OpenRaiser/Intern-Atlas huggingface dataset:
Online People Tracking and Identification with RFID and Kinect
Venue: arXiv cs.CV 2017
Year: 2017

We introduce a novel, accurate and practical system for real-time people tracking and identification. We used a Kinect V2 sensor for tracking that generates a body skeleton for up to six people in the view. We perform identification using both Kinect and passive RFID, by first measuring the velocity vector of person's skeleton and of their RFID tag using the position of the RFID reader antennas as reference points and then finding the best match between skeletons and tags. We introduce a method for synchronizing Kinect data, which is captured regularly, with irregular or missing RFID data readouts. Our experiments show centimeter-level people tracking resolution with 80% average identification accuracy for up to six people in indoor environments, which meets the needs of many applications. Our system can preserve user privacy and work with different lighting.
Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems
Venue: arXiv cs.SE 2017
Year: 2017

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.
Towards speech-to-text translation without speech recognition
Venue: arXiv cs.CL 2017
Year: 2017

We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction
Venue: arXiv cs.LG 2017
Year: 2017

Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we show how to adapt some of these techniques to create a novel chained convolutional architecture with next-step conditioning for improving performance on protein sequence prediction problems. We explore its value by demonstrating its ability to improve performance on eight-class secondary structure prediction. We first establish a state-of-the-art baseline by adapting recent advances in convolutional neural networks which were developed for vision tasks. This model achieves 70.0% per amino acid accuracy on the CB513 benchmark dataset without use of standard performance-boosting techniques such as ensembling or multitask learning. We then improve upon this state-of-the-art result using a novel chained prediction approach which frames the secondary structure prediction as a next-step prediction problem. This sequential model achieves 70.3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71.4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem. Our models are implemented using TensorFlow, an open-source machine learning software library available at TensorFlow.org; we aim to release the code for these experiments as part of the TensorFlow repository.
Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects
Venue: arXiv cs.IT 2017
Year: 2017

The problem of estimating the dynamic direction of arrival of far field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This work proposes two novel approaches able to provide accurate solutions, including at the endfire regions of the array. Firstly, a Bayesian compressive sensing Kalman filter is developed, which accounts for the predicted estimated signals rather than using the traditional sparse prior. The posterior probability density function of the received source signals and the expression for the related marginal likelihood function are derived theoretically. Next, a Gibbs sampling based approach with indicator variables in the sparsity prior is developed. This allows sparsity to be explicitly enforced in different ways, including when an angle is too far from the previous estimate. The proposed approaches are validated and evaluated over different test scenarios and compared to the traditional relevance vector machine based method. An improved accuracy in terms of average root mean square error values is achieved (up to 73.39% for the modified relevance vector machine based approach and 86.36% for the Gibbs sampling based approach). The proposed approaches prove to be particularly useful for direction of arrival estimation when the angle of arrival moves into the endfire region of the array.
Google Scholar and the gray literature: A reply to Bonato's review
Venue: arXiv cs.DL 2017
Year: 2017

Recently, a review concluded that Google Scholar (GS) is not a suitable source of information "for identifying recent conference papers or other gray literature publications". The goal of this letter is to demonstrate that GS can be an effective tool to search and find gray literature, as long as appropriate search strategies are used. To do this, we took as examples the same two case studies used by the original review, describing first how GS processes original's search strategies, then proposing alternative search strategies, and finally generalizing each case study to compose a general search procedure aimed at finding gray literature in Google Scholar for two wide selected case studies: a) all contributions belonging to a congress (the ASCO Annual Meeting); and b) indexed guidelines as well as gray literature within medical institutions (National Institutes of Health) and governmental agencies (U.S. Department of Health & Human Services). The results confirm that original search strategies were undertrained offering misleading results and erroneous conclusions. Google Scholar lacks many of the advanced search features available in other bibliographic databases (such as Pubmed), however, it is one thing to have a friendly search experience, and quite another to find gray literature. We finally conclude that Google Scholar is a powerful tool for searching gray literature, as long as the users are familiar with all the possibilities it offers as a search engine. Poorly formulated searches will undoubtedly return misleading results.
Network growth models: A behavioural basis for attachment proportional to fitness
Venue: arXiv cs.SI 2017
Year: 2017

Several growth models have been proposed in the literature for scale-free complex networks, with a range of fitness-based attachment models gaining prominence recently. However, the processes by which such fitness-based attachment behaviour can arise are less well understood, making it difficult to compare the relative merits of such models. This paper analyses an evolutionary mechanism that would give rise to a fitness-based attachment process. In particular, it is proven by analytical and numerical methods that in homogeneous networks, the minimisation of maximum exposure to node unfitness leads to attachment probabilities that are proportional to node fitness. This result is then extended to heterogeneous networks, with supply chain networks being used as an example.
Crowdsourcing the Policy Cycle
Venue: arXiv cs.CY 2017
Year: 2017

Crowdsourcing is beginning to be used for policymaking. The wisdom of crowds [Surowiecki 2005], and crowdsourcing [Brabham 2008], are seen as new avenues that can shape all kinds of policy, from transportation policy [Nash 2009] to urban planning [Seltzer and Mahmoudi 2013], to climate policy. In general, many have high expectations for positive outcomes with crowdsourcing, and based on both anecdotal and empirical evidence, some of these expectations seem justified [Majchrzak and Malhotra 2013]. Yet, to our knowledge, research has yet to emerge that unpacks the different forms of crowdsourcing in light of each stage of the well-established policy cycle. This work addresses this research gap, and in doing so brings increased nuance to the application of crowdsourcing techniques for policymaking.
The Contours of Crowd Capability
Venue: arXiv cs.CY 2017
Year: 2017

In this work we use the theory of Crowd Capital as a lens to compare and contrast a number of IS tools currently in use by organizations for crowd-engagement purposes. In doing so, we contribute to both the practitioner and research domains. For the practitioner community we provide decision-makers with a convenient and useful resource, in table-form, outlining in detail some of the differing potentialities of crowd-engaging IS. For the research community we begin to unpack some of the key properties of crowd-engaging IS, including some of the differing qualities of the crowds that these IS application engage.
A Geography of Participation in IT-Mediated Crowds
Venue: arXiv cs.CY 2017
Year: 2017

In this work we seek to understand how differences in location affect participation outcomes in IT-mediated crowds. To do so, we operationalize Crowd Capital Theory with data from a popular international creative crowdsourcing site, to determine whether regional differences exist in crowdsourcing participation outcomes. We present the early results of our investigation from data encompassing 1,858,202 observations from 28,214 crowd members on 94 different projects in 2012. Using probit regressions to isolate geographic effects by continental region, we find significant variation across regions in crowdsourcing participation. In doing so, we contribute to the literature by illustrating that geography matters in respect to crowd participation. Further, our work illustrates an initial validation of Crowd Capital Theory as a useful theoretical model to guide empirical inquiry in the fast-growing domain of IT-mediated crowds.
Prospect Theory for Enhanced Cyber-Physical Security of Drone Delivery Systems: A Network Interdiction Game
Venue: arXiv cs.GT 2017
Year: 2017

The use of unmanned aerial vehicles (UAVs) as delivery systems of online goods is rapidly becoming a global norm, as corroborated by Amazon's "Prime Air" and Google's "Project Wing" projects. However, the real-world deployment of such drone delivery systems faces many cyber-physical security challenges. In this paper, a novel mathematical framework for analyzing and enhancing the security of drone delivery systems is introduced. In this regard, a zero-sum network interdiction game is formulated between a vendor, operating a drone delivery system, and a malicious attacker. In this game, the vendor seeks to find the optimal path that its UAV should follow, to deliver a purchase from the vendor's warehouse to a customer location, to minimize the delivery time. Meanwhile, an attacker seeks to choose an optimal location to interdict the potential paths of the UAVs, so as to inflict cyber or physical damage to it, thus, maximizing its delivery time. First, the Nash equilibrium point of this game is characterized. Then, to capture the subjective behavior of both the vendor and attacker, new notions from prospect theory are incorporated into the game. These notions allow capturing the vendor's and attacker's i) subjective perception of attack success probabilities, and ii) their disparate subjective valuations of the achieved delivery times relative to a certain target delivery time. Simulation results have shown that the subjective decision making of the vendor and attacker leads to adopting risky path selection strategies which inflict delays to the delivery, thus, yielding unexpected delivery times which surpass the target delivery time set by the vendor.
Integrating Three Mechanisms of Visual Attention for Active Visual Search
Venue: arXiv cs.CV 2017
Year: 2017

Algorithms for robotic visual search can benefit from the use of visual attention methods in order to reduce computational costs. Here, we describe how three distinct mechanisms of visual attention can be integrated and productively used to improve search performance. The first is viewpoint selection as has been proposed earlier using a greedy search over a probabilistic occupancy grid representation. The second is top-down object-based attention using a histogram backprojection method, also previously described. The third is visual saliency. This is novel in the sense that it is not used as a region-of-interest method for the current image but rather as a noncombinatorial form of look-ahead in search for future viewpoint selection. Additionally, the integration of these three attentional schemes within a single framework is unique and not previously studied. We examine our proposed method in scenarios where little or no information regarding the environment is available. Through extensive experiments on a mobile robot, we show that our method improves visual search performance by reducing the time and number of actions required.
The KISS principle in Software-Defined Networking: An architecture for Keeping It Simple and Secure
Venue: arXiv cs.NI 2017
Year: 2017

Security is an increasingly fundamental requirement in Software-Defined Networking (SDN). However, the pace of adoption of secure mechanisms has been slow, which we estimate to be a consequence of the performance overhead of traditional solutions and of the complexity of the support infrastructure required. As a first step to addressing these problems, we propose a modular secure SDN control plane communications architecture, KISS, with innovative solutions in the context of key distribution and secure channel support. A comparative analysis of the performance impact of essential security primitives guided our selection of basic primitives for KISS. We further propose iDVV, the integrated device verification value, a deterministic but indistinguishable-from-random secret code generation protocol, allowing the local but synchronized generation/verification of keys at both ends of the channel, even on a per-message basis. iDVV is expected to give an important contribution both to the robustness and simplification of the authentication and secure communication problems in SDN. We show that our solution, while offering the same security properties, outperforms reference alternatives, with performance improvements up to 30% over OpenSSL, and improvement in robustness based on a code footprint one order of magnitude smaller. Finally, we also prove and test randomness of the proposed algorithms.
Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection
Venue: arXiv cs.CV 2017
Year: 2017

Face detection is one of the challenging tasks in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as face recognition, face tracking, image database management, etc. In these applications, face objects often come from an inconsequential part of images that contain variations, namely different illumination, poses, and occlusion. These variations can decrease face detection rate noticeably. Most existing face detection approaches are not accurate, as they have not been able to resolve unstructured images due to large appearance variations and can only detect human faces under one particular variation. Existing frameworks of face detection need enhancements to detect human faces under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate.
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
Venue: arXiv cs.LG 2017
Year: 2017

Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different vari- ations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two key sets of optimization heuristics are the bin assignment and the bin allocation. Usually the performance of a single static optimization heuristic can not beat that of a dynamic one which is tailored for each bin packing instance. Building such an adaptive system requires modeling the relationship between bin features and packing perform profiles. The primary drawbacks of traditional AI machine learnings for this task are the natural limitations of feature engineering, such as the curse of dimensionality and feature selection quality. We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling. We show in this paper how to build such a system by both theoretical formulation and engineering practices. Our prediction system achieves up to 89% training accuracy and 72% validation accuracy to select the best heuristic that can generate a better quality bin packing solution.
Satoshi Risk Tables
Venue: arXiv cs.CR 2017
Year: 2017

We present Bitcoin Security Tables computing the probability of success p(z,q,t) of a double spend attack by an attacker controlling a share q of the hashrate after z confirmations in time t.
Robust Stochastic Configuration Networks with Kernel Density Estimation
Venue: arXiv cs.NE 2017
Year: 2017

Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, however, the given dataset may contain noisy samples or outliers which may result in a poor learner model in terms of generalization. This paper contributes to a development of robust stochastic configuration networks (RSCNs) for resolving uncertain data regression problems. RSCNs are built on original stochastic configuration networks with weighted least squares method for evaluating the output weights, and the input weights and biases are incrementally and randomly generated by satisfying with a set of inequality constrains. The kernel density estimation (KDE) method is employed to set the penalty weights for each training samples, so that some negative impacts, caused by noisy data or outliers, on the resulting learner model can be reduced. The alternating optimization technique is applied for updating a RSCN model with improved penalty weights computed from the kernel density estimation function. Performance evaluation is carried out by a function approximation, four benchmark datasets and a case study on engineering application. Comparisons to other robust randomised neural modelling techniques, including the probabilistic robust learning algorithm for neural networks with random weights and improved RVFL networks, indicate that the proposed RSCNs with KDE perform favourably and demonstrate good potential for real-world applications.
Coverage Analysis for Millimeter Wave Networks: The Impact of Directional Antenna Arrays
Venue: arXiv cs.IT 2017
Year: 2017

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of its defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.
GDSP: A Graphical Perspective on the Distributed Storage Systems
Venue: arXiv cs.IT 2017
Year: 2017

The classical distributed storage problem can be modeled by a k-uniform {\it complete} hyper-graph where vertices represent servers and hyper-edges represent users. Hence each hyper-edge should be able to recover the full file using only the memories of the vertices associated with it. This paper considers the generalization of this problem to {\it arbitrary} hyper-graphs and to the case of multiple files, where each user is only interested in one, a problem we will refer to as the graphical distributed storage problem (GDSP). Specifically, we make progress in the analysis of minimum-storage codes for two main subproblems of the GDSP which extend the classical model in two independent directions: the case of an arbitrary graph with multiple files, and the case of an arbitrary hyper-graph with a single file.