Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF E-LEARNING ON STUDENTS SPEAKING SKILL PROGRESS: A CASE OF THE SEVENTH GRADE AT SMP PENCAWAN MEDAN

This study aims to see whether e-learning has an effect on the progress of students' speaking skill and know the process of students' speaking skill in e-learning. This research uses quantitative methods with a descriptive approach. The study population was SMP Pencawan Medan with a sample of seventh grade of junior high school students. The data was collected by first explaining the recorded material orally through the WhatsApp group which contained 20 students and after that the students collected the audio of their voice by recording. The results of this study indicate that in seventh grade student s at SMP PENCAWAN MEDAN who can understand 20 students who have experienced a decreasing in speaking during face to face leaning before pandemic than e-learning from the score table during the pandemic, from the table of score during the pandemic, there were 2 students with excellent categories, no students with nice category, 8 students with the sufficient categories and 10 students with the not satisfactory categories. The researchers find some problems, namely in pronunciation, fluently, vocabulary and accuracy. When in e-learning process, students become embarrassed to speak and become lazy to speak English, then there is a decreasing in vocabulary, a decreasing in pronunciation and a decreasing in students' speaking fluency and accuracy. Keywords: E-learning; speaking skills; progress.
Logistic Regression Based on Artificial Fish Swarm Algorithm with T-Distribution Parameters

Logistic regression is to establish a cost function in the face of a regression or classification problem, and then iteratively solve the optimal model parameters through optimization method, and then test and verify the quality of the model we solve. It is the primary approach to data classification in the field of big data and machine learning. The traditional logistic regression uses gradient descent method to solve the optimal parameter of the missing function. However, swarm intelligence algorithms, such as artificial fish swarm, can replace the traditional penalty function method to some extent to ensure the global convergence of the optimization algorithm. Artificial swarm intelligence algorithm can not only realize simple iterative optimization, but also has simple environmental adaptability and system self-regulation. In addition, guided random parallel global search makes the algorithm not easy to fall into the local optimal solution and get the global optimal solution. In this paper, the artificial fish swarm theory in the field of swarm intelligence is applied to solve the optimal parameter of the loss function and overcome the limitation of the gradient descent method to the loss function. In this paper, logistic regression and artificial fish swarm are adopted to deal with the actual data classification problem, and the validity of the algorithm is verified.
Critical Appraisal of e-learning through Mobile Devices in Medical Education

In COVID-19 pandemic, e-learning is the essential component of education system.All the schools, colleges, universities implemented e-learning program for the purposes of teaching and learning.Syllabus of new session is started for the said academic year through e-learning to minimize the academic loss and to coverup the academic session.Nowadays, medical education is full off with information technology solutions, computer--assisted learning has facilitated teaching of a wide range of topics, including basic science and advanced surgical procedures.The e-learning is totally depending upon the expertise in the handling of technology, electronic devices as well resource material available for the conduction of e-classes and one of the most important factors i.e. internet connectivity.In due course, mental status of students and teachers along with motivation from the teaching institute is also important factor for e-learning program.There were various challenges and disadvantages reported in previous research studies regarding e-learning, suggested for periodic need assessment of educational program, emphasize upon breach in between expected and existing system, resources management system, technical expert team, financial budget, mindset of the teachers and students and training sessions for development of e-learning culture.
Understanding Frank-Wolfe Adversarial Training

Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization problem to minimize the worst-case loss and is widely regarded as the most effective defense against such attacks. While projected gradient descent (PGD) has received most attention for approximately solving the inner maximization of AT, Frank-Wolfe (FW) optimization is projection-free and can be adapted to any $\ell_p$ norm. A Frank-Wolfe adversarial training approach is presented and is shown to provide as competitive level of robustness as PGD-AT for a variety of architectures, attacks, and datasets. Exploiting a representation of the FW attack we are able to derive the geometric insight that: The larger the $\ell_2$ norm of an $\ell_\infty$ attack is, the less loss gradient variation there is. It is then experimentally demonstrated that $\ell_\infty$ attacks against robust models achieve near the maximal possible $\ell_2$ distortion, providing a new lens into the specific type of regularization that AT bestows. Using FW optimization in conjunction with robust models, we are able to generate sparse human-interpretable counterfactual explanations without relying on expensive $\ell_1$ projections.
Comparison of Convolutional Neural Network Architectures and their Influence on Patient Classification Tasks Relating to Altered Mental Status

With the pervasiveness of Electronic Health Records in many hospital systems, the application of machine learning techniques to the field of health informatics has become much more feasible as large amounts of data become more accessible. In our experiment, we evaluated several different convolutional neural network architectures that are typically used in text classification tasks. We then tested those models based on 1,113 histories of present illness. (HPI) notes. This data was run over both sequential and multi-channel architectures, as well as a structure that implemented attention methods meant to focus the model on learning the influential data points within the text. We found that the multi-channel model performed the best with an accuracy of 92%, while the attention and sequential models performed worse with an accuracy of 90% and 89% respectively.
Crucial and Redundant Shares and Compartments in Secret Sharing

Secret sharing is the well-known problem of splitting a secret into multiple shares, which are distributed to shareholders. When enough or the correct combination of shareholders work together the secret can be restored. We introduce two new types of shares to the secret sharing scheme of Shamir. Crucial shares are always needed for the reconstruction of the secret, whereas mutual redundant shares only help once in reconstructing the secret. Further, we extend the idea of crucial and redundant shares to a compartmented secret sharing scheme. The scheme, which is based on Shamir's, allows distributing the secret to different compartments, that hold shareholders themselves. In each compartment, another secret sharing scheme can be applied. Using the modifications the overall complexity of general access structures realized through compartmented secret sharing schemes can be reduced. This improves the computational complexity. Also, the number of shares can be reduced and some complex access structures can be realized with ideal amount and size of shares.
Forensic exploration on windows File History

Nowadays, a proliferation of flash-memory-based storage devices makes it more difficult to recover deleted files in unallocated areas. Thus, it becomes more important for forensic examiners to find and utilize backed up data generated by specially prepared backup features. As an interesting example of them, File History (FH) included since Windows 8 is a backup feature that can be set and operated by a user. To enable FH, it is required to select a storage device for file backup operations which can be almost any type of storage devices, including a local drive, USB flash drive, network drive, etc. This special backup feature of course allows users to restore backed up files and delete old backup versions whenever they want. Therefore, it is necessary to be able to analyze forensic artifacts that show user behaviors relating to FH, during examination of Windows systems. In this paper, we deeply explore Windows FH feature from a digital forensics perspective. As a result, this paper proposes a three-step examination procedure along with detailed considerations for each step. We also analyze impacts of several anti-forensic actions that users can perform intentionally or unintentionally. Finally, this work develops an open-source tool for identifying FH related artifacts and analyzing user behaviors on backup operations.
Sentiment analysis of movie reviews based on deep learning

Abstract In recent years, with the rapid development of NLP (Natural Language Processing) and deep learning, public opinion and public opinion on the Internet have decreased a lot compared to the past. Many Internet users have changed from mere "bystanders" to disseminators of Internet information. Movie review sentiment analysis technology is an emerging category in the field of information mining. More and more people have joined the "review army". The quality of a movie is closely related to movie reviews! The manual screening method not only consumes a lot of manpower and material resources, but also is inefficient. Therefore, the use of deep learning-based sentiment analysis has become the current general trend. Based on the principle of word mosaic (word vector) and deep learning, this paper proposes a movie review sentiment analysis technology based on deep learning and machine learning word mosaic. Experiments show that the method used in this article has reached the correct rate of emotional classification of movie reviews. 83.13%, the experimental results prove the practicability and scalability of this method and the effectiveness of this method.
Defending Neural ODE Image Classifiers from Adversarial Attacks with Tolerance Randomization

Deep learned models are now largely adopted in different fields, and they generally provide superior performances with respect to classical signal-based approaches. Notwithstanding this, their actual reliability when working in an unprotected environment is far enough to be proven. In this work, we consider a novel deep neural network architecture, named Neural Ordinary Differential Equations (N-ODE), that is getting particular attention due to an attractive property—a test-time tunable trade-off between accuracy and efficiency. This paper analyzes the robustness of N-ODE image classifiers when faced against a strong adversarial attack and how its effectiveness changes when varying such a tunable trade-off. We show that adversarial robustness is increased when the networks operate in different tolerance regimes during test time and training time. On this basis, we propose a novel adversarial detection strategy for N-ODE nets based on the randomization of the adaptive ODE solver tolerance. Our evaluation performed on standard image classification benchmarks shows that our detection technique provides high rejection of adversarial examples while maintaining most of the original samples under white-box attacks and zero-knowledge adversaries.
Application and Prospect of Multimedia Technology in Piano Teaching in Colleges

Abstract The 21st century is a multimedia era, and all walks of life have been unable to separate from the Internet, cloud computing, IOT and other new media. The development of multimedia is an important product of information technology, which has occupied an important position in various fields, including the education industry. With the promotion of education informatization, Piano Teaching (hereinafter referred to as PT) in Colleges is gradually applied to the Multimedia technology (hereinafter referred to as TMT), which requires Colleges to constantly improve the relevant equipment and resources. Through TMT, we can enrich teaching resources and teaching forms, which will improve the classroom efficiency of PT. Combined with the current situation of multimedia teaching, this paper analyzes the problems of PT in Colleges. Then, this paper puts forward some suggestions.
Improving the Efficiency of Image and Video Forgery Detection Using Hybrid Convolutional Neural Networks

Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.
Towards a Global CIs’ Cyber-Physical Security Management and Joint Coordination Approach

Abstract Critical Infrastructures (CIs) face numerous cyber-physical threats that can affect citizens’ lives and habits, increase their feeling of insecurity, and influence the seamless services provision. During such incidents, but also in general for the security of CIs several internal and external stakeholders are involved, having different needs and requirements, trying to cooperate, respond and recover. Although CIs security management process is well analyzed in the literature there is a need to set a common ground among different CIs, thus reducing administration/coordination overhead and rendering the decision making and crisis management process more efficient. In this direction, this paper considers three different CIs (airport facilities, gas infrastructures, and hospitals); presents the current and emerging physical and cyber security related regulations and standards, operations, organisational and technical measure and; finally, through the discussion on gaps and best practices identified, proposes a global, cyber-physical security management and joint coordination approach. The proposed approach recommends among others that the adoption of a Holistic Security Operation Centre (HSOC) in each CI and a National Coordination Centre (NCC), supervising them, which will facilitate the communication and cooperation between the different CI operators and stakeholders, in case of an incident, that may have cascading effects to interconnected Infrastructures. The findings presented and the conclusions drawn are linked with three EU funded research projects (SATIE, SecureGas and SAFECARE), that aim to improve physical and cyber security of CIs in a seamless and cost-effective way.
SAR Image Enhancement Based on P-M Nolinear Diffusion and Coherent Enhancement Diffusion

SAR image enhancement is of great significance to SAR image processing. Aiming at the defects of traditional partial differential algorithms sensitive to noise, this paper proposes a SAR image enhancement method using both P-M nonlinear equations and coherent enhanced partial differential equations. Combining the physical model of the SAR image, the generalized diffusion process synthesizes the properties of both forward and backward diffusion, and has a good smooth enhancement effect at edges as well as smooth areas of the image. This hybrid model not only avoids noise enhancement, but also enhances image edges. Experiment results show that the proposed hybrid model has significantly improved the sharpness of enhanced SAR images with minimal distortion in image details, which is proven to be superior than applying only single one of the models.
Akuisisi Bukti Digital Viber Messenger Android Menggunakan Metode National Institute of Standards and Technology (NIST)

Viber is one of the most popular social media in the Instant Messenger application category that can be used to send text messages, make voice calls, send picture messages and video messages to other users. As many as 260 million people around the world have used this application. Increasing the number of viber users certainly brings positive and negative impacts, one of the negative impacts of this application is the use of digital forensic crime. This research simulates and removes digital crime evidence from the viber application on Android smartphones using the National Institute of Standards Technology (NIST) method, which is a method that has work guidelines on forensic policy and process standards to ensure each investigator follows the workflow the same so that their work is documented and the results can be accounted for. This study uses three forensic tools, MOBILedit Forensic Express, Belkasoft and Autopsy. The results in this study show that MOBILedit Forensic Express gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. Belkasoft gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. For Autopsy does not give the expected results in the extraction process, in other words the Autopsy application gives zero results. It can be concluded that MOBILedit Forensic Express and Belkasoft have a good performance compared to Autopsy and thus this research has been completed and succeeded in accordance with the expected goals.
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System

Designing task-oriented dialogue systems is a challenging research topic, since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility. Many previous works trained end-to-end (E2E) models with supervised learning (SL), however, the bias in annotated system utterances remains as a bottleneck. Reinforcement learning (RL) deals with the problem through using non-differentiable evaluation metrics (e.g., the success rate) as rewards. Nonetheless, existing works with RL showed that the comprehensibility of generated system utterances could be corrupted when improving the performance on fulfilling user requests. In our work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO, where the latent dialogue act is applied to avoid designing specific dialogue act representations; (2) train HDNO via hierarchical reinforcement learning (HRL), as well as suggest the asynchronous updates between dialogue policy and NLG during training to theoretically guarantee their convergence to a local maximizer; and (3) propose using a discriminator modelled with language models as an additional reward to further improve the comprehensibility. We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing improvements on the performance evaluated by automatic evaluation metrics and human evaluation. Finally, we demonstrate the semantic meanings of latent dialogue acts to show the explanability for HDNO.
Saliency-Driven Target Detection Based on Common Visual Feature Clustering for Multiple Sar Images

Saliency detection is a newly emerging tool to extract target in image processing. However, due to the loss of color in synthetic aperture radar (SAR) images, the detection result using the traditional saliency analysis is not satisfying. Therefore, a new saliency-driven target detection model based on common visual feature clustering is introduced for multiple SAR images. Firstly, Markov Random Field is applied to extract intra-image saliency map. Secondly, intensity, texture and curve features are extracted from multiple SAR images as common visual features, which can effectively compensate for the lack of color information. And then fuzzy c-means is employed to construct inter-image saliency map. Finally, an effective fusion strategy is used to combine the intra-image saliency map with the inter-image saliency map to obtain the final common saliency map. The experimental results demonstrate that the proposed model outperforms most the state-of-the-art saliency detection models.
A Benchmark for Analyzing Chart Images

Charts are a compact method of displaying and comparing data. Automatically extracting data from charts is a key step in understanding the intent behind a chart which could lead to a better understanding of the document itself. To promote the development of automatically decompose and understand these visualizations. The CHART-Infographics organizers holds the Competition on Harvesting Raw Tables from Infographics. In this paper, based on machine learning, image recognition, object detection, keypoint estimation, OCR, and others, we explored and proposed our methods for almost all tasks and achieved relatively good performance.
Soundwork

In the 1990s, thanks to digital convergence, many disciplines centrally concerned with the mediated uses of sound from a variety of different orientations—communication, musicology, speech, ethnography, history of technology, journalism, theater and drama, and art, to name a few—began to recognize the need for a new term to indicate their shared focus. Here I propose soundwork as that term, laying out the reasons why we need it, how it can change our thinking across a broad range of cultural forms, the kind of cultural and political work it can perform, and how linking sound to our understanding of lived experience changes the way that we perceive the world around us.
Study of Feature Extraction Techniques for Sensor Data Classification

Human activity recognition is a rapidly growing area in healthcare systems. The applications include fall detection, ambiguous activity, dangerous behavior, etc. It has become one of the important requirements for the elderly or neurological disorder patients. The devices included are accelerometer and gyroscope, which generate large amounts of data. Accuracy of classification algorithms for this data is highly dependent upon extraction and selection of data features. This research study has extracted time domain features, based on statistical functions as well as rotational features around three axes. Gyroscope data features are also used to enhance accuracy of accelerometer data. Three popular classification techniques are used to classify the accelerometer dataset into activity categories. Binary classification (run -1 / walk-0) is considered. The results have shown SVM and LDA when used with rotation and gyroscope data gives the highest accuracy of 92.0% whereas FDA shows 84% accuracy.