OpenRaiser/Intern-Atlas huggingface dataset:
Methods for estimating the size of Google Scholar
Venue: arXiv cs.DL 2015
Year: 2015

The emergence of academic search engines (mainly Google Scholar and Microsoft Academic Search) that aspire to index the entirety of current academic knowledge has revived and increased interest in the size of the academic web. The main objective of this paper is to propose various methods to estimate the current size (number of indexed documents) of Google Scholar (May 2014) and to determine its validity, precision and reliability. To do this, we present, apply and discuss three empirical methods: an external estimate based on empirical studies of Google Scholar coverage, and two internal estimate methods based on direct, empty and absurd queries, respectively. The results, despite providing disparate values, place the estimated size of Google Scholar at around 160 to 165 million documents. However, all the methods show considerable limitations and uncertainties due to inconsistencies in the Google Scholar search functionalities.
Disclosing the network structure of private companies on the web: the case of Spanish IBEX 35 share index
Venue: arXiv cs.DL 2015
Year: 2015

It is common for an international company to have different brands, products or services, information for investors, a corporate blog, affiliates, branches in different countries, etc. If all these contents appear as independent additional web domains (AWD), the company should be represented on the web by all these web domains, since many of these AWDs may acquire remarkable performance that could mask or distort the real web performance of the company, affecting therefore on the understanding of web metrics. The main objective of this study is to determine the amount, type, web impact and topology of the additional web domains in commercial companies in order to get a better understanding on their complete web impact and structure. The set of companies belonging to the Spanish IBEX-35 stock index has been analyzed as testing bench. We proceeded to identify and categorize all AWDs belonging to these companies, and to apply both web impact (web presence and visibility) and network metrics. The results show that AWDs get a high web presence but relatively low web visibility, due to certain opacity or less dissemination of some AWDs, favoring its isolation. This is verified by the low network density values obtained, that occur because AWDs are strongly connected with the corporate domain (although asymmetrically), but very weakly linked each other. Although the processes of AWDs creation and categorization are complex (web policy seems not to be driven by a defined or conscious plan), their influence on the web performance of IBEX 35companies is meaningful. This research measures the AWDs influence on companies under webometric terms for the first time.
The Wreath Process: A totally generative model of geometric shape based on nested symmetries
Venue: arXiv cs.AI 2015
Year: 2015

We consider the problem of modelling noisy but highly symmetric shapes that can be viewed as hierarchies of whole-part relationships in which higher level objects are composed of transformed collections of lower level objects. To this end, we propose the stochastic wreath process, a fully generative probabilistic model of drawings. Following Leyton's "Generative Theory of Shape", we represent shapes as sequences of transformation groups composed through a wreath product. This representation emphasizes the maximization of transfer --- the idea that the most compact and meaningful representation of a given shape is achieved by maximizing the re-use of existing building blocks or parts. The proposed stochastic wreath process extends Leyton's theory by defining a probability distribution over geometric shapes in terms of noise processes that are aligned with the generative group structure of the shape. We propose an inference scheme for recovering the generative history of given images in terms of the wreath process using reversible jump Markov chain Monte Carlo methods and Approximate Bayesian Computation. In the context of sketching we demonstrate the feasibility and limitations of this approach on model-generated and real data.
How Infectious Was #Deflategate?
Venue: arXiv cs.SI 2015
Year: 2015

On Monday January 19, 2015 a story broke that the National Football League (NFL) had started an investigation into whether the New England Patriots deliberately deflated the footballs they used during their championship win over the Indianapolis Colts. Like an infectious disease, discussion regarding Deflategate grew rapidly on social media sites in the hours and days after the release of the story. However, after the Super Bowl was over, the scandal slowly began to dissipate and lost much of the attention it had originally had, as interest in the NFL wained at the completion of its season. We construct a simple epidemic model for the infectiousness of the Deflategate news story. We then use data from the social media site Twitter to estimate the parameters of this model using standard techniques from the study of inverse problems. We find that the infectiousness (as measured by the basic reproduction number) of Deflategate rivals that of any infectious disease that we are aware of, and is actually more infectious than recent news stories of greater importance - both in terms of the basic reproduction number and in terms of the average amount of time the average tweeter continued to tweet about the news story.
Bringing Modern Web Applications to Disconnected Networks
Venue: arXiv cs.NI 2015
Year: 2015

Opportunistic networking is one way to realize pervasive applications while placing little demand on network infrastructure, especially for operating in less well connected environments. In contrast to the ubiquitous network access model inherent to many cloud-based applications, for which the web browser forms the user front end, opportunistic applications require installing software on mobile devices. Even though app stores (when accessible) offer scalable distribution mechanisms for applications, a designer needs to support multiple OS platforms and only some of those are suitable for opportunistic operation to begin with. In this paper, we present a web browser-based interaction framework that 1) allows users to interact with opportunistic application content without installing the respective app and 2) even supports users whose mobile OSes do not support opportunistic networking at all via minimal stand-alone infrastructure. We describe our system and protocol design, validate its operation using simulations, and report on our implementation including support for six opportunistic applications.
Simultaneous Multi Layer Access: A High Bandwidth and Low Cost 3D-Stacked Memory Interface
Venue: arXiv cs.AR 2015
Year: 2015

Limited memory bandwidth is a critical bottleneck in modern systems. 3D-stacked DRAM enables higher bandwidth by leveraging wider Through-Silicon-Via (TSV) channels, but today's systems cannot fully exploit them due to the limited internal bandwidth of DRAM. DRAM reads a whole row simultaneously from the cell array to a row buffer, but can transfer only a fraction of the data from the row buffer to peripheral IO circuit, through a limited and expensive set of wires referred to as global bitlines. In presence of wider memory channels, the major bottleneck becomes the limited data transfer capacity through these global bitlines. Our goal in this work is to enable higher bandwidth in 3D-stacked DRAM without the increased cost of adding more global bitlines. We instead exploit otherwise-idle resources, such as global bitlines, already existing within the multiple DRAM layers by accessing the layers simultaneously. Our architecture, Simultaneous Multi Layer Access (SMLA), provides higher bandwidth by aggregating the internal bandwidth of multiple layers and transferring the available data at a higher IO frequency. To implement SMLA, simultaneous data transfer from multiple layers through the same IO TSVs requires coordination between layers to avoid channel conflict. We first study coordination by static partitioning, which we call Dedicated-IO, that assigns groups of TSVs to each layer. We then provide a simple, yet sophisticated mechanism, called Cascaded-IO, which enables simultaneous access to each layer by time-multiplexing the IOs. By operating at a frequency proportional to the number of layers, SMLA provides a higher bandwidth (4X for a four-layer stacked DRAM). Our evaluations show that SMLA provides significant performance improvement and energy reduction (55%/18% on average for multi-programmed workloads, respectively) over a baseline 3D-stacked DRAM with very low area overhead.
Accelerating Non-volatile/Hybrid Processor Cache Design Space Exploration for Application Specific Embedded Systems
Venue: arXiv cs.AR 2015
Year: 2015

In this article, we propose a technique to accelerate nonvolatile or hybrid of volatile and nonvolatile processor cache design space exploration for application specific embedded systems. Utilizing a novel cache behavior modeling equation and a new accurate cache miss prediction mechanism, our proposed technique can accelerate NVM or hybrid FIFO processor cache design space exploration for SPEC CPU 2000 applications up to 249 times compared to the conventional approach.
Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering
Venue: arXiv cs.CL 2015
Year: 2015

Building unified timelines from a collection of written news articles requires cross-document event coreference resolution and temporal relation extraction. In this paper we present an approach event coreference resolution according to: a) similar temporal information, and b) similar semantic arguments. Temporal information is detected using an automatic temporal information system (TIPSem), while semantic information is represented by means of LDA Topic Modeling. The evaluation of our approach shows that it obtains the highest Micro-average F-score results in the SemEval2015 Task 4: TimeLine: Cross-Document Event Ordering (25.36\% for TrackB, 23.15\% for SubtrackB), with an improvement of up to 6\% in comparison to the other systems. However, our experiment also showed some draw-backs in the Topic Modeling approach that degrades performance of the system.
Stochastic Coalitional Better-response Dynamics and Strong Nash Equilibrium
Venue: arXiv cs.GT 2015
Year: 2015

We consider coalition formation among players in an n-player finite strategic game over infinite horizon. At each time a randomly formed coalition makes a joint deviation from a current action profile such that at new action profile all players from the coalition are strictly benefited. Such deviations define a coalitional better-response (CBR) dynamics that is in general stochastic. The CBR dynamics either converges to a strong Nash equilibrium or stucks in a closed cycle. We also assume that at each time a selected coalition makes mistake in deviation with small probability that add mutations (perturbations) into CBR dynamics. We prove that all strong Nash equilibria and closed cycles are stochastically stable, i.e., they are selected by perturbed CBR dynamics as mutations vanish. Similar statement holds for strict strong Nash equilibrium. We apply CBR dynamics to the network formation games and we prove that all strongly stable networks and closed cycles are stochastically stable.
Linear Transceiver Optimization in Multicell MIMO Based on the Generalized Benders Decomposition
Venue: arXiv cs.IT 2015
Year: 2015

We study the maximum sum rate optimization problem in the multiple-input multiple-output interfering broadcast channel. The multiple-antenna transmitters and receivers are assumed to have perfect channel state information. In this setting, finding the optimal linear transceiver design is an NP-hard problem. We show that a reformulation of the problem renders the application of generalized Benders decomposition suitable. The decomposition provides us with an optimization structure which we exploit to apply two different optimization approaches. While one approach is guaranteed to converge to a local optimum of the original problem, the other approach hinges on techniques which can be promising for devising a global optimization method.
Super-Languages: Developing Languages and Applications with XMF (Second Edition)
Venue: arXiv cs.SE 2015
Year: 2015

The aim of this book is to introduce the language XMF. This is done by defining the language, providing some examples of applications that can be written directly in the XOCL language that comes with XMF, and then by showing how XMF can be used for language engineering. The main focus of this book is on language engineering by example.
A General Architecture for Heterogeneous Language Engineering and Projectional Editor Support
Venue: arXiv cs.SE 2015
Year: 2015

Tool support for language engineering has typically prioritises concrete syntax over abstract syntax by providing meta-languages for expressing concrete syntax and then mapping concrete to abstract structures. Text-based languages are usually specified using a BNF-like language used to generate a syntax-aware editor that includes features such as keyword completion. Similarly, graphical languages are defined using a declarative graphical syntax language, producing an editor that supports features such as shapes, graphs and edges. Projectional editors invert traditional approaches by prioritising abstract over concrete syntax. This paper describes a projectional meta-tool architecture, including general purpose abstract and concrete meta-languages, that uses declarative rules to integrate the syntax and tool support for a range of heterogeneous languages. The architecture has been implemented in Racket and the paper illustrates the architecture with concrete examples.
Fast Online Clustering with Randomized Skeleton Sets
Venue: arXiv cs.AI 2015
Year: 2015

We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or k-medoid, it does not make any restrictive generative assumptions. In addition, in contrast to existing nonparametric clustering techniques such as DBScan or DenStream, it gives provable theoretical guarantees. To achieve fast clustering, we propose to represent each cluster by a skeleton set which is updated continuously as new data is seen. A skeleton set consists of weighted samples from the data where weights encode local densities. The size of each skeleton set is adapted according to the cluster geometry. The proposed technique automatically detects the number of clusters and is robust to outliers. The algorithm works for the infinite data stream where more than one pass over the data is not feasible. We provide theoretical guarantees on the quality of the clustering and also demonstrate its advantage over the existing state-of-the-art on several datasets.
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
Venue: arXiv cs.CV 2015
Year: 2015

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation
Venue: arXiv cs.CV 2015
Year: 2015

We introduce language-driven image generation, the task of generating an image visualizing the semantic contents of a word embedding, e.g., given the word embedding of grasshopper, we generate a natural image of a grasshopper. We implement a simple method based on two mapping functions. The first takes as input a word embedding (as produced, e.g., by the word2vec toolkit) and maps it onto a high-level visual space (e.g., the space defined by one of the top layers of a Convolutional Neural Network). The second function maps this abstract visual representation to pixel space, in order to generate the target image. Several user studies suggest that the current system produces images that capture general visual properties of the concepts encoded in the word embedding, such as color or typical environment, and are sufficient to discriminate between general categories of objects.
Verification of railway interlocking systems
Venue: arXiv cs.SE 2015
Year: 2015

In the railway domain, an interlocking is a computerised system that controls the railway signalling objects in order to allow a safe operation of the train traffic. Each interlocking makes use of particular data, called application data, that reflects the track layout of the station under control. The verification and validation of the application data are performed manually and is thus error-prone and costly. In this paper, we explain how we built an executable model in NuSMV of a railway interlocking based on the application data. We also detail the tool that we have developed in order to translate the application data into our model automatically. Finally we show how we could verify a realistic set of safety properties on a real-size station model by customizing the existing model-checking algorithm with PyNuSMV a Python library based on NuSMV.
Breaking Dense Structures: Proving Stability of Densely Structured Hybrid Systems
Venue: arXiv cs.SE 2015
Year: 2015

Abstraction and refinement is widely used in software development. Such techniques are valuable since they allow to handle even more complex systems. One key point is the ability to decompose a large system into subsystems, analyze those subsystems and deduce properties of the larger system. As cyber-physical systems tend to become more and more complex, such techniques become more appealing. In 2009, Oehlerking and Theel presented a (de-)composition technique for hybrid systems. This technique is graph-based and constructs a Lyapunov function for hybrid systems having a complex discrete state space. The technique consists of (1) decomposing the underlying graph of the hybrid system into subgraphs, (2) computing multiple local Lyapunov functions for the subgraphs, and finally (3) composing the local Lyapunov functions into a piecewise Lyapunov function. A Lyapunov function can serve multiple purposes, e.g., it certifies stability or termination of a system or allows to construct invariant sets, which in turn may be used to certify safety and security. In this paper, we propose an improvement to the decomposing technique, which relaxes the graph structure before applying the decomposition technique. Our relaxation significantly reduces the connectivity of the graph by exploiting super-dense switching. The relaxation makes the decomposition technique more efficient on one hand and on the other allows to decompose a wider range of graph structures.
Binaural coherent-to-diffuse-ratio estimation for dereverberation using an ITD model
Venue: arXiv cs.SD 2015
Year: 2015

Most previously proposed dual-channel coherent-to-diffuse-ratio (CDR) estimators are based on a free-field model. When used for binaural signals, e.g., for dereverberation in binaural hearing aids, their performance may degrade due to the influence of the head, even when the direction-of-arrival of the desired speaker is exactly known. In this paper, the head shadowing effect is taken into account for CDR estimation by using a simplified model for the frequency-dependent interaural time difference and a model for the binaural coherence of the diffuse noise field. Evaluation of CDR-based dereverberation with measured binaural impulse responses indicates that the proposed binaural CDR estimators can improve PESQ scores.
Simultaneously Moving Cops and Robbers
Venue: arXiv cs.DM 2015
Year: 2015

In this paper we study the concurrent cops and robber (CCCR) game. CCCR follows the same rules as the classical, turn-based game, except for the fact that the players move simultaneously. The cops' goal is to capture the robber and the concurrent cop number of a graph is defined the minimum number of cops which guarantees capture. For the variant in which it it required to capture the robber in the shortest possible time, we let time to capture be the payoff function of CCCR; the (game theoretic) value of CCCR is the optimal capture time and (cop and robber) time optimal strategies are the ones which achieve the value. In this paper we prove the following. (1) For every graph G, the concurrent cop number is equal to the "classical" cop number. (2) For every graph G, CCCR has a value, the cops have an optimal strategy and, for every epsilon>0, the robber has an epsilon-optimal strategy.
Max-Entropy Feed-Forward Clustering Neural Network
Venue: arXiv cs.LG 2015
Year: 2015

The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI datasets, comparing with a few baselines and applied purity as the measurement . The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.
A Novel Approach for Image Steganography in Spatial Domain
Venue: arXiv cs.MM 2015
Year: 2015

This paper presents a new approach for hiding information in digital image in spatial domain. In this approach three bits of message is embedded in a pixel using Lucas number system but only one bit plane is allowed for alternation. The experimental results show that the proposed method has the larger capacity of embedding data, high peak signal to noise ratio compared to existing methods and is hardly detectable for steganolysis algorithm.
Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
Venue: arXiv cs.SY 2015
Year: 2015

An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.