Reviewerly/exHarmony huggingface dataset:
Topic-based rank search with verifiable social data outsourcing

As the explosive development of social network sites, social data has successfully captured either individuals’ or entities’ attention due to having tremendous commercial value. The initial step to my various insights from social data is how to obtain authentic social data. Social data outsourcing as a new paradigm has been pervasive, in which a social data provider collects integrity social data from different social network sites and resells to data consumers on demand. However, some dishonest activities, like adding fake data, deleting/modifying raw data, drive us to consider verifiable topic-based rank search problem on social data outsourcing scenario. To guarantee the authenticity of social data, we propose two schemes. In our basic scheme, social network sites generate unforgeable auxiliary information and outsource to the social data provider as well as social data. Data consumers can probabilistically verify the correctness and completeness of the query results with the help of verification objects derived from auxiliary information. To reduce the number of the most related topic labels, we propose an enhanced scheme, in which the social network sites first cluster similar topics together, and then generate auxiliary information. Rigorous security and performance analyses prove that our proposed schemes are safe and effective. In addition, our experimental results built on a real Twitter dataset which demonstrates the efficacy and efficiency of our schemes.
Comparative Analysis of Content-Based Image Retrieval Systems

Content-based image retrieval methods in present days are used in modern social media and search engines. They give techniques for analyzing, organizing, processing and searching through million images uploaded daily in the internet. For that reason, searching is the most critical process in CBIR. It should be accurate and complete in reasonable amount of time. Proper metadata should be extracted from the images and used to meet the performance requirements. From the other hand, to meet the subsequent processing this metadata should be indexed and stored in appropriate way.This paper describes some of the most popular image extraction and analysis systems known as Content Based Image Retrieval Systems (CBIR). It reveals how examined different CBIR systems work and how closer are generated similarity results. At the end, information is summarized, and conclusions are made regarding existing solutions and methods used in these applications.
Assisted Labeling for Spam Account Detection on Twitter

Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device.Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks.In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets.More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users.Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics.Experimental results show the effectiveness of the proposed approach.
Sandwich theorems and capacity bounds for non-commutative graphs

We define non-commutative versions of the vertex packing polytope, the theta convex body and the fractional vertex packing polytope of a graph, and establish a quantum version of the Sandwich Theorem of Grötschel, Lovász and Schrijver (1986) [7]. We define new non-commutative versions of the Lovász number of a graph which lead to an upper bound of the zero-error capacity of the corresponding quantum channel that can be genuinely better than the one established by Duan, Severini and Winter (2013) [5]. We define non-commutative counterparts of widely used classical graph parameters and establish their interrelation.
Constructivist Approach to State Space Adaptation in Reinforcement Learning

Reinforcement learning (RL) is increasingly used to achieve adaptive behaviours in Internet of Things systems relying on large amounts of sensor data. To address the need for self-adaptation in such environments, techniques for detecting environment changes and re-learning behaviours appropriate to those changes have been proposed. However, with the heterogeneity of sensor inputs, the problem of self-adaptation permeates one level deeper; in order for the learnt behaviour to adapt, the underlying environment representation needs to adapt first. The granularity of the RL state space might need to be adapted to learn more efficiently, or to match the new granularity of input data. This paper proposes an implementation of Constructivist RL (Con-RL), enabling RL to learn and continuously adapt its state space representations. We propose a Multi-Layer Growing Neural Gas (ML-GNG) technique, as an extension of the GNG clustering algorithm, to autonomously learn suitable state spaces based on sensor data and learnt actions at runtime. We also create and continuously update a repository of state spaces, selecting the most appropriate one to use at each time step. We evaluate Con-RL in two scenarios: the canonical RL mountain car single-agent scenario, and a large-scale multi-agent car and ride-sharing scenario. We demonstrate its ability to adapt to new sensor inputs, to increase the speed of learning through state space optimization, and to maintain stable long-term performance.
Learning Skills for Small Size League RoboCup

In this work, we show how modern deep reinforcement learning (RL) techniques can be incorporated into an existing Skills, Tactics, and Plays (STP) architecture. STP divides the robot behavior into a hand-coded hierarchy of plays, which coordinate multiple robots, tactics, which encode high level behavior of individual robots, and skills, which encode low-level control of pieces of a tactic. The CMDragons successfully used an STP architecture to win the 2015 RoboCup competition. The skills in their code were a combination of classical robotics algorithms and human designed policies. In this work, we use modern deep RL, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, to learn skills. We compare learned skills to existing skills in the CMDragons’ architecture using a physically realistic simulator. We then show how RL can be leveraged to learn simple skills that can be combined by humans into high level tactics that allow an agent to navigate to a ball, aim and shoot on a goal.
Software Deployment on Heterogeneous Platforms: A Systematic Mapping Study

Context: Multiple types of processing units (e.g., CPUs, GPUs and FPGAs) can be used jointly to achieve better performance in computational systems. However, these units are built with fundamentally different characteristics and demand attention especially towards software deployment. Objective: The goal of this work is to summarize the state-of-the-art of software deployment on heterogeneous platforms. We provide an overview of the research area by searching for and categorizing relevant studies, as well as discussing gaps and trends of the field. We are interested in the main concerns (RQ1) and the approaches used (RQ2) when deploying software on heterogeneous platforms. Method: In order to achieve our goal, we performed a systematic mapping study, which refers to a method for reviewing literature with basis on predefined search strategies and a multi-step selection process. Results: We selected and analyzed 146 primary studies from multiple sources and found that the area of research is dominated by solution proposals. The majority of the studies discuss concerns about scheduling, the quality of the software, and its architecture. A large number of studies focuses on the problem of scheduling tasks and processes. We found approaches that are applied at different binding times (i.e., design time, runtime, orthogonal). Conclusion: The evaluation of the proposed solutions in an industrial context is missing. Also, the proposed methods have not been evaluated in development processes. Most of the methods address a particular concern, or a few concerns, while there is a lack of a holistic approach.
Orthogonal sparse fractal coding algorithm based on image texture feature

Fractal image compression coding algorithm is a novel image compression technology; however, the long encoding time and unacceptable image reconstruction quality remain the primary obstacles in practical application. The purpose of this study is to improve the coding quality from the perspective of grey level transform and feature extraction. In this study, a novel orthogonal sparse fractal coding algorithm based on image texture feature is proposed. The authors define a normalised version as the new grey description feature of the image block so that two improved methods are scientifically combined in theory and algorithm. First, orthogonal sparse grey level transform based on sparse decomposition improves image reconstruction quality and decoding speed. Then, the similarity measure matrix, which stores the variance feature between range blocks and domain blocks, is used to reduce redundancies and encoding time. Simulation results show that the proposed algorithm in this study can obtain better image reconstruction quality and speed up encoding time significantly as compared to the conventional fractal coding schemes.
TDCTFIC: A Novel Recommendation Framework Fusing Temporal Dynamics, CNN-Based Text Features and Item Correlation

The traditional recommendation system (RS) can learn the potential personal preferences of users and potential attribute characteristics of items through the rating records between users and items to make recommendations.However, for the new items with no historical rating records,the traditional RS usually suffers from the typical cold start problem. Additional auxiliary information has usually been used in the item cold start recommendation,we further bring temporal dynamics,text and relevance in our models to release item cold start.Two new cold start recommendation models TmTx(Time,Text) and TmTI(Time,Text,Item correlation) proposed to solve the item cold start problem for different cold start scenarios.While well-known methods like TimeSVD++ and CoFactor partially take temporal dynamics,comments,and item correlations into consideration to solve the cold start problem but none of them combines these information together.Two models proposed in this paper fused features such as time,text,and relevance can effectively improve the performance under item cold start.We select the convolutional neural network (CNN) to extract features from item description text which provides the model the ability to deal with cold start items.Both proposed models can effectively improve the performance with item cold start.Experimental results on three real-world data set show that our proposed models lead to significant improvement compared with the baseline methods.
Cloud Computing in Tourism

With rapid growth and development in technology, cloud computing has become a dominant platform for small businesses as well as major enterprises. Cloud computing streamlines the overall delivery of services and resources, helps keeping the costs in control, and global business is set for a huge change in the way businesses are done. The substantial transformation over the past few years has evolved tourism industry towards Tourism 3.0, where the consumers can easily connect to travel websites and interact by sharing their experiences. This considerably influences the perceptions, expectations and decisions both the actual as well as and potential travelers. This provides sufficient reasons for the tourism industry players to adopt and adept themselves with the latest advancements in the information technology, and the adoption of cloud computing is key in this regard as it provides easy access to a web platform that offers more productive, efficient, and competitive services to promote tourism as a vehicle of sustainable development.
A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks

Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods.
Towards Raw Sensor Fusion in 3D Object Detection

The paper focuses on the problem of raw data fusion in neural networks based 3D object detection architectures. Here we consider the case of autonomous driving with data from camera and LiDAR sensors. Understanding the vehicle surroundings is a crucial task in autonomous driving since any subsequent action taken is strongly dependent on it. In this paper we present an alternative method of fusing camera image information with LiDAR poinclouds at a close to raw level of abstraction. Our results suggest that our approach improves the average precision of 3D bounding box detection of cyclists (and possibly other objects) in sparse point clouds compared to the baseline architecture without low-level fusion. The proposed approach has been evaluated on the KITTI dataset containing driving scenes with corresponding camera and LiDAR data. The long-term goal of our research is to develop a neural network architecture for environment perception that fuses multi-sensor data at the earliest stages possible, thus leveraging the full benefits of possible inter-sensor synergies.
RGB-D-DIC technique for low-cost 3D displacement fields measurements

Optical displacement measurements can provide data on the behavior of structural elements without altering key parameters such as damping, stiffness, and mass. Optical techniques can also substantially simplify the equipment that is required for acquiring data effectively and can map displacements at specified locations of elements with complex geometries, such as steel connections. In addition, these techniques can provide simultaneous data at multiple points using a single imaging device (for plane measurements) or multiple devices (for three-dimensional measurements); however, these systems rely on specialized imaging sensors that are coupled with expensive optics and high-cost software licenses. In contrast, range/depth cameras (RGB-D) can provide a color and 3D imaging solution for capturing motion at an affordable cost. These cameras are widely available and applied in the video game industry using a mass production scheme; hence, they are of low cost. This paper proposes a methodology, along with an experimental validation, of a large three-dimensional displacement field measurement approach that uses artificial vision techniques. This methodology uses digital image correlation (DIC) principles and an RGB-D camera (RGB-D-DIC) for a variety of structural applications, which range from experimental modal analysis, cyclic loading tests, and creep tests to static displacement tests. A low-cost Kinect V2 camera was single- and stereo-calibrated, evaluated and implemented in a comparison test with a commercial three-dimensional DIC system. With the RGB-D camera, the proposed methodology can provide accurate displacement measurements that are comparable to those that are obtained via precise displacement measurement techniques and/or by using sensors; further, this methodology has the advantages of low cost, ease of implementation, and contactless measurement and provides three-dimensional information.
Job Recommendation System based on Machine Learning and Data Mining Techniques using RESTful API and Android IDE

In the current Capitalist world with an abundance of different state-of-the-art industries and fields cropping up, ushering in an influx of jobs for motivated and talented professionals, it is not difficult to identify your field and to persevere to get a job in the respective field but lack of information and awareness render the task difficult. This problem is being tackled by Job Recommendation systems. But not every aspect from the wide spectrum of factors is incorporated in the existing systems. For the "Job Recommendation System - Vitae" machine learning and data mining techniques were applied to a RESTful Web Server application that bridges the gap between the Frontend (Android Application) and the Backend (MongoDB instance) using APIs. The data communicated through APIs is fed into the database and the Recommendation System uses that data to synthesize the results. To make the existing systems even more reliable, here efforts have been done to come up with the idea of a system that uses a wide variety of factors and is not only a one-way recommendation system.
A Performance Evaluation Model for Mobile Applications

The advancement of use of quality product has been considered as an imperative part of software development. With the expanded interest of Mobile applications clients, the quality of the Mobile applications turns into a noteworthy concern. The number of studies has been done for the assessment of the performance of different types of Mobile applications. There is a need for a model that can be used in a generic way. The proposed model is a generic model for performance evaluation of Mobile applications that can be of any type or from any domain. The research, examines the key performance criteria's that are essential for a Mobile application success and evaluate performance using a model applying the SWARA approach.
A Video Processing Based Eye Gaze Recognition Algorithm for Wheelchair Control

Over the past few years, a lot of research has been carried out in eye gaze recognition and its applications. From controlling wheelchairs to selecting options on a screen, utilizing the gaze of an individual has become a long-sought way for performing these tasks and in turn making the life of several differently abled people easy. In this paper a novel methodology to perform iris segmentation and gaze recognition has been introduced and described. The method elaborated utilizes a segmentation algorithm which can successfully extract the iris under varying lighting conditions with the help of machine learning. All experiments were conducted using the MATLAB R2013a software and a speed improvement of almost 3.433 times was achieved as opposed to other popular methods of iris extraction. In terms of accuracy, the algorithm proved to be 86% accurate and was also adopted to control an actual wheelchair.
Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge

SUMMARY Thickness of cover over crystalline basement is an important consideration for mineral exploration in covered regions. It can be estimated from a variety of geophysical data types using a variety of inference methods. A robust method for combining such estimates to map the cover–basement interface over a region of interest is needed. Due to the large uncertainties involved, these need to be probabilistic maps. Predominantly, interpolation methods are used for this purpose, but these are built on simplifying assumptions about the inputs which are often inappropriate. The Bayesian estimate fusion is an alternative capable of addressing that issue by enabling more extensive use of domain knowledge about all inputs. This study is intended as a first step towards making the Bayesian estimate fusion a practical tool for cover thickness uncertainty mapping. The main contribution is to identify the types of data assumptions that are important for this problem, to demonstrate their importance using synthetic tests and to design a method that enables their use without introducing excessive tedium. We argue that interpolation methods like kriging often cannot achieve this goal and demonstrate that Markov chain Monte Carlo sampling can. This paper focuses on the development of statistical methodology and presents synthetic data tests designed to reflect realistic exploration scenarios on an abstract level. Intended application is for the early stages of exploration where some geophysical data are available while drill hole coverage is poor.
Maximally Correlated Principal Component Analysis Based on Deep Parameterization Learning

Dimensionality reduction is widely used to deal with high-dimensional data. As a famous dimensionality reduction method, principal component analysis (PCA) aiming at finding the low dimension feature of original data has made great successes, and many improved PCA algorithms have been proposed. However, most algorithms based on PCA only consider the linear correlation of data features. In this article, we propose a novel dimensionality reduction model called maximally correlated PCA based on deep parameterization learning (MCPCADP), which takes nonlinear correlation into account in the deep parameterization framework for the purpose of dimensionality reduction. The new model explores nonlinear correlation by maximizing Ky-Fan norm of the covariance matrix of nonlinearly mapped data features. A new BP algorithm for model optimization is derived. In order to assess the proposed method, we conduct experiments on both a synthetic database and several real-world databases. The experimental results demonstrate that the proposed algorithm is comparable to several widely used algorithms.
Lock and Load: A Model for Free Blockchain Transactions through Token Locking

Bitcoin introduced the world to blockchain-based cryptocurrencies, and Ethereum highlighted their value in building distributed applications (dApps). However, the development of blockchain-based applications has been held back by high transaction fees. In this paper, we introduce a model for free transactions on the blockchain. Rather than spending tokens for transaction fees, a token owner (known as a client) locks tokens to generate new tokens as a reward for the miner who includes the transaction in a block. This token-locking reward model eases congestion on the blockchain in the same manner as fees do in protocols like Bitcoin, but without forcing clients to sacrifice their tokens. This same design can be used to incentivize service providers. We show how a client can lock their tokens to generate new tokens for storage providers, and how this reward mechanism can help to facilitate an audit of the storage provider.