Reviewerly/exHarmony huggingface dataset:
RANCANGAN SISTEM PRESENSI MAHASISWA MENGGUNAKAN QR CODE DENGAN FITUR GEOLOCATION DAN ENKRIPSI AES

This research aims to create a student presence system using the QR Code (Quick Response Code) with AES encryption, when the learning process will begin, lecturers display QR Code for further scans by students through their smartphones that have been installed academic information system, data presences will be automatically stored in the database, the data can be known directly by lecturers and students. Data security uses AES encryption methods and in order to ensure that students are actually present then this research also implements Geolocation, if Geolocation on the student's smartphone is not the same as that in the presences system then the student is not able to perform the presences. From the results of the research, the system can run well, with the existence of this system can simplify the process of presences and increase efficiency because no more presences printed on paper.
TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression

We present TACTIC: Task-Aware Compression Through Intelligent Coding. Our lossy compression model learns based on the rate-distortion-accuracy trade-off for a specific task. By considering what information is important for the follow-on problem, the system trades off visual fidelity for good task performance at a low bitrate. When compared against JPEG at the same bitrate, our approach is able to improve the accuracy of ImageNet subset classification by 4.5%. We also demonstrate the applicability of our approach to other problems, providing a 3.4% accuracy and 4.9% mean IoU improvements in performance over task-agnostic compression for semantic segmentation.
Poisoning the Search Space in Neural Architecture Search

Deep learning has proven to be a highly effective problem-solving tool for object detection and image segmentation across various domains such as healthcare and autonomous driving. At the heart of this performance lies neural architecture design which relies heavily on domain knowledge and prior experience on the researchers' behalf. More recently, this process of finding the most optimal architectures, given an initial search space of possible operations, was automated by Neural Architecture Search (NAS). In this paper, we evaluate the robustness of one such algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search space with carefully designed ineffective operations. By evaluating algorithm performance on the CIFAR-10 dataset, we empirically demonstrate how our novel search space poisoning (SSP) approach and multiple-instance poisoning attacks exploit design flaws in the ENAS controller to result in inflated prediction error rates for child networks. Our results provide insights into the challenges to surmount in using NAS for more adversarially robust architecture search.
Design and Implementation of Admin’s CRUD System on the Cermat Website

The Cermat website is an application to assess the quality of education starting from Elementary School to Junior High School, quality assessment is carried out by student taking a quiz in accordance with national standards at the grade level, the result of the quiz will be seen, whether the students have mastered a certain lesson. In the Cermat website, teachers can give assignments or quizzes to students in their shade. Teachers can access the website using an account that has been created by the developer as admin. The large number of teachers who must access the website, causes the creation and management of accounts by developers to be less efficient work in terms of time, because developers need to input large amounts of data in a short time and are prone to errors.  In this study, a CRUD (Create, Read, Update, Delete) system design was carried out using Excel as a solution to these problems. Microsoft Excel is a spreadsheet application program that is used for managing data in the form of tables, diagrams, and others. Excel can create admin data tables quickly and efficiently, which will then be converted into a database that can be used for large amounts of admin input. This makes the developer’s work time in managing admin data easier and more efficient.
Adversarially Regularized Policy Learning Guided by Trajectory Optimization

Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems. Despite their great flexibility, the large neural networks for parameterizing control policies impose significant challenges. The learned neural control policies are often overcomplex and non-smooth, which can easily cause unexpected or diverging robot motions. Therefore, they often yield poor generalization performance in practice. To address this issue, we propose adVErsarially Regularized pOlicy learNIng guided by trajeCtory optimizAtion (VERONICA) for learning smooth control policies. Specifically, our proposed approach controls the smoothness (local Lipschitz continuity) of the neural control policies by stabilizing the output control with respect to the worst-case perturbation to the input state. Our experiments on robot manipulation show that our proposed approach not only improves the sample efficiency of neural policy learning but also enhances the robustness of the policy against various types of disturbances, including sensor noise, environmental uncertainty, and model mismatch.
Fast And Complex Moving Object Detection Using Gan In Computer Vision

Fast moving object detection and video frame interpolation usually applies convolution neural networks. But most of these models deal with difficulties due to sudden illumination change, occlusion, blur, etc. With the advancement in generative adversarial network a dense attention generative adversarial network that deals with these difficulties by combing the global and local data and providing network accuracy in focusing on moving objects is proposed. Training the neural network using wide range of video data and formulation of video interpolation as a single convolution process allowed eliminate the problems and enables high-quality video frame interpolation. Adversarial learning and GAN work in development of more satisfactory outcomes. We have to converted the video into image framesand detection of fast- moving object is done. Then the selected frames undergo series of operations for feature extraction. Further the frames to be included for video enhancement is processed and included. Finally, the image frames are converted back to video. Experimental results on various datasets ensure that this method has more effectiveoutcome.
Gaussian process mixture model for prediction based on maximum posterior distribution

Abstract In this paper, a numerical experiment was proposed, which is based on the Bayesian procedure and Gaussian Process. Gaussian Process prior distribution was supposed as a before evaluating the hyperparameters of the Gaussian Process model via GyPytorch. Finally, the Gaussian process posterior was achieved and then confidence regions were visualized in two-dimensional planes. The Gaussian Process model could perform under common or specified kernel functions, while it depends on the prior information to a great extent.
Atomic, Wireless Methodologies for Symmetric Encryption

Unified concurrent algorithms have prompted many key advances, including compose back reserves and Smalltalk. Given the flow status of steady time models, analysts amazingly want the improvement of open private key sets. We verify that Moore's Law [6] can be made cacheable, relational.
Secret Keys Extraction Using Light Weight Schemes for Data Ciphering

Abstract One of the most essential methods used to provide security services is encryption. One key is used for encoding in symmetric encryption. The symmetric encryption depends on the encryption block, switching, and replacing. Therefore, it is a problem if the received secret keys from protocols are frequents in some states or they have less randomness. In this paper, a Light Weight Multiple Key Generating (LWM) is proposed to generate the secret keys which are using Light Weight Schemes (LWS). In this work, six experiments are implemented. Three LWA are utilized, which are Xtea, RC5, and Tea algorithms. The SHA2 hash function is used to merge the chains. The diehard test is used in all experiments to determine the randomness of the secret key produced. The entropy is a measure of the uncertainty of a random variable.
A Drug Recommender System based on Sentiment Analysis of Drug Reviews using Deep Learning

In today’s digital world, the younger to older generation is dependent on internet not only for entertainment, shopping but also to live a healthy life. Artificial Intelligence and Machine Learning has also made its place in healthcare. The health forum data and the data extracted from social mediaare major sources of the experiences shared on chronic diseases and drugs.A major percentage of users are searching on theinternetfor data related to wellbeing. But study shows that most of the people die by taking wrong medicine. Tons of informationis there on theinternet which can be helpful but can also be hazardous. There are researchers who have dealt with areas like electronic items, films and restaurants yet very few have worked widely on healthcare and medical domains.In this paper, Natural Language Processing techniques are applied on the patient reviews to classify the emotions and recommend the top medicines to the patient. In this paper, CNN-LSTM gives the highest accuracy (83.06 %) than any other ML & DL Model. This paper presents that DL model works better than ML models on this dataset.
산후 한의진료를 받은 고위험산모와 일반산모의 한의진료 체중변화에 대한 후향적 분석

Objectives: This study was aimed to investigate the weight change of the high-risk group and the general maternal group and weight-relating factors in the early postpartum period.Methods: We retrospectively reviewed the medical records of those who received postpartum care from January 1, 2020, to December 31, 2020, in the postpartum care center affiliated with one Korean Medicine hospital. A total of 257 postpartum women''s medical charts were included and divided into the high-risk group and the general maternal group. We investigated the weight changes and Body mass index (BMI) of the postpartum women and compared the difference between the two groups after taking the postpartum care. Finally, we used a Pearson correlation analysis to identify the weight-relating factors in the early postpartum period.Results: All the postpartum women showed the following results; 33.81±4.03 years old as the mean age; 22.23±3.28 as pre-pregnancy BMI; 58.21±9.18 kg of pre-pregnancy weight increased into 70.75±9.70 kg in the last month of pregnancy. Of the total 257 patients, 149 (58.0%) of high-risk pregnancy experience and 108 (42.0%) of general pregnancy were included. The edema index right after childbirth was significantly higher in the high-risk group than in the general maternal group (p<0.001), but there was no significant difference in BMI. After treatment with Korean medicine treatments, body weight, BMI, and edema index decreased significantly in both groups (p<0.01). As a result of correlation analysis, weight gain during pregnancy had a significant negative correlation with pre-pregnancy weight and pre-pregnancy BMI and a significant positive correlation with weight and BMI of the last month of pregnancy. In particular, pre-pregnancy BMI and body weight showed a significant negative correlation only in the high-risk group. Postpartum weight loss was significantly positively correlated with pre-pregnancy weight, pre-pregnancy BMI, weight & BMI of the pregnancy last month, weight gain during pregnancy, and decrease in edema (p<0.01)Conclusions: The weight during pregnancy of the high-risk group increased in inverse proportion to the pre-pregnancy BMI. The level of edema right after childbirth was significantly higher than that of the general maternal group, but showed a significant decrease after 2 weeks of Korean medicine treatments. Although it implicates the need for active Korean medicine treatments in the early postpartum period, further studies with controlled groups are needed.
LibGuides: Curating your scholarly identity: References and resources

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Hubungan Disiplin Belajar dengan Motivasi Belajar Siswa pada Mata Pelajaran Fiqh Kelas IX MTs Al-Iqna Cisaga Kabupaten Ciamis

This study aims to determine the relationship between learning discipline and student learning motivation in Fiqh subjects in class IX MTs Al-Iqna, Cisaga District, Ciamis Regency. This research method is quantitative research, in data collection techniques using a questionnaire for data collection. The results of the study, the students' learning discipline in class IX MTs Al-Iqna Cisaga was classified as moderate, because the average obtained was 65.83. This figure if classified into the research scale is classified as moderate, because it is in the range of r scores 62.22 - 69.42. And student learning motivation in the class IX fiqh subject is classified as moderate, because the average obtained is 64.37. This figure if classified into the research scale is classified as moderate, because it is in the range of scores of 61.78 - 66.92. Thus there is a relationship between learning discipline and student learning motivation in class IX Fiqh subjects, because based on the results of comparing t count with t table, the value of the Coefficient table (a) is obtained by the value of t count is 3,965, while t table is 2,048 When compared to t count&gt; t table (3,965&gt; 2,048), then Ho is rejected. Thus, it can be concluded that there is a significant influence between learning discipline on student learning motivation in class IX fiqh subjects. This is 36.0% change in learning motivation variable explained by changes in learning discipline variables. This means that there are still other factors as large as 64.0% that influence it.
On the Quantum Complexity of the Continuous Hidden Subgroup Problem

The Hidden Subgroup Problem (HSP) aims at capturing all problems that are susceptible to be solvable in quantum polynomial time following the blueprints of Shor's celebrated algorithm. Successful solutions to this problems over various commutative groups allow to efficiently perform number-theoretic tasks such as factoring or finding discrete logarithms.The latest successful generalization (Eisentrager et al. STOC 2014) considers the problem of finding a full-rank lattice as the hidden subgroup of the continuous vector space Rm, even for large dimensions m. It unlocked new cryptanalytic algorithms (Biasse-Song SODA 2016, Cramer et al. EUROCRYPT 2016 and 2017), in particular to find mildly short vectors in ideal lattices.The cryptanalytic relevance of such a problem raises the question of a more refined and quantitative complexity analysis. In the light of the increasing physical difficulty of maintaining a large entanglement of qubits, the degree of concern may be different whether the above algorithm requires only linearly many qubits or a much larger polynomial amount of qubits.This is the question we start addressing with this work. We propose a detailed analysis of (a variation of) the aforementioned HSP algorithm, and conclude on its complexity as a function of all the relevant parameters. Incidentally, our work clarifies certain claims from the extended abstract of Eisentrager et al.
Noise-Resistant Quantum State Compression Readout

Qubit measurement is generally the most error-prone operation that degrades the performance of near-term quantum devices, and the exponential decay of readout fidelity severely impedes the development of large-scale quantum information processing. Given these disadvantages, we present a quantum state readout method, named \textit{compression readout}, that naturally avoids large multi-qubit measurement errors by compressing the quantum state into a single qubit for measurement. Our method generally outperforms direct measurements in terms of accuracy, and the advantage grows with the system size. Moreover, because only one-qubit measurements are performed, our method requires solely a fine readout calibration on one qubit and is free of correlated measurement error, which drastically diminishes the demand for device calibration. These advantages suggest that our method can immediately boost the readout performance of near-term quantum devices and will greatly benefit the development of large-scale quantum computing.
Saxcess Wind Power - Music for Saxophone Quartet

Comprises recording of Saxcess Wind Power - Music for Saxophone Quartet. The 34 tracks were recorded by John Neill, produced by Brent Stewart, and edited, mixed and mastered by Mike Clayton. Title supplied by Library. Quantity: 1 compact disc(s). Provenance: This recording was made for commercial release for streaming. It was copied onto a compact disc for the Library by the donor.
From information privacy to emergency privacy

If before the creation and dissemination of computers made it possible to collect, organise and transmit an indistinct series of personal information, the right to privacy was linked to the concept of private property and the means for its protection and then, in a social dimension, came to coincide with the individual’s ability to control the circulation of information relating to him – a power that often is essential to maintaining social relationship and personal freedom, With the development of technologies and the use of personal data processing, as well as the possibility of their exchange and aggregation through the Internet and the creation of databases, the needs have evolved (and are evolving) even more significantly towards a collective dimension of information privacy. The innate mutability of the concept of privacy as a concept which is strongly affected by social, cultural and technological changes, pushes, in this way, to prepare a cautious legal schematization of the institution also in light of the emergency situation generated by the diffusion of the COVID-19. It is necessary, therefore, to ask whether the existing legislation can be considered sufficiently malleable to the changed framework of protection of personal data or whether the solutions adopted can be considered legitimate and proportionally oriented to respect the new “emergency privacy”.
Traffic classification using deep learning

As Deep Learning (DL) algorithms have rapidly become a methodology of choice in various domains, they have recently entered also the field of the Internet traffic classification, successfully demonstrating impressive results. Most of the research work up to this point has focused on improving the accuracy of classification systems, yet there has been little attempt to provide (i) systematic comparison of the various DL algorithms used and (ii) analysis on where the higher accuracy come from, particularly when comparing with the traditional machine learning algorithms like C4.5. To fill this gap, we conduct experiments with four DL algorithms proposed for traffic classification, including CNN, LSTM, Stacked Auto-Encoder (SAE), and Hierarchical Attention Networks (HAN). Further, we propose to leverage and visualize hierarchical attention layers to highlight which parts of the traffic packet traces were most informative for accurate classification, which provides hints about why (and how) DL algorithms achieve the state-of-the-art level high accuracy. We view this paper as the first step towards answering the aforementioned "why" question, which is critical in understanding the real benefit and contribution of deep learning to the field of the Internet traffic classification, and advancing its state-of-the-art.
Question: ‘how about dinner?’ Answer: ‘’ what have you just agreed to?

The modern day use of emojis and emoticons has become so prevalent that it is now a new language and form of communication which must be legally acknowledged and integrated into our legal system. Our failure to anticipate and incorporate new modes and methods of communication will ultimately make our legal system less responsive to our society’s needs and further the divide between our society and legal norms. The question we must be able to answer is: ‘How about Dinner?’ Answer: ‘’. Is there a consensus on what we just agreed to? If we are to acknowledge this new language, how are we to deal with the fact that the meanings of emojis and emoticons can differ between individual, context and country?
Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger

This paper presents a simple and effective approach in low-resource named entity recognition (NER) based on multi-hop dependency trigger. Dependency trigger refer to salient nodes relative to a entity in the dependency graph of a context sentence. Our main observation is that there often exists trigger which play an important role to recognize the location and type of entity in sentence. Previous research has used manual labelling of trigger. Our main contribution is to propose use a syntactic parser to automatically annotate trigger. Experiments on two English datasets (CONLL 2003 and BC5CDR) show that the proposed method is comparable to the previous trigger-based NER model.
Fast query-by-example speech search using separable model

Traditional Query-by-Example (QbE) speech search approaches usually use methods based on frame-level features, while state-of-the-art approaches tend to use models based on acoustic word embeddings (AWEs) to transform variable length audio signals into fixed length feature vector representations. However, these approaches cannot meet the requirements of the search quality as well as speed at the same time. In this paper, we propose a novel fast QbE speech search method based on separable models to fix this problem. First, a QbE speech search training framework is introduced. Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality. Third, we modify and improve our QbE speech search model according to the proposed model inference scheme. Experiments on keywords dataset shows that our proposed method can improve the GPU Real-time Factor (RTF) from 1/150 to 1/2300 by just applying separable model scheme and outperforms other state-of-the-art methods.
Completeness of Unbounded Best-First Game Algorithms

In this article, we prove the completeness of the following game search algorithms: unbounded best-first minimax with completion and descent with completion, i.e. we show that, with enough time, they find the best game strategy. We then generalize these two algorithms in the context of perfect information multiplayer games. We show that these generalizations are also complete: they find one of the equilibrium points.