ppxscal/embedding_network huggingface dataset:
Implementation and Evaluation of Wearable Sensor Devices for Recording and Detecting Conversational Interaction
Authors: Sadanori Ito, Shoichiro Iwasawa, Megumu Tsuchikawa, Yasuyuki Sumi, Kenji Mase, Yasuhiro Katagiri, Kiyoshi Kogure, Norihiro Hagita
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

We have developed a wearable device that records the activities of human-human and human-artifact interactions. The device records personal and social everyday-life experiences in multiple modalities such as voice and visible scene, using microphone and camera to imitate human perceptibility. These sensors are used to record the perceived experiences continuously as well as to detect and index interactions from nonverbal behaviors. We employ an infrared LED ID tag system to detect the interactions in terms of the ID and its relative positions of the objects in the visual field of the camera. In this paper, we propose the "Interaction Scope" which is defined as the range of relative human/object positions that have a high probability of occurring in conversational interactions. We confirm that the interaction scope exists and can represent the interactions naturally through the analysis of experimental conversational sessions. We also demonstrate that our tag system can be effectively used as a sensor that detects and measures the proposed interaction scope.
An Agile Method of Modeling Business Process Simulation for Virtual Enterprises
Authors: Shaowu Cheng, Xiaofei Xu, Gang Wang, Quanlong Li
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2005)

The agility of modeling business process simulation (BPS) is required by virtual enterprises. Firstly with some new concepts, such as the atomic business process simulation model (BPSM) and the business process simulation federation (BPSF) etc., a frame of business process simulation model for virtual enterprises (VE-BPSM) is presented. Business processes of a member enterprise are classified into three groups: internal, external and hybrid eventtriggered processes. The inter-relationship among them is discussed. To facilitate the business process simulation model for a member enterprise (MEBPSM) to be reused, every external-event-triggered BPSM or every hybrid event-triggered BPSM is built as an atomic business process simulation model (Atomic-BPSM). The VE-BPSM can be built as a BPSF through combining Atomic-BPSM of member enterprises, so the agility of modeling BPS for a virtual enterprise could be achieved. Finally, an HLA-based method of developing a VE-BPSM is discussed.
Data profiling and the access path model: a step toward addressing insider misuse in database systems
Authors: Madhavi Gandhi
Venue:  (2005)

In recent years, there has been a significant increase in the use of complex information infrastructures by government, industry, military, and academia. The users of these infrastructures depend heavily on the availability, confidentiality, and accuracy of the information. Despite many advances in intrusion detection systems (IDS), insider misuse, in which the malicious behavior (intentional or accidental) comes from within a system, such as the operating system or a database system, is difficult to detect using IDS. This leaves the mission critical data managed in a database vulnerable to misuse. The problem of insider misuse is complex and non-trivial. The models and techniques presented in this dissertation take a step toward addressing this problem. The approach is &ldquo;data-centric&rdquo; as it focuses on protecting the mission critical data from insider misuse. This approach facilitates the enforcement of information integrity principles of least privilege and accountability. The two major aspects of the approach presented include data profiling framework to describe the data behavior and the access path model to describe access paths to this data from database users, application users, and operating system users. The access path model is enriched further to include profile information regarding the data, their users, and their access correlations. Focusing on relational database systems, a methodology is presented for data profiling based on the observed data values and data accesses over a period of time. Finally, the access path model and the data profiles are used to derive a fine-grained access policy, which forms the basis for preventing, deterring, and detecting insider misuse.
Object deformation and force feedback for virtual chopsticks
Authors: Yoshifumi Kitamura, Ken'ichi Douko, Makoto Kitayama, Fumio Kishino
Venue: Proceedings of the ACM symposium on Virtual reality software and technology (2005)

This paper proposes a virtual chopsticks system using force feedback and object deformation with FEM (finite element model). The force feedback model is established by using a leverage based on the correct chopsticks handling manner, and the force is applied to the index and middle finger. The object deformation is obtained in real-time by calculating inverse stiffness matrix beforehand. We performed experiments to compare the hardness of virtual objects. As a result, we found that a recognition rate of almost 100% can be achieved between virtual objects where the logarithmic difference in hardness is 0.4 or more, while lower recognition rates are obtained when the difference in hardness is smaller than this.
Table-Driven Code Optimizer
Authors: Sung-Lim Yun, Se-Man Oh
Venue: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (2005)

The Various optimization techniques are deployed in the compilation process of a source program for improving the program's execution speed and reducing the size of the source code[2]. Of the optimization pattern matching techniques, the string pattern matching technique involves finding an optimal pattern that corresponds to the intermediate code. However, it is deemed inefficient due to excessive time required for optimized pattern search. The tree matching pattern technique can result in many redundant comparisons for pattern determination, and there is also the disadvantage of high cost involved in constructing a code tree[8][15]. The objective of this paper is to propose a tabledriven code optimizer using the DFA(Deterministic Finite Automata)[6][11] optimization table to overcome the shortcomings of existing optimization techniques. Unlike other techniques, this is an efficient method of implementing an optimizer that is constructed with the deterministic automata, which determines the final pattern, reducing the pattern selection cost and expediting the pattern search process.
From gesture recognition to functional motion analysis: quantitative techniques for the application and evaluation of human motion
Authors: Melinda Marie Cerney
Venue:  (2005)

The quantification and analysis of human motion is a central focus of many studies in biology, anthropology, biomechanics, human factors and ergonomics. These works are primarily concerned with describing the relationships between structure and function from a quantitative perspective. Recent work has seen the application of functional analysis techniques and their associated models to such diverse areas as surveillance, human computer interaction, and game development by researchers in the areas of computer graphics, robotics, computer vision, and machine learning. The work in this dissertation is motivated by the study and quantification of gesture and human motion. This research explores the characteristics of gesture-based interactions, the development of a gesture recognition tool for virtual reality environments, the quantification and analysis of a sequence of postures as a complete motion, and the application of the motion analysis methods to a lifting and fatigue study. The result is a look at gesture and motion analysis from its role in interaction to its ability to quantify relationships between structure and function.
Mining Ontological Knowledge from Domain-Specific Text Documents
Authors: Xing Jiang, Ah-Hwee Tan
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as Text-To-Onto, has shown that CRCTOL produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures.
Caption Localisation in Video Sequences by Fusion of Multiple Detectors
Authors: Sebastien Lefevre, Nicole Vincent
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In this article, we focus on the problem of caption detection in video sequences. Contrary to most of existing approaches based on a single detector followed by an ad hoc and costly post-processing, we have decided to consider several detectors and to merge their results in order to combine advantages of each one. First we made a study of captions in video sequences to determine how they are represented in images and to identify their main features (color constancy and background contrast, edge density and regularity, temporal persistence). Based on these features, we then select or define the appropriate detectors and we compare several fusion strategies which can be involved. The logical process we have followed and the satisfying results we have obtained let us validate our contribution.
Privacy for RFID through trusted computing
Authors: David Molnar, Andrea Soppera, David Wagner
Venue: Proceedings of the 2005 ACM workshop on Privacy in the electronic society (2005)

Radio Frequency Identification (RFID) technology raises significant privacy issues because it enables tracking of items and people possibly without their knowledge or consent. One of the biggest challenges for RFID technology is to provide privacy protection without raising tag production and management cost. We introduce a new architecture that uses trusted computing primitives to solve this problem. Our design splits the RFID reader into three software modules: a Reader Core with basic functionality, a Policy Engine that controls the use of RFID-derived data, and a Consumer Agent that performs privacy audits on the RFID reader and exports audit results to third party auditors. Readers use remote attestation to prove they are running a specific Reader Core, Policy Engine, and Consumer Agent. As a result, remote attestation allows concerned individuals to verify that RFID readers comply with privacy regulations, while also allowing the reader owner to verify that the reader has not been compromised.Furthermore, industry standards bodies have suggested several mechanisms to protect privacy in which authorized readers use a shared secret to authenticate themselves to the tag. These standards have not fully addressed issues of key management. First, how is the shared secret securely provided to the legitimate reader? Second, how do we guarantee that the reader will comply with a specific privacy policy? We show how, with remote attestation, the key-issuing authority can demand such a proof before releasing shared secrets to the reader. We also show how sealed storage can protect secrets even if the reader is compromised. Finally, we sketch how our design could be implemented today using existing RFID reader hardware.
Shaping Requirements for Institutional Web Applications: Experience from an Industrial Project
Authors: Vito Perrone, Davide Bolchini, Andrea Rastellini, Luigi Dragone
Venue: Proceedings of the 13th IEEE International Conference on Requirements Engineering (2005)

In this paper we recount the experience and the lessons we learned trying to use concretely and in-depth a requirements engineering method (called AWARE) combined with a conceptual user-centered design method (called W2000) for the development of the Institutional website of the Superior Council for Public Works of the Ministry of Infrastructure and Transportation in Italy. The project is framed within its contractual context, and discussed through the process followed and the artifacts produced during requirements analysis and design. Lessons learned are depicted from a twofold perspective. From the one side they crystallize insights from the research experience of using and transferring the methods to industry, suggesting practical and methodological recommendations. From the other they express what a medium-size software company has experienced trying to use academic methods.
Building adaptable cache services
Authors: Laurent d'Orazio, Fabrice Jouanot, Cyril Labbé, Claudia Roncancio
Venue: Proceedings of the 3rd international workshop on Middleware for grid computing (2005)

Caching is crucial to improve performances in many computing systems. It is context dependent, thus many types of cache exist. As a consequence, when a cache is required, it is usually built from scratch. Such a solution is time (and money) consuming, in particular in data grid context where several caches may be required. This paper proposes ACS (Adaptable Cache Service), a framework which allows building adaptable cache services. It presents a generic cache definition and provides a description of models implemented in the framework. ACS aims at facilitating the development of cache services for any application. Our current experiences concern their use in building a data grid middleware (Gedeon project).
An Algebraic Framework for Merging Incomplete and Inconsistent Views
Authors: Mehrdad Sabetzadeh, Steve Easterbrook
Venue: Proceedings of the 13th IEEE International Conference on Requirements Engineering (2005)

View merging, also called view integration, is a key problem in conceptual modeling. Large models are often constructed and accessed by manipulating individual views, but it is important to be able to consolidate a set of views to gain a uni?ed perspective, to understand interactions between views, or to perform various types of end-to-end analysis. View merging is complicated by inconsistency of views. Once views are merged, it is useful to be able to trace the elements of the merged view back to their sources. In this paper, we propose a framework for merging incomplete and inconsistent graph-based views. We introduce a formalism, called annotated graphs, which incorporates a systematic annotation scheme capable of modeling incompleteness and inconsistency as well as providing a built-in mechanism for stakeholder traceability. We show how structure-preserving maps can capture the relationships between disparate views modeled as annotated graphs, and provide a general algorithm for merging views with arbitrary interconnections. We use the i* modeling language [31] as an example to demonstrate how our approach can be applied to existing graphbased modeling languages.
The Genetic Kernel Support Vector Machine: Description and Evaluation
Authors: Tom Howley, Michael G. Madden
Venue: Artificial Intelligence Review (2005)

The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings
The MHETA Execution Model for Heterogeneous Clusters
Authors: Mario Nakazawa, David K. Lowenthal, Wendou Zhou
Venue: Proceedings of the 2005 ACM/IEEE conference on Supercomputing (2005)

The availability of inexpensive "off the shelf" machines increases the likelihood that parallel programs run on heterogeneous clusters of machines. These programs are increasingly likely to be out of core, meaning that portions of their datasets must be stored on disk during program execution. This results in significant, per-iteration, I/O cost.This paper describes an execution model, called MHETA, which is the key component to finding an effective data distribution on heterogeneous clusters. MHETA takes into account computation, communication, and I/O costs of iterative scientific applications. MHETA uses automatically extracted information from a single iteration to predict the execution time of the remaining iterations. Results show that MHETA predicts with on average 98% accuracy the execution time of several scientific benchmarks (with and without prefetching) and one full-scale scientific program that utilize pipelined and other communication. MHETA is thus an effective tool when searching for the most effective distribution on a heterogeneous cluster.
A novel framework for energy and application-aware data gathering in wireless sensor networks
Authors: Wook Choi
Venue:  (2005)

A wireless sensor network is an application-specific information gathering platform where sensors are required to sense their vicinity (sensing coverage) continuously, consuming highly limited resources such as energy which may not often be replenishable. Thus, an important issue in sensor networks is to design energy-aware algorithms and protocols that optimize energy consumption with a goal to extend the network lifetime while meeting the user requirements such as coverage and data reporting latency. The sensitivity to these requirements varies depending on the type of applications, implying that the designed algorithms and protocols must also be application-aware . In this dissertation, we propose a novel framework for energy and application-aware data gathering in wireless sensor networks. More specifically, our framework includes two strategies: a cluster-based delay-adaptive data gathering strategy (CD-DGS) and a coverage-adaptive data gathering strategy (CA-DGS). The first strategy, called CD-DGS, is based on a two-phase clustering scheme that requests sensors to construct two types of links: direct and relay links. The direct links are used for control and forwarding time-critical sensed data. On the other hand, the relay links are used only for data forwarding based on the user delay constraints, thus allowing the sensors to opportunistically use the most energy-saving links and forming a multi-hop path. Simulation results demonstrate that CD-DGS saves a significant amount of energy for dense sensor networks by adapting to the user delay constraints. The second strategy, called CA-DGS, is based on a trade-off between sensing coverage and data reporting latency. The basic idea is to select in each round a minimum of k data reporters (sensors) which are sufficient for the desired sensing coverage (DSC) specified by the users/applications. For selecting k reporters in a round, we make use of three efficient coverage-adaptive random sensor selection (CANSEE) schemes. These reporters form a data gathering tree and are scheduled to remain active for that round only. This process incurs some delay but saves energy. We derive a probabilistic bound on k and also estimate the probability for having almost surely k data reporters in each round. Finally, we apply the Poisson sampling technique to improve the spatial regularity of the selected k sensors and propose an enhanced selection scheme, called constrained random sensor selection (CROSS). Probabilistic analysis shows that the CROSS scheme improves the connectivity of the selected sensors and reduces the variance on the sensor covered area in each round. Simulation results demonstrate that CA-DGS results in a significant conservation of energy with a small trade-off in terms of data reporting latency. In particular, the higher the network density, the higher is the energy conservation without any additional computational overhead.
A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion
Authors: Davide Scaramuzza, Agostino Martinelli, Roland Siegwart
Venue: Proceedings of the Fourth IEEE International Conference on Computer Vision Systems (2006)

In this paper, we present a flexible new technique for single viewpoint omnidirectional camera calibration. The proposed method only requires the camera to observe a planar pattern shown at a few different orientations. Either the camera or the planar pattern can be freely moved. No a priori knowledge of the motion is required, nor a specific model of the omnidirectional sensor. The only assumption is that the image projection function can be described by a Taylor series expansion whose coefficients are estimated by solving a two-step least-squares linear minimization problem. To test the proposed technique, we calibrated a panoramic camera having a field of view greater than 200 in the vertical direction, and we obtained very good results. To investigate the accuracy of the calibration, we also used the estimated omni-camera model in a structure from motion experiment. We obtained a 3D metric reconstruction of a scene from two highly distorted omnidirectional images by using image correspondences only. Compared with classical techniques, which rely on a specific parametric model of the omnidirectional camera, the proposed procedure is independent of the sensor, easy to use, and flexible.
The structure of super line graphs.
Authors: Jay Bagga, Daniela Ferrero
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

For a given graph G = (V,E) and a positive integer k, the super line graph of index k of G is the graph Sk(G) which has for vertices all the k-subsets of E(G), and two vertices S and T are adjacent whenever there exist s \varepsilon S and t \varepsilon T such that s and t share a common vertex. In the super line multigraph Lk(G) we have an adjacency for each such occurrence.We give a formula to find the adjacency matrix of L_k(G). If G is a regular graph, we calculate all the eigenvalues of L_k(G) and their multiplicities. From those results we give an upper bound on the number of isolated vertices.