Search

This paper presents an approach for acquiring academic bilingual resources from the web paper libraries. By analyzing the structured information of web pages, we first implement a customized crawler to download these pages including paper details, and then use a parser to transfer them into XML format. Based on the classic statistical method for sentence alignment, we propose an improved approach to align the initial bilingual resources, in which two factors, bilingual keyword pairs and matching patterns are introduced. Experimental results show that our sentence aligner supported by the new approach achieves performance enhancement by 7% in both precision and recall.

Information management and extraction in the field of biomedical research has become a requirement with the rapid increase in the amount of data being published in this area. In this paper, a graphical model, Conditional Random Fields has been used to extract a particular gene-gene relationship called “coexpression” from the existing literature. First, a Conditional Random Fields based model has been trained and tested on full-length papers downloaded from PubMed, to label the predicates that talk about coexpression of genes. Proper local and contextual text features at both word and sentence levels are proposed and extracted during the pre-processing step. The classification performance of the model trained based on the proposed features has been compared with the that of Support Vector Machines, Nearest Neighbor with generalization, and Neural Networks algorithms, and seen to outperform them all. In our second experiment, the proposed ranking scheme, which is based on classification results, is applied to the ranked lists of papers returned by PubMed and Google, respectively. The comparison of our ranked results to that of PubMed and Google demonstrates that our proposed ranking scheme performs better than both in distinguishing a positive paper from a negative paper. In conclusion, this paper describes a specialized classification and ranking framework that can retrieve papers that really talk about coexpression between and among genes based on mining of semantics and not just lexical search.

At present there are still some problems in the automatic test paper-making-system: The first is about the test question including graphs; The second is about the test question including formula; The third is about the difficulty degree of the test question which is set in advance and fixed, and can't be adjusted to meet the new need when the ability of examinees has changed; The forth is repetition of the same topic and focus in one test paper; the fifth is how to design the typesetting. In view of the above problems, taking the subject of “Object-Oriented Programming” for an example, a kind of self-adapted making test paper system, which can settle those problems, has been provided by the means of law of large numbers, the idea of machine learning and man-made assistant adjustment. It is simpler and more reliable than what is used nowadays.

This paper proposes a method for extracting useful expressions from English research papers. The method extracts sequences of words from research papers and refine them into phrasal expressions (PEs). We use base-phrases for acquiring such the expressions. The method extracts PEs from the set of sequences of base-phrases by using three kinds of statistical information: frequency, length, and the number of kinds of the succeeding base-phrases. In our experiment using 1,232 research papers, the precision of acquisition at the top-200 was 62.0%. The precision was higher than all of the baselines, and therefore, we confirmed the feasibility of our method.

With more appropriate linkage of digital libraries to Web resources, online-browsing of research papers would be much comfortable since many digital libraries of research papers are online and accessible from the Web. This paper proposes a browsing support system for reading research papers online with the use of OCRed text of scanned academic articles. Our digital library stores scanned document images of research papers; hence, their OCRed texts can be cheaply obtained. The proposed system extracts technical terms from the OCRed text, searches the Web for the best explanatory descriptions of the terms, and gives the terms the links to the retrieved Web pages. This paper also describes an experimental exploration of several aspects of the proposed system, including the extraction accuracy of technical terms and the precision of retrieved Web pages.

In order to generating test papers, as required in final or midsemester of any courses, test questions database management system needs effectively algorithm that will help it choose appropriate questions quickly according to the constraints proposed by the teachers. The traditional methods are not well suitable for the test papers that are restricted by multi-factors. In this paper we describe the relation like data structures meet for this need, and give algorithms for searching and updating it to generate test paper. We also present the results of a series tests that indicate that the algorithms performed well

When generating test paper, traditional distant education online examination system either merely extracts the same test questions from item bank or extracts test questions at random, or select test questions according to some classifications of student. It can not individually varies and give custom made scheme according to student's learning situation. This paper improved the traditional genetic algorithm and proposed a new method of test paper generating based on student's learning situation. It can generate personalized test paper dynamically that satisfying every student according to student's online self-condition in distance education network and factors reflected during learning such as psychological status, comprehension and application level.

Traditional algorithms of intelligent test paper generation have the disadvantages of slow convergence, low success rate and poor quality. In this article a new method of test paper generation is given..which is based on partition binary coding and improved genetic algorithm focused on improving the process of selection. This method uses independent question database. The new method is more efficient and easier to get over premature convergence than the traditional algorithms. It is proved by a number of experiments provided by this article.

Cross-site scripting defenses often focus on HTML documents, neglecting attacks involving the browser's content-sniffing algorithm, which can treat non-HTML content as HTML. Web applications, such as the one that manages this conference, must defend themselves against these attacks or risk authors uploading malicious papers that automatically submit stellar self-reviews. In this paper, we formulate content-sniffing XSS attacks and defenses. We study content-sniffing XSS attacks systematically by constructing high-fidelity models of the content-sniffing algorithms used by four major browsers. We compare these models with Web site content filtering policies to construct attacks. To defend against these attacks, we propose and implement a principled content-sniffing algorithm that provides security while maintaining compatibility. Our principles have been adopted, in part, by Internet Explorer 8 and, in full, by Google Chrome and the HTML 5 working group.

We develop a novel technique for authenticating physical documents by using random, naturally occurring imperfections in paper texture. To this end, we devised a new method for measuring the three-dimensional surface of a paper without modifying the document in any way, using only a commodity scanner. From this physical feature, we generate a concise fingerprint that uniquely identifies the document. Our method is secure against counterfeiting, robust to harsh handling, and applicable even before any content is printed on a page. It has a wide range of applications, including detecting forged currency and tickets, authenticating passports, and halting counterfeit goods. On a more sinister note, document identification could be used to de-anonymize printed surveys and to compromise the secrecy of paper ballots.

It is a multi-objective optimization problem to autogenerate a test paper from test bank. In this paper an autogenerating test paper method based on latent semantic analysis and genetic algorithm is proposed. Firstly the relationships of keywords among test items in a test bank are analyzed with LSA, and the weights of the test items are calculated. Then using the Genetic Algorithm to compose the test paper accorded to the weights. The proposed method can effectively select test items from Chinese test bank to compose a test paper.

In this paper we analyze our recent research on the use of document analysis techniques for metadata extraction from PDF papers. We describe a package that is designed to extract basic metadata from these documents. The package is used in combination with a digital library software suite to easily build personal digital libraries. The proposed software is based on a suitable combination of several techniques that include PDF parsing, low level document image processing, and layout analysis. In addition, we use the information gathered from a widely known citation database (DBLP) to assist the tool in the difficult task of author identification. The system is tested on some paper collections selected from recent conference proceedings.

How to identify a machine automatically generated paper is an active research direction. It is involved in machine learning and semantic understanding. However in this paper, we introduce a simple but effective method to quickly identify whether a paper is from a paper generator or not. We design and implement the detection system using LAMP, and Yahoo Boss OpenAPI interface. Experimental result shows that our method is useful to detect faked paper and can be easily adapted to other related work.

Due to the flush development of academic research, a great deal research results have been published in conference proceedings and journals. However, these articles need to be inspected by some professionals in specific fields. It is the most important that it must keep fair during the entire process of reviewing. However, the privacy of reviewers will be leaked out because that the reviewers must sign their comments on the reviewed papers. The leakage of the reviewers' privacy will affect the fairness of paper reviewing. In addition, the authors need to show their names to the editor of conference proceedings or journals such that it may also make the inspecting results unfair. Unfortunately, the solutions proposed in the literature cannot cope with the problems of fairness well. Therefore, in order to eliminate the drawbacks of the previous schemes, we will deeply analyze the paper review procedure to find the possible reasons that bring about these unfair results. Furthermore, we will present a generic idea, which is independent of the underlying cryptographic components, to achieve the fairness property and other requirements in a paper review scheme.

This paper presents a system for checking code numbers between on a credit card (CC) and a paper of personal information (PPI) using optical character recognition (OCR). The process starts to get both CC and PPI images from two cameras. Such both CC and PPI images are cropped with only the region of code numbers and then they are resized by linear interpolation. They are converted to binary images of value (0 or 1). Finally, the OCR is applied for checking the code numbers of the credit card and PPI. There are 100 samples for testing the credit card and PPI. Both credit cards and PPIs include 10 numbers (0 – 9). The result shows that the accuracy of the checking system on the credit card is at approximately 100%.

Paper making expert system has inherent merits and demerits in fault diagnosing. It is a very important topic of how to make the most of advantages and cover the shortages. With combining the expert system and other fault diagnosing methods, for instance fuzzy theory and artificial neural networks theory, the quality in paper manufacturing can be predicted; the safe operation of paper-making equipment and the stability of paper-making quality can be ensured; and the non-structural problem can be dealt with by making full use of advantages. All of these are practically significant to ensure the stability of the paper manufacture.

A user can get a high immersive feeling with staying in a VR-based screen booth, changing the user’s viewpoints and controlling visualized objects. However, because of its installation environment and its cost, it is not suitable for casual applications. This paper proposes a marker-based viewpoint recognition method, which calculates the position and direction of the viewpoint of a user who is looking at virtual objects. The virtual objects can then be appropriately located so that their position and orientation could meet the user’s view. A user standing in front of the projection system of this research can interactively review the shape of the object from different angles simply by moving the head with the marker, and even can check the internal view of the object. This paper presents the method and its demonstration, and discusses the feasibility of the proposed approach for casual applications.

In this paper, a good data hiding scheme has been proposed. This scheme embeds messages into a still image based on (7,4) Hamming Codes oriented wet paper codes . It embeds a segment of three secret bits into a group of seven cover pixels at a time. The experimental results show that the proposed scheme achieves high embedding efficiency and acceptable capacity.

We usually annotate unknown words and useful expressions with a pen while reading documents written in foreign languages. Although such annotations include a great deal of linguistic knowledge that is required for future reading and writing, these annotations are seldom reused. We propose a system that generates Web-based content using the annotations collected from paper documents written in a foreign language. This system provides a paper-and-pen interface, which is acceptable to most of us. The generated content is specialized for language study, which helps us to internalize the linguistic knowledge in it. The experimental results with a prototype revealed that the system could detect most of the correct annotations with some failures. The system could also visualize the intellectual activity of individuals and a group.

This paper proposes a multi-tenant workflow framework that allows users to create data analytic workflows whose tasks are efficiently scheduled and distributed in cloud computing environment. We provide a demo of an event room assignment (ERA) as a test application of the framework. The ERA dynamically and automatically assigns registered events (e.g., meetings, classes, conferences, etc.) to available rooms meeting the user requirements such as the event size, purpose, reservation period, etc. The assignment will lead to the energy efficiency with respect to the power usage (e.g., lighting, ventilation, devices, etc.), and the energy savings can be achieved without affecting people's comfort. We run the ERA with power consumption data (whose size is approximately 50GB) collected from each of over 200 rooms in a building at Dept. of Engineering, Tokyo University. Through the demonstration, we will show that the proposed framework accelerates the speed of data analysis by providing user-friendly workflow composition and parallel processing features utilizing cloud computing technologies.

Parallel computers are becoming increasingly heterogeneous and correspondingly more difficult to program as a result. Irregular applications exacerbate this problem further, given that this class of applications is more diverse and uses different performance features of a computer system than more common application classes. Thus, new approaches are necessary to achieve performance and productivity simultaneously. Domain-specific languages are becoming increasingly popular for high-performance computing, both in the domains of regular (e.g., SPIRAL) and irregular (e.g., Green-Marl) applications. However, past languages for graph computations tend to be too limited to efficiently express the wide range of irregular algorithms needed in applications. Instead, this position paper advocates adapting a language from the database community, Datalog, to the domain of high-performance irregular applications. Although the plain Datalog language is also insufficient for the class of applications targeted, extensions can be added to increase its expressiveness. Starting with a standard language also enables taking advantage of the literature on Datalog implementations, including in the contexts of parallelism and incremental execution of algorithms. Thus, this approach promises to be a good way to implement irregular applications with both productivity and performance.

The work that lead to our ICCD-1999 FlexRAM paper [4] started in 1996. At that time, there was great interest in the potential of integrating compute capabilities in large DRAM memories — an architecture called Processing-In-Memory (PIM) or Intelligent Memory. Prof. Kogge at the University of Notre Dame had been an early and persistent proponent of the technology since his EXECUBE work [6]. Prof. Patterson at UC Berkeley had been leading the Berkeley IRAM project [11], and co-organized a workshop on these architectures in June 1997 [12]. In addition, Dr. Lucas from DARPA was outlining plans for an effort in this area. Finally, some chip manufacturers were investing in a DRAM technology that could be compatible with high-speed logic — e.g., IBM's CMOS 7LD and Mitsubishi's ERAM.

Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based models provide accurate directions without additional calibration procedure of antenna array and a priori knowledge of the number of sources. In this review paper, the results achieved by the research group at the Faculty of Electronic Engineering in Nis are presented. The problem of DOA estimation of narrowband signals impinging upon different configurations of antenna arrays is addressed. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are considered, and their advantages and disadvantages are discussed. To improve the resolution of DOA estimates, sectorization model is introduced. As shown in this work, neural network-based models demonstrate high-resolution localization capabilities and much better efficiency than the MUSIC.

In the ever growing literature on countering the cyber threat, the so-called JDL model of data fusion, well established in the information fusion community, has been applied to characterize the inner structure of problems within cyber defence and their mutual relationship. The overarching goal is to provide contributions to comprehensive cyber situational awareness by producing timely situation pictures. Cyber situational awareness, however, is prerequisite to taking appropriate actions, i.e. for “defence”. In this review paper, we provide an overview of what has been proposed in this context by various authors and collect basic insights published in the open literature. By doing so, we wish to provide an overview of the current discussion which reflects our own apprehension and prioritization. Moreover, we stress our opinion where relevant research questions are to be expected.

As cloud usage continues to increase, new issues with respect to managing and securing resources in the cloud are becoming more apparent. While some people may believe that security and privacy in the cloud can be addressed without the consumer considering the physical location and internal structure of the cloud, we show that this is clearly not the case. Furthermore, we describe a mechanism by which cloud consumers can inform cloud providers of their requirements in a manner that still allows the cloud to remain dynamic and flexible. Specifically, this paper explores the analogy between human migration in the real world and virtual machine migration in an IaaS cloud environment. It addresses issues such as jurisdictional control, zone evolution, migration, and instantiation based on an examination of these analogous real-world scenarios and their applicability to the cloud.

With the intervention of technology into education, plain text learning material does not suffice anymore. Learning Management Systems (LMS) are effectively replacing the monotonous and rigid method of imparting education. However, there is a necessity of a framework to which these learning technologies can comply to. This paper discusses such a framework-SCORM. SCORM is the Advanced Distributed Learning (ADL) initiative of the U.S. Government for unifying e-learning specifications emerging from the international standards organizations into a single specification [1].

Cloud computing is the biggest buzz in the computer world these days -- maybe too big of a buzz. Cloud computing means different things to different people. Cloud computing is not a small, undeveloped branch of IT. Research firm IDC thinks that cloud computing will reach $42 billion in 2012. You can do everything on cloud from running applications to storing data off-site. You can run entire operating systems on the cloud. This paper is for anyone who may have recently heard the term "cloud computing" for the first time and needs to know what it is and how it helps them.

This paper describes wireless sensor network (WSN) and sensor fusion technology designed for ubiquitous smart living space applications. With respect to the WSN technology, a new standard from the Zigbee alliance is introduced. This new standard is designed to help constructing the network infrastructure by integrating with the TCP/IP network with the ZigBee network. A new approach for WSN deployment based on this new standard is also presented. With respect to the sensor fusion technology, this paper presents a smart sensing and control algorithm that naturally adjusts the thermal quality of the environment according to the interior and exterior environmental factors and the behavior of the inhabitants in a smart skin environment. The experiment results indicate that the proposed algorithm significantly improves the thermal comfort and effectively reduces energy consumption.

New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.

New Internet applications are coming out at an unprecedented rate under the rapid development of Internet technologies, and the network platform of sci-tech papers published is exactly a result of the information technical development. This paper introduce a method of double-element which includes rigid mechanism-process control and soft mechanism-incentive and constraint mechanism to study the academic quality control of sci-tech papers published under network environment.

Investigations carried out on artificial defects are fundamental for understanding partial discharge (PD) behavior in electrical apparatus and establishing expert system databases. In this paper, tests on artificial defects (realized both in the lab and on real transformers) for paper/oil insulation system are discusses. In particular, emphasis is given to bubbles (in free oil or between paper layers) and PD due to floating metal objects, as other PD sources have been discussed in previous works. In order to provide a thorough picture of PD phenomena in paper/oil systems, the PD patterns obtained in the lab are discussed and compared with those obtained from transformers. The results reported here indicate that recognition of PD source in paper/oil insulated systems is feasible thorough appropriate tools.

The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves - on the same data-set - on state-of-the-art Bayesian learning system KEA from a minimum 27% to a maximum 77% depending on KEA parameters tuning and specific evaluation set. Our assessment is performed by comparison with key-phrases assigned by human experts in the specific domain and freely available through ACM portal.

Because of unsatisfied efficiency and effect of the current on-line auto-generating paper, an algorithm of auto-generating paper based on KP (Knapsack Problem) was presented and the conventional method of solving KP was improved, that is, paper was separated into small packs according to itempsilas type and its difficulty and solved respectively by process of the solution to KP. The experiments show that the algorithm can auto-generate paper in terms of the strategy of generating paper made by experts and the algorithm is perfect in its efficiency and its rate of success.

This paper is a retrospective arising from a WCRE paper published in 1998 promoting a relational approach to manipulate software architecture and to help solve various problems in software analysis. That paper explains how Tarskipsilas binary relational algebra, embedded in a language such as Grok, can solve such problems. Tarskipsilas notation is elegant and often efficiently executable when the subject at hand is characterized by parts with binary relations between them. Software architecture, especially as-built architecture, is such a subject. This paper concentrates on the following three questions. What impact can a relational approach have on our understanding of software architecture? What Grok-languages exist and what are their strengths? How have Grok-like languages been used to solve problems in software architecture or software analysis?

This paper presents a quality-forecasting model based on neural network for the paper making industry with different source data transaction processes. The paper quality test and control plays an essential role in the paper making industry, which affects the whole operation process and the future paper market. Compared with other paper quality indexes, paper curl is closer to terminal clients and more difficult to pretest and control in the actual working environment. Large-scale data from production database, which would potentially affect final paper quality, have been cleansed and abstracted. Modeling based on MLP neural network was designed to compare between Quasi-Newton algorithm and Double Dogleg with early stopping regularization in different source data sets. With bootstrap accuracy estimation, the final result has been evolved which would annotate the relationship between workflow data and paper curvature in a more constructive way.

For original paper see, ibid.,p.339-46, (2008). This paper proposed an object- and task-oriented architecture for automated video surveillance in distributed sensor networks. The proposed work is implemented and evaluated in an experimental testbed consisting 25 nodes.

This paper provides comments to the paper "Visual Players Detection and Tracking in Soccer Matches". It was prepared as part of an experimental open review process for the 5th IEEE International Conference On Advanced Video and Signal Based Surveillance for which the author of this commentary paper acted as a reviewer. Due to the conference submission process, this comment was prepared for the review version of the paper and does not consider changes that the authors may have made for the print version of their work. The reviewed paper addresses the problems of automatically detecting, classifying and tracking players in soccer matches. It focuses on three separate sub-problems: estimation of a background model that represents the empty soccer field, classification of team players, and player tracking.

In this paper we present a systematic translation algorithm that maps a software architecture model to rewriting logics. We consider a nowadays typical component-based software architecture model - SAM. SAM is a formal software architecture model that integrates two formalisms - Petri nets and temporal logic. Our goal is to effectively describe the component based software architecture model SAM using a rewriting based semantics. This algorithm is implemented in Maude, a high performance declarative programming language that supports membership and rewriting logics. The contribution of this paper is we defined the translation algorithm to rewriting logic to show an interleaving semantic matching between the behavior model Petri net and rewriting logic.

Evaluation of test paper quality plays an increasingly important role in education management. But there is no effective approach to implementing it automatically. The paper proposes a multilevel fuzzy comprehensive evaluation model for test paper quality based on education statistics. The model has 2 levels. The first level includes seven basic factors: difficulty, difference, credibility, eligible rate, overlay rate, kurtosis and skewness. The second level includes the degree-3, rate-2 and normality. Degree-3 consists of difficulty, difference and credibility; rate-2 is composed of eligible rate and overlay rate; normality includes kurtosis and skewness. Experiments show that the method can evaluate test paper quality effectively.

This paper describes methods for increasing the security of data being stored in a distributed hash table (DHT) which leverages the inherent properties of the DHT to provide a secure storage substrate. The methods presented are based upon a framework referred to as "scatter, conceal, and recover" (SCAR). The standard method of securing data in a DHT is to encrypt the data using symmetrical encryption before storing it in the network. SCAR provides this level of security, but also prevents any known cryptoanalisys from being performed. It does this by dividing data into multiple blocks and scattering these blocks within the DHT. The security of SCAR is provided by the property that an attacker is unable to obtain and reassemble the data blocks correctly. However, if the attacker has access to the network communication, the likelihood of a successful attack is significantly increased. This paper defines how such attacks can be executed and provides methods for ensuring data security in spite of such attacks.

The virtual product data model in its different stages is an essential prerequisite for the virtual modeling of processes and their virtual simulation. Product design starts with conceptual design including the concepts for the future productpsilas shape and function - the gestalt. In this stage, the designers work on fuzzy product data interpreted in so-called scribbles and applied to perception modeling and shape creating. In the paper we will suppose methods to analyze paper scribbles for the creation of fuzzy-spline pre-CAD shape models. In particular, the paper discusses special geometrical issues that are concerned with the interpretation of the future productpsilas contours within paper scribbles and virtual pre-CAD models. Examples of curve and closed region modeling strategies based on a blurred 2D description will be presented. The implemented algorithms combine image-based modeling methods with usual computer-aided design (CAD) modeling strategies.

This paper describes a robot referee for "rock- paper-scissors (RPS)" sound games; the robot decides the winner from a combination of rock, paper and scissors uttered by two or three people simultaneously without using any visual information. In this referee task, the robot has to cope with speech with low signal-to-noise ratio (SNR) due to a mixture of speeches, robot motor noises, and ambient noises. Our robot referee system, thus, consists of two subsystems - a real-time robot audition subsystem and a dialog subsystem focusing on RPS sound games. The robot audition subsystem can recognize simultaneous speeches by exploiting two key ideas; preprocessing consisting of sound source localization and separation with a microphone array, and system integration based on missing feature theory (MFT). Preprocessing improves the SNR of a target sound signal using geometric source separation with a multi-channel post-filter. MFT uses only reliable acoustic features in speech recognition and masks out unreliable parts caused by interfering sounds and preprocessing. MFT thus provides smooth integration between preprocessing and automatic speech recognition. The dialog subsystem is implemented as a system-initiative dialog system for multiple players based on deterministic finite automata. It first waits for a trigger command to start an RPS sound game, controls the dialog with players in the game, and finally decides the winner of the game. The referee system is constructed for Honda ASIMO with an 8-ch microphone array. In the case with two players, we attained a 70% task completion rate for the games on average.

The Internet pervades virtually every aspect of our daily lives, and it seems there is no area that is immune from computing solutions. Computers can do things faster, with greater precision, more reliably, etc., etc., etc. Ironically, one area that most needs the mechanical rigor offered by computing solutions seems destined to abandon electronic solutions and return to paper as the operating medium of choice. As electronic voting falls from favor across America, we are concerned to hear talk of paper receipts provided to voters1. Though the department store receipt model is appealing in its simplicity, we posit that when this model is applied to voting systems, it introduces a complex combination of dangerously conflicting properties. We describe these properties and offer an alternate framework to address paper receipt concerns. We then extend this notion into a discussion of paper records and their contribution to forensics for election systems.

Structuring constitutes an important means for enhancing individual and collaborative learning processes. This paper introduces two new concepts for augmented semantic structuring of paper-based learning processes. Digital paper bookmarks can be used for individually structuring, indexing and tagging learning documents. The learner cube provides a new structuring method for collaborative face-to-face learning meetings. Both concepts rely on the Anoto technology, permitting to track and digitalize the paper-based learning process. A first pilot study indicates that digital paper bookmarks and tangible interaction are promising tools for structuring learning processes.

Based on the activity for building the foundation and application of computer, a common used automatic examination paper generating system is designed. This paper introduces how to design the database of item bank and establish the mathematic model of examination paper generating system, and discusses the method of finding the solution. To solve the model, the multiple objectives are translated into single objective by using genetic algorithm.

The rapid increase of multimedia resources makes it difficult for organizing, describing and locating. The method based on ontology is one of the resolving directions, which can simplify resource organization and improve the resource searching efficiency. Based on ontology, we developed a Multimedia Science Paper Generation System (MSPGS). It implements an ontology-based multimedia resource sharing framework. In this paper, three key processes of multimedia resource sharing framework are introduced in detail. First, the ontology based multimedia resource organization structure is proposed; then, we introduce an approach for multimedia description; finally, in order to locate the target multimedia resource accurately and efficiently, the Concept Classified Semantic Search algorithm is described. To evaluate the efficiency and precision of the sharing framework, some widely used evaluation methods such as Precision, Recall and F-Measure are used in the experiments. The experimental results show that MSPGS can work well and the proposed multimedia resource sharing framework is effective.

Automatic test generating system in distributed computing context is one of the most important links in on-line evaluation system. Although the issue has been argued long since, there is not a perfect solution to it so far. This paper proposed an innovative approach to successfully addressing such issue by the seamless integration of an algorithm and multi-agent system. In the design phase, a test paper qualification model was defined for automatic generation of test paper with higher efficiency. The algorithm first generates a quantization model of test paper then the test items are selected from the database according to the quantization model. The random selection and approximate match tactics are used in this algorithm. In the phase of implementation, the algorithm was implemented in an agent by the Java Agent Development Environment. The final simulation results validated the feasibility of the proposed approach.

We describe the architecture of a middleware system that exploits a novel technology to enhance the traditional pen and paper agenda. We describe the architecture of an application service handler to synchronize its digital agenda with its classical paper counterpart. A user may create, modify, or delete appointments in the paper agenda, and the system reproduces these modifications into the digital agenda. The system is almost transparent to the user. The same methodology has been applied to the address book of the agenda. Our system has as low an impact as possible to the classical users, whilst using state of the art technology. Our system allows the enhancement of the traditional paper agenda, transforming it into a proactive tool instead of a passive one.

Resources are presented for fostering paper-based election technology. They comprise a diverse collection of real and simulated ballot and survey images, and software tools for ballot synthesis, registration, segmentation, and ground truthing. The grids underlying the designated location of voter marks are extracted from 13,315 degraded ballot images. The actual skew angles of sample ballots, recorded as part of complete ballot descriptions compiled with the interactive ground-truthing tool, are compared with their automatically extracted parameters. The average error is 0.1 degrees. These results provide a baseline for the application of digital image analysis to the scrutiny of electoral ballots.

The use of the i∗ (iStar) framework by the requirements engineering community is many-fold. Among the several possible engineering cases, we are particularly interested here in the joint use of i∗ with other modelling frameworks to obtain what we call i∗-based frameworks. In this context, several challenges need to be overcome: theoretical, technical, methodological and community-related. In this paper, we review current i∗-based frameworks under several possible scenarios and observe that not all of these challenges are always addressed, and even more, there is lack of guidelines or well-accepted methodological design steps on how to overcome these issues. Then, we detail the several engineering artifacts and techniques whose consideration in i∗-based frameworks may help to overcome them. To illustrate the vision, we present the case of combining i∗ with data warehouse models, from the initial definition of the ontology to the final implementation using profiles. Finally, we provide a research agenda to apply the proposed vision including a final reflection on defining a maturity model as a convenient way to support forthcoming research in the topic.