Reviewerly/exHarmony huggingface dataset:
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks

This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existing methods obtain the quantized weights by performing quantization on the full-precision network weights. However, this approach would result in some mismatch: the gradient descent updates full-precision weights, but it does not update the quantized weights. To address this issue, we propose a novel method that enables direct updating of quantized weights with learnable quantization levels to minimize the cost function using gradient descent. Second, to obtain low bit-width activations, existing works consider all channels equally. However, the activation quantizers could be biased toward a few channels with high-variance. To address this issue, we propose a method to take into account the quantization errors of individual channels. With this approach, we can learn activation quantizers that minimize the quantization errors in the majority of channels. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the image classification task, using AlexNet, ResNet and MobileNetV2 architectures on CIFAR-100 and ImageNet datasets.
Study on Cyber Law’s of India

In this present age all the things are depends on internet like online dealing or transaction. As we know internet is vast source of knowledge, so it's easy to access by anyone from anywhere. Some people used internet technology for criminal activities like unauthorized access, scams etc. Above unlawful activities or the offense through internet is come under cyber crime. The term "Cyber Law" was introduced for prevention of cyber crimes. Cyber law is the part of legal systems that conciliation the legal issues of internet related crime. Cyber Law helps to prevent or reduce criminal activities, for example freedom of expressions, access to utilization of the online, and online security or online privacy. Cyber crime is also known as law of web.
Summed Batch Lexicase Selection on Software Synthesis Problems

Lexicase selection is one of the most successful parent selection methods in evolutionary computation.However, it has the drawback of being a more computationally involved process and thus taking more time compared to other selection methods, such as tournament selection.Here, we study a version of lexicase selection where test cases are combined into several composite errors, called summed batch lexicase selection; the hope being faster but still reasonable success.Runs on some software synthesis problems show that a larger batch size tends to reduce the success rate of runs, but the results are not very conclusive as the number of software synthesis problems tested was small.
Active contour driven by scalable local regional information on expandable kernel

An active contour that uses the pixel’s intensity on a set of expandable kernels along the propagating contour for image segmentation is presented in this paper. The objective is this study is to employ the scalable kernels to attract the contour to meet the desired boundary. The key characteristics of this scheme is that the kernels gradually expand to find an object’s boundary. So this scheme could penetrate to the concave boundary more effective and efficient than some other schemes. If a Gaussian kernel is applied, it could trace the object with a blurred or smooth boundary. Moreover, the directional selectivity feature enables in capturing two edge’s types with just one initial position. Its performance showed more desirable segmentation outcomes compared to the other existing active contours using regional information when segmenting the noisy image and the non-uniform (or heterogeneous) textures. Meanwhile, the level set implementation enables topological flexibility to our active contour scheme.
Improving semantic part features for person re-identification with supervised non-local similarity

In person re-IDentification (re-ID) task, the learning of part-level features benefits from fine-grained information. To facilitate part alignment, which is a prerequisite for learning part-level features, a popular approach is to detect semantic parts with the use of human parsing or pose estimation. Such methods of semantic partition do offer cues to good part alignment but are prone to noisy part detection, especially when they are employed in an off-the-shelf manner. In response, this paper proposes a novel part feature learning method for re-ID, that suppresses the impact of noisy semantic part detection through Supervised Non-local Similarity (SNS) learning. Given several detected semantic parts, SNS first locates their center points on the convolutional feature maps for use as a set of anchors and then evaluates the similarity values between these anchors and each pixel on the feature maps. The non-local similarity learning is supervised such that: each anchor should be similar to itself and simultaneously dissimilar to any other anchors, thus yielding the SNS. Finally, each anchor absorbs features from all of the similar pixels on the convolutional feature maps to generate a corresponding part feature (SNS feature). We evaluate our method with extensive experiments conducted under both holistic and partial re-ID scenarios. Experimental results confirm that SNS consistently improves re-ID accuracy using human parsing or pose estimation, and that our results are on par with state-of-the-art methods.
MODEL PEMBELAJARAN GALLERY WALK PADA MATA PELAJARAN JURNAL KHUSUS PERUSAHAAN DAGANG DI SMAN 1 REJANG LEBONG

This study is the application of the Gallery Walk model in improving student learning activities in accounting subjects of specialized journal material for trading companies in SMAN 1 Rejang Lebong. Retrieval of data using methods of observation, questionnaires, written tests and deeds ,. The study was conducted by the classroom action research (CAR) method. Data collection techniques using test and observation techniques. Quantitative and qualitative analysis techniques. The results of the study stated that there was an increase in students who were active from cycle 1, cycle 2 and cycle 3. Increased average learning outcomes followed by mastery learning. Cycle I achieved an average of 59.16 and students completing learning 45.83%, Cycle II achieved an average of 67.08 and students completing learning 54.16%, Cycle III achieved an average of 85.41 and students completing learning 100%. Learning Gallery Walk model can increase learning activities more vibrant both classical, group and individual.
Progressive image painting outside the box with edge domain

Motivated by a widely studied computer vision task: image inpainting, we became interested in a less concerned problem image outpainting. By which, contents beyond the image boundaries may be extrapolated. In recent years, deep learning methods have achieved remarkable improvements in image inpaint ing, these techniques can be considered to be applied to image outpainting as solutions. However, many of these inpainting methods generate image blocks generally resulting in blur or smooth. Recently, hallucinating edges for the missing holes before completion has been proved to be a state-of-the-art image inpainting method. Refer to the aforementioned method, we propose a three-phase outpainting model that consists of an edge generation phase, an image expansion phase and a refinement phase. In order to depict the edge lines more accurately, we adopt a comparatively effective focal loss for edge prediction. An optimization stage with a refinement network is also added since large portions outside the image need to be inferred, and discriminator in this stage works on a decreased patch size with a coarse-to-fine fashion. In addition, with recursive outpainting, an image could be expanded arbitrarily. Experiments show that an image can be effectively expanded by our method, and our outpainting method of predicting edges and then coloring is generally superior to other methods both quantitatively and qualitatively.
ANALYSIS OF THE RESULTS OF THE PROJECT TO SUPPORT THE DEVELOPMENT PROGRAMS OF RUSSIAN SCIENTIFIC JOURNALS

In the period 2014–2020 with the support of the Russian Ministry of Education and Science, an interesting project was carried out to support Russian scientific journals in their implementation of their development programs. This article describes the main stages of the project and the results achieved by the project participant. The data on the project participants in various aspects (goals, tasks, quotas on topics) are given. The main objectives of the project were: increasing the number of Russian journals in the WoS and Scopus and improving their bibliometric indicators. The article provides an analysis of changes in the bibliometric indicators of project participants, and shows that on average their indicators improved and many project participants were either included in the WoS and Scopus or submitted applications for inclusion and are awaiting expert decision.
Vul-Mirror: A Few-Shot Learning Method for Discovering Vulnerable Code Clone

It is quite co mmon for reusing code in soft development, wh ich may lead to the wide spread of the vulnerability, so automatic detection of vulnerable code clone is becoming more and more important.However, the existing solutions either cannot automatically extract the characteristics of the vulnerable codes or cannot select different algorithms according to different codes, which results in low detection accuracy.In this paper, we consider the identification of vulnerable code clone as a code recognition task and propose a method named Vul-Mirror based on a few-shot learning model for discovering clone vulnerable codes.It can not only automatically extract features of vulnerabilities, but also use the network to measure similarity.The results of experiments on open -source projects of five operating systems show that the accuracy of Vul-Mirror is 95.7%, and its performance is better than the state-of-the-art methods.
PERANCANGAN PEMBELAJARAN E-LEARNING BAHASA INGGRIS UNTUK PEMULA BERBASIS MULTIMEDIA

&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;Abstrak&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Tujuan penelitian adalah merancang aplikasi pembelajaran &lt;em&gt;e-learning&lt;/em&gt; bahasa Inggris berbasis multimedia dengan menggabungkan konsep yang terdiri dari teks, gambar, animasi, dan video. Subjek penelitian adalah siswa SMA kelas X. Dalam mengimplementasikan aplikasi pembelajaran, peneliti menggunakan Moodle. Metode pengembangan sistem menggunakan &lt;em&gt;System Development Live Cycle&lt;/em&gt; (SDLC). Untuk analisis dan perancangan sistem menggunakan metode &lt;em&gt;Unified Modelling Language&lt;/em&gt; (UML). Peneliti berhasil merancang aplikasi pembelajaran yang dapat dijadikan sebagai media pembelajaran Bahasa Inggris untuk tingkat pemula dengan tema &lt;em&gt;learning by doing.&lt;/em&gt; Aplikasi pembelajaran yang dirancang diharapkan dapat membantu meningkatkan proses belajar bahasa Inggris terutama dalam membaca, menghapal, dan mengucapkan kosakata.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="StyleAuthorBold"&gt;&lt;strong&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The purpose of the research was to design the e-learning of English multimedia-based application by combining concepts consisting of text, images, animation, and video. The research subjects were high school students in class X. In implementing the learning application, researchers used Moodle. The system development method uses the System Development Live Cycle (SDLC). For system analysis and design using the Unified Modeling Language (UML) method. Researchers succeeded in designing learning applications that can be used as a medium of learning English for the beginner level with the theme of learning by doing. Learning applications that are designed are expected to help improve the process of learning English, especially in reading, memorizing, and speaking vocabulary.&lt;/em&gt;&lt;/p&gt;
Critique of Various Algorithms for Handwritten Digit Recognition Using Azure ML Studio

Handwritten Digit Recognition is probably one of the most exciting works in the field of science and technology as it is a hard task for the machines to recognize the digits which are written by different people. The handwritten digits may not be perfect and also consist of different flavors. And there is a necessity for handwritten digit recognition in many real-time purposes. The widely used MNIST dataset consists of almost 60000 handwritten digits. And to classify these kinds of images, many machine learning algorithms are used. This paper presents an in-depth analysis of accuracies and performances of Support Vector Machines (SVM), Neural Networks (NN), Decision Tree (DT) algorithms using Microsoft Azure ML Studio.
EDUCATIONAL AND SOCIAL SUCCESS OF UNIVERSITY STUDENTS: LEARNING PROCESS AND ACHIEVEMENT CONDITIONS

The article presents the study of psychological and pedagogical conditions of educational and social success.The authors generalized the idea of the internal conditions for achieving success.The authors conclude that a high level of motivation to achieve success, high self-esteem, the ability to reflect and self-development contribute to students achieving success, as well as the formation of their social maturity.It is proved that considering the personality characteristics of students in university classes allows us to improve the advisory, diagnostic and psycho-correctional work of a psychologist, the educational work of a class teacher, and to create an educational environment that helps students achieve social success.
An Image Forgery Detection using SIFT-PCA

Digital Images plays an important role for transferring the information and are widely used in all areas of day-to-day life.Although, with the development of modern technologies, multiple software's are developed.This leads to the forgery of digital images.Copy-move Image Forgery Detection is one of the forensic techniques in which selected area of an image is get copied and then moved onto the other portion of the image.In this research, the main aim is to detect the forged region from the image.A method is proposed to detect the copymove forgery in an image, by comparing extracted key points.The SIFT (Scale Invariant Feature Transform) algorithm is used for extracting the invariant features from an image and then extract blocks by using PCA.
How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems

The use of cloud computing (CC) is rapidly increasing due to the demand for internet services and communications.The large number of services and data stored in the cloud creates security risks due to the dynamic movement of data, connected devices and users between various cloud environments.In this study, we will develop an innovative prototype for location-aware access control and data privacy for CC systems.We will apply location-aware access control policies to role-based access control of Cloud Foundry, and then analyze the impact on user privacy after implementing these policies.This innovation can be used to address the security risks introduced by inter-cloud use and communication, and will have significant impact in making citizen's personal data more secure.
Generalized Adversarially Learned Inference

Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs. We generalize these approaches to incorporate multiple layers of feedback on reconstructions, self-supervision, and other forms of supervision based on prior or learned knowledge about the desired solutions. We achieve this by modifying the discriminator's objective to correctly identify more than two joint distributions of tuples of an arbitrary number of random variables consisting of images, latent vectors, and other variables generated through auxiliary tasks, such as reconstruction and inpainting or as outputs of suitable pre-trained models. We design a non-saturating maximization objective for the generator-encoder pair and prove that the resulting adversarial game corresponds to a global optimum that simultaneously matches all the distributions. Within our proposed framework, we introduce a novel set of techniques for providing self-supervised feedback to the model based on properties, such as patch-level correspondence and cycle consistency of reconstructions. Through comprehensive experiments, we demonstrate the efficacy, scalability, and flexibility of the proposed approach for a variety of tasks. The appendix of the paper can be found at the following link: https://drive.google.com/file/d/1i99e682CqYWMEDXlnqkqrctGLVA9viiz/view?usp=sharing
Frame-synchronous Blind Audio Watermarking for Tamper Proofing and Self-Recovery

This paper presents a lifting wavelet transform (LWT)-based blind audio watermarking scheme designed for tampering detection and self-recovery. Following 3-level LWT decomposition of a host audio, the coefficients in selected subbands are first partitioned into frames for watermarking. To suit different purposes of the watermarking applications, binary information is packed into two groups: frame-related data are embedded in the approximation subband using rational dither modulation; the source-channel coded bit sequence of the host audio is hidden inside the 2nd and 3rd -detail subbands using 2N-ary adaptive quantization index modulation. The frame-related data consists of a synchronization code used for frame alignment and a composite message gathered from four adjacent frames for content authentication. To endow the proposed watermarking scheme with a self-recovering capability, we resort to hashing comparison to identify tampered frames and adopt a Reed–Solomon code to correct symbol errors. The experiment results indicate that the proposed watermarking scheme can accurately locate and recover the tampered regions of the audio signal. The incorporation of the frame synchronization mechanism enables the proposed scheme to resist against cropping and replacement attacks, all of which were unsolvable by previous watermarking schemes. Furthermore, as revealed by the perceptual evaluation of audio quality measures, the quality degradation caused by watermark embedding is merely minor. With all the aforementioned merits, the proposed scheme can find various applications for ownership protection and content authentication.
Ontology-based Model for Security Assessment: Predicting Cyberattacks through Threat Activity Analysis

The prediction of attacks is essential for the prevention of potential risk.Therefore, risk forecasting contributes a lot to the optimization of the information security budget.This article focuses on the ontology and stages of a cyberattack.It introduces the main representatives of the attacking side and describes their motivation.
Joint Attention for Automated Video Editing

Joint attention refers to the shared focal points of attention for occupants in a space. In this work, we introduce a computational definition of joint attention for the automated editing of meetings in multi-camera environments from the AMI corpus. Using extracted head pose and individual headset amplitude as features, we developed three editing methods: (1) a naive audio-based method that selects the camera using only the headset input, (2) a rule-based edit that selects cameras at a fixed pacing using pose data, and (3) an editing algorithm using LSTM (Long-short term memory) learned joint-attention from both pose and audio data, trained on expert edits. The methods are evaluated qualitatively against the human edit, and quantitatively in a user study with 22 participants. Results indicate that LSTM-trained joint attention produces edits that are comparable to the expert edit, offering a wider range of camera views than audio, while being more generalizable as compared to rule-based methods.
The Effect of Cooperative Learning Models and Learning Motivation towards the Skills of Reading Students in Public Elementary School 101883 Tanjung Morawa Sub-district

This study aims to: (1) Know the differences in reading skills of students taught with Cooperative Integrated Reading and Composition learning models compared to students taught with conventional learning models; (2) Knowing the difference in reading skills of students who have high learning motivation compared to students who have low learning motivation; (3) Knowing the interaction between learning models and learning motivation in influencing students' reading skills. The population in this study was the fifth grade students of Public Elementary School 101883 Tanjung Morawa Subdistrict, Deliserdang Regency, amounting to 54 people. The data collection in this study was through a questionnaire and a test of students' reading skills. Hypothesis testing is done by the Two Way Anova test. The results showed that: (1) The reading skills of students taught with Cooperative Integrated Reading and Composition learning models were higher than those of students who were taught with conventional learning models (Fcount = 21,164 and sig. 0,000&gt; 0.05); (2) The reading skills of students who have high learning motivation are higher than the reading skills of students who have low learning motivation (Fcount = 6.756 and sig. 0.012&gt; 0.05); (3) There is an interaction between learning models and learning motivation in influencing students' reading skills (Fcount = 7.054 and sig. 0.011&gt; 0.05).
Experimental demonstration of all-photonic quantum repeater

We for the first time demonstrate the all-photonic quantum repeater by manipulating a 12-photon interferometry. Our experiment opens a new window of opportunity for a memoryless quantum repeater with efficient photonic graph states.
Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse.

Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA's institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort.
A Framework for Measuring Information Asymmetry

Information asymmetry occurs when an imbalance of knowledge exists between two parties, such as a buyer and a seller, a regulator and an operator, and an employer and an employee. It is a key concept in several domains, in particular, in economics. We propose in this work a general logic-based framework for measuring the information asymmetry between two parties. A situation of information asymmetry is represented by a knowledge base and a set of questions. We define the notion of information asymmetry measure through rationality postulates. We further introduce a syntactic concept, called minimal question subset (MQS), to take into consideration the fact that answering some questions allows avoiding others. This concept is used for defining rationality postulates and measures. Finally, we propose a method for computing the MQSes of a given situation of information asymmetry.