ppxscal/embedding_network huggingface dataset:
Testing a Computational Account of Category-Specific Deficits
Authors: Conrad Perry
Venue: Journal of Cognitive Neuroscience (1999)

Patients displaying mild symptoms of Alzheimer's disease sometimes have more difficulty naming items from an artifact than from a natural kind category; others displaying more severe symptoms almost always have more difficulty naming items from a natural kind than from an artifact category. This paper examined a computational model of this double dissociation (Devlin, Gonnerman, Andersen, & Seidenberg, 1998). Four basic tests of the model were proposed: The model should be able to generalize to new exemplars, the model should be expandable such that training sets of a realistic size can be used, the model's performance should not be unduly affected by small changes in architecture, and the learning algorithm should produce results that are not inconsistent with any major underlying factor of semantic organization. The model was found to be deficient in all four areas. Results reported from the model may therefore have been idiosyncratic to the model and not reflect general properties of a real semantic system.
Subclass Discriminant Analysis
Authors: Manli Zhu, Aleix M. Martinez
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

Over the years, many Discriminant Analysis (DA) algorithms have been proposed for the study of high-dimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand). Unfortunately, in most problems the form of each class pdf is a priori unknown, and the selection of the DA algorithm that best fits our data is done over trial-and-error. Ideally, one would like to have a single formulation which can be used for most distribution types. This can be achieved by approximating the underlying distribution of each class with a mixture of Gaussians. In this approach, the major problem to be addressed is that of determining the optimal number of Gaussians per class, i.e., the number of subclasses. In this paper, two criteria able to find the most convenient division of each class into a set of subclasses are derived. Extensive experimental results are shown using five databases. Comparisons are given against Linear Discriminant Analysis (LDA), Direct LDA (DLDA), Heteroscedastic LDA (HLDA), Nonparametric DA (NDA), and Kernel-Based LDA (K-LDA). We show that our method is always the best or comparable to the best.
Identifying "best bet" web search results by mining past user behavior
Authors: Eugene Agichtein, Zijian Zheng
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-of-the-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques.
Relationships between broadcast and shared memory in reliable anonymous distributed systems
Authors: James Aspnes, Faith Ellen Fich, Eric Ruppert
Venue: Distributed Computing (2006)

We study the power of reliable anonymous distributed systems, where processes do not fail, do not have identifiers, and run identical programmes. We are interested specifically in the relative powers of systems with different communication mechanisms: anonymous broadcast, read-write registers, or read-write registers plus additional shared-memory objects. We show that a system with anonymous broadcast can simulate a system of shared-memory objects if and only if the objects satisfy a property we call idemdicence; this result holds regardless of whether either system is synchronous or asynchronous. Conversely, the key to simulating anonymous broadcast in anonymous shared memory is the ability to count: broadcast can be simulated by an asynchronous shared-memory system that uses only counters, but read-write registers by themselves are not enough. We further examine the relative power of different types and sizes of bounded counters and conclude with a nonrobustness result.
Dissociating Confidence and Accuracy: Functional Magnetic Resonance Imaging Shows Origins of the Subjective Memory Experience
Authors: Elizabeth F. Chua, Erin Rand-giovannetti, Daniel L. Schacter, Marilyn S. Albert, Reisa A. Sperling
Venue: Journal of Cognitive Neuroscience (2004)

Successful memory typically implies both objective accuracy and subjective confidence, but there are instances when confidence and accuracy diverge. This dissociation suggests that there may be distinct neural patterns of activation related to confidence and accuracy. We used event-related functional magnetic resonance imaging to study the encoding of novel face&ndash;name associations, assessed with a postscan memory test that included objective measures of accuracy and subjective measures of confidence. We showed specific neural activity in the left inferior prefrontal cortex associated with trials when subjects expressed high confidence that they had chosen the correct name for the face and made a correct identification. Moreover, we found that this region was also associated with imparting high confidence when subjects chose the incorrect name. However, medial temporal lobe regions showed activity only for high-confidence correct trials. Many functional magnetic resonance imaging studies have shown that the medial temporal lobe and left prefrontal regions are particularly important for the successful formation of memories by using a combination of subjective and objective measures. Our findings suggest that these regions may be differentially involved in the objective and subjective components of memory and that the origins of confidence&ndash;accuracy dissociations may be related to incomplete activation of the neural pattern seen in successful encoding. These findings may also aid understanding of eyewitness misidentifications and memory distortions.
Research on Applying Java Card to Database Encryption Systems
Authors: Han Yu, Xiamu Niu, Duohe Ma, Changxiang Shen
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

The security of a system not only depends on the quality of the encryption algorithm it used, but also on the mechanism of key management. Traditional method layered key management is complex and inefficient for a database encryption system as well. In this paper, we apply Java Card to the database encryption system to solve the problem of key management and access control. By fully utilizing the cipher support of Java Card and its advantage of safe storage and portability, the complexity of layered key management is reduced. This paper also proposes a double-factor based indemnification, access control and data transfer scheme for the database encryption system based on Java Card. The most prominent advantage of our scheme is to enhance both efficiency and reliability of the system. Security analysis and simulation experiment is also given for supporting its validity.
A Computational Study into the Evolution of Dual-Route Dynamics for Affective Processing
Authors: Paul Den Dulk, Bram T. Heerebout, R. Hans Phaf
Venue: Journal of Cognitive Neuroscience (2003)

The evolutionary justification by LeDoux (1996) for his dual-route model of fear processing was analyzed computationally by applying genetic algorithms to artificial neural networks. The evolution was simulated of a neural network controlling an agent that gathered food in an artificial world and that was occasionally menaced by a predator. Connections could not change in the agent's "lifetime," so there was no learning in the simulations. Only if the smells of food and predator were hard to distinguish and the fitness reflected time pressures in escaping from the predator did the type of dual processing postulated by LeDoux emerge in the surviving agents. Processing in the "quick and dirty" pathway of the fear system ensured avoidance of both predators and food, but a distinction between food and predator was made only in the long pathway. Elaborate processing inhibited the avoidance reaction and reversed it into an approach reaction to food, but strengthened the avoidance reaction to predators (and more finely tuned the direction of escape). It is suggested that "computational neuroethology" (Beer, 1990) may help constrain reasoning in evolutionary psychology, particularly when applied to specific neurobiological models, and in the future may even generate new hypotheses for cognitive neuroscience.
Optimal lot-sizing with capacity constraints and auto-correlated interarrival times
Authors: S. T. Enns, Li Li
Venue: Proceedings of the 36th conference on Winter simulation (2004)

There have been recent advances in using queuing relationships to determine lot sizes that minimize mean flowtimes when multiple product types are being produced at capacity-constrained resources. However, these relationships assume lot interarrival times are independent, which is not the case in most manufacturing scenarios. This study examines the performance lot-sizing optimization relationships based on GI/G/1 relationships when lot interarrival times are auto-correlated. Simulation and response surface modeling are used to experimentally determine optimal lot sizes for a sample problem. The flowtimes for "optimal" lot sizes determined analytically are found to compare poorly with with the best flowtimes obtained experimentally. An approah is then developed that uses feedback during simulation to adjust parameters within queuing heuristics that support dynamic lot-size optimization. Performance using this approach compares well with the best performance obtained using the much more difficult experimental approach.
Robust multi-logo watermarking by RDWT and ICA
Authors: Thai Duy Hien, Zensho Nakao, Yen-Wei Chen
Venue: Signal Processing (2006)

This paper proposes a new approach to watermarking multimedia products by redundant discrete wavelet transform (RDWT) and independent component analysis (ICA). For watermark security, embedded logo watermarks are encrypted to random noise signal. To embed logo watermarks, the original image is decomposed by RDWT, and watermarks are embedded into middle frequency at LH and HL sub-bands. The perceptual model is applied with a stochastic multiresolution model for adaptive watermark embedding. This is based on computation of a noise visibility function (NVF) which has local image properties. We also propose an intelligent ICA-based detector which directly extracts watermarks in spatial domain. A novel characteristic of this detection is that it does not require the transformation process to extract the watermark. The experimental results show that logo watermarks are extracted perfectly, and also demonstrate the robustness of the method.
East-West Design & Test Workshop
Authors: Vladimir Hahanov
Venue: IEEE Design Test (2006)

This report discusses the 2006 IEEE East-West Design & Test International Workshop, held 15 to 19 September 2006 in Sochi, Russia. The goal of the workshop was to exchange experience in design and test of electronic systems among scientific experts from Eastern and Western Europe, as well as other parts of the world.
A Feature Selection Algorithm for Detecting Subtype Specific Functional Sites from Protein Sequences for Smad Receptor Binding
Authors: Elena Marchiori, Walter Pirovano, Jaap Heringa, K. Anton Feenstra
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

Multiple sequence alignments are often used to reveal functionally important residues within a protein family. In particular, they can be very useful for identification of key residues that determine functional differences between protein subclasses (subtype specific sites). This paper proposes a new algorithm for selecting subtype specific sites from a set of aligned protein sequences. The algorithm combines a feature selection technique with neighbor position information for selecting and ranking a set of putative relevant sites. The algorithm is applied to a dataset of protein sequences from the MH2 domain of the SMAD family of transcriptor factors. Validation of the results on the basis of the known interaction and function of the sites shows that the algorithm successfully identifies the known (from literature) subtype specific sites and new putative ones.
GRISINO: Combining Semantic Web Services, Intelligent Content Objects and Grid Computing
Authors: Ioan Toma, Tobias Burger, Omair Shafiq, Daniel Doegl, Wernher Behrendt, Dieter Fensel
Venue: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (2006)

Future information, knowledge and content infrastructures which provide highly automatic support in fulfilling users goals will most likely rely on some form of Grid computing. In combination with Semantic Web technologies and Semantic Web services, this infrastructure will be much enhanced to form the Semantic Grid. Last but not least, one of the major driving forces for the Web and its future derivatives is actual content which can range from multimedia data (with some meta data) to "intelligent objects" i.e. content that itself, can either exhibit behavior or at least, carry semantic information that can (and must) be interpreted by the services of the Semantic Grid. This paper proposes GRISINO, an integrated infrastructure for Semantic Web Services, Intelligent Content Objects and Grid that are brought together in the search for an emerging solution that cope with rapidly increasing demands and complexity of next generation distributed applications.
An Algorithm for Approximate Multiparametric Convex Programming
Authors: Alberto Bemporad, Carlo Filippi
Venue: Computational Optimization and Applications (2006)

For multiparametric convex nonlinear programming problems we propose a recursive algorithm for approximating, within a given suboptimality tolerance, the value function and an optimizer as functions of the parameters. The approximate solution is expressed as a piecewise affine function over a simplicial partition of a subset of the feasible parameters, and it is organized over a tree structure for efficiency of evaluation. Adaptations of the algorithm to deal with multiparametric semidefinite programming and multiparametric geometric programming are provided and exemplified. The approach is relevant for real-time implementation of several optimization-based feedback control strategies.
A context-aware mobile service discovery and selection mechanism using artificial neural networks
Authors: Eyhab Al-Masri, Qusay H. Mahmoud
Venue: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet (2006)

In this paper we present SmartCon, a context-aware system for the discovery and selection of mobile services using Artificial Neural Networks (ANNs). The solution we have developed is a mobile agent-enabled system that adaptively and iteratively learns to select the best available mobile service derived from the extraction of a series of features utilizing contextual information such as the Composite Capabilities/Preferences Profile (CC/PP), service-specific, and non-uniform user-specific features which are supplied to a backpropagation neural network. Based on the features provided, the neural network classifies the most relevant mobile service. In the present work, the system is also capable through iterative learning to generalize and gather information using cognitive feedback based on user's decisions and interactivity with a mobile device. SmartCon is evaluated using a series of preliminary empirical data and results show an 87% success rate in the discovery and selection of the best or most relevant mobile service.
Rural connected communities: a project in online collaborative journalism
Authors: Marina Umaschi Bers, Michael L. Best
Venue: Proceedings of the 1999 conference on Computer support for collaborative learning (1999)

We describe a web-based collaborative educational project in an under-served rural community in Northern Thailand. The rural community enjoyed high internet connectivity via a satellite internet link installed as part of a larger research endeavor directed by Seymour Papert. Project Lighthouse is exploring the role of computers in educational reform throughout Thailand. The goal of this particular pilot project was to help villagers of all ages and backgrounds gain technological fluency, cultivate a sense of ownership and mastery, and develop knowledge and pride about their cultural heritage and history while creating an on-line magazine for their village. The constructionist methodology guided our work and helped people to view themselves not only as consumers but also as producers of information. The project employed a software environment named Pluto, an authoring tool specially designed at the MIT Media Lab to support community-based collaborative electronic publishing.
AOCMS: An Adaptive and Scalable Monitoring System for Large-Scale Clusters
Authors: Zhenghua Xue, Xiaoshe Dong, Weiguo Wu
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

In this paper, we present the design and implementation of AOCMS, an adaptive, scalable and efficient monitoring system for a large-scale cluster. We describe an adaptive architecture of AOCMS in detail, and focus on the discussion about some techniques as to enhancing the adaptation, scalability and efficiency of AOCMS. These techniques include: a solution to monitor a heterogeneous cluster; a universal applet-servlet communicating controller responsible for communication between the clients and the web server; adaptive pools providing threads or connections to the database for the monitoring tasks on demand; and an AOP-based alarm decoupling the alarming logic from the monitoring logic. Moreover, we measured the performance of AOCMS. The results show that AOCMS runs with low overheads and responds to clients quickly.
Extracting Actionable Knowledge from Decision Trees
Authors: Qiang Yang
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data.
Towards Performance-Oriented Pattern-Based Refinement of Synchronous Models onto NoC Communication
Authors: Zhonghai Lu, Ingo Sander, Axel Jantsch
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

We present a performance-oriented refinement approach that refines a perfectly synchronous communicationmodel onto Network-on-Chip (NoC) communication. We first identify four basic forms of NoC process interaction patterns at the process level, namely, producer-consumer, peers, client-server, and multicast. We propose a threestep top-down refinement method: channel refinement, protocol refinement and channel mapping. For the producer-consumer pattern, we describe it in detail. In channel refinement, we deal with interfacing multiple clock domains and use a stochastic process to model channel delay and jitter. In protocol refinement, we show how to refine communication towards application requirements such as reliability and throughput. In channel mapping, we discuss channel convergence and channel merge arising from channel overlapping. All the refinements have been conducted and validated as an integral design phase towards implementation in ForSyDe, a formal system-level design methodology based on a synchronous model of computation.
Web Document Clustering with Multi-view Information Bottleneck
Authors: Yan Gao, Shiwen Gu, Liming Xia, Yaoping Fei
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

Clustering is an important way to organize the large amount of information on the Web. In this paper, we study how to incorporate many information of web document, such as content, anchor, url etc, to improve the performance of clustering. We propose a novel algorithm: multi-view information bottleneck (MVIB), to cluster web documents with multi-type features. In this algorithm, the compatible constraint maximizing the agreement between clustering hypotheses on different views is imposed on the individual views to cluster instances. Based on the compatible constraints, the set of clustering hypotheses revealing lots of information about correct one is obtained. The final hypothesis can be deduced from these hypotheses. We study the performance of MVIB in different views setting. Experiments on two real datasets indicate that MVIB with 3-view setting based on content, anchor text and url can improve the quality of clusters more effectively.
Web Systems: Semantic Management of Distributed Web Applications
Authors: Daniel Oberle, Steffen Staab, Andreas Eberhart
Venue: IEEE Distributed Systems Online (2006)

The wealth of functionality of middleware solutions such as application servers and Web services increasingly overburdens developers and administrators. These functionalities have been developed and managed mostly with the help of administration tools and corresponding configuration files. Although this approach is flexible, it's problematic because the conceptual model underlying the configurations isn't explicit. An ontology-based approach facilitates the management and administration of application server- and Web services-based applications. The proposed design integrates the ontology infrastructure in existing application servers and has been prototypically implemented in KAON SERVER, an amalgamation of JBoss and the KAON ontology tool suite.