OpenRaiser/Intern-Atlas huggingface dataset:
A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks
Venue: arXiv cs.SI 2015
Year: 2015

Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users over time, can we tell when the network "behaved" abnormally? Furthermore, can we predict how the opinions of the users will change in the future? Do opinions evolve according to existing network opinion dynamics models? To answer such questions, it is not sufficient to study individual user behavior, since opinions can spread far beyond users' egonets. We need a method to analyze opinion dynamics of all network users simultaneously and capture the effect of individuals' behavior on the global evolution pattern of the social network. In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users, which makes SND applicable to the analysis of large-scale online social networks. In our experiments with synthetic and real-world Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. When employed for opinion prediction in Twitter, our method's accuracy is 75.63%, which is 7.5% higher than that of the next best method. Source Code: https://cs.ucsb.edu/~victor/pub/ucsb/dbl/snd/
Block Sparse Memory Improved Proportionate Affine Projection Sign Algorithm
Venue: arXiv cs.SY 2015
Year: 2015

A block sparse memory improved proportionate affine projection sign algorithm (BS-MIP-APSA) is proposed for block sparse system identification under impulsive noise. The new BS-MIP-APSA not only inherits the performance improvement for block-sparse system identification, but also achieves robustness to impulsive noise and the efficiency of the memory improved proportionate affine projection sign algorithm (MIP-APSA). Simulations indicate that it can provide both faster convergence rate and better tracking ability under impulsive interference for block sparse system identification as compared to APSA and MIP-APSA.
Proactive Eavesdropping via Jamming for Rate Maximization over Rayleigh Fading Channels
Venue: arXiv cs.IT 2015
Year: 2015

Instead of against eavesdropping, this letter proposes a new paradigm in wireless security by studying how a legitimate monitor (e.g., government agencies) efficiently eavesdrops a suspicious wireless communication link. The suspicious transmitter controls its communication rate over Rayleigh fading channels to maintain a target outage probability at the receiver, and the legitimate monitor can successfully eavesdrop only when its achievable rate is no smaller than the suspicious communication rate. We propose a proactive eavesdropping via jamming approach to maximize the average eavesdropping rate, where the legitimate monitor sends jamming signals with optimized power control to moderate the suspicious communication rate.
Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis
Venue: arXiv cs.SI 2015
Year: 2015

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progresswith contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis, the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.
Combining intermediate propositional logics with classical logic
Venue: arXiv cs.LO 2015
Year: 2015

In [17], we introduced a modal logic, called $L$, which combines intuitionistic propositional logic $IPC$ and classical propositional logic $CPC$ and is complete w.r.t. an algebraic semantics. However, $L$ seems to be too weak for Kripke-style semantics. In this paper, we add positive and negative introspection and show that the resulting logic $L5$ has a Kripke semantics. For intermediate logics $I$, we consider the parametrized versions $L5(I)$ of $L5$ where $IPC$ is replaced by $I$. $L5(I)$ can be seen as a classical modal logic for the reasoning about truth in $I$. From our results, we derive a simple method for determining algebraic and Kripke semantics for some specific intermediate logics. We discuss some examples which are of interest for Computer Science, namely the Logic of Here-and-There, Gödel-Dummett Logic and Jankov Logic. Our method provides new proofs of completeness theorems due to Hosoi, Dummett/Horn and Jankov, respectively.
Region Based Energy Balanced Inter-cluster communication Protocol for Sensor networks
Venue: arXiv cs.NI 2015
Year: 2015

Wireless sensor networks faces unbalanced energy consumption problem over time. Clustering provides an energy efficient method to improve lifespan of the sensor network. Cluster head collects data from other nodes and transmits it towards the sink node. Cluster heads which are far-off from the sink, consumes more power in transmission of information towards the sink. We propose Region Based Energy Balanced Inter-cluster communication protocol (RBEBP) to improve lifespan of the sensor network. Monitored area has been divided into regions; cluster heads are selected from specific region based on the residual energy of nodes in that region. If energy of nodes of the specific region is low, nodes from another region are selected as cluster heads. Optimized selection of cluster heads helps in improving lifespan of the sensor network. In our scheme, cluster heads which are far-off from the sink use another cluster heads as the relay nodes to transmit their data to the sink node. So energy of cluster heads deplete in a uniform way and complete area remain covered by sensor nodes. Simulation results demonstrate that RBEBP can effectively reduce total energy depletion and considerably extend lifespan of the network as compared to LEACH protocol. RBEBP also minimize the problem of energy holes in monitored area and improve the throughput of the network
EdgeCentric: Anomaly Detection in Edge-Attributed Networks
Venue: arXiv cs.SI 2015
Year: 2015

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this information to discern suspicious from typical node behavior. Our work has a number of notable contributions, including (a) formulation: while most other graph-based anomaly detection works use structural graph connectivity or node information, we focus on the new problem of leveraging edge information, (b) methodology: we introduce EdgeCentric, an intuitive and scalable compression-based approach for detecting edge-attributed graph anomalies, and (c) practicality: we show that EdgeCentric successfully spots numerous such anomalies in several large, edge-attributed real-world graphs, including the Flipkart e-commerce graph with over 3 million product reviews between 1.1 million users and 545 thousand products, where it achieved 0.87 precision over the top 100 results.
On the Computability of AIXI
Venue: arXiv cs.AI 2015
Year: 2015

How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. In this paper, we quantify this using the arithmetical hierarchy, and prove upper and corresponding lower bounds for incomputability. We show that AIXI is not limit computable, thus it cannot be approximated using finite computation. Our main result is a limit-computable ε-optimal version of AIXI with infinite horizon that maximizes expected rewards.
Application of Machine Learning Techniques in Human Activity Recognition
Venue: arXiv cs.LG 2015
Year: 2015

Human activity detection has seen a tremendous growth in the last decade playing a major role in the field of pervasive computing. This emerging popularity can be attributed to its myriad of real-life applications primarily dealing with human-centric problems like healthcare and elder care. Many research attempts with data mining and machine learning techniques have been undergoing to accurately detect human activities for e-health systems. This paper reviews some of the predictive data mining algorithms and compares the accuracy and performances of these models. A discussion on the future research directions is subsequently offered.
Distributed Virtual Resource Allocation in Small Cell Networks with Full Duplex Self-backhauls and Virtualization
Venue: arXiv cs.NI 2015
Year: 2015

Wireless network virtualization has attracted great attentions from both academia and industry. Another emerging technology for next generation wireless networks is in-band full duplex (FD) communications. Due to its promising performance, FD communication has been considered as an effective way to achieve self-backhauls for small cells. In this paper, we introduce wireless virtualization into small cell networks, and propose a virtualized small cell network architecture with FD self-backhauls. We formulate the virtual resource allocation problem in virtualized small cell networks with FD self-backhauls as an optimization problem. Since the formulated problem is a mixed combinatorial and non-convex optimization problem, its computational complexity is high. Moreover, the centralized scheme may suffer from signaling overhead, outdated dynamics information, and scalability issues. To solve it efficiently, we divide the original problem into two subproblems. For the first subproblem, we transfer it to a convex optimization problem, and then solve it by an efficient alternating direction method of multipliers (ADMM)-based distributed algorithm. The second subproblem is a convex problem, which can be solved by each infrastructure provider. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.
On the Book Thickness of 1-Planar Graphs
Venue: arXiv cs.CG 2015
Year: 2015

In a book embedding of a graph G, the vertices of G are placed in order along a straight-line called spine of the book, and the edges of G are drawn on a set of half-planes, called the pages of the book, such that two edges drawn on a page do not cross each other. The minimum number of pages in which a graph can be embedded is called the book-thickness or the page-number of the graph. It is known that every planar graph has a book embedding on at most four pages. Here we investigate the book-embeddings of 1-planar graphs. A graph is 1-planar if it can be drawn in the plane such that each edge is crossed at most once. We prove that every 1-planar graph has a book embedding on at most 16 pages and every 3-connected 1-planar graph has a book embedding on at most 12 pages. The drawings can be computed in linear time from any given 1-planar embedding of the graph.
A latent shared-component generative model for real-time disease surveillance using Twitter data
Venue: arXiv cs.SI 2015
Year: 2015

Exploiting the large amount of available data for addressing relevant social problems has been one of the key challenges in data mining. Such efforts have been recently named "data science for social good" and attracted the attention of several researchers and institutions. We give a contribution in this objective in this paper considering a difficult public health problem, the timely monitoring of dengue epidemics in small geographical areas. We develop a generative simple yet effective model to connect the fluctuations of disease cases and disease-related Twitter posts. We considered a hidden Markov process driving both, the fluctuations in dengue reported cases and the tweets issued in each region. We add a stable but random source of tweets to represent the posts when no disease cases are recorded. The model is learned through a Markov chain Monte Carlo algorithm that produces the posterior distribution of the relevant parameters. Using data from a significant number of large Brazilian towns, we demonstrate empirically that our model is able to predict well the next weeks of the disease counts using the tweets and disease cases jointly.
Fast and Scalable Structural SVM with Slack Rescaling
Venue: arXiv cs.LG 2015
Year: 2015

We present an efficient method for training slack-rescaled structural SVM. Although finding the most violating label in a margin-rescaled formulation is often easy since the target function decomposes with respect to the structure, this is not the case for a slack-rescaled formulation, and finding the most violated label might be very difficult. Our core contribution is an efficient method for finding the most-violating-label in a slack-rescaled formulation, given an oracle that returns the most-violating-label in a (slightly modified) margin-rescaled formulation. We show that our method enables accurate and scalable training for slack-rescaled SVMs, reducing runtime by an order of magnitude compared to previous approaches to slack-rescaled SVMs.
Robust Semi-Supervised Classification for Multi-Relational Graphs
Venue: arXiv cs.LG 2015
Year: 2015

Graph-regularized semi-supervised learning has been used effectively for classification when (i) instances are connected through a graph, and (ii) labeled data is scarce. If available, using multiple relations (or graphs) between the instances can improve the prediction performance. On the other hand, when these relations have varying levels of veracity and exhibit varying relevance for the task, very noisy and/or irrelevant relations may deteriorate the performance. As a result, an effective weighing scheme needs to be put in place. In this work, we propose a robust and scalable approach for multi-relational graph-regularized semi-supervised classification. Under a convex optimization scheme, we simultaneously infer weights for the multiple graphs as well as a solution. We provide a careful analysis of the inferred weights, based on which we devise an algorithm that filters out irrelevant and noisy graphs and produces weights proportional to the informativeness of the remaining graphs. Moreover, the proposed method is linearly scalable w.r.t. the number of edges in the union of the multiple graphs. Through extensive experiments we show that our method yields superior results under different noise models, and under increasing number of noisy graphs and intensity of noise, as compared to a list of baselines and state-of-the-art approaches.
Matching Mechanisms For Real-Time Computational Resource Exchange Markets
Venue: arXiv cs.GT 2015
Year: 2015

In this paper we describe matching mechanisms for a real-time computational resource exchange market, Chital, that incentivizes participating clients to perform computation for their peers in exchange for overall improved performance. The system is designed to discourage dishonest behavior via a credit system, while simultaneously minimizing the use of dedicated computing servers and the number of verifications performed by the administrating servers. We describe the system in the context of a pre-existing system (under development), Vedalia \cite{715Project}, for analyzing and visualizing product reviews, by using machine learning such as topic models. We extend this context to general computing tasks, describe a list of matching algorithms, and evaluate their performance in a simulated environment. In addition, we design a matching algorithm that optimizes the amount of time a participant could save compared to computing a task on their own, and show empirically that this algorithm results in a situation in which it is almost always optimal for a user to join the exchange than do computation alone. Lastly, we use a top-down approach to derive a theoretically near-optimal matching algorithm under certain distributional assumptions on query frequency.
Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment
Venue: arXiv cs.OH 2015
Year: 2015

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future.
Efficient Influence Maximization in Weighted Independent Cascade Model
Venue: arXiv cs.SI 2015
Year: 2015

Influence maximization(IM) problem is to find a seed set in a social network which achieves the maximal influence spread. This problem plays an important role in viral marketing. Numerous models have been proposed to solve this problem. However, none of them considers the attributes of nodes. Paying all attention to the structure of network causes some trouble applying these models to real-word applications. Motivated by this, we present weighted independent cascade (WIC) model, a novel cascade model which extends the applicability of independent cascade(IC) model by attaching attributes to the nodes. The IM problem in WIC model is to maximize the value of nodes which are influenced. This problem is NP-hard. To solve this problem, we present a basic greedy algorithm and Weight Reset(WR) algorithm. Moreover, we propose Bounded Weight Reset(BWR) algorithm to make further effort to improve the efficiency by bounding the diffusion node influence. We prove that BWR is a fully polynomial-time approximation scheme(FPTAS). Experimentally, we show that with additional node attribute, the solution achieved by WIC model outperforms that of IC model in nearly 90%. The experimental results show that BWR can achieve excellent approximation and faster than greedy algorithm more than three orders of magnitude with little sacrifice of accuracy. Especially, BWR can handle large networks with millions of nodes in several tens of seconds while keeping rather high accuracy. Such result demonstrates that BWR can solve IM problem effectively and efficiently.
Radio Resource Allocation in LTE-Advanced Cellular Networks with M2M Communications
Venue: arXiv cs.IT 2015
Year: 2015

Machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines without the need of human intervention. To support such a large number of autonomous devices, the M2M system architecture needs to be extremely power and spectrally efficient. This article thus briefly reviews the features of M2M services in the third generation (3G) long-term evolution and its advancement (LTE-Advanced) networks. Architectural enhancements are then presented for supporting M2M services in LTE-Advanced cellular networks. To increase spectral efficiency, the same spectrum is expected to be utilized for human-to-human (H2H) communications as well as M2M communications. We therefore present various radio resource allocation schemes and quantify their utility in LTE-Advanced cellular networks. System-level simulation results are provided to validate the performance effectiveness of M2M communications in LTE-Advanced cellular networks.
Execution of Compound Multi-Kernel OpenCL Computations in Multi-CPU/Multi-GPU Environments
Venue: arXiv cs.DC 2015
Year: 2015

Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of these hybrid systems as a whole. The distinct nature of the architectural and execution models in place raises several challenges, as the best hardware configuration is behaviour and workload dependent. In this paper, we address the execution of compound, multi-kernel, OpenCL computations in multi-CPU/multi-GPU environments. We address how these computations may be efficiently scheduled onto the target hardware, and how the system may adapt itself to changes in the workload to process and to fluctuations in the CPU's load. An experimental evaluation attests the performance gains obtained by the conjoined use of the CPU and GPU devices, when compared to GPU-only executions, and also by the use of data-locality optimizations in CPU environments.
Network-on-Chip with load balancing based on interleave of flits technique
Venue: arXiv cs.AR 2015
Year: 2015

This paper presents the evaluation of a Network-on-Chip (NoC) that offers load balancing for Systems-on-Chip (SoCs) dedicated for multimedia applications that require high traffic of variable bitrate communication. The NoC is based on a technique that allows the interleaving of flits from diferente flows in the same communication channel, and keep the load balancing without a centralized control in the network. For this purpose, all flits in the network received extra bits, such that every flit carries routing information. The routers use this extra information to perform arbitration and schedule the flits to the corresponding output ports. Analytic comparisons and experimental data show that the approach adopted in the network keeps average latency lower for variable bitrate flows than a network based on resource reservation when both networks are working over 80% of offered load.