Reviewerly/exHarmony huggingface dataset:
A user collaboration privacy protection scheme with threshold scheme and smart contract

Abstract Recently, the revolution of 5G networks has promoted a wide application of wireless technology, and then followed with the prosperity of Location-Based Service (LBS). However, an intricate problem called privacy violation still restricts the development of this type of service. In order to preserve users’ privacy, a number of privacy preservation strategies had been proposed. As an efficient framework used for protecting personal privacy, user collaboration is mainly proposed for dealing with the issue of service bottleneck and the issue of attack focus. However, there are still several challenges far from being perfectly solved, such as the collusion attacks as well as the willingness of collaborative users. Therefore, in order to address these challenges, in this paper, based on the conception of information division and user’s incentive, a ( t , n ) smart contract privacy preservation scheme short for ( t , n ) SCPPS is proposed. In this scheme, the query information is encrypted and split by (t, n) threshold scheme, so the collaborative users will be difficult to learn anything about the initiator, and the collusion will be difficult to constitute the integral query without t users colluding with each other. On the other hand, the combination of (t, n) threshold scheme and smart contract further provide competition for incentive, as just the earliest collaborative users that submit the partition information and get the corresponding feedback will obtain the incentive. At last, the security analyses were given, and then tests of users’ granularity and locations difference in continuous query in the real environment. Furthermore, simulation experiments are conducted to demonstrate the effectiveness of proposed scheme, and the experimental verification is executed using two different types of datasets. Thus, compared with other similar schemes, the results with brief explanations about reasons can further demonstrate the superiority of proposed scheme.
Classification, segmentation and correlation of zoned minerals

Minerals exhibit compositional zoning patterns that can be related to changes in the environment in which they grew. Using statistical methods that have been designed to segment optical images, we have developed a procedure to segment zoned crystals using elemental maps. For a single mineral phase, compositional zones in individual crystals are correlated between multiple crystals. This allows us to quantify the complexity and variability of chemical zoning between different geological samples. Specifically, we employ a simple linear iterative clustering algorithm, which splits the chemical maps into spatially constrained regions of similar chemistry. The result is a texturally segmented crystal, akin to what would be identified by the human eye. To aid the segmentation and correlation of zones, we also introduce a new method to classify multiple mineral phases within a single thin section. This is based on a finite mixture model approach, which proves very effective in removing mixed pixels that will only introduce noise into the segmentation. We provide an example using the mineral phase plagioclase. Using two contemporaneous samples from an eruptive unit on the island of St. Kitts (Eastern Caribbean) we show that a volcanic bomb (~10 cm) and a scoria clast (~2 cm) have similar rim compositions but distinctly different core compositions. Our methodology will enable a statistical characterization of 2D complexity of crystals in a variety of different geo-scientific disciplines. This will allow the genesis of different mineral phases to be directly compared.
An investigation of the effects of chaotic maps on the performance of metaheuristics

Abstract This article presents an empirical investigation of the effects of chaotic maps on the performance of metaheuristics. Particle Swarm Optimization and Simulated Annealing are modified to use chaotic maps instead of the traditional pseudorandom number generators and then compared on five common benchmark functions using nonparametric null hypothesis statistical testing. Contrary to what has often been assumed, results show that chaotic maps do not generally appear to increase the performance of swarm metaheuristics in a statistically significant way, except possibly for noisy functions. No performance differences were observed with the single‐state Simulated Annealing algorithm. Finally, it is shown that sequence effects may be responsible for the observed performance increase. These findings reveal new research directions in using chaotic maps for metaheuristics research. The MATLAB code used in this article is available in a GitHub repository for suggestions and/or corrections.
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions

Nighttime stereo depth estimation is still challenging, as assumptions associated with daytime lighting conditions do not hold any longer. Nighttime is not only about lowlight and dense noise, but also about glow/glare, flares, non-uniform distribution of light, etc. One of the possible solutions is to train a network on night stereo images in a fully supervised manner. However, to obtain proper disparity ground-truths that are dense, independent from glare/glow, and have sufficiently far depth ranges is extremely intractable. To address the problem, we introduce a network joining day/night translation and stereo. In training the network, our method does not require ground-truth disparities of the night images, or paired day/night images. We utilize a translation network that can render realistic night stereo images from day stereo images. We then train a stereo network on the rendered night stereo images using the available disparity supervision from the corresponding day stereo images, and simultaneously also train the day/night translation network. We handle the fake depth problem, which occurs due to the unsupervised/unpaired translation, for light effects (e.g., glow/glare) and uninformative regions (e.g., low-light and saturated regions), by adding structure-preservation and weighted-smoothness constraints. Our experiments show that our method outperforms the baseline methods on night images.
A Deep Primal-Dual Proximal Network for Image Restoration

Image restoration remains a challenging task in image processing. Numerous methods tackle this problem, often solved by minimizing a non-smooth penalized co-log-likelihood function. Although the solution is easily interpretable with theoretic guarantees, its estimation relies on an optimization process that can take time. Considering the research effort in deep learning for image classification and segmentation, this class of methods offers a serious alternative to perform image restoration but stays challenging to solve inverse problems. In this work, we design a deep network, named DeepPDNet, built from primal-dual proximal iterations associated with the minimization of a standard penalized likelihood with an analysis prior, allowing us to take advantage of both worlds. We reformulate a specific instance of the Condat-Vu primal-dual hybrid gradient (PDHG) algorithm as a deep network with fixed layers. The learned parameters are both the PDHG algorithm step-sizes and the analysis linear operator involved in the penalization (including the regularization parameter). These parameters are allowed to vary from a layer to another one. Two different learning strategies: "Full learning" and "Partial learning" are proposed, the first one is the most efficient numerically while the second one relies on standard constraints ensuring convergence in the standard PDHG iterations. Moreover, global and local sparse analysis prior are studied to seek a better feature representation. We apply the proposed methods to image restoration on the MNIST and BSD68 datasets and to single image super-resolution on the BSD100 and SET14 datasets. Extensive results show that the proposed DeepPDNet demonstrates excellent performance on the MNIST and the more complex BSD68, BSD100, and SET14 datasets for image restoration and single image super-resolution task.
A novel multi-image cryptosystem based on weighted plain images and using combined chaotic maps

This paper introduces a new multi-image cryptosystem based on modified Henon map and non-linear combinations of chaotic seed maps. Based on the degree of correlation between the adjacent pixels of the plain images, a unique weight is assigned to the plain images. First, the coordinates of plain images are disrupted by modified Henon map as confusion phase. In the first step of diffusion phase, the pixels content of images are changed separately by XOR operation between confused images and matrices with suitable non-linear combination of seed maps sequences. These combinations of seed maps are selected depending on the weight of plain images as well as bifurcation properties of mentioned chaotic maps. After concatenating the matrices obtained from the first step of diffusion phase, the bitwise XOR operations are applied again between newly developed matrix and the other produced matrix from the chaotic sequences of the Logistic–Tent–Sine hybrid system, as second step of diffusion phase. The encrypted image is obtained after applying shift and exchange operations. The results of the implementation using graphs and histograms show that the proposed method requires an average of 49.31 ms less processing time than the best time of some previous methods for 1024 × 1024 images. The entropy is close to the ideal value of 8, and the values of the correlation coefficient of the encrypted images are close to 0. The histogram of the encrypted images is uniform and the value of peak signal to noise ratio (PSNR) is about 5 dB more than some compared methods for 50% cropping attack. Therefore, compared to some of the existing methods, the proposed method can also be used as a secure method for encrypting gray-scale digital images.
Malicious URL Detection Using Supervised Machine Learning Techniques

short-paper Share on Malicious URL Detection Using Supervised Machine Learning Techniques Authors: Vara Vundavalli Kennesaw State University Kennesaw State UniversityView Profile , Farhat Barsha Military Institute of Science and Technology Military Institute of Science and TechnologyView Profile , Mohammad Masum KSU KSUView Profile , Hossain Shahriar Kennesaw State University Kennesaw State UniversityView Profile , Hisham Haddad KSU KSUView Profile Authors Info & Claims SIN 2020: 13th International Conference on Security of Information and NetworksNovember 2020 Article No.: 21Pages 1–6https://doi.org/10.1145/3433174.3433592Published:01 February 2021Publication History 2citation237DownloadsMetricsTotal Citations2Total Downloads237Last 12 Months139Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Incomplete Multi-view Multiple Clusterings

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on the predefined clustering criterion, but devising such a criterion that captures what users need is difficult. Due to the multiplicity of multi-view data, we can have meaningful alternative clusterings. In addition, the incomplete multi-view data problem is ubiquitous in real world but has not been studied for multiple clusterings. To address these issues, we introduce a deep incomplete multi-view multiple clusterings (DiMVMC) framework, which achieves the completion of data view and multiple shared representations simultaneously by optimizing multiple groups of decoder deep networks. In addition, it minimizes a redundancy term to simultaneously control the diversity among these representations and among parameters of different networks. Next, it generates an individual clustering from each of these shared representations. Experiments on benchmark datasets confirm that DiMVMC outperforms the state-of-the-art competitors in generating multiple clusterings with high diversity and quality.
Frobenius Norm-Based Unbiased Finite Impulse Response Fusion Filtering for Wireless Sensor Networks

This article presents a new approach to designing the Frobenius norm-based weighted unbiased finite impulse response (FIR) fusion filter for wireless sensor networks. The weighted Frobenius norm is employed as a cost function to design a local unbiased FIR filter. The design problem is converted into a constrained optimization problem subject to an equality constraint. The Lagrange multiplier method is used to derive the local FIR filter gain. An alternative Frobenius norm is introduced to determine weights for the local unbiased FIR filters in the design of a global fusion FIR filter. The developed FIR fusion filter is demonstrated to have higher robustness against uncertainties than Kalman filter-based methods, such as the optimal fusion Kalman filter, distributed Kalman filter, and distributed weighted Kalman filter, through a numerical example of moving-target tracking employing seven smart sensors and an experiment with temperature and humidity estimation using eight sensors.
On the Resource Management of Kubernetes

Kubernetes is the most popular container orchestration platform that enables users to create and run multiple containers in cloud environments. Kubernetes offers resource management to isolate the resource usage of containers on a host server because performance isolation is an important factor in terms of service quality. This paper investigates whether the resource management of Kubernetes is sufficient to isolate the performance of containers. This is different from previous studies that mostly focuses on efficient resource management rather than the study on performance interference. We evaluate the performance interference 1) between CPU-intensive and networkintensive containers, and 2) between multiple network-intensive containers. Our evaluation results show that containers experience performance degradation by 50% due to the co-located containers even under the resource management of Kubernetes. This paper also points out that the root cause of the performance interference between multiple network-intensive containers is CPU contention, not network bandwidth. As a result, Kubernetes needs to consider the CPU usage of network-related workloads in resource management in order to mitigate the performance interference.
Classification of Sign Language Characters by Applying a Deep Convolutional Neural Network

Having a massive community of almost 466 million deaf-mute people all over the world, sign language recognition has always fascinated researchers to develop sophisticated models that can successfully recognize sign languages. Because of not being a universal language, sign language differs in terms of languages and communities. Previously, various researches have been conducted on different sign languages. In this study, we considered the Sign Language MINST dataset. Previously, different classifiers like support vector machine, random forest, multilayer perceptron, etc. have been introduced for sign language recognition. Recently, shallow CNN and Capsule Networks have obtained better results. Therefore, in this research, we proposed a deep convolutional neural network model to achieve the successful identification of the sign linguistics alphabets. After implementing the model, we produced an overall accuracy of 97.62% and comparison with previous researches revealed that our proposed model outperformed all previously introduced models.
Intention Understanding in Human–Robot Interaction Based on Visual-NLP Semantics

With the rapid development of robotic and AI technology in recent years, human–robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human–robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures.
A Framework of Software Project Scope Definition Elements: An ISM-DEMATEL Approach

Software project scope definition is complicated due to the diversity and magnitude of the information needed.An inadequate scope definition often results in project failure as it continues to emerge as the major cause of delays, changes/rework, and cost and schedule overruns.Literature mentions different tools and methods to verify, quantify, and control software scope definition.However, none of these methods and tools help in defining a complete scope.Since a well-defined scope in the early stages is a core ingredient for project success, therefore, previously we developed a method that includes 45 elements of the software project scope definition.Although these elements are noteworthy for project scope, some may influence others; and should be concentrated on more when defining the scope of the software projects.The objective of this present study is that it builds on our previous research and uses Interpretive Structural Modeling (ISM) to extract the interrelationships among the elements and Decision Making Trial and Evaluation Laboratory (DEMATEL) to determine the intensity of these relationships.Experts from academia and the software development industry were consulted to identify the relationships among the elements.ISM-DEMATEL approach indicates that the project manager's competence is the main driver.Further, requirements, stakeholders' expectations, cost estimates, project schedule, resource estimation, project summary, communication, consultation, top management support are among the most influential elements that are critical to focus on.Moreover, the findings of the study provide project managers with a better understanding of the elements and their interconnections thus, helping in achieving a better scope definition.
A systematic literature Review: Risk analysis in cloud migration

The era of the industrial revolution 4.0 was an era marked by the transition of information and communication technology that was able to create new technology-based investments. Internet of things (IoT), Big Data, and Cloud Computing are the foundations that underlie this 4.0 industrial revolution. Cloud Computing is a service that provides network storage space and computer resources using an internet connection as a medium of access. Cloud Service Providers (CSP) offer attractive services, making more and more companies want to migrate to the cloud. Sometimes the migration process to cloud computing faces problems or even failures, and this is certainly a risk for cloud service users. This study will identify the types of risks and risk components in cloud migration using the Systematic Literature Review (SLR) method. The databases used in selecting articles that match the criteria include: Emerald Online, IEEE Xplore, ScienceDirect, SpringerLink, and between 2015 and 2020. The results of this study, there were 74 articles selected according to the criteria and reviewed. The output of this study shows that there are 7 types of risk in cloud migration, namely information security risk, risk of losing data access, risk of using virtual machines, errors in choosing CSPs, risk of compliance with various laws and regulations, financial risk, and management failure, the weights of 25%, 21%, 18%, 14%, 11%, 7%, and 4% respectively, as well as 5 risk components, namely threats, impacts, risk factors, vulnerabilities, and damage with a weight of 33%, 27%, 20%, 13%, and 7%.
Empirical study on tangent loss function for classification with deep neural networks

Deep neural networks have been widely applied in natural language processing and computer vision tasks, which have achieved great successes due to their powerful ability for capturing sophisticated deep features. Currently, most of the neural networks are trained with cross-entropy (CE). However, the traditional CE loss function is sensitive to randomness induced from training samples. In this paper, we propose a novel loss function, namely tangent loss (TG), aiming to make classification models more stable while achieving comparable performance. The TG loss function trains the neural network in a way that emphasizes samples whose predictions deviate greatly from the targets at each training step. We make a systematical empirical study on TG loss, which is compared with CE loss on various classification tasks. Extensive experimental results on the real-world datasets demonstrate that the TG loss function can be readily applied with the existing neural networks and improves the stability of classification models, which can obtain better or comparable classification performance than the CE loss function.
Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features

Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.