Reviewerly/exHarmony huggingface dataset:
Improving the hybrid cloud performance through disk activity-aware data access

Cloud computing has been making significant contributions to Internet of Things, Big Data, and many other cutting-edge research areas in recent years. To deal with the cloud bursting, on-premises private clouds often extend their service capacity with off-premises public clouds, which causes the migration of jobs and their corresponding data from private clouds to public clouds. For jobs executed in public clouds, promptly transferring data they need from private clouds to public clouds is essential for their quick completion as the volume of data is often large for cloud applications. The Internet connection between private clouds and public clouds is with limited bandwidth in most cases. Therefore, it will be valuable if the underlying operating system could expedite the course of reading data from hard drives to speed up the process of moving data from private clouds to public clouds. The Apache Hadoop is considered as one of the most widely used cloud platforms in the community of cloud computing. It keeps multiple replicas of data across its cluster nodes to improve data availability. We designed and implemented a new model enabling computing nodes in Hadoop to get data in request from a cluster node with the least amount of disk activity regardless of its location to hasten the course of accessing data. Experimental results show that jobs could reduce their execution time by up to 80.83% in our model. Accordingly, our model could help accelerate the completion of job execution in both private clouds and public clouds in the environment of hybrid clouds.
A Cognitive IoT Smart Surveillance Framework for Crowd Behavior Analysis

The following topics are dealt with: learning (artificial intelligence); Internet; telecommunication traffic; wireless LAN; Internet of Things; wireless channels; cloud computing; quality of service; and mobile computing; health care.
Secure and Transparent Public-key Management System for Vehicular Social Networks

Vehicular Social Networks (VSNs) are expected to become a reality soon, where commuters having common interests in the virtual community of vehicles, drivers, passengers can share information, both about road conditions and their surroundings. This will improve transportation efficiency and public safety. However, social networking exposes vehicles to different kinds of cyber-attacks. This concern can be addressed through an efficient and secure key management framework. This study presents a Secure and Transparent Public-key Management (ST-PKMS) based on blockchain and notary system, but it addresses security and privacy challenges specific to VSNs. ST-PKMS significantly enhances the efficiency and trustworthiness of mutual authentication. In ST-PKMS, each vehicle has multiple short-lived anonymous public-keys, which are recorded on the blockchain platform. However, public-keys get activated only when a notary system notarizes it, and clients accept only notarized public-keys during mutual authentication. Compromised vehicles can be effectively removed from the VSNs by blocking notarization of their public-keys; thus, the need to distribute Certificate Revocation List (CRL) is eliminated in the proposed scheme. ST-PKMS ensures transparency, security, privacy, and availability, even in the face of an active adversary. The simulation and evaluation results show that the ST-PKMS meets real-time performance requirements, and it is cost-effective in terms of scalability, delay, and communication overhead.
On the Sharing-Based Model of Steganography

Steganography has long been considered as a way of hiding the fact of secret communication. However, the fact that a message sender and a receiver communicated with each other is seldom protected. It can be dangerous because they often have very secret relationship. Noticeably, in recent two years some researchers respectively proposed robust and secure steganographic schemes which realize dependable steganography in the lossy channel of social media without degrading the security and capacity, and more relevant technologies, such as robust steganographic coding and fast steganography, were also proposed. In this review, the sharing-based model of steganography implied by social media and promoted by both the emerging advanced robust steganography and fast steganography is formally presented and compared with the dominating prisoner-warden model. It is shown that designing steganography under the sharing-based model can protect the fact of communication and the sender-receiver relationship instead of merely the fact of secret communication, improving the overall security of steganographic systems.
Towards adversarial robustness with 01 loss neural networks

Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense against adversarial attacks in machine learning. One measure of a model's robustness is the minimum distortion required to make the input adversarial. This can be approximated with the Boundary Attack (Brendel et. al. 2018) and HopSkipJump (Chen et. al. 2019) methods. We compare the minimum distortion of the 01 loss network to the binarized neural network and the standard sigmoid activation network with cross-entropy loss all trained with and without Gaussian noise on the CIFAR10 benchmark binary classification between classes 0 and 1. Both with and without noise training we find our 01 loss network to have the largest adversarial distortion of the three models by non-trivial margins. To further validate these results we subject all models to substitute model black box attacks under different distortion thresholds and find that the 01 loss network is the hardest to attack across all distortions. At a distortion of 0.125 both sigmoid activated cross-entropy loss and binarized networks have almost 0% accuracy on adversarial examples whereas the 01 loss network is at 40%. Even though both 01 loss and the binarized network use sign activations their training algorithms are different which in turn give different solutions for robustness. Finally we compare our network to simple convolutional models under substitute model black box attacks and find their accuracies to be comparable. Our work shows that the 01 loss network has the potential to defend against black box adversarial attacks better than convex loss and binarized networks.
A Secured Healthcare System Using Blockchain and Graph Theory

In the rapid growth of information technology(IT) the internet of things (IoT) devices, cloud services, and smart devices play a major role in the healthcare sector and they have led a digital transformation in the présent healthcare services. Digital healthcare services have paved the way for easier and more accessible treatments, and making humans lives far more comfortable. However, in the present healthcare sector, two major focuses must be addressed. They are data security and data ownership. Currently, the number of data breaches compromising confidential healthcare data is on the rise. Recently large no of patients are impacted by data breaches. Moreover, the average cost of healthcare data breaches is going high. AS a solution for this authors suggest a well-secured data transformation and storing method is able to minimize this issue. Therefore in this research authors propose a Blockchain and Graph theory-based system to manage digital healthcare services. Since blockchains are secured because of its transparency in the proposed solution blockchain technology is used to share the data. Moreover, the health care sector is one of the massive big data generating fields. Therefore to make the processers efficient and secure the data, the authors propose a graph-related data storing method. The main objective of this research is to secure the health care data and minimize the data breaches in both data sharing and data storing components.
Event and Activity Recognition in Aerial Videos Using Deep Neural Networks and a New Dataset

Unmanned aerial vehicles (UAVs) are now widespread available. Yet the more UAVs there are in the skies, the more video data they create. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on UAV video content understanding is of great importance. In this paper, we introduce a novel task of event recognition in unconstrained aerial videos in the remote sensing community and present a dataset for this task. Organized in a rich semantic taxonomy, the proposed dataset covers a wide range of events involving diverse environments and scales. We report results of plenty of deep networks in two ways: single-frame classification and video classification. The dataset and trained models can be downloaded from https://1cmou.github.io/ERA_Dataset/.
A Longitudinal Study Investigating the Effects of Workload and Exposure on 3D Gestural Human Computer Interaction

3D gestural input technology has the ability to expand human-computer interaction (HCI) beyond traditional input modalities. It is known that context and domain expertise are influential to gesture development, but there is little known about other individual factors such as workload and exposure. Therefore, the objective of this work is to explore the effects of workload and exposure on intuitive gesture choice and reaction time under a general HCI context. A longitudinal study was conducted to investigate the differences in intuitive mappings for high and low workload conditions as well as across three separate experimental sessions. There were no differences in the intuitive mappings for either workload conditions or different experimental sessions. However, there was a difference in reaction times between all experimental sessions indicating there was a learning effect from the first to the last session in that the participants became faster in generating intuitive mappings.
SCScan: A SVM-Based Scanning System for Vulnerabilities in Blockchain Smart Contracts

The application of blockchain has moved beyond cryptocurrencies, to applications such as credentialing and smart contracts. The smart contract allows ones to achieve fair exchange for values without relying on a centralized entity. However, as the smart contract can be automatically executed with token transfers, an attacker can seek to exploit vulnerabilities in smart contracts for illicit profits. Thus, this paper proposes a support vector machine (SVM)-based scanning system for vulnerabilities on smart contracts. Our evaluation on Ethereum demonstrate that we achieve a identification rate of over 90% based on several popular attacks.
Semirings of Evidence

Abstract In traditional justification logic, evidence terms have the syntactic form of polynomials, but they are not equipped with the corresponding algebraic structure. We present a novel semantic approach to justification logic that models evidence by a semiring. Hence justification terms can be interpreted as polynomial functions on that semiring. This provides an adequate semantics for evidence terms and clarifies the role of variables in justification logic. Moreover, the algebraic structure makes it possible to compute with evidence. Depending on the chosen semiring this can be used to model trust, probabilities, cost, etc. Last but not least the semiring approach seems promising for obtaining a realization procedure for modal fixed point logics.
Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.
Biogeochemical Exploration of Gold Mineralization and its Pathfinder Elements Using Hyperspectral Remote Sensing

Preliminary steps of mineral exploration have always included drilling and other destructive and expensive exploration techniques to quantify ore-deposits underground. This study examines how we can access information on the sub-surface geology by looking at the surface vegetation. We have targeted gold-mineralization along the Hyde-Macraes Shear Zone as a development site, which is located in the Otago Schist belt in southern New Zealand. Deep rooted plants, of the Pinus radiata species, were sampled on the field to quantify the element concentrations focusing on the pathfinder elements such as As and Fe in the host rock, in conjunction with airborne and laboratory based hyperspectral remote sensing data. In order to predict the elemental concentration from hyperspectral data Partial Least Squares Regression (PLSR) was executed.
The Mamdani Fuzzy Logic Engineering Analysis for Determining Weather Forecast

Abstract Many people rely on life in the form of climate cycles, such as farmers, fishermen, traders, and so on. This resulted in the emergence of a lot of pressure from various groups of people who needed accurate information on climate information and created various ways to carry out weather forecasts. Weather forecasts are examples of problems that often have unclear or obscure answers. Fuzzy Logic is a technique to solve this problem, so the technique used is The Mamdani as its application in the weather forecast system. Analyzing the results of weather forecasts that occur in Indonesia requires a system to know the accuracy of the data generated, so it is necessary to create a system for forecasting weather based on parameters that are inputted using the Fuzzy The Mamdani technique so that weather forecasts can be more accurate and efficient. This study predicts the weather using fuzzy The Mamdani logic techniques using the Fuzzy and Matlab toolbox as a determinant of an estimate of the weather cycle that is prioritized to be determined, namely the degree of membership functions and the rules used. Fuzzy logic with The Mamdani technique is very good to use as a weather forecast because it has an accuracy level of above 60%. Therefore, the more variables that are used as input, the better (accurate) output will be.
Efficient Detection of Robot Kidnapping in Range Finder-Based Indoor Localization Using Quasi-Standardized 2D Dynamic Time Warping

This paper proposes an augmented online approach to detect kidnapping events within range-finder-based indoor localization. The method is specifically designed for an Internet of Things (IoT)-Aided Robotics Platform that enables the system to detect kidnapping across all time instances of an indoor mobile robotic operation with high accuracy and maintain a high accuracy in the face of relocalization failures. The approach is based on similarity degree of geometry shape of the environment obtained from range scan data between two consecutive time instances. The proposed approach named Quasi-Standardized Two-Dimensional Dynamic Time Warping (QS-2DDTW) is based on the Multidimensional Dynamic Time Warping (MD-DTW) with homogeneity variance test imbued in it. A series of simulations are preformed against maximum current weight, measurement entropy, and the four metrics in metric based detector. The result shows that the proposed method yields high performance in terms of its ability to distinguish kidnapping condition from normal condition and that it has low dependency towards relocalization process, thus ensures the accuracy of detection is not disturbed by relocalization.
TSFE-Net: Two-Stream Feature Extraction Networks for Active Stereo Matching

In this paper, we propose TSFE-Net, two stream feature extraction networks for active stereo matching. First, we perform extra local contrast normalization (LCN) for dataset due to dependency between speckle intensity and distance. Second, we construct two stream feature extraction layers which consist of convolutional layers and deconvolutional layers in different scales to simultaneously learn the features of the original images and LCN images and aggregate context information to form the left and right features. Third, we convert the obtained depth map into disparity map in virtue of camera parameters to construct a supervised learning model. The TSFE-Net not only solves illumination effects between speckle intensity and distance but also reserves details of the original image. Our dataset are captured by RealSense D435 camera. We research extensive quantitative and qualitative evaluations based on a series of scenes, and achieve the end point error (EPE) accuracy of 0.335 on the TITAN XP platform only for valid pixel. The assessment results show that our network has the ability of real-time deep reconstruction for active pattern.
RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR

Leveraging the information-rich and large volume of Electronic Health Records (EHR), deep learning systems have shown great promise in assisting medical diagnosis and regulatory decisions. Although deep learning models have advantages over the traditional machine learning approaches in the medical domain, the discovery of adversarial examples has exposed great threats to the state-of-art deep learning medical systems. While most of the existing studies are focused on the impact of adversarial perturbation on medical images, few works have studied adversarial examples and potential defenses on temporal EHR data. In this work, we propose RADAR, a Recurrent Autoencoder based Detector for Adversarial examples on temporal EHR data, which is the first effort to defend adversarial examples on temporal EHR data. We evaluate RADAR on a mortality classifier using the MIMIC-III dataset. Experiments show that RADAR can filter out more than 90% of adversarial examples and improve the target model accuracy by more than \(90\%\) and F1 score by 60%. Besides, we also propose an enhanced attack by introducing the distribution divergence into the loss function such that the adversarial examples are more realistic and difficult to detect.
Automatic Chinese Text Summarization for Emergency Domain

Abstract With the rapid development of the global economy, natural disasters and emergencies frequently occur. A large amount of disaster accident data and emergency case handling measures on the Internet can be used to provide technical reference and auxiliary decision-making when the various social emergency incident occurs. This study establishes an accurate Chinese text automatic short summarization model to automatically obtain summary information from accident cases. In the proposed model, Generative Pre-Training 2.0 (GPT2), which is excellent in generating tasks, is employed as the basic network structure. Adabound algorithm is used to optimize the model so that the model is not disturbed by extreme learning rates, and it converges to the global minimum at the end of training. It solves the problem that the Adam optimization algorithm causes the model to converge to the local minimum due to the extreme learning rate. Meanwhile, Jaya algorithm is utilized to optimize the hyperparameters of the Adabound for a good performance. Experimental results demonstrated that the proposed method has a significant improvement in terms of Recall-Oriented Understudy for Gisting Evaluation (ROUGE).
Testing the performance of a single pole detection algorithm using the confusion matrix model

Abstract This study aims to examine the single pole detection algorithm using a confusion matrix model which is a specific table that makes it easy to visualize the performance of an algorithm. The algorithm tested is the YuRHoS pole detection algorithm, a new algorithm developed by researchers to detect pole objects with not poles. Methods used is by calculating three aspects of algorithm performance in machine learning, namely sensitivity, specificity, and accuracy. The value of the three aspects of performance depends on four variables, namely true positive, true negative, false positive and false negative. The calculation process is done by matching the pixel detection region with the ground-truth region. The test results for 4 (four) different single pole images found that the YuRHoS pole detection algorithm is better than other algorithms on two measurement aspects, namely specificity, and accuracy. Excellence aspects of specificity obtained because of its ability in detecting the object instead of a pole. Excellence aspects of accuracy indicated because more accurate in detecting a pole. As for sensitivity aspects, both the detection algorithms are having the same reliability in correctly predicting a pole.
Spatial-Temporal Learning-Based Artificial Intelligence for IT Operations in the Edge Network

With the rapid increase of edge network scale and the complexity of service interaction, it takes more time for operation staff to analyze anomalies from complex scenarios. To maintain the normal network operation, various key performance indicators, such as link delay, throughput, and memory usage, are monitored for timely anomaly detection and troubleshooting. We introduce artificial intelligence for IT operations to assist operators in performing anomaly detection, anomaly localization, and root cause analysis, and building an intelligent operation and maintenance platform over the software-defined networking (SON)-based edge network. In this article, the graph-based gated convolutional network for anomaly detection (GAD) is first proposed to solve the anomaly detection problem of time series data with topology information. Specifically, GAD uses a gated convolutional encoder to encode spatial-temporal time series, and a graph convolutional network is developed to capture the spatial dependence. Then, based on the features, a convolutional layer is used to decode features and reconstruct input sequence. Finally, the residual between input and reconstructed sequences is further utilized to detect anomalies. Our experimental results demonstrate that GAD outperforms the state-of-the-art anomaly detection baselines in terms of F-scores on the datasets collected by an SDN simulation platform.