Reviewerly/exHarmony huggingface dataset:
Design and Implementation of OOM Module based on Rust

The Linux kernel plays an important role in various application scenarios such as computers, mobile devices, and vehicles, in which security-sensitive information is stored. Undefined behavior in the C programming language is usually the cause of Linux kernel vulnerabilities, among which memory safety vulnerabilities are the most threatening. At present, the methods based on static/dynamic analysis and runtime software and hardware defense to ensure memory safety have the problems of poor performance, false positives, and poor compatibility. This paper presents the design and implementation of the OOM (out of memory) module based on the safe programming language Rust. We leverage the Rust FFI mechanism to design a foreign interface layer and a safe foreign interface layer to enable the reconstructed OOM module to invoke other Linux functionalities, and then use Rust to reconstruct the OOM module. The safety features of Rust language are used to avoid undefined behaviors, so as to improve the safety of the kernel. Finally, we export the C interface of the module to enable the invocation by the Linux kernel. Tests show that the OOM module performance and memory consumption after reconstruction are comparable to the original module.
New Media as Modern Communication Technologies: The Digital Dimension

The latest media nowadays is a set of interaction tools for expanding the subjects’ communicative, sensory and cognitive capabilities involving information and communication technologies that correspond to the historical period of their development. Currently, these are technical means (PC equipment with a software complex, with various means of supporting network communications), information resources and communication platforms based on digital technologies. Modern media meet the needs of long-distance, international interactive communication and integrate a number of means for the individual’s creative implementation, his social activity and participation in society’s information processes. The purpose of the academic paper is a theoretical and practical description of the concept, individual types and features of using new media as technologies that provide communicative processes in the digital mode. Methodology. In the process of preparing the present research, several methods were applied. In particular, analytical and bibliographic methods, induction, deduction, analysis, and synthesis of information were used to study scientific literature on applying new media when working with communication processes in the digital dimension. System-structural, comparative, logical-linguistic methods, abstraction, and idealization were used to study and process data. The questionnaire survey was conducted by the research’s authors in online mode for the practical clarification of media tools’ certain aspects in the field of communications. Results. Based on the research results, the features of using new media types as modern communication technologies in the digital space were studied.
Pengelompokan Ulasan Aplikasi PeduliLindungi dengan Algoritma K-Medoids

By looking at the reviews of potential users, you can see the responses of other users who have used the application first, besides the application reviews can be used as input for developers. Clustering reviews can use text mining. In this study, the Kmedoids algorithm was used to cluster reviews with Fasttext word embedding to represent the review's word units into vectors. The data used in this study amounted to 2,729 taken from the PeduliLindung application comment column on the Google Playstore. The results of the evaluation using the Davies Bouldin index or abbreviated as DBI by comparing sixteen trials and the best experiment was obtained, namely, an experiment using data without stemming, cbow architecture, using the Manhattan distance metric, and an experiment using data without stemming, cbow architecture, and using the cityblock distance metric with the same DBI value of 2.93 which resulted in two clusters of reviews. In the first cluster there were 2,183 reviews while in the second cluster there were 546 reviews.
Covid-19 Pandemic and School Dropout Rates: Empirical Evidence from Indonesia

The Covid-19 pandemic has disrupted all aspects of life, including the education aspect. The government then tackled the virus's transmission rate by issuing a policy to stop face-to-face learning activities and replace them with online learning activities. However, the inequality of information technology facilities has made it difficult for several regions in Indonesia to teach online. It more or less impacts the dropout rate, especially at the elementary school level. This study then aims to determine how much the Covid-19 pandemic affects the dropout rate in Indonesia at the elementary school level by considering other factors such as school facilities and infrastructure, children's academic abilities, and economic control variables. So far, no similar study discusses the pandemic's impact on the dropout rate measured quantitatively, especially in Indonesia. This study uses panel data analysis from 34 provinces in Indonesia from 2017 to 2021. The results indicate that the Covid-19 pandemic is associated with the dropout rate increasing at the primary school level by 36.4%.
The Importance of Text Mining for Services Management

The present investigative study is a systematic as well as scientific endeavor to understand the beneficial effect of the text mining approach for the service management of the organization. This stud uses secondary qualitative data for gaining accurate insight into the present research study. Thematic analysis is done on the collected data set for gaining accurate insight into the present study. Interpretive research philosophy is chosen for this study along with the inductive research approach. This study also uses an explanatory research design for effective insight into the beneficial effect of the text mining process for the service management of the organization. The study focuses on the process of text mining which is used for analysing the client forums, call logs, tickets of customer service, and platforms of social media, news feeds, tweets and many others. It also focuses on the business that have better insights based on client expectations and it requires the areas that focus on overall improvements. The study integrates the customer communications and reviews that can identify the new features for strengthening the product offerings. It focuses on the latest technologies that provide several opportunities for improving the customer experience those results in increased profits and revenue.
Research on application of industrial robots in automated fueling systems for small individual cars

An automated fueling system for small individual vehicles, such as 4- and 7-passenger cars, is necessary in the modern life. Currently in the car market, in terms of the characteristics of the equipped fuel tanks, it can be divided into two main groups, including the vehicles using the traditional fuel tanks and the vehicles using the cap-less fuel tanks. For the vehicles using the traditional fuel tanks, the vehicle drivers need 4 steps to pump the gasoline. They are opening the outer lid of the body, opening the screw cap on the fuel tank, putting the pump nozzle into the fuel tank and making the fuel pump. For the vehicles using the cap-less fuel tank design, the manufacturers replace the traditional screw cap with the self-opening (close) lid mechanism when there is (without) the pump nozzle. So when operating the gasoline pump, there is no step to open the screw cap on the fuel tank. This new design was pioneered by American car manufacturers such as Ford and General Motor. This paper proposes a solution for the automatic fueling for the vehicles that have the fuel tank without the screw cap. The system is built based on an industrial robot that do the task replacing for the station officer. Firstly, a special tool is designed to help the robot to operate during the automatic fueling. Next, an image processing algorithm is applied to recognize the position of the fuel tank of the vehicles. After that, an automatic fueling algorithm is proposed so that the system can do fueling automatically. The proposed solution is verified by the experiments on the real model. The experimental results show that the proposed system can be used to position the nozzle into the fuel tank with the acceptable error
Remote attestation of SEV-SNP confidential VMs using e-vTPMs

Trying to address the security challenges of a cloud-centric software deployment paradigm, silicon and cloud vendors are introducing confidential computing - an umbrella term aimed at providing hardware and software mechanisms for protecting cloud workloads from the cloud provider and its software stack. Today, Intel SGX, AMD SEV, Intel TDX, etc., provide a way to shield cloud applications from the cloud provider through encryption of the application's memory below the hardware boundary of the CPU, hence requiring trust only in the CPU vendor. Unfortunately, existing hardware mechanisms do not automatically enable the guarantee that a protected system was not tampered with during configuration and boot time. Such a guarantee relies on a hardware RoT, i.e., an integrity-protected location that can store measurements in a trustworthy manner, extend them, and authenticate the measurement logs to the user. In this work, we design and implement a virtual TPM that virtualizes the hardware RoT without requiring trust in the cloud provider. To ensure the security of a vTPM in a provider-controlled environment, we leverage unique isolation properties of the SEV-SNP hardware that allows us to execute secure services as part of the enclave environment protected from the cloud provider. We further develop a novel approach to vTPM state management where the vTPM state is not preserved across reboots. Specifically, we develop a stateless ephemeral vTPM that supports remote attestation without any persistent state on the host. This allows us to pair each confidential VM with a private instance of a vTPM completely isolated from the provider-controlled environment and other VMs. We built our prototype entirely on open-source components. Though our work is AMD-specific, a similar approach could be used to build remote attestation protocols on other trusted execution environments.
An Improved Cartoon Character Avatar Generation Algorithm Based on GAN

Aimed at solving the problems of poor diversity of generated results and generally blurred images in traditional image generation methods, an improved cartoon character avatar generation algorithm WGAN-GP based on GAN is proposed. During the process, this method is used for generating image. By using transposed convolutional layers and inserting BN layers in the middle of each convolutional layer to define activation function as Leaky_ReLU as well as using Wasserstein distance instead of JS divergence as the adversarial loss function of the network and choosing RMSProp as the optimizer, improvements of our method are realized. The experimental results show that, the improved model solves the problems that the network is difficult to converge and the model is easy to collapse, and improves its generation diversity. Meanwhile, the details and texture of the generated images are clear and close to the sample set, which proves the effectiveness of the proposed algorithm.
A first-order Rician denoising and deblurring model

In this paper, we propose a first-order variational model for the Rician denoising and deblurring.The model employs a recently developed regularizer that has proven to be effective in image restoration [32].Due to this regularizer, our model is able to suppress the staircase effect, and more importantly, it helps preserve image contrast, which considerably reduces the blurring effect in restored images.We present the maximum-minimum principle of the model and give a more accurate convex approximation than the conventional one for the fidelity term.Augmented Lagrangian method is utilized to minimize the associated functional.Synthetic and real gray and color images are tested to demonstrate the features of our model. 1.Introduction.Magnetic resonance images (MRI) are often corrupted by Rician noise.In this paper, we propose a first-order variational model to denoise and deblur images corrupted by Rician noise and Gaussian blur, both with known standard deviations.Quite a few approaches or models have been proposed to deal with image denoising problems, such as total variation based models [25], wavelet transform approaches [23], the anisotropic diffusion filter [9], Perona-Malik filter [22], and nonlinear diffusion filters [18], etc.To remove the specific Rician noise in MRI, several models have been developed [11,19].Among them, an effective total variation based model was proposed by Getreuer et.al. in [11].We have incorporated the tailored finite point method (TFPM) [13,14] with the split Bregman method [21], to get more accurate solutions to the Rician denoising model [29].For simplicity, this model is abbreviated as the TV model.Just as the well-known ROF model [25], this model also suffers from the staircase effect.
UPAYA MENINGKATKAN LITERASI MEMBACA DI MASA PANDEMI MELALUI KEGIATAN SEMINGGU SEBUKU

Reading literacy skill is related to one’s ability to analytically and critically understand the reading text. Reading literacy is not only related to knowledge and skills but also motivation, attitudes and behaviour. During the pandemic, the use of digital apps plays a critical role;. This situation also led to the emergence of a reading literacy community called ‘a week a book’ community through digital media. This community has 232 members where everyone reads a book every week. At the end of the week, the volunteers reflected on the book they read and submitted through the application. This kind of circulation enabled everyone in the community to listen to the others’ review anywhere. This reading literacy community was initiated by sociopreneur.id. This study will focus on the phenomenon of reading literacy in the sociopreneur.id application related to activities and their contribution to reading literacy during a pandemic. This activity has a positive effect on reading interest, independence, social interaction, and the reading practice of members to increase.
CHARACTERISTICS OF CHILDREN AGE OF BASIC EDUCATION

Education aims to gain knowledge and all activities that affect a person's strength, change, character and morals. The period of childhood education to a young age goes through various and changing stages. The childhood phase begins with the beginning of the first three years, the fourth year to the sixth year, the sixth year to the eighth year, the eighth year to the twelve year. Characteristics of children of basic education age are happy to play, move, work in groups and like to demonstrate things directly. In childhood, developmental and educational disorders often occur, namely learning disorders, dyslexia, dyscalculia, and dysgraphia. Thus understanding the characteristics of children is a must for teachers and parents to be able to educate and guide children in a better direction.
Iraqi Sign Language Translator system using Deep Learning

The deaf and mute use sign language by moving their hands, faces, and bodies to talk to each other or normal people. Sign language is non-verbal communication, which is the process of communication by sending and receiving messages without words between people. The number of deaf people is increasing in the world and Iraq in particular, in addition to the problems in communicating with the world and the difficulty of learning sign language by deaf and hard-of-hearing families, there must be other ways to help the deaf communicate efficiently with ordinary people and learn sign language easily. One such method is to use artificial intelligence to create translation software and recognize hand gestures. This paper presents a computer program that can translate Iraqi sign language into Arabic (text). First, the translation starts with capturing videos to make up the dataset, the proposed system uses a convolutional neural network (CNN) to classify sign language based on its features to impute the meaning of the sign. The accuracy of the part of the proposed system that translates sign language into Arabic text is 99% for words sign.
Exploring Determinants of Firm´s Innovation in Morocco: Evidence from the World Bank Enterprises Survey

This study investigates the determinants of firm's innovation in Morocco using data from the World Bank Enterprise Surveys among 1,096 enterprises for the year 2019.Using probit regression model, the results show that firm size, formal training, and informal sector competition are the main drivers of product, process, R&D investment, and foreign certification innovation.Furthermore, foreign ownership and the exporter status also significantly impact firms' innovation.The results indicate that small and medium-sized enterprises are less likely to innovate compared to large firms, and firms in the manufacturing sector are more likely to innovate compared to service sector firms.The findings of this study can be used by enterprises to develop effective strategies for innovation and by policy-makers to enhance the competitiveness of the Moroccan economy.
Android-Based Mobile Application for Vocabulary Learning

The improvements in technology have made it inevitable to use in learning vocabulary. It is an undeniable fact that learning styles affect the use of applications. Accordingly, this study is aimed to analyze the efficacy of mobile applications based on Android for learning vocabulary to tenth-level learners. At this point, the product is a mobile application for learning vocabulary completed with teachers’ and students’ books for learning guides. The researchers apply Research and Development with 20 students and five teachers as research participants randomly from two Senior High Schools. The instruments used tests, guiding interviews, guiding Observations, and questionnaire. The process data is collected by systematically searching and arranging the interview transcript, field notes, validation expert, and other materials you accumulate to support the data found. Then the researchers analyze data using triangulation data. The results of the first step prove that problem-solving abilities are shown; students need help remembering unfamiliar and mastering the meaning of foreign words. The result of the design and Development indicates that the Mobile Application was valid. The results of the product experiment prove that this form is appropriate to be applied to students in vocabulary upgrading and is claimed to be sourced in general. At the same time, the product test result shows that the essential has an important effect on students’ vocabulary. The “Vocabulary App” media improves students' vocabulary.
An Investigation of Structures Responsible for Gender Bias in BERT and DistilBERT

In recent years, large Transformer-based Pre-trained Language Models (PLM) have changed the Natural Language Processing (NLP) landscape, by pushing the performance boundaries of the state-of-the-art on a wide variety of tasks. However, this performance gain goes along with an increase in complexity, and as a result, the size of such models (up to billions of parameters) represents a constraint for their deployment on embedded devices or short-inference time tasks. To cope with this situation, compressed models emerged (e.g. DistilBERT), democratizing their usage in a growing number of applications that impact our daily lives. A crucial issue is the fairness of the predictions made by both PLMs and their distilled counterparts. In this paper, we propose an empirical exploration of this problem by formalizing two questions: (1) Can we identify the neural mechanism(s) responsible for gender bias in BERT (and by extension DistilBERT)? (2) Does distillation tend to accentuate or mitigate gender bias (e.g. is DistilBERT more prone to gender bias than its uncompressed version, BERT)? Our findings are the following: (I) one cannot identify a specific layer that produces bias; (II) every attention head uniformly encodes bias; except in the context of underrepresented classes with a high imbalance of the sensitive attribute; (III) this subset of heads is different as we re-fine tune the network; (IV) bias is more homogeneously produced by the heads in the distilled model.
THE ROLE OF TECHNOLOGY IN SUPPORTING ENGLISH LEARNING IN ELEMENTARY SCHOOLS

Currently, teachers face many challenges in teaching. This is due to the times, which causes a teacher to need to innovate in teaching methods. Learning to use technology is one of the steps that can be used by teachers in this era to be able to face these challenges, especially English teachers. This research will then be carried out using descriptive qualitative research. The data used in this study comes from the results of previous research and studies that still have relevance to the research content. The results of this study then found that learning English using multimedia technology can increase students’ motivation and interest in learning. Of course, this must be done by providing open access to teaching materials and information. In the future, multimedia technology will become more common as a learning aid. Even though there are still limitations in using technology in learning, with proper computer knowledge on the part of the teacher, learning using this technology can be carried out correctly.
Medical personal protective equipment detection based on attention mechanism and multi-scale fusion

Deep neural networks (DNNs) have shown excellent effectiveness in object detection and greatly benefit people in various physical scenes. In this paper, we focus on a meaningful physical scene, medical personal protective equipment detection, where the performance degrades for two reasons: background information interference and different detection target scales. To solve the problems above, we propose two novel modules, a deformable and attention residual with 50 layers (DAR50) feature extraction module and a criss-cross feature pyramid network (CCFPN) feature fusion module. Concretely, the DAR50 is target morphology-aware and can enhance the feature information. The CCFPN raises the multi-scale detection performance by fusing the pixel information of the feature maps and then fusing the features of different stages. Combining the two modules, we construct a novel object detection network called attention and multi-scale fusion-based regions with convolution neural network (AMS R-CNN) features. Empirically, we prove the superiority of AMS R-CNN on a medical personal protective equipment detection dataset CPPE-5 (medical personal protective equipment) and The Visual Object Classes Challenge 2007 (VOC 2007) dataset compared with several state-of-the-art methods.