OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Penetration Depth Computation between Rigid Models using Contact Space Propagation Sampling
Venue: arXiv cs.RO 2015
Year: 2015

We present a novel method to compute the approximate global penetration depth (PD) between two non-convex geometric models. Our approach consists of two phases: offline precomputation and run-time queries. In the first phase, our formulation uses a novel sampling algorithm to precompute an approximation of the high-dimensional contact space between the pair of models. As compared with prior random sampling algorithms for contact space approximation, our propagation sampling considerably speeds up the precomputation and yields a high quality approximation. At run-time, we perform a nearest-neighbor query and local projection to efficiently compute the translational or generalized PD. We demonstrate the performance of our approach on complex 3D benchmarks with tens or hundreds of thousands of triangles, and we observe significant improvement over previous methods in terms of accuracy, with a modest improvement in the run-time performance.
Visualizing the Process of Process Modeling with PPMCharts
Venue: arXiv cs.SE 2015
Year: 2015

In the quest for knowledge about how to make good process models, recent research focus is shifting from studying the quality of process models to studying the process of process modeling (often abbreviated as PPM) itself. This paper reports on our efforts to visualize this specific process in such a way that relevant characteristics of the modeling process can be observed graphically. By recording each modeling operation in a modeling process, one can build an event log that can be used as input for the PPMChart Analysis plug-in we implemented in ProM. The graphical representation this plug-in generates allows for the discovery of different patterns of the process of process modeling. It also provides different views on the process of process modeling (by configuring and filtering the charts).
Modeling Styles in Business Process Modeling
Venue: arXiv cs.SE 2015
Year: 2015

Research on quality issues of business process models has recently begun to explore the process of creating process models. As a consequence, the question arises whether different ways of creating process models exist. In this vein, we observed 115 students engaged in the act of modeling, recording all their interactions with the modeling environment using a specialized tool. The recordings of process modeling were subsequently clustered. Results presented in this paper suggest the existence of three distinct modeling styles, exhibiting significantly different characteristics. We believe that this finding constitutes another building block toward a more comprehensive understanding of the process of process modeling that will ultimately enable us to support modelers in creating better business process models.
Single-Channel Maximum-Likelihood T60 Estimation Exploiting Subband Information
Venue: arXiv cs.SD 2015
Year: 2015

This contribution presents four algorithms developed by the authors for single-channel fullband and subband T60 estimation within the ACE challenge. The blind estimation of the fullband reverberation time (RT) by maximum-likelihood (ML) estimation based on [15] is considered as baseline approach. An improvement of this algorithm is devised where an energy-weighted averaging of the upper subband RT estimates is performed using either a DCT or 1/3-octave filter-bank. The evaluation results show that this approach leads to a lower variance for the estimation error in comparison to the baseline approach at the price of an increased computational complexity. Moreover, a new algorithm to estimate the subband RT is devised, where the RT estimates for the lower octave subbands are extrapolated from the RT estimates of the upper subbands by means of a simple model for the frequency-dependency of the subband RT. The evaluation results of the ACE challenge reveal that this approach allows to estimate the subband RT with an estimation error which is in a similar range as for the presented fullband RT estimators.
How Do Global Audiences Take Shape? The Role of Institutions and Culture in Patterns of Web Use
Venue: arXiv cs.CY 2015
Year: 2015

This study investigates the role of both cultural and technological factors in determining audience formation on a global scale. It integrates theories of media choice with theories of global cultural consumption and tests them by analyzing shared audience traffic between the world's 1000 most popular Websites. We find that language and geographic similarities are more powerful predictors of audience overlap than hyperlinks and genre similarity, highlighting the role of cultural structures in shaping global media use.
Change Patterns for Model Creation: Investigating the Role of Nesting Depth
Venue: arXiv cs.SE 2015
Year: 2015

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns offers a promising perspective. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns and factors that impact the cognitive complexity of pattern usage. Insights from the field of cognitive psychology as well as observations from a pilot study suggest that the nesting depth of the model to be created has a significant impact on cognitive complexity. This paper proposes a research design to test the impact of nesting depth on the cognitive complexity of change pattern usage in an experiment.
Controlled Owicki-Gries Concurrency: Reasoning about the Preemptible eChronos Embedded Operating System
Venue: arXiv cs.LO 2015
Year: 2015

We introduce a controlled concurrency framework, derived from the Owicki-Gries method, for describing a hardware interface in detail sufficient to support the modelling and verification of small, embedded operating systems (OS's) whose run-time responsiveness is paramount. Such real-time systems run with interrupts mostly enabled, including during scheduling. That differs from many other successfully modelled and verified OS's that typically reduce the complexity of concurrency by running on uniprocessor platforms and by switching interrupts off as much as possible. Our framework builds on the traditional Owicki-Gries method, for its fine-grained concurrency is needed for high-performance system code. We adapt it to support explicit concurrency control, by providing a simple, faithful representation of the hardware interface that allows software to control the degree of interleaving between user code, OS code, interrupt handlers and a scheduler that controls context switching. We then apply this framework to model the interleaving behavior of the eChronos OS, a preemptible real-time OS for embedded micro-controllers. We discuss the accuracy and usability of our approach when instantiated to model the eChronos OS. Both our framework and the eChronos model are formalised in the Isabelle/HOL theorem prover, taking advantage of the high level of automation in modern reasoning tools.
Sequence to Sequence Learning for Optical Character Recognition
Venue: arXiv cs.CV 2015
Year: 2015

We propose an end-to-end recurrent encoder-decoder based sequence learning approach for printed text Optical Character Recognition (OCR). In contrast to present day existing state-of-art OCR solution which uses connectionist temporal classification (CTC) output layer, our approach makes minimalistic assumptions on the structure and length of the sequence. We use a two step encoder-decoder approach -- (a) A recurrent encoder reads a variable length printed text word image and encodes it to a fixed dimensional embedding. (b) This fixed dimensional embedding is subsequently comprehended by decoder structure which converts it into a variable length text output. Our architecture gives competitive performance relative to connectionist temporal classification (CTC) output layer while being executed in more natural settings. The learnt deep word image embedding from encoder can be used for printed text based retrieval systems. The expressive fixed dimensional embedding for any variable length input expedites the task of retrieval and makes it more efficient which is not possible with other recurrent neural network architectures. We empirically investigate the expressiveness and the learnability of long short term memory (LSTMs) in the sequence to sequence learning regime by training our network for prediction tasks in segmentation free printed text OCR. The utility of the proposed architecture for printed text is demonstrated by quantitative and qualitative evaluation of two tasks -- word prediction and retrieval.
Towards Cooperation by Carrier Aggregation in Heterogeneous Networks: A Hierarchical Game Approach
Venue: arXiv cs.NI 2015
Year: 2015

This paper studies the resource allocation problem for a heterogeneous network (HetNet) in which the spectrum owned by a macro-cell operator (MCO) can be shared by both unlicensed users (UUs) and licensed users (LUs). We formulate a novel hierarchical game theoretic framework to jointly optimize the transmit powers and sub-band allocations of the UUs as well as the pricing strategies of the MCO. In our framework, an overlapping coalition formation (OCF) game has been introduced to model the cooperative behaviors of the UUs. We then integrate this OCF game into a Stackelberg game-based hierarchical framework. We prove that the core of our proposed OCF game is non-empty and introduce an optimal sub-band allocation scheme for UUs. A simple distributed algorithm is proposed for UUs to autonomously form optimal coalition formation structure. The Stackelberg Equilibrium (SE) of the proposed hierarchical game is derived and its uniqueness and optimality have been proved. A distributed joint optimization algorithm is also proposed to approach the SE of the game with limited information exchanges between the MCO and the UU.
Experimental Evaluation of Distributed Node Coloring Algorithms for Wireless Networks
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we evaluate distributed node coloring algorithms for wireless networks using the network simulator Sinalgo [by DCG@ETHZ]. All considered algorithms operate in the realistic signal-to-interference-and-noise-ratio (SINR) model of interference. We evaluate two recent coloring algorithms, Rand4DColor and ColorReduction (in the following ColorRed), proposed by Fuchs and Prutkin in [SIROCCO'15], the MW-Coloring algorithm introduced by Moscibroda and Wattenhofer [DC'08] and transferred to the SINR model by Derbel and Talbi [ICDCS'10], and a variant of the coloring algorithm of Yu et al. [TCS'14]. We additionally consider several practical improvements to the algorithms and evaluate their performance in both static and dynamic scenarios. Our experiments show that Rand4DColor is very fast, computing a valid (4Degree)-coloring in less than one third of the time slots required for local broadcasting, where Degree is the maximum node degree in the network. Regarding other O(Degree)-coloring algorithms Rand4DColor is at least 4 to 5 times faster. Additionally, the algorithm is robust even in networks with mobile nodes and an additional listening phase at the start of the algorithm makes Rand4DColor robust against the late wake-up of large parts of the network. Regarding (Degree+1)-coloring algorithms, we observe that ColorRed it is significantly faster than the considered variant of the Yu et al. coloring algorithm, which is the only other (Degree+1)-coloring algorithm for the SINR model. Further improvement can be made with an error-correcting variant that increases the runtime by allowing some uncertainty in the communication and afterwards correcting the introduced conflicts.
High-Performance Computing with Quantum Processing Units
Venue: arXiv cs.ET 2015
Year: 2015

The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that assumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. We also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.
Empirical Investigation of Key Business Factors for Digital Game Performance
Venue: arXiv cs.CY 2015
Year: 2015

Game development is an interdisciplinary concept that embraces software engineering, business, management, and artistic disciplines. This research facilitates a better understanding of the business dimension of digital games. The main objective of this research is to investigate empirically the effect of business factors on the performance of digital games in the market and to answer the research questions asked in this study. Game development organizations are facing high pressure and competition in the digital game industry. Business has become a crucial dimension, especially for game development organizations. The main contribution of this paper is to investigate empirically the influence of key business factors on the business performance of games. This is the first study in the domain of game development that demonstrates the interrelationship between key business factors and game performance in the market. The results of the study provide evidence that game development organizations must deal with multiple business key factors to remain competitive and handle the high pressure in the digital game industry. Furthermore, the results of the study support the theoretical assertion that key business factors play an important role in game business performance.
A Backward/Forward Recovery Approach for the Preconditioned Conjugate Gradient Method
Venue: arXiv cs.DS 2015
Year: 2015

Several recent papers have introduced a periodic verification mechanism to detect silent errors in iterative solvers. Chen [PPoPP'13, pp. 167--176] has shown how to combine such a verification mechanism (a stability test checking the orthogonality of two vectors and recomputing the residual) with checkpointing: the idea is to verify every $d$ iterations, and to checkpoint every $c \times d$ iterations. When a silent error is detected by the verification mechanism, one can rollback to and re-execute from the last checkpoint. In this paper, we also propose to combine checkpointing and verification, but we use algorithm-based fault tolerance (ABFT) rather than stability tests. ABFT can be used for error detection, but also for error detection and correction, allowing a forward recovery (and no rollback nor re-execution) when a single error is detected. We introduce an abstract performance model to compute the performance of all schemes, and we instantiate it using the preconditioned conjugate gradient algorithm. Finally, we validate our new approach through a set of simulations.
Learning to Represent Words in Context with Multilingual Supervision
Venue: arXiv cs.CL 2015
Year: 2015

We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.
A System for Extracting Sentiment from Large-Scale Arabic Social Data
Venue: arXiv cs.CL 2015
Year: 2015

Social media data in Arabic language is becoming more and more abundant. It is a consensus that valuable information lies in social media data. Mining this data and making the process easier are gaining momentum in the industries. This paper describes an enterprise system we developed for extracting sentiment from large volumes of social data in Arabic dialects. First, we give an overview of the Big Data system for information extraction from multilingual social data from a variety of sources. Then, we focus on the Arabic sentiment analysis capability that was built on top of the system including normalizing written Arabic dialects, building sentiment lexicons, sentiment classification, and performance evaluation. Lastly, we demonstrate the value of enriching sentiment results with user profiles in understanding sentiments of a specific user group.
Learning Mid-level Words on Riemannian Manifold for Action Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Human action recognition remains a challenging task due to the various sources of video data and large intra-class variations. It thus becomes one of the key issues in recent research to explore effective and robust representation to handle such challenges. In this paper, we propose a novel representation approach by constructing mid-level words in videos and encoding them on Riemannian manifold. Specifically, we first conduct a global alignment on the densely extracted low-level features to build a bank of corresponding feature groups, each of which can be statistically modeled as a mid-level word lying on some specific Riemannian manifold. Based on these mid-level words, we construct intrinsic Riemannian codebooks by employing K-Karcher-means clustering and Riemannian Gaussian Mixture Model, and consequently extend the Riemannian manifold version of three well studied encoding methods in Euclidean space, i.e. Bag of Visual Words (BoVW), Vector of Locally Aggregated Descriptors (VLAD), and Fisher Vector (FV), to obtain the final action video representations. Our method is evaluated in two tasks on four popular realistic datasets: action recognition on YouTube, UCF50, HMDB51 databases, and action similarity labeling on ASLAN database. In all cases, the reported results achieve very competitive performance with those most recent state-of-the-art works.
Graph-based denoising for time-varying point clouds
Venue: arXiv cs.CV 2015
Year: 2015

Noisy 3D point clouds arise in many applications. They may be due to errors when constructing a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.
A framework for deadlock detection in core ABS
Venue: arXiv cs.PL 2015
Year: 2015

We present a framework for statically detecting deadlocks in a concurrent object-oriented language with asynchronous method calls and cooperative scheduling of method activations. Since this language features recursion and dynamic resource creation, deadlock detection is extremely complex and state-of-the-art solutions either give imprecise answers or do not scale. In order to augment precision and scalability we propose a modular framework that allows several techniques to be combined. The basic component of the framework is a front-end inference algorithm that extracts abstract behavioural descriptions of methods, called contracts, which retain resource dependency information. This component is integrated with a number of possible different back-ends that analyse contracts and derive deadlock information. As a proof-of-concept, we discuss two such back-ends: (i) an evaluator that computes a fixpoint semantics and (ii) an evaluator using abstract model checking.
Two Hops or More: On Hop-Limited Search in Opportunistic Networks
Venue: arXiv cs.NI 2015
Year: 2015

While there is a drastic shift from host-centric networking to content-centric networking, how to locate and retrieve the relevant content efficiently, especially in a mobile network, is still an open question. Mobile devices host increasing volume of data which could be shared with the nearby nodes in a multi-hop fashion. However, searching for content in this resource-restricted setting is not trivial due to the lack of a content index, as well as, desire for keeping the search cost low. In this paper, we analyze a lightweight search scheme, hop-limited search, that forwards the search messages only till a maximum number of hops, and requires no prior knowledge about the network. We highlight the effect of the hop limit on both search performance (i.e., success ratio and delay) and associated cost along with the interplay between content availability, tolerated waiting time, network density, and mobility. Our analysis, using the real mobility traces, as well as synthetic models, shows that the most substantial benefit is achieved at the first few hops and that after several hops the extra gain diminishes as a function of content availability and tolerated delay. We also observe that the return path taken by a response is on average longer than the forward path of the query and that the search cost increases only marginally after several hops due to the small network diameter.
Solving the Forward Position Problem of an In-Parallel Planar Manipulator in the Gauss Plane
Venue: arXiv cs.RO 2015
Year: 2015

We study determining the posture of an in-parallel planar manipulator, which has three connectors composed of revolute, prismatic and revolute joints, from specified active joint variables. We construct an ideal in the field of complex numbers, and we introduce self inversive polynomials. We provide results for an in-parallel planar manipulator, which has a base and moving platform in right triangular shape. Using Sage computer algebra system, we compute its Groebner bases. We illustrate that the single variable polynomials obtained from the Groebner bases are self reciprocal.
Nonlinear Local Metric Learning for Person Re-identification
Venue: arXiv cs.CV 2015
Year: 2015

Person re-identification aims at matching pedestrians observed from non-overlapping camera views. Feature descriptor and metric learning are two significant problems in person re-identification. A discriminative metric learning method should be capable of exploiting complex nonlinear transformations due to the large variations in feature space. In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets. Motivated by the fact that local metric learning has been introduced to handle the data which varies locally and deep neural network has presented outstanding capability in exploiting the nonlinearity of samples, we utilize the merits of both local metric learning and deep neural network to learn multiple sets of nonlinear transformations. By enforcing a margin between the distances of positive pedestrian image pairs and distances of negative pairs in the transformed feature subspace, discriminative information can be effectively exploited in the developed neural networks. Our experiments show that the proposed NLML method achieves the state-of-the-art results on the widely used VIPeR, GRID, and CUHK 01 datasets.