Reviewerly/exHarmony huggingface dataset:
Ensemble denoising for Monte Carlo renderings

Various denoising methods have been proposed to clean up the noise in Monte Carlo (MC) renderings, each having different advantages, disadvantages, and applicable scenarios. In this paper, we present Ensemble Denoising , an optimization-based technique that combines multiple individual MC denoisers. The combined image is modeled as a per-pixel weighted sum of output images from the individual denoisers. Computation of the optimal weights is formulated as a constrained quadratic programming problem, where we apply a dual-buffer strategy to estimate the overall MSE. We further propose an iterative solver to overcome practical issues involved in the optimization. Besides nice theoretical properties, our ensemble denoiser is demonstrated to be effective and robust, and outperforms any individual denoiser across dozens of scenes and different levels of sample rates. We also perform a comprehensive analysis on the selection of individual denoisers to be combined, providing important and practical guides for users.
Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.
Implementation of a Blockchain System Using Improved Elliptic Curve Cryptography Algorithm for the Performance Assessment of the Students in the E-Learning Platform

Blockchain technology allows for the decentralized creation of a propagated record of digital events, in which third parties do not control information and associated transactions. This methodology was initially developed for value transmission. Still, it now has a broad array of utilization in various industries, including health, banking, the internet of things, and several others. With its numerous added benefits, a blockchain-based learning management system is a commonly utilized methodology at academic institutes, and more specifically during and after the COVID-19 period. It also presents several potentials for decentralized, interoperable record management in the academic system in education. Integrity, authenticity, and peer-executed smart contracts (SC) are some of the qualities of a blockchain that could introduce a new degree of safety, trustworthiness, and openness to e-learning. This research proposes a unique encryption technique for implementing a blockchain system in an e-learning (EL) environment to promote transparency in assessment procedures. Our methodology may automate evaluations and provide credentials. We built it to be analytical and content-neutral in order to demonstrate the advantages of a blockchain back-end to end-users, including student and faculty members particularly during this COVID-19 era. This article explains the employment of blockchain and SC in e-learning. To improve the trust in the assessment, we propose a novel improved elliptic curve cryptography algorithm (IECCA) for data encryption and decryption. The performance of the suggested method is examined by comparing it with various existing algorithms of encryption. The evaluation of the behaviour of the presented method demonstrates that the technique shall enhance trust in online educational systems, assessment processes, educational history, and credentials.
HDP-CNN: Highway deep pyramid convolution neural network combining word-level and character-level representations for phishing website detection

Phishing has become a prevailing method for attackers to steal users' private data and commit fraud, posing a serious threat to Internet users. How to detect phishing websites has attracted great interests from both academia and industry. A popular approach is to use support vector machine (SVM) to detect phishing websites. However, this approach relies on extracting features designated by experts, and the prediction effectiveness of the model is greatly affected by the quality of feature extraction. In addition, it cannot handle features that are not identifiable. Deep learning methods therefore become popular as they do not require manual feature engineering. However, many deep learning methods can only learn feature information of uniform resource locators (URLs) at the character level, while ignoring the intrinsic connections of words. To address these limitations, we propose a novel highway deep pyramid convolution neural network (HDP-CNN), a deep convolutional network that combines character-level and word-level representation information. HDP-CNN first receives the URL string sequences as input, then performs character-level embedding and word-level embedding respectively. Afterward, it uses the Highway network to connect the character-level embedding representation and word-level embedding representation of the URL and extracts local features of different sizes from the region embedding layer. Finally, it passes them into the designed deep pyramid structure network to capture the global representation of the URL. Our experiments illustrate that the information expressed by embedding vectors of different granularities has subtle differences. By combining embedding feature information of different granularities, HDP-CNN exhibits better performance than methods based on single embedding feature information. In our experiments, we construct an imbalanced dataset that has the ratio of benign websites to phishing websites is close to 5:1. The experimental results demonstrate that our method outperforms other methods, with accuracy at 98.30%, true positive rate (TPR) at 99.18%, and true negative rate (TNR) at 94.34%.
Heterogeneous dynamical academic network for learning scientific impact propagation

Quantifying and predicting the long-term impact of both scientific papers and individual authors have important implications for many academic policy decisions, from identifying emerging trends to assessing the merits of proposals for potential funding. This paper presents SI-HDGNN, a novel heterogeneous dynamical graph neural network that explicitly models a heterogeneous, weighted, directed and attributed academic graph, enabling a prediction of the cumulative scientific impact of papers and authors by a specifically designed aggregation method. Unlike the existing feature-based or homogeneous approaches, SI-HDGNN addresses the problem by capturing the temporal–structural characteristics of the papers and authors as well as their complex interactions and long-term dependencies. Extensive experiments conducted on three large-scale multidisciplinary academic datasets demonstrate its superior performance in predicting the long-term scientific impact of both scientific papers and authors.
How do Consumers’ Gender and Rational Thinking Affect the Acceptance of Entertainment Social Robots?

Abstract In recent years, the rapid ageing of the population, a longer life expectancy and elderly people’s desire to live independently are social changes that put pressure on healthcare systems. This context is boosting the demand for companion and entertainment social robots on the market and, consequently, producers and distributors are interested in knowing how these social robots are accepted by consumers. Based on technology acceptance models, a parsimonious model is proposed to estimate the intention to use this new advanced social robot technology and, in addition, an analysis is performed to determine how consumers’ gender and rational thinking condition the precedents of the intention to use. The results show that gender differences are more important than suggested by the literature. While women gave greater social influence and perceived enjoyment as the main motives for using a social robot, in contrast, men considered their perceived usefulness to be the principal reason and, as a differential argument, the ease of use. Regarding the reasoning system, the most significant differences occurred between heuristic individuals, who stated social influence as the main reason for using a robot, and the more rational consumers, who gave ease of use as a differential argument.
A secure blockchain system for Internet of Vehicles based on 6G-enabled Network in Box

Network in Box (NIB) is a self-organizing and portable device. The six-generation wireless communication technologies (6G) can empower NIB with better spectrum efficiency by integrating satellite broadcasting. 6G-enabled NIB is promising to promote the communication efficiency of Internet of Vehicles (IoV). IoV has emerged as the concrete practice of intelligent transportation. However, IoV is vulnerable to attacks from quantum computers because they use traditional RSA and elliptic cure cryptographic systems. Therefore, it is critical to improving the security of IoV against quantum computer attacks. This paper proposes the first secure scheme based on post-quantum techniques for 6G-enabled NIB to protect IoV against quantum attacks. On the one hand, a blockchain-based public key infrastructure is proposed to authenticate the IoV devices securely. On the other hand, we design a blockchain-based multi-party key agreement and communication system to support multi-party communication among IoV devices. The extensive theoretical analysis and experimental results indicate that the proposed blockchain system based on 6G-enabled NIB can achieve high security and efficiency for IoV.
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person’s blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist’s professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher. We propose providing models in detecting and classify acute leukemia and WBCs that use a combination of SVM and CNN classifiers in their classification step to achieve optimum performance metrics.
TBDA-Net: A Task-Based Bias Domain Adaptation Network Under Industrial Small Samples

The small sample problem is an open challenge in industrial data-driven fault diagnosis. Domain adaptation (DA) has been successfully used to enhance small samples by transferring fault samples in other working conditions, but it suffers from low accuracy and poor generalization for modeling. To address this issue, in this article, a task-based bias DA network (TBDA-net) is proposed. First, an adaptive dimension alignment subnet is proposed, which overcomes the information loss of the target domain caused by the dimension alignment of different domains, thus the accuracy of modeling is improved. Second, a task-based distribution matching subnet is proposed, in which the correlation difference of domains is considered and the designed auxiliary branch is embedded in the matching network to protect the sample diversity of multisource domains, thus the generalization of modeling is improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experiment results show the TBDA-net outperforms the existing methods for modeling under industrial small samples
The MIT Supercloud Dataset

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to changes in deployment approaches of HPC clusters and the commercial cloud, as well as a new focus on approaches to optimized resource usage, allocations and deployment of new AI frameworks, and capabilities such as Jupyter notebooks to enable rapid prototyping and deployment. With these changes, there is a need to better understand cluster/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, and identifying policy violations. In this paper we introduce the MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations. We provide detailed monitoring logs from the MIT Supercloud system, which include CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This paper discusses the details of the dataset, collection methodology, data availability, and discusses potential challenge problems being developed using this data. Datasets and future challenge announcements will be available via https://dcc.mit.edu.
Fed xData: A Federated Learning Framework for Enabling Contextual Health Monitoring in a Cloud-Edge Network

Due to the rapid recent development of cloud-edge networks, smart devices can facilitate rapid access to patients’ health information. Success has been achieved in the healthcare sector with the training of a federated learning (FL) model on large amounts of the personal data of users. However, some challenges remain that other FL models have not yet addressed. Firstly, FL models with computational parameters are very complex, which results in a high communication cost in the cloud-edge network. Furthermore, trained models in the cloud are not personalized. If personalization is present, the models do not provide practical solutions to fine-tune parameters in order to accurately predict performance in health monitoring. To address the above challenges, this paper presents the Fed xData framework for contextual health monitoring in cloud-edge networks. The Fed xData framework introduces a continuous data balancing supplemented structure using the RandomOverSample method, which solves all data classes. The FL model is an encode depth convolutional network (EDCN) model designed for both server and client. It solves various problems, for instance by using the fine-tuning model to increase personalization and solving not independent and identically (Non-IID) distribution problems regarding user health. Test results based on human activity recognition indicate that Fed xData is far superior to others for use in general centralized learning models and FL models.
Multi-view support vector ordinal regression with data uncertainty

Ordinal regression (OR) is a paradigm which learns a prediction model on the data with ordered classes. Despite much progress in OR, the existing OR works learn the classifier from only one view and the multi-view learning in OR has not been considered. What is more, there may exist uncertain information in the multi-view OR data. In this paper, we put forward a novel approach, called multi-view support vector ordinal regression with uncertain data (MORU), which can improve the OR classifier by incorporating the multi-view information and handling the data uncertainty. In our method, a series of parallel hyperplanes are applied to separate the multi-view ordered data, and the uncertain information is considered in the input data. Then, we adopt a heuristic framework to solve the OR learning problem. Experimental results have illustrated that our method obtains superior performance to the existing OR techniques.
Multi-Resolution Data Fusion for Super Resolution Imaging

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution electron microscope data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A key theoretical novelty is the analysis of mismatched back-projectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features.
Utilization of Corner Filters, AES and LSB Steganography for Secure Message Transmission

This paper proposes a couple of multiple-layer message security schemes. The utilization of cryptography as well as steganography allows the attainment of an acceptable standard of information security. In both schemes, the plaintext data is first encrypted using the AES algorithm. Next, a spatial domain steganography technique is employed to conceal the encrypted sensitive data into 3D cover images. These procedures allow us to optimize for capacity. However, if a higher level of security is required, then an extra step of image processing takes place. More specifically, a corner filter is utilized on each of the 2D slides of the 3D cover images, such that LSB embedding only takes place in those corner-detected pixels. Numerical results exhibit superior performance, especially in comparison to counterpart steganography schemes found in the literature.
Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.
Influence of Communication Lines on Reliability in Mobile Communication Systems

In this study, the assessment of the reliability of the mobile communication system, in particular, the impact of the communication lines on the base station - base station controllers system, was assessed. Base station - The type of communication lines and backup conditions are taken into account when assessing the reliability of the base station control system. An algorithm has been developed to evaluate the reliability of the system and the results obtained from the calculations are presented.
Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge

The advances in machine learning have revealed its great potential for emerging mobile applications such as face recognition and voice assistant. Models trained via a Neural Network (NN) can offer accurate and efficient inference services for mobile users. Unfortunately, the current deployment of such service encounters privacy concerns. Directly offloading the model to the mobile device violates model privacy of the model owner, while feeding user input to the service compromises user privacy. To address this issue, we propose Leia, a lightweight cryptographic NN inference system at the edge. Leia is designed from two mobile-friendly perspectives. First, it leverages the paradigm of edge computing wherein the inference procedure keeps the model closer to the mobile user to foster low latency service. Specifically, Leia&#x2019;s architecture consists of two non-colluding edge services to obliviously perform NN inference on the encoded user data and model. Second, Leia&#x2019;s realization makes the judicious use of potentially constrained computational and communication resources in edge devices. We adapt the Binarized Neural Network (BNN), a trending flavor of NN with low inference overhead, and purely choose the lightweight secret sharing techniques to realize secure blocks of BNN. We implement Leia and deploy it on Raspberry Pi. Empirical evaluations on benchmark and medical datasets via various models demonstrate the practicality of Leia.