ppxscal/embedding_network huggingface dataset:
A Computational Model of Goal Appraisal
Authors: William Jarrold
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Goal appraisal is formulated as a specific sub-problem of cognitive appraisal. A computational model of goal appraisal is built via a knowledge-based system. An empirical validation study compared model versus human performance by eliciting believability judgments from naive human raters. Results showed that computer model goal appraisals were, in most cases, as believable as human-generated appraisals.
The Effect of Task Relevance on Electrophysiological Response to Emotional Stimuli
Authors: Baolin Liu, Shuai Xin, Zhixing Jin, Xiaorong Gao, Shangkai Gao, Renxin Chu, Yongfeng Huang, Beixing Deng
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

To verify whether or not the emotion processing is modulated by task relevance, in this paper two tasks are performed - Simple Task and Complex Task. In the Simple Task, negative pictures are target stimuli, while in the Complex Task white-framed negative pictures are target stimuli. Subjects are required to respond when a target stimulus is onset. The EEG (electroencephalogram) epochs are averaged and ERP (event-related potential) components are obtained. The P300 amplitude is smaller in the Complex Task than in the Simple Task, which proves that the emotion P300 is significantly modulated by task relevance. As P1 and N1 amplitudes are decreased in the Complex Task comparing with the Simple Task, we can suggest that the P1/N1 components elicited by emotional stimuli are modulated by task relevance, too.
On List Update with Locality of Reference
Authors: Susanne Albers, Sonja Lauer
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We present a comprehensive study of the list update problem with locality of reference. More specifically, we present a combined theoretical and experimental study in which the theoretically proven and experimentally observed performance guarantees of algorithms match or nearly match. In the first part of the paper we introduce a new model of locality of reference that is based on the natural concept of runs. Using this model we develop refined theoretical analyses of popular list update algorithms. The second part of the paper is devoted to an extensive experimental study in which we have tested the algorithms on traces from benchmark libraries. It shows that the theoretical and experimental bounds differ by just a few percent. Our new bounds are substantially lower than those provided by standard competitive analysis. Another result is that the elegant Move-To-Front strategy exhibits the best performance, which confirms that it is the method of choice in practice.
Open Source Workflow: A Viable Direction for BPM?
Authors: Petia Wohed, Nick Russell, Arthur H. Hofstede, Birger Andersson, Wil M. Aalst
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

With the growing interest in open source software in general and business process management and workflow systems in particular, it is worthwhile investigating the state of open source workflow management. The plethora of these offerings (recent surveys such as [4,6], each contain more than 30 such systems) triggers the following two obvious questions: (1) how do these systems compare to each other; and (2) how do they compare to their commercial counterparts. To answer these questions we have undertaken a detailed analysis of three of the most widely used open source workflow management systems [1]: jBPM, OpenWFE, and Enhydra Shark. Another obvious candidate would have been the open-source workflow management system YAWL ( www.yawlfoundation.org ). However, given the authors' close involvement in the development of YAWL, we did not include it in our evaluation.
Computed Torque Control of a Quaternion Based Space Robot
Authors: Douglas R. Isenberg, Y. P. Kakad
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

In this paper, the dynamical model for a two-link space robot is developed utilizing quaternion based rotation operators. The model is simulated and is shown to be free of orientation induced singularities. Furthermore, the limitations of computed torque control for this type of system is examined.
Mixing Greedy and Evolutive Approaches to Improve Pursuit Strategies
Authors: Juan Reverte, Francisco Gallego, Rosana Satorre, Faraón Llorens
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

The prey-predator pursuit problem is a generic multi-agent testbed referenced many times in literature. Algorithms and conclusions obtained in this domain can be extended and applied to many particular problems. In first place, greedy algorithms seem to do the job. But when concurrence problems arise, agent communication and coordination is needed to get a reasonable solution. It is quite popular to face these issues directly with non-supervised learning algorithms to train prey and predators. However, results got by most of these approaches still leave a great margin of improvement which should be exploited.In this paper we propose to start from a greedy strategy and extend and improve it by adding communication and machine learning. In this proposal, predator agents get a previous movement decision by using a greedy approach. Then, they focus on learning how to coordinate their own pre-decisions with the ones taken by other surrounding agents. Finally, they get a final decission trying to optimize their chase of the prey without colliding between them. For the learning step, a neuroevolution approach is used. The final results show improvements and leave room for open discussion.
Speaker Classification in Forensic Phonetics and Acoustics
Authors: Michael Jessen
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

Speaker classification in forensic phonetics and acoustics is relevant for several practical tasks within this discipline, including voice analysis, voice comparison, and voice lineup. Six domains of speaker characteristics commonly used in forensic speech analysis are addressed: dialect, foreign accent, sociolect, age, gender, and medical conditions. Focussing on gender plus the less-commonly used characteristic of body size, it is argued that while auditory analysis is indispensable in forensic speaker classification, acoustic analysis can provide important additional information.
A Method for Automated Web Service Selection
Authors: Hong Qing Yu, Stephan Reiff-Marganiec
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Automated Web service selection is a key challenge in Service Oriented Architecture (SOA) research. With SOA becoming more popular in industry, and more Web services becoming available in the e-business market, the main issue changes from service discovery to service selection and ranking. In this paper, we propose a novel nonfunctional property-based service selection method by modifying the Logic Scoring Preference (LSP) method with Ordered Weighted Averaging (OWA) Operators. Moreover, the dynamic mechanism for evaluating metadata based QoS criteria of each service is presented for this method.
Analysis and reduction of data spikes in thin client computing
Authors: Yang Sun, Teng Tiow Tay
Venue: Journal of Parallel and Distributed Computing (2008)

While various optimization techniques have been used in existing thin client systems to reduce network traffic, the screen updates triggered by many user operations will still result in long interactive latencies in many contemporary network environments. Long interactive latencies have an unfavorable effect on users' perception of graphical interfaces and visual contents. The long latencies arise when data spikes need to be transferred over a network while the available bandwidth is limited. These data spikes are composed of a large amount of screen update data produced in a very short time. In this paper, we propose a model to analyze the packet-level redundancy in screen update streams caused by repainting of graphical objects. Using this model we analyzed the data spikes in screen update streams. Based on the analysis result we designed a hybrid cache-compression scheme. This scheme caches the screen updates in data spikes on both server and client sides, and uses the cached data as history to better compress the recurrent screen updates in possible data spikes. We empirically studied the effectiveness of our cache scheme on some screen updates generated by one of the most bandwidth-efficient thin client system, Microsoft Terminal Service. The experiment results showed that this cache scheme with a cache of 2M bytes can reduce 26.7%-42.2% data spike count and 9.9%-21.2% network traffic for the tested data, and can reduce 25.8%-38.5% noticeable long latencies for different types of applications. This scheme costs only a little additional computation time and the cache size can be negotiated between the client and server.
Probability measures of fuzzy events and linguistic fuzzy modelling: forms expressing randomness and imprecision
Authors: Anna Walaszek-Babiszewska
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

Using notions of a linguistic variable and a probability of fuzzy events we formulate the joint probability of the events occurring in antecedents and consequences of linguistic fuzzy models. The structure of the model is given at the beginning of the task. Probabilities of fuzzy events state the rule weights. The rule weights have been taken into account in the proposed inference procedure. An example shows that a probability of fuzzy events is a crisp, numeric validation of the probability of linguistic statements describing technological situations.
Reinforcement Learning Reward Functions for Unsupervised Learning
Authors: Colin Fyfe, Pei Ling Lai
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

We extend a reinforcement learning algorithm, REINFORCE [13] which has previously been used to cluster data [10]. By using base Gaussian learners, we extend the method so that it can perform a variety of unsupervised learning tasks such as principal component analysis, exploratory projection pursuit and canonical correlation analysis.
Fast Target Set Reduction for Large-Scale Protein Function Prediction: A Multi-class Multi-label Machine Learning Approach
Authors: Thomas Lingner, Peter Meinicke
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

Large-scale sequencing projects have led to a vast amount of protein sequences, which have to be assigned to functional categories. Currently, profile hidden markov models and kernel-based machine learning methods provide the most accurate results for protein classification. However, the prediction of new sequences with these approaches is computationally expensive. We present an approach for fast scoring of protein sequences by means of feature-based protein sequence representation and multi-class multi-label machine learning techniques. Using the Pfam database, we show that our method provides high computational efficiency and that the approach is well-suitable for pre-filtering of large sequence sets.
Constant-Round Authenticated Group Key Exchange with Logarithmic Computation Complexity
Authors: Junghyun Nam, Juryon Paik, Ung Mo Kim, Dongho Won
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

Protocols for group key exchange (GKE) are cryptographic algorithms that describe how a group of parties communicating over a public network can come up with a common secret key. Due to their critical role in building secure multicast channels, a number of GKE protocols have been proposed over the years in a variety of settings. However despite many impressive achievements, it still remains a challenging problem to design a secure GKE protocol which scales very well for large groups. Our observation is that all constant-round authenticated GKE protocols providing forward secrecy thus far are not fully scalable, but have a computation complexity that scales only linearly in group size. Motivated by this observation, we propose a new and the first forward-secure authenticated GKE protocol that achieves both constant round complexity and logarithmic computation complexity. In particular, our GKE protocol is fully scalable in all key metrics when considered in the context of a broadcast network. The scalability of the protocol is achieved by using a complete binary tree structure combined with a so-called "nonce-chained authentication technique". Besides its scalability, our protocol features provable security against active adversaries under the decisional Diffie-Hellman assumption. We provide a rigorous proof of security for the protocol in a well-defined formal model of communication and adversary capabilities. The result of the current work means that forward-secure generation of session keys even for very large groups can be now done both securely and efficiently.
Stringology: Some Classic and Some Modern Problems
Authors: S. Muthukrishnan
Venue: Proceedings of the 18th annual symposium on Combinatorial Pattern Matching (2007)

We examine some of the classic problems related to suffix trees from 70's and show some recent results on sorting suffixes with small space and suffix selection. Further, we introduce modern versions of suffix sorting and their application to XML processing. The study of combinatorial aspects of strings continues to flourish, and we present several open problems with modern applications.
Time-frequency distribution of encountered waves using Hilbert-Huang transform
Authors: Ming Li, Xue-Kang Gu, Peiwei Shan
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

Time-frequency distribution (TFD) of signals gains increasing applications in various areas of sciences and engineering for processing non-stationary signals and nonlinear signals. This paper presents our work of TF analysis of encountered wave signal in ship science using the Hilbert-Huang transform (HHT). The results in this paper exhibit that the HHT based TFD of encountered wave signal has better resolution in comparison with those resulted from the traditional methods, such as short-time Fourier transform (STFT), wavelet transform (WT) TFD, and Choi-Williams TFD.
Refactoring Process Models in Large Process Repositories
Authors: Barbara Weber, Manfred Reichert
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

With the increasing adoption of process-aware information systems (PAIS), large process model repositories have emerged. Over time respective models have to be re-aligned to the real-world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Though refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Process designers either have to refactor process models by hand or cannot apply respective techniques at all. This paper proposes a set of behaviour-preserving techniques for refactoring large process repositories. This enables process designers to effectively deal with model complexity by making process models better understandable and easier to maintain.
A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders
Authors: Henning Christiansen, Christina Mackeprang Dahmcke
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required.We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and genemark.hmm.
Architecting Dependable and Secure Systems Using Virtualization
Authors: Bernhard Jansen, Harigovind V. Ramasamy, Matthias Schunter, Axel Tanner
Venue: Architecting Dependable Systems V (2008)

We outline ways of leveraging virtualization for enhancing system dependability and security, and describe the practical realization of some of these enhancements using the Xen open-source virtual machine monitor (VMM). Using combinatorial modeling, we perform reliability analysis of multiple design choices when a single physical server is used to host multiple virtual servers. The analysis shows that unless certain conditions (e.g., regarding the number of virtual servers) are met, virtualization could decrease the reliability of a single physical server. The analysis also shows that improving the reliability of the VMM is crucial to improving the reliability of a virtualized physical node. Motivated by this observation, we show how the enhancements we have implemented can be combined to produce a more reliable Xen VMM architecture, called R-Xen. The Xen VMM consists of a hypervisor core and a privileged virtual machine (VM) called Dom0. Dom0, being much bulkier than the hypervisor core, is the weak link for Xen reliability. Consequently, R-Xen focuses on improving the reliability of Dom0 through replication in which Dom0 replicas mutually monitor each other for intrusion and faults. R-Xen converts more severe Dom0 replica faults into fail-stop behavior, and rejuvenates a failed replica. The approach is transparent and does not require any modifications to regular Xen VMs (user domains).
Using Terminology Web Services for the Archaeological Domain
Authors: Ceri Binding, Douglas Tudhope
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

The AHRC funded STAR project (Semantic Technologies for Archaeological Resources) has developed web services for knowledge organisation systems (KOS) represented in SKOS RDF format, building on previous work by the University of Glamorgan Hypermedia Research Unit on terminology web services. The current service operates on a repository of multiple (English Heritage) thesauri converted to SKOS format, containing terms and concepts that would be familiar to those working within the archaeological domain. It provides facilities for search, concept browsing and semantic expansion across these specialist terminologies.
KSEQ: A New Scalable Synchronous I/O Multiplexing Mechanism for Event-Driven Applications
Authors: Hongtao Xia, Weiping Sun, Jingli Zhou, Yunhua Huang, Jifeng Yu
Venue: Proceedings of the 5th international symposium on Parallel and Distributed Processing and Applications (2007)

The performance of event-driven network applications, such as Web servers and proxies, was influenced by the scalability and efficiency of synchronous I/O multiplexing mechanism. Research shows that event-based mechanism can ensure the scalability, and using kernel-user shared memory to evade system calls can reduce a lot of system overhead. But these two features can not be combined by any solution till now, because of synchronous problem. This paper attempts to design an event notification mechanism for event-driven network applications, which using kernel-user shared event queues (KSEQ) to achieve both good scalability and low system overhead. The KSEQ works something like double buffer, and both application and kernel can write the shared data structures without the help of synchronization system calls. Experiment shows that the Squid proxy server using this mechanism presents shorter response time than other mechanisms.
The Expression Lemma
Authors: Ralf Lämmel, Ondrej Rypacek
Venue: Proceedings of the 9th international conference on Mathematics of Program Construction (2008)

Algebraic data types and catamorphisms (folds) play a central role in functional programming as they allow programmers to define recursive data structures and operations on them uniformly by structural recursion. Likewise, in object-oriented (OO) programming, recursive hierarchies of object types with virtual methods play a central role for the same reason. There is a semantical correspondence between these two situations which we reveal and formalize categorically. To this end, we assume a coalgebraic model of OO programming with functional objects. The development may be helpful in deriving refactorings that turn sufficiently disciplined functional programs into OO programs of a designated shape and vice versa.
Using a pilot study to derive a GUI model for automated testing
Authors: Qing Xie, Atif M Memon
Venue: ACM Transactions on Software Engineering and Methodology (TOSEM) (2008)

Graphical user interfaces (GUIs) are one of the most commonly used parts of today's software. Despite their ubiquity, testing GUIs for functional correctness remains an understudied area. A typical GUI gives many degrees of freedom to an end-user, leading to an enormous input event interaction space that needs to be tested. GUI test designers generate and execute test cases (modeled as sequences of user events) to traverse its parts; targeting a subspace in order to maximize fault detection is a nontrivial task. In this vein, in previous work, we used informal GUI code examination and personal intuition to develop an event-interaction graph (EIG). In this article we empirically derive the EIG model via a pilot study, and the resulting EIG validates our intuition used in previous work; the empirical derivation process also allows for model evolution as our understanding of GUI faults improves. Results of the pilot study show that events interact in complex ways; a GUI's response to an event may vary depending on the context established by preceding events and their execution order. The EIG model helps testers to understand the nature of interactions between GUI events when executed in test cases and why certain events detect faults, so that they can better traverse the event space. New test adequacy criteria are defined for the EIG; new algorithms use these criteria and EIG to systematically generate test cases that are shown to be effective on four fielded open-source applications.