ppxscal/embedding_network huggingface dataset:
High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field
Authors: Siddhartha Bhattacharyya, Ujjwal Maulik, Paramartha Dutta
Venue: Applied Soft Computing (2009)

A time efficient technique for real-time tracking of high-speed objects in a video sequence is presented in this article. The technique is primarily based on the segmentation of the optical flow field computed between the successive image frames of a video sequence, followed by the tracking of a detected point of interest (POI) within the segmented flow field. In the initial phase of the technique, the optical flow field between the first two successive image frames acquired from a video sequence, is computed. A fuzzy hostility index indicative of the degree of coherence of the moving objects in the image frames, is used to segment the optical flow field. This yields different coherent regions of interest (ROIs) in the segmented flow field. A POI is then detected in the different ROIs obtained. Tracking of the moving object is then carried out by computing the flow fields between predefined ROIs in the neighborhood of the detected POI in the subsequent image frames. Since the selected ROIs are smaller than the image frames, a fair amount of reduction in the time required for the computation of the optical flow field is achieved, thereby facilitating real-time operation. An application of the proposed technique is demonstrated on three video sequences of high-speed flying fighter aircrafts.
Multitasking workload scheduling on flexible-core chip multiprocessors
Authors: Divya P. Gulati, Changkyu Kim, Simha Sethumadhavan, Stephen W. Keckler, Doug Burger
Venue: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008)

While technology trends have ushered in the age of chip multiprocessors (CMP), a fundamental question is what size to make each core. Most current commercial designs are symmetric CMPs (SCMP) in which each core is identical and range from a simple RISC processor to a complex out-of-order x86 processor. Some researchers have proposed asymmetric CMPs (ACMP) consisting of multiple types of cores. While less of an issue for ACMPs, the fixed nature of both these architectures makes them vulnerable to mismatches between the granularity of the cores and the parallelism in the workload, which can cause inefficient execution. To remedy this weakness, recent research has proposed flexible-core CMPs (FCMP), which have the capability of aggregating multiple small processing cores to form larger logical processors. FCMPs introduce a new resource allocation and scheduling problem which must determine how many logical processors should be configured, how powerful each processor should be, and where/when each task should run. This paper introduces and motivates this problem, describes the challenges associated with it, and evaluates algorithms appropriate for multitasking on FCMPs. We also evaluate static-core CMPs of various configurations and compare them to FCMPs for various multitasking workloads.
User-Centric Annotation Management for Biological Data
Authors: Qinglan Li, Alexandros Labrinidis, Panos K. Chrysanthis
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. Although all such systems are implemented to take user input (i.e., the annotations themselves), very few systems are user-centric, taking into account user preferences on how annotations should propagate and be applied over data. In this paper, we propose to treat annotations as first-class citizens for biological data management by presenting a user ¿ centric, view ¿ based annotation framework, called ViP. Under the ViP framework we consider user preferences over the time semantics of annotations (by supporting future annotations) and over the network semantics of annotations (by supporting both implicitly-defined and explicitly-defined annotation propagation paths). In addition to novel functionality, we describe a novel caching technique which enables ViP to outperform the state of the art. We also propose to demonstrate our prototype implementation of the ViP framework. As part of the demo, we propose on the one hand to highlight the user-interface/functionality of our system and on the other hand to visualize the server/behind-the-scenes aspect.
Neuro-swarm hybridization for protein tertiary structure prediction
Authors: Ayan Datta, Veera Talukdar, Amit Konar, Lakhmi C. Jain
Venue: International Journal of Hybrid Intelligent Systems (2008)

This paper describes a new approach for predicting tertiary structure of protein using artificial neural network and particle swarm optimization technique. The paper is concetrated around the well known Ab-initio approach for global minimisation of energy function. It predicts the native structure of protein by finding the main chain dihedral angles; through the optimization of CHARMM energy function, using particle swarm optimization algorithm. Side chain dihedral angle are predicted using three layered artificial neural network, realised with back propagation algorithm. This approach has merit as it reduces the dimensionality of the search space and computationally outperforms all other classical technique in atleast 80% cases.
System support for exploration and expert feedback in resolving conflicts during integration of metadata
Authors: K. Selçuk Candan, Huiping Cao, Yan Qi, Maria Luisa Sapino
Venue: The VLDB Journal &mdash; The International Journal on Very Large Data Bases (2008)

A critical reality in integration is that knowledge obtained from different sources may often be conflicting. Conflict-resolution, whether performed during the design phase or during run-time, can be costly and, if done without a proper understanding of the usage context, can be ineffective. In this paper, we propose a novel exploration and feedback-based approach [FICSR (Pronounced as "fixer")] to conflict-resolution when integrating metadata from different sources. Rather than relying on purely automated conflict-resolution mechanisms, FICSR brings the domain expert in the conflict-resolution process and informs the integration based on the expert's feedback. In particular, instead of relying on traditional model based definition of consistency (which, whenever there are conflicts, picks a possible world among many), we introduce a ranked interpretation of the metadata and statements about the metadata. This not only enables FICSR to avoid committing to an interpretation too early, but also helps in achieving a more direct correspondence between the experts' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for exploratory feedback for conflict-resolution: within the context of a given statement of interest, (a) a preliminary ranking of candidate matches, representing different resolutions of the conflicts, informs the user about the alternative interpretations of the metadata, while (b) user feedback regarding the preferences among alternatives is exploited to inform the system about the expert's relevant domain knowledge. The expert's feedback, then, is used for resolving not only the conflicts among different sources, but also possible mis-alignments due to the initial matching phase. To enable this $${(system \stackrel{_{informs}}{\longleftrightarrow} user)}$$ feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated metadata. We also develop algorithms for efficient on-line query processing, candidate result enumeration, validity analysis, and system feedback. The results are brought together and evaluated in the Feedback-based InConSistency Resolution (FICSR) system.
Extracting Key Factors to Design Applications in Ambient Intelligence Environment
Authors: Hiroaki Kimura, Tatsuo Nakajima
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 (2008)

In an ambient intelligence environment, the design of applications influences the users behavior heavily. The purpose of this paper is to provide key factors considered necessary in developing those applications. We developed four applications applied Ambient Lifestyle Feedback Systems and evaluated those user studies. From those four user studies, we verified key factors to develop applications in an ambient intelligence environment.
Enriched random forests
Authors: Dhammika Amaratunga, Javier Cabrera, Yung-Seop Lee
Venue: Bioinformatics (2008)

Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly. In such instances, the procedure can be improved by reducing the contribution of trees whose nodes are populated by non-informative features. To some extent, this can be achieved by prefiltering, but we propose a novel, yet simple, adjustment that has demonstrably superior performance: choose the eligible subsets at each node by weighted random sampling instead of simple random sampling, with the weights tilted in favor of the informative features. This results in an ‘enriched random forest’. We illustrate the superior performance of this procedure in several actual microarray datasets. Contact: damaratu@prdus.jnj.com
Modeling queueing and channel access delay in unsaturated IEEE 802.11 random access MAC based wireless networks
Authors: Omesh Tickoo, Biplab Sikdar
Venue: IEEE/ACM Transactions on Networking (TON) (2008)

In this paper, we present an analytic model for evaluating the queueing delays and channel access times at nodes in wireless networks using the IEEE 802.11 Distributed Coordination Function (DCF) as the MAC protocol. The model can account for arbitrary arrival patterns, packet size distributions and number of nodes. Our model gives closed form expressions for obtaining the delay and queue length characteristics and models each node as a discrete time G/G/1fs queue. The service time distribution for the queues is derived by accounting for a number of factors including the channel access delay due to the shared medium, impact of packet collisions, the resulting backoffs as well as the packet size distribution. The model is also extended for ongoing proposals under consideration for 802.11e wherein a number of packets may be transmitted in a burst once the channel is accessed. Our analytical results are verified through extensive simulations. The results of our model can also be used for providing probabilistic quality of service guarantees and determining the number of nodes that can be accommodated while satisfying a given delay constraint.
State space segmentation for acquisition of agent behavior
Authors: Hiroaki Ueda, Takeshi Naraki, Yo Nasu, Kazuki Mikami, Kenichi Takahashi, Tetsuhiro Miyahara
Venue: Web Intelligence and Agent Systems (2008)

We propose a new method SSED (State Segmentation based on Euclidean Distance) to categorize continuous numeric percepts for Q-learning, where percept vectors are classified into categories and Q-learning uses categories as states to acquire rules for agent behavior. In SSED, categories are represented by hyper-spheres. A percept vector is classified into a category that covers the vector and is the nearest to it. For efficient reinforcement learning, category merging is provided with SSED, where the number of parameters to control category merging in SSED is fewer than that in fuzzy ART with category merging. In addition, match tracking is incorporated into SSED in order to specialize a category. SSED is combined with Q-learning and it is compared with some state segmentation methods. Experimental results show that Q-learning with SSED learns good rules for agent behavior more efficiently than other methods.
Contribution of exploratory methods to the investigation of extended large-scale brain networks in functional MRI: methodologies, results, and challenges
Authors: V. Perlbarg, G. Marrelec
Venue: Journal of Biomedical Imaging (2008)

A large-scale brain network can be defined as a set of segregated and integrated regions, that is, distant regions that share strong anatomical connections and functional interactions. Data-driven investigation of such networks has recently received a great deal of attention in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). We here review the rationale for such an investigation, the methods used, the results obtained, and also discuss some issues that have to be faced for an efficient exploration.
A Novel Worm Detection Model Based on Host Packet Behavior Ranking
Authors: Fengtao Xiao, Huaping Hu, Bo Liu, Xin Chen
Venue: Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems (2008)

Traditional behavior-based worm detection can't eliminate the influence of the worm-like P2P traffic effectively, as well as detect slow worms. To try to address these problems, this paper first presents a user habit model to describe the factors which influent the generation of network traffic, then a design of HPBRWD (Host Packet Behavior Ranking Based Worm detection) and some key issues about it are introduced. This paper has three contributions to the worm detection: 1) presenting a hierarchical user habit model; 2) using normal software and time profile to eliminate the worm-like P2P traffic and accelerate the detection of worms; 3) presenting HPBRWD to effectively detect worms. Experiments results show that HPBRWD is effective to detect worms.
Generalized Semi-Infinite Programming: on generic local minimizers
Authors: Harald Günzel, Hubertus Th. Jongen, Oliver Stein
Venue: Journal of Global Optimization (2008)

In this paper a basic structural problem in Generalized Semi-Infinite Programming is solved. In fact, under natural and generic assumptions we show that at any (local) minimizer the "Symmetric Reduction Ansatz" holds.
DEM and bathymetry estimation for mapping a tide-coordinated shoreline from fine spatial resolution satellite sensor imagery
Authors: A. M. Muslim, G. M. Foody
Venue: International Journal of Remote Sensing (2008)

Fine spatial resolution remotely sensed imagery has considerable potential for mapping a shoreline. Although fine spatial resolution imagery typically allows the instantaneous shoreline to be mapped with high accuracy, interest is normally focused on a reference shoreline, defined on a stable vertical datum, which is generally not apparent in the imagery unless acquired at a time carefully coordinated with the tidal characteristics of the region. To map a tide-coordinated shoreline, such as the mean sea level (MSL), information on terrain topography, bathymetry and tidal characteristics is required. In this study, IKONOS imagery was used to derive topographic and bathymetric information for an extract of the Malaysian coast and combined with a tide chart for the region to map the MSL. The digital elevation model (DEM) derived had a root mean square error (RMSE), calculated on independent control points, of ∼2.2 m while the bathymetric model had an RMSE of 0.87 m. The shoreline derived from the combination of the DEM, bathymetry and tidal information was mapped with an RMSE of 1.8 m.
The early explanatory power of NDVI in crop yield modelling
Authors: Lenny Wall, Denis Larocque, Pierre-Majorique Léger
Venue: International Journal of Remote Sensing (2008)

The objective of this paper is to study, on a weekly basis, the explanatory power of one satellite-based measurement, the Normalized Difference Vegetation Index (NDVI), for wheat yield modelling in 40 census agricultural regions (CAR) in the Canadian Prairies during the whole growing season using 16 years of NOAA AVHRR satellite data (between 1987 and 2002). We also explore the relative value of NDVI compared with a land-based measurement, the Cumulative Moisture Index (CMI). By developing a series of weekly wheat yield models over the course of the growing season, we are able to determine the accuracy of different models. Our findings indicate that NDVI possesses explanatory power 4 weeks earlier in the season than CMI.
A hybrid PSO/ACO algorithm for discovering classification rules in data mining
Authors: Nicholas Holden, Alex A. Freitas
Venue: Journal of Artificial Evolution and Applications (2008)

We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining--where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.
Improving support for locality and fine-grain sharing in chip multiprocessors
Authors: Hemayet Hossain, Sandhya Dwarkadas, Michael C. Huang
Venue: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008)

Both commercial and scientific workloads benefit from concurrency and exhibit data sharing across threads/processes. The resulting sharing patterns are often fine-grain, with the modified cache lines still residing in the writer's primary cache when accessed. Chip multiprocessors present an opportunity to optimize for fine-grain sharing using direct access to remote processor components through low-latency on-chip interconnects. In this paper, we present Adaptive Replication, Migration, and producer-Consumer Optimization (ARMCO), a coherence protocol that, to the best of our knowledge, is the first to exploit direct access to the L1 caches of remote processors (rather than via coherence mechanisms) in order to support fine-grain sharing. Our goal is to provide support for tightly coupled sharing by recognizing and adapting to common sharing patterns such as migratory, producer-consumer, multiple-reader, and multiple read-write. The protocol places data close to where it is most needed and leverages direct access when following conventional coherence actions proves wasteful. Via targeted optimizations for each of these access patterns, our proposed protocol is able to reduce the average access latency and increase the effective cache capacity at the L1 level with on-chip storage overhead as low as 0.38%. Full-system simulations of 16-processor CMPs show an average (geometric mean) speedup of 1.13 (ranging from 1.04 to 2.26) for 12 commercial, scientific, and mining workloads, with an average of 1.18 if we include 2 microbenchmarks. ARMCO also reduces the on-chip bandwidth requirements and dynamic energy (power) consumption by an average of 33.3% and 31.2% (20.2%) respectively. By evaluating optimizations at both the L1 and the L2 level, we demonstrate that when considering performance, optimization at the L1 level is more effective at supporting fine-grain sharing than that at the L2 level.
Dust or Magic, Creative work in the digital age
Authors: Bob Hughes
Venue:  (2007)

A book for the interface workers. Dust or Magic was primarily written for the young, talented people whose creative instincts are kindled by computers and live to create 'good stuff', but who are systematically betrayed by the managerial types in suits who hire them, set them absurd tasks, and sack them when their half-baked schemes go belly-up. It is also for people who simply want to know how human creativity fares in the digital age. Originally published by Addison-Wesley (under the title 'Dust or Magic, Secrets of successful multimedia design') this book is, in part, a 'secret history' of computers: a history told from the vantage point of the people who did the work. We have insiders' accounts of a range of influential products and projects, many of which were in danger of being forgotten. The scene is illuminated by recent insights into creativity and well-being from the fields of psychology and neuroscience, as well as tried-and-tested, practical strategies for workplace survival from other industries. The author, Bob Hughes, has been a 'creative' for most of his working life: first a calligrapher, then an advertising artist and copywriter before discovering computers in the mid-1980s. He now teaches at Oxford Brookes University on the MA in Interactive Media Publishing, and researches and writes about the wider impact of electronics and computers in workplaces world-wide. He also campaigns on behalf of migrants, refugees and all precarious workers. "What you are doing is stripping away the corporate bullshit from this 'revolution' - its ours not theirs. Reclaim the pixels!" - Chris McEvoy (Creator of 'Usability Must Die' www.usabilitymustdie.com). "There are many books explaining why software projects go sour; this one breaks the mold by showing how they come good." - Malcolm Cook (Senior Lecturer in Human Factors, University of Abertay) "It was incredibly engrossing. I expected to skim through it, and found myself reading it avidly, putting aside all the other work I should have been doing... It rang so true about so many things about the process of creating the virtual world we spend so much time in that I'm dying to share it with others who also create for it, or want to." - Aleen Stein (co-founder of the Voyager Company and CEO of Organa inc. www.organa.com). More information on www.idhub.com/magic
On test collections for adaptive information retrieval
Authors: Ellen M. Voorhees
Venue: Information Processing and Management: an International Journal (2008)

Traditional Cranfield test collections represent an abstraction of a retrieval task that Sparck Jones calls the ''core competency'' of retrieval: a task that is necessary, but not sufficient, for user retrieval tasks. The abstraction facilitates research by controlling for (some) sources of variability, thus increasing the power of experiments that compare system effectiveness while reducing their cost. However, even within the highly-abstracted case of the Cranfield paradigm, meta-analysis demonstrates that the user/topic effect is greater than the system effect, so experiments must include a relatively large number of topics to distinguish systems' effectiveness. The evidence further suggests that changing the abstraction slightly to include just a bit more characterization of the user will result in a dramatic loss of power or increase in cost of retrieval experiments. Defining a new, feasible abstraction for supporting adaptive IR research will require winnowing the list of all possible factors that can affect retrieval behavior to a minimum number of essential factors.