OpenRaiser/Intern-Atlas huggingface dataset:
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field
Venue: arXiv cs.CV 2018
Year: 2018

Foreground (FG) pixel labelling plays a vital role in video surveillance. Recent engineering solutions have attempted to exploit the efficacy of deep learning (DL) models initially targeted for image classification to deal with FG pixel labelling. One major drawback of such strategy is the lacking delineation of visual objects when training samples are limited. To grapple with this issue, we introduce a multi-view receptive field fully convolutional neural network (MV-FCN) that harness recent seminal ideas, such as, fully convolutional structure, inception modules, and residual networking. Therefrom, we implement a system in an encoder-decoder fashion that subsumes a core and two complementary feature flow paths. The model exploits inception modules at early and late stages with three different sizes of receptive fields to capture invariance at various scales. The features learned in the encoding phase are fused with appropriate feature maps in the decoding phase through residual connections for achieving enhanced spatial representation. These multi-view receptive fields and residual feature connections are expected to yield highly generalized features for an accurate pixel-wise FG region identification. It is, then, trained with database specific exemplary segmentations to predict desired FG objects. The comparative experimental results on eleven benchmark datasets validate that the proposed model achieves very competitive performance with the prior- and state-of-the-art algorithms. We also report that how well a transfer learning approach can be useful to enhance the performance of our proposed MV-FCN.
A high-performance analog Max-SAT solver and its application to Ramsey numbers
Venue: arXiv cs.CC 2018
Year: 2018

We introduce a continuous-time analog solver for MaxSAT, a quintessential class of NP-hard discrete optimization problems, where the task is to find a truth assignment for a set of Boolean variables satisfying the maximum number of given logical constraints. We show that the scaling of an invariant of the solver's dynamics, the escape rate, as function of the number of unsatisfied clauses can predict the global optimum value, often well before reaching the corresponding state. We demonstrate the performance of the solver on hard MaxSAT competition problems. We then consider the two-color Ramsey number $R(m,m)$ problem, translate it to SAT, and apply our algorithm to the still unknown $R(5,5)$. We find edge colorings without monochromatic 5-cliques for complete graphs up to 42 vertices, while on 43 vertices we find colorings with only two monochromatic 5-cliques, the best coloring found so far, supporting the conjecture that $R(5,5) = 43$.
Double circulant self-dual and LCD codes over Galois rings
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the existence, enumeration and asymptotic performance of self-dual and LCD double circulant codes over Galois rings of characteristic $p^2$ and order $p^4$ with $p$ and odd prime. When $p \equiv 3 \pmod{4},$ we give an algorithm to construct a duality preserving bijective Gray map from such a Galois ring to $\mathbb{Z}_{p^2}^2.$ Using random coding, we obtain families of asymptotically good self-dual and LCD codes over $\mathbb{Z}_{p^2},$ for the metric induced by the standard $\mathbb{F}_p$-valued Gray maps.
Gender-dependent emotion recognition based on HMMs and SPHMMs
Venue: arXiv cs.SD 2018
Year: 2018

It is well known that emotion recognition performance is not ideal. The work of this research is devoted to improving emotion recognition performance by employing a two-stage recognizer that combines and integrates gender recognizer and emotion recognizer into one system. Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in the two-stage recognizer. This recognizer has been tested on two distinct and separate emotional speech databases. The first database is our collected database and the second one is the Emotional Prosody Speech and Transcripts database. Six basic emotions including the neutral state have been used in each database. Our results show that emotion recognition performance based on the two-stage approach (gender-dependent emotion recognizer) has been significantly improved compared to that based on emotion recognizer without gender information and emotion recognizer with correct gender information by an average of 11% and 5%, respectively. This work shows that the highest emotion identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models. The results achieved based on the two-stage framework fall within 2.28% of those obtained in subjective assessment by human judges.
A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds
Venue: arXiv cs.CV 2018
Year: 2018

Discriminating targets moving against a cluttered background is a huge challenge, let alone detecting a target as small as one or a few pixels and tracking it in flight. In the fly's visual system, a class of specific neurons, called small target motion detectors (STMDs), have been identified as showing exquisite selectivity for small target motion. Some of the STMDs have also demonstrated directional selectivity which means these STMDs respond strongly only to their preferred motion direction. Directional selectivity is an important property of these STMD neurons which could contribute to tracking small targets such as mates in flight. However, little has been done on systematically modeling these directional selective STMD neurons. In this paper, we propose a directional selective STMD-based neural network (DSTMD) for small target detection in a cluttered background. In the proposed neural network, a new correlation mechanism is introduced for direction selectivity via correlating signals relayed from two pixels. Then, a lateral inhibition mechanism is implemented on the spatial field for size selectivity of STMD neurons. Extensive experiments showed that the proposed neural network not only is in accord with current biological findings, i.e. showing directional preferences, but also worked reliably in detecting small targets against cluttered backgrounds.
Learning model-based strategies in simple environments with hierarchical q-networks
Venue: arXiv cs.AI 2018
Year: 2018

Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN) motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff's game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff's Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent's internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies.
A Robust Secure Hybrid Analog and Digital Receive Beamforming Scheme for Efficient Interference Reduction
Venue: arXiv cs.IT 2018
Year: 2018

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction, but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, a classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, we in this paper propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. Simulation results show that the proposed scheme performs more robustly, and moreover, has a significant improvement on the receive signal to interference plus noise ratio (SINR) compared to NSP ADB scheme and DL method.
Decoupled Learning for Conditional Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Incorporating encoding-decoding nets with adversarial nets has been widely adopted in image generation tasks. We observe that the state-of-the-art achievements were obtained by carefully balancing the reconstruction loss and adversarial loss, and such balance shifts with different network structures, datasets, and training strategies. Empirical studies have demonstrated that an inappropriate weight between the two losses may cause instability, and it is tricky to search for the optimal setting, especially when lacking prior knowledge on the data and network. This paper gives the first attempt to relax the need of manual balancing by proposing the concept of \textit{decoupled learning}, where a novel network structure is designed that explicitly disentangles the backpropagation paths of the two losses. Experimental results demonstrate the effectiveness, robustness, and generality of the proposed method. The other contribution of the paper is the design of a new evaluation metric to measure the image quality of generative models. We propose the so-called \textit{normalized relative discriminative score} (NRDS), which introduces the idea of relative comparison, rather than providing absolute estimates like existing metrics.
NOOP: A Domain-Theoretic Model of Nominally-Typed OOP
Venue: arXiv cs.PL 2018
Year: 2018

The majority of industrial-strength object-oriented (OO) software is written using nominally-typed OO programming languages. Extant domain-theoretic models of OOP developed to analyze OO type systems miss, however, a crucial feature of these mainstream OO languages: nominality. This paper presents the construction of NOOP as the first domain-theoretic model of OOP that includes full class/type names information found in nominally-typed OOP. Inclusion of nominal information in objects of NOOP and asserting that type inheritance in statically-typed OO programming languages is an inherently nominal notion allow readily proving that type inheritance and subtyping are completely identified in these languages. This conclusion is in full agreement with intuitions of developers and language designers of these OO languages, and contrary to the belief that "inheritance is not subtyping," which came from assuming non-nominal (a.k.a., structural) models of OOP. To motivate the construction of NOOP, this paper briefly presents the benefits of nominal-typing to mainstream OO developers and OO language designers, as compared to structural-typing. After presenting NOOP, the paper further briefly compares NOOP to the most widely known domain-theoretic models of OOP. Leveraging the development of NOOP, the comparisons presented in this paper provide clear, brief and precise technical and mathematical accounts for the relation between nominal and structural OO type systems. NOOP, thus, provides a firmer semantic foundation for analyzing and progressing nominally-typed OO programming languages.
A Rate-Optimal Construction of Codes with Sequential Recovery with Low Block Length
Venue: arXiv cs.IT 2018
Year: 2018

An erasure code is said to be a code with sequential recovery with parameters $r$ and $t$, if for any $s \leq t$ erased code symbols, there is an $s$-step recovery process in which at each step we recover exactly one erased code symbol by contacting at most $r$ other code symbols. In earlier work by the same authors, presented at ISIT 2017, we had given a construction for binary codes with sequential recovery from $t$ erasures, with locality parameter $r$, which were optimal in terms of code rate for given $r,t$, but where the block length was large, on the order of $r^{c^t}$, for some constant $c >1$. In the present paper, we present an alternative construction of a rate-optimal code for any value of $t$ and any $r\geq3$, where the block length is significantly smaller, on the order of $r^{\frac{5t}{4}+\frac{7}{4}}$ (in some instances of order $r^{\frac{3t}{2}+2}$). Our construction is based on the construction of certain kind of tree-like graphs with girth $t+1$. We construct these graphs and hence the codes recursively.
Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks
Venue: arXiv cs.SI 2018
Year: 2018

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False Positive Rate) below $1\%$. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.
Artificial Impostors for Location Privacy Preservation
Venue: arXiv cs.SI 2018
Year: 2018

The progress of location-based services has led to serious concerns on location privacy leakage. For effective and efficient location privacy preservation (LPP), existing methods are still not fully competent. They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them through synthesizing artificial impostors (AIs). The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do not contain any target location. Two dedicated techniques are devised: the sampling-based synthesis method and population-level semantic model. They play significant roles in two critical steps of synthesizing AIs. We conduct experiments on real datasets in two cities (Shanghai, China and Asturias, Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of $97.65\%$ and $96.12\%$, and its run time of building the generators is only $230.47$ and $215.92$ seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations.
Channel Input Adaptation via Natural Type Selection
Venue: arXiv cs.IT 2018
Year: 2018

For the model of communication through a discrete memoryless channel using i.i.d. random block codes, where the channel is changing slowly from block to block, we propose a stochastic algorithm for adaptation of the generating distribution of the code in the process of continuous reliable communication. The purpose of the algorithm is to match the generating distribution $Q(x)$ to the changing channel $P(y\,|\,x)$, so that reliable communication is maintained at some constant rate $R$. This is achieved by a feedback of one bit per transmitted block. The feedback bit is determined by the joint type of the last transmitted codeword and the received block, a constant threshold $T>R$, and some conditional distribution $Φ(x\,|\,y)$. Depending on the value of the feedback bit, the system parameters $Q(x)$ and $Φ(x\,|\,y)$ are both updated according to the joint type of the last transmitted and received blocks, or remain unchanged. We show that, under certain technical conditions, the iterations of the algorithm lead to a distribution $Q(x)$, which guarantees reliable communication for all rates below the threshold $T$, provided that the discrete memoryless channel capacity of $P(y\,|\,x)$ stays above $T$.
Distributed Multi-User Wireless Charging Power Allocation
Venue: arXiv cs.IT 2018
Year: 2018

Wireless power charging enables portable devices to be permanently unplugged. Due to its low transmission power and low transmission efficiency, it requires much longer time slot to charge users compared with that for data transmission in wireless communication networks. Besides, each user's demand urgency needs to be taken into consideration for power allocation. Therefore, new algorithms are essential for wireless power allocation in multi-user wireless charging networks. In this paper, this problem is formulated as a static noncooperative game. It is shown that there exists a unique Nash equilibrium, which is the static state of the wireless power charging network. A distributed power allocation algorithm is proposed to compute the Nash equilibrium of the game. The main result of the paper consists of rigorous analysis of the distributed algorithm for power allocation. The algorithm is shown to converge to Nash equilibrium of the game with exponentially convergence rate for arbitrary initial value with synchronous scheduling. Moreover, the distributed algorithm is also convergence guaranteed with asynchronous scheduling under communication delay and packet drops. Numerical simulations prove the correctness of the analysis and demonstrate the fast convergence of the algorithm and the robustness to synchronous scheduling.
System-of-Systems Viewpoint for System Architecture Documentation
Venue: arXiv cs.SE 2018
Year: 2018

Context: The systems comprising a system of systems (SoS) are inde- pendently acquired, operated, and managed. Frequently, the architecture documentation of these existing systems addresses only a stand-alone perspective, and must be augmented to address concerns that arise in the integrated SoS. Objective: We evaluated an architecture documentation viewpoint to address the concerns of a SoS architect about a constituent system, to support SoS design and analysis involving that constituent system. Method: We performed an expert review of documentation produced by applying the viewpoint to a system, using the active review method. Results: The expert panel was able to used a view constructed using the baseline version of the viewpoint to answer questions related to all SoS architect concerns about a constituent system, except for questions concerning the interaction of the constituent system with the platform and network infrastructure. Conclusions: We found that the expert panel was unable to answer certain questions because the baseline version of the viewpoint had a gap in coverage related to relationship of software units of execution (e.g., processes or services) to computers and networks. The viewpoint was revised to add a Deployment Model to address these concerns, and is included in an appendix.
UAV-Enabled Cooperative Jamming for Improving Secrecy of Ground Wiretap Channel
Venue: arXiv cs.IT 2018
Year: 2018

This letter proposes a novel UAV-enabled mobile jamming scheme to improve the secrecy rate of ground wiretap channel. Specifically, a UAV is employed to transmit jamming signals to combat against eavesdropping. Such a mobile jamming scheme is particularly appealing since the UAV-enabled jammer can fly close to the eavesdropper and opportunistically jam it by leveraging the UAV's mobility. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory and jamming power over a given flight period. To make the problem more tractable, we drive a closed-form lower bound for the achievable secrecy rate, based on which the UAV's trajectory and transmit power are optimized alternately by an efficient iterative algorithm applying the block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that the proposed joint design can significantly enhance the secrecy rate of the considered wiretap system as compared to benchmark schemes.
Scene recognition with CNNs: objects, scales and dataset bias
Venue: arXiv cs.CV 2018
Year: 2018

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN, showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the state-of-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).