Reviewerly/exHarmony huggingface dataset:
Spectral Ranking Inferences based on General Multiway Comparisons

This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a very general and more realistic setup in which the comparison graph consists of hyper-edges of possible heterogeneous sizes and the number of comparisons can be as low as one for a given hyper-edge. Such a setting is pervasive in real applications, circumventing the need to specify the graph randomness and the restrictive homogeneous sampling assumption imposed in the commonly-used Bradley-Terry-Luce (BTL) or Plackett-Luce (PL) models. Furthermore, in the scenarios when the BTL or PL models are appropriate, we unravel the relationship between the spectral estimator and the Maximum Likelihood Estimator (MLE). We discover that a two-step spectral method, where we apply the optimal weighting estimated from the equal weighting vanilla spectral method, can achieve the same asymptotic efficiency as the MLE. Given the asymptotic distributions of the estimated preference scores, we also introduce a comprehensive framework to carry out both one-sample and two-sample ranking inferences, applicable to both fixed and random graph settings. It is noteworthy that it is the first time effective two-sample rank testing methods are proposed. Finally, we substantiate our findings via comprehensive numerical simulations and subsequently apply our developed methodologies to perform statistical inferences on statistics journals and movie rankings.
Chatbots in Healthcare

Abstract: AI automated medical chatbots are conversationally built with technology, with the potential to reduce efforts to healthcare costs and help people with basic medical remedies that help to self diagnose. We built a diagnosis bot that engages patients in the conversation for their medical query and problems to provide with medical aid based on their symptoms and profile. Our chatbot system is qualified to identify symptoms from user inputs. Based on the symptoms and severity, the bot identifies the patient’s problem and suggests a natural medicinal recovery, if the condition is severe then it suggests a meeting with the doctor. This determines that a medical chatbot can provide a somewhat accurate diagnosis to patients with simple symptom analysis and an effective conversational approach. Moreover, the relative effectiveness of this bot indicates that more automated medical products may flourish to serve a bigger role in healthcare.
THE USE OF TED TALKS VIDEOS AS LEARNING MATERIALS IN AN INTERMEDIATE LISTENING CLASS IN A PRIVATE UNIVERSITY IN YOGYAKARTA

Listening has a crucial role in developing communication skills in English as Foreign Language (EFL) context. Facilitating EFL listening activities can be conducted by using various resources such as online video providers, interactive websites, and web-based podcasts.TED Talk is one of the online video series that can be used for undergraduate students to develop their listening comprehension in learning EFL. This study aimed to investigate the use of TED Talk videos in developing EFL listening comprehension and the benefits of listening to TED Talk videos. The study was conducted in an Intermediate Listening Class at a Private University in Yogyakarta. The research subjects were 27 students. The researchers collected data by distributing questionnaires and conducting in-depth interviews with the students. Purpose sampling was employed to result in 3 students. Four students with the highest and lowest results were chosen to be examined thoroughly. The result showed that TED Talk videos helped to develop EFL listening comprehension even though not significant. Listening to TED Talk videos can also motivate undergraduate students to learn English, and can enrich the students’ general knowledge.
Augmented Reality for Teaching Storytelling in a Rural Foundation Phase Primary School: Integrating a Place-Based Approach

Augmented reality (AR) has recently become a new leading edge in mobile-assisted language learning attributed to the popularity of smartphones. The place-based approach (PBA) is a situated, context-rich teaching and learning modality characterized by its relationship to place. AR and PBA have proven to be useful in educational settings; however, few studies have observed them being used jointly. This study aims to explore the combination of AR and PBA in teaching storytelling in a foundation phase (FP) rural primary school. Based on current trends, the study also aims to design learning principles for teaching that will help language teachers integrate this innovative technology and approach into literacy teaching. The study was tested with two FP teachers and 12 FP learners in a real classroom and carried out with qualitative observations and semi-structured interviews. The findings reveal that learners are more motivated and interested in activities that have been enhanced because they encourage problem-solving, discovery, and socialization. The findings also suggest that technological interventions, particularly when learners work as a team, may have a bigger impact than non-technology-integrated instruction. These results offer demonstrable support for technology use and have useful results for future practice.
Strengthening Spatial Relations to Multi-scale Features for Few-Shot Learning

Few-Shot Learning (FSL) is challenging due to limited training samples, hindering Convolutional Neural Networks (CNNs) from capturing discriminative object features. Recent approaches combine transfer learning and meta-learning, pre-training feature backbones on labeled base data and fine-tuning on novel data. In this work, we propose a CNN architecture with a cross-scale view of objects and introduce a spatial attention module to mitigate the impact of global average pooling. Our method achieves state-of-the-art results on standard benchmark for the cross-domain (CDFSL) few-shot task, demonstrating its effectiveness.
NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology.
Event-based Blur Kernel Estimation For Blind Motion Deblurring

Motion blur can significantly reduce the quality of images, and researchers have developed various algorithms to address this issue. One common approach to deblurring is to use deconvolution to cancel out the blur effect, but this method is limited by the difficulty of accurately estimating blur kernels from blurred images. This is because the motion causing the blur is often complex and nonlinear. In this paper, a new method for estimating blur kernels is proposed. This method uses an event camera, which captures high-temporal-resolution data on pixel luminance changes, along with a conventional camera to capture the input blurred image. By analyzing the event data stream, the proposed method estimates the 2D motion of the blurred image at short intervals during the exposure time, and integrates this information to estimate a variety of complex blur motions. With the estimated blur kernel, the input blurred image can be deblurred using deconvolution. The proposed method does not rely on machine learning and therefore can restore blurry images without depending on the quality and quantity of training data. Experimental results show that the proposed method can estimate blur kernels even for images blurred by complex camera motions, outperforming conventional methods. Overall, this paper presents a promising approach to motion deblurring that could have practical applications in a range of fields.
Deep Automatic Modulation Classification Using Deformation-Insensitive Color Constellation

Automatic modulation recognition(AMC) is an intermediate process between signal detection and signal demodulation, which is an important technology in wireless communication systems. Its main purpose is to determine the modulation mode of the received wireless communication signal, thereby realizing demodulation and subsequent processing of the signal. AMC is considered as the promising methods to improve the quality of the service in cognitive radio(CR). However, AMC suffers from the phase offset of the signal and low recognition accuracy. Therefore, we proposed deformation-insensitive color constellation(DICC) to improve the recognition accuracy in AMC. In this paper, DICC is insensitive to the deformation of the constellation caused by the phase offset and able to represent the density information of points in the constellation diagram. Firstly, we use the method of phase difference to prevent the phase offset. Particularly, we use different colors to match with density information of constellation diagrams, and use deep learning models, VGG-19 and GoogleNet for classification. The results show that for the received signal constellation with carrier phase offset, it still has a high recognition accuracy and the classification accuracy is 3%-4% higher than previous methods.
Some Issues of Countering Criminal Offenses in the Field of Informatization and Communications in the Republic of Kazakhstan

According to official statistics, in recent years there has been an increase in the registration of crimes that have a pronounced «intellectual» character. The objectively recorded growth of criminal encroachments using information technology dictates the urgent need for theoretical research and generalization of law enforcement practice. The authors made an attempt on the basis of the integrated use of historical and statistical methods to show the impact of improving the substantive criminal legislation on the state of investigative and judicial practice in cases of criminal offenses in the field of informatization and communications. The history of the regulation of responsibility for cybercrime in the criminal laws of sovereign Kazakhstan is traced. As possible ways to increase the effectiveness of countering crimes committed using information systems, the authors formulated proposals to change the form of statistical reporting.
A unified model for continuous conditional video prediction

Different conditional video prediction tasks, like video future frame prediction and video frame interpolation, are normally solved by task-related models even though they share many common underlying characteristics. Furthermore, almost all conditional video prediction models can only achieve discrete prediction. In this paper, we propose a unified model that addresses these two issues at the same time. We show that conditional video prediction can be formulated as a neural process, which maps input spatio-temporal coordinates to target pixel values given context spatio-temporal coordinates and context pixel values. Specifically, we feed the implicit neural representation of coordinates and context pixel features into a Transformer-based non-autoregressive conditional video prediction model. Our task-specific models outperform previous work for video future frame prediction and video interpolation on multiple datasets. Importantly, the model is able to interpolate or predict with an arbitrary high frame rate, i.e., continuous prediction. Our source code is available at https://npvp.github.io.
Learning Multi-scale Representations with Single-stream Network for Video Retrieval

With the explosive growth of video contents in the Internet, video retrieval has become an important issue that can benefit video recommendation and copyright detection. Since the key features of a video may distribute in distant regions of a lengthy video, several works have made a success by exploiting multi-stream, multi-scale architectures to learn and merge distant features. However, a multi-stream network is costly in terms of memory and computing overhead. The number of scales and these scales are handcrafted and fixed once a model is finalized. Further, being more complicated, multi-stream networks are more prone to being overfitting and lead to poorer generalization. This paper proposes a single-stream network with built-in dilated spatial and temporal learning capability. By combining with modern techniques, including Denoising Autoencoder, Squeeze-and-Excitation Attention, and Triplet Comparative Mechanism, our model achieves state-of-the-art performance in several video retrieval tasks on the FIVR200K, CC WEB VIDEO, and EVVE datasets.
Digital Literacy and Learning Motivation: Impacts on Online Learning Outcomes in Fiqh Study

This study aims to investigate the effect of digital literacy skills and learning motivation on students' online learning outcomes in Fiqh subjects at Madrasah Tsanawiyah (MTs). The approach used in this study is quantitative with the data collection technique being a survey model. The sample includes 52 students, along with the corresponding teacher, from MTs Nurul Falah. The correlation data analysis is conducted using SPSS 25 software. The findings reveal a significant influence of digital literacy skills and learning motivation on online learning outcomes. Specifically, digital literacy skills account for a 90% influence on the online learning outcomes of MTs Nurul Falah students, while learning motivation accounts for an 88% influence. Moreover, the combined impact of both variables on online learning outcomes is 82%. The implications of this study underscore the necessity of enhancing digital literacy skills and nurturing learning motivation in online Fiqh courses at MTs Nurul Falah to improve students' learning outcomes. This can be accomplished through adequate training and support for students and teachers to develop digital literacy skills and learning motivation. Additionally, it is crucial to address the internal factors influencing students' digital literacy. Moreover, addressing factors such as technical issues and student motivation within the online learning environment is equally essential for optimizing online learning experiences.
Netizens at Odds with the Education Department: Analysis of Impoliteness Strategies on an Online Platform

The COVID-19 pandemic has prompted the Philippines, along with other nations, to restructure its educational framework to meet the evolving needs and challenges of the time. Consequently, this reorganization has led to changes in the learning delivery modalities (LDMs). The Philippine Department of Education (DepEd) has faced significant criticism on social media regarding issues related to preparation, flawed materials, and implementation. By employing a pragmatic analysis approach based on Culpeper's theory of impoliteness strategies, this study examines fifty Facebook comments criticizing the said department and its new LDMs. The findings reveal that critics employed various impoliteness strategies, including bald-on-record impoliteness, positive impoliteness, negative impoliteness, off-record impoliteness, withhold politeness, and impoliteness meta-strategy. The study's outcomes suggest that analyzing impoliteness strategies in online comments can provide insights into how such remarks serve as social actions. Researchers working with these textual forms, as well as those specializing in the study of genres and languages, can benefit from the findings of this study. The implications of the findings are discussed for policymakers, educators, and students within the Philippine education system.
Interdisciplinary integration in the context of preparation for the license exam

Higher-educational institutions have the task of preparing innovatively trained specialists who can ensure and continue the development of the pharmaceutical industry. To achieve this goal, it is important not only to create a fundamental base of knowledge and skills but also to form a system of monitoring and evaluating educational achievements, one of which is the licensing integrated exam. The aim of the work: is to reveal the peculiarities of the implementation of interdisciplinary integration in the context of preparation for the licensing integrated exam on the example of teaching the disciplines of pharmacognosy and general and pharmaceutical chemistry. One of the professionally oriented disciplines is pharmacognosy, the study of which contributes to the formation of the necessary outlook on the rational use of natural plant resources. The foundation of the theoretical basis of pharmacognosy is the accumulated knowledge of chemistry. To improve learning results and systematize knowledge in preparation for state certification, some activities are carried out, the first step of which is the systematic study and repetition of educational material using educational and methodological literature and information and communication technologies; the second stage is self-monitoring, in which tasks based on the principles of interdisciplinary integration are applied. The next stage in self-monitoring of knowledge is the use of the “pyramid” algorithm, which consists of the fact that questions to which students give incorrect answers appear several times during the testing process. Successful certification depends on the systematization and ordering of students' knowledge, the creation of training conditions, appropriate control, as well as the successful contact of the teacher with the student. Conclusions. The proposed mechanisms of integration in the educational sphere ensure the implementation of interdisciplinary connections that affect both the quality of specialized training and the results of preparation for the licensing integrated exam.
Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR

Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial control systems based on improved comparative learning SimCLR. Firstly, a feature extraction network is trained on SimCLR using unlabeled data; a linear classification layer is added to the trained feature extraction network model; and a small amount of labeled data is used for supervised training and fine-tuning of the model parameters. The trained model is simulated on the Secure Water Treatment (SWaT) dataset and the publicly available industrial control dataset from Mississippi State University, and the results show that the method has better results in all evaluation metrics compared with the deep learning algorithm using supervised learning directly, and the comparative learning has research value in industrial control system intrusion detection.
Cloud Access Security Brokers: Strengthening Cloud Security

As organizations increasingly migrate their applications and data to the cloud, the need for robust cloud security solutions becomes paramount.Cloud Access Security Brokers (CASBs) have emerged as a vital component in ensuring the secure and compliant adoption of cloud services.This paper provides an in-depth exploration of Cloud Access Security Brokers, their architecture, features, benefits, and challenges.By analysing CASBs' role in enhancing cloud security, this paper aims to demonstrate their importance in safeguarding sensitive information and mitigating risks associated with cloud-based operations.
The Role Of Accounting Information Systems In Management Decision Making At PT. Kawi Pusaka Raharja (Wings) Bantaeng District

This study aims to determine the role of accounting information systems in management decision-making at PT Kawi Pusaka Raharja (Wings) Bantaeng Regency. This research is a type of qualitative descriptive research. Data collection is done by employing observation, interviews, and documentation. The data analysis method used is data reduction, data presentation, and conclusion drawing/verification. Based on the results of the research that has been done, it can be concluded that the accounting information system at PT Kawi Pusaka Raharja (Wings) Bantaeng Regency plays an important role in management decision-making because the accounting information system that has been computerized and the quality of the accounting information system used is very effective and efficient so that management, especially managers in the company, becomes easier in making decisions. Accounting information systems have played a very important role in decision-making at PT Kawi Pusaka Raharja (Wings) Bantaeng Regency despite the problems experienced in the accounting information system process, where often all activities and transactions within the company are hampered due to poor networks. However, this does not reduce the function of the accounting information system in decision-making.
Survei periodik kepuasan layanan Perpustakaan IPDN kampus NTB tahun 2022

Users are the subject and at the same time the object of library services, because users feel the most about whether the services they receive are able to meet their needs for the necessary scientific information. Therefore, the level of user satisfaction needs to be measured periodically using various methods, one of which is through surveys. The purpose of this study was to determine the level of user satisfaction for the NTB IPDN Campus for the 2022 period in the form of a User Satisfaction Index. The method used is descriptive qualitative with secondary data sources from searches or literature studies of relevant previous research results for comparison. Study results. The User Satisfaction Index for Library services in 2022 has increased by an average of 4%, when compared to the 2021 period. The highest increase in service elements is the element of service fees/tariffs with a difference of 231 points or an increase of 7%. While the element of service with the smallest difference is found in the element of service procedures by 27 points or 1%. Listening to all the service elements (9 elements), all of them experienced an increase in user satisfaction from the 2021 survey when compared to the 2022 survey results. The conclusion of this study is that the User Satisfaction Index for library services IPDN Campus NTB in 2022 obtained an average value for all service elements of 3.326 or converted with user satisfaction index interval values entered in the performance of service units in the GOOD category
Robustness of Visual Explanations to Common Data Augmentation Methods

As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called into question. Our research investigates the response of post-hoc visual explanations to naturally occurring transformations, often referred to as augmentations. We anticipate explanations to be invariant under certain transformations, such as changes to the colour map while responding in an equivariant manner to transformations like translation, object scaling, and rotation. We have found remarkable differences in robustness depending on the type of transformation, with some explainability methods (such as LRP composites and Guided Backprop) being more stable than others. We also explore the role of training with data augmentation. We provide evidence that explanations are typically less robust to augmentation than classification performance, regardless of whether data augmentation is used in training or not.