OpenRaiser/Intern-Atlas huggingface dataset:
Relations between assumption-based approaches in nonmonotonic logic and formal argumentation
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we make a contribution to the unification of formal models of defeasible reasoning. We present several translations between formal argumentation frameworks and nonmonotonic logics for reasoning with plausible assumptions. More specifically, we translate adaptive logics into assumption-based argumentation and ASPIC+, ASPIC+ into assumption-based argumentation and a fragment of assumption-based argumentation into adaptive logics. Adaptive logics are closely related to Makinson's default assumptions and to a significant class of systems within the tradition of preferential semantics in the vein of KLM and Shoham. Thus, our results also provide close links between formal argumentation and the latter approaches.
A SAT model to mine flexible sequences in transactional datasets
Venue: arXiv cs.AI 2016
Year: 2016

Traditional pattern mining algorithms generally suffer from a lack of flexibility. In this paper, we propose a SAT formulation of the problem to successfully mine frequent flexible sequences occurring in transactional datasets. Our SAT-based approach can easily be extended with extra constraints to address a broad range of pattern mining applications. To demonstrate this claim, we formulate and add several constraints, such as gap and span constraints, to our model in order to extract more specific patterns. We also use interactive solving to perform important derived tasks, such as closed pattern mining or maximal pattern mining. Finally, we prove the practical feasibility of our SAT model by running experiments on two real datasets.
Understanding and Overcoming Biases in Customer Reviews
Venue: arXiv cs.HC 2016
Year: 2016

Our paper contributes to the literature recommending approaches to make online reviews more credible and representative. We analyze data from four diverse major online retailers and find that verified customers who are prompted (by an email) to write a review, submit, on average, up to 0.5 star higher ratings than self-motivated web reviewers. Moreover, these email-prompted reviews remain stable over time, whereas web reviews exhibit a downward trend. This finding provides support for the existence of social influence and selection biases during the submission of a web review, when social signals are being displayed. In contrast, no information about the current state of the reviews is displayed in the email promptings. Moreover, we find that when a retailer decides to start sending email promptings, the existing population of web reviewers is unaffected both in their volume as well as the characteristics of their submitted reviews. We explore how our combined findings can suggest ways to mitigate various biases that govern online review submissions and help practitioners provide more credible, representative and higher ratings to their customers.
Throughput-Optimal Multi-hop Broadcast Algorithms
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we design throughput-optimal dynamic broad- cast algorithms for multi-hop networks with arbitrary topolo- gies. Most of the previous broadcast algorithms route pack- ets along spanning trees, rooted at the source node. For large dynamic networks, computing and maintaining a set of spanning trees is not efficient, as the network-topology may change frequently. In this paper we design a class of dynamic algorithms which makes packet-by-packet schedul- ing and routing decisions and thus obviates the need for maintaining any global topological structures, such as span- ning trees. Our algorithms may be conveniently understood as a non-trivial generalization of the familiar back-pressure algorithm which makes unicast packet routing and schedul- ing decisions, based on queue-length information, without maintaining end-to-end paths. However, in the broadcast problem, it is hard to define queuing structures due to ab- sence of a work-conservation principle which results from packet duplications. We design and prove the optimality of a virtual-queue based algorithm, where a virtual-queue is de- fined for subsets of vertices. We then propose a multi-class broadcast policy which combines the above scheduling algo- rithm with a class-based in-order packet delivery constraint, resulting in significant reduction in complexity. Finally, we evaluate performance of the proposed algorithms via exten- sive numerical simulations.
Robust video object tracking via Bayesian model averaging based feature fusion
Venue: arXiv cs.CV 2016
Year: 2016

In this article, we are concerned with tracking an object of interest in video stream. We propose an algorithm that is robust against occlusion, the presence of confusing colors, abrupt changes in the object feature space and changes in object size. We develop the algorithm within a Bayesian modeling framework. The state space model is used for capturing the temporal correlation in the sequence of frame images by modeling the underlying dynamics of the tracking system. The Bayesian model averaging (BMA) strategy is proposed for fusing multi-clue information in the observations. Any number of object features are allowed to be involved in the proposed framework. Every feature represents one source of information to be fused and is associated with an observation model. The state inference is performed by employing the particle filter methods. In comparison with related approaches, the BMA based tracker is shown to have robustness, expressivity, and comprehensibility.
Steganography -- A Game of Hide and Seek in Information Communication
Venue: arXiv cs.MM 2016
Year: 2016

With the growth of communication over computer networks, how to maintain the confidentiality and security of transmitted information have become some of the important issues. In order to transfer data securely to the destination without unwanted disclosure or damage, nature inspired hide and seek tricks such as, cryptography and Steganography are heavily in use. Just like the Chameleon and many other bio-species those change their body color and hide themselves in the background in order to protect them from external attacks, Cryptography and Steganography are techniques those are used to encrypt and hide the secret data inside other media to ensure data security. This paper discusses the concept of a simple spatial domain LSB Steganography that encrypts the secrets using Fibonacci- Lucas transformation, before hiding, for better security.
A Get-Together for Deaf and Dumb Robots in Three dimensional Space
Venue: arXiv cs.RO 2016
Year: 2016

This paper proposes a strategy for a group of deaf and dumb robots, carrying clocks from different countries, to meet at a geographical location which is not fixed in advanced. The robots act independently. They can observe others, compute some locations and walk towards those locations. They can only get a snapshot of the locations of other robots but can not detect whether they are static or in motion. The robots are forgetful; once they have completed their motion they forget their previous locations and observations. Again they decide new destinations to move to. Eventually all the robots compute the same destination and meet there. There exists no global positioning system. As they stand, they agree on up and down directions. However, as they do not have any compass, the other directions are not agreed upon. They also do not agree on the clockwise direction. For determining a strategy, we imagine the robots to be points on a three dimensional plane where all the robots are mutually visible to each other always. The strategy we propose has to be obeyed by all the robots independently with respect to their own clock and compass. Initially the robots start from distinct locations. Some dead robots may be present in the system or some may die any time before or after the get together. However, the live robots are not aware of the presence of these dead robots.
Online Updating of Word Representations for Part-of-Speech Tagging
Venue: arXiv cs.CL 2016
Year: 2016

We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS) tagging evaluation, we find that online unsupervised DA performs as well as batch DA.
Image Quality Assessment for Performance Evaluation of Focus Measure Operators
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents the performance evaluation of eight focus measure operators namely Image CURV (Curvature), GRAE (Gradient Energy), HISE (Histogram Entropy), LAPM (Modified Laplacian), LAPV (Variance of Laplacian), LAPD (Diagonal Laplacian), LAP3 (Laplacian in 3D Window) and WAVS (Sum of Wavelet Coefficients). Statistical matrics such as MSE (Mean Squared Error), PNSR (Peak Signal to Noise Ratio), SC (Structural Content), NCC (Normalized Cross Correlation), MD (Maximum Difference) and NAE (Normalized Absolute Error) are used to evaluate stated focus measures in this research. . FR (Full Reference) method of the image quality assessment is utilized in this paper. Results indicate that LAPD method is comparatively better than other seven focus operators at typical imaging conditions.
pH Prediction by Artificial Neural Networks for the Drinking Water of the Distribution System of Hyderabad City
Venue: arXiv cs.NE 2016
Year: 2016

In this research, feedforward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm.The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%.Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results.
AsicBoost - A Speedup for Bitcoin Mining
Venue: arXiv cs.CR 2016
Year: 2016

AsicBoost is a method to speed up Bitcoin mining by a factor of approximately 20%. The performance gain is achieved through a high-level optimization of the Bitcoin mining algorithm which allows for drastic reduction in gate count on the mining chip. AsicBoost is applicable to all types of mining hardware and chip designs. This paper presents the idea behind the method and describes the information flow in implementations of AsicBoost.
GAL: A Global-Attributes Assisted Labeling System for Outdoor Scenes
Venue: arXiv cs.CV 2016
Year: 2016

An approach that extracts global attributes from outdoor images to facilitate geometric layout labeling is investigated in this work. The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes. First, by following a classical method, we use local features to provide initial labels for all super-pixels. Then, we develop a set of techniques to extract global attributes from 2D outdoor images. They include sky lines, ground lines, vanishing lines, etc. Finally, we propose the GAL system that integrates global attributes in the conditional random field (CRF) framework to improve initial labels so as to offer a more robust labeling result. The performance of the proposed GAL system is demonstrated and benchmarked with several state-of-the-art algorithms against a popular outdoor scene layout dataset.
Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Heterogeneous Wireless Networks
Venue: arXiv cs.IT 2016
Year: 2016

Heterogeneous wireless networks (HetNets) provide a powerful approach to meet the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design consisting of identical caching in the macro-tier and random caching in the pico-tier, and a corresponding multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive tractable expressions for the successful transmission probability in the general region as well as the high signal-to-noise ratio (SNR) and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing the design parameters, which is a very challenging mixed discrete-continuous optimization problem due to the sophisticated structure of the successful transmission probability. By using optimization techniques and exploring the structural properties, we obtain a near optimal solution with superior performance and manageable complexity. This solution achieves better performance in the general region than any asymptotically optimal solution, under a mild condition. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.
Bicycle-Sharing System Analysis and Trip Prediction
Venue: arXiv cs.CY 2016
Year: 2016

Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short-distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.
Pure Nash Equilibrium and Coordination of Players in Ride Sharing Games
Venue: arXiv cs.GT 2016
Year: 2016

In this study, we formulate positive and negative externalities caused by changes in the supply of shared vehicles as ride sharing games. The study aims to understand the price of anarchy (PoA) and its improvement via a coordination technique in ride sharing games. A critical question is whether ride sharing games exhibit a pure Nash equilibrium (pNE) since the PoA bound assumes it. Our result shows a sufficient condition for a ride sharing game to have a finite improvement property and a pNE similar to potential games. This is the first step to analyze PoA bound and its improvement by coordination in ride sharing games. We also show an example of coordinating players in ride sharing games using signaling and evaluate the improvement in the PoA.
Cooperative communications for sleep monitoring in wireless body area networks
Venue: arXiv cs.OH 2016
Year: 2016

This paper investigates the performance of cooperative receive diversity, for the wireless body area network (WBAN) radio channel, compliant with the IEEE 802.15.6 Standard, in the case of monitoring a sleeping person. Extensive WBAN measurements near the 2.4 GHz ISM band were used. Up to 7 dB and 20% improvement for two-hop communications with the use of relays are empirically demonstrated with respect to outage probability and outage duration, with 3-branch cooperative selection combining and 3-branch cooperative switch-and-examine combining.
Sparse Fourier Transform in Any Constant Dimension with Nearly-Optimal Sample Complexity in Sublinear Time
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of computing a $k$-sparse approximation to the Fourier transform of a length $N$ signal. Our main result is a randomized algorithm for computing such an approximation (i.e. achieving the $\ell_2/\ell_2$ sparse recovery guarantees using Fourier measurements) using $O_d(k\log N\log\log N)$ samples of the signal in time domain that runs in time $O_d(k\log^{d+3} N)$, where $d\geq 1$ is the dimensionality of the Fourier transform. The sample complexity matches the lower bound of $Ω(k\log (N/k))$ for non-adaptive algorithms due to \cite{DIPW} for any $k\leq N^{1-δ}$ for a constant $δ>0$ up to an $O(\log\log N)$ factor. Prior to our work a result with comparable sample complexity $k\log N \log^{O(1)}\log N$ and sublinear runtime was known for the Fourier transform on the line \cite{IKP}, but for any dimension $d\geq 2$ previously known techniques either suffered from a polylogarithmic factor loss in sample complexity or required $Ω(N)$ runtime.
Disturbance scaling in bidirectional vehicle platoons with different asymmetry in position and velocity coupling
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers a string of vehicles where the local control law uses the states of the vehicle's immediate predecessor and follower. The coupling towards the preceding vehicle can be chosen different to the coupling towards the following vehicle, which is often referred to as an asymmetric bidirectional string. Further, the asymmetry for the velocity coupling can be chosen differently to the asymmetry in the position coupling. It is investigated how the effect of disturbance on the control errors in the string depends on the string length. It is shown, that in case of symmetric position coupling and asymmetric velocity coupling, linear scaling can be achieved. For symmetric interaction in both states, i.e., in symmetric bidirectional strings, the errors scale quadratically in the number of vehicles. When the coupling in position is asymmetric, exponential scaling may occur or the system might even become unstable. The paper thus gives a comprehensive overview of the achievable performance in linear, asymmetric, bidirectional platoons. The results reveal that symmetry in the position coupling and asymmetry in velocity coupling qualitatively improves the performance of the string. Extensive numerical results illustrate the theoretical findings.
Automatic Knowledge Base Evolution by Learning Instances
Venue: arXiv cs.AI 2016
Year: 2016

Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose full-automated ontology learning algorithm to generate refined knowledge base from incomplete knowledge base and rdf-triples. Our algorithm is data-driven approach which is based on the property of each instance. Ontology class is being elaborated by generalizing frequent property of its instances. By using that developed class information, each instance can find its most relatively matching class. By repeating these two steps, we achieve fully-automated ontology evolution from incomplete basic knowledge base.
BLISP: Enhancing Backscatter Radio with Active Radio for Computational RFIDs
Venue: arXiv cs.NI 2016
Year: 2016

We demonstrate the world's first hybrid radio platform which combines the strengths of active radio (long range and robustness to interference) and Computational RFIDs (low power consumption). We evaluate the Wireless Identification and Sensing Platform (WISP), an EPC C1G2 standard-based, Computational RFID backscatter radio, against Bluetooth Low Energy (BLE) and show (theoretically and experimentally) that WISP in high channel attenuation conditions is less energy efficient per received byte than BLE. Exploiting this observation we design a simple switching mechanisms that backs off to BLE when radio conditions for WISP are unfavorable. By a set of laboratory experiments, we show that our proposed hybrid active/backscatter radio obtains higher goodput than WISP and lower energy consumption than BLE as stand-alone platforms, especially when WISP is in range of an RFID interrogator for the majority of the time. Simultaneously, our proposed platform is as energy efficient as BLE when user is mostly out of RFID interrogator range.
Entity Type Recognition using an Ensemble of Distributional Semantic Models to Enhance Query Understanding
Venue: arXiv cs.CL 2016
Year: 2016

We present an ensemble approach for categorizing search query entities in the recruitment domain. Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. Our approach instead combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using word2vec. Additionally, our approach utilizes both entity linguistic properties obtained from WordNet and ontological properties extracted from DBpedia. We evaluate our approach on a data set created at CareerBuilder; the largest job board in the US. The data set contains entities extracted from millions of job seekers/recruiters search queries, job postings, and resume documents. After constructing the distributional vectors of search entities, we use supervised machine learning to infer search entity types. Empirical results show that our approach outperforms the state-of-the-art word2vec distributional semantics model trained on Wikipedia. Moreover, we achieve micro-averaged F 1 score of 97% using the proposed distributional representations ensemble.