Miracle-dz/UniFAR-FTUs huggingface dataset:
Ceramic-based microelectrode neuronal recordings in the rat and monkey

The study of population dynamics and information coding across ensembles of neurons requires the ability to record from multiple cells in the brain. This requirement has led to the development of microelectrode arrays which permit recording from locally restricted areas of the brain with a high degree of positional accuracy. Prototype DEEP (Deep Brain Electrochemical/Electrophysiological Probes) ceramic substrate-based microelectrode arrays developed at the University of Kentucky were used for electrophysiological recordings from deep brain regions in nonhuman primate with remarkable accuracy and fidelity. The design parameters and optimization of these microelectrode arrays along with data obtained from their use in rats and monkeys are discussed.
A Survey on Various Methods Used for Detecting Duplicates in XML Data

Duplicate detection is the process of identifying multiple representations of same real world entity. eXtensible Mark-up Language is widely used in almost all applications especially data in web. Due to the wide usage of eXtensible Mark-up Language it is essential to identify duplicates in XML. Eliminating duplicates correctly has become one of the challenging issues in the areas of Customer Relationship Management (CRM) and other place where data integration is performed. Many techniques have been developed for detecting duplicates in both relational and XML data’s. Different methods are used for detecting duplicates in XML data which is represented in different formats. The efficiency will vary depending upon different methodology. This paper made a survey for detecting duplicates. The purpose of this paper is to provide a survey on different methods used for detecting duplicates in XML data. Keywords— Bayesian Network, DELPHI, dogmatiX, duplicate detection, network pruning, NM similarity, XdoI, XML, XMLDup.
An RTOS allowing the gradual migration

A description is given of a real-time operating system (RTOS) which can allow users to achieve the gradual migration from the ITRON kernel to the mu ITRON kernel. It has some risks for the user to migrate between the two ITRONs because each kernel has a different system call specification. The mu ITRON promises speed. The ITRON, on the other hand, provides functionality to make robust programs managing complex application areas. The authors are successfully developing a novel RTOS which has two system call interfaces for the user migration between the two ITRONs with no risk. This RTOS allows users to achieve functional migration from the ITRON to the mu ITRON while using the ITRON interface and almost achieving the mu ITRON speed. <<ETX>>
Beyond policy: Strategic actions to support ICT integration in Japanese schools.

Since the late 1990s technology has become a central component of national education policies in many Asian countries. In Singapore schools, for instance, technology has become central in teaching, learning and administration. On the other hand, Japanese schooling has been largely impervious to advances in educational technology. This paper aims to stimulate discourse between policy makers, teachers, researchers and the community in Japan and internationally to consider informed, meaningful strategies required for developing suitably skilled pupils for today's Digital Age. The paper begins with a summary of Singapore's technology centric education policy, called the Masterplan for IT in Education. The Japanese context is then summarised and a number of recently proposed educational reforms are discussed. It will be argued that these reforms do not lend themselves to supporting the necessary development of digital competency required of Japanese school pupils in the 21st century. To overcome the shortcomings, strategic actions to support meaningful ICT integration in education, influenced by the Singapore experience, will be proposed.
Semantic OLAP with FluentEditor and Ontorion Semantic Excel Toolchain

Semantic technologies appear as a step on the way to creating systems capable of representing the physical world as real time computational processes. In this context, the paper presents a toolchain for an ontology based knowledge management system. It consists of the ontology editor, FluentEditor and the distributed knowledge representation system, Ontorion. FluentEditor is a comprehensive tool for editing and manipulating complex ontologies that uses Controlled Natural Language (CNL). Its main feature is the usage of Controlled English as a knowledge modelling language. Ontorion is a Distributed Knowledge Management System with Natural Language interfaces (CNL) and a built-in rules engine. The Ontorion system is equipped with plugins for connection with other software environments, for example rOntorion using an R language package to access ontologies. It is exemplified with the semantic extension of On Line Analytical Processing (OLAP) using R language.
Video streaming with quality adaption using collaborative active grid networks

Due to the services and demands of the end users, Distributed Computing (Grid Technology, Web Services, and Peer-to-Peer) has been developedrapidJy in thelastyears. Theconvergence of these architectures has been possible using mechanisms such as Collaborative work and Resources Sharing. Grid computing is a platform to enable flexible, secure, controlled, scalable, ubiquitous and heterogeneous services. On the other hand, Video Streaming applications demand a greater deployment over connected Internet users. The present work uses the Acti ve Grid technology as a fundamental platform to give a solution of multimediacontentrecovery. This solution takes into account the following key concepts: collaborative work, multi-source recovery and adapti ve quality. A new archi tecture is designed to deliver video content over a Grid Network. The acti ve and passi ve roles of the nodes are important to guarantee a high quality and efficiency for the video streaming system. The acti ve sender nodes are the content suppliers, while the passive sender nodes wiU perform the backup functions, based on global resource control policies. The aim of the backup node is minirnize the time to restore the systemin caseoffailures. In this way, all participant peers work in a collaborati ve manner following a mul ti -source recovery scheme. Furthermore, Video La yered Encoding is used to manage the video data in a high scalable way, di viding the video in multiple layers. This video codification scheme enables thequality adaptation according to the availability of system resources. In addition, a buffer by sender peer and by layer is needed for an effecti ve control ofthe video retrieve. The QoS will fit considering the state of each buffer and the measurement tools provide by the Acti ve Grid on the network nodes. Ke ywords: Peer -to-Peer Grid Architecture, Services for Active Grids, Streaming Media, Layered Coding, Quality Adaptation, CoUaborative Work.
On the automatic segmentation of speech signals

For large vocabulary and continuous speech recognition, the sub-word-unit-based approach is a viable alternative to the whole-word-unit-based approach. For preparing a large inventory of subword units, an automatic segmentation is preferrable to manual segmentation as it substantially reduces the work associated with the generation of templates and gives more consistent results. In this paper we discuss some methods for automatically segmenting speech into phonetic units. Three different approaches are described, one based on template matching, one based on detecting the spectral changes that occur at the boundaries between phonetic units and one based on a constrained-clustering vector quantization approach. An evaluation of the performance of the automatic segmentation methods is given.
Time-delay sliding mode control for a servo

Summary form only given. Time delay control (TDC) is a method using the past time delay terms to eliminate the current unknown dynamics and disturbance. Sliding mode control (SMC) uses discontinuous control inputs to be insensitive to parameter variations and disturbance. We propose modifications to TDC by adopting integral SMC which guarantees the robustness, and which decreases chattering. This algorithm is implemented to control positions of a DC servo motor with a seal installed at its main axis, which produces stick-slip friction.
Computing the accessibility relation for the Ambient Calculus

We present some algorithms to compute the reductions of Ambient Calculus and to describe mobility and dynamic hierarchies of locations. The main idea is to treat each ambient program as a complex of a set-theoretical structure for describing the hierarchy of locations, a function for naming the nodes of the structure, and a function for registering the capabilities of each node. These complexes, named state-processes, can be seen as states for a propositional branching tree logic built upon the Ambient Calculus. We develop here, the algorithms for implementing the accessibility relation between the states of this logic. Our algorithms compute the evolution of the truth values for the atomical propositions during the firing of capabilities. The model presented in this paper permits to re-use algorithms for model checking temporal logic to approach the Ambient Calculus.
Automating Software Development Productivity Metrics

Measurement of software development productivity is needed in orderto control software costs, but it is discouragingly labor-intensiveand expensive. Computer aided software engineering (CASE)technologies -- especially object-oriented, integrated CASE --have the potential to support the automation of this measurement.In this paper, we describe automated analyzers for function pointand code reuse measurement. Both analyzers take advantage of theexistence of a meta-model of the application system, stored withinan object repository, which contains the necessary informationabout the application system. We also propose new metrics for codereuse analysis, including reuse leverage, reuse value and reuseclassification. The state-of-the-art automated software metricsanalyzers are illustrated in the context of an investment bankingindustry application.
A Face Recognition Method Based on Complex Network, Canny Algorithm and Image Contours

Complex network theory are introduced to image recognition field recently. But the cost in storage space and time are obvious that the efficiency of the method is reduced. Consider the methods based on image contour can achieve high identify efficiency with simple process, a Face Recognition Method Based on Complex Network and Image Contours is discussed in this paper. A set of shape contour and color contours are taken into account. The main idea of the approach is to extract the face shape contour and color contours, model them into graphs and use complex network methodology to extract a feature vector for face recognition. Experiments show that the proposed method could effectively control the scale of the complex network and adapt to the changes in the boundary shape with efficient power of face recognition. The proposed method is also proved to be scale invariant and rotation invariant.
INTERNET USE ADOPTION AMONG ACADEMICIANS: Comparing Innovative Adopters and Other Adopter Types

The current study aims to find an answer to the research question: “how does Internet use differ between academicians who are innovative adopters and other adopter types”. In particular, we focus on three measures of Internet use: years of experience on online shopping,frequency of purchase, and price spent on items purchased online by specifically focusing on Malaysian Internet users' online buying behaviour. Thus, this study concentrates on Business to Consumer (B2C) market.
Text-independent speaker verification with a multiple binary classifier model

Describes the adaptation of a text-dependent talker verification approach to a text-independent system. In the adapted model, an (N-1) set of Moody-Darken radial basis function networks is trained for each of N true talkers from a reference database. Each network within a set is trained with the corresponding true talker's parametrised speech and a single alternative talker is chosen randomly. When an identity is claimed, the (N-1) networks are all tested with the claimant's utterance. Acceptance rates are averaged over the set. Should this resulting mean exceed a true talker-dependent threshold, the unknown talker is accepted. Otherwise, the talker is rejected as an impostor. Results obtained from 100 imposture attempts show that the system has the potential of being foolproof, under ideal operating conditions. <<ETX>>
Effects of Technology-Mediated Instructional Strategies on Motivation, Performance, and Self-Directed Learning

The shift in education from an instructor-centered to a learner-centered focus requires learners to be selfdirected and motivated. However, empirical data are lacking on how to positively effect self-directed learning. Further, the motivational needs of learners are often overlooked, and there is need for more literature examining both self-directed learning and motivation in technology-mediated learning environments. The purpose of this design experiment was to attempt to positively affect motivation, performance, and self-directed learning of undergraduate students enrolled in a tuition-free, public military school. A second purpose was to use new technologies to deliver these instructional strategies as supplementary course content. The subjects in this study were undergraduate students enrolled in a tuition-free, public military school in the Northeast United States. 784 students, representing approximately 20% of the population at the academy, were randomly divided into control and experimental groups for each of 16 instructors. Interventions were developed using Keller’s ARCS model of motivation and delivered via PDA, web, and other technologies. The within-subjects research design used a mixed method approach involving both quantitative and qualitative data. Students completed four surveys to measure motivation and SDL. Course aggregate points were used to measure academic performance. The findings showed there were significant differences in motivation, academic performance, and proclivity to be self-directed learners of students who accessed the technology-mediated instructional strategies, suggesting that such motivational strategies should be incorporated into course instructional design.
Decoupled Sliding Mode Control for a Novel 3-DOF Parallel Manipulator with Actuation Redundancy

This paper presents a decoupled nonsingular terminal sliding mode controller (DNTSMC) for a novel 3-DOF parallel manipulator with actuation redundancy. According to kinematic analysis, the inverse dynamic model for a novel 3-DOF redundantly actuated parallel manipulator is formulated in the task space using Lagrangian formalism and decoupled into three entirely independent subsystems under generalized coordinates to significantly reduce system complexity. Based on the dynamic model, a decoupled sliding mode control strategy is proposed for the parallel manipulator; the idea behind this strategy is to design a nonsingular terminal sliding mode controller for each subsystem, which can drive states of three subsystems to the original equilibrium points simultaneously by two intermediate variables. Additionally, a RBF neural network is used to compensate the cross-coupling force and gravity to enhance the control precision. Simulation and experimental results show that the proposed DNTSMC can achieve better control performances compared with the conventional sliding mode controller (SMC) and the DNTSMC without compensator.
Hypermedia and Direct Instruction: Do the Paradigms Fit? A Demonstration of a Learner-Centered Hypermedia Program in Classroom Observation Skills.

The primary method of objectively measuring children's behavior in classroom and playgroup settings is through systematic behavior observation in which a child of concern is studied in relation to peers in the same situation (Walker & Hops, 1976). To gather data objectively and reliability, observers must undergo rigorous training and reliability testing. The training process must provide a method to learn a behavior code, practice in simulated and real settings until proficient, and measure coding reliability. Training time and protocols are extremely demanding. The usual training method involves an apprenticeship model where a "master" coder explains the procedures, demonstrates via videotapes, provides practices through videotapes and live observations, and undertakes reliability testing with tapes or live observations. The process can take 40-60 hours to bring a novice observer to the minimal acceptable level of 80% accuracy. It is not feasible to spend this amount of training time on one assessment procedure within most teacher education programs. Consequently, systematic observation procedures are rarely fully taught outside of research or clinical settings (Fitzgerald, 1995). To become a competent observer, one must learn a behavior code and procedures, practice in simulated and real settings until proficient, meet established standards for reliability, and learn to interpret observation data. These skills take an investment in training and practice time. Typically, observers are trained through a combination of didactic instruction, work with videotapes, guided practice with a "master" coder, and "in situ" reliability checks. This training protocol is time and person intensive; it can take 40-60 hours to bring a novice observer to the minimal acceptable level of 80% accuracy. It is not feasible to spend this amount of training time on one assessment procedure within most health care training programs. As a result, full training is rarely provided health care personnel and observational data are based on subjective, informal procedures. An alternative delivery approach for training personnel in observation skills is interactive multimedia technology. The program being demonstrated, The Classroom Behavior Record: School Version, is a complete training program in an observational procedure and code for educators and health care professionals (Fitzgerald, & Semrau, 1997). The purpose of the program is to provide efficient and effective training in a flexible, learner-centered, hypermedia format. The current program being demonstrated) utilizes a repurposed interactive videodisc (Semrau & Fitzgerald, 1996) with new software designed to enhance user recordkeeping and authentic practice while maintaining pedagogical effectiveness based on the …
Dependency analysis for knowledge validation in rule-based expert systems

Keeping knowledge consistent is an important topic in the life cycle of developing expert systems. In this paper, we focus on some major problems in knowledge validation: redundancy, subsumption, cycles, conflict, and unnecessary conditions, and describe how these problems are solved in rule-based expert systems using dependency analysis. A rule-dependency graph is developed to describe the dependency relationship among the rules contained in a knowledge base. Since each type of inconsistent knowledge presents a specific topology in the rule-dependency graph, knowledge validation can be done by examining the structure of the graph. With the aid of the rule-dependency graph, we have developed a token-flow paradigm that identifies the inconsistent structure in the rule base. The idea is effective and can be easily implemented. Properties of our method are explored. Some practical examples are also presented. <<ETX>>
Improvement of KEA Based on Lexical Chain

Keyphrases are very useful and significant for information retrieval, automatic summarizing, text clustering, etc. KEA is a traditional and classical algorithm in keyphrase automatic extraction. But it is mainly based on the statistical information without considering the semantic information. In this paper, We propose a method which combine semantic information with KEA by constructing lexical chain that based on Regets thesaurus. In our method, the semantic similarity between terms is used to construct the lexical chain, and then we use the length of the chain as a feature to build the extraction model. The experiment result shows that the performance of the system has a big improvement compare with the KEA.
Convex modeling with priors

As the study of complex interconnected networks becomes widespread across disciplines, modeling the large-scale behavior of these systems becomes both increasingly important and increasingly difficult. In particular, it is of tantamount importance to utilize available prior information about the system’s structure when building data-driven models of complex behavior. This thesis provides a framework for building models that incorporate domain specific knowledge and glean information from unlabelled data points. I present a methodology to augment standard methods in statistical regression with priors. These priors might include how the output series should behave or the specifics of the functional form relating inputs to outputs. My approach is optimization driven: by formulating a concise set of goals and constraints, approximate models may be systematically derived. The resulting approximations are convex and thus have only global minima and can be solved efficiently. The functional relationships amongst data are given as sums of nonlinear kernels that are expressive enough to approximate any mapping. Depending on the specifics of the prior, different estimation algorithms can be derived, and relationships between various types of data can be discovered using surprisingly few examples. The utility of this approach is demonstrated through three exemplary embodiments. When the output is constrained to be discrete, a powerful set of algorithms for semisupervised classification and segmentation result. When the output is constrained to follow Markovian dynamics, techniques for nonlinear dimensionality reduction and system identification are derived. Finally, when the output is constrained to be zero on a given set and non-zero everywhere else, a new algorithm for learning latent constraints in high-dimensional data is recovered. I apply the algorithms derived from this framework to a varied set of domains. The dissertation provides a new interpretation of the so-called Spectral Clustering algorithms for data segmentation and suggests how they may be improved. I demonstrate the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. Lastly, I discuss empirical methods to detect conserved quantities and learn constraints defining data sets. Thesis Supervisor: Neil Gershenfeld Title: Associate Professor
3Dwm: A Platform for Research and Development of Three-Dimensional User Interfaces

Although hugely successful, the near-ubiquitous WIMP paradigm dates back to the 1970s and has many shortcomings that could be addressed by harnessing modern computing technology in the user interface. Most importantly, the ready supply of consumer-level hardware-accelerated 3D graphics has opened the way for 3D user interfaces, i.e. interfaces that make direct use of the third dimension. However, there are many challenges to designing applications with such interfaces. In this paper, we present 3Dwm, an object-oriented software platform for the research and development of 3D user interfaces, and discuss how it addresses these challenges. 3Dwm provides a unified 3D abstraction layer that facilitates interoperability between 3D components, combats the inherent complexity of 3D interface programming, provides a reusable toolkit for application building, supports network-aware 3D visualization applications, and may potentially serve as a standardized software platform for many kinds of 3D graphics development.