OpenRaiser/Intern-Atlas huggingface dataset:
Convergence and Density Evolution of a Low-Complexity MIMO Detector based on Forward-Backward Recursion over a Ring
Venue: arXiv cs.IT 2016
Year: 2016

Convergence and density evolution of a low complexity, iterative MIMO detection based on belief propagation (BP) over a ring-type pair-wise graph are presented in this paper. The detection algorithm to be considered is effectively a forward-backward recursion and was originally proposed in [13], where the link level performance and the convergence for Gaussian input were analyzed. Presented here are the convergence proof for discrete alphabet and the density evolution framework for binary input to give an asymptotic performance in terms of average SINR and bit error rate (BER) without channel coding. The BER curve obtained via density evolution shows a good match with simulation results, verifying the effectiveness of the density evolution analysis and the performance of the detection algorithm.
Evolution of the number of GitHub users in Spain
Venue: arXiv cs.CY 2016
Year: 2016

Since we started measuring the community of GitHub users in Spain, it has kept increasing in numbers in such a way that it has grown almost 50%, to the current 12000, in less than one year. However, the reasons for this are not clear. In this paper we will try to find out what are the different components in this growth, or at least those that can be measured, in order to find out which ones are due to the measurement itself and which might be due to other reasons. In this paper we make an advance towards finding out the reasons for this growth.
Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks
Venue: arXiv cs.IT 2016
Year: 2016

Hybrid energy supply (HES) wireless networks have recently emerged as a new paradigm to enable green networks, which are powered by both the electric grid and harvested renewable energy. In this paper, we will investigate two critical but conflicting design objectives of HES networks, i.e., the grid energy consumption and quality of service (QoS). Minimizing grid energy consumption by utilizing the harvested energy will make the network environmentally friendly, but the achievable QoS may be degraded due to the intermittent nature of energy harvesting. To investigate the tradeoff between these two aspects, we introduce the total service cost as the performance metric, which is the weighted sum of the grid energy cost and the QoS degradation cost. Base station assignment and power control is adopted as the main strategy to minimize the total service cost, while both cases with non-causal and causal side information are considered. With non-causal side information, a Greedy Assignment algorithm with low complexity and near-optimal performance is proposed. With causal side information, the design problem is formulated as a discrete Markov decision problem. Interesting solution structures are derived, which shall help to develop an efficient monotone backward induction algorithm. To further reduce complexity, a Look-Ahead policy and a Threshold-based Heuristic policy are also proposed. Simulation results shall validate the effectiveness of the proposed algorithms and demonstrate the unique grid energy consumption and QoS tradeoff in HES networks.
Boolean Operations using Generalized Winding Numbers
Venue: arXiv cs.GR 2016
Year: 2016

The generalized winding number function measures insideness for arbitrary oriented triangle meshes. Exploiting this, I similarly generalize binary boolean operations to act on such meshes. The resulting operations for union, intersection, difference, etc. avoid volumetric discretization or pre-processing.
Efficient Robust Mean Value Calculation of 1D Features
Venue: arXiv cs.CV 2016
Year: 2016

A robust mean value is often a good alternative to the standard mean value when dealing with data containing many outliers. An efficient method for samples of one-dimensional features and the truncated quadratic error norm is presented and compared to the method of channel averaging (soft histograms).
Individual Bibliometric Assessment @ University of Vienna: From Numbers to Multidimensional Profiles
Venue: arXiv cs.DL 2016
Year: 2016

This paper shows how bibliometric assessment can be implemented at individual level. This has been successfully done at the University of Vienna carried out by the Bibliometrics and Publication Strategies Department of the Vienna University Library. According to the department's philosophy, bibliometrics is not only a helpful evaluation instrument in order to complement the peer review system. It is also meant as a compass for researchers in the "publish or perish" dilemma in order to increase general visibility and to optimize publication strategies. The individual assessment comprises of an interview with the researcher under evaluation, the elaboration of a bibliometric report of the researcher's publication output, the discussion and validation of the obtained results with the researcher under evaluation as well as further optional analyses. The produced bibliometric reports are provided to the researchers themselves and inform them about the quantitative aspects of their research output. They also serve as a basis for further discussion concerning their publication strategies. These reports are eventually intended for informed peer review practices, and are therefore forwarded to the quality assurance and the rector's office and finally sent to the peers. The most important feature of the generated bibliometric report is its multidimensional and individual character. It relies on a variety of basic indicators and further control parameters in order to foster comprehensibility. Researchers, administrative staff and peers alike have confirmed the usefulness of this bibliometric approach. An increasing demand is noticeable. In total, 33 bibliometric reports have been delivered so far. Moreover, similar reports have also been produced for the bibliometric assessment of two faculties with great success.
Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art
Venue: arXiv cs.HC 2016
Year: 2016

Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally, we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.
Quantitative Evaluation of Chaotic CBC Mode of Operation
Venue: arXiv cs.CR 2016
Year: 2016

The cipher block chaining (CBC) block cipher mode of operation presents a very popular way of encrypting which is used in various applications. In previous research work, we have mathematically proven that, under some conditions, this mode of operation can admit a chaotic behavior according to Devaney. Proving that CBC mode is chaotic is only the beginning of the study of its security. The next step, which is the purpose of this paper, is to develop the quantitative study of the chaotic CBC mode of operation by evaluating the level of sensibility and expansivity for this mode.
Weakly Mutually Uncorrelated Codes
Venue: arXiv cs.IT 2016
Year: 2016

We introduce the notion of weakly mutually uncorrelated (WMU) sequences, motivated by applications in DNA-based storage systems and synchronization protocols. WMU sequences are characterized by the property that no sufficiently long suffix of one sequence is the prefix of the same or another sequence. In addition, WMU sequences used in DNA-based storage systems are required to have balanced compositions of symbols and to be at large mutual Hamming distance from each other. We present a number of constructions for balanced, error-correcting WMU codes using Dyck paths, Knuth's balancing principle, prefix synchronized and cyclic codes.
Using Reed-Solomon codes in the $\left( U\mid U+V\right)$ construction and an application to cryptography
Venue: arXiv cs.CR 2016
Year: 2016

In this paper we present a modification of Reed-Solomon codes that beats the Guruwami-Sudan $1-\sqrt{R}$ decoding radius of Reed-Solomon codes at low rates $R$. The idea is to choose Reed-Solomon codes $U$ and $V$ with appropriate rates in a $\left( U\mid U+V\right)$ construction and to decode them with the Koetter-Vardy soft information decoder. We suggest to use a slightly more general version of these codes (but which has the same decoding performances as the $\left( U\mid U+V\right)$-construction) for code-based cryptography, namely to build a McEliece scheme. The point is here that these codes not only perform nearly as well (or even better in the low rate regime) as Reed-Solomon codes, their structure seems to avoid the Sidelnikov-Shestakov attack which broke a previous McEliece proposal based on generalized Reed-Solomon codes.
Fast In-Memory SQL Analytics on Graphs
Venue: arXiv cs.DB 2016
Year: 2016

We study a class of graph analytics SQL queries, which we call relationship queries. Relationship queries are a wide superset of fixed-length graph reachability queries and of tree pattern queries. Intuitively, it discovers target entities that are reachable from source entities specified by the query. It usually also finds aggregated scores, which correspond to the target entities and are calculated by applying aggregation functions on measure attributes, which are found on the target entities, the source entities and the paths from the sources to the targets. We present real-world OLAP scenarios, where efficient relationship queries are needed. However, row stores, column stores and graph databases are unacceptably slow in such OLAP scenarios. We briefly comment on the straightforward extension of relationship queries that allows accessing arbitrary schemas. The GQ-Fast in-memory analytics engine utilizes a bottom-up fully pipelined query execution model running on a novel data organization that combines salient features of column-based organization, indexing and compression. Furthermore, GQ-Fast compiles its query plans into executable C++ source codes. Besides achieving runtime efficiency, GQ-Fast also reduces main memory requirements because, unlike column databases, GQ-Fast selectively allows more dense forms of compression including heavy-weighted compressions, which do not support random access. We used GQ-Fast to accelerate queries for two OLAP dashboards in the biomedical field. It outperforms Postgres by 2-4 orders of magnitude and outperforms MonetDB and Neo4j by 1-3 orders of magnitude when all of them are running on RAM. In addition, it generally saves space due to the appropriate use of compression methods.
On the Automorphism Groups of the Z2Z4-Linear 1-Perfect and Preparata-Like Codes
Venue: arXiv cs.IT 2016
Year: 2016

We consider the symmetry group of a $Z_2Z_4$-linear code with parameters of a $1$-perfect, extended $1$-perfect, or Preparata-like code. We show that, provided the code length is greater than $16$, this group consists only of symmetries that preserve the $Z_2Z_4$ structure. We find the orders of the symmetry groups of the $Z_2Z_4$-linear (extended) $1$-perfect codes. Keywords: additive codes, $Z_2Z_4$-linear codes, $1$-perfect codes, Preparata-like codes, automorphism group, symmetry group.
Separation of Signals Consisting of Amplitude and Instantaneous Frequency RRC Pulses Using SNR Uniform Training
Venue: arXiv cs.IT 2016
Year: 2016

This work presents sample mean and sample variance based features that distinguish continuous phase FSK from QAM and PSK modulations. Root raised cosine pulses are used for signal generation. Support vector machines are employed for signals separation. They are trained for only one value of SNR and used to classify the signals from a wide range of SNR. A priori information about carrier amplitude, carrier phase, carrier offset, roll-off factor and initial symbol phase is relaxed. Effectiveness of the method is tested by observing the joint effects of AWGN, carrier offset, lack of symbol and sampling synchronization, and fast fading.
A Framework to Prevent QR Code Based Phishing Attacks
Venue: arXiv cs.CR 2016
Year: 2016

Though the rapid development and spread of Information and Communication Technology (ICT) making people's life much more easier, on the other hand it causing some serious threats to the society. Phishing is one of the most common cyber threat, that most users falls in. This research investigate on QR code based phishing attacks which is a newly adopted intrusive method and how to enhance the awareness and avoidance behavior of QR based phishing attacks through the user centric security education approaches using game based learning.
Convolutional Pose Machines
Venue: arXiv cs.CV 2016
Year: 2016

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.
Throughput Analysis and Optimization of Wireless-Powered Multiple Antenna Full-Duplex Relay Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multi-antenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semi-definite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that, beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.
Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty - An Extended Version
Venue: arXiv cs.AI 2016
Year: 2016

This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process. HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application. Finally, (iv) we show hardness results for the problem that HEALER solves. HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach
Venue: arXiv cs.CV 2016
Year: 2016

Detection of boundaries of materials stored in transparent vessels is essential for identifying properties such as liquid level and phase boundaries, which are vital for controlling numerous processes in the industry and chemistry laboratory. This work presents a computer vision method for identifying the boundary of materials in transparent vessels using the graph-cut algorithm. The method receives an image of a transparent vessel containing a material and the contour of the vessel in the image. The boundary of the material in the vessel is found by the graph cut method. In general the method uses the vessel region of the image to create a graph, where pixels are vertices, and the cost of an edge between two pixels is inversely correlated with their intensity difference. The bottom 10% of the vessel region in the image is assumed to correspond to the material phase and defined as the graph and source. The top 10% of the pixels in the vessels are assumed to correspond to the air phase and defined as the graph sink. The minimal cut that splits the resulting graph between the source and sink (hence, material and air) is traced using the max-flow/min-cut approach. This cut corresponds to the boundary of the material in the image. The method gave high accuracy in boundary recognition for a wide range of liquid, solid, granular and powder materials in various glass vessels from everyday life and the chemistry laboratory, such as bottles, jars, Glasses, Chromotography colums and separatory funnels.
On Diffusion-restricted Social Network: A Measurement Study of WeChat Moments
Venue: arXiv cs.CY 2016
Year: 2016

WeChat is a mobile messaging application that has 549 million active users as of Q1 2015, and "WeChat Moments" (WM) serves its social-networking function that allows users to post/share links of web pages. WM differs from the other social networks as it imposes many restrictions on the information diffusion process to mitigate the information overload. In this paper, we conduct a measurement study on information diffusion in the WM network by crawling and analyzing the spreading statistics of more than 160,000 pages that involve approximately 40 million users. Specifically, we identify the relationship of the number of posted pages and the number of views, the diffusion path length, the similarity and distribution of users' locations as well as their connections with the GDP of the users' province. For each individual WM page, we measure its temporal characteristics (e.g., the life time, the popularity within a time period); for each individual user, we evaluate how many of, or how likely, one's friends will view his posted pages. Our results will help the business to decide when and how to release the marketing pages over WM for better publicity.
Discussion on Mechanical Learning and Learning Machine
Venue: arXiv cs.AI 2016
Year: 2016

Mechanical learning is a computing system that is based on a set of simple and fixed rules, and can learn from incoming data. A learning machine is a system that realizes mechanical learning. Importantly, we emphasis that it is based on a set of simple and fixed rules, contrasting to often called machine learning that is sophisticated software based on very complicated mathematical theory, and often needs human intervene for software fine tune and manual adjustments. Here, we discuss some basic facts and principles of such system, and try to lay down a framework for further study. We propose 2 directions to approach mechanical learning, just like Church-Turing pair: one is trying to realize a learning machine, another is trying to well describe the mechanical learning.
Greedy Deep Dictionary Learning
Venue: arXiv cs.LG 2016
Year: 2016

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.
Learning a low-rank shared dictionary for object classification
Venue: arXiv cs.CV 2016
Year: 2016

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we propose a new fast and accurate algorithm to solve the sparse coding problems in the learning step, accelerating its convergence. The said algorithm could also be applied to FDDL and its extensions. Experimental results on widely used image databases establish the advantages of our method over state-of-the-art dictionary learning methods.
Beyond Countable Alphabets: An Extension of the Information-Spectrum Approach
Venue: arXiv cs.IT 2016
Year: 2016

A general approach is established for deriving one-shot performance bounds for information-theoretic problems on general alphabets beyond countable alphabets. It is mainly based on the quantization idea and a novel form of "likelihood ratio". As an example, one-shot lower and upper bounds for random number generation from correlated sources on general alphabets are derived.