Reviewerly/exHarmony huggingface dataset:
Source or target first? Comparison of two post-editing strategies with translation students

We conducted an experiment with translation students to assess the influence of two different post-editing (PE) strategies (reading the source segment or the target segment first) on three aspects: PE time, ratio of corrected errors and number of optional modifications per word. Our results showed that the strategy that is adopted has no influence on the PE time or ratio of corrected errors. However, it does have an influence on the number of optional modifications per word. Two other thought-provoking observations emerged from this study: first, the ratio of corrected errors showed that, on average, students correct only half of the MT errors, which underlines the need for PE practice. Second, the time logs of the experiment showed that when students are not forced to read the source segment first, they tend to neglect the source segment and almost do monolingual PE. This experiment provides new insight relevant to PE teaching as well as the designing of PE environments.
Faulty Node Detection in HDFS Using Machine Learning Techniques

The design of Hadoop has ability to elimination of fault tolerance, which consists of rescheduling the task on the defective nodes to run on other devices in the system.However, this strategy is ineffective if an error arises after most of the task has been completed.As a result, it is essential to make an early detection of the problem at the node to ensure that the resumption of the work will not result in a significant loss of both time and productivity.The ability to predict these problems provides us with the required time to move the workload onto different nodes, which helps to avoid data loss or processing time.In this paper, we propose an identification of faulty nodes from a large Hadoop distributed environment using machine learning techniques.Initially, we deployed one controller node and numerous data nodes as virtual machines in a distributed manner.The execution performs when the end-user submits a specific job to the controller node.The master node is the middleware controller that continuously communicates with other data nodes and assigns a task to each data node accordingly.However, this conventional process of HDFS that can generate data leakage or high computation whenever the specific node is heated or straggler.In our approach, we initially collect the log history of each data node and apply some statistical and a few machine learning algorithms to identify nodes' status.According to the achieved outcome of each node, we can decide to eliminate the specific node for task execution.We applied five machine learning algorithms in the extensive experimental analysis, including a Support Vector Machine (SVM).The SVM obtains 96.7% higher accuracy over the conventional machine learning classifiers for the entire execution.
Optimization of Deep-Learning Detection of Humans in Marine Environment on Edge Devices

Artificial intelligence (AI) detection techniques based on convolution neural networks (CNNs) require high computations and memory. Their deployment on embedded edge devices, with reduced resources and power budget, is highly hindered especially for applications that requires real-time inference. Several optimization methods such as pruning, quantization and using shallow networks, are mainly utilized to overcome this limitation but at the cost of degradation in detection performance. However, efficient approaches for training and inference have been recently introduced to lower such degradation. This work investigates the use of these approaches to optimize the popular You Only Look Once (YOLO) network targeting various emerging edge devices (Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU) in order to enhance the detection of humans in maritime environment.
Hiding text using the least significant bit technique to improve cover image in the steganography system

One of the highest priorities in the era of information technology is to achieve an accurate and effective system for hiding security data. One of the goals of steganography is imperceptability to intruder. So this paper work to increase the imperceptibility on image, which has weaknesses in previous studies, as well as to avoid statistical attacks such as chi-square. A method has been proposed that includes calculating the color contrasts in the homogeneous areas of the image and dividing them according to the color contrast and exploiting the data of pixels that have a high impact to embed on the two first and third bits of least significant bit (LSB) to increase the amount of embedded data, impact regions (IR) classify according to selected features extracted in advance by using the support vector machine (SVM) classifier. Work was done on standard images taken from a standard dataset (USC-SIPI) for two types of gray and color images. The results showed the worth of the proposed method through a high peak signal to noise ratio (PSNR) that reached 89.5 dB due to the distribution of data on pixels according to the proposed method
Time series reconstructing using calibrated reservoir computing

Reservoir computing, a new method of machine learning, has recently been used to predict the state evolution of various chaotic dynamic systems. It has significant advantages in terms of training cost and adjusted parameters; however, the prediction length is limited. For classic reservoir computing, the prediction length can only reach five to six Lyapunov times. Here, we modified the method of reservoir computing by adding feedback, continuous or discrete, to "calibrate" the input of the reservoir and then reconstruct the entire dynamic systems. The reconstruction length appreciably increased and the training length obviously decreased. The reconstructing of dynamical systems is studied in detail under this method. The reconstruction can be significantly improved both in length and accuracy. Additionally, we summarized the effect of different kinds of input feedback. The more it interacts with others in dynamical equations, the better the reconstructions. Nonlinear terms can reveal more information than linear terms once the interaction terms are equal. This method has proven effective via several classical chaotic systems. It can be superior to traditional reservoir computing in reconstruction, provides new hints in computing promotion, and may be used in some real applications.
Evaluating of E- Learning Programs using Gray-Related Analysis (GRA) Method

A method of learning Based on formalized teaching but electronic it is called e-learning with the help of resources.Teaching can take place inside and outside classrooms however, the use of computers and the Internet is an important part of e-learning.It encourages active and independent learning; anywhere anytime to provide courses as resources are available efficient way; Group discussions and private chats Students can interact with their peers around the world through; unlimited access to reading material.E-learning programs in GRA (Gray-related analysis) method Alternative: Factor analysis, Fuzzy integral, Transferring Time, Self-Efficacy.Evaluation Preference: Personal characteristics and computer instruction, e-Learning Environment, Participant Motivation, Web Page Linkage, Range of instructional materials.Shows that from the result it is seen that Range of Instruction Materials and is got the first rank whereas is the Participant Motivation got is having the lowest rank.The value of the dataset for Range of Instruction Materials in GRA (Gray-related analysis) method shows that it results in Camera (A5) and top ranking.
Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem.This problem occurs when a new item hits the system or a new user signs up.It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm.This paper introduces a frequent pattern mining framework for recommender systems (FPRS) -a novel approach to address this challenging task.FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm.The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items.The performed empirical evaluation confirmed its usefulness.Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.
Repairing Bugs in Python Assignments Using Large Language Models

Students often make mistakes on their introductory programming assignments as part of their learning process. Unfortunately, providing custom repairs for these mistakes can require a substantial amount of time and effort from class instructors. Automated program repair (APR) techniques can be used to synthesize such fixes. Prior work has explored the use of symbolic and neural techniques for APR in the education domain. Both types of approaches require either substantial engineering efforts or large amounts of data and training. We propose to use a large language model trained on code, such as Codex, to build an APR system -- MMAPR -- for introductory Python programming assignments. Our system can fix both syntactic and semantic mistakes by combining multi-modal prompts, iterative querying, test-case-based selection of few-shots, and program chunking. We evaluate MMAPR on 286 real student programs and compare to a baseline built by combining a state-of-the-art Python syntax repair engine, BIFI, and state-of-the-art Python semantic repair engine for student assignments, Refactory. We find that MMAPR can fix more programs and produce smaller patches on average.
Mel-Spectrogram Image-Based End-to-End Audio Deepfake Detection Under Channel-Mismatched Conditions

This work focuses on the problem of detecting fake audio clips. To improve current audio spoofing detection models, we propose a selection of multiple audio augmentations spe-cially designed to resemble audio spoofing attacks. These augmentations are experimentally found to be very useful and using them achieves a notable performance of 2.8% EER on the ASVspoof 2019 challenge evaluation set. Unlike the widely employed acoustic features, in this paper we explore the use of Mel-spectrogram image features and employ vari-ous audio codecs to achieve robustness to codec and transmission channel variability present in the ASVspoof2021 Evalu-ation set. To better handle spectral information, crucial to de-tect spoofing, we adopt the WaveletCNN and VGG16 archi-tectures which outperform all baselines. Finally, we find that robustness of countermeasure systems degrades dramatically when provided with speech samples degraded through VoIP network transmission or mismatching audio compression.
A Critical Review on Sentiment Analysis Techniques

Social media and other online forums are the platforms that have given people the voice to shape their views and opinion in regards to a specific subject. The data collected from such platforms can be useful for extracting and gaining the valuable insights for many business organizations in regards to their products and services. This review paper lists various machine learning, deep learning and natural language processing approaches that have been used to mine the sentiments. In recent years many deep learning based approaches have been adopted by the research community to analyze the sentiments because of their robust performances that they have exhibited. Since the introduction of the transformer based models a major shift has been witnessed in the field of NLP. Further in the paper we also highlight the need for further exploration of the pre-trained language models which can lead to the improvement in the analysis of static as well as dynamic data streamed over the internet.
Open-Air Microwave Entanglement Distribution for Quantum Teleportation

Microwave technology plays a central role in current wireless communications, including mobile communication and local area networks. The microwave range shows relevant advantages with respect to other frequencies in open-air transmission, such as low absorption losses and low-energy consumption, and in addition, it is the natural working frequency in superconducting quantum technologies. Entanglement distribution between separate parties is at the core of secure quantum communications. Therefore, understanding its limitations in realistic open-air settings, especially in the rather unexplored microwave regime, is crucial for transforming microwave quantum communications into a mainstream technology. Here, we investigate the feasibility of an open-air entanglement distribution scheme with microwave two-mode squeezed states. First, we study the reach of direct entanglement transmission in open air, obtaining a maximum distance of approximately 500 m with parameters feasible for state-of-the-art experiments. Subsequently, we adapt entanglement distillation and entanglement swapping protocols to microwave technology in order to reduce the environment-induced entanglement degradation. The employed entanglement distillation helps to increase quantum correlations in the short-distance low-squeezing regime by up to $46\mathrm{%}$, and the reach of entanglement increases by $14\mathrm{%}$ with entanglement swapping. Importantly, we compute the fidelity of a continuous-variable quantum teleportation protocol using open-air-distributed entanglement as a resource. Finally, we adapt this machinery to explore the limitations of quantum communication between satellites, where the impact of thermal noise is substantially reduced and diffraction losses are dominant.
Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals

Probabilistic text generators have been used to produce fake scientific papers for more than a decade. Such nonsensical papers are easily detected by both human and machine. Now more complex AI-powered generation techniques produce texts indistinguishable from that of humans and the generation of scientific texts from a few keywords has been documented. Our study introduces the concept of tortured phrases: unexpected weird phrases in lieu of established ones, such as 'counterfeit consciousness' instead of 'artificial intelligence.' We combed the literature for tortured phrases and study one reputable journal where these concentrated en masse. Hypothesising the use of advanced language models we ran a detector on the abstracts of recent articles of this journal and on several control sets. The pairwise comparisons reveal a concentration of abstracts flagged as 'synthetic' in the journal. We also highlight irregularities in its operation, such as abrupt changes in editorial timelines. We substantiate our call for investigation by analysing several individual dubious articles, stressing questionable features: tortured writing style, citation of non-existent literature, and unacknowledged image reuse. Surprisingly, some websites offer to rewrite texts for free, generating gobbledegook full of tortured phrases. We believe some authors used rewritten texts to pad their manuscripts. We wish to raise the awareness on publications containing such questionable AI-generated or rewritten texts that passed (poor) peer review. Deception with synthetic texts threatens the integrity of the scientific literature.
A Content Analysis of Malay Clothing in Malaysia

Clothing is a combination between religion and modesty, an increase in self-confidence, the look of professionalism, culture, identity, social status, values, and many more. In everyday life, clothing provides information of appearance that indicates the origin of the wearer and carries messages with a significant impact on social relationships. Many studies on Malay clothing have been conducted focusing on traditional clothing. This study would lead toward a potential framework of appropriate justification on clothing-content components of perceptual elements as a guideline in describing Malay clothing characteristics. Hence, this complements the prevailing related literature that significantly retains national social identity. Keywords: Malay, Malay Clothing, Content Analysis eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3827
Academic Document Authentication using Elliptic Curve Digital Signature Algorithm and QR Code

Paper-based documents or printed documents such as recommendation letters, academic transcripts, and diplomas are prone to forgery. Several methods have been used to protect them, such as watermarking, security holograms, or using paper with specific security features. This paper presents a document authentication system that utilizes QR code and ECDSA as the digital signature algorithm to protect this kind of document from counterfeiting. A digital signature is a well-known technique in modern cryptography used for providing data integrity and authentication. The idea proposed herein is to put a QR code in the printed documents where the QR code includes a digital signature. The signature can later be authenticated using the proposed system by uploading the document for authentication or scanning the document's QR code. The proposed system is particularly developed for digital signature generation and verification of students' final project approval documents as the case study. In traditional settings, the approval form is typically signed directly by the student's advisor dan co-advisor using handwritten signatures. However, using the conventional handwritten signature, the signature on the approval form can be falsified. Therefore, a digital signature generation and verification system is implemented herein to avoid handwritten signature falsification. The advisors can use this system to sign the approval form using a digital signature instead of a handwritten one. The signature is stored in a QR code and is generated using ECDSA with SHA-256 as the hash function. The proposed system is evaluated using documents (i.e., approval forms) with genuine and forged QR codes.Â The evaluation results showed that the system could verify the authenticity of the approval forms, which contain genuine QR codes. The approval forms that contained forged QR codes were correctly identified.
Student Engagement Mechanism of Online Learning: The Effect of Service Quality on Learning Management System

Since typical classrooms do not include discussions, collaborative learning, or interactive learning activities, engagement is a major challenge in distant learning. Online learning satisfaction levels should be measured as evaluation material for future implementation. Although online learning has many advantages, a high dropout rate remains a significant challenge. This study investigates how higher education students' engagement and satisfaction with online learning are enhanced by information, system, and service aspects. The research design was quantitative research, and we used a questionnaire to collect data. The questionnaire was designed on a five-rating interval scale. The sampling technique was simple random sampling. The target minimum sample was counted using the Slovin method, and 206 undergraduate students taking online courses were surveyed online. The model was tested using structural equation modeling partial least squares (SEM PLS). This method is useful for investigating the relationship between constructs. The model was tested with the application of the SmartPLS program. The results revealed a positive and significant effect of system quality, information quality, service quality to student engagements, and their impact on student satisfaction, both direct and indirect. This study answers the literature gap and verifies the importance of online learning quality factors on studentsâ€™ satisfaction and engagement. These results are expected to help to improve online learning in higher education settings, specifically on students' engagement and satisfaction, leading to perseverance and success.
Enhancing students’ digital literacy at EFL classroom: Strategies of teachers and school administrators

Promoting digital literacies is the apparent goal of an extensive array of educational initiatives in all educational contexts. However, very limited attention has been directed toward the strategies of promoting students’ digital literacies in the literature. Thus, this qualitative study aims to identify the strategies of EFL teachers and school administrators in promoting EFL students’ digital literacies to thrive in the new normal challenges. Semi-structured interviews with 6 EFL teachers and 6 school admins from the Arab international schools in Kuala Lumpur were gathered using a convenient sampling technique. The qualitative data were analysed and reported thematically. The findings of the study revealed that teachers reported four strategies to promote students’ digital literacy: motivating students, conducting training workshops, actual use of digital technologies in the classroom, and enhancing classroom digital infrastructure. Similarly, school admins reported four strategies: rising parents’ awareness towards the importance of digital technologies, training teachers and students, enhancing classroom infrastructure and redesigning the EFL classroom. These findings offer implications for policymakers and researchers, especially those interested in enhancing students’ digital literacies. The results require further urgent large-scale investigations to address the factors that currently constrain the promotion of digital technologies.
A Group Covert Communication Method of Digital Currency Based on Blockchain Technology

With the rapid development of the Internet, it has become a hot spot in protecting important information from illegal theft and destruction during network transmission. The characteristics of strong robustness, anonymity and distributed transmission in blockchain technology can precisely make up for the disadvantages faced by traditional covert communication in transmitting important data, such as poor robustness, easy exposure of participants and difficulty in achieving group covert transmission. Therefore, a group steganography method for digital currency based on blockchain technology is proposed in this paper. In this method, an index matrix of address interaction is designed for group covert communication, using the address interaction relationship and transaction amount to hide secret message alternately, the transaction hiding secret message is published on the blockchain. In different groups, the receivers use different keys to extract different secret message. The method proposed also introduces a fine-grained group division mechanism to control the spread of secret information and reduce the risk of secret information leakage. And the application scenarios of the method proposed are more universal, breaking the limitation of using a single digital currency for covert communication on the blockchain. In addition, the proposed method modulates the number of addresses reasonably by sensing the generation time of the block, enhancing the concealment of the method. Through practical tests on a variety of mainstream digital currencies, the experimental results show that digital currencies with smaller block generation times need to consume less embedding time.