Reviewerly/exHarmony huggingface dataset:
Twitter bot detection and their influence in hashtag manipulation

Twitter is an important platform for many activities such as communication, promotion and news. Twitter is starting to clamp down on removing content and accounts whose sole purpose is to spread disinformation at scale. The increasing number of bots on Twitter has led to a significant circulation of malicious content. It has the power of manipulating and influencing people negatively. Hence bot detection and finding their extent of influence is of prime importance. This research has used varied approaches for the detection of bots; a supervised machine learning approach which makes use of a unique feature called the bot score to determine the bot probability of a user. Secondly, using an unsupervised machine learning approach, users were divided into various clusters based on their activity. Furthermore, to assess the influence of users in hashtag manipulation, this research analyzed various categories of users in a hashtag and made promising conclusions.
Depth Completion Using a View-constrained Deep Prior

Recent work has shown that the structure of convolutional neural networks (CNNs) induces a strong prior that favors natural images. This prior, known as a deep image prior (DIP), is an effective regularizer in inverse problems such as image denoising and inpainting. We extend the concept of the DIP to depth images. Given color images and noisy and incomplete target depth maps, we optimize a randomly-initialized CNN model to reconstruct a depth map restored by virtue of using the CNN network structure as a prior combined with a view-constrained photo-consistency loss. This loss is computed using images from a geometrically calibrated camera from nearby viewpoints. We apply this deep depth prior for inpainting and refining incomplete and noisy depth maps within both binocular and multi-view stereo pipelines. Our quantitative and qualitative evaluation shows that our refined depth maps are more accurate and complete, and after fusion, produces dense 3D models of higher quality.
Conjunctive Keyword Forward Secure Ranked Dynamic Searchable Encryption Over Outsourced Encrypted Data

Cloud Computing enables individuals and organizations with extensive computing capabilities along with scalable storage services. However the security of outsourced data brings about privacy concerns when confidential data is outsourced to third party service providers. The Searchable Symmetric Encryption (SSE) is a solution towards enabling data owners to securely outsource and later retrieve the matching documents based on encrypted queries. Majority of existing SSE schemes lack support for relevance based retrieval of matching documents to queried keywords, expressive queries, and privacy of query terms and documents. In this paper we present a novel dynamic SSE scheme which retrieves documents ranked based on their relevance to the queries, and ensures forward and backward privacy definitions given by R Bost et al. Our scheme is lightweight as it allows the server to compute document-query relevance in a secure manner without affecting the entire outsourced index, especially during update operations, which is normally the case in existing state of the art. We employ an indexing mechanism which is alternate to the most frequently used Inverted Index structure but overcomes drawbacks of such an index. Experimental analysis of our scheme on RFC dataset demonstrated a sub-linear search time and efficient update operations. The proposed scheme also ensures forward and backward security during search and update operations.
Protecting the Moving User’s Locations by Combining Differential Privacy and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math>-Anonymity under Temporal Correlations in Wireless Networks

The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers. This phenomenon raises serious privacy concerns. However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users. In this way, the solutions are vulnerable to various inference attacks. Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable. In this paper, we propose a systematic solution to preserve location information. The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties. The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy. To formalize the privacy-protecting guarantee, we propose a new definition, L&amp;A-location region, based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -anonymity and differential privacy. Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory. We simulate the proposed mechanism based on a dataset collected in real practice. The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard.
Adaptive two-step procedure of providing desired visual quality of compressed image

Providing a desired visual quality of lossy compression is an important and interesting research direction, it has been studied intensively in recent years. Its purpose is to guarantee that lossy compression will not affect further image processing efficiency due to too large loss of information while providing a relatively large compression ratio (CR). A two-step method has been proposed recently, this method is able, in some conditions, to provide a desired image quality, but its use can result in unsatisfactory accuracy in other conditions, in particular, when it is desired to produce a relatively low PSNR value. In this paper, we improve the two-step method of lossy compression in cases of unsatisfactory performance. A proposed procedure quickly calculates a parameter characterizing complexity of an image to be compressed. This allows discriminating images into rather simple and quite complex ones in advance and to adapt coder parameters at both steps accordingly. Experiments have been conducted for AGU coder based on Discrete Cosine Transform (DCT). The result demonstrates that the accuracy of providing a desired quality according to a chosen quality metric can be significantly improved, in particular, in the cases of complex structure texture images. The method also provides considerable benefits for "super-simple" images, which shows that in extreme cases the proposed approach is still robust.
On Errors Generated by Unitary Dynamics of Bipartite Quantum Systems

Given a quantum channel it is possible to define the non-commutative operator graph whose properties determine a possibility of error-free transmission of information via this channel. The corresponding graph has a straight definition through Kraus operators determining quantum errors. We are discussing the opposite problem of a proper definition of errors that some graph corresponds to. Taking into account that any graph is generated by some POVM we give a solution to such a problem by means of the Naimark dilatation theorem. Using our approach we construct errors corresponding to the graphs generated by unitary dynamics of bipartite quantum systems. The cases of POVMs on the circle group ${\mathbb Z}_n$ and the additive group $\mathbb R$ are discussed. As an example we construct the graph corresponding to the errors generated by dynamics of two mode quantum oscillator.
WITHDRAWN: Threshold secret sharing and multi-authority based data access control in cloud computing

The composition of virtualization, grid computing and distributed computing is known as cloud computing, and these systems are completely interconnected group of systems and which will dynamically provision and offering the one or more unified cloud-computing resources. The cloud service provider's objective is to provide their services, to clients qualitative, more secure, abrupt, scalable, more responsive, cost-efficient, on demand and automatically provisioned services just like: storage services, computation services, network system etc. Cloud services are geographically dispersed everywhere throughout the world in a transparent way and help to improve business execution by making a commitment to decrease the expense of conveying IT assets to any association. Cloud services implementation helping to reduce in a organization is controlling IT resources the cost. These cloud services demands are day by day rapidly increase as financial model applied in cloud computing is "Pay-per-Use". In cloud computing data security and data access control issues are most important requirements, presently some schemes are given to provide the data security and data access control in cloud computing but they have limitations in some ways such that by violating the data secrecy/confidentiality because of collusion-attack and too much processing (because of using maximum no of secret keys). To counter these important security issues in cloud computing we have proposed a system that uses attribute based encryption technique, threshold secret sharing and multi-authority access control mechanism in which the data owner divides the users in group and allocates the single secret key for entire Group to Decrypt the Data and Individual User in the created Group Shares Parts of the Secret Key. Proposed system provides strong data confidentiality, & also it reduces the usage of large no of Secret Keys.
Interest-Driven Exploration With Observational Learning for Developmental Robots

It has been emphasized for a long time that real-world applications of developmental robots require lifelong and online learning. A major challenge in this field is the high sample-complexity of algorithms, which has led to the development of intrinsic motivation approaches to render learning more efficient. However, only few works have been demonstrated on real robots and although these robots are supposed to share the environment with humans, there is hardly any research to integrate intrinsic motivation with learning from an interacting teacher. In this article, we tackle the efficiency challenge by proposing a novel extrinsic–intrinsic motivation learning scheme. We specifically investigate how to combine intrinsic motivation with learning from observation to accelerate learning. Our novel scheme comprises four elements: 1) a probabilistic intrinsic motivation signal yielding the robot’s interest; 2) a probabilistic extrinsic motivation signal to expand the robot’s knowledge by learning from observation; 3) novelty detection; and 4) novelty degree methods to enable the robot to decide autonomously how and when to explore. The efficiency as well as the applicability of our methods are benchmarked in simulation experiments and demonstrated on a physical 7-degree of freedom left arm of Baxter robot.
Hardware implementation of the coding algorithm based on FPGA

Abstract In this article, the efficient implementation multiplier of polynomials irreducible polynomials modulo for cryptographic encryption and decryption using FPGA is presented. For this, the Nexys 4 board based on the Artix-7 Field Programmable Gate Array (FPGA) from Xilinx was chosen. Verilog HDL is used to describe the circuit for reducing a number modulo. The results of a timing simulation of the device are presented in the form of time diagrams for a given 8-bit number, confirming the correct operation of the device. The developed encryption algorithm on the basis of non-positional polynomial notations is intended for software, hardware, and also software and hardware implementation. The main hardware-implemented device in non-positional algorithm of the cryptographic transformation is a device for the multiplication of polynomials irreducible polynomials modulo, which produces routine calculations on data encryption. These mathematical operations are computationally intensive and fundamental arithmetic operations, which are intensively used in many fields such as cryptography, number theory, and finite field arithmetic.
An Adaptive Infrared Tracking Method for Spacebased Surveillance to a Hypersonic Cruise Vehicle

A feasible and viable means for performing surveillance of a hypersonic vehicle is space-based system, which has been used for providing tracking results of near-space vehicles. In this paper, an adaptive infrared tracking method to a hypersonic cruise vehicle (HCV) is proposed. This approach contains three inherent parts: coverage analysis of the system, a space-based bearing-only positioning, and an adaptive tracking approach. An adaptive scheme based on the coverage analysis and the positioning results is proposed to achieve high estimation performance and guarantee generality. Simulation results for an adaptive scaling unscented Kalman filter (ASUKF) indicate the effectiveness of this approach.
SLAM Self - Cruise Vehicle Based on ROS Platform

Using SLAM technology and TEB path planning based on ROS simulation platform to design an autonomous intelligent car. First, the navigation system framework is designed in Gazebo simulation environment. Relying on laser radar to scan the surrounding environment and using gmapping algorithm for SLAM mapping. Use AMCL adaptive Monte Carlo positioning method to locate the car, and perform global path planning and local path planning through move_base. After calculation, cmd_vel is released to the motion controller to control the motion of the car, so as to realize the navigation function of the car.
Variational Learning Data Fusion With Unknown Correlation

This article proposes the problem of joint state estimation and correlation identification for data fusion with unknown and time-varying correlation under the Bayesian learning framework. The considered data correlation is represented by the randomly weighted sum of positive semi-definite matrices, where the random weights depict at least three kinds of unknown correlation across single-sensor measurement components, multisensor measurements, and local estimates. Based on the variational Bayesian mechanism, the joint posterior distribution of the state and weights is derived in a closed-form iterative manner, through minimizing the Kullback-Leibler divergence. The three-case simulation shows the superiority of the proposed method in the root-mean-square error of estimation and identification.
Construction of Structural Diversity of Ensemble Learning Based on Classification Coding

The diversity of base learners is an important factor affecting the generalization accuracy of ensemble learning, and structural diversity is one of the main methods to construct diversity. The classification coding diversity (CCD) method is proposed to solve the problems of inaccurate measurement of traditional structural diversity methods and incomplete use of heterogeneous base learners. The method constructs three valued classification coding according to the performance of the basic learning device in the data block partition, then the absolute difference of those is used to measure the pairwise diversity, and the global diversity measure is used to construct the most optimal solution. The greedy algorithm is used to complete the selective integration of the basic learning device. Compared with ordinary Bagging algorithm and tree structure matching (TMD), the method of classification coding diversity(CCD) has better applicability, explanatory and computational performance than the tree structure matching (TMD) algorithm, and has higher generalization accuracy than the Bagging algorithm, especially for the two-classification problem.
Noise reduction in images using autoencoders

Pure signals can ideally exist only on paper. However, with the availability of a considerable number of state-of-the-art techniques that claim to denoise a given signal only up to an extent, it is at the same time necessary that such techniques must be compatible with a large number of devices. This paper discusses an approach to reduce the noise present in images through Image Processing and Deep Learning algorithms by implementing autoencoders. Using an autoencoder for removing noise is one such approach where the main focus is to retain the originality as autoencoders follow the backpropagation process, rather than the traditional techniques, where the output signal is ultimately the unoriginal version of the input signal. The technique discussed in the paper is interconvertible, i.e., could be used for any signal, is reliable, efficient, and compatible with a larger pool of devices.
Graph-Induced Contrastive Learning for Intra-Camera Supervised Person Re-Identification

Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view and no inter-camera identity association is labeled. It is a new setting proposed recently to reduce the burden of annotation while expect to maintain desirable Re-ID performance. However, the lack of inter-camera labels makes the ICS Re-ID problem much more challenging than the fully supervised counterpart. By investigating the characteristics of ICS, this article proposes a graph-induced contrastive learning (GCL) approach to address this issue. More specifically, we first formulate the cross-camera ID association task as a graph partitioning problem subjected to ICS-specific constraints and design a greedy agglomeration algorithm to solve it. Then, we propose a graph-induced contrastive loss that unifies both intra- and inter-camera learning into a contrastive learning framework to learn a Re-ID model. The cross-camera ID association step and the Re-ID model contrastive learning step are alternatively iterated, by which we progressively obtain a highly discriminative Re-ID model. Extensive experiments on three large-scale datasets show that our approach outperforms all previous ICS works. Especially, it gains 15.7% Rank-1 and 14.3% mAP improvements on the challenging MSMT17 dataset. Moreover, our approach performs even comparable to state-of-the-art fully supervised methods on all of the three datasets.
Effect of Architectural Composition of MLP ANN in Neural Network Learning for Signal Power Loss Prediction

This work analyzes the architectural complexity of a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) model suitable for modeling and predicting signal power loss in micro-cellular environments. The MLP neural network model with one, two, and three hidden layers respectively were trained using measurement datasets used as the target values collected from a micro-cell environment that is suitable to describe different propagation paths and conditions. The neural network training has been performed by applying different training techniques to ensure a well-trained network for good generalization and avoid over-fitting during network training. Bayesian regularization algorithm (that updates weights and biases during network training) following the Levenberg-Marquardt optimization training algorithm was used as the training algorithm. A comparative analysis of training results from one, two, and three hidden layers MLP neural networks show the best prediction result of the signal power loss using a neural network with one hidden layer. A complex architectural composition of the MLP neural network involved very high training time and higher prediction errors.
Application of Graphs for Story Generation in Video Games

The paper presents the system based on the layered hierarchical graphs for game plot description implemented in Godot Engine using the JSON format. The aim of the presented research is to create a tool that both supports the decision-making process and helps multiple writers to collaborate in creating a game world. Constructing a layered hierarchical graph provides information about game plot description, while the proposed graph generation system based on JSON data format allows designers and story writers to create game worlds. A key feature of the graph generation system is that it describes game worlds using a sequence of generation rules which represent the player's actions. On the one hand, the proposed approach helps the story writers to control the impact of the created story elements on the game world. On the other hand, it allows world designers to use procedural generation of narration chains, analyze and test the designed game plots. An additional goal of this approach strongly focused on using the JSON data format to describe actions that can be taken in world states is the impact of JSON data structures on the application of graph rules, which may be important in the problems of artificial intelligence.
Data Mining and Deep Learning

Both data mining and machine learning are becoming popular with many different applications. This chapter introduces some techniques in data mining and machine learning, including clustering, support vector machine, and neural networks. We also discuss their links to nature-inspired algorithms for optimization.
GPSC: A Grid-Based Privacy-Reserving Framework for Online Spatial Crowdsourcing

Spatial crowdsourcing (SC) allows requesters to crowdsource tasks to workers based on location proximity. To preserve privacy, the location should not be disclosed to untrustworthy entities (even the SC platform). Previous solutions to preserve workers’ location privacy require an online trusty third party (TTP), which is not practical in reality. In this paper, we design a framework that allows the SC platform to assign tasks to nearest workers in an online manner without knowing their actual locations. We propose an encryption algorithm to encrypt the locations of tasks and workers, and design an indexing method that assigns tasks to workers without losing too much privacy. We prove that there exists a trade-off between efficiency and security theoretically, which can be controlled based on user preference. We verify our method on real-world datasets and experimental results show that our method is efficient, effective and practical.