ppxscal/embedding_network huggingface dataset:
The impact of estimation error on the dynamic order admission policy in B2B MTO environments
Authors: Andy Wei-Di Wu, David Ming-Huang Chiang
Venue: Expert Systems with Applications: An International Journal (2009)

When swarming demands cause stringent capacity situations, order promising becomes a challenging job. However, a dynamic order admission policy by utilizing the concept of revenue management may find a good way to solve the problem. Unfortunately, the expected profit under different dynamic order admission policies is sensitive to the estimation error of order forecasts. In this paper, the impact of estimation error is investigated under various order structures. The post analysis is performed and shows significant statistical difference among the optimal unbiased DSKP policy, biased DSKP policy, and FCFS policy. The results reveal the robustness and superiority of DSKP policy in most scenarios.
Learning instance specific distances using metric propagation
Authors: De-Chuan Zhan, Ming Li, Yu-Feng Li, Zhi-Hua Zhou
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

In many real-world applications, such as image retrieval, it would be natural to measure the distances from one instance to others using instance specific distance which captures the distinctions from the perspective of the concerned instance. However, there is no complete framework for learning instance specific distances since existing methods are incapable of learning such distances for test instance and unlabeled data. In this paper, we propose the Isd method to address this issue. The key of Isd is metric propagation, that is, propagating and adapting metrics of individual labeled examples to individual unlabeled instances. We formulate the problem into a convex optimization framework and derive efficient solutions. Experiments show that Isd can effectively learn instance specific distances for labeled as well as unlabeled instances. The metric propagation scheme can also be used in other scenarios.
Designing hypertext tools to facilitate authoring multiple points-of-view stories
Authors: Alex Mitchell, Kevin McGee
Venue: Proceedings of the 20th ACM conference on Hypertext and hypermedia (2009)

How can authoring tools help authors create complex, innovative hypertext narrative structures? Tools for creating hypertext fiction typically represent such narratives in the form of nodes and links. However, existing tools are not particularly helpful when an author wants to create a story with a more complex structure, such as a story told from multiple points of view. In this paper, we describe our work to develop HypeDyn, a new hypertext authoring tool that provides alternative representations designed to make it easier to create complex hypertext story structures. As an initial exploration, the tool has been designed to support authoring of interactive, multiple-points-of-view stories. In order to describe the tool, we describe a simplified transformation of Rashomon into a progressively more interactive narrative. Along the way, we identify useful new representations, mechanisms, and visualizations for helping the author. We conclude with some thoughts about the design of interactive storytelling authoring tools in general.
Purely optical tomography: atlas-based reconstruction of brain activation
Authors: W. Eric Grimson, William M. Wells,  III
Venue:  (2008)

Diffuse Optical Tomography (DOT) is a relatively new method used to image blood volume and oxygen saturation in vivo. Because of its relatively poor spatial resolution (typically no better than 1-2 cm), DOT is increasingly combined with other imaging techniques, such as MRI, fMRI and CT, which provide high-resolution structural information to guide the characterization of the unique physiological information offered by DOT. This work aims at improving DOT by offering new strategies for a more accurate, efficient, and faster image processor. Specifically, after investigating the influence of Cerebral Spinal Fluid (CSF) properties on the optical measurements, we propose using a realistic segmented head model that includes a novel CSF segmentation approach for a more accurate solution of the DOT forward problem. Moreover, we outline the benefits and applicability of a Diffusion Approximation-based faster forward model solver. We also describe a new registration algorithm based on superficial landmarks which is an essential tool for the purely optical tomographic image process proposed here. A purely optical tomography of the brain during neural activity will greatly enhance DOT applicability and provide many advantages, in the sense that DOT low cost, portability and non-invasiveness would be fully exploited without the compromises due to the MRI role in the DOT forward image process. We achieve a purely optical tomography by using a generalized head model (or atlas) in place of the subject specific anatomical MRI. We validate the proposed imaging protocol by comparing measurements derived from the DOT forward problem solution obtained using the subject specific anatomical model versus these acquired using the atlas registered to the subject, using a database of 31 healthy human subjects, and focusing on a set of 12 functional regions of interest. We conclude our study presenting data obtained from 3 experimental subjects having undergone median nerve stimuli. We apply our purely optical tomography protocol to the 3 subjects and analyze the observations derived from both the DOT forward and inverse solutions. The experimental results demonstrate that it is possible to guide the DOT forward problem with a general anatomical model in place of the subject's specific head geometry to localize the macroanatomical structures of neural activity. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Promoting Metacognitive Strategy Development through Online Question-Generation Instructional Approach
Authors: Fu-Yun Yu
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

The study investigated the immediate and delayed effects of online multiple-choice question-generation instructional approach on students' metacognitive strategies development within a web-based learning environment. A 2 (question-generation versus non question-generation) x 3 (measuring times) mixed-design experimental research method was used. 132 sixth-graders participated in the study for 4 consecutive weeks. A Web-based learning system that enabled students to construct, assess, view, and practice answering multiple-choice questions was used. Results indicated that students participated in the question-generation condition reported more frequent use of metacognitive strategies while learning science during the course of the study than their counterparts in the comparison group. Weeks after concluding the study, students in the question-generation group remained to activate metacognitive strategies more frequently than the comparison group without being prompted to do so. Suggestions for classroom instructors and future research topics are proposed.
The role of AI planning as a decision support tool in power substation management
Authors: Keith Bell, Amanda Coles, Andrew Coles, Maria Fox, Derek Long
Venue: AI Communications (2009)

The management of power substations is a challenging task, with two opposing criteria: to reduce wear-and-tear on equipment and to ensure the voltage remains within a specified range. At present, a two-stage process is used. Voltage targets are determined for each time of day by electrical engineers using a time-consuming and costly manual process. Then, a reactive control system at the substation is used to satisfy these targets. In this article, we present a novel application of AI planning as part of an intelligent automated system for devising voltage targets. Within the system, both the cost and fault-tolerance implications of voltage target decisions are considered, and hence an efficient and effective set of voltage targets is produced. Using AI planning affords a great deal of flexibility and we show how the system can handle known exogenous events for a given day to reduce forecasted operational costs.
A Methodology for Action-Selection using Value-Based Argumentation
Authors: Fahd Saud Nawwab, Trevor Bench-Capon, Paul E. Dunne
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

This paper describes a method for decision making using argumentation. The method is intended to produce the decision considered most likely to promote the agent's ams ans aspirations. First, the problem situation is formulated in terms of an Action-Based Alternating Transition System, representing the actions available to the agents relevant to the situation, and their consequences, taking into account the possible effects of the choices of the other relevant agents. Next, arguments are constructed by instantiating an argumentation scheme designed to justify actions in terms of the values they promote and subjecting these instantiations to a series of critical questions to identify possible counter arguments. The resulting arguments are then organized into a Value-Based Argumentation Framework (VAF), so that a set of arguments acceptable to the agent can be identified. Finally the agent must select one of the acceptable actions to execute. The methodology is illustrated through the use of a detailed case study.
Expert system to evaluate English medium instruction in Korean Universities
Authors: Sang Ki Kim, So Young Sohn
Venue: Expert Systems with Applications: An International Journal (2009)

Recently, many Universities in Korea have been encouraging English Medium Instruction (EMI) to cope up with globalization, but many problems have emerged. We use the Structural Equation Model (SME) to understand the causal relationship among potential factors on engineering student's satisfaction on the EMI. In addition, we develop Customer Satisfaction Index (CSI) to estimate the student's satisfaction about EMI, based on the proposed SEM. This satisfaction index is used to improve the currently employed EMI. In addition, we suggest some alternatives using the cluster analysis.
Allocation Method of Total Permitted Pollution Discharge Capacity Based on Uniform Price Auction
Authors: Congjun Rao, Zhongcheng Zhang, June Liu
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

To study the allocating problem of total permitted pollution discharge capacity(TPPDC), an allocation method with variable supply based on the uniform price auction of divisible goods is proposed in this paper firstly. Then, a linear equilibrium bidding strategy of this new method is given. Lastly, the incentive compatibility and validity of this method are proved. Therefore, this method will provide valuable theoretical basis and guidance for building the pollution emission permits trade system.
Resonance and friction compensations in a micro hard drive
Authors: Wilaiporn Ngernbaht, Kongpol Areerak, Sarawut Sujitjorn
Venue: WSEAS TRANSACTIONS on SYSTEMS (2009)

This paper presents dynamic compensations in a micro hard drive. It reviews dynamic models of the drive in low-and high-frequency regions. The nonlinear friction compensation for low-frequency dynamic is achieved via a fuzzy logic controller. Forward and backward micro-step motion of the read/write head can be nicely performed. Resonance compensation for high-frequency dynamic is achieved via linear compensation. The paper presents some comparison studies of using a cascaded lead compensator, a complex lead-lag compensator, and a searched polynomial compensator. The compensated system's performance is enhanced further by stable nonlinear control. Detailed descriptions of modelling, design, simulation results, and analysis can be found in the paper.
The Computational Complexity of Ideal Semantics I: Abstract Argumentation Frameworks
Authors: Paul E. Dunne
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

We analyse the computational complexity of the recently proposed ideal semantics within abstract argumentation frameworks. It is shown that while typically less tractable than credulous admissibility semantics, the natural decision problems arising with this extension-based model can, perhaps surprisingly, be decided more efficiently than sceptical admissibility semantics. In particular the task of finding the unique maximal ideal extension is easier than that of deciding if a given argument is accepted under the sceptical semantics. We provide efficient algorithmic approaches for the class of bipartite argumentation frameworks. Finally we present a number of technical results which offer strong indications that typical problems in ideal argumentation are complete for the class PNP|| : languages decidable by polynomial time algorithms allowed to make non-adaptive queries to an np oracle.
Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation
Authors: Liang Sun, Rinkal Patel, Jun Liu, Kewei Chen, Teresa Wu, Jing Li, Eric Reiman, Jieping Ye
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network, i.e., the connectivity among different brain regions. The connectivity patterns will provide useful imaging-based biomarkers to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. In this paper, we investigate the sparse inverse covariance estimation technique for identifying the connectivity among different brain regions. In particular, a novel algorithm based on the block coordinate descent approach is proposed for the direct estimation of the inverse covariance matrix. One appealing feature of the proposed algorithm is that it allows the user feedback (e.g., prior domain knowledge) to be incorporated into the estimation process, while the connectivity patterns can be discovered automatically. We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects. Our experimental results demonstrate that the proposed algorithm is promising in revealing the brain region connectivity differences among these groups.
Probability model for data redundancy detection in sensor networks
Authors: Suman Kumar, Seung-Jong Park
Venue: Mobile Information Systems (2009)

Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.
Fast and dynamic OLAP exploration using UDFs
Authors: Zhibo Chen, Carlos Ordonez, Carlos Garcia-Alvarado
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

OLAP is a set of database exploratory techniques to efficiently retrieve multiple sets of aggregations from a large dataset. Generally, these techniques have either involved the use of an external OLAP server or required the dataset to be exported to a specialized OLAP tool for more efficient processing. In this work, we show that OLAP techniques can be performed within a modern DBMS without external servers or the exporting of datasets, using standard SQL queries and UDFs. The main challenge of such approach is that SQL and UDFs are not as flexible as the C language to explore the OLAP lattice and therefore it is more difficult to develop optimizations. We compare three different ways of performing OLAP exploration: plain SQL queries, a UDF implementing a lattice structure, and a UDF programming the star cube structure. We demonstrate how such methods can be used to efficiently explore typical OLAP datasets.
Linux LPIC 1 and LPI Certification: The Ultimate LPIC 1 Linux LPI Certification Review
Authors: Terry Sanchez-Clark
Venue:  (2007)

The Ultimate Reference & Learning Guide for the Linux Professional! LPI / LPIC 1 Certification Question Review Guide Fluency with Linux is essential for the IT professional, however, a user-friendly and thorough certification resource can be difficult to locate. Suitable for all levels of LPI study, this guide can help you to assess your current skill level, prepare for certification, and help you understand what you need to know for the certification exam, and what you can safely ignore. Set yourself apart from other candidates and show that you have what it takes to land the job. More than just a rehash of the basics, more than just documentation and sales presentations, each section is based on project knowledge and experience gained on high profile Linux installations. Key topics include: * Covers Exams 101 and 102, With real-world examples * Command line, Regular Expressions, File systems, Compilation * Configuring Medium to Large Corporate Server Hardware * Security, Permissions, Networking, Hard Drive Schemas
A framework for managing the life-cycle of event-driven, embedded applications
Authors: Johnathan M. Reason, Han Chen, Changwoo Jung, Sooyeon Kim, Paul B. Chou, Kangyoon Lee
Venue: Journal of Embedded Computing (2009)

Event-driven, embedded applications that embody the composition of many disparate components are emerging as an important class of pervasive applications. Realizing such applications often requires a breadth of expertise across many disciplines; consequently, managing the life cycle for this class of applications can be a very complex, time-intensive process. In this paper, we present a framework that eases the complexity of managing the life cycle of event-driven, embedded applications. We call this framework Rapid Integrated Solution Enablement or RISE. For solution creation, component composition and software reuse are two central concepts of RISE. Solutions in RISE are graphically composed from reusable components using a visual editor. For deployment and management of solutions, we exploit the concept of dynamic and remote deployment of components from the Open Service Gateway Initiative (OSGi). We describe the RISE architecture and discuss our prototype implementation, which follows the model-driven methodology and leverages open source technologies, such as Eclipse. Additionally, we illustrate the efficacy of RISE with an example solution for RFID supply chain logistics.
General Concept Inclusions in Fuzzy Description Logics
Authors: Giorgos Stoilos, Umberto Straccia, Giorgos Stamou, Jeff Z. Pan
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Fuzzy Description Logics (fuzzy DLs) have been proposed as a language to describe structured knowledge with vague concepts. A major theoretical and computational limitation so far is the inability to deal with General Concept Inclusions (GCIs), which is an important feature of classical DLs. In this paper, we address this issue and develop a calculus for fuzzy DLs with GCIs.
The Implementation of an Information Literacy Framework: Key Issues in Professional Development
Authors: Siu Cheung Kong, Fong Lok Lee, Shuk Fan Ip
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

Information literacy should be framed in such a way that students are empowered with the capacity for lifelong learning and assume greater autonomy over --and gain social responsibilities from --their learning. We conducted focus group discussions and in-depth interviews to collect ideas from practitioners on professional development in information literacy education. The research findings show that practitioners are concerned about the recognition of teachers and the training methods that will be used in the implementation of information literacy in schools. Professional development should focus on the content and the pedagogy of capacity building in school education. Three key issues in professional development are highlighted: realising capacity building, obtaining the consensus and support of school principals and teachers, and allowing practitioners the flexibility to organise their own development. Nine modules for the professional development of in-service and pre-service teachers are also proposed.
Physical topologies in computer networks
Authors: Omer Uysal, Zeynel Abidin Misirli
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

The research is aimed to explain physical topology that means how to connect computers in the network physically. Due to technological improvement different alternatives were invented for physical topology with the help of network hardwares such as switch, cables and connectors. In the paper, bus, star, ring, tree, mesh and cellular topology which are models of physical topology will be emphasized.
Time Series Prediction Based on Generalization Bounds for Support Vector Machine
Authors: Liming Yang, Laisheng Wang, Yitian Xu, Qun Sun
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive model (AR) concerns many important fields. The Vapnik-Chervonenkis (VC) generalization bound provides a mathematical framework for the practical models selection from finite and noisy data sets of time series dataset. In this paper, based on the VC generalization bound for Support Vector Machine (SVM), we introduce a new method of identifying the time varying parameters of an AR model, then and two SVM-based time series prediction models are formulated. Both numerical experiments and theoretical analysis show that the proposed models are feasible and effective.
Towards Learning Knowledge Objects
Authors: Amal Zouaq, Roger Nkambou, Claude Frasson
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

In this paper, we present an ontology-based approach, The Knowledge Puzzle approach, that aims to exploit principles from the AIED and Intelligent Tutoring Systems fields to produce e-Learning resources more tailored to learner's needs. We present a semi-automatic process to annotate learning material from different points of view: domain, structural and pedagogical. We then use this knowledge to generate dynamically learning knowledge objects based on instructional theories.