Reviewerly/exHarmony huggingface dataset:
Input Feature Pruning for Accelerating GNN Inference on Heterogeneous Platforms

Graph Neural Networks (GNNs) are an emerging class of machine learning models which utilize structured graph information and node features to reduce high-dimensional input data to low-dimensional embeddings, from which predictions can be made. Due to the compounding effect of aggregating neighbor information, GNN inferences require raw data from many times more nodes than are targeted for prediction. Thus, on heterogeneous compute platforms, inference latency can be largely subject to the inter-device communication cost of transferring input feature data to the GPU/accelerator before computation has even begun. In this paper, we analyze the trade-off effect of pruning input features from GNN models, reducing the volume of raw data that the model works with to lower communication latency at the expense of an expected decrease in the overall model accuracy. We develop greedy and regression-based algorithms to determine which features to retain for optimal prediction accuracy. We evaluate pruned model variants and find that they can reduce inference latency by up to 80% with an accuracy loss of less than 5% compared to non-pruned models. Furthermore, we show that the latency reductions from input feature pruning can be extended under different system variables such as batch size and floating point precision.
Accelerating Broadcast Communication with GPU Compression for Deep Learning Workloads

With the rapidly increasing model sizes, state-of-the-art Deep Learning (DL) models rely on multiple GPU nodes to run distributed training. Large message communication of GPU data between the GPUs is becoming a performance bottleneck in the overall training performance. GPU-Aware MPI libraries are widely adopted for state-of-the-art DL frameworks to improve communication performance. In the existing optimization solutions for Distributed Data-Parallel (DDP) training, the broadcast operation is often utilized to sync up the updated model parameters among all the GPUs. However, for state-of-the-art GPU-Aware MPI libraries, broadcasting large GPU data turns to overburden the training performance due to the limited bandwidth of interconnect between the GPU nodes. On the other hand, the recent research on using GPU-based compression libraries to lower the pressure on the nearly saturated interconnection and co-designing online compression with the communication pattern provides a new perspective to optimize the performance of broadcast on modern GPU clusters.In this paper, we redesign the GPU-Aware MPI library to enable efficient collective-level online compression with an optimized chunked-chain scheme for large message broadcast communication. The proposed design is evaluated to show benefits at both microbenchmark and application levels. At the microbenchmark level, the proposed design can reduce the broadcast communication latency by up to 80.9% compared to the baseline using a state-of-the-art MPI library and 55.1% compared to the existing point-to-point-based compression on modern GPU clusters. For DDP training with PyTorch, the proposed design reduces the training time by up to 15.0% and 6.4% compared to the existing chunked-chain scheme and point-to-point-based compression, respectively, while keeping similar training accuracy. To the best of our knowledge, this is the first work that leverages online GPU-based compression techniques to significantly accelerate broadcast communication for DL workloads.
The Computation of Quantum Radar cross section for the 2D Cardioid Target

The quantum radar cross section (QRCS) is a measure of the ability of a quantum radar to detect an object. Clearly, it is particularly essential for the study of invisible weapon platforms. In our work, we have displayed the computation of QRCS and classical radar cross section (CRCS) for the 2D cardioid target. Finally, we discuss the sidelobe quantum impact between CRCS and QRCS.
From Crystal-clear to Limpide: Translating English [Noun+adj] Compound Adjectives with a Figurative-intensifying Noun into French

English [Noun+Adj] compound adjectives containing an intensifying metaphor (e.g. crystal-clear) pose particular challenges for French translation, due in part to the absence of a direct equivalent construction. Our study examines morphosyntactic and conceptual-semantic translation procedures that capture how these challenges are resolved. We also explore the little-investigated aspect of translation variation (the number of different solutions for each item). We analyze the potential effects of two factors: the presence or absence of figurative intensification and the items’ frequency of use in English. Our results indicate that translators prefer different morphosyntactic procedures for different compound subtypes. Overall, an adjective constituent is most frequently retained, although complete reformulations with a noun or verb also occur. Semantically, the intensifying meaning is often rendered non-figuratively, depending on what is available in idiomatic French usage. Intensification is also frequently dropped. Translation variation is remarkably high, due in part to extensive use of near-synonyms. High-frequency items do not appear to converge on a smaller number of translations, but instead provide more opportunities for diversification.
Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents

This article presents the results of a survey conducted to elicit keywords or phrases relating to cyberbullying incidents in both English and Malay languages commonly used in Malaysian society. The keywords or phrases can be utilized as a Malay and English cyberbullying glossary in the development of an auto-detection cyberbullying tool. A set of questionnaires were distributed among 329 respondents ages 15–30 years in Malaysia in the form of an online survey over one-and-a-half-month starting 1 November 2021. This study was conducted to test the items’ reliability using Cronbach’s alpha values. There are three (3) Sections to this questionnaire; Sect. 1 is about the demographics of the respondents, Sect. 2 is related to Cyberbullying, and Sect. 3 shows a few scenarios that might be or might not be a cyberbullying incident. Findings from the analyses showed that 447 words were collected, and all of these were later grouped into five (5) categories 5; Intellectual, Physical Appearance, Insulting/Offensive, Intimidating and Others. Making offensive comments or doing insulting posts was the most cyberbullying form made by the bullies. Five (5) popular words or phrases were identified as the common cyberbullying content in Malaysian society.
Development of ‘SiPantik’ for the Teachers’ Test Items Production

The fulfillment of the intended learning outcomes depends on producing test items that adhere to the principles outlined in Principle 9-Points. In addition, the necessity of follow-up activities to optimize students’ abilities because of the effective test item measures cannot be underestimated. However, there is currently no system that offers teachers systematic direction for producing test items that adhere to the principles of item construction and item evaluation based on the principle 9-Points. Consequently, the primary purpose of this study is to establish a systematic guidance system that can generate test items that adhere to the principle 9-Points. System Development Life Cycle adapted Waterfall method used for guiding the development of the Systematic Guidance System, namely SiPantik. With SiPantik, it is certain that teachers will be able to generate effective test items efficiently and effectively. Effective test items can measure student achievement and enable teachers to arrange suitable follow-up actions based on student capacities. Finally, compliance with aspects of Pupil Assessment Management as required in Malaysian Education Quality Standards Wave 2 (SKPMg2) can be realized.
Prosthetic Parts for Protecting Mobile Robots

Mobile robotics are currently designed to be worked in difficult and complicated conditions of applications such as disasters and dangerous areas in industries. Such types of robots need to protect themselves from damage against unexpected effects. In this paper we propose five different prosthetic parts, which can be installed on the mobile robots to increase survival chances. These protection mechanisms were inspired by how some animals protect themselves in nature. The five proposed prosthetic parts are named as umbrella, four-bar mechanism, retractable roof, turtle shell, and box shell. A visual design of all five methods is included in this paper. The analysis of all the five shells is based on the ABS Plastic as the material of the shell, mainly due to its low density and adequate strength, and 1000 N distributive external static force. The results showed the ability of equipping the introduced shells into the mobile robot platform without needing mechanical modifications.
Development of a Detection System for Endangered Mammals in Negros Island, Philippines Using YOLOv5n

Many species have gone extinct as a result of human neglect and various environmental influences. Monitoring these species has proven to be a challenge due to their small population, remote habitats, and evasiveness, among other reasons. Nonetheless, they can be routinely tracked by using CCTV cameras. This project made use of a transfer learning approach to detect the presence of Malayan civets (Viverra tangalunga), Visayan leopard cats (Prionailurus javanensis sumatranus), Visayan spotted deer (Rusa alfredi) and Visayan warty pigs (Sus cebifrons) in the forests of Negros Island. We also developed a web application to record camera data and assign timestamps. The setup consisted of a Raspberry Pi 3B+ and a Raspberry Pi camera module powered by a solar power bank. The method used YOLOv5n, a lightweight object detection algorithm, to detect the four species. The trained model yielded 91% mAP, 64% mAP@0.5:0.95, and the following average precisions: 94% (Visayan warty pig), 91% (Malayan civet), 88% (Visayan leopard cat), and 91% (Visayan spotted deer) with 4.5 GFLOPs.
A Blockchain-Based E-Voting System

E-voting methods have become increasingly popular in recent years due to the rapid growth of internet and computer networks. Many e-voting schemes have been proposed by researchers that use cryptographic tool such as blind signature technique and homomorphic encryption technique. However, most of these proposed schemes use a centralised database as their data storage medium and it causes various security risks, such as data alteration by a third-party in the network due to the use of central database system. Therefore, it is a need to have a trusted entity to manage the voting process. This brings the involvement of blockchain technology in this proposed e-voting scheme which can provide a decentralised model that makes the network flexible, reliable and able to support real-time services. This study focuses on the development of a secure blockchain-based e-voting system. Finding shows that the proposed scheme is secure and efficient as it outperforms some of the existing schemes with respect to transaction cost.
A Safe Box Safety System with Facial Authentication for Multiple Users

A safe box with a security system protects personal and valuable items from being stolen, tampered or damaged. The most used security mechanism is using keys and cards, or the use of passwords, personal identification numbers (PINS), and fingerprints for personal identification. Face recognition is one of the most effective security confirmation techniques for a safe box management system. This project proposed a comprehensive face-authentication-based security system that includes software, hardware, and the Internet of Things (IoT). In the software system, the OpenCV built-in library and face recognition algorithm written in Python were implemented. A total of 150 images (i.e., 50 images per subject from 3 individuals) were registered in the database for training and testing the developed face recognition system. The control and working of the hardware are performed by a Raspberry Pi, to which a Pi camera, an infrared sensor, and a solenoid lock are connected. The IoT enables the communication between the Raspberry Pi and Telegram cloud, allowing real-time monitoring of the safe box and alerting the owner by capturing images of approaches. The developed system shows an accuracy ranging from 0.79 to 0.91, the precision range of 0.571–1, the recall range of 0.64–0.88, and the F1-score ranging from 0.6 to 0.81.
Face Illumination Reduction Using MADPIP Restoration Approach to Biometric Patient Authentication System

Despite of the rapid development of computer vision technology and the widely advancement of digital image processing systems used in many fields, some uncontrollable factors often exist during the process of image acquisition, resulting in various image defects. In particular, under poor illumination conditions, such as indoors, night-time, or cloudy days, the light reflected from the object surface may be weak; consequently, the image quality of such a low-light image may be seriously degraded due to colour distortions and noise. Reducing illumination by various techniques such as edges extraction for example the High Pass Filter is only good in performing one of the image restoration tasks, which is sharpening adjoining areas with slight difference in the brightness. However, in order to produce a complete restoration for a very low level of illuminance image, restoration process has to have a balance process of blurring and sharpening processes. The Low-Pass Filter is one of blurring filter methods that can be used to blur images. In order to find the best hybrid of image restoration algorithm, several combinations of blurring and sharpening processes have to be conducted. For that reason, this paper proposes a new method called the Multiple Adaptive Derivatives for Passive Image Processing (MADPIP) which is a module that performs a series of synergy algorithm combinations in restoring illuminated images by reducing illumination noise and revealing the facial outlines and facial features. In this work, the MADPIP global and local features extraction algorithms were investigated. It was concluded that the Zero Padding + Laplacian Masked and Border Replication + Low Pass Filter (ZPL + BRLPF) combination has the best score for the MSE assessment and thus best algorithm for global feature extraction. On the other hand, for local features extraction, the 2L derivative with 3 colour Logarithmic and HPF-Unsharp Masking with 8 scaling factors has produced the best result in retrieving the local features extraction.
Attendance Management System with Half-Covered and Full-Facial Recognition Feature

Manual way of tracking attendance is inefficient and has vulnerability in protecting personal data. Various attendance management systems have been introduced to replace the manual attendance tracking process. This includes the integration of face recognition technique in the attendance management system. However, following the outbreak of pandemic COVID-19, we are strongly advised to always put on a face mask to protect ourselves and others. This practice has caused problems to existing attendance management system with facial recognition. This is because the mask has covered the essential data that can be measured and extracted by the facial recognition algorithms. To overcome this problem, an attendance management system with half-covered and full-facial recognition feature is proposed. MobileFaceNet model is used to verify the user identity for a real-time attendance check-in. Users are able to take attendance via the application with or without a face mask.
Kutsyuruba, B., Cherkowski, S., &amp; Walker, K. D. (Eds). (2021). Leadership for Flourishing in Educational Contexts. Canadian Scholars

Kutsyuruba, B., Cherkowski, S., & Walker, K. D. (Eds). (2021). Leadership for Flourishing in Educational Contexts. Canadian Scholars. An article from journal Canadian Journal of Educational Administration and Policy / Revue canadienne en administration et politique de l'éducation (Number 201, 2022, pp. 2-115), on Érudit.
Attention-guided Boundary Refinement on Anchor-free Temporal Action Detection

Modelling temporal dependencies is important for accurate action detection. In this work, we develop a temporal attention unit to mine the global dependencies among features from different temporal locations. Additionally, based on the developed temporal attention unit, we propose an attention-guided boundary refinement module for revising action prediction results. Besides, we integrate the proposed module into a contemporary anchor-free detector for performing temporal action detection. To evaluate the proposed method, experiments are carried out on two large-scale temporal action detection datasets, namely THUMOS14 and ActivityNet1.3 datasets. Experimental results show that the action detection performance is significantly boosted by the proposed temporal attention module which outperforms several state-of-the-art methods.
Drawing and Analysis of Bounding Boxes for Object Detection with Anchor-Based Models

Supervised object detection models are trained to recognize certain objects. These models are classified into two types: single-stage detectors and two-stage detectors. The single-stage detectors just need one pass through the model to anticipate all the bounding boxes, whereas the two-stage detectors require to first estimate the image portions where the object could be located. Due to their speed and simplicity, single-stage anchor-based models are used in many industrial settings. Training such models require bounding boxes that describe the spatial location of an object, which are usually drawn by an expert. However, the question remains, how much area should be considered when drawing the bounding boxes? In this paper, we demonstrate the effects that the size and placement of a rectangular bounding box can have on the performance of the anchor-based models. For this, we first perform experiments on a synthetically generated binary dataset and then on a real-world object detection dataset. Our results show that fixing the size of the bounding boxes can help in improving the performance of the model in the case of single class object detection (approximately 50% improvement in mAP@[.5:.95] for real world dataset). Furthermore, we also demonstrate how freely available tools can be combined for obtaining the best possible semi automated object labeling pipeline.
Accuracy of Parallel Distance Mapping Algorithms When Applied to Sub-Pixel Precision Transform

The problem of distance mapping is a thoroughly studied topic in the field of image processing. Much focus has been spent on perfecting algorithms, calculating the Euclidean Distance Transform on binary images, in order to achieve the highest accuracy as efficiently as possible. Less focus has been spent on perfecting algorithms calculating the Euclidean Distance Transform on non-binary images where a much higher degree of precision is made possible by increasing the granularity of the input image. This study focuses on taking three different established methods for calculating distance mapping on binary images and applying them to non-binary images. We have found that the simpler algorithms, which simply propagate distance information according to a predetermined pattern work well with our new transform. The more analytical and in the case of the binary image, exact method, however, does not work as well with our new transform. Its way of propagating distance information is making too strict assumptions about the geometry of an image, which hold true for binary images, but not for non-binary images.
Distortion-Based Transparency Detection Using Deep Learning on a Novel Synthetic Image Dataset

Transparency detection is a hard problem, as suggested by animals and humans flying or running into glass. However, humans seem to be able to learn and improve on the task with experience, begging the question, whether computers are able to do so too. Making a computer learn and understand transparency would be beneficial for moving agents, such as robots or autonomous vehicles. Our contributions are threefold: First, we conducted a perception study to obtain insights about human transparency detection methods, when borders of transparent objects are not visible. Second, based on our study insights we created a novel synthetic dataset called DISTOPIA, which focuses on the warping properties of transparent objects, placed in a variety of natural scenes and contains over 140 000 high resolution images. Third, we modified and trained a deep neural network classification model with an attention module to detect transparency through warping. Our results show that a neural network trained on synthetic data depicting only distortion effects can solve the transparency detection problem and surpasses human performance.
RELIEF: Joint Low-Light Image Enhancement and Super-Resolution with Transformers

The goal of Single-Image Super-Resolution (SISR) is to reconstruct a High-Resolution (HR) version of a degraded Low-Resolution (LR) image. Existing Super-Resolution (SR) methods mostly assume that the LR image is a result of blurring and downsampling the HR image, while in reality LR images are often degraded by additional factors such as low-light, low-contrast, noise, and color distortion. Due to this, current State-of-the-Art (SoTA) SR methods cannot reconstruct real low-light low-resolution images, and a straightforward strategy is, therefore, to first perform Low-Light Enhancement (LLE), followed by SR, using dedicated methods for each task. Unfortunately, this approach leads to poor performance, which motivates us to propose a method for joint LLE and SR. However, since LLE and SR are both ill-posed and ill-conditioned inverse problems, the joint reconstruction task becomes highly challenging, which calls for efficient ways to leverage as much as possible of the available information in the degraded image during reconstruction. In this paper, we propose REsolution and LIght Enhancement transFormer (RELIEF), a novel Transformer-based multi-scale hierarchical encoder-decoder network with efficient cross-shaped attention mechanisms that can extract informative features from large training patches due to its strong long-range dependency modeling capabilities. This in turn leads to significant improvements in reconstruction performance on real Low-Light Low-Resolution (LLLR) images. We evaluate our method on two publicly available datasets and present SoTA results on both.
Prototype Softmax Cross Entropy: A New Perspective on Softmax Cross Entropy

In this work, we consider supervised learning for image classification. Inspired by recent results in the field of supervised contrastive learning, we focus on the loss function for the feature encoder. We show that Softmax Cross Entropy (SCE) can be interpreted as a special kind of loss function in contrastive learning with prototypes. This insight provides a completely new perspective on cross entropy, allowing the derivation of a new generalized loss function, called Prototype Softmax Cross Entropy (PSCE), for use in supervised contrastive learning. We prove both mathematically and experimentally that PSCE is superior to other loss functions in supervised contrastive learning. It only uses fixed prototypes, so no self-organizing part of contrastive learning is required, eliminating the memory bottleneck of previous solutions in supervised contrastive learning. PSCE can also be used equally successfully for both balanced and unbalanced data.