OpenRaiser/Intern-Atlas huggingface dataset:
On the Semantics and Complexity of Probabilistic Logic Programs
Venue: arXiv cs.AI 2017
Year: 2017

We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics, respectively based on stable and on well-founded models. We show that the semantics based on stable models (referred to as the "credal semantics") produces sets of probability models that dominate infinitely monotone Choquet capacities, we describe several useful consequences of this result. We then examine the complexity of inference with probabilistic logic programs. We distinguish between the complexity of inference when a probabilistic program and a query are given (the inferential complexity), and the complexity of inference when the probabilistic program is fixed and the query is given (the query complexity, akin to data complexity as used in database theory). We obtain results on the inferential and query complexity for acyclic, stratified, and cyclic propositional and relational programs, complexity reaches various levels of the counting hierarchy and even exponential levels.
Big Data Technology Accelerate Genomics Precision Medicine
Venue: arXiv cs.DB 2017
Year: 2017

During genomics life science research, the data volume of whole genomics and life science algorithm is going bigger and bigger, which is calculated as TB, PB or EB etc. The key problem will be how to store and analyze the data with optimized way. This paper demonstrates how Intel Big Data Technology and Architecture help to facilitate and accelerate the genomics life science research in data store and utilization. Intel defines high performance GenomicsDB for variant call data query and Lustre filesystem with Hierarchal Storage Management for genomics data store. Based on these great technology, Intel defines genomics knowledge share and exchange architecture, which is landed and validated in BGI China and Shanghai Children Hospital with very positive feedback. And these big data technology can definitely be scaled to much more genomics life science partners in the world.
Batch Incremental Shared Nearest Neighbor Density Based Clustering Algorithm for Dynamic Datasets
Venue: arXiv cs.DB 2017
Year: 2017

Incremental data mining algorithms process frequent updates to dynamic datasets efficiently by avoiding redundant computation. Existing incremental extension to shared nearest neighbor density based clustering (SNND) algorithm cannot handle deletions to dataset and handles insertions only one point at a time. We present an incremental algorithm to overcome both these bottlenecks by efficiently identifying affected parts of clusters while processing updates to dataset in batch mode. We show effectiveness of our algorithm by performing experiments on large synthetic as well as real world datasets. Our algorithm is up to four orders of magnitude faster than SNND and requires up to 60% extra memory than SNND while providing output identical to SNND.
Optimal Synthesis of Overconstrained 6R Linkages by Curve Evolution
Venue: arXiv cs.RO 2017
Year: 2017

The paper presents an optimal synthesis of overconstrained linkages, based on the factorization of rational curves (representing one parametric motions) contained in Study's quadric. The group of Euclidean displacements is embedded in a affine space where a metric between motions based on the homogeneous mass distribution of the end effector is used to evolve the curves such that they are fitted to a set of target poses. The metric will measure the distance (in Euclidean sense) between the two resulting vectors of the feature points displaced by the two motions. The evolution is driven by the normal velocity of the curve projected in the direction of the target points. In the end we present an example for the optimal synthesis of an overconstrained $6R$ linkage by choosing a set of target poses and explaining in steps how this approach is implemented.
Expansion of the Kullback-Leibler Divergence, and a new class of information metrics
Venue: arXiv cs.IT 2017
Year: 2017

Inferring and comparing complex, multivariable probability density functions is fundamental to problems in several fields, including probabilistic learning, network theory, and data analysis. Classification and prediction are the two faces of this class of problem. We take an approach here that simplifies many aspects of these problems by presenting a structured, series expansion of the Kullback-Leibler divergence - a function central to information theory - and devise a distance metric based on this divergence. Using the Möbius inversion duality between multivariable entropies and multivariable interaction information, we express the divergence as an additive series in the number of interacting variables, which provides a restricted and simplified set of distributions to use as approximation and with which to model data. Truncations of this series yield approximations based on the number of interacting variables. The first few terms of the expansion-truncation are illustrated and shown to lead naturally to familiar approximations, including the well-known Kirkwood superposition approximation. Truncation can also induce a simple relation between the multi-information and the interaction information. A measure of distance between distributions, based on Kullback-Leibler divergence, is then described and shown to be a true metric if properly restricted. The expansion is shown to generate a hierarchy of metrics and connects this work to information geometry formalisms. We give an example of the application of these metrics to a graph comparision problem that shows that the formalism can be applied to a wide range of network problems, provides a general approach for systematic approximations in numbers of interactions or connections, and a related quantitative metric.
Analysis of Unprotected Intersection Left-Turn Conflicts based on Naturalistic Driving Data
Venue: arXiv cs.SY 2017
Year: 2017

Analyzing and reconstructing driving scenarios is crucial for testing and evaluating automated vehicles. This research analyzed left turn / straight-driving conflicts at unprotected intersections by extracting actual vehicle motion data from a naturalistic driving database collected by the University of Michigan. Nearly 7,000 Left turn across path opposite direction (LTAP/OD) events involving heavy trucks and light vehicles were extracted and used to build a stochastic model of such LTAP/OD scenarios. Statistical analysis showed that vehicle type is a significant factor, whereas the change of season seems to have limited influence on the statistical nature of the conflict. The results can be used to build HAV testing environments to simulate the LTAP/OD crash cases in a stochastic manner, which is among the top NHTSA identified priority light-vehicle pre-crash scenarios.
Mining Anonymity: Identifying Sensitive Accounts on Twitter
Venue: arXiv cs.SI 2017
Year: 2017

We explore the feasibility of automatically finding accounts that publish sensitive content on Twitter. One natural approach to this problem is to first create a list of sensitive keywords, and then identify Twitter accounts that use these words in their tweets. But such an approach may overlook sensitive accounts that are not covered by the subjective choice of keywords. In this paper, we instead explore finding sensitive accounts by examining the percentage of anonymous and identifiable followers the accounts have. This approach is motivated by an earlier study showing that sensitive accounts typically have a large percentage of anonymous followers and a small percentage of identifiable followers. To this end, we first considered the problem of automatically determining if a Twitter account is anonymous or identifiable. We find that simple techniques, such as checking for name-list membership, perform poorly. We designed a machine learning classifier that classifies accounts as anonymous or identifiable. We then classified an account as sensitive based on the percentages of anonymous and identifiable followers the account has. We applied our approach to approximately 100,000 accounts with 404 million active followers. The approach uncovered accounts that were sensitive for a diverse number of reasons. These accounts span across varied themes, including those that are not commonly proposed as sensitive or those that relate to socially stigmatized topics. To validate our approach, we applied Latent Dirichlet Allocation (LDA) topic analysis to the tweets in the detected sensitive and non-sensitive accounts. LDA showed that the sensitive and non-sensitive accounts obtained from the methodology are tweeting about distinctly different topics. Our results show that it is indeed possible to objectively identify sensitive accounts at the scale of Twitter.
On the Futility of Learning Complex Frame-Level Language Models for Chord Recognition
Venue: arXiv cs.SD 2017
Year: 2017

Chord recognition systems use temporal models to post-process frame-wise chord preditions from acoustic models. Traditionally, first-order models such as Hidden Markov Models were used for this task, with recent works suggesting to apply Recurrent Neural Networks instead. Due to their ability to learn longer-term dependencies, these models are supposed to learn and to apply musical knowledge, instead of just smoothing the output of the acoustic model. In this paper, we argue that learning complex temporal models at the level of audio frames is futile on principle, and that non-Markovian models do not perform better than their first-order counterparts. We support our argument through three experiments on the McGill Billboard dataset. The first two show 1) that when learning complex temporal models at the frame level, improvements in chord sequence modelling are marginal; and 2) that these improvements do not translate when applied within a full chord recognition system. The third, still rather preliminary experiment gives first indications that the use of complex sequential models for chord prediction at higher temporal levels might be more promising.
Can SDN Accelerate BGP Convergence? A Performance Analysis of Inter-domain Routing Centralization
Venue: arXiv cs.NI 2017
Year: 2017

The Internet is composed of Autonomous Systems (ASes) or domains, i.e., networks belonging to different administrative entities. Routing between domains/ASes is realised in a distributed way, over the Border Gateway Protocol (BGP). Despite its global adoption, BGP has several shortcomings, like slow convergence after routing changes, which can cause packet losses and interrupt communication even for several minutes. To accelerate convergence, inter-domain routing centralization approaches, based on Software Defined Networking (SDN), have been recently proposed. Initial studies show that these approaches can significantly improve performance and routing control over BGP. In this paper, we complement existing system-oriented works, by analytically studying the gains of inter-domain SDN. We propose a probabilistic framework to analyse the effects of centralization on the inter-domain routing performance. We derive bounds for the time needed to establish data plane connectivity between ASes after a routing change, as well as predictions for the control-plane convergence time. Our results provide useful insights (e.g., related to the penetration of SDN in the Internet) that can facilitate future research. We discuss applications of our results, and demonstrate the gains through simulations on the Internet AS-topology.
Evolving Boxes for Fast Vehicle Detection
Venue: arXiv cs.CV 2017
Year: 2017

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.
Proof Diagrams for Multiplicative Linear Logic: Syntax and Semantics
Venue: arXiv cs.LO 2017
Year: 2017

Proof nets are a syntax for linear logic proofs which gives a coarser notion of proof equivalence with respect to syntactic equality together with an intuitive geometrical representation of proofs. In this paper we give an alternative $2$-dimensional syntax for multiplicative linear logic derivations. The syntax of string diagrams authorizes the definition of a framework where the sequentializability of a term, i.e. deciding whether the term corresponds to a correct derivation, can be verified in linear time. Furthermore, we can use this syntax to define a denotational semantics for multiplicative linear logic with units by means of equivalence classes of proof diagrams modulo a terminating rewriting.
Transaction Support over Redis: An Overview
Venue: arXiv cs.DC 2017
Year: 2017

This document outlines the approach to supporting cross-node transactions over a Redis cluster.
Towards the 5th Generation of Wireless Communication Systems
Venue: arXiv cs.NI 2017
Year: 2017

In this article, one first introduces the general landscape of the next generation of wireless communication systems (5G), including its drivers and requirements, and the candidate technologies that might help achieve its intended goals. The following areas, which the author considers to be of particular relevance for 5G, are then introduced: detection and access of free spectrum over bands of an heterogeneous nature, extreme densification of networks (massive base station deployments), extreme increase in the number of antennas in transmitter arrays and their interaction with a novel waveform, integration of both wireless and optical sides of telecom networks, and study of wireless networks using the magnifying glass provided by complex systems science. In particular, recent results from the author's research team are shortly discussed for each of these research areas.
The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness
Venue: arXiv cs.CC 2017
Year: 2017

Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is undergoing a resurgence of interest due to the need to analyze social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a "ground truth" community structure built into the graph somehow. The task is then to recover the ground truth knowing only the graph. Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to require exponential time. For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.
Single versus Double Blind Reviewing at WSDM 2017
Venue: arXiv cs.DL 2017
Year: 2017

In this paper we study the implications for conference program committees of using single-blind reviewing, in which committee members are aware of the names and affiliations of paper authors, versus double-blind reviewing, in which this information is not visible to committee members. WSDM 2017, the 10th ACM International ACM Conference on Web Search and Data Mining, performed a controlled experiment in which each paper was reviewed by four committee members. Two of these four reviewers were chosen from a pool of committee members who had access to author information; the other two were chosen from a disjoint pool who did not have access to this information. This information asymmetry persisted through the process of bidding for papers, reviewing papers, and entering scores. Reviewers in the single-blind condition typically bid for 22% fewer papers, and preferentially bid for papers from top institutions. Once papers were allocated to reviewers, single-blind reviewers were significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors and top institutions. The estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for top universities and companies respectively, so the result is tangible. For female authors, the associated odds multiplier of 0.78 is not statistically significant in our study. However, a meta-analysis places this value in line with that of other experiments, and in the context of this larger aggregate the gender effect is also statistically significant.
Learning to Compose with Professional Photographs on the Web
Venue: arXiv cs.CV 2017
Year: 2017

Photo composition is an important factor affecting the aesthetics in photography. However, it is a highly challenging task to model the aesthetic properties of good compositions due to the lack of globally applicable rules to the wide variety of photographic styles. Inspired by the thinking process of photo taking, we formulate the photo composition problem as a view finding process which successively examines pairs of views and determines their aesthetic preferences. We further exploit the rich professional photographs on the web to mine unlimited high-quality ranking samples and demonstrate that an aesthetics-aware deep ranking network can be trained without explicitly modeling any photographic rules. The resulting model is simple and effective in terms of its architectural design and data sampling method. It is also generic since it naturally learns any photographic rules implicitly encoded in professional photographs. The experiments show that the proposed view finding network achieves state-of-the-art performance with sliding window search strategy on two image cropping datasets.
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
Venue: arXiv cs.CV 2017
Year: 2017

We introduce a new, integrated approach to uncalibrated photometric stereo. We perform 3D reconstruction of Lambertian objects using multiple images produced by unknown, directional light sources. We show how to formulate a single optimization that includes rank and integrability constraints, allowing also for missing data. We then solve this optimization using the Alternate Direction Method of Multipliers (ADMM). We conduct extensive experimental evaluation on real and synthetic data sets. Our integrated approach is particularly valuable when performing photometric stereo using as few as 4-6 images, since the integrability constraint is capable of improving estimation of the linear subspace of possible solutions. We show good improvements over prior work in these cases.
Rethinking Information Sharing for Actionable Threat Intelligence
Venue: arXiv cs.CR 2017
Year: 2017

In the past decade, the information security and threat landscape has grown significantly making it difficult for a single defender to defend against all attacks at the same time. This called for introduc- ing information sharing, a paradigm in which threat indicators are shared in a community of trust to facilitate defenses. Standards for representation, exchange, and consumption of indicators are pro- posed in the literature, although various issues are undermined. In this paper, we rethink information sharing for actionable intelli- gence, by highlighting various issues that deserve further explo- ration. We argue that information sharing can benefit from well- defined use models, threat models, well-understood risk by mea- surement and robust scoring, well-understood and preserved pri- vacy and quality of indicators and robust mechanism to avoid free riding behavior of selfish agent. We call for using the differential nature of data and community structures for optimizing sharing.
Beyond Free Riding: Quality of Indicators for Assessing Participation in Information Sharing for Threat Intelligence
Venue: arXiv cs.CR 2017
Year: 2017

Threat intelligence sharing has become a growing concept, whereby entities can exchange patterns of threats with each other, in the form of indicators, to a community of trust for threat analysis and incident response. However, sharing threat-related information have posed various risks to an organization that pertains to its security, privacy, and competitiveness. Given the coinciding benefits and risks of threat information sharing, some entities have adopted an elusive behavior of "free-riding" so that they can acquire the benefits of sharing without contributing much to the community. So far, understanding the effectiveness of sharing has been viewed from the perspective of the amount of information exchanged as opposed to its quality. In this paper, we introduce the notion of quality of indicators (\qoi) for the assessment of the level of contribution by participants in information sharing for threat intelligence. We exemplify this notion through various metrics, including correctness, relevance, utility, and uniqueness of indicators. In order to realize the notion of \qoi, we conducted an empirical study and taken a benchmark approach to define quality metrics, then we obtained a reference dataset and utilized tools from the machine learning literature for quality assessment. We compared these results against a model that only considers the volume of information as a metric for contribution, and unveiled various interesting observations, including the ability to spot low quality contributions that are synonym to free riding in threat information sharing.