Reviewerly/exHarmony huggingface dataset:
Distributed Framework via Block-chain Smart Contracts for Smart Grid Systems against Cyber-Attacks

In this century, the demand for energy is increasing daily, and the need for energy resources has become urgent and inevitable. New ways of generating energy, such as renewable resources that depend on many sources, including the sun and wind energy will contribute to the future of humankind largely and effectively. These renewable sources are facing major challenges that cannot be ignored which also require more researches on appropriate solutions . This has led to the emergence of a new type of network user called prosumer, which causes new challenges such as the intermittent nature of renewable. Smart grids have emerged as a solution to integrate these distributed energy sources. It also provides a mechanism to maintain safety and security for power supply networks. The main idea of smart grids is to facilitate local production and consumption By customers and consumers.Distributed ledger technology (DLT) or Block-chain technology has evolved dramatically since 2008 that coincided with the birth of its first application Bitcoin, which is the first cryptocurrency. This innovation led to sparked in the digital revolution, which provides decentralization, security, and democratization of information storage and transfer systems across numerous sectors/industries. Block-chain can be applied for the sake of the durability and safety of energy systems. In this paper, we will propose a new distributed framework that provides protection based on block-chain technology for energy systems to enhance self-defense capability against those cyber-attacks.
On Reinforcement Learning for Turn-based Zero-sum Markov Games

We consider the problem of finding Nash equilibrium for two-player turn-based zero-sum games. Inspired by the AlphaGo Zero (AGZ) algorithm, we develop a Reinforcement Learning based approach. Specifically, we propose Explore-Improve-Supervise (EIS) method that combines "exploration", "policy improvement" and "supervised learning" to find the value function and policy associated with Nash equilibrium. We identify sufficient conditions for convergence and correctness for such an approach. For a concrete instance of EIS where random policy is used for "exploration", Monte-Carlo Tree Search is used for "policy improvement" and Nearest Neighbors is used for "supervised learning", we establish that this method finds an $\varepsilon$-approximate value function of Nash equilibrium in $\widetildeO(\varepsilon^-(d+4))$ steps when the underlying state-space of the game is continuous and d-dimensional. This is nearly optimal as we establish a lower bound of $\widetildeØmega (\varepsilon^-(d+2) )$ for any policy.
Binary Genetic Algorithm (BGA): A Hybrid Approach for Efficient Web Document Categorization

Textual data on the web is growing at an exponential rate and millions of categories of data are available on web. In this big data environment, finding accurate information from these millions of categories is a challenging task. Web document categorization helps in classifying the web pages in to one or more predefined categories. This paper proposed hybrid Text document categorization approach. In this paper document categorization approach is improved by feature extraction and selection approach since filtering for relevant features is done twice. Here Deep Belief Network (DBN) is used for feature extraction i.e., DBN-FE, Binary Genetic Approach is used for Feature Selection i.e., BGA-FS and three different classifiers are used for evaluation of result of proposed categorization scheme. BGA Model gives the promising result for proposed approach in comparison to other approaches.
An Adaptive Image Steganographic Scheme Using Convolutional Neural Network and Dual-Tree Complex Wavelet Transform

The technique of concealing a confidential information in a carrier information is known as steganography. When we use digital images as carriers, it is termed as image steganography. The advancements in digital technology and the need for information security have given great significance for image steganographic methods in the area of secured communication. An efficient steganographic system is characterized by a good trade-off between its features such as imperceptibility and capacity. The proposed scheme implements an edge-detection based adaptive steganography with transform domain embedding, offering high imperceptibility and capacity. The scheme employs an adaptive embedding technique to select optimal data-hiding regions in carrier image, using Canny edge detection and a Convolutional Neural Network (CNN). Then, the secret image is embedded in the Dual-Tree Complex Wavelet Transform (DTCWT) coefficients of the selected carrier image blocks, with the help of Singular Value Decomposition (SVD). The analysis of the scheme is performed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross Correlation (NCC).
A Comparative Study of Background Segmentation Approaches in Detection of Person with Gun under Adverse Weather Conditions

The ability to detect gun held in hand or other body parts is an ordinary human skill. The same detection problem presents an exceptional challenge for machine vision system. Very few works has been done in the area of automatic detection of moving persons carrying gun in hand under adverse weather condition, although it has several implication in the area of video surveillance and security. The quality of outdoor video scenes suffered from poor visibility and loss it's contrast due to adverse weather atmospheric conditions by scattering of aerosols. In this article, we present a structured comparison carried on between the state-of-art object detection algorithm. Different quality assessment matrices has been used for the evaluation of the performances of state-of-the-art methods. Incidentally relevant public datasets handling such a problem is scanty, if not absent, so far as our knowledge goes. As a result, the present article provides a newly collected several real time crime scene based video data clips from different web sources. The dataset consists of seven sets of data clips, such as, clear day, night, blur, disguise, dusty, foggy, and rainy. The proposed dataset will facilitate the research community to assess the performance of algorithms.
ResTor: A Pre-Processing Model for Removing the Noise Pattern in Flow Correlation

Flow correlation is a common approach to break the anonymity of anonymous communication. However, unpredictable network noise caused by multiple factors in open Internet raises the bar for existing correlation methods. Traditional methods comparing statistical distance of data flows and deep learning methods such as convolutional neural network behave worse because network noise changes traffic shape. In this paper, we design a pre-processing model called ResTor to perform the noise reduction before actually correlating entering and exiting flows. ResTor treats the byte accumulation sequences smoothed at fixed intervals as fitting targets, and takes advantage of the stacked auto-encoder architecture to remove noise in two phases. Experiment results show that the exiting Tor flows processed by ResTor are closer to their corresponding entering flows, thus the correlation task can be finished effectively even using traditional correlation ways: cosine distance and other statistical metrics assisted by ResTor achieve less computational overhead and higher correlation accuracy on Tor compared to the state-of-the-art method of DeepCorr, especially when traffic is obfuscated.
Does Link Prediction Help Find Feature Interactions in Software Product Lines?

An ongoing challenge for the requirements engineering of software product lines is to predict whether a new combination of features (units of functionality) will create an unwanted or even hazardous feature interaction. We thus seek to improve and automate the prediction of unwanted feature interactions early in development. In this paper we show how the detection of unwanted feature interactions in a software product line can be effectively represented as a link prediction problem. Link prediction uses machine learning algorithms and similarity scores among a graph's nodes to identify likely new edges. We here model the product line features as nodes and the unwanted interactions among the features as edges. We investigate six link-based similarity metrics, some using local and some using global knowledge of the graph, for use in this context. We evaluate our approach on a software product line benchmark in the literature, building six machine-learning models from the graph-based similarity data. Results show that the best ML algorithms achieved accuracy of 0.75 to 1 for classifying feature interactions as unwanted or wanted in this small study, and that global similarity metrics performed better than local similarity metrics here. The work shows how link-prediction models can help find missing edges, which represent unwanted feature interactions that are undocumented or unrecognized, earlier in development.
Implementation of Illumination Invariant Face Recognition for Accessing User Record in Healthcare Kiosk

The availability of health check facilities that are increasingly affordable in terms of cost and distance is very useful for the community. Therefore, the presence of a healthcare kiosk that is installed everywhere will certainly be very beneficial for the wider community. In this paper, we developed a user-login method in the healthcare kiosk without utilizing any additional tools so that it is quite efficient, using face recognition biometric technology. We added the invariant illumination feature to face recognition technology to ensure that the healthcare kiosk system will continue to work even in places with changing lighting. This feature uses the light intensity contrast adjustment in the image automatically by employing the Fuzzy Inference System (FIS) and Genetic Algorithm (GA). Based on the results of testing the user-login system, we get an accuracy of 85.57% with an ideal distance of 30-60 cm. The system works with maximum performance in some experimental conditions such as normal lighting, backlighting, direct-lighting, low-lighting, and dark. Users that use facial recognition as a login method, the facial recognition program will capture the user's face and match it to the database. If the user is registered in the database, the system will inform the user that the user is logged in successfully, otherwise, if the user is not logged in, the system will notify the user as unknown.
Transfer Learning with Augmented Vocabulary for Tweet Classification : (Grand Challenge)

In this paper, we describe our experiments and insights gathered in the process of participating in the BigMM Grand Challenge 2020, where the task was to classify a set of tweets pertaining to the #MeToo movement into five linguistic aspects. We analyzed the data set and experimented with several approaches, including classical machine learning models and state of the art deep learning architectures. We achieved our best results by applying transfer learning on a pre-trained ULMFiT model. Our best performing approach had ranked first on the leader-board when the grand challenge finished.
A Comparison of the Cognitive Difficulties Posed by SPARQL Query Constructs

This study investigated difficulties in the comprehension of SPARQL. In particular, it compared the declarative and navigational styles present in the language, and various operators used in SPARQL property paths. The study involved participants selecting possible answers given a SPARQL query and knowledgebase. In general, no significant differences were found in terms of the response time and accuracy with which participants could answer questions expressed in either a declarative or navigational form. However, UNION did take significantly longer to comprehend than both braces and vertical line in property paths; with braces being faster than vertical line. Inversion and negated property paths both proved difficult, with their combination being very difficult indeed. Questions involving MINUS were answered more accurately than those involving negation in property paths, in particular where predicates were inverted. Both involve negation, but the semantics are different. With the MINUS questions, negation and inversion can be considered separately; with property paths, negation and inversion need to be considered together. Participants generally expressed a preference for data represented graphically, and this preference was significantly correlated with accuracy of comprehension. Implications for the design and use of query languages are discussed.
Notice of Violation of IEEE Publication Principles: Denoising techniques for multi-parametric prostate MRI: A Comparative Study

Notice of Violation of IEEE Publication Principles <br><br>“Denoising Techniques for Multi-parametric Prostate MRI: A Comparative Study” <br>by Afef Latrach, Rania Trigui, Lamia Sellemi <br>in the Proceedings of the 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) September 2020 <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper copied content from the paper cited below. The original content was copied without sufficient attribution (including appropriate delineation and citation of reused content) <br><br> “A Survey of Denoising Techniques for Multi-parametric Prostate MRI” <br> by Gaurav Garg and Mamta Juneja <br>in Multimedia Tools and Applications 78, 2019, pp. 12689-12722 <br><br> <br/> Since recent years, denoising become one Of the most active area of research in image processing topic. Usually, MR images are affected by noise and artifacts during the acquisition process. Therefore, many denoising algorithms have been developed although noise elimination still an undefended challenge. In this paper, we study firstly different denoising filters for T2-Weighted prostate cancer MR images, in order to select the appropriate filter. As example of denoising filters, homomorphic, Median, Wavelet, nonlocal means, gaussian, Anisotropic, Laplacian, Cure-LET, LMMSE and bilateral. Then, we discuss the problem of evaluation of image quality which become necessary. As example of evaluation metrics, we present the PSNR, MSE and SSIM. We consider both subjective and objective quality assessment parameters for determining a final score of filters executed over 40 T2-Weighted MR images. This study concludes that Anisotropic filter should be opted for denoising T2-Weighted MR image since its details preserving capability.
Evaluation of aggregated query plans using heuristic approach

In the present era, swarm optimization techniques are quite adaptable towards achieving the flexibilities in terms of expansion of queries achieving the global optimization capabilities. The expansion of queries in general targets to achieve the desired query terms among the available query sets with proper identification of candidate keys. Practically, it is difficult to judge the potentiality of the query terms as well as large queries through traditional computing mechanisms. In such case, it is desirable to employ the optimization techniques to resolve the problems associated with expansion of queries. Also it is essential to make experimentation on parameters associated with the particle swarm optimization techniques. This analysis should focus on query expansion and link to retrieval mechanisms. To enhance the mechanisms of retrieval of information within the stipulated time period, the linked queries can be augmented involving the computational steps along with query expansion techniques. It will be a support to increase the effectiveness of retrieval mechanism of queries and eradicate the anomalies implementing the normalization. The reason of choosing particle swarm optimization in this case is to maintain the members as well as the complete population linked with the retrieval mechanism of queries and to filter the operation obtaining the optimal or near optimal solution. Accordingly, it is essential to update the present generation of particles considering as candidate solutions focusing on velocity, position which may be initialized randomly. During this process there is a great significance of search engine which aims to process huge facts as well as data. With the consistent increase of information, the task of retrieval mechanisms is to aggregate the queries and estimate the performance by simulating the data. In order to achieve better result during retrieval process, particle swarm optimization technique can be adopted to optimize the data and obtain better query formulation. The major merit in this case is its global convergence as well as robustness. Considering the mechanisms associated with data virtualization, it is seen that the logical layer integrates all types of linked data and unifies the same towards real time applications without disturbing the physical storage allocations. Accordingly some related approaches can be adopted towards real time applications. As such one of the most common approaches, heuristic approach has been proposed in this case to evaluate the performance of aggregated query plans.
Towards Personalization of Spoken Dialogue System Communication Strategies

This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Estimating wallets and bitcoins owned by Satoshi using Hash-rate and Transactions analysis

Bitcoin is designed to allow its users to send and receive payments under a pseudonym. In Bitcoin, A pseudonym is an address from/to which a user sends/receives coins. Owing to users anonymity, one-half of the transactions in Bitcoin are associated with illegal trading or money laundering. Hence, it is necessary to develop an approach that estimates the number of coins owned by Bitcoin users and identifies transactions which deal with illegal activities. Considering that every address involved in the Bitcoin transaction is stored permanently in the ledger. If an address is ever mapped to a user identity, every transaction wrote by that user will be linked. In this study, we proposed a new method to identify the number of coins owned by users, in particular, we estimate the number of bitcoins held by the Bitcoin creator Satoshi Nakamoto, by analyzing the mining hash rate, mining reward and transactions data.
Development of Laravel Digital Platform Based on MVC Design Pattern for Compii cated Data Structure-Take the Bible for Example

The digital platform can manage the documents effectively, because it can help us to organize, query, and update the documents. Also, we can query the documents on different mobile terminal devices with the help of the network. However, some documents have complex data structure, such as the Bible. In the Bible, each chapter has its own characteristic. The topic of each chapter in the Bible is introduced in its first sentence or its first paragraph. Therefore, it is necessary to use modern digital platform to organize complex data structure in the Bible. Then, this paper designs and develops a new system. This system is based on the Laravel framework with MVC design pattern. The data components of this system are divided into three parts: Model Layer, View Layer, and Controller Layer. The feature includes the architecture of interactive software system and the technology of Laravel routing engine. The result of experiment shows that the system has an excellent processing ability of complex data structure. By using the Laravel framework, both the time of development and flow of design can be reduced efficiently.
Towards Understanding the Effect of Voice on Human-Agent Negotiation

Virtual agents are increasingly being used for communication training such as public speaking-, job interviews-, as well as negotiation training. In these use-cases the agent is generally taking on the role of interviewer and its behaviour is altered according to the nonverbal cues of its human interlocutor. However, understanding how the agent's non-verbal cues influence human behaviour, perception or interactions outcomes is equally important. This contributes to appropriate behaviour generation in agents, but also to our understanding of the intricate interplay of non-verbal behaviours on human perception and interaction outcomes.
Comparative Analysis of Depth Detection Algorithms using Stereo Vision

In recent years, the use of unmanned aerial vehicles in various domains has increased exponentially. Drones are being extensively used in the fields of Agriculture, Transportation, military, etc. Different sensors are being integrated into the drones depending upon the application. Lately, LIDAR sensors are being integrated on the drone for acquiring depth-related information. Though these sensors have advantages, they are very costly and do not perform very well under high sun angles. Stereo cameras can be mounted on drones to get depth perception of the obstacles as they are cheaper and efficient. In this paper, we have developed and compared two different algorithms ( Conventional and deep learning-based) for realtime depth detection of the obstacle using stereo vision with the intention to mount the stereo camera on drone in future. The comparison is based on accuracy, range of operation and load incurred by the algorithm on the system. The coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and correlation coefficient has also been calculated which shows that Algorithm1 exhibits correlation and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0:9985 and 0:9971 respectively. This is considerably higher than Algorithm2 whose values are around 0:8779 and 0:7707 respectively.
On the Adversarial Robustness of Linear Regression

In this paper, we study the adversarial robustness of linear regression problems. Specifically, we investigate the robustness of the regression coefficients against adversarial data samples. In the considered model, there exists an adversary who is able to add one carefully designed adversarial data sample into the dataset. By leveraging this poisoned data sample, the adversary tries to boost or depress the magnitude of one targeted regression coefficient under the energy constraint of the adversarial data sample. We characterize the exact expression of the optimal adversarial data sample in terms of the targeted regression coefficient, the original dataset and the energy budget. Our experiments with synthetic and real datasets show the efficiency and optimality of our proposed adversarial strategy.