Reviewerly/exHarmony huggingface dataset:
ANALISIS KOMPARATIF UX DESIGN PADA PLATFORM EDUKASI ONLINE

User experience is the result of what the user feels and thinks when using a product or service, and makes the user experience subjective, therefore the user experience (user experience) can be tested in conjunction with other tests to get an objective assessment of the user when can be done directly with the product. This study will discuss the user experience using the User Experience Questionnaire and Focus Group Discussion methods. The process to be carried out is by using the Brainly and Quora applications obtained from the UEQ and FGD test questionnaires through interviews and data interviews from questionnaires. Based on the results of this study, the results of the analysis and comparisons with UEQ and FGD were obtained. The first test objectively used direct testing to respondents using the UEQ questionnaire which has 6 scales, namely: attractiveness, efficiency, perspective, dependability, stimulation and novelty, which are given to 11 respondents. Second, subjective testing using Focus Group Discussion for perceptions and more detailed user problems with interviews. From the second test conducted, it can be denied that the Brainly application is superior to Quora.
API Testing Strategy

API testing is different from other GUI-based application testing. It tests the programmatic interface that allows access to the data or business logic. Instead of testing the application’s look and feel, API testing tests the business logic of the remote software component and its communication mechanism. Hence, API testing is performed using special-purpose software that sends requests to the API and reads the response received.
Quantizing YOLOv5 for Real-Time Vehicle Detection

Autonomous driving has received much attention in the last decade as a key component of intelligent transportation, and vehicle detection serves as a fundamental task for autonomous driving. Although recent learning-based methods have achieved great advances in terms of accuracy, these methods are usually computationally expensive and cannot be deployed on resource-limited devices. To address this limitation, in this paper, we aim to develop an efficient yet highly accurate vehicle detection method by tailoring a network quantization method. Due to the small objects and diverse scenarios in the real world, we introduce learnable scale parameters for network quantization to achieve flexible adaption. Ablation study is conducted to demonstrate the effectiveness of our method. In addition, extensive experiments are conducted on the UA-DETRAC and KITTI datasets for performance evaluation. Compared to previous generic network quantization methods, our method produces significantly better performance. Moreover, our quantized network surpasses previous lightweight vehicle detection methods in terms of both accuracy and efficiency, establishing a new baseline for vehicle detection.
DiffWater: Underwater Image Enhancement Based on Conditional Denoising Diffusion Probabilistic Model

Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality such as color distortion, reduced contrast, and noise. Existing underwater image enhancement (UIE) methods often lack generalization capabilities, making them unable to adapt to various underwater images captured in different aquatic environments and lighting conditions. To address these challenges, a UIE method based on conditional denoising diffusion probabilistic model (DDPM) is proposed (DiffWater), which leverages the advantages of DDPM, and trains a stable and well-converged model capable of generating high-quality and diverse samples. Considering the multiple distortion issues in underwater imaging, unconditional DDPM may not achieve satisfactory enhancement and restoration results. Therefore, DiffWater utilizes the degraded underwater image with added color compensation as a conditional guide, through which the DiffWater achieves highquality restoration of degraded underwater images. Particularly, the proposed DiffWater introduces a color compensation method that performs channel-wise color compensation in the RGB color space, tailored to different water conditions and lighting scenarios, and utilizes this condition to guide the denoising process. In the experimental section, the proposed DiffWater method is tested on four real underwater image datasets and compared against existing methods. Experimental results demonstrate that DiffWater outperforms existing comparison methods in terms of enhancement quality and effectiveness, exhibiting stronger generalization capabilities and robustness
Optimizing the Non-local Means Filtering of CT Images

In this paper a general optimizing procedure is proposed for the non-local means (NLM) filter. It involves finding the optimal degree of smoothing, the size of the search window and the size of the comparison window for a series of Computed Tomography (CT) images. All of them contain Additive White Gaussian Noise (AWGN) with a particular variance and zero mean, both of which are preliminary unknown. Applying the optimization procedure over a single slice from the CT packet appears to be efficient enough in finding the optimal parameters of the filter for the rest of the CT images. Positive results are obtained from filtering a complete set of CT images from a patient’s body and the quality of the filtration is higher than that of the Gaussian and Average filters.
Challenges and limitations of synthetic minority oversampling techniques in machine learning

Oversampling is the most utilized approach to deal with class-imbalanced datasets, as seen by the plethora of oversampling methods developed in the last two decades. We argue in the following editorial the issues with oversampling that stem from the possibility of overfitting and the generation of synthetic cases that might not accurately represent the minority class. These limitations should be considered when using oversampling techniques. We also propose several alternate strategies for dealing with imbalanced data, as well as a future work perspective.
Item analyses of measuring teacher competency among pre-service teaching Chinese as a foreign language teachers

This paper emphasizes that pre-service Teaching Chinese as a Foreign Language (TCFL) teachers must be professionally trained since researchers reported a shortage of qualified TCFL teachers in different countries. This study aims to analyze the instrument for measuring pre-service TCFL teachers ’ competency using the Rasch Measurement Model regarding validity and reliability. It measures five domains: pedagogical knowledge, teaching skills, self-esteem, working attitude, and job motivation. This paper applies a quantitative research design. The researcher distributed the instrument with 70 items to 80 pre-service TCFL teachers. The Rasch Measurement Model is applied to analyzing and examining the psychometric properties by Winsteps version 3.70.0. The reliability based on Cronbach Alpha is 0.92, according to the results of the psychometric characteristics analysis. Positive Point Measure Correlation (PMC) value, infit, and outfit MNSQ value between 0.5 and 1.5, and ZSTD value range from -2.0 to 2.0 are used to assess the instrument’s construct validity. The person and item dependability are, respectively, 0.90 and 1.00. The item separation index has a value of 19.62, and the person separation index has a value of 2.97, both of which are regarded as excellent separation. The conditions for unidimensionality are met by the raw variance explained by measure, 86.7%, and unexplained variance in the first construct, 2.4%. The results demonstrate that the instrument is valid and reliable for future analysis, and the instrument is understandable by the respondents without any major problems. This study could increase public understanding and attention to the value and importance of teacher competency. In addition, a psychometrically teacher competency measuring instrument can examine the level of teacher competency of pre-service TCFL teachers and provide a result of what kind of academic and psychological components they need to improve in the future.
Timeline Binder: A Caption-based Approach for Creating Semantic Video Timeline

Video is an important type of multimedia which has myriad characteristics such as richer content and little prior structure. Major applications such as surveillance, medicine, education, entertainment, and sports heavily use videos. Internet protocol video is dominating the World’s Internet traffic. Large number of videos are made and uploaded daily. Looking for topics of one’s interest in these videos is a cumbersome task. Mostly, people are only interested in small portions of videos as opposed to the whole video. It is better to look for small but related segments of interest. Our proposed system Timeline Binder is an attempt to tackle this problem. For Timeline Binder, we have created our own dataset of online videos with subtitles. Based on those subtitles’ files, a concept vector is obtained. If a concept vector has a match for a user’s query tag, then timed segments for all related videos to that matched concept in the concept vector are combined into a single timeline thus making all related segments into a single video. In this research, we present performance measurements between Timeline Binder and other available applications such as Google2SRT and NR8. The results show that Timeline Binder performs better than Google2SRT and NR8.
Feature Extraction of Assamese Speech Based One Motion Analysis

Objectives: The present work aims to investigate the recognition of emotion from Assamese speech. Methods: This work presents a method based on the Gaussian Mixture Model (GMM) classifier and Mel-frequency cepstral coefficients (MFCC) as feature extraction technique for emotion recognition from Assamese speeches. Findings: We have conducted experiments considering different emotions: Angry, Happy, Neutral and Sad. The speech emotion recognition system database is the emotional speech samples collected manually from 20 speakers and some standard samples available on the internet. The speakers are from different districts of Assam and use different dialects of the Assamese language, such as Western (Kamrupi), Central, and Eastern. They fall under the age group of 18-26 years. The field survey consists of recordings done at Dibrugarh University and outside the campus. After the GMM training and testing process, the accuracy we obtained is 51.25%. The experiments confirmed that angry and happy emotions have high energy in the higher frequency region. In contrast, neutral and sad emotions have low energy in the higher frequency region. Novelty: This work will help predict the attitudes and actions of different speakers based on their manner of speaking. In addition, the present work will also help in other aspects of human-machine interaction in our daily life. The Assamese emotional speech database used in the work is self-collected from different dialect groups to understand the variability of emotions in dialectal perspective. Keywords: Assamese, GMM, emotion, speech, MFCC
Research on Vulnerable Road User Detection Algorithm based on Improved Deep Learning

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This paper proposes a detection algorithm based on deep learning for Vulnerable Road Users such as pedestrians and cyclists, which is improved on the basis of YOLOv5 network model. (1) Aiming at the problems of low resolution and insufficient information for small targets, a multi-scale feature fusion method is adopted to integrate shallow features with deep features. In this way, the effective information of small target is enriched, and the accuracy of target detection is improved. (2) In view of the interference of image noise, background and other factors, the channel attention method is introduced to strengthen key features and suppress the interference of noise, which can improve the model's attention to small targets and enable the network to better identify blocked targets; (3) Aiming at the problem that the computing speed of the model is difficult to achieve real-time performance, a deep separable convolution optimization method is proposed to reduce the amount of computation, so as to reduce the computing time of the model. The experimental results show that compared with the YOLOv5 model in KITTI data set, the precision of the improved model is increased by 1.26%, the mAP is increased by 0.76 percentage points, and the detection speed is increased by 2f/s.&lt;/div&gt;&lt;/div&gt;
Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

Models for text-to-image synthesis, such as DALL-E 2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public. These models are capable of producing high-quality images that depict a variety of concepts and styles when conditioned on textual descriptions. However, these models adopt cultural characteristics associated with specific Unicode scripts from their vast amount of training data, which may not be immediately apparent. We show that by simply inserting single non-Latin characters in the textual description, common models reflect cultural biases in their generated images. We analyze this behavior both qualitatively and quantitatively and identify a model’s text encoder as the root cause of the phenomenon. Such behavior can be interpreted as a model feature, offering users a simple way to customize the image generation and reflect their own cultural background. Yet, malicious users or service providers may also try to intentionally bias the image generation. One goal might be to create racist stereotypes by replacing Latin characters with similarly-looking characters from non-Latin scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we propose a novel homoglyph unlearning method to fine-tune a text encoder, making it robust against homoglyph manipulations.
Unlocking the Potential of Quantum Computing: A Comprehensive Review

Quantum computing represents a revolutionary paradigm shift in data processing, promising to revolutionize industries, solve complex computational problems, and propel scientific discovery to unprecedented heights. This research paper provides a comprehensive overview of the current state of quantum computing technology, its basic principles, and applications. The article begins by explaining the basic principles of quantum mechanics, introducing key concepts such as superposition and entrainment that underlie the enormous computing power of quantum computers. The discussion of quantum bits, or qubits, highlights the unique properties of quantum computers that allow them to perform tasks much faster than classical computers. Research papers cover various quantum computer architectures, gate-based quantum computers, adiabatic quantum computers, and quantum annealing devices. Explores challenges and advances in hardware with attention to quantum error correction and quantum breakthroughs. In addition, this paper explores various quantum algorithms and their applications in domains such as cryptography, optimization, material science, and drug discovery. It reviews progress in algorithm development and emphasizes the practical implications of quantum computing for solving real-world problems. Additionally, the research document examines the current state of the Quantum Computing industry and highlights the key players and their contributions to the field. It also touches on the economic and ethical implications of quantum computers, considering security issues and implications for existing computing paradigms. In summary, this comprehensive review reveals the transformative potential of quantum computers and offers valuable insights into current opportunities, challenges, and future prospects. As quantum computing advances, this paper serves as a valuable resource for researchers, policymakers, and industry leaders who want to understand and harness the power of quantum computing.
Application of Internet of Things and multimedia technology in English online teaching

This study aims to explore the application status of Internet of Things and multimedia technology in English online teaching and its optimization measures. The Internet of Things is a technology that connects objects together and realizes information exchange through the network, while multimedia technology refers to the integration and processing of different forms of information including text, images, sound, video and so on. Firstly, the development status of Internet of Things and multimedia technology at home and abroad, as well as the development trend and demand of online English teaching, are analyzed. Secondly, through a questionnaire survey, data on the teaching methods, content, student feedback, and teachers’ teaching methods and effects in English online teaching were collected. Through in-depth data analysis and empirical research, this paper discusses how to integrate the Internet of Things and multimedia technology to build a more efficient online English teaching model. On this basis, the problems existing in the Internet of Things and multimedia technology in English online teaching are summarized, such as uneven application of technology, lack of targeted teaching design, and insufficient interactivity. In response to these problems, a series of optimization measures are proposed, including balancing the application of technology, personalized teaching design, improving interactivity, cultivating independent learning ability and solving technical problems. Finally, the future development of English online teaching in the application of Internet of Things and multimedia technology is prospected, focusing on technological innovation and application, personalized and intelligent teaching, teaching mode and method innovation, teacher role change and evaluation system construction.
The syntactic side of autonomous categories enriched over generalised metric spaces

Programs with a continuous state space or that interact with physical processes often require notions of equivalence going beyond the standard binary setting in which equivalence either holds or does not hold. In this paper we explore the idea of equivalence taking values in a quantale V, which covers the cases of (in)equations and (ultra)metric equations among others. Our main result is the introduction of a V-equational deductive system for linear {\lambda}-calculus together with a proof that it is sound and complete. In fact we go further than this, by showing that linear {\lambda}-theories based on this V-equational system form a category that is equivalent to a category of autonomous categories enriched over 'generalised metric spaces'. If we instantiate this result to inequations, we get an equivalence with autonomous categories enriched over partial orders. In the case of (ultra)metric equations, we get an equivalence with autonomous categories enriched over (ultra)metric spaces. We additionally show that this syntax-semantics correspondence extends to the affine setting. We use our results to develop examples of inequational and metric equational systems for higher-order programming in the setting of real-time, probabilistic, and quantum computing.
Intelligent Fuzzy Clinical Decision Support System to Classify Breast Cancer—Case Study: The Wisconsin Dataset

The aim of this research work was to design, implement, and validate several fuzzy decision support systems using clusters and dynamic tables. The outcomes were evaluated with related works obtained from the literature for validating the proposed fuzzy inference systems—FISs to classify the Wisconsin breast cancer dataset. Two validation approaches were used in this work. The FISs were trained with distinct input features, according to the studies obtained from the literature. The uniqueness of this study remains in the manner of generating the membership functions and the rule base for the intelligent fuzzy clinical decision support systems. The outcomes showed that the obtained performance metrics in several issues were higher than the achieved outcomes from the literature, demonstrating better precision.
No-Reference Quality Assessment of Extended Target Adaptive Optics Images Using Deep Neural Network

This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric turbulence, tracking error, and photoelectric noise, which can lead to varying degrees of image degradation, making image processing challenging. Currently, assessing the quality and selecting frames of AO images depend on either traditional IQA methods or manual evaluation by experienced researchers, neither of which is entirely reliable. The proposed network is trained by leveraging the similarity between the point spread function (PSF) of the degraded image and the Airy spot as its supervised training instead of relying on the features of the degraded image itself as a quality label. This approach is reflective of the relationship between the degradation factors of the AO imaging process and the image quality and does not require the analysis of the image’s specific feature or degradation model. The simulation test data show a Spearman’s rank correlation coefficient (SRCC) of 0.97, and our method was also validated using actual acquired AO images. The experimental results indicate that our method is more accurate in evaluating AO image quality compared to traditional IQA methods.
Triple Riding and No-Helmet Detection

Two-wheelers or motorcycles are the most used and popular mode of transport all over the world. But, there is a significant risk involved if a motorcycle rider does not follow all the traffic rules. If a rider doesn’t wear helmet, the risk of losing life during an accident is significantly higher. The problem with the existing traffic surveillance system is that it is not so efficient as it involves intervention of humans, whose efficiency may not be ideal, and this has led to increasing road accident rates and fatality rates due to road accidents. Hence, this project aims at solving the problem by automating the task of detecting the riders without helmet and the riders who are triple riding. A traffic video as an input is given to the system. The system recognizes the motorcycles in the traffic scene by using the object detection model which is built using YOLOv3. If the system detects a two-wheeler, then another YOLOv3 object detection model is used for helmet detection. For triple riding detection, the riders are recognized with the YOLOv3 model for person detection and the number of detected riders is counted. If the number of motorcycle riders exceeds two, then it indicates triple riding, and the image of the rule violating motorcyclists is captured, saved and an alarm is buzzed. The system was tested for accuracy on a total of 100 videos. The accuracy of motorcycle detection was 99%, for helmet detection was 92% and no-helmet detection was 97%. The accuracy for one rider is 99%, for two riders is 96% and for three riders is 95%.