in developing a love of reading were divided into six indicators which each indicator achieved high percentage: visiting the library had 61.1%, library utilization got 68%, borrowed books had 54.9%, having interest in books and libraries got 87.4%, having books owned by the students got 56.6%, and the frequency of students in reading books had 83.4%. &lt;/em&gt;In addition, there was a positive impact in improving the Indonesian learning outcomes to fifth grade students from 86.12 to 86.96. The positive impacts of library affected on students’ skills observed from the work on the wall magazine. The students developed in reading activities, writing activities, observing activities, and analyzing activities. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; national school library standard, a love of reading, knowledge, skills. &lt;/em&gt;&lt;/p&gt;
Zero-shot Object Detection Through Vision-Language Embedding Alignment

Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which involves the non-semantic task of localization as well as semantic task of classification. To solve this problem, we introduce a vision-language embedding alignment method that transfers the generalization capabilities of a pretrained model such as CLIP to an object detector like YOLOv5. We formulate a loss function that allows us to align the image and text embeddings from the pretrained model CLIP with the modified semantic prediction head from the detector. With this method, we are able to train an object detector that achieves state-of-the-art performance on the COCO, ILSVRC, and Visual Genome zero-shot detection benchmarks. During inference, our model can be adapted to detect any number of object classes without additional training. We also find that standard object detection scaling can transfer well to our method and find consistent improvements across various scales of YOLOv5 models and the YOLOv3 model. Lastly, we develop a self-labeling method that provides a significant score improvement without needing extra images nor labels.
PERSPECTIVES OF SELF-DIRECTED LEARNING TRAJECTORY AMONG FIRST-AND FINAL-YEAR UNDERGRADUATE STUDENTS

Statement of the problem. The article is devoted to the problem of forming students’ representations about the trajectory of self-directed learning in order to increase their adaptability in a rapidly changing world. The need to increase students’ degree of its thoughtfulness is emphasized. This article emphasizes the need in independent and thoughtful construction of this trajectory by university students. The purpose of the article is to present the results of a study on creation of a technique identifying students’ perspectives of self-directed learning trajectory, as well as to report findings about features of these perspectives among first-and final-year undergraduate students. Research methods. To identify students’ perspectives of self-directed learning trajectory, a questionnaire was developed in accordance with the ideas of A.V. Hutorskiy, G.M. Kuleshova, E.A. Aleksandrova, Y.G. Yudina, S.V. Vdovina, I.S. Yakimanskaya, S.V. Yudakova, etc. The questionnaire identifies the following components for perspectives of self-directed learning trajectory: plans for self-directed learning, ideas about their implementations, and ideas about actions for achieving aims of self-directed learning. Research results. Statistically significant differences between perspectives of self-directed learning trajectory among first- and final-year students were revealed: the perspectives of self-directed learning trajectory and range of interests are clearer among final-year students than among first-year students. Additionally, self-directed learning motivation and time-management skills are higher in the group of final-year students in comparison with the group of first-year students. First-year students prefer workshops, lectures, conferences, summer or winter schools as ways to improve their knowledge and skills, while final-year students prefer to read books, take notes, and communicate with specialists. All students are characterized by a poor level of analysis of difficulties which they may encounter in the process of self-directed learning, and unwillingness to allocate fixed time for classes during self-directed learning. Conclusions. The guidelines for students in order to help them to build clear perspectives of self-directed learning trajectory were developed. The authors’ contribution consists in creation of the questionnaire for identification of students’ perspectives of self-directed learning trajectory and in conducting the study that expands knowledge about the psychology of students’ self-directed learning.
CYBER CRIME AMONG MATHEMATICAL SCIENCE STUDENTS: IMPLICATIONS ON THEIR ACADEMIC PERFORMANCE

Cybercrime refers to abuse and misuse of computer and internet facilities by an individual or a corporate body in the cyberspace. It is a crime that is very rampant among students of both secondary and tertiary institution in Nigeria. It is crimes that need to be fight holistically by cyber security experts because it stands to destroy the moral foundation of every nation. This research intend to evaluate the effect cybercrime has on academic performance of mathematical science students of Oyo State College of Education Lanlate, Oyo State Nigeria. This research uses a case study design method through the use of questionnaire. Random sampling with replacement was used to select 150 respondents from NCE I - III. The data collected was analysed using mean score rating. The study reveals that majority of respondent have in-depth knowledge of what cybercrime is and how it is been perpetrated. The respondent claimed to know through friends and internet. Also, respondent attributed the course to poverty, peer influence and non-prosecution of offender by the security agency. The study revealed that student who indulge in cybercrime have difficulty in concentration in the class, poor study habit and eventually drop out of school due to poor performances. Students who get addicted to cybercrime often get convicted; go to prison and eventually loss their life. The study recommends that government should include courses to treat cybercrime in the secondary and university curriculum. Also an aggressive orientation from government mass media and religious body should be done to expose innocent youth to menace of cybercrime. Also government should improve the economic situation of the country by providing job opportunity, empowerment and relief fund for youth to have a better life.
CHARACTERISTICS OF FORMATION OF READINESS FOR CHANGE COMPETENCE AMONG MANAGERS

Statement of the problem. The article establishes the relevance of the development of the readiness for change competence, discusses the behavioral and reflexive components of the competence, and gives recommendations for the development of the competence and its individual components in general. The purpose of the article is to discuss the characteristics of the behavioral and reflexive components of the readiness for change competence among managers. The theoretical and methodological basis of the study is the general theory of guideposts (D.N. Uznadze), the subject-activity approach (S.L. Rubinstein, B.G. Ananyev, V.A. Krutetsky, K.K. Platonov et al.), competence approach (Yu.G. Tatur, I.A. Zimnyaya, A.V. Khutorskoy et al.), integrative approach (M.I. Fayerman), competence approach in innovative changes management (T.A. Terekhova), innovative approach (L.A. Zhuravlev), psychological readiness for innovation (S.R. Yagolkovsky, O.V. Sovetova, M.I. Kiloshenko, E.V. Ivanova, V.E. Klochko, O.M. Krasnoryardtseva), and works devoted to psychological problems and mechanisms of formation of readiness for activity (M.I. Dyachenko, L.A. Kandybovich, R.D. Sanzhaeva, O.V. Makhova). One hundred and fifty employees of a group of companies in the sphere of IT/Telecom from Krasnoyarsk took part in the study. The following materials were used for diagnosis: the Californian Personality Inventory (CPI), T.V. Kornilova’s Questionnaire of Tolerance for Uncertainty, and A.V. Karpov’s Reflexivity Questionnaire. Statistical data processing included comparison of samples and analysis of the interdependence of characteristics. Research results. The characteristics of the formation of the readiness for change competence were identified in the following categories: senior-level management, mid-level management, and specialists. It was discovered that senior- and mid-level managers have an average value of reflexivity and high tolerance to uncertainty.The average level of reflexivity indicates the ability to plan and analyze one’s actions, to realize one’s attitude to the current situation, and to analyze the actions of other people; at the same time, reflexive processes can be irregular, and reflexive analysis can be carried out superficially. For the development of the reflexive component, it is necessary to pay attention to the development of skills for regular planning of current activities and plan implementation analysis, and to carry out corrective measures taking into account the changes taking place. Conclusion. The conclusion was made about the formation of the readiness for change competence in different categories of employees and the need to build a corporate training program that promotes the development of the competence.