OpenRaiser/Intern-Atlas huggingface dataset:
Identifying Partisan Slant in News Articles and Twitter during Political Crises
Venue: arXiv cs.SI 2017
Year: 2017

In this paper, we are interested in understanding the interrelationships between mainstream and social media in forming public opinion during mass crises, specifically in regards to how events are framed in the mainstream news and on social networks and to how the language used in those frames may allow to infer political slant and partisanship. We study the lingual choices for political agenda setting in mainstream and social media by analyzing a dataset of more than 40M tweets and more than 4M news articles from the mass protests in Ukraine during 2013-2014 - known as "Euromaidan" - and the post-Euromaidan conflict between Russian, pro-Russian and Ukrainian forces in eastern Ukraine and Crimea. We design a natural language processing algorithm to analyze at scale the linguistic markers which point to a particular political leaning in online media and show that political slant in news articles and Twitter posts can be inferred with a high level of accuracy. These findings allow us to better understand the dynamics of partisan opinion formation during mass crises and the interplay between main- stream and social media in such circumstances.
Particle Value Functions
Venue: arXiv cs.LG 2017
Year: 2017

The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponential utility and illustrate its effects on an example. This risk-sensitive value function is not always applicable to reinforcement learning problems, so we introduce the particle value function defined by a particle filter over the distributions of an agent's experience, which bounds the risk-sensitive one. We illustrate the benefit of the policy gradients of this objective in Cliffworld.
A Contract-based Incentive Mechanism for Energy Harvesting-based Internet of Things
Venue: arXiv cs.GT 2017
Year: 2017

By enabling wireless devices to be charged wirelessly and remotely, radio frequency energy harvesting (RFEH) has become a promising technology to power the unattended Internet of Things (IoT) low-power devices. To enable this, in future IoT networks, besides the conventional data access points (DAPs) responsible for collecting data from IoT devices, energy access points (EAPs) should be deployed to transfer radio frequency (RF) energy to IoT devices to maintain their sustainable operations. In practice, the DAPs and EAPs may be operated by different operators and a DAP should provide certain incentives to motivate the surrounding EAPs to charge its associated IoT device(s) to assist its data collection. Motivated by this, in this paper we develop a contract theory-based incentive mechanism for the energy trading in RFEH assisted IoT systems. The necessary and sufficient condition for the feasibility of the formulated contract is analyzed. The optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulation results demonstrate the feasibility and effectiveness of the proposed incentive mechanism.
A Tight Upper Bound on the Second-Order Coding Rate of the Parallel Gaussian Channel with Feedback
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the asymptotic expansion for the maximum rate of fixed-length codes over a parallel Gaussian channel with feedback under the following setting: A peak power constraint is imposed on every transmitted codeword, and the average error probability of decoding the transmitted message is non-vanishing as the blocklength increases. It is well known that the presence of feedback does not increase the first-order asymptotics of the channel, i.e., capacity, in the asymptotic expansion, and the closed-form expression of the capacity can be obtained by the well-known water-filling algorithm. The main contribution of this paper is a self-contained proof of an upper bound on the second-order asymptotics of the parallel Gaussian channel with feedback. The proof techniques involve developing an information spectrum bound followed by using Curtiss' theorem to show that a sum of dependent random variables associated with the information spectrum bound converges in distribution to a sum of independent random variables, thus facilitating the use of the usual central limit theorem. Combined with existing achievability results, our result implies that the presence of feedback does not improve the second-order asymptotics.
The Effect of Population Control Policies on Societal Fragmentation
Venue: arXiv cs.SI 2017
Year: 2017

Population control policies are proposed and in some places employed as a means towards curbing population growth. This paper is concerned with a disturbing side-effect of such policies, namely, the potential risk of societal fragmentation due to changes in the distribution of family sizes. This effect is illustrated in some simple settings and demonstrated by simulation. In addition, the dependence of societal fragmentation on family size distribution is analyzed. In particular, it is shown that under the studied model, any population control policy that disallows families of 3 or more children incurs the possible risk of societal fragmentation.
Connection Scan Algorithm
Venue: arXiv cs.DS 2017
Year: 2017

We introduce the Connection Scan Algorithm (CSA) to efficiently answer queries to timetable information systems. The input consists, in the simplest setting, of a source position and a desired target position. The output consist is a sequence of vehicles such as trains or buses that a traveler should take to get from the source to the target. We study several problem variations such as the earliest arrival and profile problems. We present algorithm variants that only optimize the arrival time or additionally optimize the number of transfers in the Pareto sense. An advantage of CSA is that is can easily adjust to changes in the timetable, allowing the easy incorporation of known vehicle delays. We additionally introduce the Minimum Expected Arrival Time (MEAT) problem to handle possible, uncertain, future vehicle delays. We present a solution to the MEAT problem that is based upon CSA. Finally, we extend CSA using the multilevel overlay paradigm to answer complex queries on nation-wide integrated timetables with trains and buses.
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Venue: arXiv cs.LG 2017
Year: 2017

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.
Approximation ratio of RePair
Venue: arXiv cs.DS 2017
Year: 2017

In a seminal paper of Charikar et al.~on the smallest grammar problem, the authors derive upper and lower bounds on the approximation ratios for several grammar-based compressors. Here we improve the lower bound for the famous {\sf RePair} algorithm from $Ω(\sqrt{\log n})$ to $Ω(\log n/\log\log n)$. The family of words used in our proof is defined over a binary alphabet, while the lower bound from Charikar et al. needs an alphabet of logarithmic size in the length of the provided words.
Stochastic Flow Models with Delays and Applications to Multi-Intersection Traffic Light Control
Venue: arXiv cs.SY 2017
Year: 2017

We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. We introduce two new cost metrics to better capture congestion and show that the inclusion of delays in our analysis leads to improved performance relative to models that ignore delays.
Defect prediction with bad smells in code
Venue: arXiv cs.SE 2017
Year: 2017

Background: Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect prediction is proper selection of metrics used for dataset preparation. Objective: The purpose of this research is to verify, whether code smells metrics, collected using Microsoft CodeAnalysis tool, added to basic metric set, can improve defect prediction in industrial software development project. Results: We verified, if dataset extension by the code smells sourced metrics, change the effectiveness of the defect prediction by comparing prediction results for datasets with and without code smells-oriented metrics. In a result, we observed only small improvement of effectiveness of defect prediction when dataset extended with bad smells metrics was used: average accuracy value increased by 0.0091 and stayed within the margin of error. However, when only use of code smells based metrics were used for prediction (without basic set of metrics), such process resulted with surprisingly high accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data anomalies and problems we observed when two different metric sources were used to prepare one, consistent set of data. Conclusion: Extending the dataset by the code smells sourced metric does not significantly improve the prediction effectiveness. Achieved result did not compensate effort needed to collect additional metrics. However, we observed that defect prediction based on the code smells only is still highly effective and can be used especially where other metrics hardly be used.
Hardware-Efficient Schemes of Quaternion Multiplying Units for 2D Discrete Quaternion Fourier Transform Processors
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we offer and discuss three efficient structural solutions for the hardware-oriented implementation of discrete quaternion Fourier transform basic operations with reduced implementation complexities. The first solution: a scheme for calculating sq product, the second solution: a scheme for calculating qt product, and the third solution: a scheme for calculating sqt product, where s is a so-called i-quaternion, t is an j-quaternion, and q is an usual quaternion. The direct multiplication of two usual quaternions requires 16 real multiplications (or two-operand multipliers in the case of fully parallel hardware implementation) and 12 real additions (or binary adders). At the same time, our solutions allow to design the computation units, which consume only 6 multipliers plus 6 two input adders for implementation of sq or qt basic operations and 9 binary multipliers plus 6 two-input adders and 4 four-input adders for implementation of sqt basic operation.
Rediscovery Datasets: Connecting Duplicate Reports
Venue: arXiv cs.SE 2017
Year: 2017

The same defect can be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. In the case of popular open source software, high volume of defects is reported on a regular basis. A large number of these reports are actually duplicates / rediscoveries of each other. Researchers have analyzed the factors related to the content of duplicate defect reports in the past. However, some of the other potentially important factors, such as the inter-relationships among duplicate defect reports, are not readily available in defect tracking systems such as Bugzilla. This information may speed up bug fixing, enable efficient triaging, improve customer profiles, etc. In this paper, we present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. We believe that sharing these data with the community will help researchers and practitioners to better understand the nature of defect rediscovery and enhance the analysis of defect reports.
Zero-Shot Learning by Generating Pseudo Feature Representations
Venue: arXiv cs.CV 2017
Year: 2017

Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel classes without any training instances. In this paper we present a simple but high-performance ZSL approach by generating pseudo feature representations (GPFR). Given the dataset of seen classes and side information of unseen classes (e.g. attributes), we synthesize feature-level pseudo representations for novel concepts, which allows us access to the formulation of unseen class predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to acquire understandings about attributes, then construct a cognitive repository of attributes filtered by confidence margins, and finally generate pseudo feature representations using a probability based sampling strategy to facilitate subsequent training process of class predictor. We demonstrate the effectiveness in ZSL settings and the extensibility in supervised recognition scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For several popular ZSL benchmark datasets, our approach also shows compelling results on zero-shot recognition task, especially leading to tremendous improvement to state-of-the-art mAP on zero-shot retrieval task.
A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
Venue: arXiv cs.CV 2017
Year: 2017

We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes. The process is performed in three steps, including organ detection, lesion detection and lesion segmentation. Our method applies machine learning techniques such as marginal space learning and convolutional neural networks, as well as active contour models. The method proves to be robust in its handling of extremely high lesion diversity. We tested our method on volumetric computed tomography (CT) images, including 42 volumes containing liver lesions and 86 volumes containing 595 pathological lymph nodes. Preliminary results under 10-fold cross validation show that for both the liver lesions and the lymph nodes, a total detection sensitivity of 0.53 and average Dice score of $0.71 \pm 0.15$ for segmentation were obtained.
Enhancing Physical Layer Security in Dual-Hop Multiuser Transmission
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider the Physical Layer Security(PLS) problem in orthogonal frequency division multiple access (OFDMA) based dual-hop system which consists of multiple users, multiple amplify and forward relays, and an eavesdropper. The aim is to enhance PLS of the entire system by maximizing sum secrecy rate of secret users through optimal resource allocation under various practical constraints. Specifically, the sub-carrier allocation to different users, the relay assignments, and the power loading over different sub-carriers at transmitting nodes are optimized. The joint optimization problem is modeled as a mixed binary integer programming problem subject to exclusive sub-carrier allocation and separate power budget constraints at each node. A joint optimization solution is obtained through Lagrangian dual decomposition where KKT conditions are exploited to find the optimal power allocation at base station. Further, to reduce the complexity, a sub-optimal scheme is presented where the optimal power allocation is derived under fixed sub-carrier-relay assignment. Simulation results are also provided to validate the performance of proposed schemes.
Métodos de Otimização Combinatória Aplicados ao Problema de Compressão MultiFrases
Venue: arXiv cs.CL 2017
Year: 2017

The Internet has led to a dramatic increase in the amount of available information. In this context, reading and understanding this flow of information have become costly tasks. In the last years, to assist people to understand textual data, various Natural Language Processing (NLP) applications based on Combinatorial Optimization have been devised. However, for Multi-Sentences Compression (MSC), method which reduces the sentence length without removing core information, the insertion of optimization methods requires further study to improve the performance of MSC. This article describes a method for MSC using Combinatorial Optimization and Graph Theory to generate more informative sentences while maintaining their grammaticality. An experiment led on a corpus of 40 clusters of sentences shows that our system has achieved a very good quality and is better than the state-of-the-art.
Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning in Soft and Hard Fusion Environments
Venue: arXiv cs.AI 2017
Year: 2017

Robust belief revision methods are crucial in streaming data situations for updating existing knowledge or beliefs with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments that harness soft (i.e., human or human-based) sources in addition to hard (i.e., physics-based) sensors. The objective of this paper is to investigate the most natural extension of Bayes conditioning that is suitable for evidence updating in the presence of such uncertainties. By viewing the evidence updating process as a thought experiment, an elegant strategy is derived for robust evidence updating in the presence of extreme uncertainties that are characteristic of big-data environments. In particular, utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes conditioning is derived for evidence that takes the form of a general belief function. The presented work differs fundamentally from the Conditional Update Equation (CUE) and authors own extensions of it. An overview of this development is provided via illustrative examples. Furthermore, insights into parameter selection under various fusion contexts are also provided.
Evaluating the Stream Control Transmission Protocol Using Uppaal
Venue: arXiv cs.LO 2017
Year: 2017

The Stream Control Transmission Protocol (SCTP) is a Transport Layer protocol that has been proposed as an alternative to the Transmission Control Protocol (TCP) for the Internet of Things (IoT). SCTP, with its four-way handshake mechanism, claims to protect the Server from a Denial-of-Service (DoS) attack by ensuring the legitimacy of the Client, which has been a known issue pertaining to the three-way handshake of TCP. This paper compares the handshakes of TCP and SCTP to discuss its shortcomings and strengths. We present an Uppaal model of the TCP three-way handshake and SCTP four-way handshake and show that SCTP is able to cope with the presence of an Illegitimate Client, while TCP fails. The results confirm that SCTP is better equipped to deal with this type of attack.
The Cryptographic Implications of the LinkedIn Data Breach
Venue: arXiv cs.CR 2017
Year: 2017

Data security and personal privacy are difficult to maintain in the Internet age. In 2012, professional networking site LinkedIn suffered a breach, compromising the login of over 100 million accounts. The passwords were cracked and sold online, exposing the authentication credentials millions of users. This manuscript dissects the cryptographic failures implicated in the breach, and explores more secure methods of storing passwords.
Paper2vec: Citation-Context Based Document Distributed Representation for Scholar Recommendation
Venue: arXiv cs.IR 2017
Year: 2017

Due to the availability of references of research papers and the rich information contained in papers, various citation analysis approaches have been proposed to identify similar documents for scholar recommendation. Despite of the success of previous approaches, they are, however, based on co-occurrence of items. Once there are no co-occurrence items available in documents, they will not work well. Inspired by distributed representations of words in the literature of natural language processing, we propose a novel approach to measuring the similarity of papers based on distributed representations learned from the citation context of papers. We view the set of papers as the vocabulary, define the weighted citation context of papers, and convert it to weight matrix similar to the word-word cooccurrence matrix in natural language processing. After that we explore a variant of matrix factorization approach to train distributed representations of papers on the matrix, and leverage the distributed representations to measure similarities of papers. In the experiment, we exhibit that our approach outperforms state-of-theart citation-based approaches by 25%, and better than other distributed representation based methods.