OpenRaiser/Intern-Atlas huggingface dataset:
A Fast Ellipse Detector Using Projective Invariant Pruning
Venue: arXiv cs.CV 2016
Year: 2016

Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverage a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of -1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a 3*3 matrix. Extensive experiments on three challenging data sets with 650 images demonstrate that our detector runs 20%-50% faster than the state-of-the-art algorithms with the comparable or higher precision.
A $2n^2-log(n)-1$ lower bound for the border rank of matrix multiplication
Venue: arXiv cs.CC 2016
Year: 2016

Let M_n denote the matrix multiplication tensor for nxn matrices. We use the border substitution method combined with Koszul flattenings to prove the border rank lower bound of 2n^2-log(n)-1 for M_n.
Minimizing Outage Probability by Exploiting CSI in Wireless Powered Cooperative Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this work, we address the relay selection problem for the wireless powered communication networks, where the relays harvest energy from the source radio frequency signals. A single source-destination pair is considered without a direct link. The connecting relay nodes are equipped with storage batteries of infinite size. We assume that the channel state information (CSI) on the source-relay link is available at the relay nodes. Depending on the availability of the CSI on the relay-destination link at the relay node, we propose different relay selection schemes and evaluate the outage probability. The availability of the CSI at the relay node on the relay-destination link considerably improves the performance due to additional flexibility in the relay selection mechanism. We numerically quantify the performance for the proposed schemes and compare the outage probability for fixed and equal number of wireless powered forwarding relays.
Large Scale Behavioral Analytics via Topical Interaction
Venue: arXiv cs.LG 2016
Year: 2016

We propose the split-diffuse (SD) algorithm that takes the output of an existing dimension reduction algorithm, and distributes the data points uniformly across the visualization space. The result, called the topic grids, is a set of grids on various topics which are generated from the free-form text content of any domain of interest. The topic grids efficiently utilizes the visualization space to provide visual summaries for massive data. Topical analysis, comparison and interaction can be performed on the topic grids in a more perceivable way.
Performance Guaranteed Approximation Algorithm for Minimum $k$-Connected $m$-Fold Dominating Set
Venue: arXiv cs.DM 2016
Year: 2016

To achieve an efficient routing in a wireless sensor network, connected dominating set (CDS) is used as virtual backbone. A fault-tolerant virtual backbone can be modeled as a $(k,m)$-CDS. For a connected graph $G=(V,E)$ and two fixed integers $k$ and $m$, a node set $C\subseteq V$ is a $(k,m)$-CDS of $G$ if every node in $V\setminus C$ has at least $m$ neighbors in $C$, and the subgraph of $G$ induced by $C$ is $k$-connected. Previous to this work, approximation algorithms with guaranteed performance ratio in a general graph were know only for $k\leq 3$. This paper makes a significant progress by presenting a $(2k-1)α_0$ approximation algorithm for general $k$ and $m$ with $m\geq k$, where $α_0$ is the performance ratio for the minimum CDS problem. Using currently best known ratio for $α_0$, our algorithm has performance ratio $O(\lnΔ)$, where $Δ$ is the maximum degree of the graph.
Learning to generalize to new compositions in image understanding
Venue: arXiv cs.CV 2016
Year: 2016

Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scenes that were not observed during training, possibly depending too strongly on the statistics of the text in the training data. Here we propose to describe images using short structured representations, aiming to capture the crux of a description. These structured representations allow us to tease-out and evaluate separately two types of generalization: standard generalization to new images with similar scenes, and generalization to new combinations of known entities. We compare two learning approaches on the MS-COCO dataset: a state-of-the-art recurrent network based on an LSTM (Show, Attend and Tell), and a simple structured prediction model on top of a deep network. We find that the structured model generalizes to new compositions substantially better than the LSTM, ~7 times the accuracy of predicting structured representations. By providing a concrete method to quantify generalization for unseen combinations, we argue that structured representations and compositional splits are a useful benchmark for image captioning, and advocate compositional models that capture linguistic and visual structure.
SUMMeR: Sub-Nyquist MIMO Radar
Venue: arXiv cs.IT 2016
Year: 2016

Multiple input multiple output (MIMO) radar exhibits several advantages with respect to traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, the digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this work, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets' parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas' locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.
Cast and Self Shadow Segmentation in Video Sequences using Interval based Eigen Value Representation
Venue: arXiv cs.CV 2016
Year: 2016

Tracking of motion objects in the surveillance videos is useful for the monitoring and analysis. The performance of the surveillance system will deteriorate when shadows are detected as moving objects. Therefore, shadow detection and elimination usually benefits the next stages. To overcome this issue, a method for detection and elimination of shadows is proposed. This paper presents a method for segmenting moving objects in video sequences based on determining the Euclidian distance between two pixels considering neighborhood values in temporal domain. Further, a method that segments cast and self shadows in video sequences by computing the Eigen values for the neighborhood of each pixel is proposed. The dual-map for cast and self shadow pixels is represented based on the interval of Eigen values. The proposed methods are tested on the benchmark IEEE CHANGE DETECTION 2014 dataset.
Indexing and querying color sets of images
Venue: arXiv cs.DS 2016
Year: 2016

We aim to study the set of color sets of continuous regions of an image given as a matrix of $m$ rows over $n\geq m$ columns where each element in the matrix is an integer from $[1,σ]$ named a {\em color}. The set of distinct colors in a region is called fingerprint. We aim to compute, index and query the fingerprints of all rectangular regions named rectangles. The set of all such fingerprints is denoted by ${\cal F}$. A rectangle is {\em maximal} if it is not contained in a greater rectangle with the same fingerprint. The set of all locations of maximal rectangles is denoted by $\mathcal{L}.$ We first explain how to determine all the $|\mathcal{L}|$ maximal locations with their fingerprints in expected time $O(nm^2σ)$ using a Monte Carlo algorithm (with polynomially small probability of error) or within deterministic $O(nm^2σ\log(\frac{|\mathcal{L}|}{nm^2}+2))$ time. We then show how to build a data structure which occupies $O(nm\log n+\mathcal{|L|})$ space such that a query which asks for all the maximal locations with a given fingerprint $f$ can be answered in time $O(|f|+\log\log n+k)$, where $k$ is the number of maximal locations with fingerprint $f$. If the query asks only for the presence of the fingerprint, then the space usage becomes $O(nm\log n+|{\cal F}|)$ while the query time becomes $O(|f|+\log\log n)$. We eventually consider the special case of squared regions (squares).
Quantified Spectrum Sharing: Motivation, Approach, and Benefits
Venue: arXiv cs.NI 2016
Year: 2016

A significant portion of the radio frequency spectrum remains underutilized with exclusive and static allocation of spectrum. The growing demand for spectrum has spurred a need for dynamic spectrum sharing paradigm. While the new dynamic spectrum sharing paradigm helps to improve utilization of the precious spectrum resource, there exist several obstacles on the technical, regulatory, and business fronts for the adoption of the new paradigm. In this paper, we investigate the limitations of the existing techniques and argue for quantified approach to dynamic spectrum sharing and management. We introduce a quantified approach to spectrum sharing based on defining and enforcing quantified spectrum-access rights. By discretizing the spectrum-space in the time, space, frequency dimensions, this approach enables quantifying the spectrum consumed by individual transceivers. It enables defining and enforcing a quantified spectrum-access policy in real-time. The proposed quantified approach brings in simplicity, precision and efficiency in terms of spectrum commerce and operations while addressing the key technical and regulatory challenges.
A Direct Proof of the Strong Hanani-Tutte Theorem on the Projective Plane
Venue: arXiv cs.CG 2016
Year: 2016

We reprove the strong Hanani-Tutte theorem on the projective plane. In contrast to the previous proof by Pelsmajer, Schaefer and Stasi, our method is constructive and does not rely on the characterization of forbidden minors, which gives hope to extend it to other surfaces. Moreover, our approach can be used to provide an efficient algorithm turning a Hanani-Tutte drawing on the projective plane into an embedding.
Relevant based structure learning for feature selection
Venue: arXiv cs.LG 2016
Year: 2016

Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy effect of dealing with huge number of features in typical learning problems. There is a variety of techniques for feature selection in supervised learning problems based on different selection metrics. In this paper, we propose a novel unified framework for feature selection built on the graphical models and information theoretic tools. The proposed approach exploits the structure learning among features to select more relevant and less redundant features to the predictive modeling problem according to a primary novel likelihood based criterion. In line with the selection of the optimal subset of features through the proposed method, it provides us the Bayesian network classifier without the additional cost of model training on the selected subset of features. The optimal properties of our method are established through empirical studies and computational complexity analysis. Furthermore the proposed approach is evaluated on a bunch of benchmark datasets based on the well-known classification algorithms. Extensive experiments confirm the significant improvement of the proposed approach compared to the earlier works.
Coexistence of OFDM and FBMC for Underlay D2D Communication in 5G Networks
Venue: arXiv cs.NI 2016
Year: 2016

Device-to-device (D2D) communication is being heralded as an important part of the solution to the capacity problem in future networks, and is expected to be natively supported in 5G. Given the high network complexity and required signalling overhead associated with achieving synchronization in D2D networks, it is necessary to study asynchronous D2D communications. In this paper, we consider a scenario whereby asynchronous D2D communication underlays an OFDMA macro-cell in the uplink. Motivated by the superior performance of new waveforms with increased spectral localization in the presence of frequency and time misalignments, we compare the system-level performance of a set-up for when D2D pairs use either OFDM or FBMC/OQAM. We first demonstrate that inter-D2D interference, resulting from misaligned communications, plays a significant role in clustered D2D topologies. We then demonstrate that the resource allocation procedure can be simplified when D2D pairs use FBMC/OQAM, since the high spectral localization of FBMC/OQAM results in negligible inter-D2D interference. Specifically, we identify that FBMC/OQAM is best suited to scenarios consisting of small, densely populated D2D clusters located near the encompassing cell's edge.
Physical Layer Security in MIMO Backscatter Wireless Systems
Venue: arXiv cs.IT 2016
Year: 2016

Backscatter wireless communication is an emerging technique widely used in low-cost and low-power wireless systems, especially in passive radio frequency identification (RFID) systems. Recently, the requirement of high data rates, data reliability, and security drives the development of RFID systems, which motivates our investigation on the physical layer security of a multiple-input multiple-output (MIMO) RFID system. In this paper, we propose a noise-injection precoding strategy to safeguard the system security with the resource-constrained nature of the backscatter system taken into consideration. We first consider a multi-antenna RFID tag case and investigate the secrecy rate maximization (SRM) problem by jointly optimizing the energy supply power and the precoding matrix of the injected artificial noise at the RFID reader. We exploit the alternating optimization method and the sequential parametric convex approximation method, respectively, to tackle the non-convex SRM problem and show an interesting fact that the two methods are actually equivalent for our SRM problem with the convergence of a Karush-Kuhn-Tucker (KKT) point. To facilitate the practical implementation for resource-constrained RFID devices, we propose a fast algorithm based on projected gradient. We also consider a single-antenna RFID tag case and develop a low-complexity algorithm which yields the global optimal solution. Simulation results show the superiority of our proposed algorithms in terms of the secrecy rate and computational complexity.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations. Following the common pipeline of "CNN feature extraction + region proposal + RoI classification", we mainly redesign the feature extraction part, since region proposal part is not computationally expensive and classification part can be efficiently compressed with common techniques like truncated SVD. Our design principle is "less channels with more layers" and adoption of some building blocks including concatenated ReLU, Inception, and HyperNet. The designed network is deep and thin and trained with the help of batch normalization, residual connections, and learning rate scheduling based on plateau detection. We obtained solid results on well-known object detection benchmarks: 83.8% mAP (mean average precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only 750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA Titan X GPU. Theoretically, our network requires only 12.3% of the computational cost compared to ResNet-101, the winner on VOC2012.
A short review and primer on electromyography in human computer interaction applications
Venue: arXiv cs.HC 2016
Year: 2016

The application of psychophysiology in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Electromyography (EMG) is a useful signal to estimate the emotional context of individuals, because it is relatively robust, and simple to record and analyze. Common uses are to infer emotional valence in response to a stimulus, and to index some symptoms of stress. However, in order to interpret EMG signals, they must be considered alongside data on physical, social and intentional context. Here we present a short review on the application of EMG in human-computer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.
Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Extended Version)
Venue: arXiv cs.DB 2016
Year: 2016

Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.
Bibliometrics and Information Retrieval: Creating Knowledge through Research Synergies
Venue: arXiv cs.IR 2016
Year: 2016

This panel brings together experts in bibliometrics and information retrieval to discuss how each of these two important areas of information science can help to inform the research of the other. There is a growing body of literature that capitalizes on the synergies created by combining methodological approaches of each to solve research problems and practical issues related to how information is created, stored, organized, retrieved and used. The session will begin with an overview of the common threads that exist between IR and metrics, followed by a summary of findings from the BIR workshops and examples of research projects that combine aspects of each area to benefit IR or metrics research areas, including search results ranking, semantic indexing and visualization. The panel will conclude with an engaging discussion with the audience to identify future areas of research and collaboration.
Vicious Circle Principle and Formation of Sets in ASP Based Languages
Venue: arXiv cs.AI 2016
Year: 2016

The paper continues the investigation of Poincare and Russel's Vicious Circle Principle (VCP) in the context of the design of logic programming languages with sets. We expand previously introduced language Alog with aggregates by allowing infinite sets and several additional set related constructs useful for knowledge representation and teaching. In addition, we propose an alternative formalization of the original VCP and incorporate it into the semantics of new language, Slog+, which allows more liberal construction of sets and their use in programming rules. We show that, for programs without disjunction and infinite sets, the formal semantics of aggregates in Slog+ coincides with that of several other known languages. Their intuitive and formal semantics, however, are based on quite different ideas and seem to be more involved than that of Slog+.
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups
Venue: arXiv cs.SY 2016
Year: 2016

Imbalance (on-line energy gap between contracted supply and actual demand, and associated cost) reduction is going to be a crucial service for a Power Producer and Supplier (PPS) in the deregulated energy market. PPS requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy. This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise aggregated demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance for a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a Probabilistic Programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical demand statistics. Apart from the group formation, the aggregation strategy (with the help of Bayesian Inference) also clears out the upper-limit of the required storage capacity for a formed group, fraction of which is to be utilized in on-line operation. For on-line operation, a robust energy storage scheduling method is proposed that minimizes expected imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo, Japan. Simulation results are presented to verify the effectiveness of the proposed methods.