OpenRaiser/Intern-Atlas huggingface dataset:
Platooning in the Presence of a Speed Drop: A Generalized Control Model
Venue: arXiv cs.SY 2017
Year: 2017

The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogeneous roadway segments. However, speed limit changes frequently throughout the transportation network, due to either safety-related considerations (e.g., workzone operations) or congestion management schemes (e.g., speed harmonization systems). These abrupt changes in speed limit can result in shock- wave formation and cause travel time unreliability. Therefore, designing a platooning strategy for tracking a reference velocity profile is critical to enabling end-to-end platooning. Accordingly, this study introduces a generalized control model to track a desired velocity profile, while ensuring safety in the platoon of autonomous vehicles. We define appropriate natural error terms and the target curve in the state space of the control system, which is the set of points where all error terms vanish and corresponds to the case when all vehicles move with the desired velocities and in the minimum safe distance between them. In this way, we change the tracking velocity profile problem into a state- feedback stabilization problem with respect to the target curve. Under certain mild assumptions on the Lipschitz constant of the speed drop profile, we show that the stabilizing feedback can be obtained via introducing a natural dynamics for the maximum of the error terms for each vehicle. Moreover, we show that with this stabilizing feedback collisions will not occur if the initial state of the system of vehicles is sufficiently close to the target curve. We also show that the error terms remain bounded throughout the time and space. Two scenarios were simulated, with and without initial perturbations, and results confirmed the effectiveness of the proposed control model in tracking the speed drop while ensuring safety and string stability.
Maat: A Platform Service for Measurement and Attestation
Venue: arXiv cs.CR 2017
Year: 2017

Software integrity measurement and attestation (M&A) are critical technologies for evaluating the trustworthiness of software platforms. To best support these technologies, next generation systems must provide a centralized service for securely selecting, collecting, and evaluating integrity measurements. Centralization of M&A avoids duplication, minimizes security risks to the system, and ensures correct ad- ministration of integrity policies and systems. This paper details the desirable features and properties of such a system, and introduces Maat, a prototype implementation of an M&A service that meets these properties. Maat is a platform service that provides a centralized policy-driven framework for determining which measurement tools and protocols to use to meet the needs of a given integrity evaluation. Maat simplifies the task of integrating integrity measurements into a range of larger trust decisions such as authentication, network access control, or delegated computations.
Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning
Venue: arXiv cs.MM 2017
Year: 2017

Human activity recognition is one of the important research topics in computer vision and video understanding. It is often assumed that high quality video sequences are available for recognition. However, relaxing such a requirement and implementing robust recognition using videos having reduced data rates can achieve efficiency in storing and transmitting video data. Three-dimensional video scalability, which refers to the possibility of reducing spatial, temporal, and quality resolutions of videos, is an effective way for flexible representation and management of video data. In this paper, we investigate the impact of the video scalability on multi-view activity recognition. We employ both a spatiotemporal feature extraction-based method and a deep learning-based method using convolutional and recurrent neural networks. The recognition performance of the two methods is examined, along with in-depth analysis regarding how their performance vary with respect to various scalability combinations. In particular, we demonstrate that the deep learning-based method can achieve significantly improved robustness in comparison to the feature-based method. Furthermore, we investigate optimal scalability combinations with respect to bitrate in order to provide useful guidelines for an optimal operation policy in resource-constrained activity recognition systems.
A Social Network Analysis on Blended Courses
Venue: arXiv cs.SI 2017
Year: 2017

The large-scale online management systems (e.g. Moodle), online web forums (e.g. Piazza), and online homework systems (e.g. WebAssign) have been widely used in the blended courses recently. Instructors can use these systems to deliver class content and materials. Students can communicate with the classmates, share the course materials, and discuss the course questions via the online forums. With the increased use of the online systems, a large amount of students' interaction data has been collected. This data can be used to analyze students' learning behaviors and predict students' learning outcomes. In this work, we collected students' interaction data in three different blended courses. We represented the data as directed graphs and investigated the correlation between the social graph properties and students' final grades. Our results showed that in all these classes, students who asked more answers and received more feedbacks on the forum tend to obtain higher grades. The significance of this work is that we can use the results to encourage students to participate more in forums to learn the class materials better; we can also build a predictive model based on the social metrics to show us low performing students early in the semester.
Shapley Facility Location Games
Venue: arXiv cs.GT 2017
Year: 2017

Facility location games have been a topic of major interest in economics, operations research and computer science, starting from the seminal work by Hotelling. Spatial facility location models have successfully predicted the outcome of competition in a variety of scenarios. In a typical facility location game, users/customers/voters are mapped to a metric space representing their preferences, and each player picks a point (facility) in that space. In most facility location games considered in the literature, users are assumed to act deterministically: given the facilities chosen by the players, users are attracted to their nearest facility. This paper introduces facility location games with probabilistic attraction, dubbed Shapley facility location games, due to a surprising connection to the Shapley value. The specific attraction function we adopt in this model is aligned with the recent findings of the behavioral economics literature on choice prediction. Given this model, our first main result is that Shapley facility location games are potential games; hence, they possess pure Nash equilibrium. Moreover, the latter is true for any compact user space, any user distribution over that space, and any number of players. Note that this is in sharp contrast to Hotelling facility location games. In our second main result we show that under the assumption that players can compute an approximate best response, approximate equilibrium profiles can be learned efficiently by the players via dynamics. Our third main result is a bound on the Price of Anarchy of this class of games, as well as showing the bound is tight. Ultimately, we show that player payoffs coincide with their Shapley value in a coalition game, where coalition gains are the social welfare of the users.
Privacy Preserving Identification Using Sparse Approximation with Ambiguization
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we consider a privacy preserving encoding framework for identification applications covering biometrics, physical object security and the Internet of Things (IoT). The proposed framework is based on a sparsifying transform, which consists of a trained linear map, an element-wise nonlinearity, and privacy amplification. The sparsifying transform and privacy amplification are not symmetric for the data owner and data user. We demonstrate that the proposed approach is closely related to sparse ternary codes (STC), a recent information-theoretic concept proposed for fast approximate nearest neighbor (ANN) search in high dimensional feature spaces that being machine learning in nature also offers significant benefits in comparison to sparse approximation and binary embedding approaches. We demonstrate that the privacy of the database outsourced to a server as well as the privacy of the data user are preserved at a low computational cost, storage and communication burdens.
The Health and Wealth of OSS Projects: Evidence from Community Activities and Product Evolution
Venue: arXiv cs.SE 2017
Year: 2017

Background: Understanding the condition of OSS projects is important to analyze features and predict the future of projects. In the field of demography and economics, health and wealth are considered to understand the condition of a country. Aim: In this paper, we apply this framework to OSS projects to understand the communities and the evolution of OSS projects from the perspectives of health and wealth. Method: We define two measures of Workforce (WF) and Gross Product Pull Requests (GPPR). We analyze OSS projects in GitHub and investigate three typical cases. Results: We find that wealthy projects attract and rely on the casual workforce. Less wealthy projects may require additional efforts from their more experienced contributors. Conclusions: This paper presents an approach to assess the relationship between health and wealth of OSS projects. An interactive demo of our analysis is available at goo.gl/Ig6NTR.
Improved approximate near neighbor search without false negatives for $l_2$
Venue: arXiv cs.CG 2017
Year: 2017

We present a new algorithm for the $c$--approximate nearest neighbor search without false negatives for $l_2^d$. We enhance the dimension reduction method presented in \cite{wygos_red} and combine it with the standard results of Indyk and Motwani~\cite{motwani}. We present an efficient algorithm with Las Vegas guaranties for any $c>1$. This improves over the previous results, which require $c=ω(\log\log{n})$ \cite{wygos_red}, where $n$ is the number of the input points. Moreover, we improve both the query time and the pre-processing time. Our algorithm is tunable, which allows for different compromises between the query and the pre-processing times. In order to illustrate this flexibility, we present two variants of the algorithm. The "efficient query" variant involves the query time of $O(d^2)$ and the polynomial pre-processing time. The "efficient pre-processing" variant involves the pre-processing time equal to $O(d^{ω-1} n)$ and the query time sub-linear in $n$, where $ω$ is the exponent in the complexity of the fast matrix multiplication. In addition, we introduce batch versions of the mentioned algorithms, where the queries come in batches of size $d$. In this case, the amortized query time of the "efficient query" algorithm is reduced to $O(d^{ω-1})$.
On-the-Fly Array Initialization in Less Space
Venue: arXiv cs.DS 2017
Year: 2017

We show that for all given $n,t,w \in \{1,2,...\}$ with $n<2^w$, an array of $n$ entries of $w$ bits each can be represented on a word RAM with a word length of $w$ bits in at most $nw+\lceil n(t/(2 w))^t\rceil$ bits of uninitialized memory to support constant-time initialization of the whole array and $O(t)$-time reading and writing of individual array entries. At one end of this tradeoff, we achieve initialization and access (i.e., reading and writing) in constant time with $nw+\lceil n/w^t\rceil$ bits for arbitrary fixed $t$, to be compared with $nw+Θ(n)$ bits for the best previous solution, and at the opposite end, still with constant-time initialization, we support $O(\log n)$-time access with just $nw+1$ bits, which is optimal for arbitrary access times if the initialization executes fewer than $n$ steps.
What Automated Planning can do for Business Process Management
Venue: arXiv cs.AI 2017
Year: 2017

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle.
Checking and Enforcing Security through Opacity in Healthcare Applications
Venue: arXiv cs.CR 2017
Year: 2017

The Internet of Things (IoT) is a paradigm that can tremendously revolutionize health care thus benefiting both hospitals, doctors and patients. In this context, protecting the IoT in health care against interference, including service attacks and malwares, is challenging. Opacity is a confidentiality property capturing a system's ability to keep a subset of its behavior hidden from passive observers. In this work, we seek to introduce an IoT-based heart attack detection system, that could be life-saving for patients without risking their need for privacy through the verification and enforcement of opacity. Our main contributions are the use of a tool to verify opacity in three of its forms, so as to detect privacy leaks in our system. Furthermore, we develop an efficient, Symbolic Observation Graph (SOG)-based algorithm for enforcing opacity.
Statistical Disclosure: Improved, Extended, and Resisted
Venue: arXiv cs.CR 2017
Year: 2017

Traffic analysis is a type of attack on secure communications systems, in which the adversary extracts useful patterns and information from the observed traffic. This paper improves and extends an efficient traffic analysis attack, called "statistical disclosure attack." Moreover, we propose a solution to defend against the improved (and, a fortiori, the original) statistical disclosure attack. Our solution delays the attacker considerably, meaning that he should gather significantly more observations to be able to deduce meaningful information from the traffic.
Small Satellite Constellation Separation using Linear Programming based Differential Drag Commands
Venue: arXiv cs.SY 2017
Year: 2017

We study the optimal control of an arbitrarily large constellation of small satellites operating in low Earth orbit. Simulating the lack of on-board propulsion, we limit our actuation to the use of differential drag maneuvers to make in-plane changes to the satellite orbits. We propose an efficient method to separate a cluster of satellites into a desired constellation shape while respecting actuation constraints and maximizing the operational lifetime of the constellation. By posing the problem as a linear program, we solve for the optimal drag commands for each of the satellites on a daily basis with a shrinking-horizon model predictive control approach. We then apply this control strategy in a nonlinear orbital dynamics simulation with a simple, varying atmospheric density model. We demonstrate the ability to control a cluster of 100+ satellites starting at the same initial conditions in a circular low Earth orbit to form an equally spaced constellation (with a relative angular separation error tolerance of one-tenth a degree). The constellation separation task can be executed in 71 days, a time frame that is competitive for the state-of-the-practice. This method allows us to trade the time required to converge to the desired constellation with a sacrifice in the overall constellation lifetime, measured as the maximum altitude loss experienced by one of the satellites in the group after the separation maneuvers.
3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel 3DOF pedestrian trajectory prediction approach for autonomous mobile service robots. While most previously reported methods are based on learning of 2D positions in monocular camera images, our approach uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D position plus 1D rotation within the world coordinate system). Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities. Our approach incorporates long-term temporal information (i.e. date and time) with short-term pose observations as input. A sequence-to-sequence LSTM encoder-decoder is trained, which encodes observations into LSTM and then decodes as predictions. For deployment, it can perform on-the-fly prediction in real-time. Instead of using manually annotated data, we rely on a robust human detection, tracking and SLAM system, providing us with examples in a global coordinate system. We validate the approach using more than 15K pedestrian trajectories recorded in a care home environment over a period of three months. The experiment shows that the proposed T-Pose-LSTM model advances the state-of-the-art 2D-based method for human trajectory prediction in long-term mobile robot deployments.
Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Venue: arXiv cs.CL 2017
Year: 2017

Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.
Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding
Venue: arXiv cs.CL 2017
Year: 2017

Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU component treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work leveraged history for contextual SLU. However, the previous model only paid attention to the content in history utterances without considering their temporal information and speaker roles. In the dialogues, the most recent utterances should be more important than the least recent ones. Furthermore, users usually pay attention to 1) self history for reasoning and 2) others' utterances for listening, the speaker of the utterances may provides informative cues to help understanding. Therefore, this paper proposes an attention-based network that additionally leverages temporal information and speaker role for better SLU, where the attention to contexts and speaker roles can be automatically learned in an end-to-end manner. The experiments on the benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the time-aware dynamic role attention networks significantly improve the understanding performance.
UAV-assisted Cooperative Communications with Wireless Information and Power Transfer
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we focus on a typical cooperative communication system with one pair of source and destination, where a unmanned aerial vehicle (UAV) flying from a start location to an end location serves as a mobile relay. To efficiently utilize energy in ambient environment, the UAV's transmission capability is powered exclusively by radio signal transmitted from the source via the power-splitting mechanism. In such a cooperative communication system, we study the end-to-end cooperative throughput maximization problem by optimizing the UAV's power profile, power-splitting ratio profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into two subproblems: profile optimization given trajectory and trajectory optimization given profile. The former one is solved via the dual decomposition with in-depth analysis and the latter one is solved via successive convex optimization, by which a lower bound is iteratively maximized. Then the end-to-end cooperative throughput maximization problem is solved by alternately solving the two subproblems. The numerical results show that with the proposed optimal solution, choice for the UAV's power profile and power-splitting ratio profile is more long-sighted than the greedy strategy from our previous work and the successive optimization is able to converge in a few rounds of iteration. Moreover, as for the end-to-end cooperative throughput, the proposed optimal solution outperforms both static and greedy strategies, especially for the AF protocol.
Full-Duplex Relay Selection in Cognitive Underlay Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we analyze the performance of full-duplex relay selection (FDRS) in spectrum-sharing networks. Contrary to half-duplex relaying, full-duplex relaying (FDR) enables simultaneous listening/forwarding at the secondary relay(s), thereby allowing for a higher spectral efficiency. However, since the source and relay simultaneously transmit in FDR, their superimposed signal at the primary receiver should now satisfy the existing interference constraint, which can considerably limit the secondary network throughput. In this regard, relay selection can offer an adequate solution to boost the secondary throughput while satisfying the imposed interference limit. We first analyze the performance of opportunistic FDRS with residual self-interference (RSI) by deriving the exact cumulative distribution function of its end-to-end signal-to-interference-plus-noise ratio under Nakagami-m fading. We also evaluate the offered diversity gain of relay selection for different full-duplex cooperation schemes in the presence/absence of a direct source-destination link. When the adopted RSI link gain model is sublinear in the relay power, which agrees with recent research findings, we show that remarkable diversity gain can be recovered even in the presence of an interfering direct link. Second, we evaluate the end-to-end performance of FDRS with interference constraints due to the presence of a primary receiver. Finally, the presented exact theoretical findings are verified by numerical simulations.