Reviewerly/exHarmony huggingface dataset:
Chinese–Vietnamese Pseudo-Parallel Sentences Extraction Based on Image Information Fusion

Parallel sentences play a crucial role in various NLP tasks, particularly for cross-lingual tasks such as machine translation. However, due to the time-consuming and laborious nature of manual construction, many low-resource languages still suffer from a lack of large-scale parallel data. The objective of pseudo-parallel sentence extraction is to automatically identify sentence pairs in different languages that convey similar meanings. Earlier methods heavily relied on parallel data, which is unsuitable for low-resource scenarios. The current mainstream research direction is to use transfer learning or unsupervised learning based on cross-lingual word embeddings and multilingual pre-trained models; however, these methods are ineffective for languages with substantial differences. To address this issue, we propose a sentence extraction method that leverages image information fusion to extract Chinese–Vietnamese pseudo-parallel sentences from collections of bilingual texts. Our method first employs an adaptive image and text feature fusion strategy to efficiently extract the bilingual parallel sentence pair, and then, a multimodal fusion method is presented to balance the information between the image and text modalities. The experiments on multiple benchmarks show that our method achieves promising results compared to a competitive baseline by infusing additional external image information.
Bibliografia di Linguistica Educativa in Italia – BLEI Aggiornamento 2022

This bibliography (formerly BELI - Bibliografia dell’Educazione Linguistica in Italia) is edited by the Research Center in Language Education (Centro di Ricerca sulla Didattica delle Lingue) with the patronage of the Italian Society for Didactics of Languages and Educational Linguistics (Società Italiana di Linguistica Educativa e Didattica delle Lingue - DILLE). It has been regularly updated since 1960 and is available in open access at https://www.unive.it/pag/16976.
Teaching practice in higher education. Analysis from educational models

The objective of this study is to describe the teaching practice from the experience of the students, based on the knowledge that teachers have about the subject, the pedagogical abilities or skills they possess, interpersonal relationships, personality characteristics, research associated with the chair, and planning. The method was quantitative with a descriptive scope and cross-sectional design, it was applied in 106 surveys to students of the Psychology Academic Unit of the Autonomous University of Zacatecas, in the State of Zacatecas, Mexico. A self-made instrument called "Teaching practice questionnaire, student perception" was applied with a "Cronbach's Alpha" of 0.93. It was found that if the teacher is critically trained, assesses the teaching and learning processes through dialogue, interacts with other teachers, and builds and lives values in their educational and social context, they will be in agreement with a practice close to a paradigm constructivist.
DEVELOPMENT OF INSTRUMENTS FOR ASSESSING STUDENT POLITENESS

The purpose of this study was to create and develop an instrument for assessing student politeness that was appropriate and tested for validity and reliability. The method used in this research was Research and Development (R&amp;D). This research was conducted at SMA Negeri 1 Gambut, Banjar Regency, South Kalimantan, with a random sample of 200 people. The data collection technique was in the form of a questionnaire using the Google Form application. To produce an instrument for assessing the politeness of quality students, a validation test was first carried out which was carried out by three experts, namely one linguist, and two education practitioners, namely the Civics teacher and the Counseling Guidance teacher. Data analysis used the SPSS version 23 program. Through the Product Moment test, 18 valid instruments were obtained (rcount value was 0.138 &gt; rtable) with very high reliability value category (with cronbach's alpha 0.96 α (0.6)). Thus, the 18 items of the instrument had the eligibility to assess the polite attitude of students, especially at Peat State High School, Banjar Regency, South Kalimantan.
Differential Evolution and Agglomerative-Clustering-Based Mutation Strategy for Complex Numerical Optimization Problems

Differential evolution is an evolutionary algorithm that is used to solve complex numerical optimization problems. Differential evolution balances exploration and exploitation to find the best genes for the objective function. However, finding this balance is a challenging task. To overcome this challenge, we propose a clustering-based mutation strategy called Agglomerative Best Cluster Differential Evolution (ABCDE). The proposed model converges in an efficient manner without being trapped in local optima. It works by clustering the population to identify similar genes and avoids local optima. The adaptive crossover rate ensures that poor-quality genes are not reintroduced into the population. The proposed ABCDE is capable of generating a population efficiently where the difference between the values of the trial vector and objective vector is even less than 1% for some benchmark functions, and hence it outperforms both classical mutation strategies and the random neighborhood mutation strategy. The optimal and fast convergence of differential evolution has potential applications in the weight optimization of artificial neural networks and in stochastic and time-constrained environments such as cloud computing.
Spam Detection in Social Networks Using Machine Learning Algorithms

Social networking websites have become more and more popular recently. Users use them to meet new people and communicate their most recent thoughts and actions to their existing acquaintances. The website among these that is growing the quickest is social media. Due to its popularity, many spammers attempt to flood actual users' accounts with spam messages. This paper considers three social networks, Twitter, Facebook, and Instagram, for experimentation. The classification of the data into spam and non-spam using four machine learning techniques, including SVM, KNN, decision trees, and Random Forest. The results obtained from the experiments show that the proposed approach can accurately detect spam in social networks. Implementing such algorithms could help social network platforms improve user experience by reducing the prevalence of spam and fraudulent activity
Overlapping Domain Decomposition Method with Cascadic Multigrid for Image Restoration

In the process of image restoration, it is usually necessary to solve large-scale inverse problems, where the computational cost is very high for large or high-resolution images. The domain decomposition method is one of the most effective algorithms to solve large-scale problems, which can effectively decrease the computational cost. The cascadic multigrid method has a good effect on the linear model of image restoration and can obtain high quality restored images. In this paper, the overlapping domain decomposition method (DDM) with the cascadic multigrid method (CMG) and the DDM with new extrapolation cascadic multigrid method (NECMG) are presented to solve the image restoration problems of denoising and deblurring. We first divide the image problem into some overlapping and independent subproblems. Then, each subproblem is solved independently by CMG or NECMG with the edge-preserving operator. Numerical experiments show that the new method is effective.
AVX512Crypto: Parallel Implementations of Korean Block Ciphers Using AVX-512

Cryptographic algorithms are widely used as the foundation of various security systems and applications (e.g., secure communication, blockchain systems, and cloud services). A block cipher is an essential cryptographic algorithm to achieve confidentiality. This paper proposes parallel implementations of Korean block ciphers using Advanced Vector eXtension (AVX)-512, which is a new Single instruction, multiple data (SIMD) instruction set that has recently been integrated into several high-end Intel central processing unit (CPU). Target algorithms are LEA, HIGHT, and CHAM block ciphers. Additionally, this paper also proposes applicable implementing techniques, which are designed for each algorithm. These enable to use of parallel processing instructions in AVX-512 properly for each algorithm. The proposed LEA-128 (192, 256 resp.)implementation demonstrates a performance improvement of 506.09% (323.31%, 386.76% resp.) compared to the reference code, and our HIGHT implementation exhibits a performance improvement of 520.88% compared to the reference code. In addition, CHAM-64/128 (128/256 resp.) implementation shows a performance improvement of 1,325.81% (833.61% resp.) compared to the reference code. In addition, we measured the performance with a 32MB long message. LEA-128 (192, 256 resp.) implementation showed an improvement of 556.32% (594.74%, 615.38% resp.) compared with the reference code. Also, HIGHT implementation showed 834.40%, and CHAM showed 1,332.40% (832.86% resp.) for CHAM-64/128 (CHAM-128/256 resp.), compared by the reference code. To the best of our knowledge, this is the first result of the study to optimize Korean cryptographic algorithms using the AVX-512 instruction set. The proposed methods can effectively be used in Addition, Rotation, and XOR (ARX)-based cryptographic algorithms, enabling efficient cryptographic algorithm processing in various environments such as hash-based signatures, service environments, gateway, and edge computing.
A Multi-Agent Adaptive Co-Evolution Method in Dynamic Environments

It is challenging to ensure satisfying co-evolution efficiency for the multi-agents in dynamic environments since during Actor-Critic training there is a high probability of falling into local optimality, failing to adapt to the suddenly changed environment quickly. To solve this problem, this paper proposes a multi-agent adaptive co-evolution method in dynamic environments (ACE-D) based on the classical multi-agent reinforcement learning method MADDPG, which effectively realizes self-adaptive new environments and co-evolution in dynamic environments. First, an experience screening policy is introduced based on the MADDPG method to reduce the negative influence of original environment experience on exploring new environments. Then, an adaptive weighting policy is applied to the policy network, which accordingly generates benchmarks for varying environments and assigns higher weights to those policies that are more beneficial for new environments exploration, so that to save time while promoting adaptability of the agents. Finally, different types of dynamic environments with complexity at different levels are built to verify the co-evolutionary effects of the two policies separately and the ACE-D method comprehensively. The experimental results demonstrate that, compared with a range of other methods, the ACE-D method has obvious advantages helping multi-agent adapt to dynamic environments and preventing them from falling into local optima, with more than 25% improvement in stable reward and more than 23% improvement in training efficiency. The ACE-D method is valuable and commendable to promote the co-evolutionary effect of multi-agent in dynamic environments.
Proactive and dynamic load balancing model for workload spike detection in cloud

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.
Adjectives and adverbs as stylometric analysis parameters

Abstract The present study considers the role of adjectives and adverbs in stylometric analysis and authorship attribution. Adjectives and adverbs allow both for variations in placement and order (adverbs) and variations in type (adjectives). This preliminary study examines a collection of 25 English-language blogs taken from the Schler Blog corpus, and the Project Gutenberg corpus with specific emphasis on 3 works. Within the blog corpora, the first and last 100 lines were extracted for the purpose of analysis. Project Gutenberg corpora were used in full. All texts were processed and part-of-speech tagged using the Python NLTK package. All adverbs were classified as sentence-initial, preverbal, interverbal, postverbal, sentence-final, or none-of-the-above. The adjectives were classified into types according to the universal English type hierarchy (Cambridge Dictionary Online, 2021; Annear, 1964) manually by one of the authors. Ambiguous adjectives were classified according to their context. For the adverbs, the initial samples were paired and used as training data to attribute the final samples. This resulted in 600 trials under each of five experimental conditions. We were able to attribute authorship with an average accuracy of 9.7% greater than chance across all five conditions. Confirmatory experiments are ongoing with a larger sample of English-language blogs. This strongly suggests that adverbial placement is a useful and novel idiolectal variable for authorship attribution (Juola et al., 2021). For the adjective, differences were found in the type of adjective used by each author. Percent use of each type varied based upon individual preference and subject-matter (e.g. Moby Dick had a large number of adjectives related to size and color). While adverbial order and placement are highly variable, adjectives are subject to rigid restrictions that are not violated across texts and authors. Stylometric differences in adjective use generally involve the type and category of adjectives preferred by the author. Future investigation will focus, likewise, on whether adverbial variation is similarly analyzable by type and category of adverb.
High-definition technology of AI inference scheme for object detection on edge/terminal

To detect a wide range of objects with one camera at once, real-time object detection in high-definition video is required in video artificial intelligence (AI) applications for edge/terminal, such as beyond-visual-line-of-sight (BVLOS) drone flight. Although various AI inference schemes for object detection (e.g., you-only-look-once (YOLO)) have been proposed, they typically have limitations on the input image size and thus need to shrink the input high-definition image down to the limit. This makes small objects collapsed and undetectable. This paper presents our proposal technology for solving this problem and its effective implementation, where multiple object detectors cooperate to detect small and large objects in high-definition video such as full HD and 4K.
Study on the Intention and Behavior Complying with Accounting Information System Security Policy: The Case of Vietnam

The digital-driven economy leads to many opportunities and many challenges for businesses concerning information system security.This is the reason why more managers concentrate on promulgating and enforcing accounting information system security policies.Research on AISSP compliance behavior, therefore, receives much attention from researchers.However, research on this topic is still very limited in Vietnam.In business practice, organizations usually use sanctions to deter employees who do not comply with the security policy.However, previous research results show inconsistency regarding the effectiveness of a motivated as well as a deterrent approach to compliance.These different results can be explained based on cultural differences as previous studies argued that research on information systems security policy compliance behavior based on motivation and deterrence mechanisms needs to be considered in the impact of a specific cultural factor.This motivated us to research AISSP compliance in the context of Vietnam.This study explores the cognitive factors and personal characteristics affecting the intention and behavior to comply with accounting information system security policy (AISSP) based on deterrence theory, protection motivation theory, and the theory of reasoned action.Our results demonstrate that the intention and behavior of accountants to comply with AISSP are positively influenced by the punishment severity, AISSP compliance attitude, response efficacy, and uncertainty avoidance.The results add to the body of literature and inform future research on AISSP compliance.From the management aspect, the results help organizations recognize the problems that need to be addressed to ensure AISSP compliance from accountants.
A 200 kb/s 36 µw True Random Number Generator Based on Dual Oscillators for IOT Security Application

As a module of the internet of things (IOT) information security system, the true random number generator (TRNG) plays an important role in overall performance. In this paper, a low-power TRNG based on dual oscillators is proposed. Two high-frequency cross-coupled oscillators are used to generate high-jitter clock signals, and then the SR latch with power supply below standard power supply voltage is adopted to process the oscillator output to maintain its metastability and increase jitter. The circuit is realized by an SMIC 180 nm 1P6M mixed-signal process. The experimental results show that when power supply voltage is 1.8 V, the circuit outputs a random number bit rate of 200 kb/s, the core area is 0.0039 mm2, and the power consumption is only 36 µw. The output random sequences can pass the NIST SP 800-22 test.
Dialogue Act Classification via Transfer Learning for Automated Labeling of Interviewee Responses in Virtual Reality Job Interview Training Platforms for Autistic Individuals

Computer-based job interview training, including virtual reality (VR) simulations, have gained popularity in recent years to support and aid autistic individuals, who face significant challenges and barriers in finding and maintaining employment. Although popular, these training systems often fail to resemble the complexity and dynamism of the employment interview, as the dialogue management for the virtual conversation agent either relies on choosing from a menu of prespecified answers, or dialogue processing is based on keyword extraction from the transcribed speech of the interviewee, which depends on the interview script. We address this limitation through automated dialogue act classification via transfer learning. This allows for recognizing intent from user speech, independent of the domain of the interview. We also redress the lack of training data for a domain general job interview dialogue act classifier by providing an original dataset with responses to interview questions within a virtual job interview platform from 22 autistic participants. Participants’ responses to a customized interview script were transcribed to text and annotated according to a custom 13-class dialogue act scheme. The best classifier was a fine-tuned bidirectional encoder representations from transformers (BERT) model, with an f1-score of 87%.
Motivating Accounting Information Systems Security Policy Compliance: Insight from the Protection Motivation Theory and the Theory of Reasoned Action

This study aims to explore the factors that motivate individuals to comply with accounting information systems security policies (AISSP) in Vietnam based on the Protection Motivation Theory (PMT) and the Theory of Reasoned Action (TRA).The survey method was acquired for doing the study.Our findings were interpreted based on the data collected from 226 accountants in Vietnam through a self-administrated questionnaire.The psychometric properties of the theoretical model and proposed hypotheses were assessed by the PLS-SEM technique on SmartPLS 3.2.7.The results show that the PMT and TRA model performs well in Vietnam, with a 58.1% variance in behavior to comply with the AISSP explained by the model.Direct/ indirect positive impacts on AISSP compliance behavior may have resulted from the coping appraisal, attitude to comply with AISSP, and subjective norms.This study recommends that management can boost employees' AISSP compliance behavior by instituting regular IS security awareness sessions, campaigns, and training.In addition, management might encourage employees to acquire the skills and knowledge required to secure the IS assets of the firm.The findings from this study contribute to a better understanding of the mechanism of establishing and enhancing the employees' AISSP compliance behavior in particular, as well as ISSP compliance behavior in general in organizations.
Bit depth enhancement method for low-contrast images based on sequence image fusion

To improve the visual effect of low-contrast images, a low-contrast image bit depth enhancement method based on sequence image fusion is proposed. The low-contrast image neighbourhood pixels are randomly discarded through Bernoulli trials. The corresponding weights of the local variance and deviation of the reserved pixels are obtained. The signal is used to build a denoising model, and the model is solved by the steepest descent method to complete the low-contrast image denoising process. Taking the denoised image as input, the useful information and features of the image are calculated, and the weight value is obtained. At the same time, the image is standardized, and different fusion rules are used to perform low-contrast image bit depth enhancement processing on the standardized sequence images. The experimental results show that the proposed method can effectively remove image noise, improve the image enhancement effect, and reduce the running time.
Face recognition using CNN and siamese network

Facial recognition is no longer a cutting-edge technology; it is now a part of everyday life. It has been used for various security and profiling applications around the world. Early face detection models were developed during the 1960s and were used to just classify photos of people. In past decades, the face recognition models were optimized and reengineered to identify all the people in each frame of real-time, high-resolution video input. It still has a wide variety of applications to be implemented and can be further optimized for high precision using different approaches. In this study, we have implemented two different approaches for facial detection. The first is a CNN-based approach that extracts keypoints from an image and classifies it using a KNN algorithm. The next approach uses a Siamese network to classify the input image. The initial part focuses primarily on data collection and training. The following part clearly explains the implementation of both approaches. The performance of these approaches was also evaluated and illustrated optimally.