Reviewerly/exHarmony huggingface dataset:
EFL Learners’ Perceptions about the Use of Mobile Learning during COVID-19

Mobile learning has recently emerged as a powerful and frequently used tool in the teaching-learning curriculum. Besides, it also attracted the attention of EFL teachers and learners across the globe. Its acceptance in EFL learning is mainly because of the two fundamental functions, i.e., anytime and anywhere. As a result, the pioneers and scholars who work in second or foreign language learning have largely recognized its impact and importance. Furthermore, since the outbreak of the COVID-19 pandemic, the regular traditional classes have been severely affected, and teaching has been carried out by online method. As a result, mobiles and tablets have primarily been used along with other electronic gadgets. Therefore, this paper aims to examine EFL learners' perceptions about m-learning for their learning activities during COVID-19 at Qassim University, Saudi Arabia. A questionnaire has been prepared by keeping the use of the mobile device in view that can benefit Saudi EFL learners, and distributed among 50 EFL male and female students. Later, a quantitative analysis of the responses has been done by using SPSS 26. The study identified healthy and positive perceptions of m-learning. The significant findings will be valuable for the teachers and administrators concerning m-learning in language teaching.
CLIFD: A novel image forgery detection technique using digital signatures

The paper presents a new image forgery detection technique. The proposed technique uses digital signatures; it generates a digital signature for each column and embeds the signature in the least significant bits of each corresponding column’s selected pixels. The message digest algorithm 5 (MD5) is used for digital signature generation, and the fourleast-significant-bit substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.
Benefits of Assistance over Reward Learning

Much recent work has focused on how an agent can learn what to do from human feedback, leading to two major paradigms. The first paradigm is reward learning, in which the agent learns a reward model through human feedback that is provided externally from the environment. The second is assistance, in which the human is modeled as a part of the environment, and the true reward function is modeled as a latent variable in the environment that the agent may make inferences about. The key difference between the two paradigms is that in the reward learning paradigm, by construction there is a separation between reward learning and control using the learned reward. In contrast, in assistance these functions are performed as needed by a single policy. By merging reward learning and control, assistive agents can reason about the impact of control actions on reward learning, leading to several advantages over agents based on reward learning. We illustrate these advantages in simple environments by showing desirable qualitative behaviors of assistive agents that cannot be found by agents based on reward learning.
Content-based prediction: big data sampling perspective

Today, large volumes of data are actively generated on the order of terabytes or even petabytes. Hence, processing data on such a large scale in an efficient and effective manner is extremely challenging. However, existing research studies apply machine learning algorithms by loading the entire training dataset into the computer’s main memory (RAM). This causes a problem as the data grows too big over time and can’t be supported by most of the conventional models or hardware within a single machine’s memory. Inspired by current research studies, this paper discusses the benefits of implementing two sampling techniques that could be used for machine learning models: (1) sampling with replacement and (2) reservoir sampling. In this study, 40 experiments were performed by reducing the number of data instances by 50% of the original data using random sampling of a video dataset that was more than 40 GB in size. Remark that accuracies of SVM and random forest are very competitive classifiers and give the importance score of all repeated ten rounds of the process for each of the four combinations of sampling techniques and machine learning classifiers.
“TECHNOLOGY OF USING E-LEARNING MODELING PROGRAMS IN TEACHING SPECIAL SUBJECTS IN PROFESSIONAL EDUCATION”

Nowadays, the focus on education is to radically reform professional education, that is, to develop e-learning tools based on the concept of learning outcomes. A distinctive feature of the cognitive-visual approach to the development of electronic didactics is the concentration of attention through visual support, as the formation of visual models of objects studied taking into account human psychophysiology and demonstration of practical skills of learners, effective organization of the interface with the learning object. Providing feedback on electronic didactic tools, mobile desktop and console platforms, as well as in the browser, based on the principles of design, plays an important role in preparing future engineers for professional activities. Therefore, interactive software was developed through the cognitive visualization of a simple oil distillation laboratory. This virtual learning lab was developed using the Microsoft Visual Studio software package. This, in turn, provides a certain degree of convenience in the introduction of electronic didactic tools in engineering education.
VPN Traffic Classification Based on Payload Length Sequence

In order to circumvent Internet censorship, more and more malicious users utilize Virtual Private Network (VPN) software to penetrate firewall and access restricted resources. It's important to identify and classify VPN traffic for network management and security. In this paper, 6 different kinds of VPN traffic are analyzed. Characterizing VPN traffic by Sample Entropy Fingerprint and Payload Length Sequence (PLS) separately, we apply Machine Learning (ML) algorithms to construct VPN traffic classifiers basing on these features. Comparing with Deep Packet Inspection (DPI), Sample Entropy Fingerprint and PLS, our result shows that the classifier based on PLS can achieve accurate VPN traffic classification, including obfuscated VPN traffic.
Realization of Agile Methods in Established Processes: Challenges and Barriers

This paper presents an explorative study and the results of 17 interviews with informants from different companies. Its purpose is to identify the challenges associated with implementing agile methods along with the established procedures for early design. The study exemplifies project leaders’ experiences and implementation efforts. As leaders of design projects, they have proposed the use of a new method that involves teams engaging in testing and evaluation, which aids in the understanding and introduction of change initiatives. The challenges that are identified are as follows: (1) a lack of approval not only from top managers but also from critical peers; (2) an unprepared organization that did not allow teamwork; and (3) a lack of specific company success factors to support new methods.
Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.
RANCANG BANGUN SISTEM INFORMASI PENJUALAN PADA TOKO OMG BERBASIS WEB DI KECAMATAN EMPANG KABUPATEN SUMBAWA

This study aims to design and build a sales information system at web-based Omg stores in Empang Subdistrict, Sumbawa Regency, which makes it easy for consumers or customers to obtain information on purchasing and ordering products sold by Omg stores and providing convenience in processing product data and reporting reports. . The method used in this study is qualitative and uses qualitative methods and uses the waterfall method as a method of software development. This system is designed and built using the PHP programming language by using MySql as a database. With the existence of this sales information system can facilitate buyers or customers in the process of ordering and buying at the Omg store and can facilitate shop owners in checking product data available on the store through reports provided by the shop admin.
Fragile Watermarking Framework for Tamper Detection of Color Biometric Images

Application of fragile watermarking on biometric images stored at a server or cloud ensures proper authentication and tamper detection when access to the servers was shared. In this paper, a hybrid domain fragile watermarking technique for authenticity of color biometric images, using hybridization of various transforms such as discrete cosine transform (DCT), fast discrete curvelet transform (FDCuT), and singular value decomposition (SVD) is proposed. The hybrid transform coefficients are modified according to the scrambled color watermark to obtain watermarked color biometric image. The security of this technique is strengthened with the usage of Arnold scrambling, and by using multiple secret keys. The proposed technique is analyzed on FEI Brazilian face database. The experimental results show that this technique performs better than the existing fragile watermarking techniques.
A design of sequential reversible circuits by reversible gates

Reversible logic has become increasingly important in the design of low power CMOS circuits, quantum computing and nanotechnology. In this article we work on recent sequential circuits namely RS Flip Flop JK Flop Flip Flop Flop Flip Flop Master Slave Flip Flop using some reversible gates FG (Feyman Gate), FRG (Fredkin Gate), NG (New Gate) , PG (Peres Gate), BJN (New BJN Gate), while modifying them to obtain new circuits keeping their same functionality and increasing their performances.
Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.
Reference-frame-independent measurement-device-independent quantum key distribution with imperfect sources

Reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) can remove all potential detector side-channel attacks and the requirement of real-time alignment of reference frames. However, all previous RFI-MDI-QKD implementations assume the perfect state preparation in sources, which is impractical and may lead to security loopholes. Here, we propose a RFI-MDI-QKD protocol which is robust against state preparation flaws. Comparing to the conventional six-state RFI-MDI-QKD, our scheme can be realized with only four flawed states, which improves the practical security of RFI-MDI-QKD and simplifies the experimental implementation. In addition, simulation results demonstrate that source flaws in Z basis have adverse effect on the performance of RFI-MDI-QKD while the source flaws in X and Y bases have almost no effect. We hope that this work could provide a valuable reference for practical implementations of RFI-MDI-QKD.
Multi-Branch Gabor Wavelet Layers for Pedestrian Attribute Recognition

Surveillance cameras are everywhere, keeping an eye on pedestrians as they navigate through a scene. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This is a multi-label problem and challenging even for human observers. The problem has rightly attracted attention recently from the computer vision community. In this paper, we adopt trainable Gabor wavelets (TGW) layers and use it with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a multi-branch neural network where mixed-layers, a combination of the TGW and convolutional layer, make up the building block of our 3-branch deep neural network. We test our method on publicly available challenging datasets and compare our results with state of the art.
Detecting Criminal Activities of Surveillance Videos using Deep Learning

Videos generated by surveillance cameras inside the ATM were very long. In case, any robbery had taken place inside the ATM; it became time consuming to watch the entire long video. Hence, there was a need to process these surveillance videos by extracting the priority frames from it in which suspicious activities like robbery, murder, kidnap, etc. had taken place. The objective of this paper was to propose algorithm that would generate a detect the suspicious frames from that long surveillance video for the authorities which would consists of priority information. In this paper a novel approach dealing with Convolutional Neural Networks using Deep Learning was used to sample the priority information from the surveillance videos. The priority information was the suspicious activities like robbery, murder, etc. which take place inside the ATM. The results of the CNN model effectively were able to extract suspicious activity frames from a long video and thus extract suspicious frames and create a video from it.
Optimum Median Filter Based on Crow Optimization Algorithm

A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%).
Novel Approach for Generating Hybrid Features Set to Effectively Identify Hate Speech

Automating hate speech or inappropriate text detection in social media and other internet platforms isgaining a lot of interest and becoming a valuable research topic for both industry and academia in recent years. Itis more important for applications to identify the disruptive contents, understand sentiment analysis, identify cyberbullying, detect flames, threats, hatred towards people or particular communities or groups etc. Text classificationis a very challenging task due to the nature and complexities with languages, especially its context, micro words,emojis, typo error and sarcasm present in the text. In this paper, we have proposed a model with a novel approachfor generating hybrid features for an effective feature representation to classify hate speech. We have combinedfeatures learned from deep learning methods with the semantic features like word n-grams and tweets specificsyntactic features to form hybrid feature sets. We have also improvised preprocessing steps to reduce the numberof missing embeddings to increase the vocabulary for efficient feature learning. We have experimented with thevarious neural networks for feature learning and machine learning models with hybrid features for classification.Our work delivers hybrid features and appropriate preprocessing techniques for an efficient classification of thestandard dataset of 16k annotated hate speech tweets. The combination of Long Short Term Memory (LSTM)trained on Random Embeddings for deep learning features extraction and Logistic Regression (LR) as a classifierwith the hybrid features is found to be the best model and it outperforms the state of the art reported in theliterature.
Selection of acoustic modeling unit for Tibetan speech recognition based on deep learning

The selection of the speech recognition modeling unit is the primary problem of acoustic modeling in speech recognition, and different acoustic modeling units will directly affect the overall performance of speech recognition. This paper designs the Tibetan character segmentation and labeling model and algorithm flow for the purpose of solving the problem of selecting the acoustic modeling unit in Tibetan speech recognition by studying and analyzing the deficiencies of the existing acoustic modeling units in Tibetan speech recognition. After experimental verification, the Tibetan character segmentation and labeling model and algorithm achieved good performance of character segmentation and labeling, and the accuracy of Tibetan character segmentation and labeling reached 99.98%, respectively.
PENGARUH MODEL DISCOVERY LEARNING BERBANTUAN MEDIA AUDIOVISUAL TERHADAP KETERAMPILAN MENULIS TEKS EKSPOSISI SISWA KELAS VIII SMP NEGERI 25 PADANG

ABSTRACT The purpose of this study is three. First, describe the writing skills of exposition text of class VIII students of SMP Negeri 25 Padang before using discovery learning models assisted by audiovisual media. Second, describe the writing skills of exposition text of grade VIII students of SMP Negeri 25 Padang after using discovery learning models assisted by audiovisual media. Third, describe the influence of discovery learning models assisted by audiovisual media on writing exposition text skills of eighth grade students of SMP Negeri 25 Padang. The sample in this study was class VIII.2 with 29 students. The results of this study are three, namely as follows. First, exposition text writing skills before using the discovery learning learning model assisted by audiovisual media of eighth grade students of SMP Negeri 25 Padang are in almost adequate qualifications with an average value of 53.45. Second, the skill of writing exposition text after using the discovery learning model assisted by audiovisual media of eighth grade students of SMP Negeri 25 Padang is in a qualification of more than enough with an average score of 70.11. Third, based on the t-test, the alternative hypothesis (H_1) is received at a significant level of 95% and the degree of freedom (dk) = n-1 because of tcount&gt; t table, which is 4.79&gt; 1.70. This means that there is a significant influence on the use of discovery learning models assisted by audiovisual media on the exposition writing skills of eighth grade students at SMP Negeri 25 Padang. Kata kunci: Pengaruh, Discovery Learning, Keterampilan Menulis Teks Eksposisi
Resolving Network Defense Conflicts with Zero Trust Architectures and Other End-to-End Paradigms

Network defense implies a comprehensive set of software tools to preclude malicious entities from conducting activities such as exfiltration of data, theft of credentials, blocking of services and other nefarious activities. For most enterprises at this time, that defense builds upon a clear concept of the fortress approach. Many of the requirements are based on inspection and reporting prior to delivery of the communication to the intended target. These inspections require decryption of packets and this implies that the defensive suite either impersonates the requestor, or has access to the private cryptographic keysof the servers that are the target of communication. This is in contrast to an end-to-end paradigm where known good entities can communicate directly and no other entity has access to the content unless that content is provided to them. There are many new processes that require end-to-end encrypted communication, including distributed computing, endpoint architectures, and zero trust architectures and enterprise level security. In an end-to-end paradigm, the keys used for authentication, confidentiality, and integrity reside only with the endpoints. This paper examines a formulation that allows unbroken communication, while meeting the inspection and reporting requirements of a network defense. This work is part of a broader security architecture termed Enterprise Level Security (ELS)framework.