Reviewerly/exHarmony huggingface dataset:
Enhancing Student Vocabulary through the Use of the PowToon Application

Vocabulary mastery is crucial for effective language acquisition, enabling learners to comprehend spoken and written words and articulate their thoughts. The PowToon application, with its engaging animated videos, offers an immersive and clear learning experience. This is particularly beneficial for English learners, where proficiency in listening, speaking, reading, and writing is vital. This study investigates the impact of the PowToon application on enhancing students' vocabulary. Utilizing a quantitative approach with a Quasi-Experimental Design, data were gathered through observations and tests. The analysis was based on descriptive statistics. The sample comprised 52 tenth-grade students at Tunas Pelita High School. The experimental group, which used PowToon, had a pretest average of 36.19 compared to the control group's 38.40. However, post-tests revealed a score of 82.31 for the experimental group and 71.70 for the control group, indicating a more significant vocabulary improvement in the PowToon cohort. The findings confirm that the PowToon application positively affects vocabulary acquisition. The research underscores PowToon’s potential to enhance students' comprehension, spelling, and pronunciation of English vocabulary, ultimately bolstering their overall learning outcomes.
A comparison of machine learning algorithms for credit card fraud detection

With the increasing use of credit cards for online and offline transactions, the risk of fraudulent activities has also increased significantly. In this study, we propose a machine learning-based approach to predict credit card fraud. We used a public dataset with 284,807 transactions, of which 492 were fraudulent. We experimented with various machine learning algorithms such as k-nearest neighbor, random forests, and isolation forests to develop predictive models for credit card fraud. We also performed feature selection to identify the most important features that contribute to credit card fraud prediction. Our results suggest that the proposed machine learning approach can effectively detect fraudulent transactions and can be adopted by banks and financial institutions to reduce the risk of credit card fraud.
An Efficient Image Encryption Algorithm using Chaotic S-boxes of Pseudo-random Size

In this article, we will present a new image encryption technique. This approach is an improvement to the classic Vigenère cipher. This improvement consists in generating two substitution tables of pseudo-random size. The first S-Box is used to install a confusion phase using a new substitution function. In the next phase, a not necessarily bijective substitution table is used to ensure the diffusion process in order to protect our cryptosystem from any differential attack. The results of standard security tests, on a large volume of images of different sizes and formats, clearly justify the robustness and effectiveness of our method.
Penerapan Sistem Monitoring Pergeseran Tanah berbasis Internet of Things (IoT) di Daerah Aliran Sungai (DAS) Kahayan, Kota Palangka Raya

Landslides occur as a result of a geological process caused by the displacement of rock or soil masses. In general, landslides are caused by shifting soil and excessive water content due to high rainfall. One of the processes that occur in rivers is sedimentation With the entry of sediment into the river flow, it will result in increased river surface flow and deposition which causes siltation of the river. This siltation will greatly affect the capacity of the river when the rainy season arrives, because it can cause floods. For this reason, assistance and training are needed to increase community knowledge about the use of Internet of Things (IoT) based monitoring system tools around the Kahayan river basin. IoT is a technology that allows smart objects, things, and machines to be connected via the internet so that data can be received in real time. The people of Pahandut Village were enthusiastic in participating in the socialization activities provided and felt the benefits of increasing knowledge starting from an understanding of the Internet of Things and the use of monitoring system tools for land shifts. Based on the evaluation results, it is known that participants have increased knowledge and insight into the Internet of things and its application in this case the soil shift monitoring system tool which will later be installed in residential areas around the watershed (DAS).
Person Quick-Search Approach Based on a Facial Semantic Attributes Description

Person search based on semantic attributes description presents an interest task for intelligent video surveillance applications. The main objective is to locate a suspect or to find a missing person in public areas using a semantic description (e.g. a 40-year-old asian woman) provided by an eyewitness. Such a description provides the facial soft biometric related to the facial semantic attributes (i.e. age, gender and ethnicity). In this paper, we introduced a new approach for person search named “Quick-Search” based on a facial semantic attributes description to enhance the person search task in an unconstrained environment. The main contribution of the paper is to introduce a multi-attributes score fusion method which relies on soft biometric features (age, gender, ethnicity) to improve the person search in a large dataset. An experimental study was conducted on the challenging FairFace dataset to validate the effectiveness of the proposed person search approach.
Adaptive Enhancement of Extreme Low-Light Images

Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the training set. However, this assumption often does not hold, leading to output images that contain visual imperfections such as dark regions or low contrast. To facilitate the training and evaluation of adaptive models that can overcome this limitation, we have created a dataset of 1500 raw images taken in both indoor and outdoor low-light conditions. Based on our dataset, we introduce a deep learning model capable of enhancing input images with a wide range of intensity levels at runtime, including ones that are not seen during training. Our experimental results demonstrate that our proposed dataset combined with our model can consistently and effectively enhance images across a wide range of diverse and challenging scenarios. Code is available at https://github.com/mklyu/CEL-net .
Combating Computer Vision-Based Aim Assist Tools in Competitive Online Games

This work presents a novel approach to the application of adversarial attacks to the domain of video games, specifically, the exploitation of computer vision-based aim assist tools in the first-person shooter genre. As one of the greatest issues plaguing modern shooters, aim assist (also referred to as aimbots) greatly increase the speed and accuracy of cheating players, giving them an unfair advantage over their competitors. The latest versions of these aim assisting tools make use of object detection models such as YOLO (You Only Look Once); fortunately, these models are vulnerable to attack via small perturbations to their input space which results in the misclassification of objects. The purpose of this work is to formulate an attack on a black-box object detection model which can be feasibly implemented in a commercial game environment. What makes our solution unique is the generation of attack images in the form of in-game objects rendered by the game engine itself, instead of a set of screenshots or from a generic differentiable renderer. Results show that our approach is capable of generating adversarial examples which can fool an object detection model in a black-box environment, as well as recreating the game’s original textures such that these perturbations go unnoticed by players.
Enhancing Stockfish: A Chess Engine Tailored for Training Human Players

Stockfish is a highly popular open-source chess engine known for its exceptional strength. The recent integration of a neural network called NNUE has significantly improved Stockfish’s playing ability. However, the neural network lacks the capability to explain the reasoning behind its moves. This poses a challenge for human players who seek moves that align with their playing style. The objective of this paper is to describe some modifications to Stockfish, making the engine more suitable for training human players of all skill levels. We have refactored the move search and evaluation algorithms to selectively analyze potential continuations, incorporating dynamic evaluations based on the specific nature of the position and the player’s training abilities. The engine remains strength is still very high, in some situations even better than the original. We evaluate and discuss the outcomes of these enhancements.
Mutually Imperceptible Skill Adjustment in VR for Making Hyakunin Isshu Karuta Inclusive

In this paper, we propose “Mutually imperceptible skill adjustment in VR” as a new method of skill adjustment to make multiplayer games inclusive. It is a skill adjustment in VR that is difficult for players to perceive each other’s assistance. By carefully designing the assistance to minimize their impact on the opponents’ environments, it is intended that the assistance will minimize their impact on the opponents’ skill sets and allow skill adjustment without informing the opposing players. We prototyped a system that implements the mutually imperceptible skill adjustment in VR involving visual and auditory modalities to the game of Hyakunin Isshu Karuta, a popular traditional card game in Japan in which memory and quick reaction are important. Evaluation experiments showed that the visual assistance, one of the mutually imperceptible skill adjustments, was effective for novice players, and suggested that combining multiple assistance methods could bridge the skill gap between novice and experienced players.
Anomaly data enhancement and detection for charging piles considering generative adversarial network

The accuracy of anomaly detection results is affected by factors such as low proportion of abnormal data samples and imbalanced distribution. To address the issue that the current abnormal data detection model for charging piles depends on the quality of abnormal data samples in the training set, this paper proposes a charging pile abnormal data augmentation method based on Generative Adversarial Network (GAN) and the corresponding abnormal data detection model. The proposed method first improves the traditional GAN by introducing the unsaturated activation function Leaky ReLU (LR) and proposes charging pile abnormal data augmentation method based on LR-GAN to enhance the abnormal data in the training set. Then, the LR-GAN augmented training set is used as input for the Random Forests (RF) model to establish a charging pile abnormal data detection model based on LR-GAN-RF to detect abnormal data in the charging pile charging dataset. Finally, different charging pile anomaly data sets are used to validate the feasibility and effectiveness of this method, which is verified to be highly feasible.
Application of Deep Learning Technology in College Teaching

With the twenty-first century gradually taking core literacy training as the key work content of contemporary college education, deep learning technology has become one of the most direct and effective tools, which will help students develop the ability to learn independently. By cultivating college students’ ability of deep learning, deep processing and improving learning efficiency have become the key content of college curriculum reform. College Chinese is an important course integrating tools and humanities, which is an important basic course for students to have a solid foundation. Therefore, colleges and universities must strengthen the use of students’ in-depth learning, which will improve the self-restraint of college Chinese teaching, which will strengthen students’ aesthetic experience. Through in-depth learning technology, we can help students build an ideal personality, which will strengthen the cultural heritage. Therefore, this paper analyzes the important role of the current depth learning. At the same time, based on the guidance of the goal of deep learning, this paper conducts a survey of the research status of college Chinese teaching. Finally, this paper proposes some measures for the current application of deep learning.
Higher Education

Research within the field of higher education has rapidly expanded over the past 50 years. The purpose of this study was to synthesize the research of higher education from 1970 through 2020 and identify the trends and themes in that time period. While many authors have surveyed higher education research by studying all publications (output), we reviewed the field by focusing on the publications that made the biggest impact through the number of citations (outcome). We used a bibliometric literature analysis to identify the 20 most highly cited journal articles of each decade and then measured the number of citations. This comparison of citation counts allowed us to trace the growth and changes in topics of the most interest to higher education researchers and determine which themes had the most impact on the field. Themes centering on students and learning—such as effective teaching, retention, engagement, assessment, feedback, and employability—were the most common among the high-impact articles. Our findings suggest that over time, the field of higher education has moved away from a teacher-centered approach and more towards a student-centered focus in order to encourage deep, applied learning. The results of our analysis also showed that many of the identified trends are connected to the social, political, and economic influences of the same time periods, including an increasingly diverse and growing student population and a transformation in education delivery methods.
Location Leakage in Federated Signal Maps

We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL parameters for averaging, curating local batches so as to mislead the DLG attacker, and aggregating across multiple users with different trajectories. We evaluate the performance of our algorithms through both analysis and simulation based on real-world mobile datasets, and we show that they achieve a good privacy-utility tradeoff.
Towards Privacy-Preserving and Practical Data Trading for Aggregate Statistic

Data trading is an effective way for commercial companies to obtain massive personal data to develop their data-driven businesses. However, when data owners may want to sell their data without revealing privacy, data consumers also face the dilemma of high purchase costs due to purchasing too much invalid data. Therefore, there is an urgent need for a data trading scheme that can protect personal privacy and save expenses simultaneously. In this paper, we design a priv <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC</b> y-preserving and pra <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> tical aggr <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> gate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tati <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tic trading scheme (named as ACCESS). Technically, we focus on the group-level pricing strategy to make ACCESS easier to implement. The differential privacy technique is applied to protect the data owners' privacy, and the sampling algorithm is adopted to reduce the data consumers' costs. Specifically, to provide a maximum tolerant privacy loss guarantee for the data owners, we design a decision algorithm to detect whether a conflict occurs between the consumer-specified accuracy level and the maximum tolerable privacy loss budget. Besides, to minimize the purchase cost for the data brokers, we develop a sampling-based aggregation method consisting of two sampling algorithms (called as BUSA and BKSA, respectively). BUSA enables reducing purchase costs with no additional background knowledge. Once the data broker knows the data boundary, BKSA can significantly reduce the amount of data that needs to be purchased, thereby the purchase cost is reduced. Rigorous theoretical analysis and extensive experiments (over four real-world and public datasets) further demonstrate the practicability of ACCESS.
Dual-color space network with global priors for photo retouching

Abstract There have been growing trends using deep learning-based approaches for photo retouching which aims to enhance unattractive images and make them visually appealing. However, the existing methods only considered the RGB color space, which limited the available color information for editing. To address this issue, we propose a dual-color space network that extracts color representations from multiple color spaces to provide more robust color information. Our approach is based on the observation that converting an image to a different color space generates a new image that can be further processed by a neural network. Hence, we utilize two separate networks: a transitional network and a base network, each operating in a different color space. Specifically, the input RGB image is converted to another color space (e.g., YCbCr) using color space converter (CSC). The resulting image is then passed through the transitional network to extract color representations from the corresponding color space using color prediction module (CPM). The output of the transitional network is converted back to the RGB space and fed into the base network, which operates in RGB space. By utilizing global priors from each representation in different color spaces, we guide the retouching process to produce natural and realistic results. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods on the MIT-Adobe FiveK dataset, and an in-depth analysis and ablation study highlight the advantages of our approach.
Production of U.S. SMB and HML in the Fama-French Data Library

This paper describes the construction of the size factor, SMB (small minus big), and the value minus growth factor, HML (high minus low book-to-market equity) in the Fama-French Data Library. Table 1 gives summary statistics that show how the average values of SMB and HML change because of five major CRSP data projects and the two changes in our rules that produce the factors.
Generation of MRS Abstract Predicates from Paninian USR

Semantic Representations become useful resources for various multilingual NLP applications such as Machine Translation, Multilingual Generation, cross Lingual QA, to name a few. No Semantic Representation, to our knowledge, adopts vivakṣā (Speaker’s intention) as a guiding principle for the representation. This motivates us to develop a new Semantic Representation system – Universal Semantic Representation (USR) – following Indian Grammatical Tradition (IGT) and Paninian grammar. Since USR is designed to be language-independent, we have currently taken up the task of generating English, Hindi, Tamil and Bangla from the USR. For English generation, the USR is mapped to ERG meaning representation (Flickinger, D. 1999) which is couched in Minimal Recursion Semantics (MRS). We use an off-the-shelf ACE generator that uses ERG as a resource-grammar for generating English. While designing the transfer module from USR to ERG-based MRS, we came across various Abstract Predicates (APs) in MRS representation as described in ErgSemantics_Basic (Flickinger et al., 2014). These APs are used to represent the semantic contribution of grammatical constructions or more specialized lexical entries such as compounding or the comparative use of more and so on. This paper presents the strategy for postulating the APs from the information given in USR and then reports the implementation of the transfer module keeping the focus on the postulation of APs. We get around 95% accuracy in postulating APs from USR.
A Note on Separation Axioms in Kasaj Topological Spaces

Objectives: The separation axioms are about the use of kasaj topological means to distinguish disjoint sets and distinct points and the purpose of this work is to investigate the spaces namely KSgp[KSgsp]-T0 spaces, KSgp[KSgsp]-T1 spaces, KSgp[KSgsp]-T2 spaces by utilizing KSgp[KSgsp]-open sets in Kasaj topological spaces. Also, we discuss their relationship with existing concepts in Kasaj topological spaces. Methods: Any two Kasaj topologically distinguishable points must be distinct, and any two separated points must be Kasaj topologically distinguishable. Finding: This study consists of three separation axioms namely -T0 spaces, -T1 spaces, -T2 spaces by utilizing -open sets in Kasaj topological spaces. Novelty: This study uses their relationship with existing concepts in Kasaj topological spaces. AMS Classification: 54A05,54B05,54A99. Keywords: KSgp-T0 spaces; KSgsp-T0 space; KSgp-T1 spces; KSgsp-T1 spaces; KSgp-T2 spaces; KSgsp-T2 spaces
Transitions in education: Educators, digitalisation, and datafication

This paper explores educators' experiences of the digitalisation and datafication of teaching and learning that intensified during the Covid-19 pandemic. It focuses on the transitions, responses, and agency of educators as the rules of their professional world changed. The paper uses data from four focus group discussions with 19 educators from diverse South African contexts including urban and rural, affluent and poor environments, schools, colleges, and universities. Framed by Archer's (2007, 2012) nuanced concepts of agency, the paper shows how educators working within the structures of very stratified education contexts negotiated their educational projects while the rules were being rewritten as the socio-technical systems in which they taught were-and are-being transformed in ways that are not yet fully understood. Control of the teaching and learning environment has been a key issue as it has become clear how much is outside the jurisdiction of individual educators: the entrenching of big tech in education, stakeholder arrangements including private-state partnerships, and the selection of digital tools and systems. Despite not being explicitly aware of the business models that shape the datafication of their teaching systems, educators discussed their discomfort and unease- while remaining reflexive and active agents showing the ability to reorientate a course of action even within narrow and covert parameters.