OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Online Real-time Classifier for Multi-label Data Streams
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In contrast to traditional binary and multi-class classification, multi-label classification involves association of each of the input samples with a set of target labels simultaneously. There are no real-time online neural network based multi-label classifier available in the literature. In this paper, we exploit the inherent nature of high speed exhibited by the extreme learning machines to develop a novel online real-time classifier for multi-label data streams. The developed classifier is experimented with datasets from different application domains for consistency, performance and speed. The experimental studies show that the proposed method outperforms the existing state-of-the-art techniques in terms of speed and accuracy and can classify multi-label data streams in real-time.
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks
Venue: arXiv cs.LG 2016
Year: 2016

We revisit the elegant observation of T. Cover '65 which, perhaps, is not as well-known to the broader community as it should be. The first goal of the tutorial is to explain---through the prism of this elementary result---how to solve certain sequence prediction problems by modeling sets of solutions rather than the unknown data-generating mechanism. We extend Cover's observation in several directions and focus on computational aspects of the proposed algorithms. The applicability of the methods is illustrated on several examples, including node classification in a network. The second aim of this tutorial is to demonstrate the following phenomenon: it is possible to predict as well as a combinatorial "benchmark" for which we have a certain multiplicative approximation algorithm, even if the exact computation of the benchmark given all the data is NP-hard. The proposed prediction methods, therefore, circumvent some of the computational difficulties associated with finding the best model given the data. These difficulties arise rather quickly when one attempts to develop a probabilistic model for graph-based or other problems with a combinatorial structure.
A novel online multi-label classifier for high-speed streaming data applications
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label classification. Multi-label classification problems are far more complex than binary and multi-class classification problems, as both the number of target labels and each of the target labels corresponding to each of the input samples are to be identified. The proposed work exploits the high-speed nature of the extreme learning machines to achieve real-time multi-label classification of streaming data. A new threshold-based online sequential learning algorithm is proposed for high speed and streaming data classification of multi-label problems. The proposed method is experimented with six different datasets from different application domains such as multimedia, text, and biology. The hamming loss, accuracy, training time and testing time of the proposed technique is compared with nine different state-of-the-art methods. Experimental studies shows that the proposed technique outperforms the existing multi-label classifiers in terms of performance and speed.
Approximate Bisimulation and Discretization of Hybrid CSP
Venue: arXiv cs.LO 2016
Year: 2016

Hybrid Communicating Sequential Processes (HCSP) is a powerful formal modeling language for hybrid systems, which is an extension of CSP by introducing differential equations for modeling continuous evolution and interrupts for modeling interaction between continuous and discrete dynamics. In this paper, we investigate the semantic foundation for HCSP from an operational point of view by proposing notion of approximate bisimulation, which provides an appropriate criterion to characterize the equivalence between HCSP processes with continuous and discrete behaviour. We give an algorithm to determine whether two HCSP processes are approximately bisimilar. In addition, based on that, we propose an approach on how to discretize HCSP, i.e., given an HCSP process A, we construct another HCSP process B which does not contain any continuous dynamics such that A and B are approximately bisimilar with given precisions. This provides a rigorous way to transform a verified control model to a correct program model, which fills the gap in the design of embedded systems.
Suspicious-Taint-Based Access Control for Protecting OS from Network Attacks
Venue: arXiv cs.CR 2016
Year: 2016

Today, security threats to operating systems largely come from network. Traditional discretionary access control mechanism alone can hardly defeat them. Although traditional mandatory access control models can effectively protect the security of OS, they have problems of being incompatible with application software and complex in administration. In this paper, we propose a new model, Suspicious-Taint-Based Access Control (STBAC) model, for defeating network attacks while being compatible, simple and maintaining good system performance. STBAC regards the processes using Non-Trustable-Communications as the starting points of suspicious taint, traces the activities of the suspiciously tainted processes by taint rules, and forbids the suspiciously tainted processes to illegally access vital resources by protection rules. Even in the cases when some privileged processes are subverted, STBAC can still protect vital resources from being compromised by the intruder. We implemented the model in the Linux kernel and evaluated it through experiments. The evaluation showed that STBAC could protect vital resources effectively without significant impact on compatibility and performance.
The Complexity of (List) Edge-Coloring Reconfiguration Problem
Venue: arXiv cs.DM 2016
Year: 2016

Let $G$ be a graph such that each edge has its list of available colors, and assume that each list is a subset of the common set consisting of $k$ colors. Suppose that we are given two list edge-colorings $f_0$ and $f_r$ of $G$, and asked whether there exists a sequence of list edge-colorings of $G$ between $f_0$ and $f_r$ such that each list edge-coloring can be obtained from the previous one by changing a color assignment of exactly one edge. This problem is known to be PSPACE-complete for every integer $k \ge 6$ and planar graphs of maximum degree three, but any complexity hardness was unknown for the non-list variant. In this paper, we first improve the known result by proving that, for every integer $k \ge 4$, the problem remains PSPACE-complete even if an input graph is planar, bounded bandwidth, and of maximum degree three. We then give the first complexity hardness result for the non-list variant: for every integer $k \ge 5$, we prove that the non-list variant is PSPACE-complete even if an input graph is planar, of bandwidth linear in $k$, and of maximum degree $k$.
Transparent Live Code Offloading on FPGA
Venue: arXiv cs.DC 2016
Year: 2016

Even though it seems that FPGAs have finally made the transition from research labs to the consumer devices' market, programming them remains challenging. Despite the improvements made by High-Level Synthesis (HLS), which removed the language and paradigm barriers that prevented many computer scientists from working with them, producing a new design typically requires at least several hours, making data- and context-dependent adaptations virtually impossible. In this paper we present a new framework that off-loads, on-the-fly and transparently to both the user and the developer, computationally-intensive code fragments to FPGAs. While the performance should not surpass that of hand-crafted HDL code, or even code produced by HLS, our results come with no additional development costs and do not require producing and deploying a new bit-stream to the FPGA each time a change is made. Moreover, since optimizations are made at run-time, they may fit particular datasets or usage scenarios, something which is rarely foreseeable at design or compile time. Our proposal revolves around an overlay architecture that is pre-programmed on the FPGA and dynamically reconfigured by our framework to execute code fragments extracted from the Data Flow Graph (DFG) of computational intensive routines. We validated our solution using standard benchmarks and proved we are able to off-load to FPGAs without developer's intervention.
Parallel Clustering of Graphs for Anonymization and Recommender Systems
Venue: arXiv cs.DS 2016
Year: 2016

Graph clustering is widely used in many data analysis applications. In this paper we propose several parallel graph clustering algorithms based on Monte Carlo simulations and expectation maximization in the context of stochastic block models. We apply those algorithms to the specific problems of recommender systems and social network anonymization. We compare the experimental results to previous propositions.
Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4', 10', 20' and 40' time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.
Consensus over Weighted Directed Graphs: A Robustness Perspective
Venue: arXiv cs.SY 2016
Year: 2016

The present paper investigates the robustness of the consensus protocol over weighted directed graphs using the Nyquist criterion. The limit to which a single weight can vary, while consensus among the agents can be achieved, is explicitly derived. It is shown that even with a negative weight on one of the edges, consensus may be achieved. The result obtained in this paper is applied to a directed acyclic graph and to the directed cycle graph. Graph theoretic interpretations of the limits are provided for the two cases. Simulations support the theoretical results.
Sampling-based verification of Lyapunov's inequality for piecewise continuous nonlinear systems
Venue: arXiv cs.SY 2016
Year: 2016

This paper considers a sampling-based approach to stability verification for piecewise continuous nonlinear systems via Lyapunov functions. Depending on the system dynamics, the candidate Lyapunov function and the set of initial states of interest, one generally needs to handle large, possibly non-convex or non-feasible optimization problems. To avoid such problems, we propose a constructive and systematically applicable sampling-based method to Lyapunov's inequality verification. This approach proposes verification of the decrease condition for a candidate Lyapunov function on a finite sampling of a bounded set of initial conditions and then it extends the validity of the Lyapunov function to an infinite set of initial conditions by automatically exploiting continuity properties. This result is based on multi-resolution sampling, to perform efficient state- space exploration. Using hyper-rectangles as basic sampling blocks, to account for different constraint scales on different states, further reduces the amount of samples to be verified. Moreover, the verification is decentralized in the sampling points, which makes the method scalable. The proposed methodology is illustrated through examples.
Open Call-by-Value (Extended Version)
Venue: arXiv cs.LO 2016
Year: 2016

The elegant theory of the call-by-value lambda-calculus relies on weak evaluation and closed terms, that are natural hypotheses in the study of programming languages. To model proof assistants, however, strong evaluation and open terms are required, and it is well known that the operational semantics of call-by-value becomes problematic in this case. Here we study the intermediate setting -- that we call Open Call-by-Value -- of weak evaluation with open terms, on top of which Grégoire and Leroy designed the abstract machine of Coq. Various calculi for Open Call-by-Value already exist, each one with its pros and cons. This paper presents a detailed comparative study of the operational semantics of four of them, coming from different areas such as the study of abstract machines, denotational semantics, linear logic proof nets, and sequent calculus. We show that these calculi are all equivalent from a termination point of view, justifying the slogan Open Call-by-Value.
New Rain Rate Statistics for Emerging Regions: Implications for Wireless Backhaul Planning
Venue: arXiv cs.NI 2016
Year: 2016

As demand for broadband service increases in emerging regions, high-capacity wireless links can accelerate and cost-reduce the deployment of new networks (both backhaul and customer site connection). Such links are increasingly common in developed countries, but their reliability in emerging regions is questioned where very heavy tropical rain is present. Here, we investigate the robustness of the standard (ITU-R P.837-6) method for estimating rain rates using an expanded test dataset. We illustrate how bias/variance issues cause problematic predictions at higher rain rates. We confirm (by construction) that an improved rainfall climatology can largely address these prediction issues without compromising standard ITU fit evaluation metrics.
A case study of algorithm selection for the traveling thief problem
Venue: arXiv cs.AI 2016
Year: 2016

Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain
Venue: arXiv cs.IR 2016
Year: 2016

This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
868 MHz Wireless Sensor Network - A Study
Venue: arXiv cs.NI 2016
Year: 2016

Today 2.4 GHz based wireless sensor networks are increasing at a tremendous pace, and are seen in widespread applications. Product innovation and support by many vendors in 2.4 GHz makes it a preferred choice, but the networks are prone to issues like interference, and range issues. On the other hand, the less popular 868 MHz in the ISM band has not seen significant usage. In this paper we explore the use of 868 MHz channel to implement a wireless sensor network, and study the efficacy of this channel
Simultaneous Information and Energy Flow for IoT Relay Systems with Crowd Harvesting
Venue: arXiv cs.IT 2016
Year: 2016

It is expected that the number of wireless devices will grow rapidly over the next few years due to the growing proliferation of Internet-of-Things (IoT). In order to improve the energy efficiency of information transfer between small devices, we review state-of-the-art research in simultaneous wireless energy and information transfer, especially for relay based IoT systems. In particular, we analyze simultaneous information-and-energy transfer from the source node, and the design of time-switching and power-splitting operation modes, as well as the associated optimization algorithms. We also investigate the potential of crowd energy harvesting from transmission nodes that belong to multiple radio networks. The combination of source and crowd energy harvesting can greatly reduce the use of battery power and increase the availability and reliability for relaying. We provide insight into the fundamental limits of crowd energy harvesting reliability based on a case study using real city data. Furthermore, we examine the optimization of transmissions in crowd harvesting, especially with the use of node collaboration while guaranteeing Quality-of-Service (QoS).
The 3rd Reactive Synthesis Competition (SYNTCOMP 2016): Benchmarks, Participants & Results
Venue: arXiv cs.LO 2016
Year: 2016

We report on the benchmarks, participants and results of the third reactive synthesis competition(SYNTCOMP 2016). The benchmark library of SYNTCOMP 2016 has been extended to benchmarks in the new LTL-based temporal logic synthesis format (TLSF), and 2 new sets of benchmarks for the existing AIGER-based format for safety specifications. The participants of SYNTCOMP 2016 can be separated according to these two classes of specifications, and we give an overview of the 6 tools that entered the competition in the AIGER-based track, and the 3 participants that entered the TLSF-based track. We briefly describe the benchmark selection, evaluation scheme and the experimental setup of SYNTCOMP 2016. Finally, we present and analyze the results of our experimental evaluation, including a comparison to participants of previous competitions and a legacy tool.
On Profiling Bots in Social Media
Venue: arXiv cs.SI 2016
Year: 2016

The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling framework that includes a rich set of features and classifier bank. We conduct extensive experiments to evaluate the performances of different classifiers under varying time windows, identify the key features of bots, and infer about bots in a larger Twitter population. Our analysis encompasses more than 159K bot and human (non-bot) accounts in Twitter. The results provide interesting insights on the behavioral traits of both benign and malicious bots.
Doubly stochastic large scale kernel learning with the empirical kernel map
Venue: arXiv cs.LG 2016
Year: 2016

With the rise of big data sets, the popularity of kernel methods declined and neural networks took over again. The main problem with kernel methods is that the kernel matrix grows quadratically with the number of data points. Most attempts to scale up kernel methods solve this problem by discarding data points or basis functions of some approximation of the kernel map. Here we present a simple yet effective alternative for scaling up kernel methods that takes into account the entire data set via doubly stochastic optimization of the emprical kernel map. The algorithm is straightforward to implement, in particular in parallel execution settings; it leverages the full power and versatility of classical kernel functions without the need to explicitly formulate a kernel map approximation. We provide empirical evidence that the algorithm works on large data sets.
Modeling, refining and analyzing Incomplete Büchi Automata
Venue: arXiv cs.FL 2016
Year: 2016

Software development is an iterative process which includes a set of development steps that transform the initial high level specification of the system into its final, fully specified, implementation. This report discusses the theoretical foundations that allow Incomplete Büchi Automata (IBAs) to be used in the iterative development of a sequential system.
Reprowd: Crowdsourced Data Processing Made Reproducible
Venue: arXiv cs.DB 2016
Year: 2016

Crowdsourcing is a multidisciplinary research area including disciplines like artificial intelligence, human-computer interaction, database, and social science. To facilitate cooperation across disciplines, reproducibility is a crucial factor, but unfortunately, it has not gotten enough attention in the HCOMP community. In this paper, we present Reprowd, a system aiming to make it easy to reproduce crowdsourced data processing research. We have open sourced Reprowd at http://sfu-db.github.io/reprowd/.