Reviewerly/exHarmony huggingface dataset:
3D Face Reconstruction from Front and Profile Images for Low Computational Devices

This Face reconstruction in three dimension (3D) is a difficult and advanced problem for computer vision, and our goal is to accomplish it in an efficient way to get 3D models closer to reality. Although many algorithms for construction of 3D model from two dimensional (2D) images are present, we propose an approach using front and one profile image auto-captured from video frame. The input images are used to build the UV-map and Mesh selection using various image processing techniques for small computing devices. Multiple OpenCV image processing methods are utilized for generation of the UV-map, but as its core element, it relies on the Haar Cascade face detection algorithm. For Mesh construction, it uses MediaPipe library, using Face Mesh 468 landmarks to identify the shape of the face in the image and compare it with our own dataset of mesh of most similar structure for faster 3D reconstruction. Though, we have achieved good results from the approach, we are still investigating other enhancement to make the model an identical replica.
PipePrune: Pipeline Parallel Based on Convolutional Layer Pruning for Distributed Deep Learning

Benefitting from the combination of the idea of pipeline with model parallelism and data parallelism, pipeline parallelism improves the efficiency of distributed deep learning systems significantly. However, suffering from the bubbles and gaps caused by the imbalance of different stages in pipeline, it can not output ideal performance yet. To explore the potential of pipeline parallelism further, we propose a novel approach called PipePrune, which adds a convolutional layer pruning strategy to pipeline creatively to reduce the bubbles and gaps. For the convolutional layers with heavy overheads, some unimportant kernels are pruned by the measurement of the L1-norm. This approach makes the processing overheads of different pipeline stages more balanced. The experimental results show that, compared with state-of-the-art pipeline methods, PipePrune can improve the training speeds obviously (e.g. for ResNet50 on ImageNet, about 30&#x0025; speed improvement is realized with only 1.1&#x0025; loss of training accuracies).
Scrutiny of Block-chain Technology with Use Cases

A blockchain is a distributed, decentralized database of records that allows for rapid, secure transactions without the need for centralized control. It is a method of storing data in such a way that it is difficult or impossible to alter, hack, or cheat it. Blockchain is a type of Distributive Ledger Technology (DLT) in which transactions is recorded with an immutable cryptographic signature called Hash.
Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules

In knowledge graph completion, a symbolic reasoning method establishes a human readable rule by analyzing an imperfect knowledge graph and infers knowledge omitted by an inference engine. However, the entire rules cannot be defined based on a large-scale knowledge graph. This study proposes a method, based on a knowledge graph, that can facilitate end-to-end learning and induce rules without several processing steps that require direct human involvement. The proposed method combines the concept of unification used in symbolic reasoning and deep learning for training vectors expressing symbols. It trains the vectors expressing relations of rule schemas defined to induce rules based on a given knowledge graph. Furthermore, the performance of the proposed method is evaluated against neural theorem prover and the greedy neural theorem prover, which are recently developed neuro-symbolic models, based on four benchmark datasets. The experimental results verify that the proposed method induces more significant rules in less training time. Furthermore, this study conducted an experiment on knowledge graph completion, implemented by an inference engine. Based on the experiment results, it was confirmed that the rules induced by the proposed model can indeed effectively complete missing knowledge.
Decision Making on School Closure Based on Correlation Analysis During COVID-19 Pandemic

A significant outcome of the COVID-19 pandemic has been the hyper dependency on digital tools. As schools and, at many times, businesses have converted online, the use of digital tools has increased rapidly. Since the situation of COVID-19 fluctuates, national and state-level regulations have caused schools to switch back and forth between traditional and online formats. When deciding between the two, governments should factor in the effects on mental health to prevent increased rates of depression. Our research aims to analyze the correlation between depression rates and the use of digital tools to implement an autoregressive model to predict the best times to switch between online and traditional teaching formats. Based on our findings, the autoregressive model will be implemented to predict when depression rates would peak after school closures.
StatekeepSpec: Securing Speculative Execution via forbidding Change-Of-State

Speculation-based attacks, e.g., Spectre and Meltdown, affect hundreds of millions of computers. These attacks utilize speculative execution to access privileged data and leak it through a side-channel, e.g., typical a cache channel. Existing hardware-based defense solutions require major changes to the microprocessor architecture and incur large overheads, while software-based solutions address only specific exploit techniques. In this paper, we propose StatekeepSpec, a novel defense against Spectre attacks by forbidding speculative instructions&#x0027; side effects to the cache hierarchy. Specifically, given a speculative load, if it triggers a cache hit then StatekeepSpec enforces it reading data from the cache without updating its replacement information; otherwise a cache miss occurs then StatekeepSpec enforces it reading data directly from the next level cache or memory, without updating the current level cache. As a result, StatekeepSpec blocks micro-architectural covert and side channels through the cache hierarchy caused by speculative loads. We have implemented a prototype of StatekeepSpec by modifying the design of an x86-64 processor using the Gem5 full system simulator. The evaluation results show that StatekeepSpec provides a strong security guarantee against cache based Spectre attacks. Furthermore, our simulations with 23 SPEC CPU workloads show that the performance overhead introduced by StatekeepSpec is less than 29&#x0025;.
Improving Restore Performance of Deduplication Systems via a Greedy Rewriting Scheme

Data deduplication has been widely used to improve storage space utilization, however, it is baffled by data fragmen-tation: logically consecutive chunks physically scattered across various containers. Many rewriting schemes, rewriting fragment-ed duplicate chunks into new containers, attempt to alleviate the restore performance degradation caused by fragmentation. Unfortunately, these schemes rely on a fixed threshold and fail to choose the appropriate set of old containers for rewriting, which leads to substantial redundant chunks existing in the retrieved containers when restoring backups. To address this issue, we propose a flexible threshold rewriting scheme to improve restore performance while maintaining high backup performance. We define an effectiveness metric - valid container reference counts (VCRC) - that facilitates identifying the appropriate containers for rewriting. We design a greedy-algorithm-based algorithm called F-greedy that dynamically adjusts the threshold according to the distribution of containers' VCRC, aiming to rewrite low-VCRC containers. We quantitatively evaluate F-greedy on three real-world backup datasets in terms of restore performance, backup performance, and storage overhead. The empirical results show that compared with two state-of-the-art schemes (Capping and SMR), our scheme improves the restore speed of the exiting algorithms by 1.3x - 2.4x while achieving similar backup performance.
Adaptive GRU With Regularization Item for Book Sales Prediction

Book sales forecasting is a very important practical task for the retail industry, which can solve the contradiction between supply and demand for online or offline book transactions. Currently, RNNs and their variants have been successfully used in the field of time series prediction tasks. However, the temporal change law of book sales volume data is extremely complicated and there is no significant periodicity, which makes it difficult for the existing RNN-based models to learn such change law. To solve the above problems, an Adaptive Gated Recurrent Unit (AdaGRU) model, which has an adaptive learning mechanism is proposed. On the one hand, the factor for capturing temporal information is obtained by using an update gate in the AdaGR U model. On the other hand, this factor is used to mix the intermediate values of current and historical hidden state information. The aim that this process is to learn complex temporal information that changes over time. In addition, considering that the AdaGRU model may have the problem of overfitting, a variant of the L2 regularization term is proposed to alleviate this phenomenon. Finally, the experimental results show that our proposed method overcomes the state-of-the-art model on two real-world datasets.
Partial Attention CenterNet for Bottom-Up Human Pose Estimation

The typical bottom-up human pose estimation methods can be divided into two steps, keypoint detection and grouping. The traditional keypoint regression-based methods exploit an effective backbone (like HRNet) and different prediction heads to acquire the body center and body joint. Then they utilize the offset between the body center and body joint to figure out the grouping task. In this paper, we first propose a body branch module and keypoint attention module to improve keypoint detection and keypoint regression. In body branch module, we exploit a multi-branch structure for keypoint detection and keypoint regression. Each branch represents a part of human body. In keypoint attention module, two simple yet reliable pooling layers are adopted to extract the attention areas of different kinds of keypoints. Combining these two modules, we propose a Partial Attention CenterNet for multi-person human pose estimation. The proposed method outperforms the traditional keypoint regression-based methods. Experiments have demonstrated the obvious performance improvements on COCO dataset brought by the introduced components.
Design and Implementation of Monitoring System for New Energy Data Call Based on Blockchain

With the energy revolution starting the third transformation to new energy, new energy has become essential for human energy. In order to better organize and utilize new energy, more and more research is committed to predicting the power generation capacity of new energy through the power generation data collected by intelligent systems to make better use of new energy. However, with the increase of new energy digital platforms and supporting infrastructure improvement, the distribution and management of relevant data also begin to face significant challenges. New energy data faces a substantial potential threat in distributing and using data to various digital platforms. Based on this, this paper designs and implements a new energy data call monitoring system based on blockchain to ensure the security of new energy data by tracking data operation information.
Kabuki Explanation System Based on User’s Knowledge and Interests

This paper introduces the generation of explanations for a stage-performing structural simulation system using the animation of kabuki dance that we have developed. After considering the explanation function in the context of narratives and kabuki knowledge, we present a mechanism in the aforementioned system that automatically determines the content and method of an explanation according to the degree of the user&#x2019;s interest and knowledge. Through this trial, we consider effective explanation methods for narrative generation and, in this study, the use of kabuki-related knowledge.
Authentic Assessment in a Beginning Cybersecurity Course: A Pilot Study

Authentic assessment means to examine student performance on real-world tasks. This study explores the process of developing and using authentic assessment in a basic cybersecurity course, specifically in place of written examinations. Although only a pilot study, initial results are promising. Each assessment was a learning experience, besides assessing a skill, and student perceptions of the assessment were positive. Some students also went beyond the bare minimums needed for the assessment, likely due to a sense of ownership of the IT resources. Using authentic assessments in place of exams also removed any need for exam proctoring.
Evaluation of the security level of Aruba wireless networks in an educational center

Wireless networks have made it possible to connect mobile devices, providing great advantages for today's communication. However, this has made communications a target of continuous security attacks. The risks can range from being left without connectivity in an organization, to the theft or hijacking of valuable information for institutions. To minimize the risk of a cyber-attack on a wireless network, there are complex and external methodologies to evaluate security, detect failures in the network and help find solutions.This research proposes the use of a methodology based on "ethical hacking" in order to evaluate, analyze and expose the vulnerabilities that may have a "cluster" formed by Aruba Network equipment within an institution of higher education in northeastern Mexico. In the application of this methodology a total of forty-seven vulnerabilities were found, four of them considered critical, eighteen are high risk, twenty-four have a medium score and one low risk level vulnerabilty.
On the Selection of Hyperparameters in Convolutional Neural Networks

Convolutional neural networks (CNNs) have been showing great success in a variety learning tasks, especially object detection and image recognition. In the applications of CNN models, many hyperparameters, such as kernel size, channel size, learning rate, padding, and stride step, should be tuned and the success of CNN models highly depends on the correct selection of these parameters. However, it is very time-consuming and challenging to tune hyperparameters. In most cases one has to preselect some values for the hyperparameters based on experiences. In this paper we explore the impact of kernel size and channel size on the accuracy of feature extraction and imaging classification by CNN models and provide a practical guide to set up these hyperparameters. Particularly for kernel size we found the choice could be quite flexible for an easy task while the best choice should be in a range for a difficult task, and the range shrinks as the feature to background ratio becomes smaller.
PCA Approaches for Optimal Convolution Kernels in Convolutional Neural Networks

Convolutional neural networks (CNNs) have become one of most powerful machine learning models; with enough data, their accuracy in tasks such as image-related classifications and natural language processing is unmatched. The drawback that many scientists have commented on is the fact that these networks, usually trained from randomly-initialized parameters, are black-boxes. This article introduces an innovative variant for CNNs, which incorporates principal components (PCs) derived from well-trained convolution kernels. The variant is called the principal component-incorporating CNN (PC-CNN), in which the PCs are employed either as a complete replacement for randomly-initialized convolution kernels or as an initialization for the convolution kernels to be re-trained. The objective is to help training processes converge to the global minimizer. The PC-CNN is applied for the MNIST handwritten digit dataset to prove its effectiveness.
Enhanced Data Management Framework for Cloud Based System

In recent years, the notion of cloud computing has grown in popularity. In cloud computing, data storage is a critical and significant study area. A novel approach for safe data storage has been presented to address the challenges of data security on cloud servers. The proposed technique categorizes the data according to the type of data file and then it is partitioned and encrypted before being stored on servers. This approach has been subjected to save time and improve the throughput. With this integrated solution, Data storage on the server requires lesser time. In addition, it has a higher throughput than the integrated technique.
A Hybrid Model Combined with SVM and CNN for Community Content Classification

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9&#x0025; to 1.4&#x0025; in general.
Crossed analysis of three-variable for early pre-diagnosis of COVID-19

This study presents a new analysis method of physiological variables considered vital for the early diagnosis of COVID-19: Body Temperature, Heart Rate, and Blood Saturation. The applied method was the cross-analysis of variables to obtain triage-type criteria for classifying the individual in one of the three states: Prevention (yellow), Warning (Orange), and Alarm (Red) for each particular case. As a result, an automatic analysis algorithm was developed to support the physician in preventive treatment. It is possible to generate the warning states and classify the situation when making a report according to its condition by validating the results. The algorithms are published on Github to make them available to the scientific community in general and thus solve the early diagnosis.
Efficient Comorbidity Analysis in Brain Disorders Reveals Better Diagnosis

Nowadays, brain disorders are gaining momentum faster than ever. Early detection of these disorders would be helpful in the treatment process. Also, detecting some comorbid brain disorders would be expensive and time-consuming. With advancements in machine learning (ML) and Artificial intelligence, these brain disorders and their comorbidities can be detected in the early stage. Different techniques of machine learning are used to detect Autism Spectrum Disorder (ASD), Attention deficit hyperactivity disorder (ADHD), Intellectual Disability (ID), and other brain disorders. This paper focuses on predicting ASD, ADHD, ID, and their Comorbidities via multi-stage analytical and prediction modelling. The first stage involves efficient data pre-processing. The next stage is a comorbidity analysis phase via logistic regression. In this analysis, logistic regression was applied to recognize health-related variables which are associated with ASD+ADHD+ID. These variables are Vision Test, Brain Injury, Anxiety, Down Syndrome, Blood Disorder, and Cystic Fibrosis. In the third stage, machine learning methods predict ASD, ADHD, ID for better diagnosis. For this purpose, SVM, KNN, and MLP are used. To evaluate these models, accuracy, precision, recall, and F1-score are selected.
Metricam: Fast and Reliable Social Distancing Analysis in Online Security Cameras

Distance measurements taken from 2D camera images are subject to the correct estimation of the camera&#x2019;s perspective, that is, the spatial mapping from 2D points imaged by a camera to the correspondent 3D ones in the real world. Current solutions to solve this 3D reconstruction are either dependent on the estimation of vanishing points through the detection of straight lines on targeted images or by employing sophisticated sensors and deep learning algorithms, which require expensive training on huge annotated datasets. Nevertheless, none of those approaches provide the required level of precision and accuracy for social distancing evaluation. In this paper we present Metricam, a real-time lightweight software system for security cameras that computes a 2D to 3D mapping using computational geometry and uses the DBSCAN clustering algorithm to evaluate social distancing evaluation. With Metricam, we have been able to identify several places prone to agglomeration inside the Butant&#x00E3; campus of the University of S&#x00E3;o Paulo, and provide the local authorities with valuable information to fight off the pandemic.
An Image Access Control Scheme in the Blockchain Environment

Aiming at the current storage and access problems of image resources, this article proposes a scheme for implementing fine-grained image access control in a blockchain environment. According to the characteristics of image files, this solution introduces a distributed storage system outside the blockchain, and designs a storage and access control mechanism based on identity attributes to encrypt the storage index of the image and upload it to the blockchain. Users who satisfy the attribute access structure can decrypt index information and obtain image resources, realizing a safe and effective access control scheme.
Models and Challenges Categorization in Cloud Computing

Cloud computing is one of the fastest evolving technologies and is the future in computing for companies looking for technological updation, more productivity and service providers are seeking enhanced revenue. Information technology services and infrastructure are outsourced so to make them remotely accessible through internet. Organizations utilizing cloud-computing services and models have a competitive edge to their counterparts. Different cloud service &#x0026; deployment models and strategies have been developed in due course of time with the growing demand. This alarming development leads to few challenges as well like security issues, compliance, cost, expertise, performance and managing multiple clouds and its migration. Cloud computing models are discussed and its challenges are bifurcated into two broad categories technical and managerial aspects.