OpenRaiser/Intern-Atlas huggingface dataset:
Improving the Performance of Nested Lattice Codes Using Concatenation
Venue: arXiv cs.IT 2016
Year: 2016

A fundamental problem in coding theory is the design of an efficient coding scheme that achieves the capacity of the additive white Gaussian (AWGN) channel. The main objective of this short note is to point out that by concatenating a capacity-achieving nested lattice code with a suitable high-rate linear code over an appropriate finite field, we can achieve the capacity of the AWGN channel with polynomial encoding and decoding complexity. Specifically, we show that using inner Construction-A lattice codes and outer Reed-Solomon codes, we can obtain capacity-achieving codes whose encoding and decoding complexities grow as $O(N^2)$, while the probability of error decays exponentially in $N$, where $N$ denotes the blocklength. Replacing the outer Reed-Solomon code by an expander code helps us further reduce the decoding complexity to $O(N\log^2N)$. This also gives us a recipe for converting a high-complexity nested lattice code for a Gaussian channel to a low-complexity concatenated code without any loss in the asymptotic rate. As examples, we describe polynomial-time coding schemes for the wiretap channel, and the compute-and-forward scheme for computing integer linear combinations of messages.
The SVM Classifier Based on the Modified Particle Swarm Optimization
Venue: arXiv cs.LG 2016
Year: 2016

The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particles' «regeneration» is put on the basis of the modified particle swarm optimization algorithm. At the realization of this idea, some particles change their kernel function type to the one which corresponds to the particle with the best value of the classification accuracy. The offered particle swarm optimization algorithm allows reducing the time expenditures for development of the SVM classifier. The results of experimental studies confirm the efficiency of this algorithm.
Active Cyber Defense Dynamics Exhibiting Rich Phenomena
Venue: arXiv cs.CR 2016
Year: 2016

The Internet is a man-made complex system under constant attacks (e.g., Advanced Persistent Threats and malwares). It is therefore important to understand the phenomena that can be induced by the interaction between cyber attacks and cyber defenses. In this paper, we explore the rich phenomena that can be exhibited when the defender employs active defense to combat cyber attacks. To the best of our knowledge, this is the first study that shows that {\em active cyber defense dynamics} (or more generally, {\em cybersecurity dynamics}) can exhibit the bifurcation and chaos phenomena. This has profound implications for cyber security measurement and prediction: (i) it is infeasible (or even impossible) to accurately measure and predict cyber security under certain circumstances; (ii) the defender must manipulate the dynamics to avoid such {\em unmanageable situations} in real-life defense operations.
Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention
Venue: arXiv cs.CV 2016
Year: 2016

This paper focuses on two key problems for audio-visual emotion recognition in the video. One is the audio and visual streams temporal alignment for feature level fusion. The other one is locating and re-weighting the perception attentions in the whole audio-visual stream for better recognition. The Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is employed as the main classification architecture. Firstly, soft attention mechanism aligns the audio and visual streams. Secondly, seven emotion embedding vectors, which are corresponding to each classification emotion type, are added to locate the perception attentions. The locating and re-weighting process is also based on the soft attention mechanism. The experiment results on EmotiW2015 dataset and the qualitative analysis show the efficiency of the proposed two techniques.
Adaptive-Latency DRAM (AL-DRAM)
Venue: arXiv cs.AR 2016
Year: 2016

This paper summarizes the idea of Adaptive-Latency DRAM (AL-DRAM), which was published in HPCA 2015. The key goal of AL-DRAM is to exploit the extra margin that is built into the DRAM timing parameters to reduce DRAM latency. The key observation is that the timing parameters are dictated by the worst-case temperatures and worst-case DRAM cells, both of which lead to small amount of charge storage and hence high access latency. One can therefore reduce latency by adapting the timing parameters to the current operating temperature and the current DIMM that is being accessed. Using an FPGA-based testing platform, our work first characterizes the extra margin for 115 DRAM modules from three major manufacturers. The experimental results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55C while maintaining reliable operation. AL-DRAM adaptively selects between multiple different timing parameters for each DRAM module based on its current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface; it only requires multiple different timing parameters to be specified and supported by the memory controller. Real system evaluations show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors.
Survey of Important Issues in UAV Communication Networks
Venue: arXiv cs.NI 2016
Year: 2016

Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications where human lives would otherwise be endangered. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network leads to the requirement of seamless handovers where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute towards greening of the network. This article surveys the work done towards all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.
Exploring Local Context for Multi-target Tracking in Wide Area Aerial Surveillance
Venue: arXiv cs.CV 2016
Year: 2016

Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association methods by coupling it with a local context tracker (LCT), which does not rely on motion detections. On one hand, our LCT learns neighboring spatial relation and tracks each target in consecutive frames using graph optimization. It takes the advantage of context constraints to avoid drifting to nearby targets. We generate hypotheses from sparse and dense flow efficiently to keep solutions tractable. On the other hand, we use detection association strategy to extract short tracks in batch processing. We explicitly handle merged detections by generating additional hypotheses from them. Our evaluation on wide area aerial imagery sequences shows significant improvement over state-of-the-art methods.
The Conditional Lucas & Kanade Algorithm
Venue: arXiv cs.CV 2016
Year: 2016

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.
Validating an Approach to Formalize Use Cases with Ontologies
Venue: arXiv cs.SE 2016
Year: 2016

Use case driven development methodologies put use cases at the center of the software development process. However, in order to support automated development and analysis, use cases need to be appropriately formalized. This will also help guarantee consistency between requirements specifications and the developed solutions. Formal methods tend to suffer from take up issues, as they are usually hard to accept by industry. In this context, it is relevant not only to produce languages and approaches to support formalization, but also to perform their validation. In previous works we have developed an approach to formalize use cases resorting to ontologies. In this paper we present the validation of one such approach. Through a three stage study, we evaluate the acceptance of the language and supporting tool. The first stage focusses on the acceptance of the process and language, the second on the support the tool provides to the process, and finally the third one on the tool's usability aspects. Results show test subjects found the approach feasible and useful and the tool easy to use.
Towards an Automated Requirements-driven Development of Smart Cyber-Physical Systems
Venue: arXiv cs.SE 2016
Year: 2016

The Invariant Refinement Method for Self Adaptation (IRM-SA) is a design method targeting development of smart Cyber-Physical Systems (sCPS). It allows for a systematic translation of the system requirements into the system architecture expressed as an ensemble-based component system (EBCS). However, since the requirements are captured using natural language, there exists the danger of their misinterpretation due to natural language requirements' ambiguity, which could eventually lead to design errors. Thus, automation and validation of the design process is desirable. In this paper, we (i) analyze the translation process of natural language requirements into the IRM-SA model, (ii) identify individual steps that can be automated and/or validated using natural language processing techniques, and (iii) propose suitable methods.
Rearrangement Planning via Heuristic Search
Venue: arXiv cs.RO 2016
Year: 2016

We present a method to apply heuristic search algorithms to solve rearrangement planning by pushing problems. In these problems, a robot must push an object through clutter to achieve a goal. To do this, we exploit the fact that contact with objects in the environment is critical to goal achievement. We dynamically generate goal-directed primitives that create and maintain contact between robot and object at each state expansion during the search. These primitives focus exploration toward critical areas of state-space, providing tractability to the high-dimensional planning problem. We demonstrate that the use of these primitives, combined with an informative yet simple to compute heuristic, improves success rate when compared to a planner that uses only primitives formed from discretizing the robot's action space. In addition, we show our planner outperforms RRT-based approaches by producing shorter paths faster. We demonstrate our algorithm both in simulation and on a 7-DOF arm pushing objects on a table.
Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

This paper studies the problem of robust spectrum-aware routing in a multi-hop, multi-channel Cognitive Radio Network (CRN) with the presence of malicious nodes in the secondary network. The proposed routing scheme models the interaction among the Secondary Users (SUs) as a stochastic game. By allowing the backward propagation of the path utility information from the next-hop nodes, the stochastic routing game is decomposed into a series of stage games. The best-response policies are learned through the process of smooth fictitious play, which is guaranteed to converge without flooding of the information about the local utilities and behaviors. To address the problem of mixed insider attacks with both routing-toward-primary and sink-hole attacks, the trustworthiness of the neighbor nodes is evaluated through a multi-arm bandit process for each SU. The simulation results show that the proposed routing algorithm is able to enforce the cooperation of the malicious SUs and reduce the negative impact of the attacks on the routing selection process.
A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity
Venue: arXiv cs.CL 2016
Year: 2016

Corpora and web texts can become a rich language learning resource if we have a means of assessing whether they are linguistically appropriate for learners at a given proficiency level. In this paper, we aim at addressing this issue by presenting the first approach for predicting linguistic complexity for Swedish second language learning material on a 5-point scale. After showing that the traditional Swedish readability measure, Läsbarhetsindex (LIX), is not suitable for this task, we propose a supervised machine learning model, based on a range of linguistic features, that can reliably classify texts according to their difficulty level. Our model obtained an accuracy of 81.3% and an F-score of 0.8, which is comparable to the state of the art in English and is considerably higher than previously reported results for other languages. We further studied the utility of our features with single sentences instead of full texts since sentences are a common linguistic unit in language learning exercises. We trained a separate model on sentence-level data with five classes, which yielded 63.4% accuracy. Although this is lower than the document level performance, we achieved an adjacent accuracy of 92%. Furthermore, we found that using a combination of different features, compared to using lexical features alone, resulted in 7% improvement in classification accuracy at the sentence level, whereas at the document level, lexical features were more dominant. Our models are intended for use in a freely accessible web-based language learning platform for the automatic generation of exercises.
Betting and Belief: Prediction Markets and Attribution of Climate Change
Venue: arXiv cs.MA 2016
Year: 2016

Despite much scientific evidence, a large fraction of the American public doubts that greenhouse gases are causing global warming. We present a simulation model as a computational test-bed for climate prediction markets. Traders adapt their beliefs about future temperatures based on the profits of other traders in their social network. We simulate two alternative climate futures, in which global temperatures are primarily driven either by carbon dioxide or by solar irradiance. These represent, respectively, the scientific consensus and a hypothesis advanced by prominent skeptics. We conduct sensitivity analyses to determine how a variety of factors describing both the market and the physical climate may affect traders' beliefs about the cause of global climate change. Market participation causes most traders to converge quickly toward believing the "true" climate model, suggesting that a climate market could be useful for building public consensus.
RemIX: A Distributed Internet Exchange for Remote and Rural Networks
Venue: arXiv cs.NI 2016
Year: 2016

The concept of the IXP, an Ethernet fabric central to the structure of the global Internet, is largely absent from the development of community-driven collaborative network infrastructure. The reasons for this are two-fold. IXPs exist in central, typically urban, environments where strong network infrastructure ensures high levels of connectivity. Between rural and remote regions, where networks are separated by distance and terrain, no such infrastructure exists. In this paper we present RemIX a distributed IXPs architecture designed for the community network environment. We examine this praxis using an implementation in Scotland, with suggestions for future development and research.
Routing under Balance
Venue: arXiv cs.DS 2016
Year: 2016

We introduce the notion of balance for directed graphs: a weighted directed graph is $α$-balanced if for every cut $S \subseteq V$, the total weight of edges going from $S$ to $V\setminus S$ is within factor $α$ of the total weight of edges going from $V\setminus S$ to $S$. Several important families of graphs are nearly balanced, in particular, Eulerian graphs (with $α= 1$) and residual graphs of $(1+ε)$-approximate undirected maximum flows (with $α=O(1/ε)$). We use the notion of balance to give a more fine-grained understanding of several well-studied routing questions that are considerably harder in directed graphs. We first revisit oblivious routings in directed graphs. Our main algorithmic result is an oblivious routing scheme for single-source instances that achieve an $O(α\cdot \log^3 n / \log \log n)$ competitive ratio. In the process, we make several technical contributions which may be of independent interest. In particular, we give an efficient algorithm for computing low-radius decompositions of directed graphs parameterized by balance. We also define and construct low-stretch arborescences, a generalization of low-stretch spanning trees to directed graphs. On the negative side, we present new lower bounds for oblivious routing problems on directed graphs. We show that the competitive ratio of oblivious routing algorithms for directed graphs is $Ω(n)$ in general; this result improves upon the long-standing best known lower bound of $Ω(\sqrt{n})$ given by Hajiaghayi, Kleinberg, Leighton and Räcke in 2006. We also show that our restriction to single-source instances is necessary by showing an $Ω(\sqrt{n})$ lower bound for multiple-source oblivious routing in Eulerian graphs. We also give a fast algorithm for the maximum flow problem in balanced directed graphs.
A framework for event co-occurrence detection in event streams
Venue: arXiv cs.MM 2016
Year: 2016

This paper shows that characterizing co-occurrence between events is an important but non-trivial and neglected aspect of discovering potential causal relationships in multimedia event streams. First an introduction to the notion of event co-occurrence and its relation to co-occurrence pattern detection is given. Then a finite state automaton extended with a time model and event parameterization is introduced to convert high level co-occurrence pattern definition to its corresponding pattern matching automaton. Finally a processing algorithm is applied to count the occurrence frequency of a collection of patterns with only one pass through input event streams. The method proposed in this paper can be used for detecting co-occurrences between both events of one event stream (Auto co-occurrence), and events from multiple event streams (Cross co-occurrence). Some fundamental results concerning the characterization of event co-occurrence are presented in form of a visual co- occurrence matrix. Reusable causality rules can be extracted easily from co-occurrence matrix and fed into various analysis tools, such as recommendation systems and complex event processing systems for further analysis.
Back to the past: on the shoulders of an academic search engine giant
Venue: arXiv cs.DL 2016
Year: 2016

A study released by the Google Scholar team found an apparently increasing fraction of citations to old articles from studies published in the last 24 years (1990-2013). To demonstrate this finding we conducted a complementary study using a different data source (Journal Citation Reports), metric (aggregate cited half-life), time spam (2003-2013), and set of categories (53 Social Science subject categories and 167 Science subject categories). Although the results obtained confirm and reinforce the previous findings, the possible causes of this phenomenon keep unclear. We finally hypothesize that first page results syndrome in conjunction with the fact that Google Scholar favours the most cited documents are suggesting the growing trend of citing old documents is partly caused by Google Scholar.
Spider and the Flies : Focused Crawling on Tumblr to Detect Hate Promoting Communities
Venue: arXiv cs.SI 2016
Year: 2016

Tumblr is one of the largest and most popular microblogging website on the Internet. Studies shows that due to high reachability among viewers, low publication barriers and social networking connectivity, microblogging websites are being misused as a platform to post hateful speech and recruiting new members by existing extremist groups. Manual identification of such posts and communities is overwhelmingly impractical due to large amount of posts and blogs being published every day. We propose a topic based web crawler primarily consisting of multiple phases: training a text classifier model consisting examples of only hate promoting users, extracting posts of an unknown tumblr micro-blogger, classifying hate promoting bloggers based on their activity feeds, crawling through the external links to other bloggers and performing a social network analysis on connected extremist bloggers. To investigate the effectiveness of our approach, we conduct experiments on large real world dataset. Experimental results reveals that the proposed approach is an effective method and has an F-score of 0.80. We apply social network analysis based techniques and identify influential and core bloggers in a community.