OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic Global Optimization Algorithms: A Systematic Formal Approach
Venue: arXiv cs.AI 2017
Year: 2017

As we know, some global optimization problems cannot be solved using analytic methods, so numeric/algorithmic approaches are used to find near to the optimal solutions for them. A stochastic global optimization algorithm (SGoal) is an iterative algorithm that generates a new population (a set of candidate solutions) from a previous population using stochastic operations. Although some research works have formalized SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive and systematic formal approach for studying SGoals. First, we present the required theory of probability (σ-algebras, measurable functions, kernel, markov chain, products, convergence and so on) and prove that some algorithmic functions like swapping and projection can be represented by kernels. Then, we introduce the notion of join-kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a formal structure (a set with a σ-algebra that contains strict ε-optimal states) for studying SGoals, and we develop kernels, like sort and permutation, on such structure. Finally, we present some popular SGoals in terms of the developed theory, we introduce sufficient conditions for convergence of a SGoal, and we prove convergence of some popular SGoals.
Smarter Cities with Parked Cars as Roadside Units
Venue: arXiv cs.NI 2017
Year: 2017

Real-time monitoring of traffic density, road congestion, public transportation, and parking availability are key to realizing the vision of a smarter city and, with the advent of vehicular networking technologies such as IEEE 802.11p and WAVE, this information can now be gathered directly from the vehicles in an urban area. To act as a backbone to the network of moving vehicles, collecting, aggregating, and disseminating their information, the use of parked cars has been proposed as an alternative to costly deployments of fixed Roadside Units. In this paper, we introduce novel mechanisms for parking vehicles to self-organize and form efficient vehicular support networks that provide widespread coverage to a city. These mechanisms are innovative in their ability to keep the network of parked cars under continuous optimization, in their multi-criteria decision process that can be focused on key network performance metrics, and in their ability to manage the battery usage of each car, rotating roadside unit roles between vehicles as required. We also present the first comprehensive study of the performance of such an approach, via realistic modeling of mobility, parking, and communication, thorough simulations, and an experimental verification of concepts that are key to self-organization. Our analysis brings strong evidence that parked cars can serve as an alternative to fixed roadside units, and organize to form networks that can support smarter transportation and mobility.
Dynamic Distributed Mobility Management System based on Multiple Mix-Zones over Road Networks
Venue: arXiv cs.NI 2017
Year: 2017

Vehicle have access to the internet for communications to facilitate the need of mobility management and point of interest distribution in emerging Intelligent Transportation System (ITS) . Therefore, its obvious that by changing the road side unit frequently , may require fast handover management to mobile user (MU) for the configuration of new IP address , however the ongoing session remain un-disturbed . Recent study has shown that , the current version of IP mobility protocols are the centralized solutions, In centralized environment data traffic and data management is being routed to an anchor entity.In some situations , the vehicles may be routed in the form of group to pass from one RSU to another RSU.In response the traffic and mobility exchanged messages are increased and it will dramatically affect the performance of network.To cope with these challenges a new construct named DDMM (Dynamic Distributed Mobility management) has been anticipated by IETF DDMM organization. It has been designed on the basis of network-based CMM protocol, which is known as Proxy Mobile IPv6(PMIPv6).However , it has been realized that there can be significance difference among network based DDMM and PMIPv6 with respect to handover latency and packet loss.Therefore , we have envisioned fast handover for network-based DDMM (Dynamic Distributed Mobility management) which is based on fast handover for PMIPv6(PFMIPv6).However , it has been required to include modifications on PFMIPV6 to take on DDMM. We have designed some important additions to support this model, when MU hold IP flows and has many different anchor entities.In addition we also derived analytical expressions to check and made comparison about the handover performance of the given DDMM schemes.It has been revealed that DDMM has outperformed in terms of handover latency , session recovery and packet loss as compared with previous schemes.
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of overlapping sound events using pitch cues and exploiting the stereo (multichannel) audio signal available at their ears to spatially localize these events. Traditionally SED systems have only been using mono channel audio, motivated by the human listener we propose to extend them to use multichannel audio. The proposed SED system is compared against the state of the art mono channel method on the development subset of TUT sound events detection 2016 database. The usage of spatial and harmonic features are shown to improve the performance of SED.
Detection of App Collusion Potential Using Logic Programming
Venue: arXiv cs.CR 2017
Year: 2017

Android is designed with a number of built-in security features such as app sandboxing and permission-based access controls. Android supports multiple communication methods for apps to cooperate. This creates a security risk of app collusion. For instance, a sandboxed app with permission to access sensitive data might leak that data to another sandboxed app with access to the internet. In this paper, we present a method to detect potential collusion between apps. First, we extract from apps all information about their accesses to protected resources and communications. Then we identify sets of apps that might be colluding by using rules in first order logic codified in Prolog. After these, more computationally demanding approaches like taint analysis can focus on the identified sets that show collusion potential. This "filtering" approach is validated against a dataset of manually crafted colluding apps. We also demonstrate that our tool scales by running it on a set of more than 50,000 apps collected in the wild. Our tool allowed us to detect a large set of real apps that used collusion as a synchronization method to maximize the effects of a payload that was injected into all of them via the same SDK.
ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN is based on a power-of-two weight representation and, as a result, performs only shift and addition operations. Furthermore, ShiftCNN substantially reduces computational cost of convolutional layers by precomputing convolution terms. Such an optimization can be applied to any CNN architecture with a relatively small codebook of weights and allows to decrease the number of product operations by at least two orders of magnitude. The proposed architecture targets custom inference accelerators and can be realized on FPGAs or ASICs. Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be converted without retraining into ShiftCNN with less than 1% drop in accuracy when the proposed quantization algorithm is employed. RTL simulations, targeting modern FPGAs, show that power consumption of convolutional layers is reduced by a factor of 4 compared to conventional 8-bit fixed-point architectures.
Image Captioning with Object Detection and Localization
Venue: arXiv cs.CV 2017
Year: 2017

Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperforms previous benchmark models.
Risk-Informed Interference Assessment for Shared Spectrum Bands: A Wi-Fi/LTE Coexistence Case Study
Venue: arXiv cs.NI 2017
Year: 2017

Interference evaluation is crucial when deciding whether and how wireless technologies should operate. In this paper we demonstrate the benefit of risk-informed interference assessment to aid spectrum regulators in making decisions, and to readily convey engineering insight. Our contributions are: we apply, for the first time, risk assessment to a problem of inter-technology spectrum sharing, i.e. Wi-Fi/LTE in the 5 GHz unlicensed band, and we demonstrate that this method comprehensively quantifies the interference impact. We perform simulations with our newly publicly-available tool and we consider throughput degradation and fairness metrics to assess the risk for different network densities, numbers of channels, and deployment scenarios. Our results show that no regulatory intervention is needed to ensure harmonious technical Wi-Fi/LTE coexistence: for the typically large number of channels available in the 5 GHz band, the risk for Wi-Fi from LTE is negligible, rendering policy and engineering concerns largely moot. As an engineering insight, Wi-Fi coexists better with itself in dense, but better with LTE, in sparse deployments. Also, both main LTE-in-unlicensed variants coexist well with Wi-Fi in general. For LTE intra-technology inter-operator coexistence, both variants typically coexist well in the 5 GHz band, but for dense deployments, implementing listen-before-talk causes less interference.
Conditional Transition Systems with Upgrades
Venue: arXiv cs.SE 2017
Year: 2017

We introduce a variant of transition systems, where activation of transitions depends on conditions of the environment and upgrades during runtime potentially create additional transitions. Using a cornerstone result in lattice theory, we show that such transition systems can be modelled in two ways: as conditional transition systems (CTS) with a partial order on conditions, or as lattice transition systems (LaTS), where transitions are labelled with the elements from a distributive lattice. We define equivalent notions of bisimilarity for both variants and characterise them via a bisimulation game. We explain how conditional transition systems are related to featured transition systems for the modelling of software product lines. Furthermore, we show how to compute bisimilarity symbolically via BDDs by defining an operation on BDDs that approximates an element of a Boolean algebra into a lattice. We have implemented our procedure and provide runtime results.
Clique Gossiping
Venue: arXiv cs.DC 2017
Year: 2017

This paper proposes and investigates a framework for clique gossip protocols. As complete subnetworks, the existence of cliques is ubiquitous in various social, computer, and engineering networks. By clique gossiping, nodes interact with each other along a sequence of cliques. Clique-gossip protocols are defined as arbitrary linear node interactions where node states are vectors evolving as linear dynamical systems. Such protocols become clique-gossip averaging algorithms when node states are scalars under averaging rules. We generalize the classical notion of line graph to capture the essential node interaction structure induced by both the underlying network and the specific clique sequence. We prove a fundamental eigenvalue invariance principle for periodic clique-gossip protocols, which implies that any permutation of the clique sequence leads to the same spectrum for the overall state transition when the generalized line graph contains no cycle. We also prove that for a network with $n$ nodes, cliques with smaller sizes determined by factors of $n$ can always be constructed leading to finite-time convergent clique-gossip averaging algorithms, provided $n$ is not a prime number. Particularly, such finite-time convergence can be achieved with cliques of equal size $m$ if and only if $n$ is divisible by $m$ and they have exactly the same prime factors. A proven fastest finite-time convergent clique-gossip algorithm is constructed for clique-gossiping using size-$m$ cliques. Additionally, the acceleration effects of clique-gossiping are illustrated via numerical examples.
An effective and secure user authenticated protocol for Location based services in road networks
Venue: arXiv cs.CR 2017
Year: 2017

Road network is employed for exchanging the information among the vehicles where accidents and traffic information can be delivered, or receive services by an infrastructure. Although wireless communication systems yield an efficient traffic system and provide ease to the drivers, there is a chance of a traffic disturbance and risk to drivers through malicious information. So, there should be examined a way to limit the chance of an intermediate attack. This paper presents an efficient user based authentication protocol for location based services to secure address configuration for IPv6-based mix-zones over the road network. This protocol authenticates to inspect vehicles actions confidentially and have the following characteristics (1) Anonymous authentication: a message issuer can be authenticated. (2) Privacy: Communication content is confidential. The cost must be reduced through the address configuration scheme to improve the scalability. (3) Efficiency: it attains quick message verification, low storage requirements, and in case of a dispute, provides cost efficient identity tracking. Vehicles movement, the variation of velocity and distance are considered to maintain as many common users as possible by reducing the cost. The performance evaluation and cost analysis show that our framework can reduce the cost and gain outperformed results. This model can achieve reliability and efficiency with packet rate information. This user authenticated key establishment protocol has comparatively shorter time response, diminishes cost, less packet loss information and enhanced privacy preservation against malicious attacks compared with existing methods.
What Does a Belief Function Believe In ?
Venue: arXiv cs.AI 2017
Year: 2017

The conditioning in the Dempster-Shafer Theory of Evidence has been defined (by Shafer \cite{Shafer:90} as combination of a belief function and of an "event" via Dempster rule. On the other hand Shafer \cite{Shafer:90} gives a "probabilistic" interpretation of a belief function (hence indirectly its derivation from a sample). Given the fact that conditional probability distribution of a sample-derived probability distribution is a probability distribution derived from a subsample (selected on the grounds of a conditioning event), the paper investigates the empirical nature of the Dempster- rule of combination. It is demonstrated that the so-called "conditional" belief function is not a belief function given an event but rather a belief function given manipulation of original empirical data.\\ Given this, an interpretation of belief function different from that of Shafer is proposed. Algorithms for construction of belief networks from data are derived for this interpretation.
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
Venue: arXiv cs.CV 2017
Year: 2017

Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface. In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small. But high speed scanning places a significant burden on the projector electronics to produce contone patterns inside of short exposure intervals. Binary patterns are, therefore, of great value, but converting contone patterns into binary comes with significant risk. As such, this paper introduces a contone-to-binary conversion algorithm for deriving binary patterns that best mimic their contone counterparts. Experimental results will show a greater than 3 times reduction in pattern noise over traditional halftoning procedures.
Source Forager: A Search Engine for Similar Source Code
Venue: arXiv cs.SE 2017
Year: 2017

Developers spend a significant amount of time searching for code: e.g., to understand how to complete, correct, or adapt their own code for a new context. Unfortunately, the state of the art in code search has not evolved much beyond text search over tokenized source. Code has much richer structure and semantics than normal text, and this property can be exploited to specialize the code-search process for better querying, searching, and ranking of code-search results. We present a new code-search engine named Source Forager. Given a query in the form of a C/C++ function, Source Forager searches a pre-populated code database for similar C/C++ functions. Source Forager preprocesses the database to extract a variety of simple code features that capture different aspects of code. A search returns the $k$ functions in the database that are most similar to the query, based on the various extracted code features. We tested the usefulness of Source Forager using a variety of code-search queries from two domains. Our experiments show that the ranked results returned by Source Forager are accurate, and that query-relevant functions can be reliably retrieved even when searching through a large code database that contains very few query-relevant functions. We believe that Source Forager is a first step towards much-needed tools that provide a better code-search experience.
Renewal-Theoretic Packet Collision Modeling under Long-Tailed Heterogeneous Traffic
Venue: arXiv cs.IT 2017
Year: 2017

Internet-of-things (IoT), with the vision of billions of connected devices, is bringing a massively heterogeneous character to wireless connectivity in unlicensed bands. The heterogeneity in medium access parameters, transmit power and activity levels among the coexisting networks leads to detrimental cross-technology interference. The stochastic traffic distributions, shaped under CSMA/CA rules, of an interfering network and channel fading makes it challenging to model and analyze the performance of an interfered network. In this paper, to study the temporal interaction between the traffic distributions of two coexisting networks, we develop a renewal-theoretic packet collision model and derive a generic collision-time distribution (CTD) function of an interfered system. The CTD function holds for any busy- and idle-time distributions of the coexisting traffic. As the earlier studies suggest a long-tailed idle-time statistics in real environments, the developed model only requires the Laplace transform of long-tailed distributions to find the CTD. Furthermore, we present a packet error rate (PER) model under the proposed CTD and multipath fading of the interfering signals. Using this model, a computationally efficient PER approximation for interference-limited case is developed to analyze the performance of an interfered link.
Myhill-Nerode Relation for Sequentiable Structures
Venue: arXiv cs.FL 2017
Year: 2017

Sequentiable structures are a subclass of monoids that generalise the free monoids and the monoid of non-negative real numbers with addition. In this paper we consider functions $f:Σ^*\rightarrow {\cal M}$ and define the Myhill-Nerode relation for these functions. We prove that a function of finite index, $n$, can be represented with a subsequential transducer with $n$ states.
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
Venue: arXiv cs.CV 2017
Year: 2017

Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.
Depthwise Separable Convolutions for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves new state-of-the-art results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new "super-separable" convolution operation that further reduces the number of parameters and computational cost for obtaining state-of-the-art results.