Reviewerly/exHarmony huggingface dataset:
Deep Structured Instance Graph for Distilling Object Detectors

Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation. Specifically, aiming at solving the feature imbalance problem while further excavating the missing relation inside semantic instances, we design a graph whose nodes correspond to instance proposal-level features and edges represent the relation between nodes. To further refine this graph, we design an adaptive background loss weight to reduce node noise and background samples mining to prune trivial edges. We transfer the entire graph as encoded knowledge representation from teacher to student, capturing local and global information simultaneously. We achieve new state-of-the-art results on the challenging COCO object detection task with diverse student-teacher pairs on both one- and two-stage detectors. We also experiment with instance segmentation to demonstrate robustness of our method. It is notable that distilled Faster R-CNN with ResNet18-FPN and ResNet50-FPN yields 38.68 and 41.82 Box AP respectively on the COCO benchmark, Faster R-CNN with ResNet101-FPN significantly achieves 43.38 AP, which outperforms ResNet152-FPN teacher about 0.7 AP. Code: https://github.com/dvlab-research/Dsig.
A Technical Survey on Cryptography Techniques in Cloud Computing Environment

Cloud computing could be a virtualization-based technology that permits to make configure and customise purposes by using an online affiliation. The cloud technology includes a development platform, hard disk, package application and info. Cryptography Technique is one in all the main aspects of Cloud Computing, improving the protection of the cloud storage system.Cryptographical techniques are used to guarantee secrecy and integrity of knowledge within the presence of an adversary. It's related to the method of changing normal plain text into unintelligible text and vice-versa. It's a way of storing and transmittal information in a very specific kind in order that only those for whom it's intended can read and process it. In this paper, we have present a survey of numerous cryptography Technique in Cloud Computing.
Pengaruh Pembelajaran Jarak Jauh Kelas 7.3 terhadap Prestasi Belajar pada Mata Pelajaran IPS Di SMP Islam Al-Ikhlas Bekasi

This study aims to reveal the effect of Distance Learning (X) on Social Studies Subject Learning Achievement (Y). This research method is descriptive quantitative. In determining the sample used is a saturated sample and the sample used for this study as many as 29 people. The conclusions from the results of this study reveal: (1) Distance learning and learning achievement are normally distributed in distance education of 0.222 and sig. (2-talied) of 0.001 > 0.05, Ho is accepted. Thus the distance learning data is normally distributed. While the learning achievement score is 0.247 and sig. (2-talied) of 0.000 > 0.05, Ho is accepted. (2) The effect of distance learning on learning achievement in social studies subjects is a positive influence. The results of this study using the SPSS 26.0 program using the regression equation: Y = 96.689 + -0.165 X. From the analysis results obtained thit = 6.578 and p-value = 0.351> 0.05 or Ho is rejected. (3) Based on the test results, the correlation coefficient value using the Summery model table (R²) is 0.180, which means the contribution of variance given by the X variable to Y. While the coefficient of determination from the table, namely R Square = 0.032, which contains that 03.2 % of learning achievement variables. From the results of the calculation of the GAP above that distance learning can provide a very rapid achievement of student achievement in school or outside school. And independent learning can be organized systematically in presenting learning materials, providing guidance to students, and supervision for the success of learning.
Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks.

The aim of the CASE 2021 Shared Task 1 (H\urriyeto\u{g}lu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language. Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding: That the multilingual aspect of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to tackle the different languages separately. Our code is available at this https URL
LibGuides: OA and OER Resources in Women's and Gender Studies: Open Access Feminist Journals

A LibGuide on OA and OER resources benefitting the Women and Gender Studies Section of ACRL. This page lists feminist journals that are open access.
A Concentration Bound for LSPE($\lambda$)

The popular LSPE($\lambda$) algorithm for policy evaluation is revisited to derive a concentration bound that gives high probability performance guarantees from some time on.
Sosialisasi Pemanfaatan Caklingking Sebagai Salah Satu Media Pembelajaran Siswa SD

Caklingking is one of the traditional games that used to be played a lot. Usually caklingking is played together and uses stones that are formed to be played. Based on the problems in the field, that students no longer play caklingking in everyday life. In addition, students do not know that caklingking can be a medium for learning mathematics. Because of this problem, it is necessary to socialize the use of caklingking in elementary school student learning and the use of this game in daily life. The purpose of this service is to socialize the use of caklingking in elementary school student learning and the use of this game in everyday life. The implementation method used is to provide direct socialization to students of knowledge about the use of caklingking. This activity was attended by 20 students of SDN 14 Pangkalpinang. This outreach activity has been successfully implemented.
Implementation of Text Compression using Adaptive Shannon-Fano Algorithm

This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a collection of characters that have different types of characters, variants, and the number of characters. This research presents algorithm that support the steps of making adaptive Shannon-fano compression applications. The length of the character determines the variant value, compression ratio, and the number of input character types. Based on the results of test results, no error occurs according to the comparison of the original text input and the decompression results. A higher appearance frequency of a character causes a greater compression ratio of the resulting file; the analysis shows that a higher number of types of input characters causes a lower compression ratio, which proves that the proposed method in real-time data compression improves the effectiveness and efficiency of the compression process.
Algoritma C4.5 Dalam Data Mining Untuk Menentukan Klasifikasi Penerimaan Calon Mahasiswa Baru

Conducting a graduation classification of prospective freshmen at a college should be best not only by the written exam value criteria but also the interview test, and other things that can serve as assessment parameters. In this study an increased parameters for classifying potential students in the scarlet hyperlinate's post-collegiate bud with deep learning methods using a c4 algorithc.5. With this method of classification, it is hoped to help academicians determine the criteria of active and exemplary freshman candidates. From experiment with the rapidminer's software on the data of students who have registered from 2016 to 2020, obtained an active student classification defined by the value of the interview being the first node, coupled with the value of an academic potential test. In the meantime, it is found that what can affect a student's performance is the school's origin and duration of college. Students who continue studying => 4 years tend to have grades and achievements under those who continue to study at three to four years. Based on research, score of accuracy at 81.32%.
Advance and Secure Voting System

Electronic Voting Machines ('EVM') are being used in India in the current scenario, usually for state elections, these EVMs have been used since 1999. When compared to the old paper ballot systems, the EVMs decrease the time for both casting a vote and announcing the results, there is no Tampering and protection given for EVMs until 2004 after 2004 Supreme Court and Election Commission decided to implement EVMs with Voter Checked Paper Audit Trail (VVPAT) system, but it also has some difficulties such as missing names in the voting list and many more. An online system that includes procedures such as registration of voters, casting of votes, counting of votes, and announcing results will be a good solution to replace the current system, and the suggested system in this study would be beneficial to the voters by using some tools such as their own or government-arranged system. In addition, the scheme proposed would also reduce the possibility of corruption. The framework is proposed following interviews between officials of two agencies, India's National Database and Registration Authority (NADRA) and India's Election Commission (ECP).
Contextualized English Reading Proficiency Toolkit (CERPT): Enhance Learners’ English Reading Proficiency

The Department of Education has introduced numerous intervention and remediation programs to address the reading needs of learners. Despite these, data showed that majority of the learners still have reading problems when they reach higher grade levels. Henceforth, the study experimented on the innovation – Contextualized English Reading Proficiency Toolkit (CERPT) to help the learners of Kibacania Elementary School improved their reading ability level. All the pupils were exposed to CERPT. A pre-experimental research design was employed in this study. Findings revealed that the learners’ reading ability level enhanced from frustration to instructional. There was a significant difference in the learners’ reading ability levels before and after exposure to CERPT. Thus, the study commends the use of CERPT to help in the improvement of the learners’ reading ability level.
Neknjiževni tekstovi u bukvarima – metodičke mogućnosti u savremenom društvenom kontekstu

The aim of the paper is to determine the representation of non-literary texts in spelling books and their compliance with the Program of Teaching and Learning for the first grade of primary education (2017). Nine spelling books from different publishers from the Textbook Catalogue for the first and fifth grades of primary education (2019) were analyzed. The study shows the types and functions of programming non-literary texts: texts from spelling book, popular and informative texts. In addition to spelling books, these texts can be found in magazines, encyclopedias, but also on the Internet. Contemporary social context, online teaching and the use of modern technologies affect on the revised access to teaching content, as well as the selection of non-literary texts that we will show in one part of the study. We have come to the conclusion that in the spelling books most represented are non-literary spelling books texts, that there are informative and the least popular texts, and that there is no compliance of selected non-literary texts in the spelling books with the Program with suggestions on how to use them functionally in revised social circumstances.
A Review on Privacy and Security Issues in Healthcare System

— Hospitals in India as well as athwart the globe have moved or are in the course of moving away from paper-based labor-intensive information processing to Hospital Information System (HIS) as well as Electronic Health Record (EHR) with the intention of endowing with operational and well-organized healthcare services in a flawless fashion. Extensive exploitation of digital records in healthcare field has offered plausibly inestimable assistance by instantaneous entrance to medical records virtually from everywhere in the world. Relating Health Information System to the network and building Electronic Health Record accessible over the Internet locate the medical records defenseless to security intimidations and menaces the privacy of patients. Information security along with privacy is an apprehension of mounting magnitude in the healthcare zone. The championship of digital medical records, provider consolidation, amplified regulation, as well as the escalating necessity for material sandwiched amid consumers, suppliers, and financiers, all signal the prerequisite for enhanced information security. Consequently, in this day and age the defiant stare of promotion has been transferred interrogating on the security and privacy of those records which are engendered from IoT maneuvers and being amassed in cloud milieu. We unfalteringly review the research work on health data privacy and security, issued in information schemes and non-information schemes together with medical informatics, law, communal healthiness, , and widespread trade magazines and hearsays. We present the security challenges in the cloud for eHealth domain and recently proposed solutions. Into the bargain, we formulate an exhaustive reading on the prevailing elucidations to security as well as privacy issues, along with the uncluttered encounters as well as exploration disputes for the forthcoming efforts.
PENERAPAN VIDEO CONTENT PEMBELAJARAN TEMATIK DALAM PENINGKATAN HASIL BELAJAR MENGAJAR DARING DI MADRASAH IBTIDAIYAH NEGERI 1 TEMBILAHAN

This study discusses the application of learning video content in improving online teaching and learning outcomes. The focus of this research is on three things, namely the use of thematic learning video content media at Madrasah Ibtidaiyah Negeri 1 Tembilahan, the effectiveness of thematic learning using learning content videos, and student learning outcomes at Madrasah Ibtidaiyah Negeri 1 Tembilahan on video material using thematic learning content media. The method used is descriptive qualitative. This study concludes that the application of video content as a learning media is considered effective in increasing the effect of online learning, as evidenced by the results of interviews with principals, teachers, and students that this shows student performance is improving. The use of learning video content can also increase students' attention, make the learning atmosphere more fun, and attract students' interest in learning and make it easier for students. The benefits of this kind of learning media can be feedback to students so that it can help students obtain the availability of enlightening images and audio. Despite the shortcomings, there are still some videos that are not suitable for public viewing and hate speech that often appears in the comments column. As a suggestion in this study, schools should provide teachers with socialization activities related to the use of video media so that they all understand better that video media will affect student interest and learning outcomes. Keywords: application, video content, online learning.
Implementing SAMOS in Mandala

Public Blockchains (BC) in support of Smart Contracts (SC), like Ethereum, enable everyone to coordinate in a decentralized approach the management of scarce, valuable resources (like cryptocurrencies). Such BCs allow for building SCs that own resources and manage permissions describing who is allowed to interact with these resources and what actions can be applied to them. However, programming languages and runtime systems used in current BCs lack a secure, flexible, and straightforward way to implement permissions within their SCs. This unnecessary complexity can lead to erroneous implementations, allowing unauthorized access. The best-known incident related to a permission problem was the “Parity Hack”, which led to the “loss” of tokens, valued at approximately 31 M USD. SAMOS designed a concept to achieve an improved access control representation. It provides a secure path to a capability-based permission management system by using opaque and substructural types. This paper presents an implementation of SAMOS in the functional SC language Mandala, which offers a mechanism to enforce the key properties of opaque and substructural types during the SC execution by using an on-chain soundness checker.
Intermediate Layers Matter in Momentum Contrastive Self Supervised  Learning

We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrastive (MoCo) method. To this end, in addition to the contrastive loss, we minimize the mean squared error between the intermediate layer representations or make their cross-correlation matrix closer to an identity matrix. Both loss objectives either outperform standard MoCo, or achieve similar performances on three diverse medical imaging datasets: NIH-Chest Xrays, Breast Cancer Histopathology, and Diabetic Retinopathy. The gains of the improved MoCo are especially large in a low-labeled data regime (e.g. 1% labeled data) with an average gain of 5% across three datasets. We analyze the models trained using our novel approach via feature similarity analysis and layer-wise probing. Our analysis reveals that models trained via our approach have higher feature reuse compared to a standard MoCo and learn informative features earlier in the network. Finally, by comparing the output probability distribution of models fine-tuned on small versus large labeled data, we conclude that our proposed method of pre-training leads to lower Kolmogorov-Smirnov distance, as compared to a standard MoCo. This provides additional evidence that our proposed method learns more informative features in the pre-training phase which could be leveraged in a low-labeled data regime.
L1-Norm Distance Discriminant Analysis with Multiple Adaptive Graphs and Sample Reconstruction

Linear discriminant analysis (LDA) is sensitive to noise and its performance may decline greatly. Recursive discriminative subspace learning method with an L1-norm distance constraint (RDSL) formulates LDA with the maximum margin criterion and becomes robust to noise by applying L1-norm and slack variables. However, the method only considers inter-class separation and intra-class compactness and ignores the intra-class manifold structure and the global structure of data. In this paper, we present L1-norm distance discriminant analysis with multiple adaptive graphs and sample reconstruction (L1-DDA) to deal with the problem. We use multiple adaptive graphs to preserve intra-class manifold structure and simultaneously apply the sample reconstruction technique to preserve the global structure of data. Moreover, we use an alternating iterative technique to obtain projection vectors. Experimental results on three real databases demonstrate that our method obtains better classification performance than RDSL.
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic question-answer pairs that are used for training. However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size. This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated. In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from question generation and domain-invariant learning to answer out-of-domain questions in settings with limited text corpora. Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective. By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to state-of-the-art baselines.
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization

Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better responses, some approaches have been proposed, such as feeding extra information by collecting large-scale datasets with human annotations, designing neural conversational models (NCMs) with complex architecture and loss functions, or filtering out untrustworthy samples based on a dialogue attribute, e.g., Relatedness or Genericness. In this paper, we follow the third research branch and present a data filtering method for open-domain dialogues, which identifies untrustworthy samples from training data with a quality measure that linearly combines seven dialogue attributes. The attribute weights are obtained via Bayesian Optimization (BayesOpt) that aims to optimize an objective function for dialogue generation iteratively on the validation set. Then we score training samples with the quality measure, sort them in descending order, and filter out those at the bottom. Furthermore, to accelerate the filter-train-evaluate'' iterations involved in BayesOpt on large-scale datasets, we propose a training framework that integrates maximum likelihood estimation (MLE) and negative training method (NEG). The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively. Specifically, MLE is applied to maximize the log-likelihood of newly maintained samples, while NEG is used to minimize the log-likelihood of newly removed ones. Experimental results on two datasets show that our method can effectively identify untrustworthy samples, and NCMs trained on the filtered datasets achieve better performance.
Adaptive Logarithmic-Power Algorithm for Preserving the Brightness in Contrast Distorted Images

The digital images get distorted due to nonuniform light conditions or improper acquisition settings of the digital camera.Such factors lead to distorted contrast objects.In this work, we proposed adaptive enhancement algorithm to improve the contrast while preserving the mean brightness in the image.The method developed is a combination of discrete wavelet transform and gamma correction.Firstly, the gamma scale is computed from multi-scale decomposition using 2Ddiscrete wavelet transform.The value of scale parameter in gamma was computed from combination of logarithmic and power function.Secondly, the gamma correction is implemented to improve the contrast in the image.Lastly, bilateral filtering is utilized for smoothness of edges in the image.The approach effectively preserved the brightness and optimized the contrast in the image.The objective quality measures used as Peak SNR, AMBE, entropy, entropy based contrast measure and median absolute deviation is computed and compared with other state-ofthe-art techniques.