ppxscal/embedding_network huggingface dataset:
Multi-format Notifications for Multi-tasking
Authors: Julie S. Weber, Mark W. Newman, Martha E. Pollack
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I (2009)

We studied people's perception of and response to a set of visual and auditory notifications issued in a multi-task environment. Primary findings show that participants' reactive preference ratings of notifications delivered in various contexts during experimentation appear to contradict their reflective, overall ratings of the notification formats when elicited independently of contextual information, indicating a potential difficulty in people's abilities to articulate their preferences in the absence of context. We also found people to vary considerably in their preferences for different notification formats delivered in different contexts, such that simple approaches to selecting notification delivery formats will be dissatisfying to users a substantial portion of the time. These findings can inform the designs of future systems: rather than target the general user alone, they should strive to better understand each user individually.
Models and Optimization Methods of Spatial Query on Multi-platforms
Authors: Chunhui Zhou, Xinyan Zhu, Qianqing Qin
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

In this paper, the spatial query models of multi platforms are analyzed. A 2-level MBR filter spatial join method (2FSJM) is proposed, which improves the performance of multi-platforms, including Oracle Spatial, ArcSDE and the open source platform OGR/GEOS. The experiments on multi-platforms are implemented. The results show that the improvement is valid.
Hypothesis-margin Model Incorporating Structure Information for Feature Selection
Authors: Ming Yang, Ping Yang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

Iterative search margin based algorithm (Simba) has been proven effective for feature selection. However, the previously proposed model does not effectively utilize the structure information hidden in data which may have a great impact on the generalization performance of post-analysis classifiers. In this paper, we introduce a novel hypothesis-margin model incorporating structure information for feature selection(Ssimba_FS). In the newly developed model, the structure information induced by clustering algorithms is incorporated into the existing hypothesis margin model for feature selection, and meanwhile the contribution of the structure information can be effectively adjusted by a trade-off parameter. Based on Ssimba_FS, we present a novel algorithm for feature selection(Ssimba). By Ssimba, an effectively ranked feature list can be obtained, futher a compact and relevant feature subset can be directly generated from the ranked feature list. The experiments on 6 real-life benchmark datasets show that the classifiers induced by the algorithm of this paper has better or comparable classification performance than those established by Simba in most cases.
Binary Decomposition Methods for Multipartite Ranking
Authors: Johannes Fürnkranz, Eyke Hüllermeier, Stijn Vanderlooy
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

Bipartite ranking refers to the problem of learning a ranking function from a training set of positively and negatively labeled examples. Applied to a set of unlabeled instances, a ranking function is expected to establish a total order in which positive instances precede negative ones. The performance of a ranking function is typically measured in terms of the AUC. In this paper, we study the problem of multipartite ranking, an extension of bipartite ranking to the multi-class case. In this regard, we discuss extensions of the AUC metric which are suitable as evaluation criteria for multipartite rankings. Moreover, to learn multipartite ranking functions, we propose methods on the basis of binary decomposition techniques that have previously been used for multi-class and ordinal classification. We compare these methods both analytically and experimentally, not only against each other but also to existing methods applicable to the same problem.
The Application of Unascertained Measure Model to the Safety Evaluation of Bofang Coal Seam-Roof Stabiity
Authors: Zhanglin Guo, Wu Yang, Hao Liu
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

Based on the unascertained theory, the paper elaborated exhaustively the modeling processes of the unascertained measure model. Combined with the unascertained measure model, the safety factors affecting coal seam-roof stability are analyzed and evaluated. According to the reality of Bofang coal seam-roof stability, we can collect the measured data, determine the safety evaluation index, calculate for example by using the model, and evaluate the safety level of coal seam-roof stability lying in six points of the third coal in Bofang coal-mine. The result is found to be in accordance with the reality, after it is verified .The method is designed to enhance the effectiveness of coal seam-roof stability safety evaluation, as provided the proper safety measures basis for the departments concerned.
Navigation in smart environments using mediated reality tools
Authors: Jorge Torres-Solis, Mei Guan, Elaine Biddiss, Tom Chau
Venue: Technology and Health Care (2009)

Topographical Disorientation (TD) is the lack or loss of orientation and navigation abilities. People living with TD face functional challenges in everyday situations. Smart mediated reality environments present potential solutions for cognitive conditions like TD. In this article, we introduce a novel mediated reality location aware environment. It was hypothesized that tools which offer different positional information affect the navigation performance of a user. The objective of this study was to investigate preferred assistive tools for indoor navigation for use in a proposed mediated reality wayfinding system. These tools may eventually be used to assist patients with TD. To this purpose, we designed a novel wayfinding metric that can be used in the assessment of navigation tasks similar to a scavenger hunt. This novel metric is based on a relative energy expenditure ratio and is independent of navigation route complexity. We investigated four sets of tools (minimap, locator, coordinate display and routing compass) that can be used in a smart mediated reality environment to provide relevant wayfinding information. These tools were designed using different combinations of spatial knowledge (landmark, route or survey), graphical presentation (compass, text, icon, top/side view) and reference frames (egocentric or allocentric). Each tool was evaluated objectively and subjectively. The locator and minimap tools emerged as preferred interfaces, providing the most relevant wayfinding information while minimizing energy expenditure during navigation tasks.
Sketch-Based Interfaces and Modeling (SBIM): Sketch-based parameterization of L-systems using illustration-inspired construction lines and depth modulation
Authors: Fabricio Anastacio, Przemyslaw Prusinkiewicz, Mario Costa Sousa
Venue: Computers and Graphics (2009)

We present a sketch-based interface for parameter control of rule-based models. It allows intuitive specification and creation of plant structures with L-systems. Construction lines inspired by concept sketches are employed as a way to define and manipulate global-to-local characteristics of L-system models. The overall structure, posture and proportions of the plant are initially sketched by the user as 2D construction line arrangements. They are automatically encoded as a set of positional functions controlling internode lengths, branching angles, organ sizes, and stem shape. The depth in the stem is specified while sketching its construction line, by modulating input pen pressure as depth values. This is also inspired by line depth modulation techniques used in traditional illustration. The resulting positional functions are then used to parameterize pre-defined L-system templates representing phyllotactic patterns guiding the positioning of lateral organ surfaces such as leaves and petals. Results are presented for single monopodial plant structures, all generated from simple input construction line sketches.
Constructing a parser evaluation scheme
Authors: Laura Rimell, Stephen Clark
Venue: Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation (2008)

In this paper we examine the process of developing a relational parser evaluation scheme, identifying a number of decisions which must be made by the designer of such a scheme. Making the process more modular may help the parsing community converge on a single scheme. Examples from the shared task at the COLING parser evaluation workshop are used to highlight decisions made by various developers, and the impact these decisions have on any resulting scoring mechanism. We show that quite subtle distinctions, such as how many grammatical relations are used to encode a linguistic construction, can have a significant effect on the resulting scores.
Failures and successes in collaborative inquiry: learning the physics of electricity with agent-based models
Authors: Suneeta A. Pathak, Beaumie Kim, Michael J. Jacobson, BaoHui Zhang
Venue: Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1 (2009)

This paper presents a process-oriented case study of successes and failures in collaborative inquiry. The interactions of pairs were recorded and transcribed while they were engaged in learning activities, mediated by agent-based NetLogo electricity models. Transcripts of learner interactions were coded for engagements in science inquiry. The purpose of this paper is to articulate the dynamics of collaborative science inquiry approach resulting from varied scaffolding and consistent scaffolding in learning activities. Our findings indicate that students under a varied scaffolding approach were more deeply engaged in inquiry process and performed better on model-based explanations.
Training collaboration skills to improve group dynamics
Authors: Rosanna Costaguta, Analía Amandi
Venue: Proceedings of the 2008 Euro American Conference on Telematics and Information Systems (2008)

Computer Supported Collaborative Learning (CSCL) systems have recognized advantages. However, using these systems does not guarantee an effective collaborative learning. Success or failure of the learning experience depends on the collaborative skills the students show in the group. This work presents a multiagent model applied to CSCL environment, which aims both at recognizing conflicts occurring in group dynamics and at providing personalized training of collaborative skills demonstrated by group members. Conflicts are recognized by applying the Interaction Process Analysis method. Personalization is achieved through Bayesian networks that consider students' collaborative characteristics to elucidate the most suitable training strategy. The model was implemented in a distance learning environment and showed great efficacy.
Trends Analysis of Topics Based on Temporal Segmentation
Authors: Wei Chen, Parvathi Chundi
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

Extracting interesting information from large unstructured document sets is a time consuming task. In this paper, we describe an approach to analyze the temporal trends of a given topic in a time-stamped document set based on time series segmentation. We consider topics containing multiple keywords and use a fuzzy set based method to compute a numeric value to measure the relevance of a document set to the given topic. The measure of relevance is then used to assign a discrepancy score to a segmentation of the time period associated with the document set. The discrepancy score of a segmentation represents the likelihood of the topic across all segments in a segmentation. Given a user specified value k, we then define a min different k segmentation to capture the k-segmentation with the maximum possible discrepancy score and describe a dynamic-programming based algorithm to compute it. The proposed approach is illustrated by several experiments using a subset of the TDT-Pilot Corpus data set. Our experiments show that the min difference k segmentation successfully highlights the temporal trends of a topic using k segments.
A distributed constraint optimization solution to the P2P video streaming problem
Authors: Theodore Elhourani, Nathan Denny, Michael Marefat
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

The future success of application layer video multicast depends on the availability of video stream distribution methods that can scale in the number of stream senders and receivers. Previous work on the problem of application layer video streaming has not effectively addressed scalability in the number of receivers and senders. Therefore, new solutions that are amenable to analysis and can achieve scalable P2P video streaming are needed. In this work we propose the use of automated negotiation algorithms to construct video streaming trees at the application layer. We show that automated negotiation can effectively solve the problem of distributing a video stream to a large number of receivers.
Robust Watermarking in Slides of Presentations by Blank Space Coloring: A New Approach
Authors: Tsung-Yuan Liu, Wen-Hsiang Tsai
Venue: Transactions on Data Hiding and Multimedia Security IV (2009)

A new robust method to embed imperceptibly a watermark image into the slides of a presentation is proposed. The watermark is partitioned into blocks and embedded into the space characters existing in the slides in a repeating pseudo-random sequence. The embedding is achieved by changing the colors of the space characters into new ones which are results of encoding the contents and indices of the blocks. The embedded watermark is resilient against many common modifications on slides, including copying and pasting of slides; insertion, deletion and reordering of slides; slide design changes; and file format conversions. A security key is used during embedding and extraction of a watermark, such that if an offending presentation contains slides taken from presentations watermarked with different security keys, each watermark can be extracted reliably in turn with the respective key using a weighted voting scheme also proposed in this study. Experiments conducted in Microsoft PowerPoint confirm the feasibility of the proposed method. On average, a recognizable watermark of size 64×64 can be extracted from a presentation containing five watermarked slides. The proposed method is useful for various applications of information hiding involving slides, including slide copyright protection, slide authentication, covert communication through slides, etc.
Fast subspace tracking algorithm based on the constrained projection approximation
Authors: Amir Valizadeh, Mahmood Karimi
Venue: EURASIP Journal on Advances in Signal Processing (2009)

We present a new algorithm for tracking the signal subspace recursively. It is based on an interpretation of the signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the constrained projection approximation subspace tracking (CPAST) algorithm, guarantees the orthonormality of the estimated signal subspace basis at each iteration. Thus, the proposed algorithm avoids orthonormalization process after each update for postprocessing algorithms which need an orthonormal basis for the signal subspace. To reduce the computational complexity, the fast CPAST algorithm is introduced which has O(nr) complexity. In addition, for tracking the signal sources with abrupt change in their parameters, an alternative implementation of the algorithm with truncated window is proposed. Furthermore, a signal subspace rank estimator is employed to track the number of sources. Various simulation results show good performance of the proposed algorithms.
Trojan Side-Channels: Lightweight Hardware Trojans through Side-Channel Engineering
Authors: Lang Lin, Markus Kasper, Tim Güneysu, Christof Paar, Wayne Burleson
Venue: Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems (2009)

The general trend in semiconductor industry to separate design from fabrication leads to potential threats from untrusted integrated circuit foundries. In particular, malicious hardware components can be covertly inserted at the foundry to implement hidden backdoors for unauthorized exposure of secret information. This paper proposes a new class of hardware Trojans which intentionally induce physical side-channels to convey secret information. We demonstrate power side-channels engineered to leak information below the effective noise power level of the device. Two concepts of very small implementations of Trojan side-channels (TSC) are introduced and evaluated with respect to their feasibility on Xilinx FPGAs. Their lightweight implementations indicate a high resistance to detection by conventional test and inspection methods. Furthermore, the proposed TSCs come with a physical encryption property, so that even a successful detection of the artificially introduced side-channel will not allow unhindered access to the secret information.
Learning to Rank Using Semantic Features in Document Retrieval
Authors: Tian Weixin, Zhu Fuxi
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

This paper describes an approach to retrieve documents adopting machine learning method. The application of machine learning to document retrieval, which can be so called “learning to rank”, has been a hot research topic in the information retrieval and machine learning communities recently. One of the characters that discriminates this work from other studies is the use of semantic features while classifying the documents. Firstly, we extract the semantic structures from collection and index documents on these structures. Later, we construct a SVM classifier and generate it using pairwise training data. Lastly, we use this SVM to judge the relevance of documents according to given queries. In the extracting of semantic structures phrase, an analysis system that we developed previously is used. The analysis system is based on modifying relations (MR) and on the help of a knowledge base called MRKB. We give a general introduction about the system in this paper. The experiment is done on the benchmark dataset OHSUMED, and the experimental results shows that the proposed method outperforms other approaches based barely on string frequency.
Global models of document structure using latent permutations
Authors: Harr Chen, S. R. K. Branavan, Regina Barzilay, David R. Karger
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

We present a novel Bayesian topic model for learning discourse-level document structure. Our model leverages insights from discourse theory to constrain latent topic assignments in a way that reflects the underlying organization of document topics. We propose a global model in which both topic selection and ordering are biased to be similar across a collection of related documents. We show that this space of orderings can be elegantly represented using a distribution over permutations called the generalized Mallows model. Our structure-aware approach substantially outperforms alternative approaches for cross-document comparison and single-document segmentation.
Enabling High Data Throughput in Desktop Grids through Decentralized Data and Metadata Management: The BlobSeer Approach
Authors: Bogdan Nicolae, Gabriel Antoniu, Luc Bougé
Venue: Proceedings of the 15th International Euro-Par Conference on Parallel Processing (2009)

Whereas traditional Desktop Grids rely on centralized servers for data management, some recent progress has been made to enable distributed, large input data, using to peer-to-peer (P2P) protocols and Content Distribution Networks (CDN). We make a step further and propose a generic, yet efficient data storage which enables the use of Desktop Grids for applications with high output data requirements, where the access grain and the access patterns may be random. Our solution builds on a blob management service enabling a large number of concurrent clients to efficiently read/write and append huge data that are fragmented and distributed at a large scale. Scalability under heavy concurrency is achieved thanks to an original metadata scheme using a distributed segment tree built on top of a Distributed Hash Table (DHT). The proposed approach has been implemented and its benefits have successfully been demonstrated within our BlobSeer prototype on the Grid'5000 testbed.
A Color Management Process for Real Time Color Reconstruction of Multispectral Images
Authors: Philippe Colantoni, Jean-Baptiste Thomas
Venue: Proceedings of the 16th Scandinavian Conference on Image Analysis (2009)

We introduce a new accurate and technology independent display color characterization model for color rendering of multispectral images. The establishment of this model is automatic, and does not exceed the time of a coffee break to be efficient in a practical situation. This model is a part of the color management workflow of the new tools designed at the C2RMF for multispectral image analysis of paintings acquired with the material developed during the CRISATEL European project. The analysis is based on color reconstruction with virtual illuminants and use a GPU (Graphics processor unit) based processing model in order to interact in real time with a virtual lighting.
Orientation Plasticity in Visual Cortex of Mice Reared under Single-Orientation Exposure
Authors: Takamasa Yoshida, Toshiki Tani, Shigeru Tanaka
Venue: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning (2009)

To examine whether orientation selectivity in the mouse visual cortex can change depending on visual experience, we reared juvenile and adult mice under single-orientation exposure using cylindrical-lens-fitted goggles that extremely elongate visual images vertically. Immediately after goggle rearing, we performed optical imaging of intrinsic signals in the visual cortex of the mouse, while presenting 6 oriented grating stimuli. The distribution of preferred orientations was markedly biased toward the exposed vertical orientation in juvenile goggle-reared mice, whereas the distribution in normally reared mice showed a maximum at horizontal orientation and a minimum at vertical orientation. In contrast, no significant differences in the orientation distribution were found between 1-week goggle-reared and normally reared adult mice. However, in 2- or 3-week goggle-reared adult mice, the relative area maximally responding to the vertical orientation was slightly larger than that in normally reared adult mice whereas the horizontal bias was preserved. The present study demonstrated that postnatal visual experience can modify orientation selectivity in both juvenile and adult mice.