Reviewerly/exHarmony huggingface dataset:
HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation

The mainstream domain adaptation (DA) methods transfer the supervised source domain knowledge to the unsupervised or semi-supervised target domain, so as to assist the classification task in the target domain. Usually the supervision only contains the class label of the object. However, when human beings recognize a new object, they will not only learn the class label of the object, but also correlate the object to its parent class, and use this information to learn the similarities and differences between child classes. Our model utilizes hierarchical relations via making the parent class label of labeled data (all the source domain data and part of target domain data) as a part of supervision to guide prototype learning module vbfd to learn the parent class information encoding, so that the prototypes of the same parent class are closer in the prototype space, which leads to better classification results. Inspired by this mechanism, we propose a Hierarchical relation aided Semi-Supervised Domain Adaptation (HSSDA) mehtod which incorporates the hierarchical relations into the Semi-Supervised Domain Adaptation (SSDA) method to improve the classification results of the model. Our model performs well on the DomainNet dataset, and gets the state-of-the-art results in the semi-supervised DA problem.
Quantum algorithm for querying causality of multiloop scattering amplitudes

The first application of a quantum algorithm to Feynman loop integrals is reviewed. The connection between quantum computing and perturbative quantum field theory is feasible due to fact that the two on-shell states of a Feynman propagator are naturally encoded in a qubit. The particular problem to be addressed is the identification of the causal singular configurations of multiloop Feynman diagrams. The identification of such configurations is carried out through the implementation of a modified Grover's quantum algorithm for querying multiple solutions over unstructured datasets.
Mobile Development of Android-Based Beginner E-Voting System

&lt;p&gt;&lt;em&gt;One of the implementations of Pancasila democracy in Indonesia is the implementation of the election of leaders, by voting. In addition to within the scope of government, the implementation can also be carried out within the scope of educational institutions such as schools or campuses, where voters are referred to as novice voters. Elections are still carried out conventionally which requires a large nominal budget, as well as problems or fraud that often occur during elections. So in this study, a digital voting system was created using an electronic device, namely an Android-based mobile e-voting system that can be accessed via a smartphone. the existence of this system is considered to be able to provide convenience and comfort in the implementation of the voting process and provide an important role to the generation of novice voters who are now entering the digital era where technology is developing rapidly, namely to continue to cultivate democratic values in it. Based on the data obtained, smartphone users now have a percentage value of 66.31% among the public and 33.69% of people do not own a smartphone. While the survey results related to the operating system market pairs in Indonesia, accounting for 91.56% of Android users, show that the majority of smartphone users in Indonesia use Android, with this the e-voting system has the potential to be used by the general public, especially novice voters. For this reason, this e-voting system aims to provide convenience and comfort in the implementation of the election process.&lt;/em&gt;&lt;/p&gt;
System-Integrated Approach

The article conducts a comparative analysis of the concepts of two types of combinations claiming durability and indestructibility: “system” as an artificial-technical combination and “complex” as a natural-historical one. It is argued that the system-complex approach in science is an oxymoron that has no real denotation.
An analysis of software design understanding &amp; motivation of engineering students

Software engineering is now a well-recognized discipline in higher education. One challenge of software engineering education is to design teaching activities that empower students motivation and engagement. In this paper we present a study aimed at evaluating the understanding, motivation and perception of students with respect to software design activities. We first describe a project-based course currently taught to second-year students at IMT Atlantique, a French engineering school, and then present a questionnaire-based experiment. We asked the 44 students enrolled to the aforementioned course in the 2021 edition to answer three questionnaires (before and after the design activities and at the end of the course). The objective of this experiment is threefold: 1) evaluate the student's perception of the usefulness of the software design phase and the activity of modeling of software systems; 2) assess the evolution, if any, of this perception along the course; 3) evaluate if our course fosters student engagement. Results show that students are motivated with a good engagement all along the project and the perception of usefulness and pertinence of software design evolves in a slightly positive way.
International Collaboration to Develop a Remote Monitoring Web App for COVID-19 Patients in Armenia: Design and Development With Agile Methodology

COVID-19 has led to over 500 million cases and 6.2 million deaths around the world. Low- and middle-income countries (LMICs) like Armenia face unique infrastructure, financial, and capacity challenges that in many cases result in worse outcomes. Health care facilities across Armenia experienced a shortage of resources, including hospital beds and oxygen, which was further exacerbated by the war with neighboring Azerbaijan. Without a framework for home-based care, health care facilities were severely strained by COVID-19 patients who had prolonged oxygen requirements but were otherwise clinically stable.This paper describes our approach to establishing an international collaboration to develop a web app to support home monitoring of patients with COVID-19 with persistent oxygen requirements.The app was developed using a rapid, coordinated, and collaborative approach involving an international group of clinicians, developers, and collaborators. Health screening, monitoring, and discharge forms were developed into a lightweight OpenMRS web app and customized for the local Armenian context.The software was designed and developed over 2 months using human-centered design and agile sprints. Once live, 5087 patient records were created for 439 unique patients.This project suggests a promising framework for designing and implementing remote monitoring programs in LMICs, despite pandemic and geopolitical challenges.
The Position of Prescriptive Grammar Education in the Liberal Arts Writing Class: Focusing on the Expansion of Grammar Awareness

The purpose of this paper is to propose a plan to cultivate writing ability that establishes the principle of prescriptive grammar education and applies it to text reading and writing. The principles of prescriptive grammar education should be the process of discovering learners' own grammar, learning the system and structure of grammar, reflecting its principles in other language forms, and understanding and expanding the validity of the criteria involved in the macroscopic interpretation framework. Applying the prescriptive grammar education principle to liberal arts writing classes means that these three principles are consistently applied to the acceptance and production of text. Throughout the writing class, learners check their language skills and strategic skills, read the longitudinal and lateral hierarchies between the language items that make up their thoughts, and carry out the meta-cognitive strategy process by thinking for the possibility of expanding the validity of self-interpretation
Dark-Channel Enhanced-Compensation Net: An end-to-end inner-reflection compensation method for immersive projection system

Immersive projection display system is widely adopted in virtual reality and various exhibition halls. How to maintain high display quality in an immersive projection environment with uneven illumination and the color deviation caused by the inter-reflection of light is still a challenging task. In this paper, we innovatively propose a deep learning-based radiation compensation for an L-shaped projector-camera system. This method employs complex reflection phenomena to simulate the light transport processing in an L-shaped environment, we also designed a Dark-Channel Enhanced-Compensation Net (DECNet) which composed of a convolutional neural network called Compensation Net, a DarkChannelNet and another subnet (such as sensing network) aiming at achieving high-quality reproduction of projected display images. The final output of DECNet is the compensation image to be projected. It is always a critical problem to establish appropriate evaluation and analysis indexes throughout the research of light pollution compensation algorithms. In this paper, PSNR, SSIM, and RMSE are proposed to quantitatively analyze the image quality. The experimental results show that this method has certain advantages in reducing the inter-reflection of the projection plane. And our method could also well replace the traditional process using the backlight transmission matrix. It can be concluded to a certain that this method can be extended to other more complex projection environments with strong scalability and inclusiveness.
Deep Neural Networks and Auditory Imagery

Recent advances on deep neural networks, especially in combination with generative models, provide the means to deal with the large amounts of data and noisiness in the neuroimaging domain. This chapter provides an overview of state-of-the-art techniques to learn complex features from brain signal recordings that capture processes underlying auditory imagery. An introduction to important deep learning techniques and their application to auditory and neuroimaging data are given. Special attention is paid to generative models and reverse inference. These models allow to build new research paradigms, such as stimulus reconstruction from multiple sensory modalities. We discuss how models trained on sensory data can be used for inference about imagery processes, which are not directly associated with the auditory stimulus and thus make complex demands to machine learning models that traditionally require large amounts of annotated data. Deep neural networks generally require large amounts of training data, while the amount and size of available datasets are small. This motivates a description of requirements for new datasets in the field. Based on these considerations, a final section discusses possible future improvements for deep neural networks in the auditory imagery domain, such as biologically inspired computational models.
NashAE: Disentangling Representations Through Adversarial Covariance Minimization

We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no assumptions on the number or distribution of the individual latent variables to be extracted). In this method which we call NashAE, high-dimensional feature disentanglement is accomplished in the low-dimensional latent space of a standard autoencoder (AE) by promoting the discrepancy between each encoding element and information of the element recovered from all other encoding elements. Disentanglement is promoted efficiently by framing this as a minmax game between the AE and an ensemble of regression networks which each provide an estimate of an element conditioned on an observation of all other elements. We quantitatively compare our approach with leading disentanglement methods using existing disentanglement metrics. Furthermore, we show that NashAE has increased reliability and increased capacity to capture salient data characteristics in the learned latent representation.
A Novel Named Entity Recognition Algorithm for Hot Strip Rolling Based on BERT-Imseq2seq-CRF Model

Named entity recognition is not only the first step of text information extraction, but also the key process of constructing domain knowledge graphs. In view of the large amount of text data, complex process flow and urgent application needs in the hot strip rolling process, a novel named entity recognition algorithm based on BERT-Imseq2seq-CRF model is proposed in this paper. Firstly, the algorithm uses the BERT preprocessing language model to mine the dependencies in the domain text and obtain the corresponding representation vector. Then, the representation vector is sent to the encoder layer, and the output vector is input to the decoder at the same time, on the premise that the original model only considers the semantic vector. The Teacher-Forcing mechanism is integrated into the decoder layer to randomly modify the labeling results, and error accumulation is avoided to guarantee the sequence recognition effect. Finally, the validity of the labeling results is checked according to the conditional random field constraints, and the overall labeling quality of the algorithm is improved. The experimental results show that this model can efficiently and accurately predict the physical label of hot strip rolling, and the model performance index is better than other models, with the F1-Score reaching 91.47%. This model further provides technical support for information extraction and domain knowledge graph construction of hot strip rolling.
Design and research of computer aided translation software based on deep learning

With the rapid progress of computer technology, software technology based on deep learning presents a diversified development trend. Machine learning technology is based on deep learning technology. It can optimize the performance of computer programs to ensure the enhancement of machine translation ability [1]. The progress of machine learning technology makes computer-aided translation software come into being. The purpose of this paper is to design a computer-aided translation software system using deep learning technology. The writing method of this paper includes theoretical analysis and practical basis, and the basic conclusion of practice is obtained through theoretical analysis. The results show that deep learning can indeed help improve the efficiency and update speed of auxiliary translation software. On this basis, this paper mainly introduces the functional design and main implementation methods of computer-aided translation software.
TECHNOLOGY'S CONTRIBUTION TO EDUCATION

We are living in the 21st century.Education is now a primary need for every human being.In the past, when there were sages, they taught in asharams in the forests.We learned the stories of the Pandavas, Ramayan, Shri Krushna, and their friends.Dronacharya, Sandipani, and Vashishatah each had their own ashram where they taught their students about life and various weapons.All the students who want to get an education must go to these ashrams and spend some years there.They could have been wealthy or impoverished, kings or commoners.A change in technology has taken place everywhere in the last 10 years.Everywhere we face the need for technology, from early in the morning until I go to sleep, technology is useful for more effective teaching, learning, and understanding.In the pandemic situation, CID-19, technology is one of the most important resources.Using technology is necessary in schools.It helps keep students engaged in the learning process.WhatsApp, Zoom Meeting, Google Meet, Facebook Live, YouTube, and other social media do the job wonderfully.It is very hard to teach concepts of anything by traditional methods.Using technology is easier and more practical.The availability of technology has spread to urban as well as rural areas.Parents agreed to allow them to use mobile phones with their kids.As we know, everybody has to walk hand in hand.It will not be an exaggeration to say that without technology, human beings will survive in this century.
Research on English teaching ability evaluation algorithm based on big data fuzzy mean

This paper focuses on the systematic analysis of the English teaching ability assessment algorithm based on fuzzy K-value clustering under big data. The previous English teaching ability assessment algorithm can not accurately evaluate and estimate, and the analysis speed is slow, which can not meet the actual needs of English teaching ability assessment. Big data technology is used to construct the parameter index model of English teaching ability assessment structure, and quantitative recursive analysis method is also used to analyze English teaching ability systematically, so as to fully realize the optimization and adjustment of English teaching ability assessment algorithm by merging with k-means clustering algorithm. Big data fuzzy K-means clustering has strong data analysis for English teaching ability assessment algorithm, which can greatly improve the accuracy of English teaching ability assessment and rationally allocate teaching resources, thus promoting the sustainable development of English teaching ability assessment algorithm.
Chaotic manta ray foraging optimization based on elite hybridization and reverse learning

Aiming at the shortcomings of manta ray foraging optimization, such as facile to sink into local optimal and sluggish convergence velocity, an improved manta ray foraging optimization for high-dimensional conundrums was proposed, which is named Chaotic Manta Ray Foraging Optimization based on Elite Hybridization and Reverse Learning (ERMRFO). Firstly, the initial population is initialized by Circle mapping, which makes the initial population traverse the space more randomly. Secondly, a multi-elite hybridization strategy was used to improve the poor particles. Finally, an adaptive reverse learning strategy combining Lévy flight and Gaussian variation was used to increase population diversity. Five high-dimensional test functions are selected for experiments, and the results show that the improved algorithm has better ability of escaping local optimum and faster convergence speed.
Significance of Elliptic Curve Cryptography in Blockchain IoT

Today, blockchain has emerged as peer-to-peer distributed ledger technology for recording transactions, maintained by many peers without any central trusted regulatory authority through distributed public-key cryptography and consensus mechanism. It has not only given birth to cryptocurrencies, but it has also resolved various security, privacy, and transparency issues of decentralized systems. This paper discusses blockchain basics and blockchain security components, digital signature, and elliptic curve cryptography (ECC). In addition to the core idea of blockchain, we focus on ECC’s significance in incorporating blockchain with IoT. We also discuss why RSA and other key generation, encryption, decryption, signing and verification mechanisms are not suitable for IoT applications. We will also analyse various possible IoT applications where the ECC algorithm is more efficient than other algorithms because of latency, memory, security, and privacy assurance. At the end of the paper, we will explain the comparative analysis of some essential key generation algorithms along with ECC and RSA.
STRATEGIES USED BY THE AUTHORITIES IN DEALING WITH ONLINE PURCHASING FRAUD

Cases of online purchase fraud are escalating and this has created concerns about the level of security available for online users. Previous studies have provided solutions to deal with the risk of fraud when using the Internet but no study has focused on discussions about the strategies employed by the Malaysian authorities when dealing with online purchase fraud. Therefore, this study aims to discuss the strategies employed by Malaysian authorities to make online purchasing safe for online users. This qualitative study employed the interview method and content analysis to collect and analyse data, respectively. The documents analysed included journal articles by past researchers, while interviews were conducted with government and non-government agencies that are directly involved with online users. Findings show that the authorities have disseminated consumer information and education through the media, provided channels for lodging complaints, communicated directly with sales service providers, conducted monitoring and inspections, enforced the disclosure of eight vital pieces of information according to P.U (A) 458, encouraged the procurement of the SSM BizTrust certificate, established collaborations with related agencies and conducted research. It is hoped that the suggested improvements can be implemented so that the implemented strategies can have a more positive effect.
Interpretation of Students' Cultural Confidence and Design of Online Evaluation System from the Perspective of Cluster Big Data

The "three-integration" type of cultural self-confidence education for college students integrates cultural self-confidence into students' moral cultivation, academic improvement and ability reserve, guides students' development direction through correct cultural values, and enriches students' cultural self-confidence awareness through rich practical activities. Four kinds of cluster scheduling structures under the background of big data are introduced respectively: centralized structure, double-layer structure, distributed structure and hybrid structure, and the reasons, applicable scenarios, advantages and disadvantages of each structure are introduced. Put forward scientific, practical and multi-dimensional suggestions and countermeasures. Provide intellectual support for the government to cultivate industrial clusters, improve regional influence, and formulate effective policies.
New Constructions of Existential Unforgeable Aggregate Signature Scheme from CSP

In future, hundreds of years of mathematical problems that the security of public key cryptography algorithms rely on may be defeated by quantum algorithms. How can a digital signature scheme gracefully balance security and efficiency? This study uses the conjugate search problem and the left self-distributive system to combine and uses the RSA-like algorithm as the underlying structure to propose a new aggregated signature scheme. We, through the EUF game, under the random metaphor model, prove that the security of the scheme satisfies the adaptation unforgeability under selective message attack, the scheme can be finally reduced to the discrete logarithm problem or large prime number decomposition problem. In addition, we can achieve antiquantum attack and exhaustive attack by performing matrix calculations on the message, defining and changing the structure of the matrix by encoding, and setting thresholds for the matrix dimension and the length of the private key. In terms of efficiency, the message signature implementation is linear compared with the expansion rate in terms of storage and computing overhead, and the generation and verification of the final signature pair have nothing to do with the number of users. In addition, the length of the signature is fixed and the size is only the length of a single group, which effectively reduces the generation of public and private key pairs and saves a lot of storage space. The storage space and computational complexity are also effectively improved compared with other solutions.