Reviewerly/exHarmony huggingface dataset:
Towards De-Anonymization of Google Play Search Rank Fraud

Search rank fraud, the fraudulent promotion of products hosted on peer-review sites, is driven by expert workers recruited online, often from crowdsourcing sites. In this paper we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fraud de-anonymization</i> problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in peer-review sites. We collect and study data from crowdsourced search rank fraud jobs, and survey the capabilities and behaviors of 58 search rank fraud workers recruited from 6 crowdsourcing sites. We collect a gold standard dataset of Google Play user accounts attributed to 23 crowdsourced workers and analyze their fraudulent behaviors in the wild. We propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dolos</small> , a fraud de-anonymization system that leverages traits and behaviors we extract from our studies, to attribute detected fraud to crowdsourcing site workers, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">different</i> workers, and use stylometry and supervised learning to attribute them to crowdsourcing site profiles. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dolos</small> correctly identified the owners of 95 percent of fraud worker-controlled communities, and uncovered fraud workers who promoted as many as 97.5 percent of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dolos</small> identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dolos</small> significantly outperformed adapted dense subgraph detection and loopy belief propagation competitors, on two new coverage scores that measure the quality of detected community partitions.
Blockchain based Secure Crypto-currency system with Quantum Key Distribution Protocol

A block chain is referred to as a growing list of records which are linked using cryptography. It is simple and open ledger that records all the transactions in block structures. These block structures are bound with each other by using Quantum Cryptographic protocols. The block chain is democratic system in which concerned parties get access by using a key to perform transaction. The well-known type of Quantum Cryptography protocol is Quantum Key Distribution (QKD). With the help of QKD our Crypto-currency system is secured when passage in between transmitting end and receiving end shouldn't be intrude by third party. Secrecy of the system depends upon different factors like efficient and optimized key rate, secure carrier of modulating signal. In this work the transparency and immunity of block chain based crypto-currency system is analyzed with simulation of six state QKD Protocol. The generation of key rate is observed to ensure the path for production of better crypto-currency system. A Mathematical model is used to obtain the desire constraints so that linear relationship can be achieved. At application level this study contributes to the implementation of crypto-currency system through six state QKD protocol.
Edge Fuzzy Clustering by Eliminating Undesirable Features in Garment Texture Image Segmentation

Edge computing allows data to be processed at the nearest termination, reducing the requirement to transfer data back and forth between the cloud during the procedure of online knowledge graph construction for fashion design resources. In this paper, we proposed a DBI driven clustering technique for garment texture image segmentation installed on terminations which has two merits. One is that the technique embeds DBI index into the Shannon entropy term in the objective function to control feature weighting learning such that the learnt feature weights become more accurate. Another merit is that we propose a feature eliminating principle to filter undesirable features to thoroughly reduce the side effects caused by undesirable features. The proposed clustering technique is verified on different kinds of datasets including garment texture images and the experimental results indicate its promising performance.
Constrained Markov order surrogates

We describe a surrogates algorithm for symbolic time series which consists of constrained permutation of strings and exactly preserves the nth-order Markov properties of the original data. In the context of hypothesis tests, each surrogate generated by the algorithm provides another realisation of the same Markov process posited in the null hypothesis. We prove uniformity, convergence and range, and validate numerically the properties of the algorithm with traditional hypothesis tests. Finally we apply it to real-world data from U.S. business cycles. The swapping algorithm can also be interpreted as a constrained realisation of a random walk in a network.
Quantum effects in two-qubit systems interacting with two-mode fields: Dissipation and dipole-dipole interplay effects

We explore two coupled dipole qubits interacting with two-mode cavity fields through non-degenerate two-photon processes and under the influence of intrinsic noise. Our investigation is based on the influence of the coupling constants between different constituents of the system on the quantum coherence and entropy squeezing. We investigate the phase space information, entanglement, and purity via the Husimi function. It is found that the robustness of the positivity and the negativity of the superposition of generalized coherent state is highly sensitive to the coupling constants. The phase space quantum information, the matter-light entanglement, and the stationary mixture state can be controlled by the two-qubit coupling as well as the coupling to the environment. The effect of the intrinsic dissipation and the two-qubit coupling on the generated squeezing via the qubit-system variable squeezing or the entropy squeezing is investigated. They lead to revoke the squeezing phenomenon.
Recent Progress in Blockchain in Public Finance and Taxation

Blockchain technology is an open-source decentralized (peer-to-peer) transaction model with offers transparency among participants without third party validation. It is impacting any traditional industries. This paper reviews its potential in the public financial sector along with its technical challenges and limitations. We also raise and identify potential questions in the current trend, challenges and future directions for Blockchain technology pertaining to its application to national taxation. The work also includes core components and underlying infrastructure to address its associated challenges.
Intelligent Resource Management in Intercloud, Fog, and Edge: Tutorial and New Directions

This article 1) constructs a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cloud intelligence</i> model that specifies the desirable intelligent properties of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cloud agents</i> , 2) provides a tutorial on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agent-based cloud, fog, and edge resource management</i> , and 3) paves the way for designing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intelligent interclouds</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fogs</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edges</i> . An intelligent intercloud is a “cloud of clouds” populated by a society of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agents</i> that automates intercloud resource management activities. By contributing a tutorial on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agent-based cloud resource management techniques</i> , this article provides researchers with the foundational knowledge for devising intelligent intercloud resource management techniques. By contributing an architectural blueprint, and suggesting and comparing different intelligent resource management techniques, this article provides an overall design together with pointers to and guidelines for constructing the components of an intelligent intercloud. This article describes a proof-of-concept prototype of an intelligent intercloud and provides an application example of the prototype. The need of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT</i> devices for reduced response time triggered technological advancements towards <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fog</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge</i> computing. By providing a tutorial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on agent-based fog and edge resource management techniques</i> , this article provides researchers with the foundational knowledge for devising <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intelligent fog and edge agents</i> . Relevant techniques are also suggested for improving and optimizing the performances of future intercloud, fog, and edge agents.
Modeling and Simulation of a Multi-Robot System Architecture

A Multi-Robot System (MRS) is the infrastructure of an intelligent cyber-physical system, where the robots understand the need of the human, and hence cooperate together to fulfill this need. Modeling the MRS is a crucial aspect of designing the proper system architecture, because this model can be used to simulate and measure the performance of the proposed architecture. However, an MRS architecture modeling is a very difficult problem, as it contains many dependent behaviors that dynamically change due to the current state of the system. In this paper, we introduce a general-purpose MRS case study, where the humans initiate requests that are achieved by the available robots. These requests require different plans that use the current capabilities of the available robots. After proposing an architecture that defines the solution components, three steps are followed. Firstly, modeling these components via a proper Architecture Description Language (ADL). Business Process Model and Notation (BPMN) is used as an ADL to represent the behaviors of every component, which is an essential need to model the solution. Secondly, simulating these components behaviors and interaction in form of software agents. Java Agent Development (JADE) middleware is used to develop the proposed model. JADE is based on a reactive agent approach, therefore it can simulate the dynamically interaction among the solution components. Finally, analyze the performance of the solution by defining a number of quantitative measurements, which can be obtained while simulating the system model, therefore the solution can be analyzed and compared to another architecture.
Model-Driven Cyber Range Training: A Cyber Security Assurance Perspective

Security demands are increasing for all types of organisations, due to the ever-closer integration of computing infrastructures and smart devices into all aspects of the organisational operations. Consequently, the need for security-aware employees in every role of an organisation increases in accordance. Cyber Range training emerges as a promising solution, allowing employees to train in both realistic environments and scenarios and gaining hands-on experience in security aspects of varied complexity, depending on their role and level of expertise. To that end, this work introduces a model-driven approach for Cyber Range training that facilitates the generation of tailor-made training scenarios based on a comprehensive model-based description of the organisation and its security posture. Additionally, our approach facilitates the auto- mated deployment of such training environments, tailored to each defined scenario, through simulation and emulation means. To further highlight the usability of the proposed approach, this work also presents scenarios focusing on phishing threats, with increasing level of complexity and difficulty.
Network Embedding: on Compression and Learning

Recently, network embedding that encodes structural information of graphs into a vector space has become popular for network analysis. Although recent methods show promising performance for various applications, the huge size of graphs may hinder a direct application of the existing network embedding method to them. This paper presents NECL, a novel efficient Network Embedding method as answers to the following two questions: 1) Is there an ideal network Compression designed specifically for embedding? 2) Does the network compression significantly boost the network representation Learning? For the first problem, we propose a neighborhood similarity based graph compression method that compresses the input graph to a smaller graph without losing any/much information about its global structure and the local proximity of its vertices. For the second problem, we employ the compressed graph for network embedding instead of the original large graph to bring down the embedding cost. NECL is a general meta-strategy to improve the efficiency of all of the state-of-the-art graph embedding algorithms based on random walks, including DeepWalk and Node2vec, without losing their effectiveness. Extensive experiments validate the efficiency of NECL method that yields an average improvement of 23 -57% embedding time, including walking and learning time, without decreasing classification accuracy as evaluated on single and multi-label classification tasks on large real-world graphs.
Hyperparameters Search Methods for Machine Learning Linear Workflows

Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems. In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows. We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space, when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space.
Efficient Hybrid Central Processing Unit/ Input–Output Resource Scheduling for Virtual Machines

The use of virtualization technology in industrial control is increasing. However, virtual instances or virtual machines (VMs) are confronted with the unreasonable resource allocation in the industrial control cyber range, thereby highlighting the increasing importance of resource scheduling. In the Xen open source system, the input-output (IO)-intensive task response is extended because the system does not distinguish between CPU- and IO-intensive tasks. Therefore, this article presents an improved task performance through the hybrid scheduling of CPU and network IO resources. This method uses Cap- and Timeslice-scheduling algorithms for CPU resource scheduling. First, the Cap-scheduling algorithm uses historical data to train a recurrent neural network model for classification and then utilizes the heuristic method to set the upper limit of cap value for VMs. Second, the Timeslice-scheduling algorithm adjusts the timeslice based on Q-learning to shorten the execution time of the overall tasks. This article proposes an IOb-scheduling algorithm for network bandwidth scheduling. The part that does not exceed the average bandwidth is eliminated and distributed to other VMs by monitoring the bandwidth usage of each VM, thereby improving the utilization of bandwidth. Experimental results showed that the proposed CPU/IO scheduling algorithms improved the overall benchmark performance substantially.
Sentiment Analysis of Amazon Mobile Reviews

Sentiment analysis is used to derive the emotion/opinion that is being conveyed in a text. This helps in determining whether the author's intent is positive or negative. Its applications are vast and help in analyzing product reviews, popularity of a brand, and in determining people's opinions on any subject. Due to the complexities involved in the human language such as subjectivity, metaphors, sarcasm, and multiple sentiments, it becomes difficult to categorize our opinions, computationally. The goal of this project is to conduct sentiment analysis on Amazon product reviews using various natural language processing (NLP) techniques and classification algorithms. The dataset consists of 400,000 reviews of unlocked mobile phones sold on Amazon.com . We will achieve the result by preprocessing the reviews and converting them to clean reviews, after which using word embedding, the word reviews were converted into numerical representations. Then, we finally fit the numerical representations of reviews to the Naïve Bayes, logistic regression, and random forest algorithm. The results and accuracy of all these classifiers are compared in this paper. This will be helpful for a brand/company to understand the general opinion toward their product which in turn will help them in evaluating the improvements required.
Vision-aware target recognition toward autonomous robot by Kinect sensors

Target recognition is a key module in modern human–computer interaction (HCI) and computer vision systems It is pervasively used in many domains like autonomous vehicles and robot, remote operation, and video surveillance. However, due to the complicated environment and object occlusion, target recognition is still a challenging task. In this paper, we propose a novel target recognition algorithm toward autonomous robot by leveraging the Kinect sensors. More specifically, we utilize the Kinect sensors to capture scenario image in real-time. Subsequently, we present an improved HSV-based image segmentation algorithm to decompose the captured image, where morphological operation is employed for foreground target extraction. Afterward, we leverage Spatial Pyramid (SP)-based scheme for visual feature extraction. Then, we adopt a new distance metric for target matching. Comprehensive experimental results have shown the effectiveness of our proposed method.
Fluctuating Power Logic: SCA Protection by $V_{DD}$ Randomization at the Cell-level

In this paper, we propose a novel cell-level side channel countermeasure fluctuating power logic (FPL), which diffuses the correlation between the real power consumption and the fixed data transitions by employing a cascade voltage logic. The countermeasure further acts as a cell-level V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DD</inf> randomizer, making it a strong candidate for implementing algorithmic countermeasure and exploiting its noise generation capabilities. This proposed scheme is illustrated by a standard flip-flop. HSPICE based simulation results show that the modified flip-flop is resistant against power analysis at the cost of doubled power dissipation. Two illustrative case studies of PRESENT and AES substitutions have been explored. The resistance against Side-Channel Analysis(SCA) is evaluated by the correlation power analysis. The new logic outperforms other counterparts in consideration of both security and cost, which renders it as a practical solution for resource-constrained systems. The proposed cell-level countermeasure can naturally mitigate other SCA such as electromagnetic analysis.
Relevant Experiences in Replay Buffer

The sampling bias problem creates instability in reinforcement learning. Reusing past experiences with experience replay helps reinforcement learning overcome possible sampling bias. After the success of deep reinforcement learning, other variations of experience replay have been proposed and further improved learning performance. However, how to select samples to store in experience replay methods has not been well investigated. In this paper, we examine if there exists relevant (or significant) experiences to be preferably replayed. For systematic discovery of relevant experiences, we adopt the DRL-Monitor and discuss how it improves the efficiency of learning. Comparing with traditional experience replay and prioritized experience replay, we demonstrate improved learning performance in different Atari games.
The Evolution of Reinforcement Learning

In both the natural and artificial realms, evolution and reinforcement learning are parallel adaptive processes that work on different scales but with similar feedback mechanisms. This makes combined and coordinated study of these phenomena synergistic. In the natural world, evolution is antecedent to all forms of learning but can, nonetheless, be influenced by them. In artificial intelligence, evolutionary computation and reinforcement learning were initially developed independently and in parallel but numerous studies of their interactions exist. As reinforcement learning gains prominence in machine learning, and as it is integrated into more complex learning systems such as deep neural networks, greater understanding of how it has and could be evolved becomes increasingly relevant. This survey covers major trends in the evolution of reinforcement learning and suggests important directions for future research.
Fuzzy Logic Controller with Fuzzylab Python Library and the Robot Operating System for Autonomous Robot Navigation: A Practical Approach

The navigation system of a robot requires sensors to perceive its environment to get a representation. Based on this perception and the state of the robot, it needs to take an action to made a desired behavior in the environment. The actions are defined by a system that processes the obtained information and which can be based on decision rules defined by an expert or obtained by a training or optimization process. Fuzzy logic controllers are based on fuzzy logic on which degrees of truth are used on system variables and has a rulebase that stores the knowledge about the operation of the system. In this paper a fuzzy logic controller is made with the Python fuzzylab library which is based on the Octave Fuzzy Logic Toolkit, and with the Robot Operating System (ROS) for autonomous navigation of the TurtleBot3 robot on a simulated and a real environment using a LIDAR sensor to get the distance of the objects around the robot.