Reviewerly/exHarmony huggingface dataset:
Blogpost Website

As the name implies "Blog Post Website" is an educational website where there will be many blogs from a variety of topics. The main purpose of our website is to create a powerful platform where users can read various blogs and at the same time, users can publish their content or blogs on the website by creating an account, other users can log in / sign in. They can engage by doing likes, commenting on blogs. We will be adding this feature to increase engagement in our platform. Our Blogpost website is a website that is used for the chronological listing of the blog posts. Contains the most recent content first followed by previously updated content. In this there are many sections and posts written by a large number of authors. People can just create an account and start publishing their blogs. Blogs are a new and exciting way to communicate and to learn. Personal talk has evolved into a new and useful tool. Blogs have become an important form of emotional release and information for a growing number of people. With this website we are trying to enhance the learning perspective through blogs.
Lockdown: A Great Period for E-Learning

Abstract: The year 2020 was a lockdown or shutdown period for the world in general. The year marked a great priority to eLearning i.e. electronic learning, virtual learning, web-based learning or online learning. The modes of e–learning are mobiles, laptops, PC’s, Tablets via internet. Due to this pandemic, all institutions i.e. schools, colleges, universities remained close but continued to provide learning via internet to help students with the studies, providing proper education continuously. It was a unique and challenging platform for teachers to provide proper education to students without many facilities in a developing nation. Moreover, such e- learning benefits most of the drop-outs or distance learning students as well. Several courses are highlighted on the internet, for instance, SWAYAM, edX, Coursera and more. E-learning provides positive as well as negative impact, at the good end it has provided students a ‘home based free learning’ while negative impact implies health issues. Keywords: Pandemic, COVID-19, SWAYAM, edX, Coursera.
The Use of Code-Switching in EFL Classroom Instruction and Interaction

This study investigates code-switching in an EFL online classroom interaction. The research data were gathered by using fieldnotes, questionnaires, and semi-structured interviews on one hundred and twenty students and three English teachers. It demonstrates that both teachers and students code-switched during online English lessons. Tag switching and establishing continuity with the previous speaker were mostly used which reached 38.4%, inter-sentential switching15.5%, and intra-sentential 7.7%. respectively. In terms of the language, the findings show that classroom interaction was dominated by the use of Bahasa Indonesia 74.0%, English 52.8%, and Sundanese 16.5%. Code-switching was effective for teachers and students in EFL learning. It proved to 1) significantly influence students' activeness, 2) encourage and give better feedback to students through alternative reinforcement, and 3) encourage better teachers’ and students’ negotiation. With regard to efficiency, conveying meaning of the materials and bringing the project as well as assignment were essential. Nevertheless, some issues came up as a consequence of the Covid -19 outbreaks. Notably, some factors affecting the results were 1) the internet connection, 2) the students’ attendance, and 3) balance. Those results suggest that the potential next research is more likely to respond to students' need to campaign online learning to be better.&#x0D; Some issues come up as consequent of covid-19 outbreaks. Notably, some factors mentioned: (1) the stable or unstable internet connection, (2) the presence of students, and (3) balance. Those results suggest that the potential next research is more likely to respond students’ need to campaign online learning to be better. &#x0D; &#x0D; Keywords: codeswitching, EFL, online learning interaction and instruction
Calibration of camera internal parameters based on grey wolf optimization improved by levy flight and mutation

Traditional calibration technology has been widely used in measurement and monitoring; however, there are limitations of poor calibration accuracy, which can not meet the accuracy requirements in some scenarios. About this problem, we proposed a grey wolf optimization algorithm based on levy flight and mutation mechanism to solve camera internal parameters in this paper. The algorithm is based on the actual nonlinear model, which takes the minimum average value of reprojection error as the objective function. The grey wolf position is randomly generated within a given range. Then, the grey wolf optimization algorithm based on levy flight and mutation mechanism is used to iteratively calculate the optimal position, which is the internal parameters of cameras. The two groups of experimental data were performed to verify the algorithm. The result shows better effectiveness and calibration accuracy of the proposed algorithm compared with other optimization methods.
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration

In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks. These approaches utilize a loss function that penalizes the intensity differences between the fixed and moving images, along with a suitable regularizer on the deformation. In this paper, we argue that the relative failure of supervised registration approaches can in part be blamed on the use of regular U-Nets, which are jointly tasked with feature extraction, feature matching, and estimation of deformation. We introduce one simple but crucial modification to the U-Net that disentangles feature extraction and matching from deformation prediction, allowing the U-Net to warp the features, across levels, as the deformation field is evolved. With this modification, direct supervision using target warps begins to outperform self-supervision approaches that require segmentations, presenting new directions for registration when images do not have segmentations. We hope that our findings in this preliminary workshop paper will re-ignite research interest in supervised image registration techniques. Our code is publicly available from https://github.com/balbasty/superwarp.
Hand-Written Text Recognition Methods: Review Study

The great and rapid development of information technology, Artificial Intelligent, and its daily uses in all areas of our life, requires keeping up with distinguished software tools especially those used in data entry. The handwritten text is an important example to speed up the process of using any software. Researchers around the world provided many techniques to convert handwritten text into an understandable form for the computer system. This paper provides a study about used methods and approaches for Handwritten Text Recognition (HTR), and general overview of the properties of HTR approaches, and the advantages and disadvantages of each one.
Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.
Preventing Identity Theft: Perspectives on Technological Solutions from Industry Insiders

An estimated 26 million American citizens per year have been victims of an identity-based crime. This study contributes to the scholarship on financial crimes facilitated through identity-based criminal activity by examining the views on technological approaches to the prevention of identity theft among 50 professionals working in the identity-based crime victim services, including those from the public sector and private industry. The professionally diverse sample included private investigators, fraud examiners, victim service providers, and executives of firms offering victim services and protection services. Data were collected during a series of focus groups held at professional conferences. The paper reports on the views held by focus group participants on specific technological solutions to identity theft victimization, including biometric scanning, dark web scanning, subscription -based monitoring programs, and broader thematic observations about the current environment for protecting information, emerging sources of risk, and policy recommendations identified by focus group participants. Perspectives from insiders about the pros and cons of each approach can provide researchers and enforcement agencies with enhanced capacity to avoid empty technological promises that fail to protect victims from subsequent victimizations.
An Analysis of Madrasah Aliyah Students’ Ability in Writing Recount Text

Students' ability in writing varies greatly; some students can write recount text, but on the other hand, others don't have that capability. This study focus on five aspects of writing. They are Grammar, Vocabulary, Mechanic, Organization, and Fluency. This study aims to find out the ability of Tenth Grade Students of MAN 4 Kota Pekanbaru in writing recount text. The data collecting technique is using a writing test. The research findings are as follows: first, the Tenth Grade student of MAN 4 Kota Pekanbaru's ability to write recount text is at a good level with an average score of 64.75. Second, the student's average score in terms of Grammar is 61.80, Vocabulary is 69, Mechanic is 70.02, Organization is 61.80, and Fluency is 52.40. This component is the most challenging problem the students face in writing recount texts. This result concluded that the tenth-grade students of MAN 4 Pekanbaru have a relatively good level of writing recount texts. But with an average score of 62.75, they need to practice more in writing a text.
Sparse Adversarial Video Attacks via Superpixel-Based Jacobian Computation

Adversarial examples have aroused great attention during the past years owing to their threat to the deep neural networks (DNNs). Recently, they have been successfully extended to video models. Compared with image cases, the sparse adversarial perturbations in the videos can not only reduce the computation complexity, but also guarantee the crypticity of adversarial examples. In this paper, we propose an efficient attack to generate adversarial video perturbations with large sparsity in both the temporal (inter-frames) and spatial (intra-frames) domains. Specifically, we select the key frames and key pixels according to the gradient feedback of the target models by computing the forward derivative, and then add the perturbations on them. To overcome the problem of dimensional explosion in the video, we introduce super-pixels to decrease the number of pixels that need to compute gradients. The proposed method is finally verified under both the white-box and black-box settings. We estimate the gradients using natural evolution strategy (NES) in the black-box attacks. The experiments are conducted on two widely used datasets: UCF101 and HMDB51 versus two mainstream models: C3D and LRCN. Results show that compared with the state-of-the-art method, our method can achieve the similar attacking performance, but it pollutes only <1% pixels and costs less time to finish the attacks.
Joint Client Selection and Resource Allocation for Federated Learning in Mobile Edge Networks

Federated Learning (FL) has received widespread attention in 5G mobile edge networks (MENs) due to its ability to facilitate collaborative learning of machine learning models without revealing user privacy data. However, FL training is both time and energy consuming. Constrained by the instability and limited resources of clients in MENs, it is challenging to optimize both learning time and energy consumption for FL. This paper studies the problem of client selection and resource allocation to minimize the energy consumption and learning time of multiple FL jobs competing for resources. Because minimizing learning time and minimizing energy consumption are conflicting objectives, we design a decoupling algorithm to optimize them separately and efficiently. Simulations based on popular models and learning datasets show the effectiveness of our approach, reducing up to 75.7% energy consumption and 38.5% learning time compared to prior work.
The Internet of things technology in the rehabilitation for the disabled in China: A survey

According to the WORLD Disabled Persons federation (WPF), there are a large number of disabled people in the world, accounting for over 400 million in the Asia-Pacific region alone. This paper discusses the application of the Internet of Things technology in the rehabilitation of special populations, aiming at achieving high allocation of resources without changing personnel allocation, realizing innovation to improve economic benefits without changing personnel allocation, and rationally optimizing resource allocation to achieve maximum. Firstly, we give an overview of the Internet of Things technology and its application, and introduce the sensor, RFID, embedded system and other technologies. Then, we discuss the application of Internet of Things technology in the rehabilitation of disabled people, from the rehabilitation needs of disabled people and the application of Internet of Things technology in the field of rehabilitation. Then, from the statistical analysis of the application of Internet of Things technology in the rehabilitation field in the past 10 years, we obtained the shortcomings of the application of Internet of Things technology in the rehabilitation field and some space for further exploration. Finally, we believe that the application of Internet of Things technology to the rehabilitation management of persons with disabilities will be a breakthrough in the rehabilitation management of persons with disabilities, and have important reference value for the rehabilitation management of persons with disabilities worldwide. We also hope that understanding, respecting, caring and helping people with disabilities will increasingly become a global consensus and action.
E-Voting With Blockchain Technology

Creating an E-Voting system which satisfies every legal requirement of lawmaker or Election Commission has been a challenge for a long time especially in a country like India. The E-Voting were 1st used in 1982 in kerela(India). Since then many questions have been raised against EVM. In every Election, losing party accuses the winning party of hacking the EVM. But the Blockchain Technology offers infinite range of security and assurance that each and every vote given by you is going to right place and to the right person. This paper aims to evaluate every aspect of blockchain based E-Voting and how blockchain based Evoting is different from traditional E-Voting system. Also we elucidate the requirement for the Blockchain based E-Voting system. This paper describes the need of making E- Voting system and identifies the legal and technical limitations of using blockchain. This is a simple and user- friendly web app developed for Indian voting system which is very bigger. Blockchain technology provides scalability and confidentiality and it is more secure than the other traditional voting system. The hash code generated by the user is unique and can be use only one time in one election. User cannot give the vote second time. Blockchain Technology uses peer to peer technology that’s why it is highly secure.
LatentKeypointGAN: Controlling Images via Latent Keypoints -- Extended Abstract

Generative adversarial networks (GANs) can now generate photo-realistic images. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN internally conditioned on a set of keypoints and associated appearance embeddings providing control of the position and style of the generated objects and their respective parts. A major difficulty that we address is disentangling the image into spatial and appearance factors with little domain knowledge and supervision signals. We demonstrate in a user study and quantitative experiments that LatentKeypointGAN provides an interpretable latent space that can be used to re-arrange the generated images by re-positioning and exchanging keypoint embeddings, such as generating portraits by combining the eyes, and mouth from different images. Notably, our method does not require labels as it is self-supervised and thereby applies to diverse application domains, such as editing portraits, indoor rooms, and full-body human poses.
Problems Encountered by School-Based Feeding Program Beneficiaries and Their Academic Performance

Based Feeding Program beneficiaries and their academic performance in selected public school of Subic. This study was limited only to the three hundred forty (340) SBFP recipients of grade 4-6 elementary students in the District of Subic who were randomly selected. The study used descriptive survey method design.&#x0D; The major findings include the following: There was significant difference between the academic performance of students before and after the implementation of the school-based feeding program. Social, Behavioral and Economic problems encountered had low negative relationship with academic performance of beneficiaries before and after the implementation. The null hypothesis was rejected; hence there is significant relationship between the problems encountered and the academic performance of SBFP.&#x0D; The following recommendations are Employed parents may not take their child’s health nutritional status for granted by ensuring enough time to supervise their feeding habits of their child. School feeding coordinators may also look into the problems of the beneficiaries and not just providing them food during feeding time. School advisers and teachers may collaborate with parents/guardian of the SBFP beneficiaries in minimizing and eliminating the sources and cause of problems to attain higher level, if not the highest level of academic performance.
Eye Tracking Technique for Controlling Computer Game Objects

The study explored the employment of associate in accessible eye tracer with keyboard and mouse input devices for video games. An interactive game has been developed using unity with multiple balls objects and by hitting they could collect more point for each player. It has been used different techniques to hit the balls using mouse, keyboard, and mixed. Eye tracker input has been help to increase the performance of collected the player points. The research explains how the eye tacking techniques can be used in widely in video game and it is very interactive. Finally, we examine the use of visual observation in relevancy the keyboard and mouse input control and show the difference. Our results indicate that the employment of a watch huntsman will increase the immersion of a computer game and considerably improve the video game technology.
On the Computation of the Automorphisms Group of Some Optimal Codes Using Genetic Algorithm

Many research papers in coding theory have recently focused on designing high-rate codes or improving codes that exist through a better understanding and then improving the coding and decoding algorithm. As a result, this paper aims to investigate the computation of the Automorphisms groups of some optimal codes (e.g., some linear circulant codes where their distance meets the lower bound and nonlinear Nordstrom-Robinson (24, 28, 6) code). These Automorphisms groups provide information about the structure of the code, which aids in both the design and enhancement and improvement of decoding algorithms. A new genetic algorithm-based method is proposed, with a detailed description of its components, the fitness function, selection, crossover, and mutation, and is used to find an important collection of Automorphisms; the results obtained have shown that the proposed method is effective in finding stabilizers set for some types of codes.
Provably Optimal Control for Multiplicative Amplitude Control Noise

We provide a technique to obtain provably optimal control sequences for quantum systems under the influence of time-correlated multiplicative control noise. Utilizing the circuit-level noise model introduced in [Phys. Rev. Research 3, 033229(2021)], we show that we can map the problem of finding such a sequence to a convex optimization problem with guaranteed optimality that follows from the convexity. We also show that this technique is compatible with more general off-axis time-correlated dephasing noise. In spite of losing provable optimality, numerically optimized control sequences under this scenario can still achieve nearly optimal performance when the control noise is strong relative to the dephasing contribution. This approach will enable the development of optimal quantum logic gates in systems where noise due to amplitude drifts in the control is strong relative to dephasing such as in ion-trap based quantum computers or in the limit of fast control.
Assessment of factors affecting the software process improvement in small organizations

Software process improvement implies a set of complex and systematic activities of software engineering. It requires theory and models established in management, technical and social sciences. The improvement is based on the assumption that the organization if it owns mature and capable processes, would be able to deliver quality software on time and in line with predicted costs. The maturity models are initially aimed for implementation in enterprise software organizations, government organizations and within the military industry. Their complexity and the size make them difficult to use in small software organizations and companies. In such organizations the interest for use and the efforts to make an efficient and effective organization is always presented, though. In this paper, the basic and derived capability maturity models are described and cases from their implementation are analyzed, along with assessment of results of such projects in business practices. The problem of the software process improvement in small organizations is described, extracting the risks and recommendations for its enhancement. These recommendations are provided in order to set up a foundation for implementation of these models in a specific managerial and organizational environment characterized by small organizations.