OpenRaiser/Intern-Atlas huggingface dataset:
Ashwin: Plug-and-Play System for Machine-Human Image Annotation
Venue: arXiv cs.CV 2016
Year: 2016

We present an end-to-end machine-human image annotation system where each component can be attached in a plug-and-play fashion. These components include Feature Extraction, Machine Classifier, Task Sampling and Crowd Consensus.
9-1-1 DDoS: Threat, Analysis and Mitigation
Venue: arXiv cs.CR 2016
Year: 2016

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or $100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.
Extraction of Skin Lesions from Non-Dermoscopic Images Using Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Melanoma is amongst most aggressive types of cancer. However, it is highly curable if detected in its early stages. Prescreening of suspicious moles and lesions for malignancy is of great importance. Detection can be done by images captured by standard cameras, which are more preferable due to low cost and availability. One important step in computerized evaluation of skin lesions is accurate detection of lesion region, i.e. segmentation of an image into two regions as lesion and normal skin. Accurate segmentation can be challenging due to burdens such as illumination variation and low contrast between lesion and healthy skin. In this paper, a method based on deep neural networks is proposed for accurate extraction of a lesion region. The input image is preprocessed and then its patches are fed to a convolutional neural network (CNN). Local texture and global structure of the patches are processed in order to assign pixels to lesion or normal classes. A method for effective selection of training patches is used for more accurate detection of a lesion border. The output segmentation mask is refined by some post processing operations. The experimental results of qualitative and quantitative evaluations demonstrate that our method can outperform other state-of-the-art algorithms exist in the literature.
Feasibility Study on Disaster Management with Hybrid Network of LTE and Satellite Links
Venue: arXiv cs.NI 2016
Year: 2016

We are highly vulnerable to either natural or artificial catastrophes and therefore, Public Protection and Disaster Relief (PPDR) operators need reliable wireless communications for successful operations especially in critical rescue missions. PPDR dedicated or commercial terrestrial networks have always been used which at most times lead to unsuccessful operations. This is due to the fact these networks are all infrastructure-based which can be destroyed, fail to deliver the required service or the networks are not able to support and sustain the sudden traffic surge. Long-Term Evolution (LTE) is earmarked as the future candidate technology for PPDR purpose and so much have been put into it in terms of research, perhaps suitable architecture that will meet mission-critical requirements can be developed. This can only work if terrestrial networks will always be available. Unfortunately, in worst case scenarios, infrastructures might get damaged totally or might be destroyed by subsequent disasters. As a result, adequate guarantees can only be possible in the hypothesis of very high financial involvement. Fortunately, considering availability, coverage ubiquity and reliability, satellite technologies have lately proven good. So, to maximize the high channel performance of terrestrial networks and the availability and reliability of non-terrestrial networks, the solution lies in a hybrid system. It is on this ground that this work deals with the integration of LTE and satellite networks in both infrastructure-based and infrastructure-less topologies for PPDR purpose. It is aim at providing people trapped in disaster and field operators with a transparent accessibility and guaranteed coverage even when infrastructures are damaged. The requirements are defined and the model simulated. The network is able to provide network coverage, enhanced capacity and promised greater resilience.
Machine Learning with Guarantees using Descriptive Complexity and SMT Solvers
Venue: arXiv cs.LG 2016
Year: 2016

Machine learning is a thriving part of computer science. There are many efficient approaches to machine learning that do not provide strong theoretical guarantees, and a beautiful general learning theory. Unfortunately, machine learning approaches that give strong theoretical guarantees have not been efficient enough to be applicable. In this paper we introduce a logical approach to machine learning. Models are represented by tuples of logical formulas and inputs and outputs are logical structures. We present our framework together with several applications where we evaluate it using SAT and SMT solvers. We argue that this approach to machine learning is particularly suited to bridge the gap between efficiency and theoretical soundness. We exploit results from descriptive complexity theory to prove strong theoretical guarantees for our approach. To show its applicability, we present experimental results including learning complexity-theoretic reductions rules for board games. We also explain how neural networks fit into our framework, although the current implementation does not scale to provide guarantees for real-world neural networks.
DELTA: Data Extraction and Logging Tool for Android
Venue: arXiv cs.OH 2016
Year: 2016

In the past few years, the use of smartphones has increased exponentially, and so have the capabilities of such devices. Together with an increase in raw processing power, modern smartphones are equipped with a wide variety of sensors and expose an extensive set of API (Accessible Programming Interface). These capabilities allow us to extract a wide spectrum of data that ranges from information about the environment (e.g., position, orientation) to user habits (e.g., which apps she uses and when), as well as about the status of the operating system itself (e.g., memory, network adapters). This data can be extremely valuable in many research fields such as user authentication, intrusion detection and detection of information leaks. For these reasons, researchers need to use a solid and reliable logging tool to collect data from mobile devices. In this paper, we first survey the existing logging tools available on the Android platform, comparing the features offered by different tools and their impact on the system, and highlighting some of their shortcomings. Then, we present DELTA - Data Extraction and Logging Tool for Android, which improves the existing Android logging solutions in terms of flexibility, fine-grained tuning capabilities, extensibility, and available set of logging features. We performed a full implementation of DELTA and we run a thorough evaluation on its performance. The results show that our tool has low impact on the performance of the system, on battery consumption, and on user experience. Finally, we make the DELTA source code and toolset available to the research community.
Entropy of the Sum of Two Independent, Non-Identically-Distributed Exponential Random Variables
Venue: arXiv cs.IT 2016
Year: 2016

In this letter, we give a concise, closed-form expression for the differential entropy of the sum of two independent, non-identically-distributed exponential random variables. The derivation is straightforward, but such a concise entropy has not been previously given in the literature. The usefulness of the expression is demonstrated with examples.
Image Denoising Via Collaborative Support-Agnostic Recovery
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly structured patches are grouped together to be utilized for collaborative processing. In the proposed collaborative schemes, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group. This provides very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of SSIM and PSNR, demonstrate the superiority of the proposed algorithm.
Stealing Machine Learning Models via Prediction APIs
Venue: arXiv cs.CR 2016
Year: 2016

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis. The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., "steal") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.
Pinpoint Influential Posts and Authors
Venue: arXiv cs.SI 2016
Year: 2016

This research presents an analytical model that aims to pin-point influential posts across a social web comprised of a corpus of posts. The model employs the Latent Dirichlet Al-location algorithm to associate posts with topics, and the TF-IDF metric to identify the key posts associated with each top-ic. The model was demonstrated in the domain of customer relationship by enabling careful monitoring of evolving "storms" created by individuals which tend to impact large audiences (either positively or negatively). Future research should be engaged in order to extend the scope of the corpus by including additional relevant publicly available sources.
The Role of Context Selection in Object Detection
Venue: arXiv cs.CV 2016
Year: 2016

We investigate the reasons why context in object detection has limited utility by isolating and evaluating the predictive power of different context cues under ideal conditions in which context provided by an oracle. Based on this study, we propose a region-based context re-scoring method with dynamic context selection to remove noise and emphasize informative context. We introduce latent indicator variables to select (or ignore) potential contextual regions, and learn the selection strategy with latent-SVM. We conduct experiments to evaluate the performance of the proposed context selection method on the SUN RGB-D dataset. The method achieves a significant improvement in terms of mean average precision (mAP), compared with both appearance based detectors and a conventional context model without the selection scheme.
Learning-Based View Synthesis for Light Field Cameras
Venue: arXiv cs.CV 2016
Year: 2016

With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
Knowledge management and measurement in Public Sector Organizations
Venue: arXiv cs.OH 2016
Year: 2016

Knowledge Management (KM) is a strategic component that enables development, growth and continuous improvement of Public Sector Organizations (PSO). This thesis is bounded to this specific context. Indeed, we critically and comprehensively study the factors that characterize KM strategies and those that foster its development and success in PSO, then finally we propose metrics to measure and evaluate, in order to continuous and systematically improve KM practices in PSO. Main problems are related to the lack of academic literature that explains the elements that KM address in the given context. In addition, it is identified as a problem the lack of criteria for measuring and evaluating KM in PSO, from a different viewpoint than business perspective. The contribution of this thesis is that it provides valuable elements for an academic debate on the previous factors, strategies and metrics to promote KM initiatives in PSO. To achieve the research objective of this thesis we performed a comprehensive systematic literature review in order to discover what the KM critical success factors are, and also we integrated and proposed some metrics to evaluate and assess the performance of KM in PSO. We conducted an in-depth study among different Public Sector Organizations in order to learn what are the critical success factors that must be first considered before implement KM initiatives. based on this. Finally, based on this methodological proposal and as a result of this research, we were able to analyze and explain the critical success factors, we identified some strategies to encourage the success of KM and integrated a proposal of metrics, from different approaches, for KM in PSO.
A LSE and Sparse Message Passing-Based Channel Estimation for mmWave MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we propose a novel channel estimation algorithm based on the Least Square Estimation (LSE) and Sparse Message Passing algorithm (SMP), which is of special interest for Millimeter Wave (mmWave) systems, since this algorithm can leverage the inherent sparseness of the mmWave channel. Our proposed algorithm will iteratively detect exact the location and the value of non-zero entries of sparse channel vector without its prior knowledge of distribution. The SMP is used to detect exact the location of non-zero entries of the channel vector, while the LSE is used for estimating its value at each iteration. Then, the analysis of the Cramer-Rao Lower Bound (CRLB) of our proposed algorithm is given. Numerical experiments show that our proposed algorithm has much better performance than the existing sparse estimators (e.g. LASSO), especially when mmWave systems have massive antennas at both the transmitters and receivers. In addition, we also find that our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels in just a few turbo iterations.
A centralized reinforcement learning method for multi-agent job scheduling in Grid
Venue: arXiv cs.DC 2016
Year: 2016

One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability.
Effect of Wideband Beam Squint on Codebook Design in Phased-Array Wireless Systems
Venue: arXiv cs.IT 2016
Year: 2016

Analog beamforming with phased arrays is a promising technique for 5G wireless communication at millimeter wave frequencies. Using a discrete codebook consisting of multiple analog beams, each beam focuses on a certain range of angles of arrival or departure and corresponds to a set of fixed phase shifts across frequency due to practical hardware considerations. However, for sufficiently large bandwidth, the gain provided by the phased array is actually frequency dependent, which is an effect called beam squint, and this effect occurs even if the radiation pattern of the antenna elements is frequency independent. This paper examines the nature of beam squint for a uniform linear array (ULA) and analyzes its impact on codebook design as a function of the number of antennas and system bandwidth normalized by the carrier frequency. The criterion for codebook design is to guarantee that each beam's minimum gain for a range of angles and for all frequencies in the wideband system exceeds a target threshold, for example 3 dB below the array's maximum gain. Analysis and numerical examples suggest that a denser codebook is required to compensate for beam squint. For example, 54% more beams are needed compared to a codebook design that ignores beam squint for a ULA with 32 antennas operating at a carrier frequency of 73 GHz and bandwidth of 2.5 GHz. Furthermore, beam squint with this design criterion limits the bandwidth or the number of antennas of the array if the other one is fixed.
On Natural Deduction for Herbrand Constructive Logics I: Curry-Howard Correspondence for Dummett's Logic LC
Venue: arXiv cs.LO 2016
Year: 2016

Dummett's logic LC is intuitionistic logic extended with Dummett's axiom: for every two statements the first implies the second or the second implies the first. We present a natural deduction and a Curry-Howard correspondence for first-order and second-order Dummett's logic. We add to the lambda calculus an operator which represents, from the viewpoint of programming, a mechanism for representing parallel computations and communication between them, and from the viewpoint of logic, Dummett's axiom. We prove that our typed calculus is normalizing and show that proof terms for existentially quantified formulas reduce to a list of individual terms forming an Herbrand disjunction.
Postprocessing for Iterative Differentially Private Algorithms
Venue: arXiv cs.DS 2016
Year: 2016

Iterative algorithms for differential privacy run for a fixed number of iterations, where each iteration learns some information from data and produces an intermediate output. However, the algorithm only releases the output of the last iteration, and from which the accuracy of algorithm is judged. In this paper, we propose a post-processing algorithm that seeks to improve the accuracy by incorporating the knowledge on the data contained in intermediate outputs.
Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
Venue: arXiv cs.AI 2016
Year: 2016

Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.
A Neural Network Alternative to Non-Negative Audio Models
Venue: arXiv cs.SD 2016
Year: 2016

We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements.