Reviewerly/exHarmony huggingface dataset:
Blockchain-Based PKI System and Its Application in Internet of Things

With the continuous construction and development of the Internet of things, security and trust have become issues that cannot be ignored. Through transparent and trusted rules, blockchain can build a traceable chain data structure, which can effectively solve the problems of trust and security in the Internet of things. In this paper, a public key infrastructure suitable for the Internet of things is constructed, based on the characteristics of blockchain technology such as decentralization and tamper proof. The digital certificate is stored and verified in the way of blockchain, which can realize the decentralization of PKI, solve the problem of single point failure and trust problem of root CA, and improve the efficiency of certificate configuration.
NegMAS: A Platform for Automated Negotiations

Alongside the widespread adoption of AI technology throughout the business world, automated negotiation is similarly gaining more interest within the multiagent system (MAS) research community. This interest has prompted the development of research-oriented automated negotiation platforms like GENIUS. This paper introduces NegMAS, Negotiations Managed by Agent Simulations/Negotiation MultiAgent System, which was developed to facilitate research and development of agents that negotiate in dynamic situations characterized by interrelated utility functions with all negotiation related decisions managed by agents.
A Reliability Aware Algorithm for Workflow Scheduling on Cloud Spot Instances Using Artificial Neural Network

IT infrastructures are rapidly growing due to the increased demand for computing power used by applications. Furthermore, modern cloud data centers are hosting various advanced applications based on the user's needs. The goal of this paper is to maximize the reliability of running workflow applications considering spot instances revocation without imposing fault-tolerance overhead. For this purpose, we use an Artificial Neural Network algorithm (ANN) to define a failure prediction module for the cloud spot instances. Indeed, we introduce a novel workflow scheduling algorithm, named Reliability Aware and modified HEFT (Heterogeneous Earliest Finish Time) for minimizing the makespan of a given workflow subject to a specified reliability of the application. To evaluate our ANN based prediction model, we have used a benchmarking data set. The results of the model training demonstrate that the prediction accuracy is about 96 percent. Our experiments illustrate that although makespan does not improve significantly in all experiments, an acceptable level of reliability is achieved.
Foreground Objects Detection by U-Net with Multiple Difference Images

In video surveillance, robust detection of foreground objects is usually done by subtracting a background model from the current image. Most traditional approaches use a statistical method to model the background image. Recently, deep learning has also been widely used to detect foreground objects in video surveillance. It shows dramatic improvement compared to the traditional approaches. It is trained through supervised learning, which requires training samples with pixel-level assignment. It requires a huge amount of time and is high cost, while traditional algorithms operate unsupervised and do not require training samples. Additionally, deep learning-based algorithms lack generalization power. They operate well on scenes that are similar to the training conditions, but they do not operate well on scenes that deviate from the training conditions. In this paper, we present a new method to detect foreground objects in video surveillance using multiple difference images as the input of convolutional neural networks, which guarantees improved generalization power compared to current deep learning-based methods. First, we adjust U-Net to use multiple difference images as input. Second, we show that training using all scenes in the CDnet 2014 dataset can improve the generalization power. Hyper-parameters such as the number of difference images and the interval between images in difference image computation are chosen by analyzing experimental results. We demonstrate that the proposed algorithm achieves improved performance in scenes that are not used in training compared to state-of-the-art deep learning and traditional unsupervised algorithms. Diverse experiments using various open datasets and real images show the feasibility of the proposed method.
Analysis of Good Teaching Practices With Mobile Devices at the University: Design and Validation of the APMU Scale

The purpose of this paper was to validate the scale of Analysis of M-learning Practices in the University (APMU), designed to evaluate good teaching practices with mobile devices in the university environment. Different strategies were used such as expert judgement, exploratory factor analysis, confirmatory factor analysis and reliability analysis from a sample of 1125 university professors. The scale was composed of 16 items configured in five factors: mobile devices, digital competence, knowledge construction, cooperative work and education. Finally, the scale presented good psychometric properties, gathering a unique and reliable instrument to evaluate good teaching practices with mobile devices at the University.
A Blockchain-based Medical Data Marketplace with Trustless Fair Exchange and Access Control

The unprecedented growth of decentralized technologies and the abundance of healthcare data creates numerous opportunities for the digital healthcare industry and poses major challenges for data security. In this paper, we propose a novel decentralized blockchain-based medical data marketplace in which medical record sellers can sell their data to interested buyers, e.g., pharmaceutical corporations. Sellers use a smart contract to exchange their records with buyers for a digital currency. In our model, sellers can enforce flexible access control policy on the encrypted records while allowing the buyers to verify the correctness of the encrypted records without revealing any information about the records using the developed zk-SNARK protocol. In addition, sellers acquire a proof-of-delivery to redeem buyers' contingent payments by exchanging a record access key for a buyer signature using the developed trustless zero-knowledge contingent payment protocol. Our security analysis proves that our model is secure against malicious behaviors of both dishonest sellers/buyers. Performance evaluation indicates that the GAS cost on Etherume blockchain and the computational cost of the cryptographic operations are low.
Experience-Based Prediction of Unknown Environments for Enhanced Belief Space Planning

Autonomous navigation missions require online decision making abilities, in order to choose from a given set of candidate actions an action that will lead to the best outcome. In a partially observable setting, decision making under uncertainty, also known as belief space planning (BSP), involves reasoning about belief evolution considering realizations of future observations. Yet, when candidate actions lead the robot to an unknown environment the decision making mission becomes a very challenging problem since without a map it is hard to foresee future observations. In this paper we develop a data-driven approach for predicting a distribution over an unexplored map, generating future observations, and combining these observations within BSP. We examine our approach and compare it to existing BSP methods in a Gazebo simulation, and demonstrate it often yields improved performance.
Computer-Aided Detection Breast Cancer in Whole Slide Image

The diagnosis of the whole slide images of breast cancer is highly dependent on the experience of the pathologist, and the diagnosis results are prone to large human errors. Along with the rapid develop of the technique of computer and image manipulation, the computer-aided diagnosis is gradually used in medicine. Based on visual perception, we propose a model for rapid detection and refined segmentation of breast cancer regions in this paper. We adopt a classification model ResNet101 or MobileNetV2 to quickly filter out obvious non-cancer areas. Then, the semantic segmentation model U-Net is used to refine the segmentation. Experimental results show that our approach can quickly classify the lesion, ResNet101 model patch classification accuracy rate reaches 98.3%, MobileNetV2 model in the case of relatively few parameters, the patch classification accuracy rate reached 97.2%. When the results are not much different, the MobileNetV2 model can quickly classify the patch image. In accurately locating the lesion area, the FROC score best reached 79.5%. The results are competitive compared to the results of other state-of-the-art methods.
Linking Personally Identifiable Information from the Dark Web to the Surface Web: A Deep Entity Resolution Approach

The information privacy of the Internet users has become a major societal concern. The rapid growth of online services increases the risk of unauthorized access to Personally Identifiable Information (PII) of at-risk populations, who are unaware of their PII exposure. To proactively identify online at-risk populations and increase their privacy awareness, it is crucial to conduct a holistic privacy risk assessment across the internet. Current privacy risk assessment studies are limited to a single platform within either the surface web or the dark web. A comprehensive privacy risk assessment requires matching exposed PII on heterogeneous online platforms across the surface web and the dark web. However, due to the incompleteness and inaccuracy of PII records in each platform, linking the exposed PII to users is a non-trivial task. While Entity Resolution (ER) techniques can be used to facilitate this task, they often require ad-hoc, manual rule development and feature engineering. Recently, Deep Learning (DL)-based ER has outperformed manual entity matching rules by automatically extracting prominent features from incomplete or inaccurate records. In this study, we enhance the existing privacy risk assessment with a DL-based ER method, namely Multi-Context Attention (MCA), to comprehensively evaluate individuals' PII exposure across the different online platforms in the dark web and surface web. Evaluation against benchmark ER models indicates the efficacy of MCA. Using MCA on a random sample of data breach victims in the dark web, we are able to identify 4.3% of the victims on the surface web platforms and calculate their privacy risk scores.
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning

Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation. However, standard DRL methods often suffer from poor sample efficiency, partially because they aim to be entirely problem-agnostic. In this work, we introduce a novel approach to exploration and hierarchical skill learning that derives its sample efficiency from intuitive assumptions it makes about the behavior of objects both in the physical world and simulations which mimic physics. Specifically, we propose the Hypothesis Proposal and Evaluation (HyPE) algorithm, which discovers objects from raw pixel data, generates hypotheses about the controllability of observed changes in object state, and learns a hierarchy of skills to test these hypotheses. We demonstrate that HyPE can dramatically improve the sample efficiency of policy learning in two different domains: a simulated robotic blockpushing domain, and a popular benchmark task: Breakout. In these domains, HyPE learns high-scoring policies an order of magnitude faster than several state-of-the-art reinforcement learning methods.
A semi-supervised linear-nonlinear prediction system for tumbler strength of iron ore sintering process with imbalanced data in multiple working modes

An iron ore sintering is a process that provides qualified sinter for the blast-furnace iron-making process. The tumbler strength is an important physical quality index of the sinter. Precise prediction of the tumbler strength is essential for solving the problem of how to improve it. In this study, a semi-supervised prediction system was devised for the tumbler strength of an iron ore sintering process. First, the process was briefly described with an analysis of the process characteristics. The characteristics of the existence of imbalanced data in multiple working modes, the lack of labeled samples, and the coexistence of linear–nonlinear components were taken into consideration for building the system. Then, the configuration of the prediction system was devised based on the characteristics. The system consists of three parts: working-modes decomposition based on a Gaussian mixture model (GMM) considering the existence multiple working modes, a GMM based just-in-time learning for nearest-samples selection in the relevant working modes considering the existence of imbalanced data, and the development of a semi-supervised linear–nonlinear least-square learning network considering the existence of the linear–nonlinear component and lack of labeled samples. Finally, comparisons of simulation results using actual run process data with scarce and abundant labeled samples verified the effectiveness of the proposed system. In addition, results of industrial application also verified the effectiveness of the prediction system.
Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning

A fully autonomous car does not yet exist. But the vehicles have continued to gain in range in recent years. The main reason? The dazzling progress made in artificial intelligence, in particular by specific algorithms, known as machine learning. These example-based machine learning methods are used in particular for recognizing objects in photos. The algorithms developed for the detection and identification must respond robustly to the various disturbances observed and take into account the variability in the signs' appearance. Variations in illumination generate changes in apparent color, shadows, reflections, or backlighting. Besides, geometric distortions or rotations may appear depending on the viewing angle and the panels' scale. Their appearance may also vary depending on their state of wear and possible dirt, damage. In this work, to improve the accuracy of detection and classification of sign road partially covered by snow, we use the Fast Region-based Convolutional Network method (Fast R-CNN) model. To train the detection model, we collect an image dataset composed of multi-class of road signs. Our model can simultaneously multi-class of a road sign in nearly real-time.
Self-supervised Simultaneous Alignment and Change Detection

This study proposes a self-supervised method for detecting scene changes from an image pair. For mobile cameras such as drive recorders, to alleviate the camera viewpoints' difference, image alignment and change detection must be optimized simultaneously because they depend on each other. Moreover, lighting condition makes the scene change detection more difficult because it widely varies in images taken at different times. To solve these challenges, we propose a self-supervised simultaneous alignment and change detection net-work (SACD-Net). The proposed network is robust specifically in differences of camera viewpoints and lighting conditions to simultaneously estimate warping parameters and multi-scale change probability maps while change regions are not taken into account of calculation of the feature consistency and semantic losses. Based on comparative analysis between our self-supervised and the previous supervised models as well as ablation study of the losses of SACD-Net, the results show the effectiveness of the proposed method using a synthetic dataset and our new real dataset.
Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows

Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions.These workflows are also known as stream workflows.The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs.Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time.This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application.In this paper, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost.The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound.
Generation of Synthetic Data for Handwritten Word Alteration Detection

Fraudsters often alter handwritten contents in a document in order to achieve illicit purposes. At times, this may result in financial and mental loss to an individual or an organization. Hence, ink analysis is necessary to identify such an alteration. Convolution Neural Network (CNN) can be used to identify such cases of alteration, as CNN has emerged as a monumental success in the field of computer vision for varieties of classification tasks. But, CNN requires large amount of labeled data for training. Hence, there is a need to generate a large dataset for the experiments relating to handwritten word alteration detection. Collection, digitization, and cropping of a large number of altered and unaltered handwritten words are tedious and time consuming. To overcome such an issue, an approach for synthetic word data generation is presented in this paper for handwritten word alteration detection experiments. This scheme is designed in such a way that the synthetically generated words are very similar to the original ones. In order to achieve this, handwritten character data set is prepared using 10 blue and 10 black pens. These handwritten characters are used for creating synthetic word alteration data set. The presented approach uses relatively less number of handwritten character images to create a huge word alteration data set. Further, deep learning models are trained on the synthetically generated data set for word alteration detection.
Deep Convolutional Variational Autoencoder for Anomalous Sound Detection

Anomalous sound detection (ASD) is one of the most important fields in industrial facility maintenance. For this task, semi-supervised approaches are preferred thanks to their simplicity and no training data labels required. These methods train an autoencoder (AE) with only normal sound data and detect anomalies based on anomaly scores of actual samples. In this paper, we propose applying the convolutional variational autoencoder (CVAE) to ASD task. Through experiments using machine sound data, the CVAE is proven to be effective in detecting abnormal sound and outperform existing methods.
Identification and Mitigation Tool For Cross-Site Request Forgery (CSRF)

Most organizations use web applications for sharing resources and communication via the internet and information security is one of the biggest concerns in most organizations. Web applications are becoming vulnerable to threats and malicious attacks every day, which lead to violation of confidentiality, integrity, and availability of information assets.We have proposed and implemented a new automated tool for the identification and mitigation of Cross-Site Request Forgery (CSRF) vulnerability. A secret token pattern based has been used in the automated tool, which applies effective security mechanism on PHP based web applications, without damaging the content and its functionalities, where the authenticated users can perform web activities securely.
Clusterwise functional linear regression models

Classical clusterwise linear regression is a useful method for investigating the relationship between scalar predictors and scalar responses with heterogeneous variation of regression patterns for different subgroups of subjects. This paper extends the classical clusterwise linear regression to incorporate multiple functional predictors by representing the functional coefficients in terms of a functional principal component basis. We estimate the functional principal component coefficients based on M-estimation and K-means clustering algorithm, which can classify the data into clusters and estimate clusterwise coefficients simultaneously. One advantage of the proposed method is that it is robust and flexible by adopting a general loss function, which can be broadly applied to mean regression, median regression, quantile regression and robust mean regression. A Bayesian information criterion is proposed to select the unknown number of groups and shown to be consistent in model selection. We also obtain the convergence rate of the set of estimators to the set of true coefficients for all clusters. Simulation studies and real data analysis show that the proposed method is easily implemented, and it consequently improves previous works and also requires much less computing burden than existing methods.
Ship Segmentation on High-Resolution Sar Image by a 3D Dilated Multiscale U-Net

Targets detection and segmentation in a synthetic aperture radar (SAR) image is a vital step for its interpretation. It is quite challenging for most conventional methods due to complex background and the speckle. Furthermore, the sizes of targets in a scene are variable. Inspired by the success of neural networks in computer vision, In this paper, we propose a 3D dilated multi-scale U-shape convolutional neural network (3DDM-UNet). In the proposed method, we first build a 3D image block via a multiscale stationary wavelet transform to exploit the structural information of targets with various sizes. Then, the built 3D image block is fed into a 3D dilated multiscale U-Net. To train the proposed network, we build a dataset from a scene of SAR image with various sizes and shapes of ship targets. Finally, the trained network is employed to the testing set to obtain the segmentation results. Experimental results on test images show that the proposed method achieved better performance than conventional methods.
A modified Hybrid Reciprocal Velocity Obstacles approach for multi-robot motion planning without communication

Ensuring a safe online motion planning despite a large number of moving agents is the problem addressed in this paper. Collision avoidance is achieved without communication between the agents and without global localization system. The proposed solution is a modification of the Hybrid Reciprocal Velocity Obstacles (HRVO) combined with a tracking error estimation, in order to adapt the Velocity Obstacle paradigm to agents with kinodynamic constraints and unreliable velocity estimates. This solution, evaluated in simulation and in real test scenario with three dynamic unicycle type robots, shows an improvement over HRVO.