OpenRaiser/Intern-Atlas huggingface dataset:
Understanding the Spatial and Temporal Activity Patterns of Subway Mobility Flows
Venue: arXiv cs.CY 2017
Year: 2017

In urban transportation systems, mobility flows in the subway system reflect the spatial and temporal dynamics of working days. To investigate the variability of mobility flows, we analyse the spatial community through a series of snapshots of subway stations over sequential periods. Using Shanghai as a case study, we find that the spatial community snapshots reveal dynamic passenger activities. Adopting a dual-perspective, we apply spatial and temporal models separately to explore where and when individuals travel for entertainment. In the two models, microblog topics and spatial facilities such as food venues and entertainment businesses are used to characterise the spatial popularity of each station and people's travelling perceptions. In the studied case, the city centre is characterised by greater social influence, and it is better described by the spatial model. In the temporal model, shorter travel distances motivate individuals to start their trips earlier. Interestingly, as the number of food-related facilities near the starting station increases, until it exceeds 1563, the speed of people's journeys slows down. This study provides a method for modelling the effects of social features on mobility flows and for predicting the spatial-temporal mobility flows of newly built subway stations.
Low Cost Monitoring and Intruders Detection using Wireless Video Sensor Networks
Venue: arXiv cs.CR 2017
Year: 2017

There is a growing interest in the use of video sensor networks in surveillance applications in order to detect intruders with low cost. The essential concern of such networks is whether or not a specified target can pass or intrude the monitored region without being detected. This concern forms a serious challenge to wireless video sensor networks of weak computation and battery power. In this paper, our aim is to prolong the whole network lifetime while fulfilling the surveillance application needs. We present a novel scheduling algorithm where only a subset of video nodes contribute significantly to detect intruders and prevent malicious attacker to predict the behavior of the network prior to intrusion. Our approach is chaos-based, where every node based on its last detection, a hash value and some pseudo-random numbers easily computes a decision function to go to sleep or active mode. We validate the efficiency of our approach through theoretical analysis and demonstrate the benefits of our scheduling algorithm by simulations. Results show that in addition of being able to increase the whole network lifetime and to present comparable results against random attacks (low stealth time), our scheme is also able to withstand malicious attacks due to its fully unpredictable behavior.
Pilot Reuse Factor with Large Scale Fading Precoding for Massive MIMO
Venue: arXiv cs.IT 2017
Year: 2017

The fundamental limitation of massive MIMO technology is pilot contamination effect. This effect occurs during uplink training when terminals use the same orthogonal signals. In this paper, a pilot reuse factor with large scale fading precoding is proposed to mitigate the pilot contamination effect. The pilot reuse factor is designed to assign unique orthogonal signals to the adjacent cells. These unique orthogonal signals are reused only within the cell and hence, intra-pilot contamination is the only concern. Large scale fading precoding is then used to mitigate the intra-pilot contamination effect. The average achievable sum rate is computed for different pilot reuse factors. Experimental results through MATLAB simulation show that a higher pilot reuse factor gives better average achievable sum rates.
Remote State Estimation over Packet Dropping Links in the Presence of an Eavesdropper
Venue: arXiv cs.SY 2017
Year: 2017

This paper studies remote state estimation in the presence of an eavesdropper. A sensor transmits local state estimates over a packet dropping link to a remote estimator, while an eavesdropper can successfully overhear each sensor transmission with a certain probability. The objective is to determine when the sensor should transmit, in order to minimize the estimation error covariance at the remote estimator, while trying to keep the eavesdropper error covariance above a certain level. This is done by solving an optimization problem that minimizes a linear combination of the expected estimation error covariance and the negative of the expected eavesdropper error covariance. Structural results on the optimal transmission policy are derived, and shown to exhibit thresholding behaviour in the estimation error covariances. In the infinite horizon situation, it is shown that with unstable systems one can keep the expected estimation error covariance bounded while the expected eavesdropper error covariance becomes unbounded. An alternative measure of security, constraining the amount of information revealed to the eavesdropper, is also considered, and similar structural results on the optimal transmission policy are derived. In the infinite horizon situation with unstable systems, it is now shown that for any transmission policy which keeps the expected estimation error covariance bounded, the expected amount of information revealed to the eavesdropper is always lower bounded away from zero. An extension of our results to the transmission of measurements is also presented.
NetClus: A Scalable Framework for Locating Top-K Sites for Placement of Trajectory-Aware Services
Venue: arXiv cs.DB 2017
Year: 2017

Facility location queries identify the best locations to set up new facilities for providing service to its users. Majority of the existing works in this space assume that the user locations are static. Such limitations are too restrictive for planning many modern real-life services such as fuel stations, ATMs, convenience stores, cellphone base-stations, etc. that are widely accessed by mobile users. The placement of such services should, therefore, factor in the mobility patterns or trajectories of the users rather than simply their static locations. In this work, we introduce the TOPS (Trajectory-Aware Optimal Placement of Services) query that locates the best k sites on a road network. The aim is to optimize a wide class of objective functions defined over the user trajectories. We show that the problem is NP-hard and even the greedy heuristic with an approximation bound of (1-1/e) fails to scale on urban-scale datasets. To overcome this challenge, we develop a multi-resolution clustering based indexing framework called NetClus. Empirical studies on real road network trajectory datasets show that NetClus offers solutions that are comparable in terms of quality with those of the greedy heuristic, while having practical response times and low memory footprints. Additionally, the NetClus framework can absorb dynamic updates in mobility patterns, handle constraints such as site-costs and capacity, and existing services, thereby providing an effective solution for modern urban-scale scenarios.
Graph Based Relational Features for Collective Classification
Venue: arXiv cs.IR 2017
Year: 2017

Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.
The DTM-signature for a geometric comparison of metric-measure spaces from samples
Venue: arXiv cs.CG 2017
Year: 2017

In this paper, we introduce the notion of DTM-signature, a measure on R + that can be associated to any metric-measure space. This signature is based on the distance to a measure (DTM) introduced by Chazal, Cohen-Steiner and Mérigot. It leads to a pseudo-metric between metric-measure spaces, upper-bounded by the Gromov-Wasserstein distance. Under some geometric assumptions, we derive lower bounds for this pseudo-metric. Given two N-samples, we also build an asymptotic statistical test based on the DTM-signature, to reject the hypothesis of equality of the two underlying metric-measure spaces, up to a measure-preserving isometry. We give strong theoretical justifications for this test and propose an algorithm for its implementation.
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
Venue: arXiv cs.LG 2017
Year: 2017

One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.
PowerAlert: An Integrity Checker using Power Measurement
Venue: arXiv cs.CR 2017
Year: 2017

We propose PowerAlert, an efficient external integrity checker for untrusted hosts. Current attestation systems suffer from shortcomings in requiring complete checksum of the code segment, being static, use of timing information sourced from the untrusted machine, or use of timing information with high error (network round trip time). We address those shortcomings by (1) using power measurements from the host to ensure that the checking code is executed and (2) checking a subset of the kernel space over a long period of time. We compare the power measurement against a learned power model of the execution of the machine and validate that the execution was not tampered. Finally, power diversifies the integrity checking program to prevent the attacker from adapting. We implement a prototype of PowerAlert using Raspberry pi and evaluate the performance of the integrity checking program generation. We model the interaction between PowerAlert and an attacker as a game. We study the effectiveness of the random initiation strategy in deterring the attacker. The study shows that \power forces the attacker to trade-off stealthiness for the risk of detection, while still maintaining an acceptable probability of detection given the long lifespan of stealthy attacks.
The Word Problem of $\mathbb{Z}^n$ Is a Multiple Context-Free Language
Venue: arXiv cs.FL 2017
Year: 2017

The \emph{word problem} of a group $G = \langle Σ\rangle$ can be defined as the set of formal words in $Σ^*$ that represent the identity in $G$. When viewed as formal languages, this gives a strong connection between classes of groups and classes of formal languages. For example, Anisimov showed that a group is finite if and only if its word problem is a regular language, and Muller and Schupp showed that a group is virtually-free if and only if its word problem is a context-free language. Above this, not much was known, until Salvati showed recently that the word problem of $\mathbb{Z}^2$ is a multiple context-free language, giving first such example. We generalize Salvati's result to show that the word problem of $\mathbb{Z}^n$ is a multiple context-free language for any $n$.
Trace Properties from Separation Logic Specifications
Venue: arXiv cs.PL 2017
Year: 2017

We propose a formal approach for relating abstract separation logic library specifications with the trace properties they enforce on interactions between a client and a library. Separation logic with abstract predicates enforces a resource discipline that constrains when and how calls may be made between a client and a library. Intuitively, this can enforce a protocol on the interaction trace. This intuition is broadly used in the separation logic community but has not previously been formalised. We provide just such a formalisation. Our approach is based on using wrappers which instrument library code to induce execution traces for the properties under examination. By considering a separation logic extended with trace resources, we prove that when a library satisfies its separation logic specification then its wrapped version satisfies the same specification and, moreover, maintains the trace properties as an invariant. Consequently, any client and library implementation that are correct with respect to the separation logic specification will satisfy the trace properties.
An Overview of Multi-Processor Approximate Message Passing
Venue: arXiv cs.IT 2017
Year: 2017

Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network.
Dynamic Switch-Controller Association and Control Devolution for SDN Systems
Venue: arXiv cs.NI 2017
Year: 2017

In software-defined networking (SDN), as data plane scale expands, scalability and reliability of the control plane have become major concerns. To mitigate such concerns, two kinds of solutions have been proposed separately. One is multi- controller architecture, i.e., a logically centralized control plane with physically distributed controllers. The other is control devolution, i.e., delegating control of some flows back to switches. Most of existing solutions adopt either static switch-controller association or static devolution, which may not adapt well to the traffic variation, leading to high communication costs between switches and controller, and high computation costs of switches. In this paper, we propose a novel scheme to jointly consider both solutions, i.e., we dynamically associate switches with controllers and dynamically devolve control of flows to switches. Our scheme is an efficient online algorithm that does not need the statistics of traffic flows. By adjusting a parameter, we can make a trade- off between costs and queue backlogs. Theoretical analysis and extensive simulations show that our scheme yields much lower costs or latency compared to other schemes, as well as balanced loads among controllers.
Decentralized 2-D Control of Vehicular Platoons under Limited Visual Feedback
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we consider the two dimensional (2-D) predecessor-following control problem for a platoon of unicycle vehicles moving on a planar surface. More specifically, we design a decentralized kinematic control protocol, in the sense that each vehicle calculates its own control signal based solely on local information regarding its preceding vehicle, by its on-board camera, without incorporating any velocity measurements. Additionally, the transient and steady state response is a priori determined by certain designer-specified performance functions and is fully decoupled by the number of vehicles composing the platoon and the control gains selection. Moreover, collisions between successive vehicles as well as connectivity breaks, owing to the limited field of view of cameras, are provably avoided. Finally, an extensive simulation study is carried out in the WEBOTS realistic simulator, clarifying the proposed control scheme and verifying its effectiveness.
Next Generation Crowdsourcing for Collective Intelligence
Venue: arXiv cs.HC 2017
Year: 2017

New techniques leveraging IT-mediated crowds such as Crowdsensing, Situated Crowdsourcing, Spatial Crowdsourcing, and Wearables Crowdsourcing have now materially emerged. These techniques, here termed next generation Crowdsourcing, serve to extend Crowdsourcing efforts beyond the heretofore dominant desktop computing paradigm. Employing new configurations of hardware, software, and people, these techniques represent new forms of organization for IT-mediated crowds. However, it is not known how these new techniques change the processes and outcomes of IT-mediated crowds for Collective Intelligence purposes? The aim of this exploratory work is to begin to answer this question. The work ensues by outlining the relevant findings of the first generation Crowdsourcing paradigm, before reviewing the emerging literature pertaining to the new generation of Crowdsourcing techniques. Premised on this review, a collectively exhaustive and mutually exclusive typology is formed, organizing the next generation Crowdsourcing techniques along two salient dimensions common to all first generation Crowdsourcing techniques. As a result, this work situates the next generation Crowdsourcing techniques within the extant Crowdsourcing literature, and identifies new research avenues stemming directly from the analysis.
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
Venue: arXiv cs.LG 2017
Year: 2017

In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games. The purpose of this study is twofold. First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU). The activation of the SiLU is computed by the sigmoid function multiplied by its input. Second, we suggest that the more traditional approach of using on-policy learning with eligibility traces, instead of experience replay, and softmax action selection with simple annealing can be competitive with DQN, without the need for a separate target network. We validate our proposed approach by, first, achieving new state-of-the-art results in both stochastic SZ-Tetris and Tetris with a small 10$\times$10 board, using TD($λ$) learning and shallow dSiLU network agents, and, then, by outperforming DQN in the Atari 2600 domain by using a deep Sarsa($λ$) agent with SiLU and dSiLU hidden units.
Universal Semantic Parsing
Venue: arXiv cs.CL 2017
Year: 2017

Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions. Our code and data can be downloaded at https://github.com/sivareddyg/udeplambda.
Towards Developing an Easy-To-Use Scripting Environment for Animating Virtual Characters
Venue: arXiv cs.GR 2017
Year: 2017

This paper presents the three scripting commands and main functionalities of a novel character animation environment called CHASE. CHASE was developed for enabling inexperienced programmers, animators, artists, and students to animate in meaningful ways virtual reality characters. This is achieved by scripting simple commands within CHASE. The commands identified, which are associated with simple parameters, are responsible for generating a number of predefined motions and actions of a character. Hence, the virtual character is able to animate within a virtual environment and to interact with tasks located within it. An additional functionality of CHASE is supplied. It provides the ability to generate multiple tasks of a character, such as providing the user the ability to generate scenario-related animated sequences. However, since multiple characters may require simultaneous animation, the ability to script actions of different characters at the same time is also provided.
Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle
Venue: arXiv cs.SY 2017
Year: 2017

Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy inference rules of the adaptive controller. Simulations and experiments are conducted to evaluate the performance of the proposed controller. The results demonstrate superior performance of the developed controller in terms of improved transient response, robustness to model uncertainty and fast online learning.