OpenRaiser/Intern-Atlas huggingface dataset:
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Venue: arXiv cs.AI 2017
Year: 2017

The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.
Consensus in the Age of Blockchains
Venue: arXiv cs.CR 2017
Year: 2017

The blockchain initially gained traction in 2008 as the technology underlying bitcoin, but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: first protocols based on proof-of-work (PoW), second proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and third hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
Bayesian Paragraph Vectors
Venue: arXiv cs.CL 2017
Year: 2017

Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences. In this work, we propose a novel interpretation for neural-network-based paragraph vectors by developing an unsupervised generative model whose maximum likelihood solution corresponds to traditional paragraph vectors. This probabilistic formulation allows us to go beyond point estimates of parameters and to perform Bayesian posterior inference. We find that the entropy of paragraph vectors decreases with the length of documents, and that information about posterior uncertainty improves performance in supervised learning tasks such as sentiment analysis and paraphrase detection.
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
Venue: arXiv cs.CV 2017
Year: 2017

This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is a U-Net like network that consists of a convolutional encoder-decoder followed by a pixel-wise classification layer. The output is a map with the same size of the input where pixels have the following labels \{'0': Non eddy, '1': anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU activation function instead of the classical ReLU+BN and we use an overlap based loss function instead of the cross entropy loss. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on https://github.com/redouanelg/EddyNet.
StreetX: Spatio-Temporal Access Control Model for Data
Venue: arXiv cs.DB 2017
Year: 2017

Cities are a big source of spatio-temporal data that is shared across entities to drive potential use cases. Many of the Spatio-temporal datasets are confidential and are selectively shared. To allow selective sharing, several access control models exist, however user cannot express arbitrary space and time constraints on data attributes using them. In this paper we focus on spatio-temporal access control model. We show that location and time attributes of data may decide its confidentiality via a motivating example and thus can affect user's access control policy. In this paper, we present StreetX which enables user to represent constraints on multiple arbitrary space regions and time windows using a simple abstract language. StreetX is scalable and is designed to handle large amount of spatio-temporal data from multiple users. Multiple space and time constraints can affect performance of the query and may also result in conflicts. StreetX automatically resolve conflicts and optimizes the query evaluation with access control to improve performance. We implemented and tested prototype of StreetX using space constraints by defining region having 1749 polygon coordinates on 10 million data records. Our testing shows that StreetX extends the current access control with spatio-temporal capabilities.
Energy Efficiency and Asymptotic Performance Evaluation of Beamforming Structures in Doubly Massive MIMO mmWave Systems
Venue: arXiv cs.IT 2017
Year: 2017

Future cellular systems based on the use of millimeter waves will heavily rely on the use of antenna arrays both at the transmitter and at the receiver. For complexity reasons and energy consumption issues, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated. This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for a cellular multiuser MIMO (MU-MIMO) system with large number of antennas. Analytical formulas for the asymptotic achievable spectral efficiency and for the global energy efficiency of several beamforming structures are derived in the large number of antennas regime. Using the most recently available data for the energy consumption of phase shifters and switches, we show that fully-digital beamformers may actually achieve a larger energy efficiency than lower-complexity solutions, as well as that low-complexity beam-steering purely analog beamforming may in some cases represent a good performance-complexity trade-off solution.
Optimised Maintenance of Datalog Materialisations
Venue: arXiv cs.DB 2017
Year: 2017

To efficiently answer queries, datalog systems often materialise all consequences of a datalog program, so the materialisation must be updated whenever the input facts change. Several solutions to the materialisation update problem have been proposed. The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries. We show that this can be a considerable source of overhead even on very small updates. In contrast, the Counting algorithm does not evaluate the rules 'backwards', but it can handle only nonrecursive rules. We present two hybrid approaches that combine DRed and B/F with Counting so as to reduce or even eliminate 'backward' rule evaluation while still handling arbitrary datalog programs. We show empirically that our hybrid algorithms are usually significantly faster than existing approaches, sometimes by orders of magnitude.
Differential Performance Debugging with Discriminant Regression Trees
Venue: arXiv cs.AI 2017
Year: 2017

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs. We propose a new algorithm for DRT learning that first clusters the data into functional clusters, capturing different asymptotic performance classes, and then invokes off-the-shelf decision tree learning algorithms to explain these clusters. We focus on linear functional clusters and adapt classical clustering algorithms (K-means and spectral) to produce them. For the K-means algorithm, we generalize the notion of the cluster centroid from a point to a linear function. We adapt spectral clustering by defining a novel kernel function to capture the notion of linear similarity between two data points. We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm significantly outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification.
Depth First Always On Routing Trace Algorithm
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, we discussed current limitation in the electronic-design-automotation (EDA) tool on tracing the always on routing. We developed an algorithm to efficiently track the secondary power routing and accurately estimate the routing quality using approximate voltage drop as the criteria. The fast check can identify potential hotspot issues without going through sign-off checks. It helps designers to capture issues at early stages and fix the issues with less design effort. We also discussed some limitations to our algorithm.
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single IMU
Venue: arXiv cs.GR 2017
Year: 2017

This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user's full-body locomotion and the IMU's data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character's full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.
Ensuring Liveness Properties of Distributed Systems (A Research Agenda)
Venue: arXiv cs.LO 2017
Year: 2017

Often fairness assumptions need to be made in order to establish liveness properties of distributed systems, but in many situations these lead to false conclusions. This document presents a research agenda aiming at laying the foundations of a theory of concurrency that is equipped to ensure liveness properties of distributed systems without making fairness assumptions. This theory will encompass process algebra, temporal logic and semantic models, as well as treatments of real-time. The agenda also includes developing a methodology that allows successful application of this theory to the specification, analysis and verification of realistic distributed systems, including routing protocols for wireless networks. Contemporary process algebras and temporal logics fail to make distinctions between systems of which one has a crucial liveness property and the other does not, at least when assuming justness, a strong progress property, but not assuming fairness. Setting up an alternative framework involves giving up on identifying strongly bisimilar systems, inventing new induction principles, developing new axiomatic bases for process algebras and new congruence formats for operational semantics, and creating new treatments of time and probability. Even simple systems like fair schedulers or mutual exclusion protocols cannot be accurately specified in standard process algebras (or Petri nets) in the absence of fairness assumptions. Hence the work involves the study of adequate language or model extensions, and their expressive power.
Linking Sequences of Events with Sparse or No Common Occurrence across Data Sets
Venue: arXiv cs.LG 2017
Year: 2017

Data of practical interest - such as personal records, transaction logs, and medical histories - are sequential collections of events relevant to a particular source entity. Recent studies have attempted to link sequences that represent a common entity across data sets to allow more comprehensive statistical analyses and to identify potential privacy failures. Yet, current approaches remain tailored to their specific domains of application, and they fail when co-referent sequences in different data sets contain sparse or no common events, which occurs frequently in many cases. To address this, we formalize the general problem of "sequence linkage" and describe "LDA-Link," a generic solution that is applicable even when co-referent event sequences contain no common items at all. LDA-Link is built upon "Split-Document" model, a new mixed-membership probabilistic model for the generation of event sequence collections. It detects the latent similarity of sequences and thus achieves robustness particularly when co-referent sequences share sparse or no event overlap. We apply LDA-Link in the context of social media profile reconciliation where users make no common posts across platforms, comparing to the state-of-the-art generic solution to sequence linkage.
Unified Spectral Clustering with Optimal Graph
Venue: arXiv cs.LG 2017
Year: 2017

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to severe information loss and performance degradation. First, predefined similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a unified framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.
Overlaying Quantitative Measurement on Networks: An Evaluation of Three Positioning and Nine Visual Marker Techniques
Venue: arXiv cs.GR 2017
Year: 2017

We report results from an experiment on ranking visual markers and node positioning techniques for network visualizations. Inspired by prior ranking studies, we rethink the ranking when the dataset size increases and when the markers are distributed in space. Centrality indices are visualized as node attributes. Our experiment studies nine visual markers and three positioning methods. Our results suggest that direct encoding of quantities improves accuracy by about 20% compared to previous results. Of the three positioning techniques, circular was always in the top group, and matrix and projection switch orders depending on two factors: whether or not the tasks demand symmetry, or the nodes are within closely proximity. Among the most interesting results of ranking the visual markers for comparison tasks are that hue and area fall into the top groups for nearly all multi-scale comparison tasks; Shape (ordered by curvature) is perhaps not as scalable as we have thought and can support more accurate answers only when two quantities are compared; Lightness and slope are least accurate for quantitative comparisons regardless of scale of the comparison tasks. Our experiment is among the first to acquire a complete picture of ranking visual markers in different scales for comparison tasks.
Targeted Advertising Based on Browsing History
Venue: arXiv cs.IR 2017
Year: 2017

Audience interest, demography, purchase behavior and other possible classifications are ex- tremely important factors to be carefully studied in a targeting campaign. This information can help advertisers and publishers deliver advertisements to the right audience group. How- ever, it is not easy to collect such information, especially for the online audience with whom we have limited interaction and minimum deterministic knowledge. In this paper, we pro- pose a predictive framework that can estimate online audience demographic attributes based on their browsing histories. Under the proposed framework, first, we retrieve the content of the websites visited by audience, and represent the content as website feature vectors; second, we aggregate the vectors of websites that audience have visited and arrive at feature vectors representing the users; finally, the support vector machine is exploited to predict the audience demographic attributes. The key to achieving good prediction performance is preparing representative features of the audience. Word Embedding, a widely used tech- nique in natural language processing tasks, together with term frequency-inverse document frequency weighting scheme is used in the proposed method. This new representation ap- proach is unsupervised and very easy to implement. The experimental results demonstrate that the new audience feature representation method is more powerful than existing baseline methods, leading to a great improvement in prediction accuracy.
A Case Study of the 2016 Korean Cyber Command Compromise
Venue: arXiv cs.CY 2017
Year: 2017

On October 2016 the South Korean cyber military unit was the victim of a successful cyber attack that allowed access to internal networks. Per usual with large scale attacks against South Korean entities, the hack was immediately attributed to North Korea. Also, per other large-scale cyber security incidents, the same types of 'evidence' were used for attribution purposes. Disclosed methods of attribution provide weak evidence, and the procedure Korean organizations tend to use for information disclosure lead many to question any conclusions. We will analyze and discuss a number of issues with the current way that South Korean organizations disclose cyber attack information to the public. A time line of events and disclosures will be constructed and analyzed in the context of appropriate measures for cyber warfare. Finally, we will examine the South Korean cyber military attack in terms previously proposed cyber warfare response guidelines. Specifically, whether any of the guidelines can be applied to this real-world case, and if so, is South Korea justified in declaring war based on the most recent cyber attack.
UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks. Nonetheless, the chosen CNN features are always pre-trained in different task and individual components in tracking systems are learned separately, thus the achieved tracking performance may be suboptimal. Besides, most of these trackers are not designed towards real-time applications because of their time-consuming feature extraction and complex optimization details.In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT). Specifically, The UCT treats feature extractor and tracking process both as convolution operation and trains them jointly, enabling learned CNN features are tightly coupled to tracking process. In online tracking, an efficient updating method is proposed by introducing peak-versus-noise ratio (PNR) criterion, and scale changes are handled efficiently by incorporating a scale branch into network. The proposed approach results in superior tracking performance, while maintaining real-time speed. The standard UCT and UCT-Lite can track generic objects at 41 FPS and 154 FPS without further optimization, respectively. Experiments are performed on four challenging benchmark tracking datasets: OTB2013, OTB2015, VOT2014 and VOT2015, and our method achieves state-of-the-art results on these benchmarks compared with other real-time trackers.
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Venue: arXiv cs.LG 2017
Year: 2017

The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496x with the same model accuracy. This results in up to a 1.51x improvement over the state-of-the-art.