ppxscal/embedding_network huggingface dataset:
Technical note: A wireless telemetric method of monitoring clinical acidosis in dairy cows
Authors: Toby Mottram, John Lowe, Michael McGowan, Nancy Phillips
Venue: Computers and Electronics in Agriculture (2008)

Ruminant animals convert forage containing cellulose by bacterial fermentation into nutrients. The health of the bacterial culture in the rumen is essential for the health and productivity of the animal. Over a number of years fistulated animals have been used to study the rumen and its bacterial population. It has been shown that techniques to maintain the pH of the rumen between 7 and 5.5 pH are essential for the health of the dairy cow. The rumen pH has been recorded by using sensors suspended in the rumen at intervals or exceptionally with data recorders. However, fistulation of an animal requires surgery and is only suitable for a few research animals. This paper describes the development of a telemetric bolus that measured and recorded pH continuously. When interrogated by wireless the bolus transmitted the recorded data to an operator standing beside the cow with a receiving station. Boluses were placed in fistulated animals so that a comparison could be made with a laboratory instrument. Data are presented that show a close correlation between the calibrated laboratory instrument and the bolus at time intervals when the instrument was inserted. From this it can be assumed that the bolus accurately records the temporal variation in rumen pH. Data are presented to show the diurnal change in rumen pH over a 6-week period. Methods of increasing the lifetime and accuracy of the bolus are discussed.
MArCo: Building an Artificial Conflict Mediator to Support Group Planning Interactions
Authors: Patricia Azevedo Tedesco
Venue: International Journal of Artificial Intelligence in Education (2003)

The emphasis on building co-operative/collaborative environments has brought out the matter of group interactions. This, in its turn, has highlighted the issue of conflicts, inherent to group problem solving. If well employed, conflicts can act as triggers of cognitive changes, and thus help to refine the group's solution to the task. In this paper, we present a computational framework for detecting and mediating Meta-Cognitive conflicts. The theoretical framework presented here enables a computational system to analyse the ongoing interaction, to detect and mediate conflicts. In order to do so, we consider our model of strategic changes, a model of the group, and the history of the interaction. The objective of the mediation is to suggest courses of action that provoke articulation and reflection, and thus lead to more refined solutions. In order to diagnose which changes are happening to the group plan we have built a model of strategic operations, which describes what types of changes happen to a plan as well as how changes to one component of the strategy (beliefs, intentions, ordering relations, context beliefs and goals) impact the other ones. MArCo, our prototype embedding the artificial conflict mediator, shows how our conceptual framework has been put to practical use.
Temporal dimension for job submission description language
Authors: Omar Aldabbas, Mai Alfawair, Hussein Zedan, Antonio Cau
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

A grid job's requirements description is very important, and raises interesting issues for resource management and scheduling within a grid environment. Several languages such as Globus Resource Specification language and European Data Grid JDL have been developed for describing grid jobs, the latest of which is Job Submission Description Language (JSDL). None of the current job description languages deal with applications or events, and all have limitation with regard to issues of time. In this paper we propose an extension of JSDL to cope with jobs' granularity, their composition, event and time. This extension will enable JSDL to support the description of application and their jobs flow, and allow the system to interrupt job according to event or time. It will also give the user the flexibility to determine the job execution times that assist to complete his/her job; expectation for job execution time is very difficult especially in heterogeneous environment. The new extension enables user to define and describe; more than one job and their relationship at the same time, event occurrence at any time during the job execution time and handle it, and job time. Our extensions have been evaluated on a simulated grid of 100 nodes which has shown better performance and excellent scheduling results of the grid. The introduction of event has also increased the reliability aspect of the grid.
A Simple Lexicographic Ranker and Probability Estimator
Authors: Peter Flach, Edson Takashi Matsubara
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Given a binary classification task, a ranker sorts a set of instances from highest to lowest expectation that the instance is positive. We propose a lexicographic ranker, LexRank, whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data. When using the odds ratio to rank the attribute values we obtain a restricted version of the naive Bayes ranker. We systematically develop the relationships and differences between classification, ranking, and probability estimation, which leads to a novel connection between the Brier score and ROC curves. Combining LexRank with isotonic regression, which derives probability estimates from the ROC convex hull, results in the lexicographic probability estimator LexProb. Both LexRank and LexProb are empirically evaluated on a range of data sets, and shown to be highly effective.
Comparing State-of-the-Art Collaborative Filtering Systems
Authors: Laurent Candillier, Frank Meyer, Marc Boullé
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Collaborative filtering aims at helping users find items they should appreciate from huge catalogues. In that field, we can distinguish user-based, item-based and model-based approaches. For each of them, many options play a crucial role for their performances, and in particular the similarity function defined between users or items, the number of neighbors considered for user- or item-based approaches, the number of clusters for model-based approaches using clustering, and the prediction function used.In this paper, we review the main collaborative filtering methods proposed in the litterature and compare them on the same widely used real dataset called MovieLens, and using the same widely used performance measure called Mean Absolute Error (MAE). This study thus allows us to highlight the advantages and drawbacks of each approach, and to propose some default options that we think should be used when using a given approach or designing a new one.
A Free Terrain Model for Trajectory K---Anonymity
Authors: Aris Gkoulalas-Divanis, Vassilios S. Verykios
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

This paper introduces a privacy model for location based services that utilizes collected movement data to identify parts of the user trajectories, where user privacy is at an elevated risk. To protect the privacy of the user, the proposed methodology transforms the original requests into anonymous counterparts by offering trajectory K---anonymity. As a proof of concept, we build a working prototype that implements our solution approach and is used for experimentation and evaluation purposes. Our implementation relies on a spatial DBMS that carries out part of the necessary analysis. Through experiments we demonstrate the effectiveness of our approach to preserve the K---anonymity of the users for as long as the requested services are in progress.
An Adaptive Image Steganography Based on Depth-Varying Embedding
Authors: Junhui He, Shaohua Tang, Tingting Wu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Steganographic technique is a means of covert communication. In this paper, an adaptive image steganography with high capacity and good security is proposed. Based on local complexity of a cover image, varying-depth embedding is used to improve the imperceptibility and decrease distortions in it. Experimental results show that the steganographic technique may provide higher capacity and be resistant to several well-known steganalytic methods.
An Extended Implementation of the Great Deluge Algorithm for Course Timetabling
Authors: Paul Mcmullan
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Course Scheduling consists of assigning lecture events to a limited set of specific timeslots and rooms. The objective is to satisfy as many soft constraints as possible, while maintaining a feasible solution timetable. The most successful techniques to date require a compute-intensive examination of the solution neighbourhood to direct searches to an optimum solution. Although they may require fewer neighbourhood moves than more exhaustive techniques to gain comparable results, they can take considerably longer to achieve success. This paper introduces an extended version of the Great Deluge Algorithm for the Course Timetabling problem which, while avoiding the problem of getting trapped in local optima, uses simple Neighbourhood search heuristics to obtain solutions in a relatively short amount of time. The paper presents results based on a standard set of benchmark datasets, beating over half of the currently published best results with in some cases up to 60% of an improvement.
Interacting with virtual and augmented worlds
Authors: Nadia Magnenat-Thalmann
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

For more than three decades, the main focus was to be able to model realistic decors, lights and living beings, particularly humans that should look realistic. Actually, we are aiming for interacting with these worlds in a sensitive and meanful way. In this invited talk, I will present the research that we are developping on many aspects at MIRALab: interactive modelling of virtual humans, interactive clothes modelling and animation, touching textiles, haptic interaction with hair, gaze interaction with Virtual Humans in Augmented Reality, talking and being recognized by virtual humans with memory and personality models. In this short summary, we describe mainly the problematic of modelling any body size and shape and also dress it up, all in real-time.
Left posterior temporal regions are sensitive to auditory categorization
Authors: Rutvik Desai, Einat Liebenthal, Eric Waldron, Jeffrey R. Binder
Venue: Journal of Cognitive Neuroscience (2008)

Recent studies suggest that the left superior temporal gyrus and sulcus (LSTG/S) play a role in speech perception, although the precise function of these areas remains unclear. Here, we test the hypothesis that regions in the LSTG/S play a role in the categorization of speech phonemes, irrespective of the acoustic properties of the sounds and prior experience of the listener with them. We examined changes in functional magnetic resonance imaging brain activation related to a perceptual shift from nonphonetic to phonetic analysis of sine-wave speech analogs. Subjects performed an identification task before scanning and a discrimination task during scanning with phonetic (P) and nonphonetic (N) sine-wave sounds, both before (Pre) and after (Post) being exposed to the phonetic properties of the P sounds. Behaviorally, experience with the P sounds induced categorical identification of these sounds. In the PostP > PreP and PostP > PostN contrasts, an area in the posterior LSTG/S was activated. For both P and N sounds, the activation in this region was correlated with the degree of categorical identification in individual subjects. The results suggest that these areas in the posterior LSTG/S are sensitive neither to the acoustic properties of speech nor merely to the presence of phonetic information, but rather to the listener's awareness of category representations for auditory inputs.
Hardware/Software Partitioning of Core-Based Systems Using Pulse Coupled Neural Networks
Authors: Zhengwei Chang, Guangze Xiong
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Hardware/software partitioning of System-on-chip (SoC partitioning) has a significant effect on the cost and performance of the SoC. Given an embedded system specification and an available core library, the goal of low power SoC partitioning is to select appropriate intellectual-property (IP) cores or software components for the SoC, such that the power consumption of the SoC is minimized under price and timing constraints. SoC partitioning is first formulated to the constrained single-pair shortest-path problem in a directed, weighted graph, and then a novel discrete pulse coupled neural network (PCNN) approach is proposed to get the optimal solution. Autowaves in PCNN are designed specially to meet the constraints and find the optimal path in the constructed graph. Experimental results are given to demonstrate the feasibility and effectiveness of the proposed method.
A cerebellar associative memory approach to option pricing and arbitrage trading
Authors: S. D. Teddy, E. M. -K. Lai, C. Quek
Venue: Neurocomputing (2008)

Option pricing is a process to obtain the theoretical fair value of an option based on the factors affecting its price. The classical approaches to option pricing include the Black-Scholes pricing formula and the binomial pricing model. These techniques, however, employ complex and rigid statistical formulations that are not easily comprehensible to novice investors. More recently, non-parametric and computational methods of option valuation that are able to construct a model of the pricing formula from historical data have been proposed in the literature. However, most of these models functioned as black-boxes and may not be able to efficiently and accurately capture the complex market dynamics and characteristics of the option data. This paper proposes a novel brain-inspired cerebellar associative memory model for pricing American-style call options on British pound vs. US dollar currency futures. The proposed model, named PSECMAC, constitutes a local learning model that is inspired by the neurophysiological aspects of the human cerebellum. The PSECMAC-based option-pricing model is subsequently applied in a mis-priced option arbitrage trading system. Simulation results show an encouraging return on investment of 23.1% for some of the traded options.
Tying Ontologies to Domain Contents for CSCL
Authors: Seiji Isotani, Riichiro Mizoguchi
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

One of the main problems facing the development of ontology-aware authoring systems (OAS) is to link domain-independent knowledge (ontologies) with content of a specific domain. In collaborative learning (CL), this problem hinders the development of OAS that aids the design of pedagogically sound CL sessions with strong technological support. In this paper, we propose a framework to connect an ontology that represents CL explicitly with domain content without asking end-users to create their own ontologies from scratch.
PuzzledCircular food
Authors: Peter Winkler
Venue: Communications of the ACM (2008)

Welcome to three new challenging mathematical puzzles. Solutions to the first two will be published next month; the third is as yet unsolved, so you may need extra luck with that one. Here, I concentrate on circular food, so you might want to eat something before jumping in.
An Asynchronous Cellular Automaton Implementing 2-State 2-Input 2-Output Reversed-Twin Reversible Elements
Authors: Jia Lee, Ferdinand Peper, Susumu Adachi, Kenichi Morita
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

Reversible computers usually work in a synchronous mode, i.e., in the presence of clock signals, but in the light of the asynchronous nature of microscopic physical phenomena this may be an anomaly. The alternative, an asynchronous mode of operation, has therefore attracted attention from researchers, witness the proposal of a reversible circuit element in (Morita 2001) that works in such a mode. Simplicity of circuit elements like this is an important design objective since it correlates positively with the efficiency by which they may be realized physically. In this paper, we present two mutually inverse logic elements that compare favorably to other circuit elements in terms of their number of states and their number of input and output lines. We show that the proposed circuit elements can perform universal computation by embedding circuits made of them in asynchronous cellular automata.
A Watermarking Algorithm for Gray-Level Watermark Based on Local Feature Region and SVD
Authors: Wei Wang, Chengxi Wang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

In this paper, a novel gray-level image watermarking algorithm based on local feature region and Singular Value Decomposition (SVD) is presented for copyright protection. A new way which utilizes the Binary Coded Decimal (BCD) method and encodes the gray-level secret information into bit string is used. It employs the singular values of SVD to extract the most valuable features of the watermark image and reduces lots of redundant information. The feature regions of the host image, which are used for information hiding, can be correctly detected by the improved Harris-Laplace corner detector even after signal processing and geometric attacks. The application of the proposed scheme makes the gray-level watermark capable of transparent hiding. Simulation results show that our method performs better than traditional methods under different attacks.
GPU Implementation of a Clustering Based Image Registration
Authors: Seung-Hun Yoo, Yun-Seok Lee, Sung-Up Jo, Chang-Sung Jeong
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

This paper presents GPU(Graphics Processing Unit) implementation of a clustering based image registration method. Since the image registration is an important process in image analysis tasks such as image restoration and image fusion, fast image registration can improve the overall application execution speed. Recently, the commodity GPU is being used in not only 3D graphics rendering but also in general-purpose computation due to an increase in the good price/performance ratio and hardware programmability as well as the huge computing power and speed of the GPU. We implemented clustering-based image registration method on GPU using only transformation of texture coordinations in vertex program and re-sampling in fragment program. Finally, GPU-based image registration speed up nearly 50 percent compared with CPU.
Probabilistic passage models for semantic search of genomics literature
Authors: Jay Urbain, Nazli Goharian, Ophir Frieder
Venue: Journal of the American Society for Information Science and Technology (2008)

We explore unsupervised learning techniques for extracting semantic information about biomedical concepts and topics, and introduce a passage retrieval model for using these semantics in context to improve genomics literature search. Our contributions include a new passage retrieval model based on an undirected graphical model (Markov Random Fields), and new methods for modeling passage-concepts, document-topics, and passage-terms as potential functions within the model. Each potential function includes distributional evidence to disambiguate topics, concepts, and terms in context. The joint distribution across potential functions in the graph represents the probability of a passage being relevant to a biologist's information need. Relevance ranking within each potential function simplifies normalization across potential functions and eliminates the need for tuning of passage retrieval model parameters. Our dimensional indexing model facilitates efficient aggregation of topic, concept, and term distributions. The proposed passage-retrieval model improves search results in the presence of varying levels of semantic evidence, outperforming models of query terms, concepts, or document topics alone. Our results exceed the state-of-the-art for automatic document retrieval by 14.46% (0.3554 vs. 0.3105) and passage retrieval by 15.57% (0.1128 vs. 0.0976) as assessed by the TREC 2007 Genomics Track, and automatic document retrieval by 18.56% (0.3424 vs. 0.2888) as assessed by the TREC 2005 Genomics Track. Automatic document retrieval results for TREC 2007 and TREC 2005 are statistically significant at the 95% confidence level (p = .0359 and .0253, respectively). Passage retrieval is significant at the 90% confidence level (p = 0.0893). &copy; 2008 Wiley Periodicals, Inc.
A Term Association Inference Model for Single Documents: A Stepping Stone for Investigation through Information Extraction
Authors: Sukanya Manna, Tom Gedeon
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

In this paper, we propose a term association model which extracts significant terms as well as the important regions from a single document. This model is a basis for a systematic form of subjective data analysis which captures the notion of relatedness of different discourse structures considered in the document, without having a predefined knowledge-base. This is a paving stone for investigation or security purposes, where possible patterns need to be figured out from a witness statement or a few witness statements. This is unlikely to be possible in predictive data mining where the system can not work efficiently in the absence of existing patterns or large amount of data. This model overcomes the basic drawback of existing language models for choosing significant terms in single documents. We used a text summarization method to validate a part of this work and compare our term significance with a modified version of Salton's [1].
A PID Parameters Tuning Algorithm Inspired by the Small World Phenomenon
Authors: Xiaohu Li, Haifeng Du, Jian Zhuang, Sunan Wang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Based on the small world phenomenon in social networks, a local search operator and a random long-range search operator with decimal-coding are proposed, then a decimal-coding small world optimization algorithm (DSWA) is explored, which validity and feasibility are testified by the simulation of PID parameters tuning. Compared with the corresponding genetic algorithm (GA) and particle swarm optimization algorithm (PSO), the simulation results indicate that DSWA can avoid trapping to local optimum in a certain extend, and it also can find high quality parameters for PID controller.