ppxscal/embedding_network huggingface dataset:
The Differential Ant-Stigmergy Algorithm for Large Scale Real-Parameter Optimization
Authors: Peter Korošec, Jurij Šilc
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

The optimization problem treated here is to find x, which optimizes cost function F(x), where x = { x1, x2, ..., xD} is a set of real parameters and D represents the dimension of the cost function. Domains of the real parameters are defined by their lower and upper bounds: $x_j^{low}$, $x_j^{upp}$; 1 ≤ j ≤ D. In this paper we consider only high-dimensional cost functions with real parameters; D up to 1000.
Jet engine's speed controller with constant pressure chamber
Authors: Alexandru Nicolae Tudosie
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

The paper deals with an automatic system meant to control a jet engine's rotation speed, through the fuel injection's control, based on a constant pressure chamber controller. One has established the non-linear mathematical model (based on the motion equations of the system's parts), the linear model and the non-dimensional linear model, as well as the simplified model, the transfer function and the block-diagram. One had also performed a stability study and established the system's stability domains, as well as some quality studies.
Semiempirical molecular orbital study of glycine solvation and of binding calcium carbonate on glycine polypeptides
Authors: A. Chrissanthopoulos, E. Dalas
Venue: Journal of Computational Methods in Sciences and Engineering (2007)

Semiempirical molecular orbital calculations have been performed for the amino acid glycine and glycine polypeptide - calcium carbonate systems. We identified the amino acid active sites, which bind to the calcium carbonate species and observed the influence of the solvation effect on the structure stabilization and on the nucleation process.
Winner determination in combinatorial auctions with logic-based bidding languages
Authors: Joel Uckelman, Ulle Endriss
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

We propose the use of logic-based preference representation languages based on weighted propositional formulas for specifying bids in a combinatorial auction. We then develop several heuristics for a branch-and-bound search algorithm for determining the winning bids in this framework and report on their empirical performance. The logic-based approach is attractive due to its high degree of flexibility in designing a range of different bidding languages within a single conceptual framework.
Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard
Authors: Jindae Kim, Changsoo Ok, Soundar R. Kumara, Shang-Tae Yee
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

Real-time vehicle location information enables to facilitate more efficient decision-making in dynamic automotive shipment yard environment. This paper proposes a multiagent-based decentralized decision-making model for the vehicle deployment planning in a shipment yard. A multiagent architecture is designed to facilitate decentralized algorithms and coordinate different agents dynamically. The results of computational experiments show that the proposed deployment model outperforms a current deployment practice with respect to the deployment performance measures.
Mobile Route Planning
Authors: Peter Sanders, Dominik Schultes, Christian Vetter
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

We provide an implementation of an exact route planning algorithm on a mobile device that answers shortest-path queries in a road network of a whole continent instantaneously, i.e., with a delay of about 100 ms which is virtually not observable for a human user. Our main algorithmic contribution of is a highly compressed blocked representation of the underlying hierarchical graph and a new fast yet compact route reconstruction data structure. Our representation exploits the locality properties of the graph using a very simple algorithm that does not use any a priori information.
Text understanding via semantic structure analysis
Authors: Eduard Hovy
Venue:  (2007)

To understand and manage large collections of documents about the same topic, people have to interpret various aspects/levels of information simultaneously and integrate them to achieve a coherent overall picture. To support these diverse tasks, we need tools to extract the "important" information and combine the extracted information to a coherent overview. There exists substantial need for overview corpus analysis of large document sets in a variety of applications, including discussion, political debate, or public comment. Most of these applications exhibit tight causal and/or hierarchical relations among and within the texts, so that structure analysis plays an important role in document understanding. This kind of text also frequently contains subjective, opinionated, and biased language, necessitating opinion analysis and clustering. Prior research has addressed many of these topics, but never together. In this work, we focus on the semantic structure of sentences and discourses to identify "important" information to achieve balanced extraction and find inter-relations between information units. We apply a domain-independent sentence structure analysis based on frame semantics, and provide a discourse-level structure analysis for subjective or argumentative texts. By integrating all this analysis, we provide a novel approach to identifying frame and argument structures, classifying arguments, and integrating the structure for the whole data collection. Each structure identification and classification module separately shows substantial improvement over the baseline, and the integration module shows the value of utilizing the individual analyses to automatically build a visual and categorized summary of the large document collection. This work points the way to future multifaceted text collection analysis technology that will be required to assist people managing increasingly large and diversified sets of documents.
Topic 9 Parallel and Distributed Programming
Authors: Luc Moreau, Emmanuel Jeannot, George Bosilca, Antonio J. Plaza
Venue: Proceedings of the 13th European international conference on Parallel Processing (2007)

Developing parallel or distributed applications is a hard task and it requires advanced algorithms, realistic modeling, efficient design tools, high performance languages and libraries, and experimental evaluation. This topic provides a forum for presentation of new results and practical experience in this domain. It emphasizes research that facilitates the design and development of correct, high-performance, portable, and scalable parallels program.
Research and Application of Learning Activity Management System in College and University E-Learning
Authors: Li Yan, Jiumin Yang, Zongkai Yang, Sanya Liu, Lei Huang
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

LAMS (learning activity management system) is a very flexible learning design tool. With the combination of LAMS and the content, teachers/lecturers can easily organize the learning activities for the students in e-learning. This paper focuses on the application and functions of LAMS in college and university e-learning. Through formal research and evaluation of the impact of LAMS on e-learning in 3 disciplines (carried out in Huazhong Normal University), the presentation summarizes both the merits and the downsides of the application of LAMS in college university e-learning and concludes with reflection on how further work will go on.
Visualisation of Clinical and Non-clinical Characteristics of Patients with Behavioural and Psychological Symptoms of Dementia
Authors: Victor Vickland, Henry Brodaty
Venue: Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics (2008)

We created a computer model with a user-friendly graphical interface that could tell an accurate and engaging story about the management of patients with behavioural and psychological symptoms of dementia (BPSD) in Alzheimer’s disease. The agent-based model was built using the AnyLogic development platform and calibrated with data on prevalence and incidence of dementia in the Australian population. The graphical user interface of the model consisted of multiple screens and was intended as a tool for information visualisation and virtual experiments. The dynamic progression of the symptoms and each patient’s individual characteristics such as age, gender and place of residence were integrated into one model. The preliminary results indicate that simulation modelling approach and application of multiple icons in graphical representation of each individual patient are efficient tools for information visualisation in agent-based model. The dual graphical representation of individual patients allowed for simultaneous display of clinical e.g. symptom-specific and non-clinical e.g. accommodation characteristics.
Inference in the Promedas Medical Expert System
Authors: Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn Leisink, Hilbert J. Kappen, Jan P. Neijt
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-to-date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. We show that Belief Propagation (BP) can be successfully applied as approximate inference algorithm in the Promedas network. In some cases, however, we find errors that are too large for this application. We apply a recently developed method that improves the BP results by means of a loop expansion scheme. This method, termed Loop Corrected (LC) BP, is able to improve the marginal probabilities significantly, leaving a remaining error which is acceptable for the purpose of medical diagnosis.
Atom probe tomography of Ni silicides: First stages of reaction and redistribution of Pt
Authors: D. Mangelinck, K. Hoummada, O. Cojocaru-Mirédin, E. Cadel, C. Perrin-Pellegrino, D. Blavette
Venue: Microelectronic Engineering (2008)

The silicide formation and the redistribution of Pt after deposition and after a heat treatment at 290^oC of Ni"1"-"xPt"x films on Si have been analysed by atom probe tomography assisted by femtosecond laser pulses. Two phases with different composition were found to form during deposition at room temperature: a NiSi layer with a relatively constant thickness of approximately 2nm and a particle of Ni"2Si. The shape of the Ni"2Si particle is in accordance with nucleation followed by lateral growth formation. After heat treatment, two silicide phases Ni"2Si and NiSi were found together with the Ni"1"-"xPt"x solid solution. The redistribution of Pt at the Ni"1"-"xPt"x/Ni"2Si interface is a clear illustration of the snowplow effect. A segregation of Pt at the Ni"2Si/NiSi interface has been observed and is attributed to interfacial segregation. The effect of the redistribution of Pt on the silicide formation is discussed.
Can Social Tags Help You Find What You Want?
Authors: Khasfariyati Razikin, Dion Hoe-Lian Goh, Alton Y. Chua, Chei Sian Lee
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

One of the uses of social tagging is to associate freely selected terms (tags) to resources for sharing resources among tag consumers. This enables tag consumers to locate new resources through the collective intelligence of other tag creators, and offers a new avenue for resource discovery. This paper investigates the effectiveness of tags as resource descriptors determined through the use of text categorisation using Support Vector Machines. Two text categorisation experiments were done for this research, and tags and web pages from del.icio.us were used. The first study concentrated on the use of terms as its features. The second study used both terms and its tags as part of its feature set. The results indicate that the tags were not always reliable indicators of the resource contents. At the same time, the results from the terms only experiment were better compared to the experiment with terms and tags. A deeper analysis of a sample of tags and documents were also conducted and implications of this research are discussed.
Supporting Address Autoconfiguration for IPv6 in IEEE 802.16e Based Network
Authors: Kyunghye Lee, Miyoung Kim, Gye-Young Kim, Youngsong Mun
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

Introduction of IPv6 protocol in IEEE 802.16e based network without any modification could occur many problems due to IEEE 802.16 standard's point-to-multipoint and connection oriented features and absent of the native multicast support. In this paper, we propose a method for supporting address autoconfiguration and DAD mechanism to the IPv6 nodes in IEEE 802.16e based network. The proposed scheme uses multicast and broadcast service (MBS) and the service's transport connection identifiers (MCIDs) and assigns them to the IPv6 node as soon as the nodes complete the layer 2 handover. Using the proposed scheme, IPv6 nodes in IEEE 802.16e network can exchange the ND messages sent to the multicast address. We analyze the network resource usages for the existing method and the proposed scheme. Through the performance evaluation, we see that the proposed scheme can reduce the whole network resource usage up to maximum 32 percent.
Target tracking based on a distributed particle filter in underwater sensor networks
Authors: Yan Huang, Wei Liang, Hai-bin Yu, Yang Xiao
Venue: Wireless Communications Mobile Computing (2008)

In this paper, based on a distributed particle filter, two tracking algorithms are proposed for tracking mobile targets in cluster-based underwater sensor networks (USNs). Both tracking algorithms run local particle filter sequentially at each cluster along target trajectories, but they adopt different methods of selecting measurements from sensor nodes to balance the information contribution against the cost. Performance metrics are proposed and discussed in terms of tracking performance, communication cost, energy cost, and tracking response time. Simulations are conducted to quantitatively compare the proposed algorithms as well as another tracking algorithm based on extended Kalman filter (EKF). Our results indicate that one tracking algorithm achieves higher tracking accuracy while the other achieves dramatic reduction of communication cost, energy cost, and tracking response time. Furthermore, performance of two tracking algorithms has been studied in terms of detection threshold and sensor density. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Fast Point Multiplication on Elliptic Curves without Precomputation
Authors: Marc Joye
Venue: Proceedings of the 2nd international workshop on Arithmetic of Finite Fields (2008)

Elliptic curves find numerous applications. This paper describes a simple strategy to speed up their arithmetic in right-to-left methods. In certain settings, this leads to a non-negligible performance increase compared to the left-to-right counterparts.
Semi-systolic Modular Multiplier over GF(2m)
Authors: Hyun-Sung Kim, Sung-Woon Lee
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

The key operations for the public-key cryptosystems are modular inversion, division, and exponentiation. The modular multiplication is considered to be the basic arithmetic for them. This paper proposes a new algorithm and it's semi-systolic array architecture to compute the modular multiplication over GF(2 m ). They are based on the standard basis representation and use the property of irreducible all one polynomial as a modulus. The architecture has the critical path with 1-D XOR per cell and has the latency with m+1. These properties are better than the existing multipliers. Since the proposed multiplier has regularity, modularity and concurrency, it is suitable for VLSI implementation and can be easily utilized for the crypto-processor chip design.
Recoding patterns of sensory input: Higher-order features and the function of nonlinear dendritic trees
Authors: Paul A. Rhodes
Venue: Neural Computation (2008)

Here analytical and simulation results are presented characterizing the recoding arising when overlapping patterns of sensor input impinge on an array of model neurons with branched thresholded dendritic trees. Thus, the neural units employed are intended to capture the integrative behavior of pyramidal cells that sustain isolated Na+ or NMDA spikes in their branches. Given a defined set of sensor vectors, equations were derived for the probability of firing of both branches and neurons and for the expected overlap between the neural firing patterns triggered by two afferent patterns of given overlap. Thus, both the sparseness of the neural representation and the orthogonalization of overlapping vectors were computed. Simulations were then performed with an array of 1000 neurons comprising 30,000 branches to verify the analytical results and confirm their applicability to systems (which include any practicable artificial system) in which the combinatorically possible branches and neurons are severely subsampled. A means of readout and a measure of discrimination performance were provided so that the accuracy of discrimination among overlapping sensor vectors could be optimized as a function of neuron structure parameters. Good performance required both orthogonalization of the afferent patterns, so that discrimination was accurate and free of interference, and maintenance of a minimum level of neural activity, so that some neurons fired in response to each sensor pattern. It is shown that the discrimination performance achieved by arrays of neurons with branched dendritic trees could not be reached with single-compartment units, regardless of how many of the latter are used. The analytical results furnish a benchmark against which to measure further enhancements in the performance of subsequent simulated systems incorporating local neural mechanisms which, while often less amenable to closed-form analysis, are ubiquitous in biological neural circuitry.
Monitoring of swarming sounds in bee hives for early detection of the swarming period
Authors: S. Ferrari, M. Silva, M. Guarino, D. Berckmans
Venue: Computers and Electronics in Agriculture (2008)

Beekeeping, known as one of the oldest forms of agriculture, in its complexity requires control for honey production with what modern technology can offer. Honey is included in animal production implying that farmers have interest in big productions according to the best blooming time, the presence of parasites, the genetic strain of his bees and the swarming periods of the honeybees (queen and her workers leaving the hive). This last fact has a big economic interest for the beekeeper as swarming means honey loss since bees start collecting the honey to migrate. Here for a method that enables the prediction of the swarming is required to prevent the queen from leaving the hives. In this experiment an acoustic method based on labelling of sounds is proposed to predict the swarming period. Three hives were monitored during 270h. The microphones were sited inside the hives together with a temperature and humidity sensor. The sounds were recorded with a sample rate of 2kHz, and analyzed via Matlab and Cool Edit Pro. During this period 9 swarming activities occurred. Swarming is indicated by an increase in the power spectral density at about 110Hz; approaching to swarm the sound augmented in amplitude and frequency to 300Hz, occasionally a rapid change occurred from 150Hz to 500Hz. Another finding indicating the initiation of a swarming period is the raise in temperature from 33^oC to 35^oC until the actual time of swarming when the temperature starts dropping to 32^oC. With more activity, ventilation from bee wings causes drop of temperature. Less information comes from the correlation between sound and humidity since this parameter is too much influenced by the external conditions and no significant variation occurred according to a swarm. This increase of temperature, together with the changes in acoustical features of the sound recorded in the hive, may be used as a predictor for swarming of the bees to reduce honey loss.
Developing a Sub Room Level Indoor Location System for Wide Scale Deployment in Assisted Living Systems
Authors: Gerald Bauer, Paul Lukowicz
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The work described in this paper is part of the MonAMI European project that aims to facilitate and demonstrate large scale real life deployment of AAL technologies. A major concern of the project is the ability to locate and track people within their homes with sensor setups that are cheap, easily installed into hundreds of homes, and can be operated for extended time periods with no or little maintenance. This paper describes such a system. It is based on a standard ceiling mounted camera, an on body accelerometer and simple yet robust image processing. We describe the system concept and the current implementation based on a commercial wide angle webcam and the motion sensor of the Apple iPhone/Nokia N95 mobile phone. We also report on initial results with operating the system in our lab.
Solving Regression by Learning an Ensemble of Decision Rules
Authors: Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

We introduce a novel decision rule induction algorithm for solving the regression problem. There are only few approaches in which decision rules are applied to this type of prediction problems. The algorithm uses a single decision rule as a base classifier in the ensemble. Forward stagewise additive modeling is used in order to obtain the ensemble of decision rules. We consider two types of loss functions, the squared- and absolute-error loss, that are commonly used in regression problems. The minimization of empirical risk based on these loss functions is performed by two optimization techniques, the gradient boosting and the least angle technique. The main advantage of decision rules is their simplicity and good interpretability. The prediction model in the form of an ensemble of decision rules is powerful, which is shown by results of the experiment presented in the paper.
Seeker Optimization Algorithm
Authors: Chaohua Dai, Yunfang Zhu, Weirong Chen
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

A novel swarm intelligence paradigm called seeker optimization algorithm (SOA) for the real-parameter optimization is proposed in this paper. The SOA is based on the concept of simulating the act of humans' intelligent search with their memory, experience, and uncertainty reasoning. In this sense, the individual of this population is called seeker or searcher just from which the new algorithm' name is derived. After given start point, search direction, search radius, and trust degree, every seeker moves to a new position (next solution) based on his social learning, cognitive learning, and uncertainty reasoning. The algorithm's performance was studied using several typically complex functions. In almost all cases studied, SOA is superior to continuous genetic algorithm (GA) and particle swarm optimization (PSO) in all optimization quality, robustness and efficiency.