OpenRaiser/Intern-Atlas huggingface dataset:
Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras
Venue: arXiv cs.CR 2016
Year: 2016

The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86% overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.
Multiuser Detection in Multibeam Satellite Systems: Theoretical Analysis and Practical Schemes
Venue: arXiv cs.IT 2016
Year: 2016

We consider the rates achievable by a user in a multibeam satellite system for unicast applications, and propose alternatives to the conventional single-user symbol-by-symbol detection applied at user terminals. Single-user detection is known to suffer from strong degradation when the terminal is located near the edge of the coverage area of a beam, and when aggressive frequency reuse is adopted. For this reason, we consider multiuser detection, and take into account the strongest interfering signal. We also analyze two additional transmission strategies requiring modifications at medium access control layer. We describe an information-theoretic framework to compare the different strategies by computing the information rate of the user in the reference beam. Furthermore, we analyze the performance of coded schemes that could approach the information-theoretic limits. We show that classical codes from the DVB-S2(X) standard are not suitable when multiuser detection is adopted, and we propose two ways to improve the performance, based on the redesign of the code and of the bit mapping.
A New Approach for Helper Selection and Performance Analysis in Poisson CoopMAC Networks
Venue: arXiv cs.NI 2016
Year: 2016

The cooperative medium access control (CoopMAC) protocol in the presence of randomly-distributed nodes and shadowing is considered. The nodes are assumed to be distributed according to a homogeneous two-dimensional Poisson point process. A new approach is proposed for helper selection and throughput performance analysis which depends on the shadowing parameters as well as the distribution of helpers. In the proposed protocol, the potential helpers are divided into several tiers based on their distances from the source and destination in a way that the lower the tier index, the higher its priority. When there are several helpers of the same tier, the helper that is less affected by shadowing is chosen for cooperation. Upper and lower bounds are derived for the average cooperative throughput of the proposed CoopMAC protocol. It is observed that the proposed scheme readily outperforms the conventional CoopMAC protocol in having larger average throughput. It is also seen that the cooperative throughput of the proposed scheme approaches the upper bound when the density of nodes increases.
Applications of Online Deep Learning for Crisis Response Using Social Media Information
Venue: arXiv cs.CL 2016
Year: 2016

During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. We test our models using a crisis-related real-world Twitter dataset.
A novel and effective scoring scheme for structure classification and pairwise similarity measurement
Venue: arXiv cs.CV 2016
Year: 2016

Protein tertiary structure defines its functions, classification and binding sites. Similar structural characteristics between two proteins often lead to the similar characteristics thereof. Determining structural similarity accurately in real time is a crucial research issue. In this paper, we present a novel and effective scoring scheme that is dependent on novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recognition and computer vision. Our method is significantly better than the current state of the art methods in terms of family match of pairs of protein structures and other statistical measurements. The effectiveness of our method is tested on standard benchmark structures. A web service is available at http://research.buet.ac.bd:8080/Comograd/score.html where you can get the similarity measurement score between two protein structures based on our method.
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints
Venue: arXiv cs.CV 2016
Year: 2016

Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution focus on the luminance channel information and do not capture interactions between color channels. In this work, we extend sparsity based super-resolution to multiple color channels by taking color information into account. Edge similarities amongst RGB color bands are exploited as cross channel correlation constraints. These additional constraints lead to a new optimization problem which is not easily solvable; however, a tractable solution is proposed to solve it efficiently. Moreover, to fully exploit the complementary information among color channels, a dictionary learning method is also proposed specifically to learn color dictionaries that encourage edge similarities. Merits of the proposed method over state of the art are demonstrated both visually and quantitatively using image quality metrics.
Tutorial on Answering Questions about Images with Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
Venue: arXiv cs.LG 2016
Year: 2016

Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering. However, robotic skill learning methods typically make one of several trade-offs to enable practical real-world learning, such as requiring manually designed policy or value function representations, initialization from human-provided demonstrations, instrumentation of the training environment, or extremely long training times. In this paper, we propose a new reinforcement learning algorithm for learning manipulation skills that can train general-purpose neural network policies with minimal human engineering, while still allowing for fast, efficient learning in stochastic environments. Our approach builds on the guided policy search (GPS) algorithm, which transforms the reinforcement learning problem into supervised learning from a computational teacher (without human demonstrations). In contrast to prior GPS methods, which require a consistent set of initial states to which the system must be reset after each episode, our approach can handle randomized initial states, allowing it to be used in environments where deterministic resets are impossible. We compare our method to existing policy search techniques in simulation, showing that it can train high-dimensional neural network policies with the same sample efficiency as prior GPS methods, and present real-world results on a PR2 robotic manipulator.
5G-ICN : Delivering ICN Services over 5G using Network Slicing
Venue: arXiv cs.NI 2016
Year: 2016

The challenging requirements of 5G--from both the applications and the architecture perspectives--motivate the need to explore the feasibility of delivering services over new network architectures. As 5G proposes application-centric network slicing, which enables the use of new data planes realizable over a programmable compute, storage, and transport infrastructure, we consider Information-centric Networking (ICN) as a candidate network architecture to realize 5G objectives. This can co-exist with end-to-end IP services that are offered today. To this effect, we first propose a 5G-ICN architecture and compare its benefits (i.e., innovative services offered by leveraging ICN features) to current 3GPP-based mobile architectures. We then introduce a general application-driven framework that emphasizes on the flexibility afforded by Network Function Virtualization (NFV) and Software Defined Networking (SDN) over which 5G-ICN can be realized. We specifically focus on the issue of how mobility-as-a-service (MaaS) can be realized as a 5G-ICN slice, and give an in-depth overview on resource provisioning and inter-dependencies and -coordinations among functional 5G-ICN slices to meet the MaaS objectives.
Non-Intrusive Load Monitoring: A Review and Outlook
Venue: arXiv cs.OH 2016
Year: 2016

With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly. NILM estimates the power consumption of individual devices given their aggregate consumption. In this way, the combined consumption must only be monitored at a single, central point in the household, providing various advantages such as reduced cost for metering equipment. In this paper we discuss the fundamental building-blocks of NILM, first giving a taxonomy of appliance models and device signatures and then explaining common supervised and unsupervised learning methods. Furthermore, we outline a fundamental algorithm that tackles the task of NILM. Subsequently, this paper reviews recent research that has brought novel insight to the field and more effective techniques. Finally, we formulate future challenges in the domain of NILM and smart meters.
A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage
Venue: arXiv cs.SE 2016
Year: 2016

The test suite is essential for fault detection during software development. First-order mutation coverage is an accurate metric to quantify the quality of the test suite. However, it is computationally expensive. Hence, the adoption of this metric is limited. In this study, we address this issue by proposing a realistic model able to estimate first-order mutation coverage using only higher-order mutation coverage. Our study shows how the estimation evolves along with the order of mutation. We validate the model with an empirical study based on 17 open-source projects.
MopEye: Monitoring Per-app Network Performance with Zero Measurement Traffic
Venue: arXiv cs.NI 2016
Year: 2016

Mobile network performance measurement is important for understanding mobile user experience, problem diagnosis, and service comparison. A number of crowdsourcing measurement apps (e.g., MobiPerf [4, 6] and Netalyzr [5, 7]) have been embarked for the last few years. Unlike existing apps that use active measurement methods, we employ a novel passive-active approach to continuously monitor per-app network performance on unrooted smartphones without injecting additional network traffic. By leveraging the VpnService API on Android, MopEye, our measurement app, intercepts all network traffic and then relays them to their destinations using socket APIs. Therefore, not only MopEye can measure the round-trip time accurately, it can do so without injecting additional traffic. As a result, the bandwidth cost (and monetary cost of data usage) for conducting such a measurement is eliminated, and the measurement can be conducted free of user intervention. Our evaluation shows that MopEye's RTT measurement is very close to result of tcpdump and is more accurate than MobiPerf. We have used MopEye to conduct a one-week measurement revealing multiple interesting findings on different apps' performance.
Informative Path Planning and Mapping with Multiple UAVs in Wind Fields
Venue: arXiv cs.RO 2016
Year: 2016

Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as the dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents a systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.
A Decision Procedure for String Logic with Equations, Regular Membership and Length Constraints
Venue: arXiv cs.LO 2016
Year: 2016

In this paper, we consider the satisfiability problem for string logic with equations, regular membership and Presburger constraints over length functions. The difficulty comes from multiple occurrences of string variables making state-of-the-art algorithms non-terminating. Our main contribution is to show that the satisfiability problem in a fragment where no string variable occurs more than twice in an equation is decidable. In particular, we propose a semi-decision procedure for arbitrary string formulae with word equations, regular membership and length functions. The essence of our procedure is an algorithm to enumerate an equivalent set of solvable disjuncts for the formula. We further show that the algorithm always terminates for the aforementioned decidable fragment. Finally, we provide a complexity analysis of our decision procedure to prove that it runs, in the worst case, in factorial time.
Improved Lower Bound on DHP: Towards the Equivalence of DHP and DLP for Important Elliptic Curves Used for Implementation
Venue: arXiv cs.CR 2016
Year: 2016

In 2004, Muzereau et al. showed how to use a reduction algorithm of the discrete logarithm problem to Diffie-Hellman problem in order to estimate lower bound on Diffie-Hellman problem on elliptic curves. They presented their estimates for various elliptic curves that are used in practical applications. In this paper, we show that a much tighter lower bound for Diffie-Hellman problem on those curves can be achieved, if one uses the multiplicative group of a finite field as an auxiliary group. Moreover, improved lower bound estimates on Diffie-Hellman problem for various recommended curves are also given which are the tightest; thus, leading us towards the equivalence of Diffie-Hellman problem and the discrete logarithm problem for these recommended elliptic curves.
Recognizing and Presenting the Storytelling Video Structure with Deep Multimodal Networks
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a novel approach for temporal and semantic segmentation of edited videos into meaningful segments, from the point of view of the storytelling structure. The objective is to decompose a long video into more manageable sequences, which can in turn be used to retrieve the most significant parts of it given a textual query and to provide an effective summarization. Previous video decomposition methods mainly employed perceptual cues, tackling the problem either as a story change detection, or as a similarity grouping task, and the lack of semantics limited their ability to identify story boundaries. Our proposal connects together perceptual, audio and semantic cues in a specialized deep network architecture designed with a combination of CNNs which generate an appropriate embedding, and clusters shots into connected sequences of semantic scenes, i.e. stories. A retrieval presentation strategy is also proposed, by selecting the semantically and aesthetically "most valuable" thumbnails to present, considering the query in order to improve the storytelling presentation. Finally, the subjective nature of the task is considered, by conducting experiments with different annotators and by proposing an algorithm to maximize the agreement between automatic results and human annotators.
On the Joint Impact of Hardware Impairments and Imperfect CSI on Successive Decoding
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, a spatial multiplexing multiple-input multiple-output (MIMO) system when hardware along with RF imperfections occur during the communication setup is analytically investigated. More specifically, the scenario of hardware impairments at the transceiver and imperfect channel state information (CSI) at the receiver is considered, when successive interference cancellation (SIC) is implemented. Two popular linear detection schemes are analyzed, namely, zero forcing SIC (ZF-SIC) and minimum mean-square error SIC (MMSE-SIC). New analytical expressions for the outage probability of each SIC stage are provided, when independent and identically distributed Rayleigh fading channels are considered. In addition, the well-known error propagation effect between consecutive SIC stages is analyzed, while closed-form expressions are derived for some special cases of interest. Finally, useful engineering insights are manifested, such as the achievable diversity order, the performance difference between ZF- and MMSE-SIC, and the impact of imperfect CSI and/or the presence of hardware impairments to the overall system performance.
Linear Combinations of Unordered Data Vectors
Venue: arXiv cs.LO 2016
Year: 2016

Data vectors generalise finite multisets: they are finitely supported functions into a commutative monoid. We study the question if a given data vector can be expressed as a finite sum of others, only assuming that 1) the domain is countable and 2) the given set of base vectors is finite up to permutations of the domain. Based on a succinct representation of the involved permutations as integer linear constraints, we derive that positive instances can be witnessed in a bounded subset of the domain. For data vectors over a group we moreover study when a data vector is reversible, that is, if its inverse is expressible using only nonnegative coefficients. We show that if all base vectors are reversible then the expressibility problem reduces to checking membership in finitely generated subgroups. Moreover, checking reversibility also reduces to such membership tests. These questions naturally appear in the analysis of counter machines extended with unordered data: namely, for data vectors over $(\mathbb{Z}^d,+)$ expressibility directly corresponds to checking state equations for Coloured Petri nets where tokens can only be tested for equality. We derive that in this case, expressibility is in NP, and in P for reversible instances. These upper bounds are tight: they match the lower bounds for standard integer vectors (over singleton domains).
The recurrence function of a random Sturmian word
Venue: arXiv cs.DM 2016
Year: 2016

This paper describes the probabilistic behaviour of a random Sturmian word. It performs the probabilistic analysis of the recurrence function which can be viewed as a waiting time to discover all the factors of length $n$ of the Sturmian word. This parameter is central to combinatorics of words. Having fixed a possible length $n$ for the factors, we let $α$ to be drawn uniformly from the unit interval $[0,1]$, thus defining a random Sturmian word of slope $α$. Thus the waiting time for these factors becomes a random variable, for which we study the limit distribution and the limit density.
VoxML: A Visualization Modeling Language
Venue: arXiv cs.CL 2016
Year: 2016

We present the specification for a modeling language, VoxML, which encodes semantic knowledge of real-world objects represented as three-dimensional models, and of events and attributes related to and enacted over these objects. VoxML is intended to overcome the limitations of existing 3D visual markup languages by allowing for the encoding of a broad range of semantic knowledge that can be exploited by a variety of systems and platforms, leading to multimodal simulations of real-world scenarios using conceptual objects that represent their semantic values.
Lifted Message Passing for the Generalized Belief Propagation
Venue: arXiv cs.AI 2016
Year: 2016

We introduce the lifted Generalized Belief Propagation (GBP) message passing algorithm, for the computation of sum-product queries in Probabilistic Relational Models (e.g. Markov logic network). The algorithm forms a compact region graph and establishes a modified version of message passing, which mimics the GBP behavior in a corresponding ground model. The compact graph is obtained by exploiting a graphical representation of clusters, which reduces cluster symmetry detection to isomorphism tests on small local graphs. The framework is thus capable of handling complex models, while remaining domain-size independent.