Reviewerly/exHarmony huggingface dataset:
The Effects of Background Music on The Screen-Based Reading Material Among University Students: An Eye Tracking Study

Reading performance is a crucial skill that can predict success in education. Many factors can influence reading performances, such as background music and perceiving the sentences while reading. This study explores the effects of music on screen-based reading materials among health sciences university students. One hundred twelve participants from The National University of Malaysia, Kuala Lumpur Campus, participated in this study. Participants were asked to read and understand the general theme text given on the computer screen. The participants were segregated into two groups according to their preferred way of reading, either music or non-music. Then, participants in the music group randomly listened to rap or classic music (rap = 86 BPM or classic = 161 BPM) using a Sony headphone (MDR-XB450AP) while reading. The reading duration was recorded, and reading comprehension was determined by the correct answers to four questions based on the text. A webcam-based GazeRecorder software was used to measure the eye-tracking metrics (dwell time, first view time, and number of AOI views) on the area of interest (AOI). Reading speed is significantly associated with reading comprehension (rs = -0.1933, p = 0.0412). Reading duration, reading speed, and reading comprehension were not considerably different while listening to and not listening to music. The results show a similar pattern in all eye-tracking metrics. However, the reading duration while listening to rap music (2.68 ± 1.94 minutes) was significantly shorter (U = 299.5, p = 0.02642) compared to classical music (3.42 ± 1.91 minutes). Moreover, reading speed is significantly higher (U = 299.5, p = 0.02642) while listening to rap music (139.38 ± 102.96 wpm) as compared to classical music (109.47 ± 51.51 wpm). Music seems not to affect reading performance and eye-tracking metrics. Despite this, rap music can improve reading performance by reducing the reading duration and increasing reading speed.
DESIGN OF KIP KULIAH SELECTION SYSTEM AND RECIPIENT DETERMINATION USING SUPPORT VECTOR MACHINE (SVM)

KIP Kuliah is tuition assistance from the government for high school graduates or equivalent with good academic potential but has economic limitations. In recent years it has been seen that the Indonesian government has always tried to increase the quota for KIP Kuliah recipients. In this study, the Support Vector Machine (SVM) method was applied to create a system for selecting and determining KIP Kuliah recipients. To obtain the best model to be used in the system, the training and testing data are divided into three data distribution schemes, namely 60/40, 70/30, and 80/20. After the training and testing process was carried out using the SVM method with various parameter variations, then the best accuracy rate of 94.59% is obtained in the 80/20 data sharing scheme for the nonlinear SVM model with the RBF kernel. With this system, it is hoped that the KIP Kuliah selection process at the tertiary level can run effectively, efficiently and the results of the determination are more targeted.
Dual Cross-Attention Multi-Stage Embedding Network for Low-Light Image Enhancement

The low-light image enhancement task aims to improve the visibility of information in the dark to obtain more data and utilize it, while also improving the visual quality of the image. In this paper, we propose a dual cross-attention multi-stage embedding network (DCMENet) for fast and accurate enhancement of low-light images into high-quality images with high visibility. The problem that enhanced images tend to have more noise in them, which affects the image quality, is improved by introducing an attention mechanism in the encoder–decoder structure. In addition, the encoder–decoder can focus most of its attention on the dark areas of the image and better attend to the detailed features in the image that are obscured by the dark areas. In particular, the poor performance of the Transformer when the dataset size is small is solved by fusing the CNN-Attention and Transformer in the encoder. Considering the purpose of the low-light image enhancement task, we raise the importance of recovering image detail information to the same level as reconstructing the lighting. For features such as texture details in images, cascade extraction using spatial attention and pixel attention can reduce the model complexity while the performance is also improved. Finally, the global features obtained by the encoder–decoder are fused into the shallow feature extraction structure to reconstruct the illumination while guiding the network for the focused extraction of information in the dark. The proposed DCMENet achieves the best results in both objective quality assessment and subjective evaluation, while for the computer vision tasks working in low-light environments as well, the enhanced images using the DCMENet proposed in this paper show the best performance.
On The Blind Denoising Efficiency of Image Denoising Algorithms Through Robustness, Image Quality and Computational Burden

The main goal of the image denoising is to recover the original image while attaining the structure of the image as much as possible. When the image denoising task is blind, we have no a priori information about the original image. Thus, we cannot measure the degradation level in the image directly; instead, noise variance can be estimated by the denoising algorithm. According to the estimated value, denoising is performed. Such algorithms are supposed to be robust to varying and high levels of noise interference. Moreover, in time-constrained real-world applications, they must balance the tradeoff between image quality and computation time. In this study, we assess the performance of the image denoising algorithms armored for these goals. We are aimed to determine the optimal performance yielded by such algorithms and the noise bounds wherein each algorithm is superior. After the experimental work, important conclusions are drawn.
Appendix B: Processing Libraries for Android

Processing can be extended with libraries written by members of the community to add new functionality. This chapter goes over some of the libraries available for the Android mode and ends with a guide on how we can create our own libraries.
Live Wallpapers

After exploring the details of drawing and interaction with Processing for Android, we will conclude the second part of the book with this chapter on live wallpapers. We will learn how to load images from the camera to create a gallery wallpaper and then how to use particle systems to implement an animated background for our phone or tablet.
Getting Started with the Android Mode

In this chapter, we will introduce the Processing software and its Android mode, the community behind these projects, and how we can start using Processing to create apps for Android devices.
Touchscreen Interaction

This chapter goes into the details of the touchscreen support in Processing for Android. We will learn how to capture single- and multi-touch events in our sketch; how to process these events to implement touch-based interactions such as selection, scrolling, swipe, and pinch; as well as how to use the virtual keyboard.
Creating XR Experiences

In this final chapter, we will go step by step through in the development of a full-featured VR drawing app with Processing for Android, as an example of using Processing to create interactive experiences in "Extended Reality." We will apply all the techniques we have learned so far, including gaze-controlled movement and user interface in immersive environments.
Basics of AR

After VR, the next stop in our journey through Extended Reality is AR. As with VR, Processing for Android includes a library allowing us to easily code AR apps for users to interact with virtual objects placed in real space.
Reading Sensor Data

Android devices are capable of capturing data from the physical world using many different sensors, including accelerometer, gyroscope, and GPS. In this chapter, we will learn how to access this data from our Processing sketch using the Android SDK and the Ketai library.
Appendix A: Gradle and Android Studio Integration

In this appendix, we will learn to export Processing sketches as Gradle projects that we can then compile from the command line or import into Android Studio.
Visualizing Physical Activity

In this chapter, we will go over some of the body sensors available on smartwatches, and wearable devices in general, and will learn the techniques we have at our disposal to read and use data from these sensors in real time.
Basics of VR

In this chapter, we will learn some of the basic techniques to create VR apps with Processing. These techniques cover object selection, interaction, and movement in VR space, and using eye coordinates to create static frames of reference to facilitate user experience.
Driving Graphics and Sound with Sensor Data

With the basics of reading sensor data already covered in the previous chapter, we will now learn how to use data from the accelerometer, magnetometer, and gyroscope to generate interactive graphics and sound in our Processing sketches.
Improving Pathology Localization: Multi-series Joint Attention Takes the Lead

Automated magnetic resonance imaging (MRI) pathology localization can significantly reduce inter-reader variability and the time expert radiologists need to make a diagnosis. Many automated localization pipelines only operate on a single series at a time and are unable to capture inter-series relationships of pathology features. However, some pathologies require the joint consideration of multiple series to be accurately located in the face of highly anisotropic volumes and unique anatomies. To efficiently and accurately localize a pathology, we propose a Multi-series jOint ATtention localization framework (MOAT) for MRI, which shares information among different MRI series to jointly predict the pathological location(s) in each MRI series. The framework allows different MRI series to share latent representations with each other allowing each series to get location guidance from the others and enforcing consistency between the predicted locations. Extensive experiments on three knee MRI pathology datasets, including medial compartment cartilage (MCC) high-grade defects, medial meniscus (MM) tear and displaced fragment/flap (DF) with 2729, 2355, and 4608 studies respectively, show that our proposed method outperforms the state of the art approaches by 3.4 to 8.0 mm on L1 distance, 6 to 27% on specificity and 5 to 14% on sensitivity across different pathologies.
Runtime Monitoring DNN-Based Perception

Deep neural networks (DNNs) are instrumental in realizing complex perception systems. As many of these applications are safety-critical by design, engineering rigor is required to ensure that the functional insufficiency of the DNN-based perception is not the source of harm. In addition to conventional static verification and testing techniques employed during the design phase, there is a need for runtime verification techniques that can detect critical events, diagnose issues, and even enforce requirements. This tutorial aims to provide readers with a glimpse of techniques proposed in the literature. We start with classical methods proposed in the machine learning community, then highlight a few techniques proposed by the formal methods community. While we surely can observe similarities in the design of monitors, how the decision boundaries are created vary between the two communities. We conclude by highlighting the need to rigorously design monitors, where data availability outside the operational domain plays an important role.
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking

Parametric verification of linear temporal properties for stochastic models requires to compute the satisfaction probability of a certain property as a function of the parameters of the model. Smoothed model checking (smMC) [8] infers the satisfaction function over the entire parameter space from a limited set of observations obtained via simulation. As observations are costly and noisy, smMC leverages the power of Bayesian learning based on Gaussian Processes (GP), providing accurate reconstructions with statistically sound quantification of the uncertainty. In this paper we propose Stochastic Variational Smoothed Model Checking (SV-smMC), which exploits stochastic variational inference (SVI) to approximate the posterior distribution of the smMC problem. The strength and flexibility of SVI, a stochastic gradient-based optimization making inference easily parallelizable and enabling GPU acceleration, make SV-smMC applicable both to Gaussian Processes (GP) and Bayesian Neural Networks (BNN). SV-smMC extends the smMC framework by greatly improving scalability to higher dimensionality of parameter spaces and larger training datasets, thus overcoming the well-known limits of GP.
Face Mask Detection, Temperature Scanning, and Sanitization Automation

The unprecedented pandemic caused by COVID-19 led to several deaths and affected millions of people. The effective way of preventing the spread of this virus is wearing a face mask in public places. To ensure the safety of everyone, everything around us is expected to be contactless. In this paper, we developed a Raspberry Pi-based model that prevents the spread of this virus by detecting the people who are not wearing face masks and by checking the temperature of their body. A computerized hand sanitization is also performed. The model is developed using deep learning approaches and Python modules such as OpenCV, TensorFlow, and Keras. This model can be used to inspect people at public places like universities, schools, movie theaters, malls, etc.
ANALISIS KEPERLUAN PEMBANGUNAN LAMAN WEB TATABAHASA BAHASA TAMIL TAHUN EMPAT SJKT [NEED ANALYSIS FOR THE DEVELOPMENT OF TAMIL GRAMMAR WEBSITE YEAR FOUR SJKT]

The teacher’s ability to diversify teaching materials and activities can help students in the process of learning Tamil grammar. This is so because the aspect of grammar is one of the important elements that must be learned and mastered by the student. Therefore, teachers should be creative and innovative in diversifying the design of teaching and learning materials in school. In relation to that, the aim of this study is to analyses the teacher’s needs for the development of the Year Four SJKT Tamil grammar website. The development of a grammar website can be a medium for independent grammar learning because it can be accessed via the internet outside of the classroom. Theory constructivism was used in the development of the grammar website. A needs analysis using a questionnaire was conducted on 230 teachers of the Tamil language subject Year Four SJKT in Peninsular Malaysia. The data obtained from the inquiry questions were analysed through descriptive statistics using the Statistical Package for Social Science (SPSS) version 26. In conclusion, the development of Year Four Tamil grammar website can help in increase student’s interest and achievement in learning Tamil grammar. The implication is that learning based on grammar websites is an interactive learning process that can improve the effectiveness of self-learning. In addition, this website complements teaching at school because it creates a fun learning environment.
PassSum: Leveraging paths of abstract syntax trees and self‐supervision for code summarization

Abstract Code summarization is to provide a high‐level comment for a code snippet that typically describes the function and intent of the given code. Recent years have seen the successful application of data‐driven code summarization. To improve the performance of the model, numerous approaches use abstract syntax trees (ASTs) to represent the structural information of the code, which is considered by most researchers to be the main factor that distinguishes code from natural language. Then, such data‐driven methods are trained on large‐scale labeled datasets to obtain a model with strong generalization capabilities that can be applied to new examples. Nevertheless, we argue that state‐of‐the‐art approaches suffer from two key weaknesses: (1) inefficient encoding of ASTs; (2) reliance on a large labeled corpus for model training. As a result, such drawbacks lead to (1) oversized model, slow training, information loss and instability; (2) inability to be applied to programming languages with only a small amount of labeled data. In light of these weaknesses, we propose PassSum, a code summarization approach that addresses the aforementioned weaknesses via (1) a novel input representation which contains an efficient AST encoding method; (2) introducing three pretraining objectives and pretraining our model with a large amount of (easy‐to‐obtain) unlabeled data under the guidance of self‐supervised learning. Experimental results on code summarization for Java, Python, and Ruby methods demonstrate the superiority of PassSum to state‐of‐the‐art methods. Further experiments demonstrate that the input representation we use has both temporal and spatial advantages in addition to performance leadership. In addition, pretraining is also shown to make the model more generalizable with less labeled data, and also to speed up the convergence of the model during training.
D-SAV360: A Dataset of Gaze Scanpaths on 360° Ambisonic Videos

Understanding human visual behavior within virtual reality environments is crucial to fully leverage their potential. While previous research has provided rich visual data from human observers, existing gaze datasets often suffer from the absence of multimodal stimuli. Moreover, no dataset has yet gathered eye gaze trajectories (i.e., scanpaths) for dynamic content with directional ambisonic sound, which is a critical aspect of sound perception by humans. To address this gap, we introduce D-SAV360, a dataset of 4,609 head and eye scanpaths for 360° videos with first-order ambisonics. This dataset enables a more comprehensive study of multimodal interaction on visual behavior in virtual reality environments. We analyze our collected scanpaths from a total of 87 participants viewing 85 different videos and show that various factors such as viewing mode, content type, and gender significantly impact eye movement statistics. We demonstrate the potential of D-SAV360 as a benchmarking resource for state-of-the-art attention prediction models and discuss its possible applications in further research. By providing a comprehensive dataset of eye movement data for dynamic, multimodal virtual environments, our work can facilitate future investigations of visual behavior and attention in virtual reality.
Cross-Blockchain Technology for an Interoperable and Scalable Digital Contact Tracing

The COVID-19 pandemic emphasizes the significance of contact tracing for virus control but raises privacy concerns. Blockchain technology offers potential solutions, yet challenges exist for safeguarding sensitive information and enabling interoperability with other chains. This research explores using Polkadot's cross-blockchain feature for decentralized and privacyoriented contact tracing. Our proposed solution stores personal data on a private blockchain, accessible to authorized entities only. Encryption ensures data security. Additionally, the Polkadot network's interoperability enables sharing data with health authorities or other blockchain networks. This study demonstrates the benefits and limitations of cross-blockchain contact tracing, urging further research and development. An effective and privacy-respecting contact tracing solution is attainable with the right approach.
Optimizasyon Problemleri için Gelişmiş Salp Sürüsü Algoritması

Salp Swarm Algorithm (SSA) is metaheuristic optimization algorithm inspired by the biological characteristics and colony strategies of salp swarms. There are a wide variety of studies conducted with SSA in the literature. In these studies, it was also emphasized that SSA has very critical main disadvantages. The most important of these disadvantages is the imbalance of exploration and exploitation. In this study, an equilibrium operator is developed using the ikeda chaotic map. Thanks to this improvement, the performance of the SSA algorithm has been increased and early convergence and stuck to local optima problems has been overcome. To evaluate the success of the proposed method, ten different fixed dimension benchmark problems and three popular engineering design optimization problems are solved. The reliability of the proposed method has been verified by comparing it with four well-known metaheuristic approaches and the original SSA. Experimental study results confirmed that the proposed method outperforms the compared methods.
Expert System for Corn Disease Identification Using Case Based Reasoning Method

Corn is the second type of food after rice. However, currently, the level of corn productivity is experiencing problems, with pests and corn diseases. The process of controlling these pests and diseases, if not handled as early as possible, will result in crop failure for corn farmers. Identifying the types of pests and diseases that attack corn plants, is carried out by experts in the field of agriculture, but this process requires quite a long time. Therefore, we need a system that can help farmers diagnose diseases in corn plants, so that the control process can be carried out optimally, quickly and on target. This study aims to build an expert system to identify diseases in corn plants by implementing the Case-Based Reasoning (CBR) method. CBR is a reasoning method on a computer that utilizes old cases to solve new cases. The process of identifying the type of disease with the CBR method is carried out by the user inputting the symptoms experienced by the corn plant into the system, then the system calculates the value of similarity between new cases and old cases using the nearest neighbor method. The system is made with 17 diseases and 56 symptoms, each symptom has a weight. Based on the test results, shows that the system can identify the types of diseases in corn plants following the rule of 100% with a similarity accuracy rate of 75.00%.
Klasifikasi Efektivitas Kebijakan Relokasi Pusat Jajanan Kota Sukabumi Menggunakan Algoritma Naïve Bayes Classifier

The program that is being discussed in Sukabumi City is the construction and arrangement of Jalan Ir. H. Juanda or “Dago” as a hawker center for the City of Sukabumi accompanied by a policy of relocating street vendors to Jalan Dewi Sartika. The effectiveness of this policy is still questionable because it has an unfavorable impact and creates polemics for various parties and there is no research that measures the efficiency and effectiveness of implementing the street vendor relocation policy. This research was conducted to classify and measure the effectiveness of the relocation policy for hawker centers in Sukabumi City based on the classification of the opinions of the people of Sukabumi City and traders in the relocation area by creating a system that can measure the effectiveness of the policy using the Naïve Bayes Classifier algorithm with the Knowledge Discovery in Database research method. The Naïve Bayes Classifier algorithm is a data mining algorithm that applies Bayes's Theorem [1]. The final result of this research is that from 368 opinion data, 52.45% or 193 opinions are classified as "Effective" and 47.55% or 175 opinions are classified as "Ineffective" with an accuracy value of 97.02%. The policy of relocating hawker centers in Sukabumi City can be concluded as Effective, because the Effective value is greater than the Ineffective value even though it has a very thin difference in value with a very high accuracy value