ppxscal/embedding_network huggingface dataset:
Learning to create data-integrating queries
Authors: Partha Pratim Talukdar, Marie Jacob, Muhammad Salman Mehmood, Koby Crammer, Zachary G. Ives, Fernando Pereira, Sudipto Guha
Venue: Proceedings of the VLDB Endowment (2008)

The number of potentially-related data resources available for querying --- databases, data warehouses, virtual integrated schemas --- continues to grow rapidly. Perhaps no area has seen this problem as acutely as the life sciences, where hundreds of large, complex, interlinked data resources are available on fields like proteomics, genomics, disease studies, and pharmacology. The schemas of individual databases are often large on their own, but users also need to pose queries across multiple sources, exploiting foreign keys and schema mappings. Since the users are not experts, they typically rely on the existence of pre-defined Web forms and associated query templates, developed by programmers to meet the particular scientists' needs. Unfortunately, such forms are scarce commodities, often limited to a single database, and mismatched with biologists' information needs that are often context-sensitive and span multiple databases. We present a system with which a non-expert user can author new query templates and Web forms, to be reused by anyone with related information needs. The user poses keyword queries that are matched against source relations and their attributes; the system uses sequences of associations (e.g., foreign keys, links, schema mappings, synonyms, and taxonomies) to create multiple ranked queries linking the matches to keywords; the set of queries is attached to a Web query form. Now the user and his or her associates may pose specific queries by filling in parameters in the form. Importantly, the answers to this query are ranked and annotated with data provenance, and the user provides feedback on the utility of the answers, from which the system ultimately learns to assign costs to sources and associations according to the user's specific information need, as a result changing the ranking of the queries used to generate results. We evaluate the effectiveness of our method against "gold standard" costs from domain experts and demonstrate the method's scalability.
Convergence and optimization of the parallel method of simultaneous directions for the solution of elliptic problems
Authors: J. R. Galo, I. I. Albarreal, M. C. Calzada, J. L. Cruz, E. Fernández-Cara, M. Marín
Venue: Journal of Computational and Applied Mathematics (2008)

For the solution of elliptic problems, fractional step methods and in particular alternating directions (ADI) methods are iterative methods where fractional steps are sequential. Therefore, they only accept parallelization at low level. In [T. Lu, P. Neittaanmaki, X.C. Tai, A parallel splitting-up method for partial differential equations and its applications to Navier-Stokes equations, RAIRO Model. Math. Anal. Numer. 26 (6) (1992) 673-708], Lu et al. proposed a method where the fractional steps can be performed in parallel. We can thus speak of parallel fractional step (PFS) methods and, in particular, simultaneous directions (SDI) methods. In this paper, we perform a detailed analysis of the convergence and optimization of PFS and SDI methods, complementing what was done in [T. Lu, P. Neittaanmaki, X.C. Tai, A parallel splitting-up method for partial differential equations and its applications to Navier-Stokes equations, RAIRO Model. Math. Anal. Numer. 26 (6) (1992) 673-708]. We describe the behavior of the method and we specify the good choice of the parameters. We also study the efficiency of the parallelization. Some 2D, 3D and high-dimensional tests confirm our results.
Efficient algorithms for segmentation of item-set time series
Authors: Parvathi Chundi, Daniel J. Rosenkrantz
Venue: Data Mining and Knowledge Discovery (2008)

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets---Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.
Social signals, their function, and automatic analysis: a survey
Authors: Alessandro Vinciarelli, Maja Pantic, Hervé Bourlard, Alex Pentland
Venue: Proceedings of the 10th international conference on Multimodal interfaces (2008)

Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes towards other human (and virtual) participants in the current social context. As such, SSP integrates both engineering (speech analysis, computer vision, etc.) and human sciences (social psychology, anthropology, etc.) as it requires multimodal and multidisciplinary approaches. As of today, SSP is still in its early infancy, but the domain is quickly developing, and a growing number of works is appearing in the literature. This paper provides an introduction to nonverbal behaviour involved in social signals and a survey of the main results obtained so far in SSP. It also outlines possibilities and challenges that SSP is expected to face in the next years if it is to reach its full maturity.
GridR: An R-based tool for scientific data analysis in grid environments
Authors: Dennis Wegener, Thierry Sengstag, Stelios Sfakianakis, Stefan Rüping, Anthony Assi
Venue: Future Generation Computer Systems (2009)

In this paper, we describe an analysis tool based on the statistical environment R, GridR, which allows using the collection of methodologies available as R packages in a grid environment. It provides the user with transparent and seamless access to large-scale distributed computational services and data repositories within the secure and reliable framework of a grid system. The aim of GridR, which was initiated in the context of the EU project Advancing Clinico-Genomics Trials on Cancer (ACGT), is to provide a powerful framework for the analysis of clinico-genomic trials involving large amount of data (e.g. microarray-based clinical trials). As a proof of the concept, an example of microarray-based analysis taken from the literature was reproduced using GridR.
SeqMap
Authors: Hui Jiang, Wing Hung Wong
Venue: Bioinformatics (2008)

Summary: SeqMap is a tool for mapping large amount of short sequences to the genome. It is designed for finding all the places in a reference genome where each sequence may come from. This task is essential to the analysis of data from ultra high-throughput sequencing machines. With a carefully designed index-filtering algorithm and an efficient implementation, SeqMap can map tens of millions of short sequences to a genome of several billions of nucleotides. Multiple substitutions and insertions/deletions of the nucleotide bases in the sequences can be tolerated and therefore detected. SeqMap supports FASTA input format and various output formats, and provides command line options for tuning almost every aspect of the mapping process. A typical mapping can be done in a few hours on a desktop PC. Parallel use of SeqMap on a cluster is also very straightforward. Contact: whwong@stanford.edu
Modelling a plasma system for soliton and shockwaves with a splitting scheme and a second and a third order high Resolution scheme
Authors: R. Naidoo
Venue: WSEAS Transactions on Computers (2008)

A splitting and a fully discrete second order and a third order semi discrete schemes are modified and adapted for a numerical solution of a hyperbolic system of a one dimensional electrostatic plasma fluid equation. Illustrations as to how the splitting and the NNT (fully discrete) and the semi-discrete (SD3) schemes capture the formation and evolution of ion acoustic solitons and shockwaves were performed. In this study we perform a comparison between a fully discrete NNT and the semi discrete SD3 high resolution schemes and the splitting scheme which is constructed for the first time for a one dimensional plasma systems in the present study. The results indicate that the splitting scheme demonstrates clear superiority over the NNT and SD3 schemes in the soliton solution where the numerical noise of the electron waves is reduced significantly. For the shock wave solution the NNT and SD3 schemes are similar to the splitting scheme but exhibit oscillations at the contact discontinuity. However the splitting scheme exhibit a smaller computational time than the NNT and SD3 schemes. It is thus advocated that in a one dimensional plasma system for solution and shockwaves simulations the splitting scheme and NNT /SD3 schemes be utilised respectively.
Evolutionary multi criteria design optimization of robot grippers
Authors: R. Saravanan, S. Ramabalan, N. Godwin Raja Ebenezer, C. Dharmaraja
Venue: Applied Soft Computing (2009)

This paper explores the use of intelligent techniques to obtain optimum geometrical dimensions of a robot gripper. The optimization problem considered is a non-linear, complex, multi-constraint and multicriterion one. Three robot gripper configurations are optimized. The aim is to find Pareto optimal front for a problem that has five objective functions, nine constraints and seven variables. The problem is divided into three cases. Case 1 has first two objective functions, the case 2 considers last three objective functions and case 3 deals all the five objective functions. Intelligent optimization algorithms namely Multi-objective Genetic Algorithm (MOGA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE) are proposed to solve the problem. Normalized weighting objective functions method is used to select the best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure (SSM) and ratio of non-dominated individuals (RNIs)) are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead (OO) and algorithm effort are used to find the computational effort of MOGA, NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.
Multimodal recognition of personality traits in social interactions
Authors: Fabio Pianesi, Nadia Mana, Alessandro Cappelletti, Bruno Lepri, Massimo Zancanaro
Venue: Proceedings of the 10th international conference on Multimodal interfaces (2008)

This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.
A Study on Community Formation in Collaborative Tagging Systems
Authors: Christo Dichev, Jinsheng Xu, Darina Dicheva, Jinghua Zhang
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

Current collaborative tagging systems do not allow community members to easily view available data related to their communities. In this paper, we present our study on community formation centered around common interests, utilizing data from del.icio.us. We propose an approach to clustering tags and users based on their similarity. We also report some practical results related to implicit grouping of tags and users.
Dynamic Source Selection to Handle Changes of User's Interest in Continuous Query
Authors: Kosuke Ohki, Yousuke Watanabe, Hiroyuki Kitagawa
Venue: Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS (2008)

The volume of stream data delivered from information sources has been increasing. A demand for efficient processing of stream data has become more and more important. Stream processing systems [1] can continuously process stream data according to user's requests. A request is usually specified as a continuous query written in SQL-like language.
SBI: a semantic framework to support business intelligence
Authors: Denilson Sell, Dhiogo Cardoso da Silva, Fabiano Duarte Beppler, Marcio Napoli, Fernando Benedet Ghisi, Roberto C. S. Pacheco, José Leomar Todesco
Venue: Proceedings of the first international workshop on Ontology-supported business intelligence (2008)

Despite the importance of analytical tools to organizations, they still lack the inference power needed to solve the requests of decision makers in a flexible and smarter way. We present a framework called SBI -- Semantic Business Intelligence - in which we apply ontologies on the description of business rules and concepts in order to support semantic-analytical functionalities that extend traditional OLAP operations. Such approach enables developers to customize BI solutions according to organizations' specific analytical requirements and allows developers align BI solutions to the latest business analytic requirements. We present how semantic inference is supported using batch and on-the-fly based strategies. In addition, we show how such semantic infrastructure makes the access to heterogeneous data sources transparent. Finally, we illustrate the benefits of our approach through Extracta, an analytical tool that relies on SBI ontologies and modules. Extracta enables an easy access to information and provides novel exploratory funcionalities based on semantics towards faster and smarter decisions.
A comprehensive GIS-based poultry litter management system for nutrient management planning and litter transportation
Authors: M. S. Kang, P. Srivastava, T. Tyson, J. P. Fulton, W. F. Owsley, K. H. Yoo
Venue: Computers and Electronics in Agriculture (2008)

Confined poultry (broiler) production in Alabama results in about 1.8 million tons of litter annually. Because poultry production mainly occurs in the Appalachian Plateau region of north Alabama, this region suffers from excessive land application of litter, resulting in buildup of phosphorus (P) in soils and runoff of pathogens and P from land-applied areas. Conversely, the Black Belt region of south-central Alabama suffers from depressed agricultural economy because of poor soil fertility. In order to achieve optimal poultry litter management in Alabama, litter produced in the concentrated production areas within the Appalachian Plateau region must be optimally distributed in this region or transported south for utilization in the Black Belt region. Hence, a comprehensive system to provide improved waste management strategies (optimal land application and transportation) for sustainable on- and off-farm litter utilization in Alabama is needed. The primary focus of this study was to develop a comprehensive, geographic information system (GIS)-integrated poultry litter decision support system (PLDSS) for on- and off-farm management of poultry litter in the Appalachian Plateau and Black Belt regions of Alabama. The PLDSS is a user-friendly, comprehensive system that supports nutrient management planning, transportation analysis, and data management for poultry litter management using ArcObjects, Visual Basic for Applications (VBA), and Network Analyst in ArcGIS. Important functional components and analytical capability of ArcGIS 9.1 were implemented in several sets of customized tools. Because the PLDSS allows efficient development of nutrient management plan, it can help to reduce over-application of poultry litter. Also, through PLDSS' transportation analysis, the litter can be optimally transported to nutrient deficit areas. Therefore, the PLDSS will help poultry producers with optimal, cost-effective and environmentally sound poultry litter management in Alabama.
FPGA-based System for Real-Time Video Texture Analysis
Authors: Dimitris Maroulis, Dimitris K. Iakovidis, Dimitris Bariamis
Venue: Journal of Signal Processing Systems (2008)

This paper describes a novel system for real-time video texture analysis. The system utilizes hardware to extract second-order statistical features from video frames. These features are based on the Gray Level Co-occurrence Matrix (GLCM) and describe the textural content of the video frames. They can be used in a variety of video analysis and pattern recognition applications, such as remote sensing, industrial and medical. The hardware is implemented on a Virtex-XCV2000E-6 FPGA programmed in VHDL. It is based on an architecture that exploits the symmetry and the sparseness of the GLCM and calculates the features using integer and fixed point arithmetic. Moreover, it integrates an efficient algorithm for fast and accurate logarithm approximation, required in feature calculations. The software handles the video frame transfers from/to the hardware and executes only complementary floating point operations. The performance of the proposed system was experimentally evaluated using standard test video clips. The system was implemented and tested and its performance reached 133 and 532 fps for the analysis of CIF and QCIF video frames respectively. Compared to the state of the art GLCM feature extraction systems, the proposed system provides more efficient use of the memory bandwidth and the FPGA resources, in addition to higher processing throughput, that results in real time operation. Furthermore, its fundamental units can be used in any hardware application that requires sparse matrix representation or accurate and efficient logarithm estimation.
The structure of narrative: The case of film scripts
Authors: Fionn Murtagh, Adam Ganz, Stewart McKie
Venue: Pattern Recognition (2009)

We analyze the style and structure of story narrative using the case of film scripts. The practical importance of this is noted, especially the need to have support tools for television movie writing. We use the Casablanca film script, and scripts from six episodes of CSI (Crime Scene Investigation). For analysis of style and structure, we quantify various central perspectives discussed in McKee's book, Story: Substance, Structure, Style, and the Principles of Screenwriting. Film scripts offer a useful point of departure for exploration of the analysis of more general narratives. Our methodology, using Correspondence Analysis and hierarchical clustering, is innovative in a range of areas that we discuss. In particular this work is groundbreaking in taking the qualitative analysis of McKee and grounding this analysis in a quantitative and algorithmic framework.
Measurement-based approaches for accurate simulation of 802.11-based wireless networks
Authors: Anand Kashyap, Samrat Ganguly, Samir R. Das
Venue: Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (2008)

In this work, we address the issue of unrealistic simulations of wireless networks using a measurement-based approach. The idea is to use empirical modeling using measurement data as a mechanism to model physical layer behavior. We demonstrate the power of this approach for 802.11-based networks using ns2, a packet-level network simulator. Specifically, we develop two versions of the ns2 simulator that model the wireless physical layer with different levels of fidelity. In both versions, the deferral and reception model are built using measurements. For propagation modeling, one version uses direct measurements and the other uses an empirically derived model. In validation experiments with a 12-node mesh testbed, both these versions were found to be reasonably accurate (85 percentile errors within about 10% of the capacity) relative to regular simulations (85 percentile errors within 50% of capacity).
Efficient top-k processing over query-dependent functions
Authors: Lin Guo, Sihem Amer Yahia, Raghu Ramakrishnan, Jayavel Shanmugasundaram, Utkarsh Srivastava, Erik Vee
Venue: Proceedings of the VLDB Endowment (2008)

We study the efficient evaluation of top-k queries over data items, where the score of each item is dynamically computed by applying an item-specific function whose parameter value is specified in the query. For example, online retail stores rank items by price, which may be a function of the quantity being queried: "Stay 3 nights, get a 15% discount on double-bed rooms." Similarly, while ranking possible routes in online maps by predicted congestion level, the score (congestion) is a function of the time being queried, e.g., "At 5PM on a Friday in Palo Alto, the congestion level on 101 North is high." Since the parameter---the number of nights or the time the online map is queried, in the above examples---is only known at query time, and online applications have stringent response-time requirements, it is infeasible to evaluate every item-specific function to determine the item scores, especially when the number of items is large. Further, space considerations make it infeasible to pre-compute and store the score of each item for each value of the input parameter. In this paper, we develop a novel technique that compresses the (large) set of item scores for all parameter values by dividing the parameter range into intervals, taking into account the expected query workload. This compressed representation is then used to do top-k pruning of query results. Our experiments show that the proposed techniques are scalable and efficient.