ppxscal/embedding_network huggingface dataset:
Security Weakness in a Provable Secure Authentication Protocol Given Forward Secure Session Key
Authors: Mijin Kim, Heasuk Jo, Seungjoo Kim, Dongho Won
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Shi, Jang and Yoo recently proposed a provable secure key distribution and authentication protocol between user, service provider and key distribution center(KDC). The protocol was based on symmetric cryptosystem, challenge-response, Diffie-Hellman component and hash function. Despite the claim of provable security, the protocol is in fact insecure in the presence of an active adversary. In this paper, we present the imperfection of Shi et al.'s protocol and suggest modifications to the protocol which would resolve the problem.
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model
Authors: Leila Ben Othman, François Rioult, Sadok Ben Yahia, Bruno Crémilleux
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

Handling missing values when tackling real-world datasets is a great challenge arousing the interest of many scientific communities. Many works propose completion methods or implement new data mining techniques tolerating the presence of missing values. It turns out that these tasks are very hard. In this paper, we propose a new typology characterizing missing values according to relationships within the data. These relationships are automatically discovered by data mining techniques using generic bases of association rules. We define four types of missing values from these relationships. The characterization is made for each missing value. It differs from the well-known statistical methods which apply a same treatment for all missing values coming from a same attribute. We claim that such a local characterization enables us perceptive techniques to deal with missing values according to their origins: the way in which we deal with the missing values should depend on their origins (e.g., attribute meaningless w.r.t. other attributes, missing values depending on other data, missing values by accident). Experiments on a real-world medical dataset highlight the interests of such a characterization.
iROVER: improving system combination with classification
Authors: D. Hillard, B. Hoffmeister, M. Ostendorf, R. Schlüter, H. Ney
Venue: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers on XX (2007)

We present an improved system combination technique, iROVER, Our approach obtains significant improvements over ROVER, and is consistently better across varying numbers of component systems. A classifier is trained on features from the system lattices, and selects the final word hypothesis by learning cues to choose the system that is most likely to be correct at each word location. This approach achieves the best result published to date on the TC-STAR 2006 English speech recognition evaluation set.
A noise-induced stability in the real-time robotic system for object handling
Authors: Hiroaki Wagatsuma
Venue: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning (2009)

In various engineering fields, separation between signal and noise is one of the important issues for the robustness of systems that are working in the real environment, and the noise reduction has been discussed when designing a robust system. On the other hand, some researches reported that adding a noise contributes to having multiple internal states and enhancing a transition between the states in the case of non-linear and biological systems, such as the stochastic resonance. It leads to a hypothesis of neuro-mimetic models in which the noise enhances their performance. We have developed the robotic platform as a combination between the real-time simulator of neural dynamics and the robotic device operating in the real world. According to communicative interruptions and time lags, the real-time simulator has the limitation in ability to control the robot, especially in time domain. The robot frequently fails in making an action with respect to the previous sensor data if the calculation is done in the proper timing, providing a deadlock behavior. We here investigated the effect of the noise induction for escaping the deadlock and completion of the ball-handling task, and reported that a self-biased noise helps a enlargement of the range of delay in the system for exhibiting proper performances. By focusing on the temporal aspect of the noise effect in non-linear systems, our research approach may benefit to the implementation and development of biological models in the real system.
Discovering Trends and Relationships among Rules
Authors: Chaohai Chen, Wynne Hsu, Mong Li Lee
Venue: Proceedings of the 20th International Conference on Database and Expert Systems Applications (2009)

Data repositories are constantly evolving and techniques are needed to reveal the dynamic behaviors in the data that might be useful to the user. Existing temporal association rules mining algorithms consider time as another dimension and do not describe the behavior of rules over time. In this work, we introduce the notion of trend fragment to facilitate the analysis of relationships among rules. Two algorithms are proposed to find the relationships among rules. Experiment results on both synthetic and real-world datasets indicate that our approach is scalable and effective.
Ontology Based Information Integration of Heterogeneous Resources
Authors: Xia Li, Bei Wu
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

This paper proposed an approach to describe and manage the e-learning resources. The learning resources is firstly described in xml format using the share content object reference model standard, then the binding xml file and learning resource content is produced. Secondly, the described learning resources are put into the database and management according to the content package specification and content partition approach. Finally, the learning resource scenario is analyzed in details
Semi-blind Fingerprinting Utilizing Ordinary Existing Watermarking Techniques
Authors: Mitsuo Okada, Yasuo Okabe, Tetsutaro Uehara
Venue: Proceedings of the 8th International Workshop on Digital Watermarking (2009)

We propose a concept of feasible blind fingerprinting called "semi-blind fingerprinting" which provides as secure content trading as conventional blind fingerprinting methods. In a basic model of conventional fingerprinting for content trading, user's security could be guaranteed only under the premise that a content provider is trustworthy. Such premise makes a scheme unpractical. To overcome this defect, various blind methods have been proposed in which cryptography is used in order to protect the information on a user. However, computation cost and robustness have not been well considered as a scheme should be feasible, though it provides high-level of protection. A semi-blind scheme fulfills a need for both feasibility and robustness. Under this scheme, feasibility can be achieved by applying newly proposed functions called "Image Division" instead of using cryptography. In order to gain robustness, a protocol for watermarking has been so designed that any well-developed existing watermarking technique can be applied with no restriction. We will not only propose the idea but also implement and evaluate several image division functions as well as an optimized embedding program in order to prove validity of the proposed method.
Multicriteria Attractiveness Evaluation of Decision and Association Rules
Authors: Izabela Szczech
Venue: Transactions on Rough Sets X (2009)

The work is devoted to multicriteria approaches to rule evaluation. It analyses desirable properties (in particular the property M, property of confirmation and hypothesis symmetry) of popular interestingness measures of decision and association rules. Moreover, it analyses relationships between the considered interestingness measures and enclosure relationships between the sets of non-dominated rules in different evaluation spaces. It's main result is a proposition of a multicriteria evaluation space in which the set of non-dominated rules will contain all optimal rules with respect to any attractiveness measure with the property M. By determining the area of rules with desirable value of a confirmation measure in the proposed multicriteria evaluation space one can narrow down the set of induced rules only to the valuable ones. Furthermore, the work presents an extension of an apriori-like algorithm for generation of rules with respect to attractiveness measures possessing valuable properties and shows some applications of the results to analysis of rules induced from exemplary datasets.
Experimenting with Sound Immersion in an Arts and Crafts Museum
Authors: Fatima-Zahra Kaghat, Cécile Prado, Areti Damala, Pierre Cubaud
Venue: Proceedings of the 8th International Conference on Entertainment Computing (2009)

Technical museums are goods targets for experimenting with sound immersion and soundscape authoring. This paper presents an immersive sound system emitting audio content. Experimentations were conducted with a wired, proof-of-concept prototype and two wireless devices. Our system takes into consideration the position of museum visitors as well as their orientation and visual vector. In contrast with other approaches, tracking and rendering are executed locally and in real-time by the visitor's device.
Arabic script-based languages deserve to be studied linguistically
Authors: Martin Kay
Venue: Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages (2004)

Arabic script-based languages are attracting increased attention for reasons that are regrettably far from their intrinsic linguistic interest. At the same time, statistical and corpus-based approaches to language processing are acquiring such dominance that it is becoming difficult for the advocates of other methods even to receive a hearing. I will argue that this is an alarming trend against which computational linguists, and especially those studying these languages, should resist with great determination. My argument for this position rests on the following observations: 1. Unless the role of quantum mechanics and chaos in the workings of ordinary language has been grossly underestimated, nothing about the subject is probabilistic in any fundamental sense. 2. The statistics are a surrogate for knowledge of the world and artificial intelligence and the performance of any system based on an approach that reduces this to numerical annotations on linguistic structures can only hope to reach a very low asymptote. 3. Thanks to Zipf's law, corpus annotation is subject to a severe law of diminishing returns to which the linguist's search for significant generalizations is not subject. 4. To the various levels of linguistic analysis and to the indefinite range of subjects and propositions that texts may treat, there correspond different notions of locality, each requiring its own statistical models. 5. Most importantly, most of the linguistic properties that must be considered for text processing are not emergent properties of the texts at all but crucially depend on l'arbitraire du signe, the arbitrary relation between a symbol and what it symbolizes.
Dynamic displays
Authors: Chris Harrison, Scott Hudson
Venue: Crossroads (2009)

While touchscreens allow extensive programmability and have become ubiquitous in today's gadgetry, such configurations lack the tactile sensations and feedback that physical buttons provide. As a result, these devices require more attention to use than their button-enabled counterparts. Still, the displays provide the ultimate interface flexibility and thus afford a much larger design space to application developers.
Incremental Naïve Bayesian Spam Mail Filtering and Variant Incremental Training
Authors: Phimphaka Taninpong, Sudsanguan Ngamsuriyaroj
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

This paper proposes an incremental spam mail filtering using Naïve Bayesian classification which gives simplicity and adaptability. To keep the training set to a limited size and small, the sliding window is applied and the training set is updated when new emails are received. In effect, features in the training set are incrementally updated, and the model would be adaptive to a new spam pattern. In addition, we present three incremental training schemes: a window containing only the most recent emails, a window containing the previous batch of emails, and a window containing all already seen emails. The proposed model is evaluated using two spam corpora: Trec05p-1 [12] and Trec06p [13]. In our experiments, the window size is varied, the processing time per message, and the ham and spam misclassification rates are measured. The results show that the third incremental training scheme gives the best outcomes, and the window size significantly affects the misclassification rates and the processing time.
Demo abstract: Embedded Virtual Machines for wireless industrial automation
Authors: Rahul Mangharam, Miroslav Pajic, Shivakumar Sastry
Venue: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks (2009)

The factory of the future is the Wireless Factory - fully programmable, nimble and adaptive to planned mode changes and unplanned faults. Today automotive assembly lines loose over $22,000 per minute of downtime. The systems are rigid, difficult to maintain, operate and diagnose. Our goal is to demonstrate the initial architecture and protocols for all-wireless factory control automation. Embedded wireless networks have largely focused on open-loop sensing and monitoring. To address actuation in closed-loop wireless control systems there is a strong need to re-think the communication architectures and protocols for reliability, coordination and control. As the links, nodes and topology of wireless systems are inherently unreliable, such time-critical and safety-critical applications require programming abstractions where the tasks are assigned to the sensors, actuators and controllers as a single component rather than statically mapping a set of tasks to a specific physical node at design time. To this end, we introduce the Embedded Virtual Machine (EVM), a powerful and flexible runtime system where virtual components and their properties are maintained across node boundaries. EVM-based algorithms introduce new capabilities such as provably minimal graceful degradation during sensor/actuator failure, adaptation to mode changes and runtime optimization of resource consumption. Through the design of a micro-factory we aim to demonstrate the capabilities of EVM-based wireless networks.
Exponential Stability of Cellular Neural Networks with Time-varying Delay
Authors: Xueli Wu, Xiangting Meng, Xuan Lv
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

A novel method is proposed in this note for exponential stability of cellular neural networks with time-varying delay. New delay-dependent exponential stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, a numerical example is given to demonstrate the effect of the proposed method.
Context-Aware Preference Model Based on a Study of Difference between Real and Supposed Situation Data
Authors: Chihiro Ono, Yasuhiro Takishima, Yoichi Motomura, Hideki Asoh
Venue: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH (2009)

We propose a novel approach for constructing statistical preference models for context-aware recommender systems. To do so, one of the most important but difficult problems is acquiring sufficient training data in various contexts/situations. Particularly, some situations require a heavy workload to set them up or to collect subjects under those situations. To avoid this, often a large amount of data in a supposed situation is collected, i.e., a situation where the subject pretends/imagines that he/she is in a specific situation. Although there may be difference between the preference in the real situation and the supposed situation, this has not been considered in existing researches. Here, to study the difference, we collected a certain amount of corresponding data. We asked subjects the same question about preference both in the real and the supposed situation. Then we proposed a new model construction method using a difference model constructed from the correspondence data and showed the effectiveness through the experiments.
Learning to prevent failure states for a dynamically balancing robot
Authors: Jeremy Searock, Brett Browning
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

To achieve robust autonomy, robots must avoid getting stuck in states from which they cannot recover without external aid. While this is the role of the robot's control algorithms, these are often imperfect. We examine how to detect failures by observing the robot's internal sensors over time. For such cases, triggering a response when detecting the onset of a failure can increase the operational range of the robot. Concretely, we explore the use of supervised learning techniques to create a classifier that can detect a potential failure and trigger a response for a dynamically balancing robot. We present a fully implemented system, where the results clearly demonstrate an improved safety margin for the robot.
The Study on Construction of E-government Platform Based on SOA and Efficiency Comparison of Serving Model
Authors: Chunnian Liu, Hua Xiao
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Low entry cost and high expected returns in the E-Government boom in the recent decades in China. As E-government becomes a very active research area, a lot of solutions aiming to provide citizens, business partners and administrative staff with information more accessible, effective and accountable are being deployed. Yet, how to build a more effective serving platform of E-government and how to better serve citizens and enterprises still remains an open question. In this case, this paper analyzes the construction of E-government platform based on SOA and serving models of E-government, and makes an efficiency comparison of serving model.
An Enhanced Cross-Layer Protocol for Energy Efficiency in Wireless Sensor Networks
Authors: Jaehyun Kim, Jaiyong Lee, Seoggyu Kim
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

In wireless sensor networks, the simplified and energy efficient protocol should be designed in order to maximize the network lifetime because of its stringent resource constraints, ultra power limitation, and tiny embedded devices. In this paper, we propose an enhanced cross-layer protocol for energy efficiency in wireless sensor networks by integrating medium access control and routing protocol. Our proposed protocol utilizes a synchronous medium access control scheme by using the adaptive duty cycling technique to improve energy efficiency and solve long end-to-end delay problem. We also design a tree-based energy aware routing algorithm to prolong the network lifetime in our protocol. Simulation results show that the proposed protocol outperforms other existing algorithms in terms of energy efficiency and latency.
Multi-robot exploration of an unknown environment, efficiently reducing the odometry error
Authors: Ioannis M. Rekleitis, Gregory Dudek, Evangelos E. Milios
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

This paper deals with the intelligent exploration of an unknown environment by autonomous robots. In particular, we present an algorithm and associated analysis for collaborative exploration using two mobile robots. Our approach is based on robots with range sensors limited by distance. By appropriate behavioural strategies, we show that odometry (motion) errors that would normally present problems for mapping can be severely reduced. Our analysis includes polynomial complexity bounds and a discussion of possible heuristics.
Relation detection between named entities: report of a shared task
Authors: Cláudia Freitas, Diana Santos, Cristina Mota, Hugo Gonçalo Oliveira, Paula Carvalho
Venue: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (2009)

In this paper we describe the first evaluation contest (track) for Portuguese whose goal was to detect and classify relations between named entities in running text, called ReRelEM. Given a collection annotated with named entities belonging to ten different semantic categories, we marked all relationships between them within each document. We used the following fourfold relationship classification: identity, included-in, located-in, and other (which was later on explicitly detailed into twenty different relations). We provide a quantitative description of this evaluation resource, as well as describe the evaluation architecture and summarize the results of the participating systems in the track.
Kansei Engineering Framework to Construct a Kansei Service Model: Extension of Kansei Information Related Ontology
Authors: Kentarou Fukuta, Takashi Uozumi, Tomomasa Nagashima
Venue: Proceedings of the 2009 International Conference on Biometrics and Kansei Engineering (2009)

Advanced development of a system operating between humans and computers (human friendly system) is more important than the evolution of computer hardware. The salient obstacle to its progress is "How do we acquire and process Kansei? How do we produce a user model and a network service using Kansei?" For preliminary investigation, we constructed fatigue expression models based on a Bayesian network (BN) using psychological and physiological data. We proposed a convergent inference system framework using multiple BNs based on ontology and agent technology. Then we described a Kansei engineering framework for Kansei information processing in a ubiquitous home and smart home. As described herein, we design new ontological categories to construct the service model using Kansei information. Results show that the Kansei modeling using our framework addresses the inference procedure and the inference result of Kansei information as an instance of ontology.