OpenRaiser/Intern-Atlas huggingface dataset:
Security Strength Indicator in Fallback Authentication: Nudging Users for Better Answers in Secret Questions
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we describe ongoing work that focuses on improving the strength of the answers to security questions. The ultimate goal of the proposed research is to evaluate the possibility of nudging users towards strong answers for ubiquitous security questions. In this research we are proposing a user interface design for fallback authentication to encourage users to design stronger answers. The proposed design involves visual feedback to the user based on mnemonics which attempts to give visual feedback to the user on the strength of the answer provided and guide the user to creatively design a stronger answer.
Light Source Point Cluster Selection Based Atmosphere Light Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Atmosphere light value is a highly critical parameter in defogging algorithms that are based on an atmosphere scattering model. Any error in atmosphere light value will produce a direct impact on the accuracy of scattering computation and thus bring chromatic distortion to restored images. To address this problem, this paper propose a method that relies on clustering statistics to estimate atmosphere light value. It starts by selecting in the original image some potential atmosphere light source points, which are grouped into point clusters by means of clustering technique. From these clusters, a number of clusters containing candidate atmosphere light source points are selected, the points are then analyzed statistically, and the cluster containing the most candidate points is used for estimating atmosphere light value. The mean brightness vector of the candidate atmosphere light points in the chosen point cluster is taken as the estimate of atmosphere light value, while their geometric center in the image is accepted as the location of atmosphere light. Experimental results suggest that this statistics clustering method produces more accurate atmosphere brightness vectors and light source locations. This accuracy translates to, from a subjective perspective, more natural defogging effect on the one hand and to the improvement in various objective image quality indicators on the other hand.
Ordered Pooling of Optical Flow Sequences for Action Recognition
Venue: arXiv cs.CV 2017
Year: 2017

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more effectively than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the state-of-the-art on UCF101 and HMDB datasets.
Achievable Rate Region of the Zero-Forcing Precoder in a 2 X 2 MU-MISO Broadcast VLC Channel with Per-LED Peak Power Constraint and Dimming Control
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider the 2 X 2 multi-user multiple-input-single-output (MU-MISO) broadcast visible light communication (VLC) channel with two light emitting diodes (LEDs) at the transmitter and a single photo diode (PD) at each of the two users. We propose an achievable rate region of the Zero-Forcing (ZF) precoder in this 2 X 2 MU-MISO VLC channel under a per-LED peak and average power constraint, where the average optical power emitted from each LED is fixed for constant lighting, but is controllable (referred to as dimming control in IEEE 802.15.7 standard on VLC). We analytically characterize the proposed rate region boundary and show that it is Pareto-optimal. Further analysis reveals that the largest rate region is achieved when the fixed per-LED average optical power is half of the allowed per-LED peak optical power. We also propose a novel transceiver architecture where the channel encoder and dimming control are separated which greatly simplifies the complexity of the transceiver. A case study of an indoor VLC channel with the proposed transceiver reveals that the achievable information rates are sensitive to the placement of the LEDs and the PDs. An interesting observation is that for a given placement of LEDs in a 5 m X 5 m X 3 m room, even with a substantial displacement of the users from their optimum placement, reduction in the achievable rates is not significant. This observation could therefore be used to define "coverage zones" within a room where the reduction in the information rates to the two users is within an acceptable tolerance limit.
Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network
Venue: arXiv cs.DC 2017
Year: 2017

Scalability is an important characteristic of cloud computing. With scalability, cost is minimized by provisioning and releasing resources according to demand. Most of current Infrastructure as a Service (IaaS) providers deliver threshold-based auto-scaling techniques. However, setting up thresholds with right values that minimize cost and achieve Service Level Agreement is not an easy task, especially with variant and sudden workload changes. This paper has proposed dynamic threshold based auto-scaling algorithms that predict required resources using Long Short-Term Memory Recurrent Neural Network and auto-scale virtual resources based on predicted values. The proposed algorithms have been evaluated and compared with some of existing algorithms. Experimental results show that the proposed algorithms outperform other algorithms.
Sampling and Reconstruction Using Bloom Filters
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we address the problem of sampling from a set and reconstructing a set stored as a Bloom filter. To the best of our knowledge our work is the first to address this question. We introduce a novel hierarchical data structure called BloomSampleTree that helps us design efficient algorithms to extract an almost uniform sample from the set stored in a Bloom filter and also allows us to reconstruct the set efficiently. In the case where the hash functions used in the Bloom filter implementation are partially invertible, in the sense that it is easy to calculate the set of elements that map to a particular hash value, we propose a second, more space-efficient method called HashInvert for the reconstruction. We study the properties of these two methods both analytically as well as experimentally. We provide bounds on run times for both methods and sample quality for the BloomSampleTree based algorithm, and show through an extensive experimental evaluation that our methods are efficient and effective.
LanideNN: Multilingual Language Identification on Character Window
Venue: arXiv cs.CL 2017
Year: 2017

In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. Monolingual language identification assumes that the given document is written in one language. In multilingual language identification, the document is usually in two or three languages and we just want their names. We aim one step further and propose a method for textual language identification where languages can change arbitrarily and the goal is to identify the spans of each of the languages. Our method is based on Bidirectional Recurrent Neural Networks and it performs well in monolingual and multilingual language identification tasks on six datasets covering 131 languages. The method keeps the accuracy also for short documents and across domains, so it is ideal for off-the-shelf use without preparation of training data.
A Digital Fuzzy Edge Detector for Color Images
Venue: arXiv cs.CV 2017
Year: 2017

Edge detection is a classic problem in the field of image processing, which lays foundations for other tasks such as image segmentation. Conventionally, this operation is performed using gradient operators such as the Roberts or Sobel operator, which can discover local changes in intensity levels. These operators, however, perform poorly on low contrast images. In this paper, we propose an edge detector architecture for color images based on fuzzy theory and the Sobel operator. First, the R, G and B channels are extracted from an image and enhanced using fuzzy methods, in order to suppress noise and improve the contrast between the background and the objects. The Sobel operator is then applied to each of the channels, which are finally combined into an edge map of the origin image. Experimental results obtained through an FPGA-based implementation have proved the proposed method effective.
An Asynchronous Parallel Approach to Sparse Recovery
Venue: arXiv cs.LG 2017
Year: 2017

Asynchronous parallel computing and sparse recovery are two areas that have received recent interest. Asynchronous algorithms are often studied to solve optimization problems where the cost function takes the form $\sum_{i=1}^M f_i(x)$, with a common assumption that each $f_i$ is sparse; that is, each $f_i$ acts only on a small number of components of $x\in\mathbb{R}^n$. Sparse recovery problems, such as compressed sensing, can be formulated as optimization problems, however, the cost functions $f_i$ are dense with respect to the components of $x$, and instead the signal $x$ is assumed to be sparse, meaning that it has only $s$ non-zeros where $s\ll n$. Here we address how one may use an asynchronous parallel architecture when the cost functions $f_i$ are not sparse in $x$, but rather the signal $x$ is sparse. We propose an asynchronous parallel approach to sparse recovery via a stochastic greedy algorithm, where multiple processors asynchronously update a vector in shared memory containing information on the estimated signal support. We include numerical simulations that illustrate the potential benefits of our proposed asynchronous method.
Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks
Venue: arXiv cs.SY 2017
Year: 2017

Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs) with competing charging stations to exchange data, such as electricity price, energy demand, and time of arrival. EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies, and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs charging demand estimation is done much more accurately. Then, the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices are communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e. optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.
Scalable, Trie-based Approximate Entity Extraction for Real-Time Financial Transaction Screening
Venue: arXiv cs.CL 2017
Year: 2017

Financial institutions have to screen their transactions to ensure that they are not affiliated with terrorism entities. Developing appropriate solutions to detect such affiliations precisely while avoiding any kind of interruption to large amount of legitimate transactions is essential. In this paper, we present building blocks of a scalable solution that may help financial institutions to build their own software to extract terrorism entities out of both structured and unstructured financial messages in real time and with approximate similarity matching approach.
Computing Scores of Forwarding Schemes in Switched Networks with Probabilistic Faults
Venue: arXiv cs.DC 2017
Year: 2017

Time-triggered switched networks are a deterministic communication infrastructure used by real-time distributed embedded systems. Due to the criticality of the applications running over them, developers need to ensure that end-to-end communication is dependable and predictable. Traditional approaches assume static networks that are not flexible to changes caused by reconfigurations or, more importantly, faults, which are dealt with in the application using redundancy. We adopt the concept of handling faults in the switches from non-real-time networks while maintaining the required predictability. We study a class of forwarding schemes that can handle various types of failures. We consider probabilistic failures. For a given network with a forwarding scheme and a constant $\ell$, we compute the {\em score} of the scheme, namely the probability (induced by faults) that at least $\ell$ messages arrive on time. We reduce the scoring problem to a reachability problem on a Markov chain with a "product-like" structure. Its special structure allows us to reason about it symbolically, and reduce the scoring problem to #SAT. Our solution is generic and can be adapted to different networks and other contexts. Also, we show the computational complexity of the scoring problem is #P-complete, and we study methods to estimate the score. We evaluate the effectiveness of our techniques with an implementation.
Balanced Dynamic Content Addressing in Trees
Venue: arXiv cs.NI 2017
Year: 2017

Balancing the load in content addressing schemes for route-restricted networks represents a challenge with a wide range of applications. Solutions based on greedy embeddings maintain minimal state information and enable efficient routing, but any such solutions currently result in either imbalanced content addressing, overloading individual nodes, or are unable to efficiently account for network dynamics. In this work, we propose a greedy embedding in combination with a content addressing scheme that provides balanced content addressing while at the same time enabling efficient stabilization in the presence of network dynamics. We point out the trade-off between stabilization complexity and maximal permitted imbalance when deriving upper bounds on both metrics for two variants of the proposed algorithms. Furthermore, we substantiate these bounds through a simulation study based on both real-world and synthetic data.
Cost-Effective Active Learning for Deep Image Classification
Venue: arXiv cs.CV 2017
Year: 2017

Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing active learning methods in two aspects. First, we incorporate deep convolutional neural networks into active learning. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high confidence samples from the unlabeled set for feature learning. Specifically, these high confidence samples are automatically selected and iteratively assigned pseudo-labels. We thus call our framework "Cost-Effective Active Learning" (CEAL) standing for the two advantages.Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification datasets, i.e., face recognition on CACD database [1] and object categorization on Caltech-256 [2].
Generalized Index Coding Problem and Discrete Polymatroids
Venue: arXiv cs.IT 2017
Year: 2017

The index coding problem has been generalized recently to accommodate receivers which demand functions of messages and which possess functions of messages. The connections between index coding and matroid theory have been well studied in the recent past. Index coding solutions were first connected to multi linear representation of matroids. For vector linear index codes discrete polymatroids which can be viewed as a generalization of the matroids was used. It was shown that a vector linear solution to an index coding problem exists if and only if there exists a representable discrete polymatroid satisfying certain conditions. In this work we explore the connections between generalized index coding and discrete polymatroids. The conditions that needs to be satisfied by a representable discrete polymatroid for a generalized index coding problem to have a vector linear solution is established. From a discrete polymatroid we construct an index coding problem with coded side information and shows that if the index coding problem has a certain optimal length solution then the discrete polymatroid satisfies certain properties. From a matroid we construct a similar generalized index coding problem and shows that the index coding problem has a binary scalar linear solution of optimal length if and only if the matroid is binary representable.
Towards a Decidable LogicWeb via Length-Bounded Derivations
Venue: arXiv cs.LO 2017
Year: 2017

LogicWeb has traditionally lacked devices for dealing with intractable queries. We address this limitation by adopting length-bounded inference, a form of approximate reasoning. A length-bounded inference is of the form $prov(P,G,n)$ which is a success if a query $G$ can be proved from the web page $P$ within $n$ proof steps. It thus makes LogicWeb decidable and more tractable. During the process, we propose a novel module language for logic programming as a device for structuring programs and queries.
On OR Many-Access Channels
Venue: arXiv cs.IT 2017
Year: 2017

OR multi-access channel is a simple model where the channel output is the Boolean OR among the Boolean channel inputs. We revisit this model, showing that employing Bloom filter, a randomized data structure, as channel inputs achieves its capacity region with joint decoding and the symmetric sum rate of $\ln 2$ bits per channel use without joint decoding. We then proceed to the "many-access" regime where the number of potential users grows without bound, treating both activity recognition and message transmission problems, establishing scaling laws which are optimal within a constant factor, based on Bloom filter channel inputs.
Symbolic Regression Algorithms with Built-in Linear Regression
Venue: arXiv cs.LG 2017
Year: 2017

Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude) faster than SR algorithms based on vanilla genetic programming. However, a systematic comparison of these algorithms on a common set of problems is still missing. In this paper we conceptually and experimentally compare several representatives of such algorithms (GPTIPS, FFX, and EFS). They are applied as off-the-shelf, ready-to-use techniques, mostly using their default settings. The methods are compared on several synthetic and real-world SR benchmark problems. Their performance is also related to the performance of three conventional machine learning algorithms --- multiple regression, random forests and support vector regression.
Dictionary Learning from Incomplete Data
Venue: arXiv cs.LG 2017
Year: 2017

This paper extends the recently proposed and theoretically justified iterative thresholding and $K$ residual means algorithm ITKrM to learning dicionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low rank component in the data and provides a strategy for recovering this low rank component again from incomplete data. Several synthetic experiments show the advantages of incorporating information about the corruption into the algorithm. Finally, image inpainting is considered as application example, which demonstrates the superior performance of ITKrMM in terms of speed at similar or better reconstruction quality compared to its closest dictionary learning counterpart.
Power and Execution Time Measurement Methodology for SDF Applications on FPGA-based MPSoCs
Venue: arXiv cs.DC 2017
Year: 2017

Timing and power consumption play an important role in the design of embedded systems. Furthermore, both properties are directly related to the safety requirements of many embedded systems. With regard to availability requirements, power considerations are of uttermost importance for battery operated systems. Validation of timing and power requires observability of these properties. In many cases this is difficult, because the observability is either not possible or requires big extra effort in the system validation process. In this paper, we present a measurement-based approach for the joint timing and power analysis of Synchronous Dataflow (SDF) applications running on a shared memory multiprocessor systems-on-chip (MPSoC) architecture. As a proof-of-concept, we implement an MPSoC system with configurable power and timing measurement interfaces inside a Field Programmable Gate Array (FPGA). Our experiments demonstrate the viability of our approach being able of accurately analyzing different mappings of image processing applications (Sobel filter and JPEG encoder) on an FPGA-based MPSoC implementation.