Reviewerly/exHarmony huggingface dataset:
Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs

Abstract Training deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed. Our goal is (1) to enhance this by enabling second-order optimization methods with fewer hyperparameters for large-scale neural networks and (2) to compare optimizers for specific tasks to suggest users the best one for their problem. We introduce a novel second-order optimization method that requires the effect of the Hessian on a vector only and avoids the huge cost of explicitly setting up the Hessian for large-scale networks. We compare the proposed second-order method with two state-of-the-art optimizers on five representative neural network problems, including regression and very deep networks from computer vision or variational autoencoders. For the largest setup, we efficiently parallelized the optimizers with Horovod and applied it to a 8 GPU NVIDIA A100 (DGX-1) machine with 80% parallel efficiency.
A Framework for Providing Augmented Reality as a Service Provided by Cloud Computing for E-Learning

The main objective of the proposed study is to develop an e-learning system using augmented reality technology one of the main problems faces using AR in education is the huge computational power needed to transfer 2D animation to enrich learning facilities. Such problem increases when using smart mobile devices that suffer from hardware limitation. A promising framework is used to utilize cloud services to support augmented reality applications on the cloud. Such method significantly reduces consumption of memory and processing units when dealing with large size videos or images. Hence the augmented reality processing is speeded up to meet the requirements of E-learning systems.&#x0D; The proposed work was conducted on 100 students from different academic levels in the first semester of the year 2022. Three experiments were conducted for different fields of education including two-dimensional images using Unity Program (3D Software) to draw 3D objects and Vufoira software development kit. The experimental results showed promising results as the application has the flexibility to work on different platforms. Moreover the consumed memory to run the application is reduced significantly. The results also showed high performance for the application when drawing complex 3D images and when dealing with different animations.&#x0D; The study supported with a detailed questioner that proofs the importance of AR in the field of E-learning.
E-Learning and Education 4.0: Revolution in Education of 21st Century

The world is now experiencing hyperlapse of innovation, transformation and technological advancement that fundamentally reorients human existence. Every aspect of life is changing significantly in a distinctive way as a consequence of globalization, liberalization, technological dynamism and revolution. The fourth industrial revolution has further propelled changes in every facet of society, with education being the key requisite of socialization, and civilization has been driven by this quantum leap. The inculcation of digitalization, artificial intelligence, virtual reality, blockchain, robotics, and animation as the crucial component of IR 4.0 into the education system and classroom offers a glimpse into the present and future of vision and opportunity for acquiring valuable skills for a new generation. There is a pressing need for contemporary education to be creative, analytical, competitive and research-oriented, which ultimately strengthens individuals’ capacity to meet the challenges and needs of the IR 4.0 age. Recently, the world has recognized the unprecedented impact of the COVID-19 pandemic, which has drastically disrupted the world. During times of lockdown, academic institutions have been compelled to shutdown, denying access to education to a large number of students. Around this period of havoc, educational institutions and academicians began to rely more on online education to ensure the perpetuation of education. The introduction of Education 4.0 and the adoption of an AI-based learning approach have revolutionized the education system and have both had profound effects on education. The paradigm shift to blended and flipped classroom learning has alchemized education with technology for the collaborative and cocreative ecosystem of education.
Task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning

This article advocates for the leveraging of machine learning to develop a workload manager that will improve the efficiency of modern data centres. The proposals stem from an existing tool that allows training deep reinforcement agents for this purpose. However, it incorporates several major improvements. It confers the ability to model heterogeneous data centres and then it proposes a novel learning agent that can not only choose the most adequate job for scheduling, but also determines the best compute resources for its execution. The evaluation experiments compare the performance of this learning agent against well known heuristic algorithms, revealing that the former is capable of improving the scheduling.
Smart Clustering of HPC Applications Using Similar Job Detection Methods

In order for supercomputer resources to be effectively used, it is necessary to constantly analyze various aspects of the operation of modern HPC systems. One of the most significant aspects is the efficiency of execution of parallel applications running on a supercomputer. To study this, system administrators need to constantly monitor and analyze the entire flow of running jobs. This is a very difficult task, and there are several reasons for this - a large number and a significant variety of executed applications; the extreme complexity of the structure of modern HPC systems, and, as a result, a huge number of characteristics that need to be evaluated for each job. One way to make this analysis easier is to cluster similar jobs. Such clustering allows you to infer the behavior and performance issues of all jobs in the cluster by examining only one of these jobs, and it also helps to better understand the structure of the supercomputer job flow as a whole. In this paper, we propose a new method that allows solving this clustering task with high accuracy. This smart clustering method analyzes both static information on the executable files and dynamic data about the behavior of applications during their execution. Using the Lomonosov-2 supercomputer as an example, we demonstrate how this method can help in practice to facilitate the analysis of the execution efficiency of supercomputing applications.
Data Rivers: Carving Out the Public Domain in the Age of GPT-4

For an increasing number of data-reliant technologies (GPT4 is but one example of a tool made possible by scraping data from the internet), the erection of IP ‘fences’ is an existential threat. European and British regulators are alert to it: so-called ‘text and data mining’ exceptions are at the heart of intense debates. In the US, these debates are taking place in court hearings structured around ‘fair use’. While the concerns of the corporations developing these tools are being heard, there is currently no reliable mechanism for members of the public to exert influence on the (re)-balancing of the rights and responsibilities that shape our ‘data rivers’. Yet the existential threat that stems from restricted public access to such tools is arguably greater. When it comes to developing successful data ecosystems fit for technologies such as GPT4, much can be learned from age-old river management practices, with one important proviso: data not only carries traces of our past. It is also a powerful tool to envisage different futures. If data-powered technologies such as GPT4 are to live up to their potential, we would do well to invest in bottom-up empowerment infrastructure. Such infrastructure could not only facilitate the valorisation of and participation in the public domain. It could also help steer the (re)-development of ‘copyright as privilege’ in a way that is better able to address the varied circumstances of today’s original content creators.
Exploiting Video Classification Using Deep Learning Models for Human Activity Recognition

One of the most important and challenging research areas in the fields of Computer Vision is Human Activity Recognition (HAR). It has various applications including human-computer interaction, Intelligent driving. Intelligent video surveillance, human–robot interaction, ambient assisted living, etc. The major issues faced in this domain are feature extraction and feature selection which ends up in using handcrafted feature representation-based methods. This issue has been successfully addressed by Deep Learning techniques in various state-of-the-art classification methods for images and videos. But still which method is suited in which condition depends on the kind of dataset used for analysis. Deep Learning models like Convolutional Neural Networks (CNN), Variants of CNN, Recurrent Neural Networks (RNNs), and other fusion methods have shown promising results for a specific kind of activity related to HAR. Activity in HAR can be categorized as Gesture, Action, Interaction (Single-User, Multiple-User, Group). Each category has its own set of issues. This research aims to focus on Video Classification in bench marked datasets like UCF50, KTH, and Wizmann for Human gesture and Action Recognition which will then be confined to recognize the particular activity performed in the datasets. CNN and RNN fusion models such as Long Term Recurrent Convolutional Networks (LRCN) and ConvLSTM. These models performed efficiently of all the benchmarked datasets and hence can be utilized for Human Activity Recognition in arbitrary video dataset.
Comparative Study of Machine Learning Algorithms for Prediction of SQL Injections

Web apps are the most extensively utilized digital platforms due to their cross-platform compatibility and the fact that they do not need users to install anything in order to use them, making the usage of online applications on a vast scale, and therefore, the security risk of web apps is increasing. SQL injection is one of the most dangerous security assaults, causing damage to a company's reputation, financial losses, and the privacy of its clients. Various classification algorithms can be used to determine whether a particular code is malicious or plain. Some of the neural network and machine learning algorithms are Naive Bayes classifier, LSTM, MLP, and SVM which can be used for the detection of SQL Injection attacks. We compared various algorithms on a common dataset in this study to see how well they performed.
A Deep Learning Based Human Activity Recognition System for Monitoring the Elderly People

Human Activity Recognition (HAR) is an arena that looks at uncooked time-series signals from embedded sensors in smartphones and wearable devices to figure out what people are doing. It has become very popular in a lot of smart home environments, especially to keep an eye on people's behavior in ambient assisted living to help the elderly and get them back on their feet. The system goes through a series of steps to get data, remove noise and distortions, find features, choose features, and classify them. Recently, a number of state-of-the-art techniques have proposed ways to extract and choose features from data. These techniques have been classified using traditional Machine learning. Although many techniques use simple feature extraction processes, they are unable to identify complicated actions. Many HAR systems now use Deep Learning algorithms to swiftly discover and categorize characteristics as high-performance computers become more common. In this study, video shortening is utilized to discover key frames and select relevant sequences in two datasets. The selected frames are resized using adaptive frame cropping, where this image is given as an input to the proposed Convolutional Neural Network (CNN). The experiments are conducted on various activities in terms of classification rate. From the results, it is proved that CNN achieved 98.22% of classification rate on Weizmann action dataset.
Story Telling: Learning to Visualize Sentences Through Generated Scenes

Visual learning has been helping people to store information for a longer period of time. It helps us to process information primarily through visuals and improves learning. Natural language text descriptions are given as an input, an ML model is built for image classification using Convolutional Neural Network (CNN), and the text is tokenized and given to the POS tagger. Further, the tagged objects are displayed in the form of a visual 2D scene in the Blender application. Thus, we propose a text-to-2D scene generation system which incorporates user interaction for preprocessing the output of the generated scene for the storyDB dataset. The dataset consists of abstract images required for the identification of children with autism. Our approach is an attempt to improve the memorization skill to remember and visualize the object next time when the word is heard by the child.
The Role of Digitalization in Achieving Cybersecurity in the Moroccan Banking System

The present study aims to identify both the process of digitalization of the banking system and to test the impact of digitalization on the realization of cybersecurity in the Moroccan banking system and operations, by studying the dimensions of digitalization, which are obviously represented in its impact on achieving cybersecurity in the banking system. Indeed, the banking systems of our modern era are facing complex cyberattacks, which are difficult to address with traditional means of protection, which is why various banks are resorting to the use of cybersecurity systems based on artificial intelligence aiming at securing their assets and their infrastructure to reduce the potential risk level of risks. To implement the objectives of this study, we test the impact of digitization on achieving cybersecurity in the banking system. We studied the subvariables that facilitate the process of measuring the effectiveness of each variable, and tested the impact of digitization on achieving cybersecurity in the Moroccan banking system. This study enabled us to confirm that digitization has a major role in achieving cybersecurity for the Moroccan banking system. The analysis of this article was performed using the Statistical Package for the Social Sciences (SPSS V. 26) for basic descriptive statistics and (Smart PLS 3.2.7) for SEM-PLS modelling.
The impact of 3D stereoscopic visualisation on performance in electron skin apposition techniques using VERT

Abstract Introduction: The Virtual Environment for Radiotherapy Training (VERT) is a simulator used to train radiotherapy students cost-effectively with limited risk. VERT is available as a two-dimensional (2D) and a more costly three-dimensional (3D) stereoscopic resource. This study aimed to identify the specific benefits afforded by stereoscopic visualisation for student training in skin apposition techniques. Method: Eight participants completed six electron skin apposition setups in both 2D and 3D views of VERT using a 7 cm × 10 cm rectangular applicator setup to 100 cm focus skin distance (FSD). The standard deviation (SD) of the mean distance from each corner of the applicator to the virtual patient’s skin surface [which we define as apposition precision (AP)] was measured along with the time taken to achieve each setup. Participants then completed a four-question Likert-style questionnaire concerning their preferences and perceptions of the 2D and 3D views. Results: There was little difference in mean setup times with 218·43 seconds for 2D and 211·29 seconds for 3D (3·3% difference). There was a similarly small difference in AP with a mean SD of 5·61 mm for 2D and 5·79 mm for 3D (3·2% difference) between views. The questionnaire results showed no preference for the 3D view over the 2D. Conclusion: These findings suggest that the 2D and 3D views result in similar setup times and precision, with no user preference for the 3D view. It is recommended that the 2D version of VERT could be utilised in similar situations with a reduced logistical and financial impact.
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation

Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution operations do not provide the geometric invariance required for the descriptor. To address this issue, we propose the Sparse Deformable Descriptor Head (SDDH), which learns the deformable positions of supporting features for each keypoint and constructs deformable descriptors. Furthermore, SDDH extracts descriptors at sparse keypoints instead of a dense descriptor map, which enables efficient extraction of descriptors with strong expressiveness. In addition, we relax the neural reprojection error (NRE) loss from dense to sparse to train the extracted sparse descriptors. Experimental results show that the proposed network is both efficient and powerful in various visual measurement tasks, including image matching, 3D reconstruction, and visual relocalization.
Design and research of substation inspection mode based on digital twins

Aiming at the problems of single inspection mode and poor real time performance in current substations, a substation inspection visualization system based on digital twins is proposed. The development process of substation twin system is introduced. Based on the digital twin five-dimensional model, the connection between physical entities and twin entities is established. The modeling of physical entities, real-time data mapping and result analysis are described respectively. At the same time, the verification and analysis of the system results can prove that the model based on the digital twin system can effectively carry out virtual real synchronization and prove the effectiveness of the system.
The Multiple Model Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

In an intelligent transportation system (ITS), the single motion model is not enough to meet the needs of simulation systems (or practical applications) and guarantee good tracking performance for multiple maneuvering extended target tracking (ETT). This article presents multiple models Poisson multi-Bernoulli mixture (MM-PMBM) filter for ETT to address this problem. To make multiple models’ transform in an accurate manner, the jump Markov system (JMS) is timely introduced in filtering recursion. After that, the model probability of target is recursively updated and used to estimate the target position. In addition, we derive the closed-form solution to the proposed filter based on the Gamma Gaussian inverse-Wishart (GGIW) implementation. The approach to handling the merging problem of GGIW components via Kullback–Leibler divergence (KLD) minimization is also presented, resulting in better utilization of available components. The simulation results demonstrate that the proposed filter has superior performance in comparison with several state-of-the-art multitarget filters.
A Down-sampling Method Based on The Discrete Wavelet Transform for CNN Classification

Convolutional neural networks usually have an upper bound for the input size due to a limited hardware resource, which causes the inconsistent size problem that the images are generally too large to match the input requirement. Therefore, an efficient down-sampling method is in need urgently. Existing methods remove a great amount of information when reducing the size, resulting in accuracy degradation. We start with the Grid Channels Stack method and propose a novel down-sampling method which based on the discrete wavelet transform. Our method not only retains more information than the commonly used bilinear interpolation but also suppresses the distortion impact compared to the Grid Channels Stack method. We evaluate the proposed method with the popular ResNet-18 network on the CIFARIOO and the MINI-ImageNet datasets. The improved performance demonstrates the effectiveness of our method, especially for the various compression rate occasions, and we believe it will be a promising down-sampling method for the CNN domain.
Sharing Visual Secrets Among Multiple Groups With Enhanced Performance

An almost unexplored research topic in visual secret sharing (VSS), i.e., sharing different visual secrets among various groups, is investigated in this article. Generally, multiple groups are cooperated together to encrypt multiple visual secrets, where every group is correlated with a VSS scheme to share one secret and each participant receives only one shadow. Such a cooperative system is referred to as cooperative VSS (CVSS). In this paper, we develop a general construction for CVSS with improved computational overhead. The basis matrices for generating the shadows are easily obtained. To improve the recovered image quality, an efficient XOR-based CVSS (XCVSS) is given. Furthermore, to fulfill the requirement of implementing complex sharing policy, the XCVSS is extended to collaborate general access structure (GAS) VSS schemes together. Theoretical and experimental results and comparisons are illustrated to demonstrate the advantages of the proposed techniques.
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation

The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context modeling capacity, which is critical to semantic segmentation. In this paper, we propose a parametric-free patch rotate operation to reorganize the pixels spatially. It first divides the feature map into multiple groups and then rotates the patches within each group. Based on the proposed patch rotate operation, we design a novel segmentation network, named PRSeg, which includes an off-the-shelf backbone and a lightweight Patch Rotate MLP decoder containing multiple Dynamic Patch Rotate Blocks (DPR-Blocks). In each DPR-Block, the fully connected layer is performed following a Patch Rotate Module (PRM) to exchange spatial information between pixels. Specifically, in PRM, the feature map is first split into the reserved part and rotated part along the channel dimension according to the predicted probability of the Dynamic Channel Selection Module (DCSM), and our proposed patch rotate operation is only performed on the rotated part. Extensive experiments on ADE20K, Cityscapes and COCO-Stuff 10K datasets prove the effectiveness of our approach. We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.