ppxscal/embedding_network huggingface dataset:
Remote monitoring of hypersaline environments in San Francisco Bay, CA, USA
Authors: J. B. Dalton, L. J. Palmer-Moloney, D. Rogoff, C. Hlavka, C. Duncan
Venue: International Journal of Remote Sensing (2009)

As part of a historic remediation project, approximately 61 km2 of salt evaporation ponds in the southern portion of San Francisco Bay, CA (USA) are scheduled for restoration to natural tidal marsh habitat over the next several decades. We have investigated the correlation of remotely sensed infrared spectral information with in situ field measurements and sampling, and evaluated the usefulness of a remote sensing approach to monitor salinity and population distributions of microbial communities in the hypersaline ponds. The Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument operated by the Jet Propulsion Laboratory has created a ten-year archive of spectral information concerning these ponds. We utilized spectral signatures of microbial populations that are sensitive to salinity, and trained a supervised classification algorithm to identify physical parameters from an AVIRIS scene based upon microbe spectra gathered in the field using a portable visible to near-infrared (VNIR) spectrometer. Our results indicate that automated analyses of hyperspectral observations are capable of detecting variations in microbial populations and discriminating corresponding salinity levels.
Sequential element design with built-in soft error resilience
Authors: Ming Zhang, Subhasish Mitra, T. M. Mak, Norbert Seifert, Nicholas J. Wang, Quan Shi, Kee Sup Kim, Naresh R. Shanbhag, Sanjay J. Patel
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2006)

This paper presents a built-in soft error resilience (BISER) technique for correcting radiation-induced soft errors in latches and flip-flops. The presented error-correcting latch and flip-flop designs are power efficient, introduce minimal speed penalty, and employ reuse of on-chip scan design-for-testability and design-for-debug resources to minimize area overheads. Circuit simulations using a sub-90-nm technology show that the presented designs achieve more than a 20-fold reduction in cell-level soft error rate (SER). Fault injection experiments conducted on a microprocessor model further demonstrate that chip-level SER improvement is tunable by selective placement of the presented error-correcting designs. When coupled with error correction code to protect in-pipeline memories, the BISER flip-flop design improves chip-level SER by 10 times over an unprotected pipeline with the flip-flops contributing an extra 7-10.5% in power. When only soft errors in flips-flops are considered, the BISER technique improves chip-level SER by 10 times with an increased power of 10.3%. The error correction mechanism is configurable (i.e., can be turned on or off) which enables the use of the presented techniques for designs that can target multiple applications with a wide range of reliability requirements.
Automated debugging of recommender user interface descriptions
Authors: Alexander Felfernig, Gerhard Friedrich, Klaus Isak, Kostyantyn Shchekotykhin, Erich Teppan, Dietmar Jannach
Venue: Applied Intelligence (2009)

Customers interacting with online selling platforms require the assistance of sales support systems in the product and service selection process. Knowledge-based recommenders are specific sales support systems which involve online customers in dialogs with the goal to support preference forming processes. These systems have been successfully deployed in commercial environments supporting the recommendation of, e.g., financial services, e-tourism services, or consumer goods. However, the development of user interface descriptions and knowledge bases underlying knowledge-based recommenders is often an error-prone and frustrating business. In this paper we focus on the first aspect and present an approach which supports knowledge engineers in the identification of faults in user interface descriptions. These descriptions are the input for a model-based diagnosis algorithm which automatically identifies faulty elements and indicates those elements to the knowledge engineer. In addition, we present results of an empirical study which demonstrates the applicability of our approach.
A Physics Instructional Design Platform Based on Information Technology
Authors: Yongjiang Zhong, Shaochun Zhong, Ju Liu, Xiaolong Liu
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

An informatized instructional design platform for physics is programmed in this paper which has embedded subject characters and instructional strategies, in order to help to solve an important problem, that is former platforms lack the combination with a specific subject.
A regression framework for learning to rank in web information retrieval
Authors: Rohini K. Srihari
Venue:  (2008)

Machine learning approaches for learning ranking functions have been generating much interest from both the web information retrieval community and the machine learning community recently. It has the promise of improved relevancy of search engines and reduced demand for manual parameter tuning. We focus on developing a regression framework for learning to rank with complex loss functions. More specifically, this framework first applies functional iterative or boosting algorithm to compute updates for a given loss function and then fit the updates with a standard regression base learner. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: (1) absolute relevance judgments arising from explicit labeling of query-document pairs; and (2) relative relevance judgments extracted from user click-throughs of search results or converted from the absolute relevance judgments. Within the framework, we propose three novel ranking algorithms and illustrate their application to web search ranking. The first one is to calibrate the existing point-wise(univariant) regression loss to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we present an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. It is an improvement over the existing approach within the category of learning to rank using point-wise regression loss. The second is an extension of gradient boosting methods for point-wise regression loss to complex(multi-variant) loss functions. It is based on optimization of quadratic upper bounds of the loss functions which allows us to present a rigorous convergence analysis of the algorithm. We illustrate an application of this approach in pair-wise preference learning to rank for Web search by combining both preference data and labeled data. The third one is a list-wise approach based on minimum effort optimization that takes into account the entire training data within a query at each iteration. We tackle this optimization problem using functional iterative methods where the update at each iteration is computed by solving an isotonic regression problem. This more global approach results in faster convergency and signficantly improved performance of the learned ranking functions over existing state-of-the-art methods.Experimental results are carried out using both data sets obtained from a commercial search engine and widely used IR benchmarking data, namely OHSUMED and TREC. Our results show significant improvements of our proposed methods over existing state-of-the-art methods.
On-line Reputation Systems: The Effects of Feedback Comments and Reactions on Building and Rebuilding Trust in On-line Auctions
Authors: Sonja Utz, Uwe Matzat, Chris Snijders
Venue: International Journal of Electronic Commerce (2009)

Previous research on reputation systems primarily focused on their trust-building function. The present research addresses their trust-rebuilding function-specifically, the role of the short text comments given in reaction to negative feedback. Rebuilding trust is often necessary because on-line markets are noisy environments. The results of two experimental studies of eBay users show that trustworthiness judgments are influenced by the text comments accompanying negative feedback and also by whether a trust violation is perceived as competence-based or morality-based. Plain apologies were more successful than denials in repairing trust. These effects were mediated by the perceived believability of the comments. Thus, to avoid the detrimental effect of noise on trust, operators of on-line marketplaces should encourage text feedback comments and reactions.
Adaptive finite element simulation of ventricular fibrillation dynamics
Authors: Peter Deuflhard, Bodo Erdmann, Rainer Roitzsch, Glenn Terje Lines
Venue: Computing and Visualization in Science (2009)

The paper communicates simulation results (computations and visualizations) for the dynamics of ventricular fibrillation caused by irregular excitation in the frame of the monodomain model with an action potential model due to Aliev&#x2013;Panfilov for a human 3D geometry. The numerical solution of this challenging multiscale reaction&#x2013;diffusion problem is attacked by algorithms which are fully adaptive in both space and time (code library KARDOS). The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation and the plateau phases (in the regular cycle) as well as after a reentrant excitation (in the irregular cycle).
Case Study of a 65-nm SoC Design
Authors: Andrew Chang
Venue: IEEE Design Test (2009)

Editor's note:IC design flow is increasing in complexity. Adopting new technologies and advanced IP blocks from multiple sources is not sufficient to solve the problem. Special management and system integration plans are necessary. This article describes a set of management and integration initiatives adopted by MediaTek to address this problem.&#x2014;Yervant Zorian, Virage Logic
Evaluating word prediction: framing keystroke savings
Authors: Keith Trnka, Kathleen F. McCoy
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Researchers typically evaluate word prediction using keystroke savings, however, this measure is not straightforward. We present several complications in computing keystroke savings which may affect interpretation and comparison of results. We address this problem by developing two gold standards as a frame for interpretation. These gold standards measure the maximum keystroke savings under two different approximations of an ideal language model. The gold standards additionally narrow the scope of deficiencies in a word prediction system.
Scaling agile software development through lean governance
Authors: Scott W. Ambler
Venue: Proceedings of the 2009 ICSE Workshop on Software Development Governance (2009)

Agile project teams are potentially easy to govern than traditional project teams due to the greater visibility and opportunities to steer provided to stakeholders. Unfortunately, traditional IT governance strategies prove to be at odds to agile's collaborative, value-focused strategies. As a result a lean approach based on enablement, collaboration, and motivation is required to effectively govern agile teams.
A mixed spectrum management framework for the future wireless service based on techno-economic analysis: The Korean spectrum policy study
Authors: Junseok Hwang, Hyenyoung Yoon
Venue: Telecommunications Policy (2009)

The evolution of radio technology and various services has increased the world's dependence on wireless communications. The demand for and value of spectrum resources therefore are also increasing. Spectrum efficiency is the most important factor in managing spectrum scarcity. However, under the current spectrum management approach, it is difficult to adopt innovative technologies that improve spectrum efficiency and flexible usage in the current dynamic wireless market. Recently, there have been several approaches to improve efficient use of spectrum resources, and each approach has its own advantages and disadvantages. Therefore, this research first discusses current issues and analyzes relative social welfare based on the different characteristics of technology and market conditions to compare various attributes of each approach. Based on the techno-economic simulation results, this paper introduces a mixed spectrum management framework for the future wireless service and support policy makers' decision making. Furthermore, the mixed spectrum policy to spectrum management in Korea is proposed to find a more realistic and efficient spectrum management policy.
Advanced AI techniques for web mining
Authors: Ioan Dzitac, Ioana Moisil
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

The World Wide Web has evolved in less than two decades as the major source of data and information for all domains. Web has become today not only an accessible and searchable information source but also one of the most important communication channels, almost a virtual society. Web mining is a challenging activity that aims to discover new, relevant and reliable information and knowledge by investigating the web structure, its content and its usage. Though the web mining process is similar to data mining, the techniques, algorithms and methodologies used to mine the web encompass those specific to data mining, mainly because the web has a great amount of unstructured data and the changes are frequent and rapid. This paper is structured into two sections. The first one briefly discusses the different web mining tasks and the second one is focusing on advanced artificial intelligence (AI) methods for information retrieval and web search, link analysis, opinion mining and web usage mining.
Audio-Visual Identity Verification and Robustness to Imposture
Authors: Walid Karam, Chafic Mokbel, Hanna Greige, Gérard Chollet
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

The robustness of talking-face identity verification (IV) systems is best evaluated by monitoring their behavior under impostor attacks. We propose a scenario where the impostor uses a still face picture and a sample of speech of the genuine client to transform his/her speech and visual appearance into that of the target client. We propose MixTrans, an original text-independent technique for voice transformation in the cepstral domain, which allows a transformed audio signal to be estimated and reconstructed in the temporal domain. We also propose a face transformation technique that allows a frontal face image of a client to be animated, using principal warps to deform defined MPEG-4 facial feature points based on determined facial animation parameters. The robustness of the talking-face IV system is evaluated under these attacks. Results on the BANCA talking-face database clearly show that such attacks represent a serious challenge and a security threat to IV systems.
Context-aware adaptive data stream mining
Authors: Pari Delir Haghighi, Arkady Zaslavsky, Shonali Krishnaswamy, Mohamed Medhat Gaber, Seng Loke
Venue: Intelligent Data Analysis (2009)

In resource-constrained devices, adaptation of data stream processing to variations of data rates and availability of resources is crucial for consistency and continuity of running applications. However, to enhance and maximize the benefits of adaptation, there is a need to go beyond mere computational and device capabilities to encompass the full spectrum of context-awareness. This paper presents a general approach for context-aware adaptive mining of data streams that aims to dynamically and autonomously adjust data stream mining parameters according to changes in context and situations. We perform intelligent and real-time analysis of data streams generated from sensors that is under-pinned using context-aware adaptation. A prototype of the proposed architecture is implemented and evaluated in the paper through a real-world scenario in the area of healthcare monitoring.
Card Sorts to Acquire Requirements
Authors: Neil Maiden
Venue: IEEE Software (2009)

Requirements authors and trainers recommend numerous acquisition techniques, yet most requirements projects use just a few. This column presents a different technique, card sorts. More and more projects need to acquire requirements to select between available solutions. This is where card sorts come in.
Current issues and challenges of supply chain management
Authors: Ales Groznik, Peter Trkman
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

Efficient supply chain management is crucial for survival and success in a turbulent world. Current economic crisis increases its importance even further. The paper reviews latest findings in the most relevant areas, namely the importance of a proper supply chain strategy that is a pre-condition for business process renovation and mitigation of supply chain connected risks. Performance measurement is also a pre-condition for proper management. Supply chain frameworks and standards that provide guidelines in facing those challenges are reviewed. Finally, the benefits of supply chain informatization are shown.
Cognitive Constructor: An Intelligent Tutoring System Based on a Biologically Inspired Cognitive Architecture (BICA)
Authors: Alexei V. Samsonovich, Kenneth A. de Jong, Anastasia Kitsantas, Erin E. Peters, Nada Dabbagh, M. Layne Kalbfleisch
Venue: Proceeding of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference (2008)

Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process of learning. Recent studies in cognitive architectures funded by the DARPA IPTO BICA Program have identified the key potential of feasible today artificial intelligence as bootstrapped cognitive growth (i.e., gradual acquisition of knowledge and skills using previously acquired knowledge and skills), up to a human level of intelligence in a selected domain. This approach is not limited to laboratory settings and short-term paradigms, it is intended for a long-term, open-ended learning scenario in real-world settings. Several cognitive architectures were designed for this purpose, among which is GMU BICA, a self-aware biologically inspired cognitive architecture. Here we describe a computational model of student learning based on GMU BICA and its use as an ITS called Cognitive Constructor, which has two components called a Science Microworld and a Pedagogical Agent (GMU BICA agent). Results of our analysis show that the system will be useful in elementary school education.
Textual substitution methods for image compression
Authors: Bruno Carpentieri
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

Textual substitution methods are one-dimensional compression methods that maintain a constantly changing dictionary of strings to adaptively compress a stream of characters by replacing common substrings with indices (pointers) into a dictionary. Lempel and Ziv proved that the proposed schemes were practical as well as asymptotically optimal for a general source model. Two-dimensional (i.e. images) applications of textual substitution methods have been widely studied in the past. Substantially those applications involve first the application of a linearization strategy to the input data, and then the encoding of the resulting monodimensional vector using LZ type one dimensional methods. In this paper we discuss the textual substitution methods for image compression, with particular attention to the AVQ class of algorithms, and review recent advances in the field.
Application of Swarm Intelligence in E-Learning Systems
Authors: Elżbieta Kukla
Venue: Proceeding of the 2008 conference on New Trends in Multimedia and Network Information Systems (2008)

E-learning systems are intended to offer their users didactic material tailored to their actual needs, preferences and abilities. This idea is consistent with the general principles of constructivism. Additionally it offers a student great flexibility in organizing his own curriculum and his learning scenario in the scope of particular course. However, it has also some side effects causing that students have many problems with making curriculum adapted to their needs or choosing subsequent course elements while complying the course. Many of e-learning courses designers, teachers and students emphasize the necessity of guidance in effective traversing a “knowledge network”. The chapter presented below explores some of the recent solutions in this subject. They were carefully selected to represent the widest possible range of research concerning the application of swarm intelligence to recommendation in e-learning systems.
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Authors: Enrique Amigó, Julio Gonzalo, Javier Artiles, Felisa Verdejo
Venue: Information Retrieval (2009)

There is a wide set of evaluation metrics available to compare the quality of text clustering algorithms. In this article, we define a few intuitive formal constraints on such metrics which shed light on which aspects of the quality of a clustering are captured by different metric families. These formal constraints are validated in an experiment involving human assessments, and compared with other constraints proposed in the literature. Our analysis of a wide range of metrics shows that only BCubed satisfies all formal constraints. We also extend the analysis to the problem of overlapping clustering, where items can simultaneously belong to more than one cluster. As Bcubed cannot be directly applied to this task, we propose a modified version of Bcubed that avoids the problems found with other metrics.
Adaptive Reward Mechanism for Sustainable Online Learning Community
Authors: Ran Cheng, Julita Vassileva
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Abundance of user contributions does not necessarily indicate sustainability of an online community. On the contrary, excessive contributions in the systems may result in information overload and user withdrawal. We propose a user-and community-adaptive reward mechanism aiming to regulate the quantity of the contributions and encourage users to moderate the quality of contributions themselves. The mechanism has been applied and evaluated in an online community supporting undergraduate students to share course-related web-resources.
A Unified Hierarchy for Functional Dependencies, Conditional Functional Dependencies and Association Rules
Authors: Raoul Medina, Lhouari Nourine
Venue: Proceedings of the 7th International Conference on Formal Concept Analysis (2009)

Conditional Functional Dependencies (CFDs) are Functional Dependencies (FDs) that hold on a fragment relation of the original relation. In this paper, we show the hierarchy between FDs, CFDs and Association Rules (ARs): FDs are the union of CFDs while CFDs are the union of ARs. We also show the link between Approximate Functional Dependencies (AFDs) and approximate ARs. In this paper, we show that all those dependencies are indeed structurally the same and can be unified into a single hierarchy of dependencies. A benefit of this hierarchy is that existing algorithms which discover ARs could be adapted to discover any kind of dependencies and, moreover, generate a reduced set of dependencies. We also establish the link between the problem of finding equivalent pattern tableaux of a CFD and the problem of finding keys of a relation.