OpenRaiser/Intern-Atlas huggingface dataset:
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images
Venue: arXiv cs.CV 2016
Year: 2016

Content-Based Image Retrieval (CBIR) systems have been widely used for a wide range of applications such as Art collections, Crime prevention and Intellectual property. In this paper, a novel CBIR system, which utilizes visual contents (color, texture and shape) of an image to retrieve images, is proposed. The proposed system builds three feature vectors and stores them into MySQL database. The first feature vector uses descriptive statistics to describe the distribution of data in each channel of RGB channels of the image. The second feature vector describes the texture using eigenvalues of the 39 sub-bands that are generated after applying four levels 2D DWT in each channel (red, green and blue channels) of the image. These wavelets sub-bands perfectly describes the horizontal, vertical and diagonal edges that exist in the multi-resolution analysis of the image. The third feature vector describes the basic shapes that exist in the skeletonization version of the black and white representation of the image. Experimental results on a private MYSQL database that consists of 10000 images, using color, texture, shape and stored relevance feedbacks, showed 96.4% average correct retrieval rate in an efficient recovery time.
Towards Policy Enforcement Point as a Service (PEPS)
Venue: arXiv cs.CR 2016
Year: 2016

In this paper, we coin the term Policy Enforcement as a Service (PEPS), which enables the provision of innovative inter-layer and inter-domain Access Control. We leverage the architecture of Software-Defined-Network (SDN) to introduce a common network-level enforcement point, which is made available to a range of access control systems. With our PEPS model, it is possible to have a `defense in depth' protection model and drop unsuccessful access requests before engaging the data provider (e.g. a database system). Moreover, the current implementation of access control within the `trusted' perimeter of an organization is no longer a restriction so that the potential for novel, distributed and cooperative security services can be realized. We conduct an analysis of the security requirements and technical challenges for implementing Policy Enforcement as a Service. To illustrate the benefits of our proposal in practice, we include a report on our prototype PEPS-enabled location-based access control.
Learning Human-Robot Handovers Through $π$-STAM: Policy Improvement With Spatio-Temporal Affordance Maps
Venue: arXiv cs.RO 2016
Year: 2016

Human-robot handovers are characterized by high uncertainty and poor structure of the problem that make them difficult tasks. While machine learning methods have shown promising results, their application to problems with large state dimensionality, such as in the case of humanoid robots, is still limited. Additionally, by using these methods and during the interaction with the human operator, no guarantees can be obtained on the correct interpretation of spatial constraints (e.g., from social rules). In this paper, we present Policy Improvement with Spatio-Temporal Affordance Maps -- $π$-STAM, a novel iterative algorithm to learn spatial affordances and generate robot behaviors. Our goal consists in generating a policy that adapts to the unknown action semantics by using affordances. In this way, while learning to perform a human-robot handover task, we can (1) efficiently generate good policies with few training episodes, and (2) easily encode action semantics and, if available, enforce prior knowledge in it. We experimentally validate our approach both in simulation and on a real NAO robot whose task consists in taking an object from the hands of a human. The obtained results show that our algorithm obtains a good policy while reducing the computational load and time duration of the learning process.
Transmission Strategies for Remote Estimation with an Energy Harvesting Sensor
Venue: arXiv cs.IT 2016
Year: 2016

We consider the remote estimation of a time-correlated signal using an energy harvesting (EH) sensor. The sensor observes the unknown signal and communicates its observations to a remote fusion center using an amplify-and-forward strategy. We consider the design of optimal power allocation strategies in order to minimize the mean-square error at the fusion center. Contrary to the traditional approaches, the degree of correlation between the signal values constitutes an important aspect of our formulation. We provide the optimal power allocation strategies for a number of illustrative scenarios. We show that the most majorized power allocation strategy, i.e. the power allocation as balanced as possible, is optimal for the cases of circularly wide-sense stationary (c.w.s.s.) signals with a static correlation coefficient, and sampled low-pass c.w.s.s. signals for a static channel. We show that the optimal strategy can be characterized as a water-filling type solution for sampled low-pass c.w.s.s. signals for a fading channel. Motivated by the high-complexity of the numerical solution of the optimization problem, we propose low-complexity policies for the general scenario. Numerical evaluations illustrate the close performance of these low-complexity policies to that of the optimal policies, and demonstrate the effect of the EH constraints and the degree of freedom of the signal.
Open-Ended Visual Question-Answering
Venue: arXiv cs.CL 2016
Year: 2016

This thesis report studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework. As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations.
Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments
Venue: arXiv cs.NE 2016
Year: 2016

Evolutionary algorithms have been successfully applied to a variety of optimisation problems in stationary environments. However, many real world optimisation problems are set in dynamic environments where the success criteria shifts regularly. Population diversity affects algorithmic performance, particularly on multiobjective and dynamic problems. Diversity mechanisms are methods of altering evolutionary algorithms in a way that promotes the maintenance of population diversity. This project intends to measure and compare the performance effect a variety of diversity mechanisms have on an evolutionary algorithm when facing an assortment of dynamic problems.
Uplink Transmission Design with Massive Machine Type Devices in Tactile Internet
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we study how to design uplink transmission with massive machine type devices in tactile internet, where ultra-short delay and ultra-high reliability are required. To characterize the transmission reliability constraint, we employ a two-state transmission model based on the achievable rate with finite blocklength channel codes. If the channel gain exceeds a threshold, a short packet can be transmitted with a small error probability; otherwise there is a packet loss. To exploit frequency diversity, we assign multiple subchannels to each active device, from which the device selects a subchannel with channel gain exceeding the threshold for transmission. To show the total bandwidth required to ensure the reliability, we optimize the number of subchannels and bandwidth of each subchannel and the threshold for each device to minimize the total bandwidth of the system with a given number of antennas at the base station. Numerical results show that with 1000 devices in one cell, the required bandwidth of the optimized policy is acceptable even for prevalent cellular systems. Furthermore, we show that by increasing antennas at the BS, frequency diversity becomes unnecessary, and the required bandwidth is reduced.
Hardening Cassandra Against Byzantine Failures
Venue: arXiv cs.DC 2016
Year: 2016

Cassandra is one of the most widely used distributed data stores these days. Cassandra supports flexible consistency guarantees over a wide-column data access model and provides almost linear scale-out performance. This enables application developers to tailor the performance and availability of Cassandra to their exact application's needs and required semantics. Yet, Cassandra is designed to withstand benign failures, and cannot cope with most forms of Byzantine attacks. In this work, we present an analysis of Cassandra's vulnerabilities and propose protocols for hardening Cassandra against Byzantine failures. We examine several alternative design choices and compare between them both qualitatively and empirically by using the Yahoo! Cloud Serving Benchmark (YCSB) performance benchmark. We include incremental performance analysis for our algorithmic and cryptographic adjustments, supporting our design choices.
A General Framework for Content-enhanced Network Representation Learning
Venue: arXiv cs.SI 2016
Year: 2016

This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods rely solely on the network structure, i.e., the linkage relationships between nodes, but ignore the rich content information associated with it, which is common in real world networks and beneficial to describing the characteristics of a node. In this paper, we propose content-enhanced network embedding (CENE), which is capable of jointly leveraging the network structure and the content information. Our approach integrates text modeling and structure modeling in a general framework by treating the content information as a special kind of node. Experiments on several real world net- works with application to node classification show that our models outperform all existing network embedding methods, demonstrating the merits of content information and joint learning.
Analysis of Downlink and Uplink Decoupling in Dense Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

Decoupling uplink (UL) and downlink (DL) is a new architectural paradigm where DL and UL are not constrained to be associated to the same base station (BS). Building upon this paradigm, the goal of the present paper is to provide lower, albeit tight bounds for the ergodic UL capacity of a decoupled cellular network. The analysis is performed for a scenario consisting of a macro BS and a set of small cells (SCs) whose positions are selected randomly according to a Poisson point process of a given spatial density. Based on this analysis simple bounds in closed form expressions are defined. The devised bounds are employed to compare the performance of the decoupled case versus a set of benchmark cases, namely the coupled case, and the situations of having either a single macro BS or only SCs. This comparison provides valuable insights regarding the behavior and performance of such networks, providing simpler expressions for the ergodic UL capacity as a function of the distances to the macro BS and the density of SCs. These expressions constitute a simple guide to the minimum degree of densification that guarantees the Quality of Service (QoS) objectives of the network, thus, providing a valuable tool to the network operator of significant practical and commercial value.
Very Deep Convolutional Networks for End-to-End Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional LSTMs to build very deep recurrent and convolutional structures. Our models exploit the spectral structure in the feature space and add computational depth without overfitting issues. We experiment with the WSJ ASR task and achieve 10.5\% word error rate without any dictionary or language using a 15 layer deep network.
Optimal Download Cost of Private Information Retrieval for Arbitrary Message Length
Venue: arXiv cs.IT 2016
Year: 2016

A private information retrieval scheme is a mechanism that allows a user to retrieve any one out of $K$ messages from $N$ non-communicating replicated databases, each of which stores all $K$ messages, without revealing anything about the identity of the desired message index to any individual database. If the size of each message is $L$ bits and the total download required by a PIR scheme from all $N$ databases is $D$ bits, then $D$ is called the download cost and the ratio $L/D$ is called an achievable rate. For fixed $K,N\in\mathbb{N}$, the capacity of PIR, denoted by $C$, is the supremum of achievable rates over all PIR schemes and over all message sizes, and was recently shown to be $C=(1+1/N+1/N^2+\cdots+1/N^{K-1})^{-1}$. In this work, for arbitrary $K, N$, we explore the minimum download cost $D_L$ across all PIR schemes (not restricted to linear schemes) for arbitrary message lengths $L$ under arbitrary choices of alphabet (not restricted to finite fields) for the message and download symbols. If the same $M$-ary alphabet is used for the message and download symbols, then we show that the optimal download cost in $M$-ary symbols is $D_L=\lceil\frac{L}{C}\rceil$. If the message symbols are in $M$-ary alphabet and the downloaded symbols are in $M'$-ary alphabet, then we show that the optimal download cost in $M'$-ary symbols, $D_L\in\left\{\left\lceil \frac{L'}{C}\right\rceil,\left\lceil \frac{L'}{C}\right\rceil-1,\left\lceil \frac{L'}{C}\right\rceil-2\right\}$, where $L'= \lceil L \log_{M'} M\rceil$.
Multi-User MIMO with flexible numerology for 5G
Venue: arXiv cs.IT 2016
Year: 2016

Flexible numerologies are being considered as part of designs for 5G systems to support vertical services with diverse requirements such as enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine type communication. Different vertical services can be multiplexed in either frequency domain, time domain, or both. In this paper, we investigate the use of spatial multiplexing of services using MU-MIMO where the numerologies for different users may be different. The users are grouped according to the chosen numerology and a separate pre-coder and FFT size is used per numerology at the transmitter. The pre-coded signals for the multiple numerologies are added in the time domain before transmission. We analyze the performance gains of this approach using capacity analysis and link level simulations using conjugate beamforming and signal-to-leakage noise ratio maximization techniques. We show that the MU interference between users with different numerologies can be suppressed efficiently with reasonable number of antennas at the base-station. This feature enables MU-MIMO techniques to be applied for 5G across different numerologies.
Leveraging Recurrent Neural Networks for Multimodal Recognition of Social Norm Violation in Dialog
Venue: arXiv cs.CL 2016
Year: 2016

Social norms are shared rules that govern and facilitate social interaction. Violating such social norms via teasing and insults may serve to upend power imbalances or, on the contrary reinforce solidarity and rapport in conversation, rapport which is highly situated and context-dependent. In this work, we investigate the task of automatically identifying the phenomena of social norm violation in discourse. Towards this goal, we leverage the power of recurrent neural networks and multimodal information present in the interaction, and propose a predictive model to recognize social norm violation. Using long-term temporal and contextual information, our model achieves an F1 score of 0.705. Implications of our work regarding developing a social-aware agent are discussed.
NEDindex: A new metric for community structure in networks
Venue: arXiv cs.SI 2016
Year: 2016

There are several metrics (Modularity, Mutual Information, Conductance, etc.) to evaluate the strength of graph clustering in large graphs. These metrics have great significance to measure the effectiveness and they are often used to find the strongly connected clusters with respect to the whole graph. In this paper, we propose a new metric to evaluate the strength of graph clustering and also study its applications. We show that our proposed metric has great consistency which is similar to other metrics and easy to calculate. Our proposed metric also shows consistency where other metrics fail in some special cases. We demonstrate that our metric has reasonable strength while extracting strongly connected communities in both simulated (in silico) data and real data networks. We also show some comparative results of our proposed metric with other popular metric(s) for Online Social Networks (OSN) and Gene Regulatory Networks (GRN).
In The Wild Residual Data Research and Privacy
Venue: arXiv cs.CY 2016
Year: 2016

As the world becomes increasingly dependent on technology, researchers in both industry and academia endeavor to understand how technology is used, the impact it has on everyday life, the artifact life-cycle and overall integrations of digital information. In doing so, researchers are increasingly gathering 'real-world' or 'in-the-wild' residual data, obtained from a variety of sources, without the explicit consent of the original owners. This data gathering raises significant concerns regarding privacy, ethics and legislation, as well as practical considerations concerning investigator training, data storage, overall security and data disposal. This research surveys recent studies of residual data gathered in-the-wild and analyzes the challenges that were confronted. Amalgamating these insights, the research presents a compendium of practices for addressing the issues that can arise in-the-wild when conducting residual data research. The practices identified in this research can be used to critique current projects and assess the feasibility of proposed future research.
Finding Euler Tours in the StrSort Model
Venue: arXiv cs.DS 2016
Year: 2016

We present a first algorithm for finding Euler tours in undirected graphs in the StrSort model. This model is a relaxation of the semi streaming model. The graph is given as a stream of its edges and can only be read sequentially, but while doing a pass over the stream we are allowed to write out another stream which will be the input for the next pass. In addition, items in the stream are sorted between passes. This model was introduced by Aggarwal et al. in 2004. Here we apply this model to the problem of finding an Euler tour in a graph (or to decide that the graph does not admit an Euler tour). The algorithm works in two steps. In the first step, a single pass is conducted while a linear (in the number of vertices $n$) amount of RAM is required. In the second step, $\mathcal O(\log(n))$ passes are conducted while only $\mathcal O(\log(n))$ RAM is required. We use an alteration of the algorithm of Atallah and Vishkin from 1984 for finding Euler tours in parallel. It finds a partition of edge-disjoint circuits and arranges them in a tree expressing their connectivity. Then the circuits are merged according to this tree. In order to minimize the needed amount of RAM, we evade the need to store the entire tree and use techniques suggested by Aggarwal et al. to exchange information concerning the merging of circuits.
Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance
Venue: arXiv cs.SY 2016
Year: 2016

We propose a method to efficiently compute the forward stochastic reach (FSR) set and its probability measure for nonlinear systems with an affine disturbance input, that is stochastic and bounded. This method is applicable to systems with an a priori known controller, or to uncontrolled systems, and often arises in problems in obstacle avoidance in mobile robotics. When used as a constraint in finite horizon controller synthesis, the FSR set, and its probability measure facilitates probabilistic collision avoidance, in contrast to methods which presume the obstacles act in a worst-case fashion and generate hard constraints that cannot be violated. We tailor our approach to accommodate rigid body constraints, and show convexity is assured so long as the rigid body shape of each obstacle is also convex. We extend methods for multi-obstacle avoidance through mixed integer linear programming (with linear robot and obstacle dynamics) to accommodate chance constraints that represent the FSR set probability measure. We demonstrate our method on a rigid-body obstacle avoidance scenario, in which a receding horizon controller is designed to avoid several stochastically moving obstacles while reaching the desired goal. Our approach can provide solutions when approaches that presume a worst-case action from the obstacle fail.
Complexity and Stop Conditions for NP as General Assignment Problems, the Travel Salesman Problem in $\mathbb{R}^2$, Knight Tour Problem and Boolean Satisfiability Problem
Venue: arXiv cs.DS 2016
Year: 2016

This paper presents stop conditions for solving General Assignment Problems (GAP), in particular for Travel Salesman Problem in an Euclidian 2D space the well known condition Jordan's simple curve and opposite condition for the Knight Tour Problem. The Jordan's simple curve condition means that a optimal trajectory must be simple curve, i.e., without crossing but for Knight Tour Problem we use the contrary, the feasible trajectory must have crossing in all cities of the tour. The paper presents the algorithms, examples and some results come from Concorde's Home page. Several problem are studied to depict their properties. A classical decision problem SAT is studied in detail.
A Chain-Detection Algorithm for Two-Dimensional Grids
Venue: arXiv cs.AI 2016
Year: 2016

We describe a general method of detecting valid chains or links of pieces on a two-dimensional grid. Specifically, using the example of the chess variant known as Switch-Side Chain-Chess (SSCC). Presently, no foolproof method of detecting such chains in any given chess position is known and existing graph theory, to our knowledge, is unable to fully address this problem either. We therefore propose a solution implemented and tested using the C++ programming language. We have been unable to find an incorrect result and therefore offer it as the most viable solution thus far to the chain-detection problem in this chess variant. The algorithm is also scalable, in principle, to areas beyond two-dimensional grids such as 3D analysis and molecular chemistry.