Reviewerly/exHarmony huggingface dataset:
Web-Based Digital Tool: A Way Forward for Synchronous and Asynchronous Computer-Mediated Communicative Language Learning

During the advent of the COVID-19 pandemic in 2020, institutions of higher learning were forced to shut down and physical classes were prohibited to curb the spread of the virus. It initiated lecturers and instructors all over the world to start learning and using web-based digital tools to teach their students online. This phenomenon still continues in this COVID-19 endemic period whereby lecturers, including language instructors are beginning to blend their physical classes with online ones via web-based digital tools such as Padlet. Padlet is a digital notice board that is accessible online with features that enable its user to post texts, images, links, audios, videos, documents and comments, as well as interact on a shared "wall" at the same time and at different times. In order words, it affords both synchronous and asynchronous computer-mediated communication (SAS CMC). This paper therefore aims to report a group of undergraduate students' feedback of their experience using Padlet for SAS computer-mediated communicative English language learning. Three themes which emerged from analysis of their written responses are usability, experience and usefulness in which majority of them gave positive comments with reference to the themes. Hence, it can be concluded that web-based digital tools such as Padlet has the potential to enable SAS CMC, increase language learners' interest and motivation to learn the target language and eventually improve their competency in the language.
Formation of an Original Database and Development of Innovative Deep Learning Algorithms for Detecting Face Impersonation in Online Exams

The issue related to the risk of identity impersonation, where one person can be replaced by another in online exam surveillance systems, poses challenges. This study focuses on the effectiveness of detecting attempts of identity impersonation through face substitution during online exams, with the aim of ensuring the integrity of assessments. The goal is to develop facial recognition algorithms capable of precisely detecting these impersonations, training them on a tailored database rather than biased generic data. An original database of student faces has been created. An algorithm leveraging advanced deep learning techniques such as depthwise separable convolution has been developed and evaluated on this database. We achieved very high levels of precision, reaching an accuracy rate of 98% in face detection and recognition.
Histogram-Based Gradient Boosting Tree: A Federated Learning Approach for Collaborative Fault Diagnosis

Data-driven approaches have been widely applied in fault diagnosis. However, having sufficient labels to effectively develop fault diagnosis models in real-world manufacturing scenarios has remained a challenge. The key issue is to break down data silos and expand the source of data availability while safeguarding data privacy. To address abovementioned gap, a federated learning model using histogram-based gradient boosting tree, is proposed for collaborative fault diagnosis under privacy-preserving. During model training process, only an update of relevant local model parameters is communicated, thus protecting data privacy while contributing to the increased accuracy of the global model. Meanwhile, a data compensation mechanism is introduced to alleviate the disparity problem in data volume across participants. Two case studies are carried out and further compared with traditional situations to reveal the effectiveness of the proposed method. It is envisioned that this developed method can be useful for industrial practitioners to integrate their data for model training while maintaining data privacy, and promoting collaboration in digital manufacturing.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening

Image-text retrieval is a fundamental task to model a connection between images and natural language. Under its flourishing development in performance, most current methods suffer from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -related time complexity, which hinders their application in practice to a certain extent. Targeting efficiency improvement, we propose a simple and effective keyword-guided pre-screening framework for image-text retrieval. Specifically, we convert the image and text data into keywords and perform keyword matching across the modalities to exclude a large number of irrelevant gallery samples prior to the retrieval network. For the keyword prediction, we transfer it into a multi-label classification problem and propose a multi-task learning scheme by appending the multi-label classifiers to the image-text retrieval network to achieve a lightweight and high-performance keyword prediction. For keyword matching, we introduce the inverted index from the search engine and thus create a win-win situation on both time and space complexities for the pre-screening. Extensive experiments on the two widely-used datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., Flickr30K and MS-COCO, verify the effectiveness of the proposed framework. The proposed framework equipped with only two embedding layers achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1) querying time complexity, while improving the retrieval efficiency and maintaining performance, when applied prior to the common image-text retrieval methods.
Knowledge Distillation Based Semantic Communications For Multiple Users

Deep learning (DL) has shown great potential in revolutionizing the traditional communications system. Many applications in communications have adopted DL techniques due to their powerful representation ability. However, the learning-based methods can be dependent on the training dataset and perform worse on unseen interference due to limited model generalizability and complexity. In this paper, we consider the semantic communication (SemCom) system with multiple users, where there is a limited number of training samples and unexpected interference. To improve the model generalization ability and reduce the model size, we propose a knowledge distillation (KD) based system where Transformer based encoder-decoder is implemented as the semantic encoder-decoder and fully connected neural networks are implemented as the channel encoder-decoder. Specifically, four types of knowledge transfer and model compression are analyzed. Important system and model parameters are considered, including the level of noise and interference, the number of interfering users and the size of the encoder and decoder. Numerical results demonstrate that KD significantly improves the robustness and the generalization ability when applied to unexpected interference, and it reduces the performance loss when compressing the model size.
Hyperparameters Optimization of Deep Learning Models for Unsupervised Lung Cancer Detection

Lung cancer remains a significant global cause of mortality, affecting populations worldwide. Deep Learning (DL) systems show promise in early detection using clinical data to reduce mortality rates. However, these systems heavily rely on large amounts of annotated anomalous data, and heavily depends on selecting appropriate hyperparameters that define the network’s structure and learning process.In this study, we propose an optimization scheme based on Tree Parzen Estimator (TPE) and Bayesian optimization (BO) algorithms for hyperparameter optimization in unsupervised lung cancer detection. First, we used a fast residual attention GAN-based model. Then, we employed the Mixup consistency regularization technique to encourage the discriminator to attend the pixel-level details of the input data. Furthermore, a new cost function for the discriminator is defined based on the mixup to enhance the output. This study is evaluated in the context of lung cancer detection. When compared to empirical optimization, both TPE and Bayesian optimization demonstrate significant improvements in the precision of the fast Residual Attention GAN model used in this study. Various metrics, including precision, f1-score, and the area under the curve (AUC) are employed to assess the system’s efficiency.
Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System (DUSX) Images for Automated Kidney Stone Detection

In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT and US. Among these imaging techniques, is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer-aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R-CNN architectures, which are common CNN-based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R-CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1-score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans.
Pipelined Ensemble Architecture for Mortality Prediction on MIMIC III

Automated healthcare decision support has seen a huge rise with the improved data collection models in hospitals and also improved machine learning based techniques that exhibit high possibilities for automation. Automating healthcare systems as an aide for clinical practitioners can ensure fast and more accurate results for the patients and can also aid in hospital administration. Mortality prediction has been one of the major and critical factors that determines the type of treatment and the level of resources that has to be allocated for a patient. This work presents a pipelined ensemble architecture that can be used for effective prediction of mortality levels of a patient. The pipeline model has been designed in multiple levels to ensure improvement of quality of the medical data and effective prediction. The pipelined architecture model has been compared with existing state-of-the-art model, and the results indicate high performance with 92% accuracy levels, ensuring the model is suitable for use in real time mortality prediction.
Sentiment Analysis of Nepali COVID-19 Tweets using BERT-LSTM

The global impact of COVID-19 has significantly reshaped the day-by-day lives of individuals worldwide. COVID-19 is one of the top deadly diseases and has tragically claimed the lives of millions across the globe. The people are affected not only by the physical infection but also mentally. Among the various social media platforms, Twitter is a widely utilized medium, reflecting a substantial surge in discussions about the coronavirus. These discussions encompass a spectrum of positive, negative, and neutral sentiments. The sentiments acknowledged by individuals, encapsulated in their posts and tweets across this platform, offer valuable insights into their emotional states and perspectives. In this investigation, the people's sentiments using Nepali COVID-19-related Twitter datasets are inspected. For this, the approach involves a two-step process. Initially, the multilingual BERT(m-bert) model will utilize whose output is used for subsequent downstream tasks. Secondly, m-BERT's output is connected to the LSTM layer to categorize the people's sentiments. The model was trained and tested using publically available NepCov19Tweets datasets. These tweets were split into three groups (positive, negative, and neutral). The appraisal outcomes for NepCOV19Tweets demonstrate that the proposed model comes up with outstanding performance when compared to the existing model, achieving an average accuracy of 76.04%, 80.03% recall, a precision of 77.12%, and an F1-score of 76%.
Outlier Identification and Reconstruction for Photovoltaic Power Data with High Proportion of Outliers

Currently, the authenticity of historical data on photovoltaic power is compromised due to artificial power restrictions and equipment failure during measurement and communication. To address this issue and ensure reliable follow-up research, this paper proposes a method for identifying and reconstructing outliers in photovoltaic power data with high outlier proportions. Initially, the characteristics of PV output power and its influencing factors are analyzed, leading to the classification of outlier types. Next, various basic outlier identification models are integrated using the LSCP framework, along with a local selection strategy to improve sensitivity to local outliers. Thereafter, lightweight gradient boosting machine-based reconstruction models are established for discontinuous and continuous outliers, and their hyperparameters optimized using a random search algorithm. Finally, verification at a photovoltaic power station with a high proportion of outliers demonstrates that the proposed algorithm can identify and repair outliers in an accurate and cost-effective manner.
RETRACTED: Joshi et al. An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices. Mathematics 2023, 11, 1675

The journal and authors retract the article entitled “An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices” [...]
Wireless Sensor Networks for Sports Injury Management and Player Safety Enhancement

Injuries in sports may be better managed, and player safety can be improved by using wireless sensor networks. All aspects of a player's performance may be tracked in realtime because of these networks' capacity for collecting, analyzing, and communicating data. Heart rate, body temperature, acceleration, and joint movement are just a few of the physiological and biomechanical factors investigated in this paper and relates to the use of wearable sensors and smart devices to monitor these variables. The data are sent wirelessly to a central system where sophisticated analytics are used to identify risks for injuries, track trends in performance, and allow for prompt action. Given the potential benefits to players' health and performance, the proposed system might bring to sports medicine, it's easy to view why it's getting so much attention. It suggests wireless sensor networks and the Internet of Things may work together to improve sports safety and injury prevention.
R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video

&lt;abstract&gt; &lt;p&gt;The security of civilians and high-profile officials is of the utmost importance and is often challenging during continuous surveillance carried out by security professionals. Humans have limitations like attention span, distraction, and memory of events which are vulnerabilities of any security system. An automated model that can perform intelligent real-time weapon detection is essential to ensure that such vulnerabilities are prevented from creeping into the system. This will continuously monitor the specified area and alert the security personnel in case of security breaches like the presence of unauthorized armed people. The objective of the proposed system is to detect the presence of a weapon, identify the type of weapon, and capture the image of the attackers which will be useful for further investigation. A custom weapons dataset has been constructed, consisting of five different weapons, such as an axe, knife, pistol, rifle, and sword. Using this dataset, the proposed system is employed and compared with the faster Region Based Convolution Neural Network (R-CNN) and YOLOv4. The YOLOv4 model provided a 96.04% mAP score and frames per second (FPS) of 19 on GPU (GEFORCE MX250) with an average accuracy of 73%. The R-CNN model provided an average accuracy of 71%. The result of the proposed system shows that the YOLOv4 model achieves a higher mAP score on GPU (GEFORCE MX250) for weapon detection in surveillance video cameras.&lt;/p&gt; &lt;/abstract&gt;
RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research

In this article, we present RISE—a R obotics I ntegration and S cenario-Management E xtensible-Architecture—for designing human–robot dialogs and conducting Human–Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.
A Study on Students' Perception: Enhancing Speaking Skill for ESL Learners through YouTube

Technological advancement with its extensive use in every field of life has also encouraged instructors to apply innovative techniques in enhancing English speaking skills. Vlogs, tutorials and lectures are the latest technique which is gaining popularity. The current research intends to explore the perceptions of ESL students towards the use of vlogs, tutorials and lectures on YouTube. The researchers used a cross-sectional survey by adopting qualitative and quantitative procedures. The population of the study comprised ESL students studying in the Virtual University of Lahore and 100 ESL students were selected as sampling. The data were collected through a close-ended questionnaire, later analysed comprehensively and presented in tables and figures. The findings of the study illustrated that ESL students have shown a positive inclination towards the use of vlogs, tutorials and lectures on YouTube for enhancing speaking skills. In the end, the researchers also recommended some suggestions for future researchers.
CHALLENGES OF ONLINE STUDYING: THE THEORY AND THE PRACTICE

In the article we’d the aim to study challenges of on-line studying, to show the technical characteristics of online learning, to single out the pedagogical and psychological features of online learning. So, we described teaching method which are in a great degree enriched by the integration of information technologies into them; also, we defined the concept of “information and communication technology”; we showed the most commonly used information and communication technology’s method for assessing learner’s listening skills and giving feedback; we presented the field of information and different communication technologies.&#x0D; The technical characteristics of online learning are: virtuality / conventionality of educational space and time; interactivity of educational methods and non-linearity of the communicative process, dispersion of its structure, synchronicity of communicative acts; the presence of an electronic signal, which determines the possibility of constant access to the Internet; distance and indirectness of online learning; preservation of the transcript of the educational process, the opportunity for teachers and students to reflect on their own activities; ease of updating and changing the content of text material and perceived educational material, which determine the efficiency of students’ performance of tasks and the ways of solving problems; more effective process of subject-subject interaction, openness in communication; conditional socialization; the possibility of simultaneous electronic search and syndication and the process of perceiving new information.&#x0D; We also singled out the pedagogical and psychological features of online learning, such as: significant limitation of the person’s sensory experience to visual and auditory ones; a sense of a space and time, which is more different from a real image of the world; the presence of students in a state of continuous distracted attention, the feeling of being constantly overloaded with unnecessary educational material; the appearance of negative emotions in the case of rather limited access to wider cyberspace; the implementation of permanent recording of the results of cyber activity; multifacetedness and multiplicity of personality, virtual identity, disinhibition of “online self”; alignment of personal statuses and profiles, unlimited access to various interactions; change of personal priorities and behavior, feeling of complete “browsing”, “web surfing”, “filter bubble”.
How Do Internet Slang and Memes Affect Chinese Teenagers?

An increasing number of young people in this era of new media have access to the web, which is both simpler and more convenient to use. Internet users have contributed to the emergence of a new language and cultural phenomenon known as Internet slang and memes. Previous studies have addressed and evaluated the many spread phenomena of Internet slang and memes in the last decade, but there has been little focus given to teenagers, despite the fact that this demographic makes up an increasingly large proportion of Internet users. The influence that Internet slang and memes have on Chinese teenagers is the focus of this research. The research question is to analyze how the characteristics of online slang and memes affect Chinese teenagers. A review of the relevant literature and analysis of case studies form the basis of the research method. The research concludes that Internet slang and memes, with their simplicity, diversity, and sustainability, have a greater negative impact on teenagers language expression, perception of thinking ways, and mental health. The most feasible strategy to address the issue is the implementation of proactive steps by regulatory bodies to regulate and improve the digital environment. Parents and educators should take a balanced and patient approach to guiding teenagers to develop competence in utilizing language in a standardized manner.