OpenRaiser/Intern-Atlas huggingface dataset:
Higher Order Statistics in Switched Diversity Systems
Venue: arXiv cs.IT 2015
Year: 2015

We analyze the level crossing rate (LCR) and the average fade duration of the output signal-to-noise-ratio (SNR) in generalized switched diversity systems. By using a common approach, we study these higher order statistics for two different kinds of configurations: (1) Colocated diversity, i.e. receiver equipped with multiple antennas, and (2) Distributed diversity, i.e. relaying link with multiple single-antenna threshold-based decode-and-forward (DF) relays. In both cases, we consider the switched diversity combining strategies Selection Combining and Switch \& Stay Combining (SSC). Whenever using threshold-based techniques such as DF or SSC, the output SNR is a discontinuous random process and hence classic Rice approach to calculate the LCR is not applicable. Thus, we use an alternative formulation in terms of the one and two-dimensional cumulative distribution functions of the output SNR. Our results are general, and hold for any arbitrary distribution of fading at the different diversity branches. Moreover, we develop a general asymptotic framework to calculate these higher order statistics in high mean SNR environments which only needs of the univariate probability density function.
On Automated Lemma Generation for Separation Logic with Inductive Definitions
Venue: arXiv cs.LO 2015
Year: 2015

Separation Logic with inductive definitions is a well-known approach for deductive verification of programs that manipulate dynamic data structures. Deciding verification conditions in this context is usually based on user-provided lemmas relating the inductive definitions. We propose a novel approach for generating these lemmas automatically which is based on simple syntactic criteria and deterministic strategies for applying them. Our approach focuses on iterative programs, although it can be applied to recursive programs as well, and specifications that describe not only the shape of the data structures, but also their content or their size. Empirically, we find that our approach is powerful enough to deal with sophisticated benchmarks, e.g., iterative procedures for searching, inserting, or deleting elements in sorted lists, binary search tress, red-black trees, and AVL trees, in a very efficient way.
Estimating the Impact of Unknown Unknowns on Aggregate Query Results
Venue: arXiv cs.DB 2015
Year: 2015

It is common practice for data scientists to acquire and integrate disparate data sources to achieve higher quality results. But even with a perfectly cleaned and merged data set, two fundamental questions remain: (1) is the integrated data set complete and (2) what is the impact of any unknown (i.e., unobserved) data on query results? In this work, we develop and analyze techniques to estimate the impact of the unknown data (a.k.a., unknown unknowns) on simple aggregate queries. The key idea is that the overlap between different data sources enables us to estimate the number and values of the missing data items. Our main techniques are parameter-free and do not assume prior knowledge about the distribution. Through a series of experiments, we show that estimating the impact of unknown unknowns is invaluable to better assess the results of aggregate queries over integrated data sources.
Rule Of Thumb: Deep derotation for improved fingertip detection
Venue: arXiv cs.CV 2015
Year: 2015

We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network. This is integrated with an in-plane image derotation scheme, DeROT, to tackle the problem of per-frame fingertip detection in depth images. The method reduces the complexity of learning in the space of articulated poses which is demonstrated by using two distinct state-of-the-art learning based hand pose estimation methods applied to fingertip detection. Significant classification improvements are shown over the baseline implementation. Our framework involves no tracking, kinematic constraints or explicit prior model of the articulated object in hand. To support our approach we also describe a new pipeline for high accuracy magnetic annotation and labeling of objects imaged by a depth camera.
DTLS Performance in Duty-Cycled Networks
Venue: arXiv cs.NI 2015
Year: 2015

The Datagram Transport Layer Security (DTLS) protocol is the IETF standard for securing the Internet of Things. The Constrained Application Protocol, ZigBee IP, and Lightweight Machine-to-Machine (LWM2M) mandate its use for securing application traffic. There has been much debate in both the standardization and research communities on the applicability of DTLS to constrained environments. The main concerns are the communication overhead and latency of the DTLS handshake, and the memory footprint of a DTLS implementation. This paper provides a thorough performance evaluation of DTLS in different duty-cycled networks through real-world experimentation, emulation and analysis. In particular, we measure the duration of the DTLS handshake when using three duty cycling link-layer protocols: preamble-sampling, the IEEE 802.15.4 beacon-enabled mode and the IEEE 802.15.4e Time Slotted Channel Hopping mode. The reported results demonstrate surprisingly poor performance of DTLS in radio duty-cycled networks. Because a DTLS client and a server exchange more than 10 signaling packets, the DTLS handshake takes between a handful of seconds and several tens of seconds, with similar results for different duty cycling protocols. Moreover, because of their limited memory, typical constrained nodes can only maintain 3-5 simultaneous DTLS sessions, which highlights the need for using DTLS parsimoniously.
Massive MIMO in Real Propagation Environments: Do All Antennas Contribute Equally?
Venue: arXiv cs.IT 2015
Year: 2015

Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy consumption. Here we investigate the possibilities to reduce the required number of RF chains, by performing antenna selection. While this approach is not a very effective strategy for theoretical independent Rayleigh fading channels, a substantial reduction in the number of RF chains can be achieved for real massive MIMO channels, without significant performance loss. We evaluate antenna selection performance on measured channels at 2.6 GHz, using a linear and a cylindrical array, both having 128 elements. Sum-rate maximization is used as the criterion for antenna selection. A selection scheme based on convex optimization is nearly optimal and used as a benchmark. The achieved sum-rate is compared with that of a very simple scheme that selects the antennas with the highest received power. The power-based scheme gives performance close to the convex optimization scheme, for the measured channels. This observation indicates a potential for significant reductions of massive MIMO implementation complexity, by reducing the number of RF chains and performing antenna selection using simple algorithms.
On-line Survival Analysis of Power Electronic Converters Using Step Noise-Cox Processes
Venue: arXiv cs.SY 2015
Year: 2015

This paper is focused on survival analysis of electrical components. The main goal of this paper is to develop a method for on-line estimation of the Mean Time To Failure (MTTF) of electrical components under dynamic stress levels. The proposed method models the variations of the stress levels as a stochastic process. Hence, a stochastic failure rate function can be developed for each electrical component. Later, this function is used as the underlying rate of a doubly stochastic Poisson process (known as Cox processes). Furthermore, this Cox process is used for on-line estimation of the Mean Residual Life (MRL) using the observed stress levels. The proposed method provides a good estimate of the age and life expectancy of each component. An experimental case study is provided to demonstrate the proposed method.
The challenges of SVM optimization using Adaboost on a phoneme recognition problem
Venue: arXiv cs.CL 2015
Year: 2015

The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.
Dynamic Channel Access for Energy Efficient Data Gathering in Cognitive Radio Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Wireless sensor networks (WSNs) operating in the license-free spectrum suffer from uncontrolled interference as those spectrum bands become increasingly crowded. The emerging cognitive radio sensor networks (CRSNs) provide a promising solution to address this challenge by enabling sensor nodes to opportunistically access licensed channels. However, since sensor nodes have to consume considerable energy to support CR functionalities, such as channel sensing and switching, the opportunistic channel accessing should be carefully devised for improving the energy efficiency in CRSN. To this end, we investigate the dynamic channel accessing problem to improve the energy efficiency for a clustered CRSN. Under the primary users' protection requirement, we study the resource allocation issues to maximize the energy efficiency of utilizing a licensed channel for intra-cluster and inter-cluster data transmission, respectively. With the consideration of the energy consumption in channel sensing and switching, we further determine the condition when sensor nodes should sense and switch to a licensed channel for improving the energy efficiency, according to the packet loss rate of the license-free channel. In addition, two dynamic channel accessing schemes are proposed to identify the channel sensing and switching sequences for intra-cluster and inter-cluster data transmission, respectively. Extensive simulation results demonstrate that the proposed channel accessing schemes can significantly reduce the energy consumption in CRSNs.
STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and Synchrony
Venue: arXiv cs.NE 2015
Year: 2015

There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimick biology. They use neural networks which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology, they allow us to re-think our current paradigm of computation. The ultimate goal is to develop brain inspired general purpose computation architectures that can breach the current bottleneck introduced by the Von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The presented framework provides both linear and non linear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of non-linear differential equations leading to chaotic attractors.
The Tangent Search Engine: Improved Similarity Metrics and Scalability for Math Formula Search
Venue: arXiv cs.IR 2015
Year: 2015

With the ever-increasing quantity and variety of data worldwide, the Web has become a rich repository of mathematical formulae. This necessitates the creation of robust and scalable systems for Mathematical Information Retrieval, where users search for mathematical information using individual formulae (query-by-expression) or a combination of keywords and formulae. Often, the pages that best satisfy users' information needs contain expressions that only approximately match the query formulae. For users trying to locate or re-find a specific expression, browse for similar formulae, or who are mathematical non-experts, the similarity of formulae depends more on the relative positions of symbols than on deep mathematical semantics. We propose the Maximum Subtree Similarity (MSS) metric for query-by-expression that produces intuitive rankings of formulae based on their appearance, as represented by the types and relative positions of symbols. Because it is too expensive to apply the metric against all formulae in large collections, we first retrieve expressions using an inverted index over tuples that encode relationships between pairs of symbols, ranking hits using the Dice coefficient. The top-k formulae are then re-ranked using MSS. Our approach obtains state-of-the-art performance on the NTCIR-11 Wikipedia formula retrieval benchmark and is efficient in terms of both index space and overall retrieval time. Retrieval systems for other graphical forms, including chemical diagrams, flowcharts, figures, and tables, may also benefit from adopting our approach.
Information Spread Over an Internet-mediated Social Network: Phases, Speed, Width, and Effects of Promotion
Venue: arXiv cs.SI 2015
Year: 2015

In this study, we looked at the effect of promotion in the speed and width of spread of information on the Internet by tracking the diffusion of news articles over a social network. Speed of spread means the number of readers that the news has reached in a given time, while width of spread means how far the story has travelled from the news originator within the social network. After analyzing six stories in a 30-hour time span, we found out that the lifetime of a story's popularity among the members of the social network has three phases: Expansion, Front-page, and Saturation. Expansion phase starts when a story is published and the article spreads from a source node to nodes within a connected component of the social network. Front-page phase happens when a news aggregator promotes the story in its front page resulting to the story's faster rate of spread among the connected nodes while at the same time spreading the article to nodes outside the original connected component of the social network. Saturation phase is when the story ages and its rate of spread within the social network slows down, suggesting popularity saturation among the nodes. Within these three phases, we observed minimal changes on the width of information spread as suggested by relatively low increase of the width of the spread's diameter within the social network. We see that this paper provides the various stakeholders a first-hand empirical data for modeling, designing, and improving the current web-based services, specifically the IT educators for designing and improving academic curricula, and for improving the current web-enabled deployment of knowledge and online evaluation of skills.
To HTTP/2, or Not To HTTP/2, That Is The Question
Venue: arXiv cs.NI 2015
Year: 2015

As of February, 2015, HTTP/2, the update to the 16-year-old HTTP 1.1, is officially complete. HTTP/2 aims to improve the Web experience by solving well-known problems (e.g., head of line blocking and redundant headers), while introducing new features (e.g., server push and content priority). On paper HTTP/2 represents the future of the Web. Yet, it is unclear whether the Web itself will, and should, hop on board. To shed some light on these questions, we built a measurement platform that monitors HTTP/2 adoption and performance across the Alexa top 1 million websites on a daily basis. Our system is live and up-to-date results can be viewed at http://isthewebhttp2yet.com/. In this paper, we report our initial findings from a 6 month measurement campaign (November 2014 - May 2015). We find 13,000 websites reporting HTTP/2 support, but only 600, mostly hosted by Google and Twitter, actually serving content. In terms of speed, we find no significant benefits from HTTP/2 under stable network conditions. More benefits appear in a 3G network where current Web development practices make HTTP/2 more resilient to losses and delay variation than previously believed.
The Polylingual Labeled Topic Model
Venue: arXiv cs.CL 2015
Year: 2015

In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA. The model accounts for multiple languages with separate topic distributions for each language while restricting the permitted topics of a document to a set of predefined labels. We explore the properties of the model in a two-language setting on a dataset from the social science domain. Our experiments show that our model outperforms LDA and Labeled LDA in terms of their held-out perplexity and that it produces semantically coherent topics which are well interpretable by human subjects.
Comparison between GFDM and VOFDM
Venue: arXiv cs.IT 2015
Year: 2015

This document provides a comparison of the transmission techniques used in Generalized Frequency Division Multiplexing (GFDM) and Vector-OFDM (VOFDM). Within the document both systems are coarsely described and common and distinct properties are highlighted.
YARBUS : Yet Another Rule Based belief Update System
Venue: arXiv cs.CL 2015
Year: 2015

We introduce a new rule based system for belief tracking in dialog systems. Despite the simplicity of the rules being considered, the proposed belief tracker ranks favourably compared to the previous submissions on the second and third Dialog State Tracking challenges. The results of this simple tracker allows to reconsider the performances of previous submissions using more elaborate techniques.
Soft Skills and Software Development: A Reflection from the Software Industry
Venue: arXiv cs.SE 2015
Year: 2015

We review the literature relating to soft skills and the software engineering and information systems domain before describing a study based on 650 job advertisements posted on well-known recruitment sites from a range of geographical locations including, North America, Europe, Asia and Australia. The study makes use of nine defined soft skills to assess the level of demand for each of these skills related to individual job roles within the software industry. This work reports some of the vital statistics from industry about the requirements of soft skills in various roles of software development phases. The work also highlights the variation in the types of skills required for each of the roles. We found that currently although the software industry is paying attention to soft skills up to some extent while hiring but there is a need to further acknowledge the role of these skills in software development. The objective of this paper is to analyze the software industry soft skills requirements for various software development positions, such as system analyst, designer, programmer, and tester. We pose two research questions, namely, (1) What soft skills are appropriate to different software development lifecycle roles, and (2) Up to what extend does the software industry consider soft skills when hiring an employee. The study suggests that there is a further need of acknowledgment of the significance of soft skills from employers in software industry.
A Reinforcement Learning Approach to Online Learning of Decision Trees
Venue: arXiv cs.LG 2015
Year: 2015

Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore, RLDT optimizes a long term return, providing a better alternative to the traditional myopic greedy approach to growing decision trees. We demonstrate that this approach performs as well as batch learning algorithms and other online decision tree learning algorithms, while making significantly fewer queries about the features of the data points. We also show that RLDT can effectively handle concept drift.
Building an OSS Quality Estimation Model with CATREG
Venue: arXiv cs.SE 2015
Year: 2015

Open Source Software (OSS) has been a popular form in software development. In this paper, we use statistical approaches to derive OSS quality estimation models. Our objective is to build estimation models for the number of defects with metrics at project levels. First CATREG (Categorical regression with optimal scaling) is used to obtain quantifications of the qualitative variables. Then the independent variables are validated using the stepwise linear regression. The process is repeated to acquire optimal quantifications and final regression formula. This modeling process is performed based on data from the OSS communities and is proved to be practically valuable.
A Decision Support Tool for Assessing the Maturity of Software Product Line Process
Venue: arXiv cs.SE 2015
Year: 2015

The software product line aims at the effective utilization of software assets, reducing the time required to deliver a product, improving the quality, and decreasing the cost of software products. Organizations trying to incorporate this concept require an approach to assess the current maturity level of the software product line process in order to make management decisions. A decision support tool for assessing the maturity of the software product line process is developed to implement the fuzzy logic approach, which handles the imprecise and uncertain nature of software process variables. The proposed tool can be used to assess the process maturity level of a software product line. Such knowledge will enable an organization to make crucial management decisions. Four case studies were conducted to validate the tool, and the results of the studies show that the software product line decision support tool provides a direct mechanism to evaluate the current software product line process maturity level within an organization.
Clues on Software Engineers Learning Styles
Venue: arXiv cs.SE 2015
Year: 2015

The Myers-Briggs Type Indicator (MBTI) has proved to be a useful instrument for understanding student learning preferences and has enable comparisons of the learning preferences for various personality types. Regarding learning styles, there is no one best combination of characteristics, since each preference has its own advantages and disadvantages. Therefore, it is a fallacy to think that professors can devise a single teaching technique that would always appeal to all students at the same time. The ideas presented in this paper have been taken into account in two 4th year courses, named Software Requirements and Software Design in which the students develop their capstone projects. The results of this investigation may help college instructors to understanding the preferred leaning style of software engineers.
Mobile-Based Video Caching Architecture Based on Billboard Manager
Venue: arXiv cs.NI 2015
Year: 2015

Video streaming services are very popular today. Increasingly, users can now access multimedia applications and video playback wirelessly on their mobile devices. However, a significant challenge remains in ensuring smooth and uninterrupted transmission of almost any size of video file over a 3G network, and as quickly as possible in order to optimize bandwidth consumption. In this paper, we propose to position our Billboard Manager to provide an optimal transmission rate to enable smooth video playback to a mobile device user connected to a 3G network. Our work focuses on serving user requests by mobile operators from cached resource managed by Billboard Manager, and transmitting the video files from this pool. The aim is to reduce the load placed on bandwidth resources of a mobile operator by routing away as much user requests away from the internet for having to search a video and, subsequently, if located, have it transferred back to the user.