Reviewerly/exHarmony huggingface dataset:
Design of Imitative Control Modalities for a 3 Degree of Freedom Robotic Arm

This research article focuses on enabling real-time Human-Robot Interaction (HRI) with two input control modalities designed to control a robotic arm. The robotic arm comprises of 3 Degrees of Freedom (DOF) and is controlled using the following 2 strategies: Inertial Sensor-glove and Image-based visual servoing. The Mathematical model of the given 3 DOF robotic arm was derived and the two proposed methods were explicitly designed for the same. The inertial sensor-glove, worn by the user, is embedded with inertial sensors that provide data to track the motion of the user's arm. The Image-based visual servoing modality is a mono-vision based approach which tracks the motion of a target object held by the user using a camera and operates similar to a mouse-pointer but in 3 dimensions. Fitts's Targeting tasks were performed to analyse the performance of these Human-Robot Interactive input modalities.
Energy Estimation Based on Path Tracking for a Differential Drive Wheeled Mobile Robot

To improve the energy efficiency of mobile robots and increase their time of operation, a comprehensive energy model is needed. Having such a model requires a lot of complex analysis and design time. There has been a lot of research into optimizing the power consumption of mobile robots but have not benefited from the advantages of languages to model complex cyber-physical systems. In this work, we used the Simscape ™ MATLAB ® environment to simplify and speed up the design of an energy consumption model of a differential drive mobile robot. We also estimated the energy consumption of the mobile in a different path tracking scenario. Our results show that is possible to obtain a good accuracy of path following with acceptable energy consumption.
Galactic Swarm Optimization Based Adaptive Digital Image Watermarking for Optimal Positions Detection

Over recent decades, myriad technologies of imaging and their applications have been skyrocketed. With the emerging of such technologies, images acquisition, storage, and distribution led to easier and fast processing and transmission. But, on the other hand, several issues of malicious copying, unauthorized manipulation, and felonious distribution are growing every day. In order to cope with these issues, as a promising technology, digital image watermarking has appeared. In general, the optimization techniques are in demand for providing optimal embedding for watermarks. In this paper, an adaptive blind approach of digital image watermarking has been proposed in which the algorithm of galactic swarm optimization (GSO) is used for detecting the optimal positions to embed a watermark in the high and middle frequencies of the discrete wavelet transformation (DWT) of the cover image. To prove the high performance of the presented approach, various experiments and comparisons were conducted. The acquired results showed robustness against several kinds of attacks and preserved a high imperceptibility.
Iterative Parameterized Consensus Approach for Clustering and Visualization of Crime Analysis

Crime exists across the world in various forms such as drug trafficking, murder, extortion, human trafficking, money laundering, fraud, robbery, assault and many more. These crimes have diverse statistical trends that change with time. Crime rate is an evaluating measure of crimes complied to weigh the effectiveness of a criminal offense management rule, and also the impact of the policy on the danger of crime victimization. The necessity of crime rate analysis focuses on assisting the police in safeguarding a locality and makes it a safe zone for people to live in. Generally, crime is associated with a location as much as it is related to the victim and the criminal. Thus, the geological position of the crime committed is an important aspect of criminal activity. This paper performs a consensus clustering-based micro-study of crime rate analysis over the years in Atlanta. Consensus Clustering (CC) generally called clustering ensembles, plays a significant role in categorizing and maintaining either categorical or numerical data. It brings together several grouping solutions to get stable, novel and accurate results. We present these results of our experiments aimed at evaluating the effectiveness of the methodology in discovering crime rate clusters that aid in decreasing the crime with respect to the zones.
WITHDRAWN: Content piracy: A large scale logo dataset for classification through deep learning

Content piracy is increasing and spreading rapidly across different regions considering the kind of online infrastructure that we have these days. The primary aim of these online infrastructure is to provide the platform where-in authorized and legal content is been pushed from the service provider till the end-user. Over a period of time, the digital online infrastructure system has been misused by pirates to copy the original content and retransmit this pirated content using the same digital online infrastructure system. One of the methods to check if it is a pirated content is through visual analytics of broadcast logo. In this paper we will be discussing on the openly available datasets of TV broadcast channel logos (Indian channels), development of new scalable TV broadcast channel logo corpus across different regions and genres. A total of 450 TV broadcast channel logos have been collected of different regional language for genres like (Sports, Movies, Kids and Cartoon, Entertainment etc.) Each Logo is subjected for various data augmentation techniques to expand the logo corpus and hence strengthening the deep learning logo classification. Further this paper discusses about using this logo corpus on YOLO v2 state of art object detection algorithm and recognizes different logo classes. Experimental results are recorded for different inference logos of varying pixel context.
Parametric Software Defect Patterns Based on Test Perspective

Software defects are the root causes that lead software unreliable, thus the reduction of software defects is the key way to improve software reliability. How to improve the software reliability using the defect data accumulated? To solve this problem, this paper adopts the theory of sense group division of the software defect data into blocks of semantics, which is difference between fixed and variable meaning group, using a fixed / variable / free parameters in the form of software defect extraction patterns from the software defect data. The extraction of software defect mode which is consistent with the characteristics and forms of software defect data mainly produced by software testing, which can better guide the development and testing of similar software industry, provides a new idea for how to use the software defect data better in tester's perspective.
Altcoin-Bitcoin Arbitrage

We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitcoin arbitrage alpha is significant and discuss it in the context of liquidity considerations as well as its implications for cryptoasset trading.
Reliable Multiview Learning for Spectral Clustering

The purpose of multiview clustering is to improve clustering performance by using multiview information. At present, the research of multiview mainly focuses on the multiview similarity learning. However, the existing multiview similarity learning methods have two drawbacks. First of all, similarity learning is learned based on the fixed graph learned from each view separately, thus ignoring the potential relationship between the views. Secondly, the real data is often destroyed. When the data are contaminated with noise or outlier, the learned similarity graph is unreliable. Therefore, this paper proposes a Reliable Multiview Graph Learning method (RMGL). This method can deal with noisy data. By constructing a data recovery model to restore the true distribution structure of data from each view, the reliability of similarity learning could be guaranteed. Then, a reliable neighbor similarity is obtained with multiview adaptive graph learning. Experimental results on several datasets show that the proposed method is superior to the existing methods on clustering performance and has strong robustness to noisy data.
TechTreat using Machine Learning

Now-a-days the world is transcending towards e-commerce, where people get products delivered at the doorstep. This paper presents an innovative method to simplify the treatment process of a patient by using Optical Character Recognition (OCR) technique. OCR extracts the text present in an image after scanning. This can be used to identify the text written for a input image, when a doctor's prescription is given as input, the medicine name prescribed by the doctor can be identified and on that account the medicine can be shopped by using the application seemingly. The extracted text from prescription can also be used to create the reminder to take the medicine on timely manner, similarly reminding to buy the medicines again when the set or packet is about to end. This would additionally help the patient to track his medicine consumption by analyzing the graph generated by the app. Furthermore by scanning the medicine name with OCR technique, one can detail of the medicine and also the purpose for which the person consumed.
Breaking Down Information Silos with Big Data: A Legal Analysis of Data Sharing

In the digital society, individuals play different roles depending on the situation they are placed in: they are consumers when they purchase a good, citizens when they vote for elections, content providers when they post information on a platform, and data subjects when their data is collected. Public authorities have thus far regulated citizens and the data collected on their different roles in silos (e.g., bankruptcy registrations, social welfare databases), resulting in inconsistent decisions, redundant paperwork, and delays in processing citizen requests. Data silos are considered to be inefficient both for companies and governments. Big data and data analytics are disrupting these silos allowing the different roles of individuals and the respective data to converge. In practice, this happens in several countries with data sharing arrangements or ad hoc data requests. However, breaking down the existing structure of information silos in the public sector remains problematic. While big data disrupts artificial silos that may not make sense in the digital society and promotes a truly efficient digitalization of data, removing information out of its original context may alter its meaning and violate the privacy of citizens. In addition, silos ensure that citizens are not assessed in one field by information generated in a totally different context. This chapter discusses how big data and data analytics are changing information silos and how digital technology is challenging citizens’ autonomy and right to privacy and data protection. This chapter also explores the need for a more integrated approach to the study of information, particularly in the public sector.
An Advanced Approach for Link-Based Spam Detection Using Machine Learning

A great deal of research has been done to discover highlights, which could classify spam. Many have tried to include content-based features to get accuracy. Although the results were positive, the overhead of extracting the content-based features was high and complicated too. This work focuses on finding structural elements of a web graph containing URLs of a domain, to replace the content-based features. The purpose of this work is to discover less complicated features that could expand the precision and decrease the weight on the search engine's computation. The obtained results demonstrate that even though the issue cannot be disposed of yet, a ton may as of now be done to improve the search engine's response to user-submitted queries. The result produced from our approach has an F-Score of 0.9829 and an accuracy of almost 99%. This shows that the link properties of a web graph could tell us a lot about the nature of the web itself. Combining this approach with content analysis could give rather great results.
Reasearch on Similar Flight Numbers of LD Algorithm

Flight number is the main identification mark for aircraft operation management in the control area, but similar flight numbers may cause control risk problems. This paper establishes an efficient method for identifying similar flight numbers by studying the rules of identifying similar flight numbers. Based on the relevant regulations of China Civil Aviation Administration and the identification rules of the experimental analysis in this paper, the procedure of identifying similar flight numbers is optimized and improved. Similar flight number identification method uses specially optimized LD algorithm to better adapt to the screening of similar flight numbers, and to improve the accuracy and effectiveness of similar flight number screening. Finally, by experiments and horizontal comparison analysis with other similarity calculation methods, it is proved that this method can effectively detect similar flight numbers and achieve the objective of assisting air traffic controllers to complete their work safely and efficiently.
Language Person Search with Pair-Based Weighting Loss

Language person search, which means retrieving specific person images with natural language description, is becoming a research hotspot in the area of person re-identification. Compared with person re-identification which belongs to image retrieval task, language person search poses challenges due to heterogeneous semantic gap between different modal data of image and text. To solve this problem, most existing methods employ softmax-based classification loss in order to embed the visual and textual features into a common latent space. However, pair-based loss, as a successful approach of metric learning, is hardly mentioned in this task. Recently, pair-based weighting loss for deep metric learning has shown great potential in improving the performance of many retrieval-related tasks. In this paper, to better correlate person image with given language description, we introduce pair-based weighting loss which encourages model to assign appropriate weights to different image-text pairs. We have conducted extensive experiments on the dataset CUHK-PEDES and the experimental results validated the effectiveness of our proposed method.
A Novel Collaborative Filtering Framework Based on Variational Self-Attention GAN

It is difficult for users to find the required information promptly in the massive data. The collaborative filtering is an effective way to help users get the proper information. To achieve better performance, an improved framework based on variational Generative Adversarial Networks with self-attention (VGCF) is proposed. In VGCF, self-attention mechanism and Variational Autoencoders are combined to form self-attention variational encoder, which improves the ability of acquiring explicit and implicit features of sparse data and obtains the personal preference and the correlation between users. By utilizing Generative Adversarial Networks for prediction, the generative model uses the compression matrix obtained by self-attention variational encoder to generate the predicted user-items interaction matrix. The discriminative model provides a better approximation for the posterior and maximum-likelihood assignment, which makes the generated result closer to the real data distribution. Finally, we show that the performance of VGCF is significantly better than the state-of-the-art recommendation methods on several real-world datasets.
Review on Zero-Knowledge Proof Method

Zero-knowledge proofs (ZKPs) enable knowledge to be verified while not revealing that knowledge. They, thus, have the potential to revolutionize the approach knowledge which is collected, used and transacted with. Every transaction encompasses a “verifier” and a “prover.” In every transaction using ZKPs, the prover makes an attempt to prove issue to the verifier while not telling the verifier anything about this thing. By providing the ultimate output, the prover proves that they are ready to cipher one thing while not revealing the input or the process method. Meanwhile, the verifier solely learns concerning the output. In this paper, we work on zero-knowledge proof and analyze all the zero-knowledge proof, and the major focus will be on the main parameters involved in evaluating zero-knowledge proof, comparison of different types of technologies which can be used in the zero-knowledge proof and with a brief difference of all the different techniques of it.
How Good Is a Strategy in a Game with Nature?

We consider games with two antagonistic players—Éloïse (modelling a program) and Abélard (modelling a Byzantine environment)—and a third, unpredictable and uncontrollable player, which we call Nature. Motivated by the fact that the usual probabilistic semantics very quickly leads to undecidability when considering either infinite game graphs or imperfect-information, we propose two alternative semantics that lead to decidability where the probabilistic one fails: one based on counting and one based on topology.
Classifier Belief Optimization for Visual Categorization

Classifier belief represents the confidence of a classifier making judgment about a special instance. Based on classifier belief, we propose an approach to realize classifier belief optimization. Through enriching prior knowledge and thus reducing the scope of candidate classes, our approach improves classification accuracy. A feature perturbation strategy containing an objective optimization is developed to automatically generate labeled instances. Moreover, we propose a classifier consensus strategy (CCS) for classifier optimization. CCS enables a given classifier to take full advantage of the test data to enrich prior knowledge. Experiments on three benchmark datasets and three classical classifiers justify the validity of the proposed approach. We improve the classification accuracy of a linear SVM by 6%.
Knowledge Graph Fact Prediction Via Knowledge-Enriched Tensor Factorization

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License Plate recognition in fog weather based on deep learning

Abstract The traditional license plate recognition is generally carried out under normal conditions, but there is little work to carry out license plate recognition in fog.With the deterioration of the global environment, the probability of winter fog increases. Therefore, efficient and accurate license plate recognition under fog conditions is the research focus of this paper. This paper introduces the defogging of license plate images in foggy weather and the detailed work of license plate recognition through deep learning, which can improve the accuracy of license plate recognition in foggy weather and is conducive to ensuring the driving safety of vehicles in foggy weather.
Determining the Degree of Relevance of Content on Social Networks Using Machine Learning Techniques and N-Grams

Today, the use of social networks has revolutionized the way users exchange ideas, opinions, and information. Thanks to this paradigm shift in the way users interact, large companies and public characters have begun to pay particular attention to the opinion generated about their products and/or services, acts and/or events within social networks. This activity is known as online reputation analysis (ORA); an activity carried out mainly by expert users in image analysis who are at the same time able to make strategic decisions that help improve the reputation of a company or public character. The relevance of this activity in recent years has motivated the scientific community to propose automatic methods that support the work of an ORA. In this study, an automatic method is proposed to determine when a tweet is important within a predefined category of messages. The proposed method is based on the use of n-grams to establish the importance of the contents generated in Twitter. The experiments carried out show that the occurrence of certain terms allows an automatic classification model to effectively determine (F-measure = 0.7) when a tweet is or is not important for an ORA.
Digital Image splicing Detection Using RIC-LBP Feature Extraction Technique

In this paper, we proposed rotation invariant co-occurrence among adjacent local binary pattern (RIC-LBP) based feature extraction technique for forgery detection. We use Standard Deviation filter (STD) to highlights the image pixel variation, RIC-LBP operator for feature extraction, and Logistic Regression Classifiers (LRC) for forgery detection to know the internal statistics of the image. LRC is a machine learning technique so directly used as a classifier on the entire data set. So it differs from SVM classifier. In this proposed work, we used two datasets, Columbia and DSO-1, to evaluate our proposed work. It gives better results compare to various state of the art.
Smart Contract-Defined Secondary Control and Co-Simulation for Smart Solar Inverters using Blockchain Technology

This paper proposes a cooperative control approach using blockchain technology for solar inverters in a photovoltaic system. Moreover, a co-simulation method for smart inverters and a blockchain network is studied. The blockchain assisted-smart inverter (BASI) consists of a solar inverter and an internet of things device as a client node of a blockchain network, which can fully utilize emerging blockchain technologies such as distributed ledger, security functions, and smart contract. The control includes a primary-level droop control in a BASI and smart contract-defined secondary-level supervisory control in a secured blockchain network. The concept of the proposed smart contract-defined control approach is validated by simulation studies using the cyber-physical co-simulation testbed built-in MATLAB/Simulink in a PC interfacing with a Hyperledger-Fabric blockchain software implemented in a PC.
Research on Optimal Cooperative Attack Strategy of Multiple UAVs

Abstract Aiming at the problem of the optimal coordinated attack strategy of multi-UAV system against multiple moving targets in a known environment, a PMMAXQ layered reinforcement learning algorithm applied to multi-UAV cooperative attack is proposed. Firstly, the problem of multi-UAV coordinated strike, the successful strike conditions and the movement strategy of moving targets are described. Then the moving targets are allocated rationally through the 0-1 planning method. Finally, the MAXQ algorithm is improved by the Bayesian probability statistical formula. The simulation results show that, compared with the MAXQ algorithm, the PMMAXQ algorithm converges faster and the average cumulative reward value that the UAV can obtain is higher.