Reviewerly/exHarmony huggingface dataset:
Exploring Single Primary School Teacher's Pedagogical Practices and Attitude Toward Students Learning

Primary education is the foundation step of formal education, including various subjects, e.g., languages, science, mathematics, English, Urdu, and social studies, which require different subject teachers. Single-teacher schools face unique challenges such as limited resources (staff, teachers, etc.), a lack of support systems, and isolation, all of which can significantly impact pedagogical practices and attitudes. The study was opted to explore the relationship between pedagogical practices and attitudes of single primary school teachers. The study employed a descriptive research approach, and a survey technique was used for data collection. The population and sample were the same, i.e., 74 single primary school teachers (both male and female) in District Sargodha. Two questionnaires validated through experts' opinions and pilot tested with 0.92 Cronbach Alpha value) were used for data collection through personal visits. Data analysis was carried out using frequencies, percentages, mean scores, and Pearson correlation. The findings were that the overall pedagogical practices and attitudes of single primary school teachers were effective, except for the factor of teacher behavior and a moderate relationship between the teacher's pedagogical practices and attitudes. It is recommended that single teachers be relocated from overcrowded to understaffed primary schools to improve teaching practices and attitudes toward student learning.
Training and coaching of public speaking skills for elementary school children in Makassar City

Children residing on Jalan Mirah Seruni, RT 05 RW 03, Pandang District, Panakukkang Village lack a foundation for effective communication. Insufficient communication skills impact their interactions with peers and parents, as well as their overall self-confidence. The objective of this community service is to enhance public speaking, communication skills, and boost self-confidence among these children. The PKM implementation method encompasses four stages: 1) preparation, 2) training, 3) mentoring and coaching, and 4) monitoring and evaluation. The outcomes of this program include heightened knowledge, improved public speaking and communication skills, leading to increased self-confidence and enthusiasm for public appearances.
Deformable Spatial-Temporal Attention for Lightweight Video Super-Resolution

Video super-resolution (VSR) aims to recover high-resolution video frames from their corresponding low-resolution video frames and their adjacent consecutive frames. Although some progress has been made, most existing methods typically use the spatial-temporal information of two adjacent reference frames to aid in enhancing the video frame super-resolution reconstruction effect. This makes it impossible for these methods to achieve satisfactory results. To solve this problem. We propose a deformable spatial-temporal attention (DSTA) module for video super-resolution. The deformable spatial-temporal attention module improves the reconstruction effect by aggregating favorable spatial-temporal information from multiple reference frames into the current frame. To speed up the model training, we select only the first s highly relevant feature points as the attention scheme. Experimental results show that our method with fewer network parameters has strong video super-resolution performance.
User-Aware Prefix-Tuning Is a Good Learner for Personalized Image Captioning

Image captioning bridges the gap between vision and language by automatically generating natural language descriptions for images. Traditional image captioning methods often overlook the preferences and characteristics of users. Personalized image captioning solves this problem by incorporating user prior knowledge into the model, such as writing styles and preferred vocabularies. Most existing methods emphasize the user context fusion process by memory networks or transformers. However, these methods ignore the distinct domains of each dataset. Therefore, they need to update the entire caption model parameters when meeting new samples, which is time-consuming and calculation-intensive. To address this challenge, we propose a novel personalized image captioning framework that leverages user context to consider personality factors. Additionally, our framework utilizes the prefix-tuning paradigm to extract knowledge from a frozen large language model, reducing the gap between different language domains. Specifically, we employ CLIP to extract the visual features of an image and align the semantic space using a query-guided mapping network. By incorporating the transformer layer, we merge the visual features with the user’s contextual prior knowledge to generate informative prefixes. Moreover, we employ GPT-2 as the frozen large language model. With a small number of parameters to be trained, our model performs efficiently and effectively. Our model outperforms existing baseline models on Instagram and YFCC100M datasets across five evaluation metrics, demonstrating its superiority, including twofold improvements in metrics such as BLEU-4 and CIDEr.
Online Class-Incremental Learning in Image Classification Based on Attention

Online class-incremental learning aims to learn from continuous and single-pass data streams. However, during the learning process, catastrophic forgetting often occurs, leading to the loss of knowledge about previous classes. Most of the current methods to solve the problem of catastrophic forgetting do not fully exploit the semantic information in single-pass data streams. To effectively address this issue, we propose an Attention-based Dual-View Consistency (ADVC) strategy. Specifically, we generate an attention score for each region through the attention mechanism to identify those important features that are useful for classification. This enables the model to thoroughly explore the semantic information in the single-pass data stream and improve prediction accuracy. At the same time, we consider the effectiveness of sample retrieval from another perspective (the samples themselves) by using data augmentation methods in memory to generate variants of old classes of samples to alleviate the problem of catastrophic forgetting. Through extensive experimental results, we demonstrate that our approach outperforms state-of-the-art methods on a range of benchmark datasets.
Accurate Facial Landmark Detector via Multi-scale Transformer

Facial landmark detection is an essential prerequisite for many face applications, which has attracted much attention and made remarkable progress in recent years. However, some problems still need to be solved urgently, including improving the accuracy of facial landmark detectors in complex scenes, encoding long-range relationships between keypoints and facial components, and optimizing the robustness of methods in unconstrained environments. To address these problems, we propose a novel facial landmark detector via multi-scale transformer (MTLD), which contains three modules: Multi-scale Transformer, Joint Regression, and Structure Loss. The proposed Multi-scale Transformer focuses on capturing long-range information and cross-scale representations from multi-scale feature maps. The Joint Regression takes advantage of both coordinate and heatmap regression, which could boost the inference speed without sacrificing model accuracy. Furthermore, in order to explore the structural dependency between facial landmarks, we design the Structure Loss to fully utilize the geometric information in face images. We evaluate the proposed method through extensive experiments on four benchmark datasets. The results demonstrate that our method outperforms state-of-the-art approaches both in accuracy and efficiency.
Eye Frame Recommender System using Convolutional Neural Networks

Abstract—In the dynamic field of retail analytics, this project represents a groundbreaking foray into online eyewear shopping. At its core, it aims to create a sophisticated Image Recommender System powered by Convolutional Neural Networks (CNNs). This innovation is driven by a growing demand for intuitive and visually-driven product searches, challenging traditional text- based search methods. The project recognizes the evolving con- sumer preference for immersive and visually engaging shopping experiences, prompting a shift away from conventional search paradigms. The Image Recommender System, therefore, becomes a pivotal solution, breaking free from the limitations of text-based searches and aligning with the modern shopper’s incli- nation towards visual stimuli. It’s a strategic response to make eyewear discovery more intuitive and personalized, ushering in a transformative era in online shopping. Index Terms—Visual Search, Retail Analytics,CNN.
Financial Development: The Role of GDP per Capita Dynamics in Low- &amp; Middle-Income Economies

This study delves into the complex role of economic growth on financial development within low- and middle-income economies (LMIE). It analyzes a dataset spanning 85 countries from 1980 to 2020, collapsed into 9 periods, yielding 765 observations. Employing General Method of Moments (GMM), including two-step system GMM and forward-orthogonal deviations IV/GMM methods, the research uncovers intriguing dynamics. Positive shifts in GDP per capita are found to correspond with heightened financial development, whereas negative changes exhibit an adverse relationship. Government consumption yields mixed results, and inflation negatively impacts financial development, while trade openness and favorable terms of trade exhibit positive associations. These findings underscore the significance of economic development, price stability, trade openness, and financial sector stability in nurturing financial development in LMIE. Therefore, policymakers are encouraged to prioritize strategies aimed at fostering economic growth, income augmentation, and economic resilience, particularly in times of economic turmoil. This research offers a unique contribution by dissecting economic growth into positive and negative changes, providing insights into their distinct impacts on financial development using GMM estimation. It empowers policymakers to leverage economic growth shifts and trade reforms for inclusive financial development.
Cascaded-Scoring Tracklet Matching for Multi-object Tracking

Multi-object tracking (MOT) aims at locating the object of interest in a successive video sequence and associating the same moving object frame by frame. Most existing approaches to MOT lack the integration of both motion and appearance information, which limits the effectiveness of tracklet association. The conventional approaches for tracklet association often struggle when dealing with scenarios involving multiple objects with indistinguishable appearances and irregular motions, leading to suboptimal performance. In this paper, we introduce an appearance-assisted feature warper (AFW) module and a motion-guided based target aware (MTA) module to efficiently utilize the appearance and motion information. Additionally, we introduce a cascaded-scoring tracklet matching (CSTM) strategy that seamlessly integrates the two modules, combining appearance features with motion information. Our proposed online MOT tracker is called CSTMTrack. Through extensive quantitative and qualitative results, we demonstrate that our tracker achieves efficient and favorable performance compared to several other state-of-the-art trackers on the MOTChallenge benchmark.
Evaluation of Prostate Cancer via Machine Learning

By training computers with machine learning technique, patients can be prevented from being exposed to unnecessarily difficult examinations. In recent years, machine learning-based disease assessment approach has gained importance in terms of the benefits it provides to clinical methods. There is a remarkable increase in studies in this direction. There are a limited number of clinical guiding parameters in predicting some types of cancer, and this limitation pushes the patients under treatment to a very frustrating process. For this reason, apart from ordinary procedure of the traditional medicine, an alternative approach to predict the any type of cancer is making a computer-based evaluation that has become a highly studied method in recent years. In this study, a machine learning (ML) approach will be used to evaluate prostate cancer, which is the second most common cancer-related death in men worldwide. For this purpose, the K-Nearest Neighbor (kNN) algorithm based on ML will be used with feature selection, which is a dimension reduction technique. An open source database, Kaggle, was used for the evaluation. The accuracy value of the used algorithm was found 88%.
Identifying changes in popular news media articles about the transgender community from 1990 to 2023

In the past few decades, newspaper articles about transgender people and the transgender community have become increasingly more common. At the same time, language used in this type of media has continued to evolve. In order to better understand the way public discourse around the trans community has changed, this study uses a linguistic analysis of newspaper articles from the ten most widely circulated newspapers in the United States. A sampling of articles from each year from 1990 to 2023 was analyzed using the Linguistic Inquiry and Word Count (LIWC) program to capture the authors’ attitudes. Correlation analyses were used with the LIWC categories to explore if any of the linguistic terms changed over the study period. In addition, change in the use of the terms “transsexual” and “transgender” was tracked over the same 34 year period. Results showed a statistically significant decrease in negative language used in articles about transgender people over time and a significant change in the use of the terms “transsexual” vs. “transgender”. Linguistic changes in these newspaper articles parallel changing attitudes about the trans community in mainstream American culture at large.
Tuned Compositional Feature Replays for Efficient Stream Learning

Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning to classify objects by training on nonrepeating video frames in temporal order (online stream learning), models that learn well from shuffled datasets catastrophically forget old knowledge upon learning new stimuli. We propose a new continual learning algorithm, compositional replay using memory blocks (CRUMB), which mitigates forgetting by replaying feature maps reconstructed by combining generic parts. CRUMB concatenates trainable and reusable memory block vectors to compositionally reconstruct feature map tensors in convolutional neural networks (CNNs). Storing the indices of memory blocks used to reconstruct new stimuli enables memories of the stimuli to be replayed during later tasks. This reconstruction mechanism also primes the neural network to minimize catastrophic forgetting by biasing it toward attending to information about object shapes more than information about image textures and stabilizes the network during stream learning by providing a shared feature-level basis for all training examples. These properties allow CRUMB to outperform an otherwise identical algorithm that stores and replays raw images while occupying only 3.6% as much memory. We stress-tested CRUMB alongside 13 competing methods on seven challenging datasets. To address the limited number of existing online stream learning datasets, we introduce two new benchmarks by adapting existing datasets for stream learning. With only 3.7%-4.1% as much memory and 15%-43% as much runtime, CRUMB mitigates catastrophic forgetting more effectively than the state-of-the-art. Our code is available at https://github.com/MorganBDT/crumb.git.
Application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) Model to Acceptance of Electronic Learning Media

The utilization of online learning media at IDE LPKIA is highly recommended by the institution for its lecturers and students to support teaching and learning activities. SDLR is one of the online learning applications implemented at IDE LPKIA. The success of implementing information technology certainly has its measures. One way to measure the level of success in utilizing information technology is by conducting testing on the technology’s acceptance by users. One of the models used to measure the level of technology acceptance is by applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model. The testing was conducted by distributing questionnaires to students, consisting of 42 statements from the total constructs used. The validity and reliability tests were given to 50 respondents. From the results obtained, all statements were deemed valid and reliable, with the obtained p-values showing Fig.s less than 0.05. The sample used consisted of 205 respondents, for hypothesis testing. The results of the testing stated that effort expectancy, social influence, facilitating condition, hedonic motivation, habit, and user satisfaction have a significant influence on behavior intention, and likewise, behavior intention has a significant influence on use behavior. As for the moderation variables, gender and experience less than 3 years have a significant effect on the relationship between hedonic motivation, facilitating condition, and behavior intention. On the other hand, experience exceeding 3 years has an effect on the relationship between habit and behavior intention.
Wheel Odometry-Based Localization for Autonomous Wheelchair

Localization is a fundamental requirement for an autonomous vehicle system. One of the most often used systems for autonomous vehicle localization is the global positioning system (GPS). Nevertheless, the functionality of GPS is strongly dependent on the availability of satellites, making it unreliable in some situations. As a result, autonomous vehicles must possess autonomous self-localization capabilities to ensure their independent operation. Odometry techniques are employed to achieve vehicle localization by predicting the vehicle position and orientation based on sensor measurements of the vehicle motion. One of the approaches employed in odometry is known as wheel odometry. Wheel odometry has a lower degree of reliance on the surrounding environment than visual odometry and laser odometry. This study aims to evaluate the performance of wheel odometry implementation for an autonomous wheelchair in the context of the localization process. The differential drive kinematic model is employed to determine the predicted pose of a wheelchair. This prediction is derived from the measurement of the linear and angular velocity of the wheelchair. Several experiments have been conducted to evaluate the performance of wheel odometry-based localization. Prior to experimenting, calibration procedures have also been performed to ensure accurate measurements of the sensor.
Visual Target Locking during Fast Ground Maneuver using Enhanced ORB Predictive Particle Filter

Maintaining the visual lock on a target during rapid ground maneuvers is critical for various applications, including autonomous vehicles, robotics, and defense systems. Existing visual tracking algorithms face challenges in maintaining accurate and reliable target locks in dynamic and unpredictable maneuvers, especially on previously unknown targets. One technique developed to address this issue is the ORB-enhanced particle filter (ORBPF). However, ORBPF is unable to handle quick maneuvers because the prediction method uses a constant velocity (CV) dynamic model. This research focuses on addressing the limitations of existing ORBPF tracking algorithms for fast ground maneuver objects using the constant-velocity and constant-turn (CVCT) dynamic model approach to predict the next movement. By incorporating motion prediction into the current ORBPF framework, the proposed system anticipates future target positions and adaptively adjusts tracking parameters to maintain stable locking. Functional testing was performed using an object tracking benchmark to compare ORBPF-CVCT with standard ORBPF in specific scenarios. Results show that the standard ORBPF-CV achieved RMSE up to 345,518 px while our proposed ORBPF-CVCT achieved up to 191,387 px. These results show that the proposed method is able to track fast maneuver objects better than the previous ORBPF method.
Integrating Extractive and Abstractive Models for Code Comment Generation

Code comments play an essential role in aiding developers understand and maintain source code. Current code comment generation techniques can be classified into categories: extractive methods and abstractive methods. Extractive methods use text retrieval techniques to extract important code tokens to constitute comments. Such comments contain important factual details articulated explicitly in code tokens, but are poor in naturalness. Abstractive methods usually regard code comment generation as a neural machine translation task. By leveraging powerful deep learning-based language models, abstractive methods can generate comments that resemble human writing. However, compared with natural language, programming language code is more complex. Comments generated by abstractive methods often leave out important factual details. In this paper, we propose a novel method for code comment generation by integrating extractive and abstractive models. Our extractive model is built on the Latent Semantic Analysis (LSA) model, effectively extracting important factual details in code snippets. Meanwhile, our abstractive model is built on a deep learning-based encoder-decoder model, enabling it to generate concise and human-written-like comments. We evaluate the effectiveness of our method, called ICS, by conducting extensive experiments on the CodeSearchNet dataset involving six programming languages. The results demonstrate that ICS outperforms state-of-the-art techniques in three widely used metrics: BLEU, METEOR, and ROUGE-L. Moreover, the outcomes of the human evaluation indicate that the comments generated by ICS exhibit superior naturalness and informativeness, and closely align with the provided code snippets.
An Acceleration Architecture of Polynomial Multiplication based on NTT for CKKS Homomorphic Evaluation

The Number Theoretic Transform (NTT) is currently widely used to accelerate polynomial multiplication in finite fields, which is the computational bottleneck of lattice-based fully homomorphic encryption schemes. The key challenge of NTT hardware structure implementation lies in the large amount of data storage and the complex pattern of data access. This paper proposes an efficient data access pattern of NTT algorithm. Benefiting from the specific pattern optimization of different stages of NTT, the use of hardware resource can be reduced by 46%~80%, and the performance based on FPGA is increased up to 4.9x compared with state-of-the-art NTT implementation. Furthermore, we apply the proposed hardware structure to realize the homomorphic evaluation of the CKKS homomorphic scheme, whose performance is improved by 7300x compared with software implementation.