ppxscal/embedding_network huggingface dataset:
Flexible hard real-time scheduling for deliberative AI systems
Authors: Yanching Chu, Alan Burns
Venue: Real-Time Systems (2008)

Constructing deliberative real-time AI systems is challenging due to the high execution-time variance in AI algorithms and the requirement of worst-case bounds for hard real-time guarantees, often resulting in poor use of system resources. Using a motivating case study, the general problem of resource usage maximization is addressed. We approach the issues by employing a hybrid task model for anytime algorithms, which is supported by recent advances in fixed priority scheduling for imprecise computation. In particular, with a novel scheduling scheme based on Dual Priority Scheduling, hard tasks are guaranteed by schedulability analysis and scheduled in favor of optional and anytime components which are executed whenever possible for enhancing system utility. Simulation studies show satisfactory performance on the case study with the application of the scheduling scheme. We also suggest how aperiodic tasks can be scheduled effectively within the framework and how tasks can be prioritized based on their utilities by an efficient algorithm. These works form a comprehensive package of scheduling model, analysis, and algorithms based on fixed priority scheduling, providing a versatile platform where real-time AI applications can be suitably facilitated.
iEPG: an ego-centric electronic program guide and recommendation interface
Authors: Chris Harrison, Brian Amento, Larry Stead
Venue: Proceeding of the 1st international conference on Designing interactive user experiences for TV and video (2008)

Conventional program guides present television shows in a list view, with metadata displayed in a separate window. However, this linear presentation style prevents users from fully exploring and utilizing the diverse, descriptive, and highly connected data associated with television programming. Additionally, despite the fact that program guides are the primary selection interface for television shows, few include integrated recommendation data to help users decide what to watch. iEPG presents a novel interface concept for navigating the multidimensional information space associated with television programming, as well as an effective visualization for displaying complex ratings data. Results from a user study indicate people appreciate the ability to search for content in non-linear ways and are receptive to recommendation systems and unconventional EPG visualizations.
Retargetable Processor System Integration into Multi-Processor System-on-Chip Platforms, 1 edition
Authors: Andreas Wieferink, Heinrich Meyr, Rainer Leupers
Venue:  (2008)

The ever increasing complexity of modern electronic devices together with the continually shrinking time-to-market and product lifetimes pose enormous chip design challenges to meet flexibility, performance and energy efficiency constraints. As a consequence, the current trend is towards programmable platforms (Multi-Processor System-on-Chip Platforms, MP-SoC), which are tailored to the respective target application. In the usual case, a new platform is designed by selecting and assembling standard platform elements. However, best results can only be achieved if the processor cores and the communication modules themselves are also optimized for the target application. Effective exploration is only possible if accurate module simulators are generated automatically based on abstract specifications. As a matter of fact, CoWare s BusCompiler allows generating accurate simulators for communication modules, and modeling languages such as LISA enable the same for processor cores. In Retargetable Processor System Integration into Multi-Processor System-on-Chip Platforms, such originally independent approaches are combined in order to enable the development of highly optimized programmable platforms. The first chapters of this book summarize the state of the art in all three involved fields separately: general system level design, communication modeling, and processor modeling. The main chapters then present a methodology and the associated tooling for enabling design space exploration as well as a successive refinement flow for the design of optimized MP-SoCs with a high degree of automation.
Evaluation of the Navigation through Image Parts in the ImageNotion Application
Authors: Andreas Walter, Gabor Nagypal, Simone Braun
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)

In our work on the ImageNotion methodology and tools, we apply semantic technologies on image archives. In this paper, we show evaluation results on our work on the user interface for semantic image search and expected navigation through image parts. We conducted an online survey with more than hundred participants. A unique feature of our evaluation is that our evaluators filled the survey based on a concrete, working semantic application, i.e., based on the publicly available online version of our system.
BioMAJ
Authors: Olivier Filangi, Yoann Beausse, Anthony Assi, Ludovic Legrand, Jean-Marc Larré, Véronique Martin, Olivier Collin, Christophe Caron, Hugues Leroy, David Allouche
Venue: Bioinformatics (2008)

Large-and medium-scale computational molecular biology projects require accurate bioinformatics software and numerous heterogeneous biological databanks, which are distributed around the world. BioMAJ provides a flexible, robust, fully automated environment for managing such massive amounts of data. The JAVA application enables automation of the data update cycle process and supervision of the locally mirrored data repository. We have developed workflows that handle some of the most commonly used bioinformatics databases. A set of scripts is also available for post-synchronization data treatment consisting of indexation or format conversion (for NCBI blast, SRS, EMBOSS, GCG, etc.). BioMAJ can be easily extended by personal homemade processing scripts. Source history can be kept via html reports containing statements of locally managed databanks. Availability: http://biomaj.genouest.org. BioMAJ is free open software. It is freely available under the CECILL version 2 license. Contact: biomaj@genouest.org
Large scale portfolio optimization with piecewise linear transaction costs
Authors: Marina Potaptchik, Levent Tuncel, Henry Wolkowicz
Venue: Optimization Methods Software (2008)

We consider the fundamental problem of computing an optimal portfolio based on a quadratic mean-variance model for the objective function and a given polyhedral representation of the constraints. The main departure from the classical quadratic programming formulation is the inclusion in the objective function of piecewise linear, separable functions representing the transaction costs. We handle the non-smoothness in the objective function by using spline approximations. The problem is first solved approximately using a primal-dual interior-point method applied to the smoothed problem. Then, we crossover to an active set method applied to the original non-smooth problem to attain a high accuracy solution. Our numerical tests show that we can solve large scale problems efficiently and accurately.
Tango or Waltz?: putting ballroom dance style into tempo detection
Authors: Björn Schuller, Florian Eyben, Gerhard Rigoll
Venue: EURASIP Journal on Audio, Speech, and Music Processing (2008)

Rhythmic information plays an important role in Music Information Retrieval. Example applications include automatically annotating large databases by genre, meter, ballroom dance style or tempo, fully automated D.J.-ing, and audio segmentation for further retrieval tasks such as automatic chord labeling. In this article, we therefore provide an introductory overview over basic and current principles of tempo detection. Subsequently, we show how to improve on these by inclusion of ballroom dance style recognition. We introduce a feature set of 82 rhythmic features for rhythm analysis on real audio. With this set, data-driven identification of the meter and ballroom dance style, employing support vector machines, is carried out in a first step. Next, this information is used to more robustly detect tempo. We evaluate the suggested method on a large public database containing 1.8 k titles of standard and Latin ballroom dance music. Following extensive test runs, a clear boost in performance can be reported.
Crop classification by support vector machine with intelligently selected training data for an operational application
Authors: Ajay Mathur, Giles M. Foody
Venue: International Journal of Remote Sensing (2008)

The accuracy of a supervised classification is dependent to a large extent on the training data used. The aim in training is often to capture a large training set to fully describe the classes spectrally, commonly with the requirements of a conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes, especially if using a support vector machine (SVM) as the classifier. An SVM seeks to fit an optimal hyperplane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. An approach to identify informative training samples was demonstrated for the classification of agricultural classes in south-western part of Punjab state, India. A small, intelligently selected, training dataset was acquired in the field with the aid of ancillary information. This dataset contained the data from training sites that were predicted before the classification to be amongst the most informative for an SVM classification. The intelligent training collection scheme yielded a classification of comparable accuracy, ∼91%, to one derived using a larger training set acquired by a conventional approach. Moreover, from inspection of the training sets it was apparent that the intelligently defined training set contained a greater proportion of support vectors (0.70), useful training sites, than that acquired by the conventional approach (0.41). By focusing on the most informative training samples, the intelligent scheme required less investment in training than the conventional approach and its adoption would have reduced the total financial outlay in classification production and evaluation by ∼26%. Additionally, the analysis highlighted the possibility to further reduce the training set size without any significant negative impact on classification accuracy.
An improved SIC based turbo equalizer
Authors: Aruna Tripathy, Sant Saran Pathak
Venue: WSEAS TRANSACTIONS on COMMUNICATIONS (2007)

This paper investigates a new soft interference canceller (SIC) based turbo equalizer. The SIC is configured as a decision feedback (DF) equalizer that uses two linear transversal filters. Both the filters update their taps as per the least mean square (LMS) algorithm. This receiver is different from the receivers discussed in literature in the sense that it uses the soft outputs of both the equalizer and the decoder in order to provide an improved estimate of the post cursors. It also uses the variance of the soft data bit estimates in its computation of the log-likelihood ratios (LLR). Probability distribution function for the soft data bit estimate is derived. This SIC based turbo equalizer is shown to result in a faster convergence and improved error floor at the decoder output. This study is carried out for a two typical highly frequency selective channels in order to establish the better performance of the proposed receiver.
Iris recognition based on multialgorithmic fusion
Authors: Fenghua Wang, Jiuqiang Han, Xianghua Yao
Venue: WSEAS Transactions on Information Science and Applications (2007)

Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. This paper proposes an iris recognition method based on multialgorithmic fusion. The proposed method combines the phase information based algorithm and zero-crossing representation based algorithm at the matching score level. The fusion rule based on support vector machine (SVM) is applied to generate a fused score which is used to make the fial decision. The experimental results on CASIA and UBIRIS iris image databases show that the proposed multialgorithmic fusion method can bring obvious performance improvement compared with any single algorithm, and the results also demonstrate that the fusion rule based on SVM can achieve better performance than conventional 1 fusion rules.
Supporting Customer Searches in E-marketplaces by Means of Fuzzy Logic-Based Machine Learning
Authors: J. Albusac, L. M. López-López, J. M. Murillo, J. J. Castro-Schez
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

Machine learning algorithms can be used to discover patterns in data into e-commerce C2C portals and this knowledge can then be useful to help customers to refine their searches and to choose what to buy. In this work, it is proposed a new machine learning algorithm based on fuzzy logic. We describe the proposed algorithm for supporting customer searches in e-Marketplaces and show the results obtained on simulated examples.
Trend and Challenge on System-on-a-Chip Designs
Authors: Yen-Kuang Chen, S. Y. Kung
Venue: Journal of Signal Processing Systems (2008)

The success of system-on-a-chip (SoC) hinges upon a well-concerted integrated approach from multiple disciplines, such as device, design, and application. From the device perspective, rapidly improving VLSI technology allows the integration of billions of transistors on a single chip, thus permitting a wide range of functions to be combined on one chip. From the application perspective, numerous killer applications have been identified, which can make full use of the aforementioned functionalities provided by a single chip. From the design perspective, however, with greater device integration, system designs become more complex and are increasingly challenging to design. Moving forward, novel approaches will be needed to meet these challenges. This paper explores several new design strategies, which represent the current design trends to deal with the emerging issues. For example, recognizing the stringent requirements on power consumption, memory bandwidth/latency, and transistor variability, novel power/thermal management, multi-processor SoC, reconfigurable logic, and design for verification and testing have now been incorporated into modern system design. In addition, we look into some plausible solutions. For example, further innovations on scalable, reusable, and reliable system architectures, IP deployment and integration, on-chip interconnects, and memory hierarchies are all anticipated in the near future.
Minimizing broadcast latency and redundancy in ad hoc networks
Authors: Rajiv Gandhi, Arunesh Mishra, Srinivasan Parthasarathy
Venue: IEEE/ACM Transactions on Networking (TON) (2008)

Network wide broadcasting is a fundamental operation in ad hoc networks. In broadcasting, a source node sends a message to all the other nodes in the network. In this paper, we consider the problem of collision-free broadcasting in ad hoc networks. Our objective is to minimize the latency and the number of transmissions in the broadcast. We show that minimum latency broadcasting is NP-complete for ad hoc networks. We also present a simple distributed collision-free broadcasting algorithm for broadcasting a message. For networks with bounded node transmission ranges, our algorithm simultaneously guarantees that the latency and the number of transmissions are within O(1) times their respective optimal values. Our algorithm and analysis extend to the case when multiple messages are broadcast from multiple sources. Experimental studies indicate that our algorithms perform much better in practice than the analytical guarantees provided for the worst case.
Gain and loss of phosphorylation sites in human cancer
Authors: Predrag Radivojac, Peter H. Baenziger, Maricel G. Kann, Matthew E. Mort, Matthew W. Hahn, Sean D. Mooney
Venue: Bioinformatics (2008)

Motivation: Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem. Results: In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a signi.cant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancercausing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations. Contact: sdmooney@iupui.edu
On the Design of a SIP-Based Binding Middleware for Next Generation Home Network Services
Authors: Mourad Alia, Andre Bottaro, Fatoumata Camara, Briac Hardouin
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)

This paper proposes a two-layer component-based middleware framework that copes with the complexity of managing and constructing efficient and useful SIP-based home services. In the first layer, the device integration framework overcomes the heterogeneity of media home devices by providing protocol-independent components that reify the underlying devices. At the second layer, the binding framework allows constructing open mobile media bindings between SIP and non SIP communication protocol endpoints including media home devices. The openness of our framework is motivated by the need of constructing highly flexible home services such as context aware adaptation, session mobility, media session enrichment and QoS. Our framework is implemented as part of a context-aware adaptive middleware on top of the OSGi platform and an illustrative use case is shown.
A Low-Latency Multi-layer Prefix Grouping Technique for Parallel Huffman Decoding of Multimedia Standards
Authors: Tsung-Han Tsai, Chun-Nan Liu
Venue: Journal of Signal Processing Systems (2008)

Huffman coding is a popular and important lossless compression scheme for various multimedia applications. This paper presents a low-latency parallel Huffman decoding technique with efficient memory usage for multimedia standards. First, the multi-layer prefix grouping technique is proposed for sub-group partition. It exploits the prefix characteristic in Huffman codewords to solve the problem of table size explosion. Second, a two-level table lookup approach is introduced which can promptly branch to the correct sub-group by level-1 table lookup and decode the symbols by level-2 table lookup. Third, two optimization approaches are developed; one is to reduce the branch cycles and the other is parallel processing between two-level table lookup and direct table lookup approaches to fully utilize the advantage of VLIW parallel processing. An AAC Huffman decoding example is realized on the Parallel Architecture Core DSP (PAC DSP) processor. The simulation results show that the proposed method can further improve about 89% of decoding cycles and 33% of table size comparing to the linear search method.
Learning to hash: forgiving hash functions and applications
Authors: Shumeet Baluja, Michele Covell
Venue: Data Mining and Knowledge Discovery (2008)

The problem of efficiently finding similar items in a large corpus of high-dimensional data points arises in many real-world tasks, such as music, image, and video retrieval. Beyond the scaling difficulties that arise with lookups in large data sets, the complexity in these domains is exacerbated by an imprecise definition of similarity. In this paper, we describe a method to learn a similarity function from only weakly labeled positive examples. Once learned, this similarity function is used as the basis of a hash function to severely constrain the number of points considered for each lookup. Tested on a large real-world audio dataset, only a tiny fraction of the points (~0.27%) are ever considered for each lookup. To increase efficiency, no comparisons in the original high-dimensional space of points are required. The performance far surpasses, in terms of both efficiency and accuracy, a state-of-the-art Locality-Sensitive-Hashing-based (LSH) technique for the same problem and data set.
Routing based on delivery distributions in predictable disruption tolerant networks
Authors: Jean-Marc François, Guy Leduc
Venue: Ad Hoc Networks (2009)

This article studies disruption tolerant networks (DTNs) where each node knows the probabilistic distribution of contacts with other nodes. It proposes a framework that allows one to formalize the behaviour of such a network. It generalizes extreme cases that have been studied before where (a) either nodes only know their contact frequency with each other or (b) they have a perfect knowledge of who meets who and when. This paper then gives an example of how this framework can be used; it shows how one can find a packet forwarding algorithm optimized to meet the 'delay/bandwidth consumption' tradeoff: packets are duplicated so as to (statistically) guarantee a given delay or delivery probability, but not too much so as to reduce the bandwidth, energy, and memory consumption.
Keyword query cleaning
Authors: Ken Q. Pu, Xiaohui Yu
Venue: Proceedings of the VLDB Endowment (2008)

Unlike traditional database queries, keyword queries do not adhere to predefined syntax and are often dirty with irrelevant words from natural languages. This makes accurate and efficient keyword query processing over databases a very challenging task. In this paper, we introduce the problem of query cleaning for keyword search queries in a database context and propose a set of effective and efficient solutions. Query cleaning involves semantic linkage and spelling corrections of database relevant query words, followed by segmentation of nearby query words such that each segment corresponds to a high quality data term. We define a quality metric of a keyword query, and propose a number of algorithms for cleaning keyword queries optimally. It is demonstrated that the basic optimal query cleaning problem can be solved using a dynamic programming algorithm. We further extend the basic algorithm to address incremental query cleaning and top-k optimal query cleaning. The incremental query cleaning is efficient and memory-bounded, hence is ideal for scenarios in which the keywords are streamed. The top-k query cleaning algorithm is guaranteed to return the best k cleaned keyword queries in ranked order. Extensive experiments are conducted on three real-life data sets, and the results confirm the effectiveness and efficiency of the proposed solutions.