ppxscal/embedding_network huggingface dataset:
Interactive open access publishing and collaborative peer review for improved scientific communication and quality assurance
Authors: Ulrich Pöschl, Thomas Koop
Venue: Information Services and Use (2008)

The traditional ways of scientific publishing and peer review do not live up to the needs of efficient communication and quality assurance in today's highly diverse and rapidly developing world of science. Therefore, new opportunities for enhanced scientific quality assurance are among the most important advantages and perspectives of open access to scientific publications. Open access gives referees more information to work with; it enables interactive and transparent forms of review and discussion open to all interested members of the scientific community and the public; and it facilitates the development and implementation of new metrics for the impact and quality of scientific publications. The effects and advantages of open access can be efficiently and flexibly combined with the strengths of traditional scientific publishing and peer review. Among the successful initiatives pursuing this approach are the interactive open access journal Atmospheric Chemistry and Physics (ACP, www.atmos-chem-phys.net) and a growing number of sister journals published by the European Geosciences Union (EGU, www.egu.eu). They are practicing a two-stage publication process with public peer review and interactive discussion, which has been designed to resolve the dilemma between rapid scientific exchange and thorough quality assurance.
Bounds on FSM Switching Activity
Authors: Eleftheria Athanasopoulou, Christoforos N. Hadjicostis
Venue: Journal of Signal Processing Systems (2008)

This paper obtains lower and upper bounds for the switching activity on the state lines of a finite state machine (FSM) that is driven by typical input sequences. More specifically, the paper provides bounds on the average Hamming distance which is in turn proportional to the switching activity and the overall power dissipation in the system. By introducing the concepts of a distance matrix and a weight matrix, and by exploiting the symmetry of the distance matrix, we are able to obtain bounds that are provably tighter than existing bounds and, as demonstrated by our experimental results, they can offer significant improvements in many cases of interest. Since our bounds are independent of the state assignment and the actual implementation, they can be used at an early stage of the FSM design to indicate the largest/smallest possible power consumption.
An adaptive and trustworthy software testing framework on the grid
Authors: Yaohang Li, Yong-Duan Song
Venue: The Journal of Supercomputing (2008)

Grid computing, which is characterized by large-scale sharing and collaboration of dynamic distributed resources has quickly become a mainstream technology in distributed computing and is changing the traditional way of software development. In this article, we present a grid-based software testing framework for unit and integration test, which takes advantage of the large-scale and cost-efficient computational grid resources to establish a testbed for supporting automated software test in complex software applications. Within this software testing framework, a dynamic bag-of-tasks model using swarm intelligence is developed to adaptively schedule unit test cases. Various high-confidence computing mechanisms, such as redundancy, intermediate value checks, verification code injection, and consistency checks are employed to verify the correctness of each test case execution on the grid. Grid workflow is used to coordinate various test units for integration test. Overall, we expect that the grid-based software testing framework can provide efficient and trustworthy services to significantly accelerate the testing process with large-scale software testing.
Controlled dynamic systems and Carleman operator
Authors: V. F. Zadorozhny
Venue: Cybernetics and Systems Analysis (2008)

Controlled dynamic systems on a compact set and thus with invariant measure are considered. This allows reducing differential dynamic systems to Fredholm-type integral equations. An algorithm of constructing a vector field of maximum descent rate along a trajectory is presented. The algorithm is reduced to a numerical moment-type procedure.
Relationships between remotely sensed surface parameters associated with the urban heat sink formation in Santiago, Chile
Authors: M. A. Pena
Venue: International Journal of Remote Sensing (2008)

This work addresses the remotely sensed urban heat sink formation in Santiago City, Chile. The main aim was to analyse the relationship that surface temperature exhibits with land cover types, as well as with vegetation cover fraction, surface moisture and albedo of Santiago City and its rural surroundings. Information was extracted from the processing of Landsat Enhanced Thematic Mapper Plus (ETM+) data. Surface temperature of the main land cover classes and correlation coefficients between surface temperature and the above-mentioned surface parameters were analysed. The results show that the hottest surfaces are mainly concentrated in the northern rural valley, wherein the predominance of bare or sparsely vegetated land covers determine a low vegetation cover fraction and surface moisture content. The correlation coefficients obtained confirm the higher warming rate that the rural valley exhibits in comparision with the city temperatures at the image acquisition time, which is one of the main causes of urban heat sink formation.
Global optimization of case-based reasoning for breast cytology diagnosis
Authors: Hyunchul Ahn, Kyoung-jae Kim
Venue: Expert Systems with Applications: An International Journal (2009)

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.
Rule-based curation and preservation of data: A data grid approach using iRODS
Authors: Mark Hedges, Tobias Blanke, Adil Hasan
Venue: Future Generation Computer Systems (2009)

Research is generating large quantities of digital material, much of it irreplaceable, and there is a pressing need to maintain long-term access to it. Not only is the quantity of data growing in size, it is becoming much more diverse and complex, significantly complicating the issues around its curation. Automation of curation is key if a scalable solution is to be found. We describe an approach to automation in which digital curation policies and strategies are represented as rules, which are implemented in data grids based on the iRODS middleware.
Dynamic XML Generation according to a Given Schema
Authors: Debarshi Raha
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

eXtensible Markup Language (XML) is a favorable format for exchanging information in the internet world. Most of the times XML needs to be generated according to a given schema agreed by all business partners so that everybody can correctly consume the information it contains. Thus it becomes critical to create the XML according to the given well defined schema. This paper presents a technique to create the XML according to a given schema dynamically.
Robust and efficient identification of biomarkers by classifying features on graphs
Authors: TaeHyun Hwang, Hugues Sicotte, Ze Tian, Baolin Wu, Jean-Pierre Kocher, Dennis A. Wigle, Vipin Kumar, Rui Kuang
Venue: Bioinformatics (2008)

Motivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis. Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets. Availability: Supplementary results and source code are available at http://compbio.cs.umn.edu/Feature_Class. Contact: kuang@cs.umn.edu Supplementary information:Supplementary data are available at Bioinformatics online.
MobHinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networks
Authors: Rossano Schifanella, André Panisson, Cristina Gena, Giancarlo Ruffo
Venue: Proceedings of the 2008 ACM conference on Recommender systems (2008)

We focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and ad-hoc communications. In such a domain, knowledge representation and users profiling can be hard; remote servers can be often unreachable due to client mobility; and feedback ratings collected during random connections to other users' ad-hoc devices can be useless, because of natural differences between human beings. Our approach is based on so called Affinity Networks, and on a novel system, called MobHinter, that epidemically spreads recommendations through spontaneous similarities between users. Main results of our study are two fold: firstly, we show how to reach comparable recommendation accuracies in the mobile domain as well as in a complete knowledge scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem.
Semantic Interoperability in the BRITE Project: Ontologies as a Tool for Collaboration, Cooperation and Knowledge Management
Authors: Timo Herborn, Ansgar Mondorf, Babak Mougouie, Maria A. Wimmer
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems: (2008)

Ontologies have been investigated for a while and are being studied as an instrument to enable cross-organizational semantic interoperability. The project BRITE (Business Register Interoperability Throughout Europe) aims at exploring ontologies as an instrument to effectively enable European Business Registers (BRs) to respond to changes imposed on them by European Union laws and new market requirements. In BRITE, ontologies are seen as a key instrument and technology to dissolve administrative, technical, cultural and lingual barriers of BRs on one hand, and as means to provide semantic interoperability on the other hand. The paper shows various ontology components of BRITE. It defines the inter-relation among these components and focuses on the domain ontology functionalities. We conclude with a critical reflection and valuable lessons learnt.
Improved classification and segmentation of hyperspectral images using spectral warping
Authors: B. Demir, S. Erturk
Venue: International Journal of Remote Sensing (2008)

This letter describes a method to increase hyperspectral image classification accuracy (CA) and segmentation accuracy (SA) using spectral warping, which is a nonlinear transformation that warps the frequency content of a signal. In the proposed approach, the frequency content corresponding to spectral data for the hyperspectral image was nonlinearly transformed along the spectral axis using warping. Classification and segmentation algorithms were estimated for the transformed spectral values to show the impact of warping. Experimental results are provided for different values of the warping parameter and it is shown that applying spectral warping increases CA and SA for appropriate warping parameters.
Towards a meaningful MRA of traffic matrices
Authors: David Rincón, Matthew Roughan, Walter Willinger
Venue: Proceedings of the 8th ACM SIGCOMM conference on Internet measurement (2008)

Most research on traffic matrices (TM) has focused on finding models that help with inference, but not with other important tasks such as synthesis of TMs, traffic prediction, or anomaly detection. In this paper we approach the problem of a general model for traffic matrices, and argue that such a model must be sparse, i.e., have a small number of parameters in comparison to the size of the TM. A Multi-Resolution Analysis (MRA) of TMs can provide such a sparse representation. The Diffusion Wavelet (DW) transform is a good choice as a MRA tool here, because it inherently adapts to the structure of the underlying network. The paper describes our construction of the two-dimensional version of the DW transform and shows how to use it for our proposed MRA of TMs. The results obtained with operational networks confirm the sparseness of the DW-based TM analysis approach and its applicability to other TM-related tasks.
New aspects in numerical representations involved in DNA repeats detection
Authors: Petre G. Pop
Venue: WSEAS Transactions on Signal Processing (2008)

The presence of repeated sequences is a fundamental feature of biological genomes. The detection of tandem repeats is important in biology and medicine as it can be used for phylogenic studies and disease diagnosis. A major difficulty in identification of repeats arises from the fact that the repeat units can be either exact or imperfect, in tandem or dispersed, and of unspecified length. Many of the methods for detecting repeated sequences are part of the digital signal processing field. These methods involve the application of a kind of transformation. Applying a transform technique requires mapping the symbolic domain into the numeric domain in such a way that no additional structure is placed on the symbolic sequence beyond that inherent to it. Therefore the numerical representation of genomic signals is very important. This paper presents results obtained by combining grey level spectrograms with two novel numerical representations to isolate position and length of DNA repeats.
Distributed cooperative caching
Authors: Enric Herrero, José González, Ramon Canal
Venue: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008)

This paper presents the Distributed Cooperative Caching, a scalable and energy-efficient scheme to manage chip multiprocessor (CMP) cache resources. The proposed configuration is based in the Cooperative Caching framework [3] but it is intended for large scale CMPs. Both centralized and distributed configurations have the advantage of combining the benefits of private and shared caches. In our proposal, the Coherence Engine has been redesigned to allow its partitioning and thus, eliminate the size constraints imposed by the duplication of all tags. At the same time, a global replacement mechanism has been added to improve the usage of cache space. Our framework uses several Distributed Coherence Engines spread across all the nodes to improve scalability. The distribution permits a better balance of the network traffic over the entire chip avoiding bottlenecks and increasing performance for a 32-core CMP by 21% over a traditional shared memory configuration and by 57% over the Cooperative Caching scheme. Furthermore, we have reduced the power consumption of the entire system by using a different tag allocation method and by reducing the number of tags compared on each request. For a 32-core CMP the Distributed Cooperative Caching framework provides an average improvement of the power/performance relation (MIPS3/W) of 3.66x over a traditional shared memory configuration and 4.30x over Cooperative Caching.
Watermarking sexually reproducing diploid organisms
Authors: Dominik Heider, Daniel Kessler, Angelika Barnekow
Venue: Bioinformatics (2008)

Summary: DNA watermarks are used for hiding messages or for authenticating genetically modified organisms. Recently, we presented an algorithm called DNA-Crypt for generating DNA-based watermarks that can be integrated into the genome by using the characteristics of the degenerative genetic code. DNA-Crypt generates the watermark by replacing single bases and thus creating synonymous codons that encrypt the hidden information. Mutations within the integrated DNA sequence can be corrected using several mutation correction codes, to keep the hidden information intact. This method has successfully been tested in asexually replicating organisms like bacteria or yeast, where the watermark is duplicated with every cell division. It has been shown that DNA watermarks produced by DNA-Crypt do not influence the transcription or translation of a protein. In sexually reproducing diploid organisms, additional problems can occur, e.g. recombination events can destroy hidden information. Using population predictions as well as statistical analyses we identified a coupled Y-chromosomal/mitochondrial DNA watermarking procedure as the most appropriate for diploid organisms. We developed a mitochondria adapted version of DNA-Crypt, which is called Project Mito that can be used in combination with the original program. Availability: http://www.uni-muenster.de/Biologie.NeuroVer/Tumorbiologie/DNA-Crypt/index.html Contact: barneko@uni-muenster.de Password: WWUTB Requirements: Java 5.0 or higher Supplementary information:Supplementary data are available at Bioinformatics online.
Image annotation via graph learning
Authors: Jing Liu, Mingjing Li, Qingshan Liu, Hanqing Lu, Songde Ma
Venue: Pattern Recognition (2009)

Image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search. In this paper, we propose a graph learning framework for image annotation. First, the image-based graph learning is performed to obtain the candidate annotations for each image. In order to capture the complex distribution of image data, we propose a Nearest Spanning Chain (NSC) method to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities. Second, the word-based graph learning is developed to refine the relationships between images and words to get final annotations for each image. To enrich the representation of the word-based graph, we design two types of word correlations based on web search results besides the word co-occurrence in the training set. The effectiveness of the proposed solution is demonstrated from the experiments on the Corel dataset and a web image dataset.
Behavior Models for Computer Programs: Understand and Improve Software Behavior by ProgramAnalysis, Statistical Modeling, and Adaptive Management
Authors: Chengliang Zhang
Venue:  (2008)

In many senses, today's complex software systems arevery similar to a live organism. This motivates us tostudy software by investigating its behavior. Whiletraditional modular-based analysis identifies staticdata and control flow, the actual behavior isdetermined not just by individual program modules butmore so by their run-time interactions driven byprogram inputs and influenced by the executionenvironment. Program behavior analysis augmentstraditional compiler analysis by analyzing theprogram traces. It builds statistical models toidentify large-scale, aggregate patterns and exposeopportunities for program optimization. This bookdescribes three behavior models and their use inimproving program performance. The new models andtechniques extend the programmer's toolbox with newprimitives for measuring and modifying compositeprogram behavior, a task that has become necessaryfor the production of efficient and reliable softwareon modern computer systems. The behavior models areespecially useful for professionals working onprogram performance optimization. They are alsoenlightening for the programmers who care about theperformance of their software system.