OpenRaiser/Intern-Atlas huggingface dataset:
Coded Caching with Low Subpacketization Levels
Venue: arXiv cs.IT 2016
Year: 2016

Caching is popular technique in content delivery networks that allows for reductions in transmission rates from the content-hosting server to the end users. Coded caching is a generalization of conventional caching that considers the possibility of coding in the caches and transmitting coded signals from the server. Prior results in this area demonstrate that huge reductions in transmission rates are possible and this makes coded caching an attractive option for the next generation of content-delivery networks. However, these results require that each file hosted in the server be partitioned into a large number (i.e., the subpacketization level) of non-overlapping subfiles. From a practical perspective, this is problematic as it means that prior schemes are only applicable when the size of the files is extremely large. In this work, we propose a novel coded caching scheme that enjoys a significantly lower subpacketization level than prior schemes, while only suffering a marginal increase in the transmission rate. In particular, for a fixed cache size, the scaling with the number of users is such that the increase in transmission rate is negligible, but the decrease in subpacketization level is exponential.
A note on "Approximation schemes for a subclass of subset selection problems", and a faster FPTAS for the Minimum Knapsack Problem
Venue: arXiv cs.CC 2016
Year: 2016

Pruhs and Woeginger prove the existence of FPTAS's for a general class of minimization and maximization subset selection problems. Without losing generality from the original framework, we prove how better asymptotic worst-case running times can be achieved if a $ρ$-approximation algorithm is available, and in particular we obtain matching running times between maximization and minimization subset selection problems. We directly apply this result to the Minimum Knapsack Problem, for which the original framework yields an FPTAS with running time $O(n^5/ε)$, where $ε$ is the required accuracy and $n$ is the number of items, and obtain an FPTAS with running time $O(n^3/ε)$, thus improving the running time by a quadratic factor in the worst case.
Using Kernel Methods and Model Selection for Prediction of Preterm Birth
Venue: arXiv cs.LG 2016
Year: 2016

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.
A Multiple Kernel Learning Approach for Human Behavioral Task Classification using STN-LFP Signal
Venue: arXiv cs.CV 2016
Year: 2016

Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patients behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.
Behavior and path planning for the coalition of cognitive robots in smart relocation tasks
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we outline the approach of solving special type of navigation tasks for robotic systems, when a coalition of robots (agents) acts in the 2D environment, which can be modified by the actions, and share the same goal location. The latter is originally unreachable for some members of the coalition, but the common task still can be accomplished as the agents can assist each other (e.g. by modifying the environment). We call such tasks smart relocation tasks (as the can not be solved by pure path planning methods) and study spatial and behavior interaction of robots while solving them. We use cognitive approach and introduce semiotic knowledge representation - sign world model which underlines behavioral planning methodology. Planning is viewed as a recursive search process in the hierarchical state-space induced by sings with path planning signs reside on the lowest level. Reaching this level triggers path planning which is accomplished by state of the art grid-based planners focused on producing smooth paths (e.g. LIAN) and thus indirectly guarantying feasibility of that paths against agent's dynamic constraints.
Visual Tracking via Shallow and Deep Collaborative Model
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a robust tracking method based on the collaboration of a generative model and a discriminative classifier, where features are learned by shallow and deep architectures, respectively. For the generative model, we introduce a block-based incremental learning scheme, in which a local binary mask is constructed to deal with occlusion. The similarity degrees between the local patches and their corresponding subspace are integrated to formulate a more accurate global appearance model. In the discriminative model, we exploit the advances of deep learning architectures to learn generic features which are robust to both background clutters and foreground appearance variations. To this end, we first construct a discriminative training set from auxiliary video sequences. A deep classification neural network is then trained offline on this training set. Through online fine-tuning, both the hierarchical feature extractor and the classifier can be adapted to the appearance change of the target for effective online tracking. The collaboration of these two models achieves a good balance in handling occlusion and target appearance change, which are two contradictory challenging factors in visual tracking. Both quantitative and qualitative evaluations against several state-of-the-art algorithms on challenging image sequences demonstrate the accuracy and the robustness of the proposed tracker.
Event-Driven Implicit Authentication for Mobile Access Control
Venue: arXiv cs.NI 2016
Year: 2016

In order to protect user privacy on mobile devices, an event-driven implicit authentication scheme is proposed in this paper. Several methods of utilizing the scheme for recognizing legitimate user behavior are investigated. The investigated methods compute an aggregate score and a threshold in real-time to determine the trust level of the current user using real data derived from user interaction with the device. The proposed scheme is designed to: operate completely in the background, require minimal training period, enable high user recognition rate for implicit authentication, and prompt detection of abnormal activity that can be used to trigger explicitly authenticated access control. In this paper, we investigate threshold computation through standard deviation and EWMA (exponentially weighted moving average) based algorithms. The result of extensive experiments on user data collected over a period of several weeks from an Android phone indicates that our proposed approach is feasible and effective for lightweight real-time implicit authentication on mobile smartphones.
Diagnostic Prediction Using Discomfort Drawings with IBTM
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we use a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. The number of output diagnostic labels is determined by using mean-shift clustering on the discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. Additionally, we generate synthetic discomfort drawings with IBTM given a diagnostic label, which results in typical cases of symptoms. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
Open and Regionalised Spectrum Repositories for Emerging Countries
Venue: arXiv cs.NI 2016
Year: 2016

TV White Spaces have recently been proposed as an alternative to alleviate the spectrum crunch, characterised by the need to reallocate frequency bands to accommodate the ever-growing demand for wireless communications. In this paper, we discuss the motivations and challenges for collecting spectrum measurements in developing regions and discuss a scalable system for communities to gather and provide access to White Spaces information through open and regionalised repositories. We further discuss two relevant aspects. First, we propose a cooperative mechanism for sensing spectrum availability using a detector approach. Second, we propose a strategy (and an architecture) on the database side to implement spectrum governance. Other aspects of the work include discussion of an extensive measurement campaign showing a number of white spaces in developing regions, an overview of our experience on low-cost spectrum analysers, and the architecture of zebra-rfo, an application for processing crowd-sourced spectrum data.
Multi-Tenant Radio Access Network Slicing: Statistical Multiplexing of Spatial Loads
Venue: arXiv cs.NI 2016
Year: 2016

This paper addresses the slicing of Radio Access Network (RAN) resources by multiple tenants, e.g., virtual wireless operators and service providers. We consider a criterion for dynamic resource allocation amongst tenants, based on a weighted proportionally fair objective, which achieves desirable fairness/protection across the network slices of the different tenants and their associated users. Several key properties are established, including: the Pareto optimality of user association to base stations, the fair allocation of base stations resources, and the gains resulting from dynamic resource sharing across slices, both in terms of utility gains and capacity savings. We then address algorithmic and practical challenges in realizing the proposed criterion. We show that the objective is NP-hard, making an exact solution impractical, and design a distributed semi-online algorithm which meets performance guarantees in equilibrium and can be shown to quickly converge to a region around the equilibrium point. Building on this algorithm, we devise a practical approach with limited computational, information, and handoff overheads. We use detailed simulations to show that our approach is indeed near-optimal and provides substantial gains both to tenants (in terms of capacity savings) and end-users (in terms of improved performance).
Comments on "Approximate Characterizations for the Gaussian Source Broadcast Distortion Region"
Venue: arXiv cs.IT 2016
Year: 2016

Recently, Tian et al. [1] considered joint source-channel coding of transmitting a Gaussian source over $K$-user Gaussian broadcast channel, and derived an outer bound on the admissible distortion region. In [1], they stated "due to its nonlinear form, it appears difficult to determine whether it is always looser than the trivial outer bound in all distortion regimes with bandwidth compression". However, in this correspondence we solve this problem and prove that for the bandwidth expansion case ($K\geq2$), this outer bound is strictly tighter than the trivial outer bound with each user being optimal in the point-to-point setting; while for the bandwidth compression or bandwidth match case, this outer bound actually degenerates to the trivial outer bound. Therefore, our results imply that on one hand, the outer bound given in [1] is nontrivial only for Gaussian broadcast communication ($K\geq2$) with bandwidth expansion; on the other hand, unfortunately, no nontrivial outer bound exists so far for Gaussian broadcast communication ($K\geq2$) with bandwidth compression.
Reaching Majority Agreement in a Disconnected Network
Venue: arXiv cs.DC 2016
Year: 2016

We investigate the problem of reaching majority agreement in a disconnected network. We obtain conditions under which such an agreement is certainly possible/impossible, and observe that these coincide in the ternary case.
Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study
Venue: arXiv cs.RO 2016
Year: 2016

Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 +/- 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration.
The Design and Implementation of a Rekeying-aware Encrypted Deduplication Storage System
Venue: arXiv cs.DC 2016
Year: 2016

Rekeying refers to an operation of replacing an existing key with a new key for encryption. It renews security protection, so as to protect against key compromise and enable dynamic access control in cryptographic storage. However, it is non-trivial to realize efficient rekeying in encrypted deduplication storage systems, which use deterministic content-derived encryption keys to allow deduplication on ciphertexts. We design and implement REED, a rekeying-aware encrypted deduplication storage system. REED builds on a deterministic version of all-or-nothing transform (AONT), such that it enables secure and lightweight rekeying, while preserving the deduplication capability. We propose two REED encryption schemes that trade between performance and security, and extend REED for dynamic access control. We implement a REED prototype with various performance optimization techniques and demonstrate how we can exploit similarity to mitigate key generation overhead. Our trace-driven testbed evaluation shows that our REED prototype maintains high performance and storage efficiency.
An algorithm for motif-based network design
Venue: arXiv cs.SI 2016
Year: 2016

A determinant property of the structure of a biological network is the distribution of local connectivity patterns, i.e., network motifs. In this work, a method for creating directed, unweighted networks while promoting a certain combination of motifs is presented. This motif-based network algorithm starts with an empty graph and randomly connects the nodes by advancing or discouraging the formation of chosen motifs. The in- or out-degree distribution of the generated networks can be explicitly chosen. The algorithm is shown to perform well in producing networks with high occurrences of the targeted motifs, both ones consisting of 3 nodes as well as ones consisting of 4 nodes. Moreover, the algorithm can also be tuned to bring about global network characteristics found in many natural networks, such as small-worldness and modularity.
On Varying Topology of Complex Networks and Performance Limitations of Community Detection Algorithms
Venue: arXiv cs.SI 2016
Year: 2016

One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are interested in the size of networks, the size of community structures, the average connectivity of nodes and the ratio of inter-intra cluster edges. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms that fail to incorporate varying topological features of different networks.
Towards Automated Performance Bug Identification in Python
Venue: arXiv cs.SE 2016
Year: 2016

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"ıve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs.
Bounds on the Rate of Linear Locally Repairable Codes over Small Alphabets
Venue: arXiv cs.IT 2016
Year: 2016

Locally repairable codes (LRC) have recently been a subject of intense research due to theoretical appeal and their application in distributed storage systems. In an LRC, any coordinate of a codeword can be recovered by accessing only few other coordinates. For LRCs over small alphabet (such as binary), the optimal rate-distance trade-off is unknown. In this paper we provide the tightest known upper bound on the rate of linear LRCs of a given relative distance, an improvement over any previous result, in particular \cite{cadambe2013upper}.
A Deep Primal-Dual Network for Guided Depth Super-Resolution
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.
Attribute Learning for Network Intrusion Detection
Venue: arXiv cs.CR 2016
Year: 2016

Network intrusion detection is one of the most visible uses for Big Data analytics. One of the main problems in this application is the constant rise of new attacks. This scenario, characterized by the fact that not enough labeled examples are available for the new classes of attacks is hardly addressed by traditional machine learning approaches. New findings on the capabilities of Zero-Shot learning (ZSL) approach makes it an interesting solution for this problem because it has the ability to classify instances of unseen classes. ZSL has inherently two stages: the attribute learning and the inference stage. In this paper we propose a new algorithm for the attribute learning stage of ZSL. The idea is to learn new values for the attributes based on decision trees (DT). Our results show that based on the rules extracted from the DT a better distribution for the attribute values can be found. We also propose an experimental setup for the evaluation of ZSL on network intrusion detection (NID).