Reviewerly/exHarmony huggingface dataset:
Dynamic Binary Translation for SGX Enclaves

Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for shielding user-level applications from the OS. While enclaves are a useful starting point, code running in the enclave requires additional checks whenever control or data is transferred to/from the untrusted OS. The enclave-OS interface on SGX, however, can be extremely large if we wish to run existing unmodified binaries inside enclaves. This paper presents Ratel, a dynamic binary translation engine running inside SGX enclaves on Linux. Ratel offers complete interposition, the ability to interpose on all executed instructions in the enclave and monitor all interactions with the OS. Instruction-level interposition offers a general foundation for implementing a large variety of inline security monitors in the future. We take a principled approach in explaining why complete interposition on SGX is challenging. We draw attention to 5 design decisions in SGX that create fundamental trade-offs between performance and ensuring complete interposition, and we explain how to resolve them in the favor of complete interposition. To illustrate the utility of the Ratel framework, we present the first attempt to offer binary compatibility with existing software on SGX. We report that Ratel offers binary compatibility with over 200 programs we tested, including micro-benchmarks and real applications such as Linux shell utilities. Runtimes for two programming languages, namely Python and R, tested with standard benchmarks work out-of-the-box on Ratel without any specialized handling.
Strategy of Education Provider Organizations in Utilizing Digital Literature (Study at IT Baitul Muslim High School, East Lampung, Indonesia)

Advances in technology and information have opened up great opportunities for the community, especially for the world of education. Therefore, educational institutions or organizations must have strategies in dealing with changes in student interaction patterns on information technology devices and how schools face the development of the digital era. This research is a research that explores the strategy of education providers in the use of digital literacy as a medium to improve the quality of learning. The research method used in this research is descriptive qualitative analysis with a qualitative approach that focuses on substantive and formal theories as well as events that exist and are recognized as true. The strategy of the education provider organization in utilizing digital literacy at the IT Baitul Muslim High School in East Lampung has been going well so far, because the digital literacy program had been implemented in the school environment long before the COVID-19 pandemic hit. So that the learning process with digital literacy is nothing new for schools and students. Although in this case the improvement of teacher competence continues to be pursued as one of the school's strategies to improve teacher competence to actualize learning and the potential of students in digital literacy learning
Pelatihan Dan Pendampingan Evaluasi Website Menggunakan Sortsite 5.3.5 Pada Website “Mal Pelayanan Publik” Dinas Penanaman Modal Dan Pelayanan Terpadu Satu Pintu Kota Samarinda

The use of the website as a means to bring public services closer to the community is indeed a must for the government in the digital era. The website plays an important role as a medium for the government to disseminate public information. Therefore, government websites that are built and funded through public funds must be able to reach all levels of society and have good performance. Based on that, this Community Service Program seeks to make a small contribution to the Samarinda City Government, especially the Samarinda City DPMPTSP related to training and assistance in evaluating the Public Service Mall website using SortSite 5.3.5. The implementation of the Community Service Program starts from the stages of problem identification, verification, provision of materials, implementation of training and mentoring and evaluation of activities. Based on the observation, it can be said that the training and mentoring program has successfully increase the skill and knowledge of DPMPTSP website operator.&#x0D; &#x0D; Keywords: Training, Mentoring, Website Evaluation, Samarinda City DPMPTS
Neural Network based Approaches for Aspect-Based Sentiment Analysis

The research of Aspect-based Sentiment Analysis which is a process that has a more specific focus than general sentiment analysis is trending upwards in numbers. Stemming from Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), novel approaches introduced new components like Graph Convolutional Networks (GCNs) and Transformers that improved the overall accuracy dramatically. Along with summarizing the models, the focus of this survey will be on comparing the several novel methods. Although this paper found that Dependency graph enhanced dual-transformer network (DGEDT) coupled with Bidirectional Encoder Representations from Transformers (BERT) is the best performing model thus far, this paper also identified challenges that needed to be addressed in order to better evaluate current and future models.
Real-Time Recognition Method of Video Basketball Technical Action Based on Target Detection Algorithm

Aiming at the problems of poor real-time recognition efficiency and low recognition accuracy of video basketball technical movements, a real-time recognition method of video basketball technical movements based on the target detection algorithm is proposed. This study firstly collects video basketball technical action images and detects their edge contour features. Through machine vision block template matching processing, the video basketball technology motion capture results are output. Then, according to the target probability of video basketball technical action detection, the basketball video sample set is estimated and constructed, the sequence of basketball video is obtained, and the basketball video target detection algorithm is designed. Finally, the similarity between the two vectors of video basketball technical action targets is measured to realize real-time recognition of video basketball technical actions. The experimental results show that the method in this study has high accuracy in extracting feature points of basketball video image decomposition, the real-time recognition time of video basketball technical action is up to 4s, and the recognition error of basketball rotation projection technology is small, which is close to the actual value.
Pelatihan Geogebra bagi Guru di Kota Balikpapan

Mathematics is frequently thought as a hard course by students because of some reasons. One of the reasons is that students cannot visualize the material given by their teacher. On the other hand, only a few numbers of teachers who can explain the course using appropriate figures. Moreover, during a presentation or making a quiz, they cannot present those things very well which confused the students further. Facing this challenge, therefore, we organized a training to enhance the Mathematics teachers’ competency in Balikpapan City so they could attract students’ attention and improve comprehension by giving the right visualization. In this training, we used GeoGebra, which is an open-source program and user-friendly. For the result, this training performed that the participants then could graph some functions, construct a 3-D space, and make a presentation using GeoGebra.
Instant disembodiment of virtual body parts

Abstract Evidence from multisensory body illusions suggests that body representations may be malleable, for instance, by embodying external objects. However, adjusting body representations to current task demands also implies that external objects become disembodied from the body representation if they are no longer required. In the current web-based study, we induced the embodiment of a two-dimensional (2D) virtual hand that could be controlled by active movements of a computer mouse or on a touchpad. Following initial embodiment, we probed for disembodiment by comparing two conditions: Participants either continued moving the virtual hand or they stopped moving and kept the hand still. Based on theoretical accounts that conceptualize body representations as a set of multisensory bindings, we expected gradual disembodiment of the virtual hand if the body representations are no longer updated through correlated visuomotor signals. In contrast to our prediction, the virtual hand was instantly disembodied as soon as participants stopped moving it. This result was replicated in two follow-up experiments. The observed instantaneous disembodiment might suggest that humans are sensitive to the rapid changes that characterize action and body in virtual environments, and hence adjust corresponding body representations particularly swiftly.
Organizational structures for preserving documentary monuments in the leading libraries of Ukraine

The purpose of the article is to study the current state and reveal the organizational and structural features of the divisions of documentary monuments as a library scientific and cultural project based on the analysis of the experience of creation, functioning and development trends of such divisions by the leading libraries of Ukraine. A content analysis of the official sites of 38 leading scientific libraries was carried out, including 13 library institutions with national and state status, and 25 ones – with regional. Summarizing the results of the content analysis made it possible to determine the level of effectiveness of creating and functioning organizational structures of documentary monuments of the leading library institutions of Ukraine, as well as the factors that contribute to the improvement of the effectiveness of the organizational and structural construction of activities with documentary monuments. Among such factors, one can note the status, level, type and kind of the library institution; library origin (as a rule, libraries with a long history have more chances to accumulate rare, valuable and unique publications, and thus to create appropriate divisions).
Aplikasi Sistem Informasi Buku Induk Siswa pada MA Putri Taruna Al-Quran

Each educational institution records important data for each student for both school and outside school needs. Important student data is recorded in a book called Student's Main Book. Some schools use computers as a substitute for student books, namely using Microsoft Excel. However the use of Ms. Excel sometimes causes problems, such as what happened at MA Putri Taruna Al-Quran, often experiencing failure to input data to the central server in the form of excel or csv to fill in the national exam data, data loss and damage often occurs, then on the computer there are many duplicate files so that difficulty recognizing which file contains the main data. To overcome these problems, a website-based information system was built. This information system will be equipped with features that can present reports according to the desired column and can also be exported to several extensions. Making this system will be done by several methods such as the method of observation, interviews and literature reviews so as to produce an application called Student Master Book.&#x0D;
Cultural foundations of a national system of education in a COVID-19 pandemic

The article aims to characterize the key components of cultural constants of the upbringing system from the national perspective. The objectives are to analyze the differences between traditional and prospective elements of the national upbringing system. Using general scientific (analysis, comparison), philosophical and scientific (synergetic, dialectics) and culturological (structural and functional, axiological and semiotic) methods allow characterizing culturological foundations of the national upbringing system. Currently, there is an urgent need to develop new priorities of the national policy of upbringing of children, youth, and in some cases the older generation in the context of new challenges facing society.
Tree Search Algorithms For Chinese Chess

Computer Games are an important aspect of Artificial Intelligence (AI) which has produced many representative algorithms of optimal strategies. Therefore, many AI researchers have applied game theory to various board games in an attempt to find the essence of games and grasp the core of AI behaviour. AlphaGo is a very famous and typical chess-playing program that uses Monte Carlo tree search (MCTS) to estimate the value of each node in the search tree and optimize the possible results. Chinese chess is one of the traditional chess versions with its typical strategy. However, due to its late start and high complexity, there are many more technical problems to be solved. In this paper, we recommend two commonly used search frameworks for Chinese chess: the minimax algorithm and the alpha-beta pruning algorithm. On this basis, to deal with the complex and changeable situation, the Chinese chess algorithm based on Monte Carlo tree search and appropriate neural network evaluation function is studied.
Research on NFT in the Context of Blockchain

A non-fungible token (NFT) is a unit of data on what is known as a blockchain digital ledger, where each token can represent a unique piece of digital data. Since they are not interchangeable, non-homogeneous tokens can represent digital files, such as paintings, sounds, movies, projects in games, or other forms of creative work. Blockchain allows users on the C or B side to place personal data or business data on the blockchain network, instead of homogenous token, thus becoming the next hot spot in the Internet industry. 2021 was perhaps the most critical year for the industry so far. In this paper, 2020 can be defined as a year of construction and preparation, which saw great progress and positive results on the most important indicators and macro indicators for the industry. There are more wallets and transactions interacting with DApps than ever before, and the demand for NFT and games has increased dramatically.
The Advance of Deep Learning Based Named Entity Recognition

Named Entity Recognition is a well-known research direction in the area of deep learning. It takes an essential role in natural language processing. The goal of the Named Entity Recognition is to identify and separate the named entities, such as a person, location, or name, from the entire text. In addition, the deep learning model has achieved remarkable achievements in many other areas, and the deep learning-based named entity recognition method has reached an F score of over 90. The paper summarizes the development of named entity recognition and puts forward a recurrent neural network-based named entity recognition algorithm. The result shows that improving the performance of the Named Entity Recognition model by simply enriching the number and variety of the input datasets or providing the model with substantial computing resources for training is nearly impossible without a significant breakthrough. There still requires another way to improve the NER model in future research.
Trusted Computing and Privacy Protection Method for Computer IoT Nodes Based on Fuzzy Logic Blockchain

With the continuous growth of the social economy, the trusted computing and privacy protection methods of IoT nodes are constantly innovating and changing, from the traditional single IoT node protection method to the new interactive IoT nodes supplemented by various network resources. That is, the item information server encrypts the detailed information of the item layer by layer with the session key of the adjacent node according to the node sequence of the response path. By completing the complete interactive design and the protection knot in the unique illusory environment, the application and discussion of privacy protection is achieved in the context of Internet of Things, creating a design concept for trusted computing and privacy protection methods for IoT nodes, and discussing the rationality of interactive design in the process of trusted computing and privacy protection methods for IoT nodes. The simulation results show that the application of fuzzy logic blockchain is very extensive, and the trusted computing and privacy protection methods of IoT nodes have a broader platform and a more open environment.
Drug trade analysis model based on location judgment

According to the current spread of new drug problem, this paper established the blocks the growth model of correction, to complete the determination of drug eruption time node, then based on the geographical position the drug eruption time model is established in this paper, using the fisher discriminant method, by geographical location, the estimated time of drug eruption and USES the multi-layer perceptron neural network to predict counties of drug eruption time, Finally, the robustness analysis is completed. This article establishes drugs.
Computer-Aided Design Methods in Virtual Reality Product Development

In order to improve the effectiveness of virtual reality product design, this paper combines the computer-aided design method to construct a virtual reality product development system and analyzes the elements, architecture, and calculation steps of the virtual reality model in detail. Moreover, from the research on the color appearance phenomenon, this paper leads to the improvement direction of improving the virtual reality model. In addition, this paper discusses the advantages of the virtual reality product design model and its feasibility as a PCS link space to undertake the task of color appearance transformation. The experimental research analysis shows that the virtual reality product design system based on computer-aided technology proposed in this paper has good effects and can effectively improve the effect of virtual reality product design.
Weakly-Supervised and Unsupervised Video Anomaly Detection

As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Encoder, Recurrent Neural Network, etc. are broadly adopted for AD. Some renowned and new datasets are reviewed. Furthermore, we also proposed several future directions of research in video anomaly detection.
A novel multi-party semiquantum private comparison protocol of size relationship with d-dimensional single-particle states

By using d-level single-particle states, the first multi-party semiquantum private comparison (MSQPC) protocol which can judge the size relationship of private inputs from more than two classical users within one execution of protocol is put forward. This protocol requires the help of one quantum third party (TP) and one classical TP, both of whom are allowed to misbehave on their own but cannot conspire with anyone else. Neither quantum entanglement swapping nor unitary operations are necessary for implementing this protocol. TPs are only required to perform d-dimensional single-particle measurements. The correctness analysis validates the accuracy of the compared results. The security analysis verifies that both the outside attacks and the participant attacks can be resisted.
MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks

Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models. Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labelled musical form data. Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.
Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods

Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure. The alternating minimization (AM) approaches split the composition structure of DNNs and have drawn great interest in the deep learning and optimization communities. In this paper, we propose a unified framework for analyzing the convergence rate of AM-type network training methods. Our analysis is based on the non-monotone $j$-step sufficient decrease conditions and the Kurdyka-Lojasiewicz (KL) property, which relaxes the requirement of designing descent algorithms. We show the detailed local convergence rate if the KL exponent $\theta$ varies in $[0,1)$. Moreover, the local R-linear convergence is discussed under a stronger $j$-step sufficient decrease condition.
Beyond Supervised Continual Learning: a Review

Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine learning where the usual assumption of stationary data distribution is relaxed or omitted. When naively applying, e.g., DNNs in CL problems, changes in the data distribution can cause the so-called catastrophic forgetting (CF) effect: an abrupt loss of previous knowledge. Although many significant contributions to enabling CL have been made in recent years, most works address supervised (classification) problems. This article reviews literature that study CL in other settings, such as learning with reduced supervision, fully unsupervised learning, and reinforcement learning. Besides proposing a simple schema for classifying CL approaches w.r.t. their level of autonomy and supervision, we discuss the specific challenges associated with each setting and the potential contributions to the field of CL in general.