Reviewerly/exHarmony huggingface dataset:
An Approach Based on Situation Analysis and Separation of Environment Perception to Implement Adaptive Strategies

In the existing research, most software adaptation is triggered by changes in the environment. However, the environment of a software system is characterized by uncertainty and complexity. For a software system, the same situation is reflected by different kinds of data in different environments. Unfortunately, traditional adaptive strategies can only be applied to specific environments. When the environment changes, the adaptive strategy also changes accordingly. At the same time, the existing adaptive strategies lack intuition. Therefore, the problem to be solved is how to simplify the specification and implementation of adaptive logic, while ensuring that the software system can adapt to a variety of deployment environments, and also tolerate the uncertainty in the environment. To solve the above problems, we propose our method, an adaptive strategy implementation method based on situation analysis and separation of environment perception. Our method is implemented by the MAPE-K loop, we consider the similar environmental states as one kind of situation in general, and then the adaptive strategy in this situation is defined. It separates the specific environmental data from the adaptive logic of how the software adapts to different situations. Besides, this paper also provides a case study - Intelligent Medical Assistance System to evaluate the feasibility and effectiveness of our method.
ElGamal-type encryption for optimal dynamic quantizer in encrypted control systems

This study considers a quantizer design problem with controller encryption for minimizing performance degradation caused by encryption. It is difficult to design an optimal dynamic quantizer that converts real numbers to plaintexts for encrypted control systems with ElGamal encryption because the plaintext space of ElGamal encryption is intermittent and does not include zero and negative numbers. A variant of ElGamal encryption is proposed to apply a conventional optimal dynamic quantizer for encrypted control systems. The proposed multiplicative homomorphic cryptosystem, wherein the plaintext space is consecutive integers within a certain range, can handle zero and negative integers properly. Numerical simulations demonstrate that the optimal dynamic quantizer with the proposed cryptosystem improves the control performance of an encrypted regulator.
Using Amnesia to Detect Credential Database Breaches

Abstract Known approaches for using decoy passwords (honeywords) to detect credential database breaches suffer from the need for a trusted component to recognize decoys when entered in login attempts, and from an attacker’s ability to test stolen passwords at other sites to identify user-chosen passwords based on their reuse at those sites. Amnesia is a framework that resolves these difficulties. Amnesia requires no secret state to detect the entry of honeywords and additionally allows a site to monitor for the entry of its decoy passwords elsewhere. We quantify the benefits of Amnesia using probabilistic model checking and the practicality of this framework through measurements of a working implementation.
Collective De-Anonymization of Social Networks With Optional Seeds

As Internet users interacting with their different friends in different social networks, the de-anonymization problem has been raising improving concern. Since the assailants may de-anonymize a social network by matching it with a correlated sanitized network and identifying anonymized user identities, multifarious arts study on the theoretical conditions or practical algorithms for correctly de-anonymizing a social network. Except for the structural information of these social networks, there has also been bounteous works taking advantage of some pre-identified seed nodes for reference in the anonymized network. In this paper, we systematically probe the theoretical conditions and algorithmic approaches for correctly matching two different-sized social networks by leveraging the multi-hop neighborhood relationships. A limited number of seeds are also taken into consideration as auxiliary information. To this end, we introduce the de-anonymization problem with the aid of the collectiveness and the collective adjacency disagreements, which are the collection of disagreements of different multi-hop adjacency matrices. We theoretically demonstrate that minimizing the collective adjacency disagreements can help match two social networks even in a very sparse circumstance, as it significantly enlarges the difference between the mismatched node pairs and the correctly matched pairs. Besides, the seeds is proved to bring positive influence in improving the de-anonymization accuracy. Algorithmically, we relax the domain of the matching function to continuum and adopt the conditional gradient descending method on the collective-form objective, to efficiently minimize the collective adjacency disagreements of two networks. We conduct tremendous experiments on different networks with or without seeds, the results of which return desirable de-anonymization accuracies and reveal the advantages of the collectiveness: the collectiveness manifests rich structural information, thereby most nodes can be correctly matched with their correspondences even in some sparse networks, where merely utilizing the 1-hop adjacency relationships might fail to work.
Quantum Cryptography and Blockchain System: Fast and Secured Digital Communication System

Brief introduction about quantum cryptography and blockchain system: Fast and secured digital communication network-based applications followed by its background and motivation are explained. Short review on the idea is enclosed with aiming network-based application for data exchange with quantum blockchain technology. The main motivation about this quantum blockchain technology in data communication is to enable fast and reliable communication channel in future reverence. In order to avoid data theft in exchange of data and fast mode of exchanging the data communicated over the Internet, quantum cryptography is fused with blockchain technology
A Pragmatic Approach to Face Recognition using a Novel Deep Learning Algorithm

This paper is based on face detection or recognition. As we know that this technology is growing and making a great impact in today’s world. And it is because of the usage of this technology in wide range of area. It’s basically used in large campuses for aided vigilance and surveillance. In the market there are large numbers of algorithm for this technology to be used and implemented. Many facial recognition methods are used, including Principal Factor Analysis, Linear Discriminant Analysis, Independent Component Analysis, Artificial Neural Networks and many others. The innovation of new approaches of face recognition technologies is a major part for building a much newer and stronger algorithm. Basically, three approaches can be used as follows, model based, appearance based and hybrid based.
A Hybrid Collaborative Recommendation System Based On Matrix Factorization And Deep Neural Network

The paper explores a modified recommender system that is established based on the combination of matrix factorization and deep neural network that work on the implicit feedbacks of users and also auxiliary information of both users and items. Recent works show the effectiveness of deep neural network on recommendation systems. Proposed models aim at discovering additional relationships by using auxiliary information to explore the internal relationship between users and also the relationships of items among themselves. Experiments show 0.5556 and 0.8036 in NDCG and HR with the model which is an improvement compared to other popular collaborative filtering methods.
Online content regulation policy in Kenya: potential challenges and possible solutions

The digital explosion experienced in Africa has rendered old media laws and ethics inapplicable in dealing with cases of abuse and misuse on social media platforms. As a result, authorities in most...
Supervisor synthesis of POMDP via automata learning

Partially observable Markov decision process (POMDP) is a comprehensive modeling framework that captures uncertainties from sensing noises, actuation errors, and environments. Traditional POMDP planning finds an optimal policy for reward maximization. However, for safety-critical applications, it is often necessary to guarantee system performance described by high-level temporal logic specifications. Hence, we are motivated to develop a supervisor synthesis framework for POMDP with respect to given formal specifications. We propose an iterative learning-based algorithm, which can learn a permissive policy in the form of a deterministic finite automaton. A human–robot collaboration case study validates the proposed algorithm.
Land of the lost: privacy patterns' forgotten properties

Privacy patterns describe core aspects of privacy-enhancing solutions to recurring problems and can, therefore, be instrumental to the privacy-by-design paradigm. However, the privacy patterns domain is still evolving. While the main focus is currently put on compiling and structuring high-quality privacy patterns in catalogs, the support for developers to select suitable privacy patterns is still limited. Privacy patterns selection-support means, in essence, the quick and easy scoping of a collection of patterns to the most applicable ones based on a set of predefined criteria. To evaluate patterns against these criteria, a thorough understanding of the privacy patterns landscape is required. In this paper, (i) we show that there is currently a lack of extensive support for privacy patterns selection due to the insufficient understanding of pattern properties, (ii) we propose additional properties that need to be analyzed and can serve as a first step towards a robust selection criteria, (iii) we analyze and present the properties for 70 privacy patterns, and (iv) we discuss a potential approach of how such a selection-support method can be realized.
Metis: An Integrated Morphing Engine CPU to Protect Against Side Channel Attacks

Power consumption and electromagnetic emissions analyses are well established attack avenues for secret values extraction in a large range of embedded devices.Countermeasures against these attacks are approached at different levels, from modified logic styles, to changes in the software implementations.In this work, we propose a microarchitectural modification to a compact RISC-V SoC, the OpenTitan open source silicon root of trust, providing a code morphing countermeasure against power and electromagnetic emissions side channel attacks.Our approach allows the countermeasure to be applied transparently, without the need for any software modification to the cryptographic primitive running on OpenTitan.Our microarchitecture integration of a morphing engine also allows us to provide transparent protection to memory operations.We validate our approach through measurements on an actual FPGA prototype on a Xilinx Artix-7.Our integrated morphing engine increases the FPGA resource consumption by less than 8%, plus the resources required by an RNG of choice, with respect to the original OpenTitan SoC.Our design shows a side channel attack resistance improvement of at least 250× in the Measurements-To-Disclose metric with respect to the unprotected design.We benchmark the performance of our proposed architecture on all the ISO/IEC standard symmetric block ciphers, including, among the other AES, reducing the execution time overhead by 21× to 141× with respect to a continuously morphing software solution.
A Novel Framework for Blockchain Based Driving License Management and Driver’s Reputation System for Bangladesh

In this paper, we reviewed the challenges and drawbacks of current licensing process such as lack of transparency, accountability, difficulties to track the provenance of information and assert traceability. As a solution to the issues we proposed a novel framework based on blockchain. We leveraged the key features of blockchain and smart contract to develop a better driving license management and drivers reputation system. We automated several tedious steps for a more efficient system. We explained our proposed framework, compared performance with existing traditional and digital systems before concluding with our future work.
Multi-target detection method based on YOLOv4 convolutional neural network

Abstract In order to solve the problems of low detection accuracy, false detection and high miss detection rate of small targets in target detection tasks, this paper is a multi-target detection method based on YOLOv4 convolutional neural network. The proposed method is based on YOLOv4. The semantic information of high-level features is first propagated to the low-level network through FPN sampling, and then it is fused with the high-resolution information of the underlying features to improve the detection effect of small target detection objects. The information transmission path from the bottom to the top is enhanced by downsampling the feature pyramid, and finally the feature maps of different layers are fused to achieve relevant predictions. Experiments prove that the method proposed in this paper has good results.
Attention-aware conditional generative adversarial networks for facial age synthesis

Generative adversarial networks (GANs) have recently achieved impressive results in facial age synthesis. However, these methods usually select an autoencoder-style generator. And the bottleneck layer in the encoder-decoder generally gives rise to blurry and low-quality generation. To address this limitation, we propose a novel attention-aware conditional generative adversarial network (ACGAN). First, we utilize two different attention mechanisms to improve generation quality. On one hand, we integrate channel attention modules into the generator to enhance the discriminative representation power. On the other hand, we introduce a position attention mask to well-process images captured with various backgrounds and illuminations. Second, we deploy a local discriminator to enhance the central face region with informative details. Third, we adopt three types of losses to achieve accurate age generation and preserve personalized features: 1) The adversarial loss aims to synthesize photo-realistic faces with expected aging effects; 2) The identity loss intends to keep identity information unchanged; 3) The attention loss tries to improve the accuracy of attention mask regression. To assess the effectiveness of the proposed method, we conduct extensive experiments on several public aging databases. Experimental results on MORPH, CACD, and FG-NET show the effectiveness of the proposed framework.
Image deraining with Adversarial Residual Refinement Network

Prior image deraining works mainly have two problems: (1) they do not generalize well to various datasets; (2) too much detail information is lost in the heavy rain area of the rain image. To overcome these two problems, we propose a new two-stage Adversarial Residual Refinement Network (ARRN) to deal with heavy rain images. Specifically, for the first problem, we first introduce a new implicit rain model to model a rain image as a composition of a background image and a residual image. Based on the proposed implicit model, we then propose the ARRN which consists of an image decomposition stage and an image refinement stage. For the second problem, a new attention Wasserstein Generative Adversarial Networks (WGAN) loss in the refinement stage is introduced to force the network to focus on refining heavily degraded areas. Comprehensive experiments demonstrate the effectiveness of the proposed approach.
Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.
Implementation of an Intelligent Online Job Portal Using Machine Learning Algorithms

Business intelligence and analytics are data management solutions implemented in companies and enterprises to collect historical and present data, while using statistics and software to analyze raw information, and deliver insights for making better future decisions. In the circumstances of today's world, to survive and established own business need an analytical and find an easiest way or intelligence business model. The main objective is to examine the performance of various Machine Learning algorithms in order to perform with the system of an online job portal. This proposed module integrated with three phase such as, the Clusters similar kind of job search phase (CSK) is a way of knowing the demand is to create a visual graph showing clusters of similar kinds of job searched by the job seekers in the website of the job portal, the email notifications send phase (ENS) is responsible to send email notifications to the job seekers when a job circular is posted in the website, extract the job circular phase (EJC) is the way to extract the job circular post from the career section of each of the company's website. The result shows the successful clustering of similar job search, email notification send to specific people and extracts the information from the web.
Hand Gesture Recognition-Based Control of Motorized Wheelchair using Electromyography Sensors and Recurrent Neural Network

Abstract Mobility has been identified to be a major characteristic of living things. Humans who are deprived of efficient mobility either by natural or man-made factors loose significant relationship with their environment. The growing demand to produce effective rehabilitation devices for the aged population and disabled individuals, have spurred us to develop a reliable and easy to use biosignal based auto control wheelchair. This is to ensure independent mobility of persons with disabilities and the aged. In this paper, a Recurrent Neural Network (RNN) architecture called Long Short Term Memory (LSTM) is engaged for the classification EMG signals to the corresponding hand-gesture category. The LSTM model in this study yielded a validation accuracy that provides a basis for an improved solution towards real-time deployment.
Comparative Study of Digital Image Forgery Detection Techniques

In present time, digital images is considered to be an important channel of information. Digital images are available on magazine covers, in papers, in courts as confirmation, e-Government administrations and everywhere throughout the internet implying one of the significant ways for correspondence. With the access of powerful image processing software and human knowledge which can easily manipulate the digital image, authenticating digital images is troublesome. Hence, an automatic computer-based system is required to be developed which can identify the originality of input image. There are three phases of image processing like pre-processing, features extraction and forged area detection on image using features extracted. In this way, an innovation has been made to cover included algorithms in the course of recent years, characterize them in bunches having comparable strategies to take care of issues. In current work, an inclusive survey of various image forgeries, its types and comparison of various detection techniques are presented. The advantages and drawbacks of each method are also highlighted. Finally, it summarizes the key conclusions and future scope of work.
Diffusion, Pre-smoothing and Gradient Descent

Nonlinear diffusion of images, both isotropic and anisotropic, has become a well-established and well-understood denoising tool during the last three decades. Moreover, it is a component of partial differential equation methods for various further tasks in image analysis. For the analysis of such methods, their understanding as gradient descents of energy functionals often plays an important role. Often the diffusivity or diffusion tensor field for nonlinear diffusion is computed from pre-smoothed image gradients. What was not clear so far was whether nonlinear diffusion with this pre-smoothing step still is the gradient descent for some energy functional. This question is answered to the negative in the present paper. Triggered by this result, possible modifications of the pre-smoothing step to retain the gradient descent property of diffusion are discussed.
Importance sampling imputation algorithms in quantile regression with their application in CGSS data

As a popular sampling tool used for Monte Carlo computing, importance sampling (IS) has been used in a wide variety of application areas recently. In large-scale survey data such as Chinese General Social Survey (CGSS), missing data, a high level of skewness and heteroscedastic variances commonly occur. Most of the existing literatures on survey data analysis focused on modeling the conditional mean without deep investigation about missing data. In this paper, we study an IS imputation algorithm and its modified algorithm with inverse probability weighting arrangement in quantile regression with missing covariates. We make full use of the observed data compared with the existing complete cases analysis and inverse probability weighting method, and also improve the computational efficiency of multiple imputation and EM-based algorithms. The quantile regression framework allows us to obtain an overall picture of covariates’ effects, highlights the changing relationships according to the explored quantile of interest, and solves the problems of skewness and heterogeneity. Through simulation studies, we investigate the performances of both IS and ISW with other existing algorithms. Finally, we apply our algorithms to part of annual income data from CGSS in 2010. We build three kinds of quantile regression models based on all the subjects, urban areas subjects and rural areas subjects, respectively.