ppxscal/embedding_network huggingface dataset:
Arts and humanities e-science-Current practices and future challenges
Authors: Tobias Blanke, Mark Hedges, Stuart Dunn
Venue: Future Generation Computer Systems (2009)

This article offers an analysis of UK arts and humanities e-Science practices in order to identify current trends. It also considers challenges of how arts and humanities disciplines fit into the overall e-Science agenda. We will discuss a first phase of early experimentation projects in 2007 and continue with a second phase since 2007, which more systematically investigates methodologies and technologies that could provide answers to grand challenges in digital arts and humanities research.
The CellML Model Repository
Authors: Catherine M. Lloyd, James R. Lawson, Peter J. Hunter, Poul F. Nielsen
Venue: Bioinformatics (2008)

Summary: The CellML Model Repository provides free access to over 330 biological models. The vast majority of these models are derived from published, peer-reviewed papers. Model curation is an important and ongoing process to ensure the CellML model is able to accurately reproduce the published results. As the CellML community grows, and more people add their models to the repository, model annotation will become increasingly important to facilitate data searches and information retrieval. Availability: The CellML Model Repository is publicly accessible at http://www.cellml.org/models Contact: c.lloyd@auckland.ac.nz
Modelling societal transitions with agent transformation
Authors: Michel Schilperoord, Jan Rotmans, Noam Bergman
Venue: Computational Mathematical Organization Theory (2008)

Transition models explain long-term and large-scale processes fundamentally changing the structure of a societal system. Our concern is that most transition models are too static. Although they capture a move of focus from static equilibria to transitions between dynamic equilibria, they are still rooted in an "equilibriumist" approach. Improvement is possible with agent-based models that give attention to endogenous system processes called "transformation processes". These models can render far more dynamic pictures of societal systems in transition, and are no longer remote from descriptions in the emerging transition literature.
A cognitive scheme for gateway protection in wireless sensor network
Authors: Waleed Youssef, Mohamed Younis
Venue: Applied Intelligence (2008)

In wireless sensor networks, sensor readings are gathered at a gateway for processing and forwarding to a remote command center. The potential closeness of the gateway to dangerous events, e.g. fires, exposes it to damage and thus risks making the network dysfunctional. Therefore, protecting the gateway by repositioning it away from safety-hazardous spots is critical for the operation of the network. However, moving the gateway too far from the sensors that report on active events would have negative effect on the network performance, e.g. throughput and energy consumption. Therefore, balancing the gateway safety and network performance goals will be necessary. In this paper, we present GRISP, a novel Gateway Relocation algorithm for Improved Safety and Performance. GRISP employs an evolutionary neural network model to assess the safety of the gateway at the various locations. The model is then used to direct the search in an area of interest for a safer position that would enhance or at least maintain an acceptable level of network performance. In addition, GRISP guides the gateway during the move by finding safe paths leading to the new location. Our experimental validation results demonstrate the effectiveness of GRISP.
Aerosol size distribution using Lidar data and a typical Lidar assembly
Authors: Hamed Parsiani, Javier Mèndez
Venue: WSEAS TRANSACTIONS on SYSTEMS (2008)

An algorithm is developed and detailed in this paper which determines atmospheric aerosol parameters such as backscatter and extinction coefficients, aerosol optical thickness, and the aerosol size distribution. The algorithm uses the power profile data obtained from Lidar at the City University of New York (CUNY). The aerosol optical thickness at 20 km range has been validated using a Sun-photometer. The assembly of a typical Lidar system capable of providing the power profile at three standard wavelengths of 355, 532, and 1064 nm is elaborated upon, by actually detailing the Lidar system under construction at the University of Puerto Rico at Mayaguez, as one of several Lidars to be established in the Caribbean.
Model inversion for chlorophyll estimation in open canopies from hyperspectral imagery
Authors: P. Kempeneers, P. J. Zarco-Tejada, P. R. J. North, S. de Backer, S. Delalieux, G. Sepulcre-Canto, F. Morales, J. A. N. van Aardt, R. Sagardoy, P. Coppin, P. Scheunders
Venue: International Journal of Remote Sensing (2008)

This paper presents the results of estimation of leaf chlorophyll concentration through model inversion, from hyperspectral imagery of artificially treated orchard crops. The objectives were to examine model inversion robustness under changing viewing conditions, and the potential of multi-angle hyperspectral data to improve accuracy of chlorophyll estimation. The results were compared with leaf chlorophyll measurements from laboratory analysis and field spectroscopy. Two state-of-the-art canopy models were compared. The first is a turbid medium canopy reflectance model (MCRM) and the second is a 3D model (FLIGHT). Both were linked to the PROSPECT leaf model. A linear regression using a single band was also performed as a reference. The different techniques were able to detect nutrient deficiencies that caused stress from the hyperspectral data obtained from the airborne AHS sensor. However, quantitative chlorophyll retrieval was found largely dependent on viewing conditions for regression and the turbid medium model inversion. In contrast, the 3D model was successful for all observations. It offers a robust technique to extract chlorophyll quantitatively from airborne hyperspectral data. When multi-angular data were combined, the results for both the turbid medium and 3D model increased. Final RMSE values of 5.8 µg cm-2 (MCRM) and 4.7 µg cm-2 (FLIGHT) were obtained for chlorophyll retrieval on canopy level.
Implications of psychological phenomenons for recommender systems
Authors: Erich Christian Teppan
Venue: Proceedings of the 2008 ACM conference on Recommender systems (2008)

Internet users often face the challenge of identifying the most suitable product out of some product assortment available on a certain e-sales platform. Recommender systems can substantially alleviate this typically complex task. Since the rise of such systems a lot of effort has been done in developing different recommendation approaches and algorithms, which all of them have certain strengths and weaknesses. What has been widely ignored by the recommender community so far are the potentials and impacts of psychological and decision theoretical phenomenons, which already have been investigated and applied in the field of marketing. Such phenomenons promise big capability to support users in decision making when facing a comparison situation. This paper concentrates on two classes of phenomenons, which are decoy effects and serial position effects. Tightly coupled to these phenomenons is the problem of getting the utility function of a recommender right, as this function serves both as the basis of result set calculation as well as the fundament of exploitation of above mentioned phenomenons. Putting all these aspects together an extended architecture for recommender systems will be proposed in the end of the paper.
Improving evolvable hardware by applying the speciation technique
Authors: Keum-Sung Hwang, Sung-Bae Cho
Venue: Applied Soft Computing (2009)

Evolvable hardware (EHW) has recently become a highly attractive topic of study because it offers a way of adapting hardware to a given embedded environment. However, it is not easy to evolve hardware efficiently and effectively, so many challenges continue to exist when trying to solve problems. In this paper, we propose a method that uses the speciation technique to enable diverse circuits to evolve efficiently by the process of one-step evolution. As a result of studying the landscape contained in the EHW example, we have found complicated spaces contain many peaks that can lead to deceptions when using the evolving process, and the speciation technique profits from the evolution of EHW. We also studied that the speciated hardware ensemble might be a good candidate for more complex and rigorous function. In the experiments, we applied the fitness sharing method as the speciation technique, and obtained diverse hardware modules, then ascertained the efficiency of these structures. We also show that several useful extra functions and better overall performance can be obtained by analyzing diverse circuits with the speciation technique.
Towards distributed storage resource management using flow control
Authors: Ajay Gulati, Irfan Ahmad
Venue: ACM SIGOPS Operating Systems Review (2008)

Deployment of shared storage systems is increasing with rapid adoption of virtualization technologies to provide flexible sharing, isolation, better management and high utilization of resources. Quality of service (QoS) in such environments is quite desirable for meeting IO demands of virtual machines. The lack of QoS support at typical storage arrays, simultaneous access by multiple hosts and concerns regarding under-utilization of resources makes this problem quite challenging. In this paper, we study the problem of providing fairness among hosts accessing a storage array in a distributed manner while maintaining high efficiency. Towards this goal, we investigate whether local latency estimates at each host can be used to detect overload and whether limiting host issue queue lengths can provide fairness across hosts. In principle, the approach is similar to mechanisms used by TCP at each host for flow control. Initial experiments and simulation results for control mechanism provide encouragement to develop a complete framework.
Scheduling shared scans of large data files
Authors: Parag Agrawal, Daniel Kifer, Christopher Olston
Venue: Proceedings of the VLDB Endowment (2008)

We study how best to schedule scans of large data files, in the presence of many simultaneous requests to a common set of files. The objective is to maximize the overall rate of processing these files, by sharing scans of the same file as aggressively as possible, without imposing undue wait time on individual jobs. This scheduling problem arises in batch data processing environments such as Map-Reduce systems, some of which handle tens of thousands of processing requests daily, over a shared set of files. As we demonstrate, conventional scheduling techniques such as shortest-job-first do not perform well in the presence of cross-job sharing opportunities. We derive a new family of scheduling policies specifically targeted to sharable workloads. Our scheduling policies revolve around the notion that, all else being equal, it is good to schedule nonsharable scans ahead of ones that can share IO work with future jobs, if the arrival rate of sharable future jobs is expected to be high. We evaluate our policies via simulation over varied synthetic and real workloads, and demonstrate significant performance gains compared with conventional scheduling approaches.
Automatic Extraction of Super-Function From Bilingual Corpus
Authors: Manabu Sasayama, Fuji Ren, Shingo Kuroiwa
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Extraction of a large Super-Function (SF) is one of the most important factor in realizing SF based machine translation. This paper presents a method to automatically extract SF from a Japanese-English bilingual corpus. The extraction process matches Japanese noun and English noun in each bilingual sentence in a bilingual corpus using a bilingual dictionary. The experimental results show that this method performs very well in automatically extracting SF for machine translation. Then, we discuss a problem of SF based machine translation from the result of the evaluation experiment using extracted SF.
Score calculation from final tournament tables
Authors: Hüseyin Pehlivan, Vasif V. Nabiyev
Venue: Computers and Operations Research (2009)

In this paper, we deal with the issue of determining the scores of all matches involved in a football tournament. The final table of the tournament, which shows the standings of the teams, is taken as the initial data of the problem. This is a different kind of combinatorial problems which require the construction of valid initial states according to some given final state. We use a rules-based method to solve the problem, analyzing the search space and introducing the notion of black&white graphs. The table data is firstly used to compute possible results of all played matches. Based on the results as well as the total number of scored and conceded goals, possible scores of the matches are then computed. The solution strategy is experimented on several final tables from previous World Cup tournaments. Other experiments are conducted for various team standings, measuring the time required to process some specific data of up to 10 teams.
Quality inspection of textile artificial textures using a neuro-symbolic hybrid system methodology
Authors: Vianey Guadalupe Cruz Sánchez, Osslan Osiris Vergara Villegas, Gerardo Reyes Salgado, Humberto De Jesús Ochoa Domínguez
Venue: WSEAS Transactions on Computers (2008)

In the industrial sector there are many processes where the visual inspection is essential, the automation of that processes becomes a necessity to guarantee the quality of several objects. In this paper we propose a methodology for textile quality inspection based on the texture cue of an image. To solve this, we use a Neuro-Symbolic Hybrid System (NSHS) that allow us to combine an artificial neural network and the symbolic representation of the expert knowledge. The artificial neural network uses the CasCor learning algorithm and we use production rules to represent the symbolic knowledge. The features used for inspection has the advantage of being tolerant to rotation and scale changes. We compare the results with those obtained from an automatic computer vision task, and we conclude that results obtained using the proposed methodology are better.
Iterative Neighbourhood Similarity Computation for Collaborative Filtering
Authors: Yun Zhang, Peter Andreae
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

Collaborative filtering recommender systems make predictions based on the preferences of users considered like-minded to the target user (user-based), or the popularities of items similar to the target item (item-based). There have been several approaches of combining user-based and item-based collaborative filtering. However, they are predominantly along the lines of averaging user-based and item-based predictions in a close-to-linear fashion, thus behave like smoothing mechanisms and only work well on sparse datasets. This article proposes a new way of combining user and item based collaborative filtering in a nonlinear fashion. The goal of the approach is to improve recommendation accuracy on regular datasets, by means of a more sensible neighbourhood similarity computation method that guides the user similarity computation using the items’ similarities to the item that is being predicted.
A re-examination of perpendicular drought indices
Authors: Abduwasit Ghulam, Qiming Qin, Timothy Kusky, Zhao-Liang Li
Venue: International Journal of Remote Sensing (2008)

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco-systems through in-depth analysis of their advantages and constraints. Spatio-temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)-based PDI and MPDI are compared against field-measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0-20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions.
Stabilization of multidimensional behaviors
Authors: Paula Rocha
Venue: Multidimensional Systems and Signal Processing (2008)

We consider three types of stabilizability defined for discrete nD systems within the behavioral framework, namely: trajectory stabilizability, set-controllability to a stable behavior and stabilizability by interconnection. As a first step, we introduce and fully characterize the underlying stability notion. Then, we formalize the definitions of these properties and investigate what is the relationship among them.
Designing a knowledge-based system for strategic planning: A balanced scorecard perspective
Authors: Hao-Chen Huang
Venue: Expert Systems with Applications: An International Journal (2009)

First developed by Kaplan and Norton [Kaplan R. S., & Norton D. P. (1992). The balance scorecard - measures that drive performance. Harvard Business Review, 70(1), 71-79], balanced scorecard (BSC) provides an integrated view of overall organizational performance and strategic objectives. BSC integrates financial measures with other key performance indicators to create a perspective that incorporates both financial and non-financial aspects. BSC has proven a powerful tool for strategic planning and communicating strategy that assists in strategy implementation. Successful strategy implementation is based on effective strategic planning. Owing to the strategic planning being a virtual necessity in business, this work proposes an integrated approach for the balanced scorecard tool and knowledge-based system using the analytic hierarchy process (AHP) method, and then develops an intellectual BSC knowledge-based system for strategic planning that sets or selects firm management or operational strategies based on the following perspectives: learning and growth, internal/business process, customer, and financial performance. This system can help determine specific strategy weights. The intellectual BSC knowledge-based system facilities efficient automated strategic planning.
Financial distress early warning based on group decision making
Authors: Jie Sun, Hui Li
Venue: Computers and Operations Research (2009)

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts' experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts' knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.
Automatic extraction of translations from web-based bilingual materials
Authors: Qibo Zhu, Diana Inkpen, Ash Asudeh
Venue: Machine Translation (2007)

This paper describes the framework of the StatCan Daily Translation Extraction System (SDTES), a computer system that maps and compares web-based translation texts of Statistics Canada (StatCan) news releases in the StatCan publication The Daily. The goal is to extract translations for translation memory systems, for translation terminology building, for cross-language information retrieval and for corpus-based machine translation systems. Three years of officially published statistical news release texts at http://www.statcan.ca were collected to compose the StatCan Daily data bank. The English and French texts in this collection were roughly aligned using the Gale-Church statistical algorithm. After this, boundary markers of text segments and paragraphs were adjusted and the Gale-Church algorithm was run a second time for a more fine-grained text segment alignment. To detect misaligned areas of texts and to prevent mismatched translation pairs from being selected, key textual and structural properties of the mapped texts were automatically identified and used as anchoring features for comparison and misalignment detection. The proposed method has been tested with web-based bilingual materials from five other Canadian government websites. Results show that the SDTES model is very efficient in extracting translations from published government texts, and very accurate in identifying mismatched translations. With parameters tuned, the text-mapping part can be used to align corpus data collected from official government websites; and the text-comparing component can be applied in prepublication translation quality control and in evaluating the results of statistical machine translation systems.
Efficient interval partitioning for constrained global optimization
Authors: Chandra Sekhar Pedamallu, Linet Özdamar, Tibor Csendes, Tamás Vinkó
Venue: Journal of Global Optimization (2008)

A new efficient interval partitioning approach to solve constrained global optimization problems is proposed. This involves a new parallel subdivision direction selection method as well as an adaptive tree search. The latter explores nodes (intervals in variable domains) using a restricted hybrid depth-first and best-first branching strategy. This hybrid approach is also used for activating local search to identify feasible stationary points. The new tree search management technique results in improved performance across standard solution and computational indicators when compared to previously proposed techniques. On the other hand, the new parallel subdivision direction selection rule detects infeasible and suboptimal boxes earlier than existing rules, and this contributes to performance by enabling earlier reliable deletion of such subintervals from the search space.
Poly-si extensions and etching residues as a reliability risk
Authors: Peter Jacob
Venue: Microsystem Technologies (2008)

Even in latest technologies, poly-silicon-related failures are well-known as a reliability risk. Due to the fact, that polysilicon reoxidizes in subsequent processing, physical failures frequently do not occur as time-zero functional failures. However, the thin reoxidation can be cracked by voltage pulses during field application. This paper describes the most important mechanisms like particles, stringers, trimming-fuse recombinations and polySi-extensions with respect to failure diagnosis, screening and reliability aspects.