ppxscal/embedding_network huggingface dataset:
Object-Orientation Is Evil to Mobile Game: Experience from Industrial Mobile RPGs
Authors: Weishan Zhang, Dong Han, Thomas Kunz
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

Mobile gaming is playing an important role in the entertainment industry. Good performance is a critical requirement for mobile games in order to achieve acceptable running speed although mobile devices are limited by scarce resources. Object-oriented programming is the prevalent programming paradigm and this is true for mobile game development as well. As the origin of object-orientation (OO) is not targeting the embedded software domain, there is suspicion as to OO's usability for embedded software, especially with respect to mobile games. Questions arise like how OO and to what degree OO will affect the performance, executable file size, and how optimization strategies can improve the qualities of mobile game software. In this paper we investigate these questions within the mobile Role-Playing-Game (RPG) domain using five industrial mobile games developed with OO. We re-implemented these five RPGs with a structural programming style, by reducing the inheritance relationships, removing excessive classes and interfaces. Some additional optimizations are also applied during the re-implementation, such as the tackling of performance bottleneck methods, using more efficient algorithms. New games after optimizations run on average almost 25% faster than the corresponding original games, with a maximum of 34.62% improvement; the memory usage is decreased by more than 10% on average and 17.56% as a maximum; we also achieved a 59% code reduction and a 71% Jar file decrease after optimization. Therefore if developers are aiming for mobile game performance, we conclude that they should use as few OO features as possible. Structural programming can be a very competitive alternative.
Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management
Authors: Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzalez, David Hachen, Brett Lantz, Alec Pawling, Timothy Schoenharl, Gábor Szabó, Pu Wang, Ping Yan
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

We describe a prototype emergency and disaster information system designed and implemented using DDDAS concepts. The system is designed to use real-time cell phone calling data from a geographical region, including calling activity --- who calls whom, call duration, services in use, and cell phone location information --- to provide enhanced situational awareness for managers in emergency operations centers (EOCs) during disaster events. Powered-on cell phones maintain contact with one or more within-range cell towers so as to receive incoming calls. Thus, location data about all phones in an area are available, either directly from GPS equipped phones, or by cell tower, cell sector, distance from tower and triangulation methods. This permits the cell phones of a geographical region to serve as an ad hoc mobile sensor net, measuring the movement and calling patterns of the population. A prototype system, WIPER, serves as a test bed to research open DDDAS design issues, including dynamic validation of simulations, algorithms to interpret high volume data streams, ensembles of simulations, runtime execution, middleware services, and experimentation frameworks [1].
Blind Image Watermark Analysis Using Feature Fusion and Neural Network Classifier
Authors: Wei Lu, Wei Sun, Hongtao Lu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Over the past two decades, great efforts have been made to develop digital watermarking techniques for multimedia copyright protection and authentication. However, most of watermark detection methods are designed based on the corresponding specific watermark embedding procedures. In this paper, we propose a general blind watermarking analysis scheme to recognize whether images are watermarked no matter what kind of watermark embedding schemes are used. In the proposed method, multiscale feature fusion are used to construct statistical characteristics between non-watermarked images and watermarked images. Then, RBF neural networks are used to classify these characteristics. Numerical simulations show that the proposed scheme describes intrinsic statistical characteristics and the proposed blind watermark analysis method is effective.
Verifiable Multi-secret Sharing Schemes for Multiple Threshold Access Structures
Authors: Christophe Tartary, Josef Pieprzyk, Huaxiong Wang
Venue: Information Security and Cryptology: Third SKLOIS Conference, Inscrypt 2007, Xining, China, August 31 - September 5, 2007, Revised Selected Papers (2007)

A multi-secret sharing scheme allows several secrets to be shared amongst a group of participants. In 2005, Shao and Cao developed a verifiable multi-secret sharing scheme where each participant's share can be used several times which reduces the number of interactions between the dealer and the group members. In addition some secrets may require a higher security level than others involving the need for different threshold values. Recently Chan and Chang designed such a scheme but their construction only allows a single secret to be shared per threshold value.In this article we combine the previous two approaches to design a multiple time verifiable multi-secret sharing scheme where several secrets can be shared for each threshold value. Since the running time is an important factor for practical applications, we will provide a complexity comparison of our combined approach with respect to the previous schemes.
Surgical Manipulator with Balloon for Stabilizing Fetus in Utero under Ultrasound Guidance
Authors: Noriaki Yamanaka, Hiromasa Yamashita, Kiyoshi Matsumiya, Hongen Liao, Ken Masamune, Toshio Chiba, Takeyoshi Dohi
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

This paper describes a surgical manipulator to stabilize intrauterine fetus with ultrasound image guidance. The manipulator includes an outline of 4 mm in diameter, a mechanism with 7 joints, and a set of balloons. The manipulator is arranged as straight form and the balloon is fold to be a minimum size before the insertion. Accuracy evaluation of bending performance showed that the standard deviations were ±3.6 degrees on wired-driven mechanism and ±1.6 degrees on linkage-driven mechanism. Experimental results also demonstrated high repeatability of the mechanisms. In feasibility experiments, ultrasound images in 2D and 3D modes were examined for guiding the manipulator. The 2D images provided wide view and easily viewable display of the balloon inflation. The 3D images provided easily viewable display of bending motion of the arm and the relative position of a phantom. It is possible to operate the manipulator in utero under the ultrasound guidance in 2D and 3D by switching them in each procedural stage.
The RSO Algorithm for Reducing Number of Set Operations in Association Rule Mining
Authors: Muhammad Sarwar, Onaiza Maqbool
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Association rule mining is a data mining technique used to find interesting associations between items in a transaction database. Well known algorithms for association rule mining include Apriori and FP-tree. Apriori is a level wise algorithm and works by scanning the database multiple times. In an attempt to optimize the performance of Apriori, many variations of basic Apriori algorithm have been proposed. These variations exploit different approaches including reducing the number of database scans performed, using special data structures, using bitmaps and granular computing. Majority of these approaches are improvements in implementation of the same basic algorithm. In this paper we propose the RSO (Reduced Set Operations) algorithm, based on reducing the number of set operations performed. RSO is an algorithmic improvement to the basic Apriori algorithm; it is not an implementation improvement. Our analysis shows that RSO is asymptotically faster than Apriori. Experimental results also validate the efficiency of our algorithm.
Polarizability functions of diatomic homonuclear molecules: Semiempirical approach
Authors: M. A. Buldakov, V. N. Cherepanov
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

A semiempirical method is discussed to construct the polarizability functions for a diatomic homonuclear molecule as a piecewise-continuous function that exhibits physically correct behavior at small and large internuclear distances and agrees with the polarizability function near the nuclear equilibrium position of the molecule. The method is applied to calculate the polarizability functions of N$_2$ and O$_2$ molecules in the range of internuclear distances (0, &infin;).
Thai Sign Language Translation Using Fuzzy C-Means and Scale Invariant Feature Transform
Authors: Suwannee Phitakwinai, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

Visual communication is important for a deft and/or mute person. It is also one of the tools for the communication between human and machines. In this paper, we develop an automatic Thai finger-spelling sign language translation system using Fuzzy C-Means (FCM) and Scale Invariant Feature Transform (SIFT) algorithms. We collect key frames from several subjects at different times of day and for several days. We also collect testing Thai finger-spelling words video from 4 subjects. The system achieves 79.90% and 51.17% correct alphabet translation and the correct word translation, respectively, with the SIFT threshold of 0.7 and 1 nearest neighbor prototype. However, when we change the number of nearest neighbor prototypes to 3, the system yields 82.19% and 55.08% correct alphabet and correct word translation, respectively, at the same SIFT threshold. These results are comparable with the manually-picked Rframe translation system.
Semantics Preservation of Sequence Diagram Aspects
Authors: Jon Oldevik, Øystein Haugen
Venue: Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications (2008)

Although some techniques for aspect oriented modelling focus on semantic-based composition, there has been a general lack of focus on how to preserve semantics. We address semantics preservation in the context of sequence diagram aspects. We define semantics preservation based on two properties: monotonicity of aspect composition with respect to refinement, and preservation of events in the composed result. We analyse and compare existing sequence diagram aspect techniques with respect to semantics preservation and find that both syntactic based and semantic based aspect weaving techniques have serious challenges to meet semantics preservation.
Time- and Amplitude-Based Voice Source Correlates of Emotional Portrayals
Authors: Irena Yanushevskaya, Michelle Tooher, Christer Gobl, Ailbhe Ní Chasaide
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

A detailed analysis of glottal source parameters is presented for emotional portrayals which included both low and high activation states: neutral, bored, sad, and happy, surprised, angry. Time- and amplitude-based glottal source parameters, F0, RG, RK, RA, OQ, FA, EE, and RD were analysed. The results show statistically significant differentiation of all emotions in terms of all the glottal parameters analysed. Results furthermore suggest that the dynamics of the individual parameters are likely to be important in differentiating among the emotions.
Analysis and Visualization of the Dynamics of Recurrent Neural Networks for Symbolic Sequences Processing
Authors: Matej Makula, Ľubica Beňušková
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Recurrent neural networks unlike feed-forward networks are able to process inputs with time context. The key role in this process is played by the dynamics of the network, which transforms input data to the recurrent layer states. Several authors have described and analyzed dynamics of small sized recurrent neural networks with two or three hidden units. In our work we introduce techniques that allow to visualize and analyze the dynamics of large recurrent neural networks with dozens units, reveal both stable and unstable points (attractors and saddle points), which are important to understand the principles of successful task processing. As a practical example of this approach, dynamics of the simple recurrent network trained by two different training algorithms on context-free language a n b n was studied.
An Agent-Based Multi-issue Negotiation Model
Authors: Ying Wang, Huamei Sun, Guorui Jiang
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

Most of the current agent systems focus on single issue, such as price, whereas multi-issue negotiation is very essential to electronic commerce. In the multi-issue model, MAUT (Multi-attribute Utility Theory) or its derived theory is mainly used as the evaluation method. However, the application of this theory may cause deviation. To resolve the problem, a two stage negotiation model is presented in this paper. In the first stage, the negotiation theory is used to negotiate over the most important issue or the issues combination. In the second stage we reverse auction the product each issue at one time according to the issue importance from the smallest to the most important using the adjusted auction theory.
Completeness and Logical Full Abstraction in Modal Logics for Typed Mobile Processes
Authors: Martin Berger, Kohei Honda, Nobuko Yoshida
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

We study an extension of Hennessy-Milner logic for the ¿-calculus which gives a sound and complete characterisation of representative behavioural preorders and equivalences over typed processes. New connectives are introduced representing actual and hypothetical typed parallel composition and hiding. We study three compositional proof systems, characterising the May/Must testing preorders and bisimilarity. The proof systems are uniformly applicable to different type disciplines. Logical axioms distill proof rules for parallel composition studied by Amadio and Dam. We demonstrate the expressiveness of our logic through verification of state transfer in multiparty interactions and fully abstract embeddings of program logics for higher-order functions.
Life Story Generation Using Mobile Context and Petri Net
Authors: Young-Seol Lee, Sung-Bae Cho
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

People mainly organize their experience as a kind of narratives. Story generated from user's information in mobile environment can help share his experience with other people and recall his meaningful memory. In this paper, we propose a method that generates a story with Petri net and user contexts. In order to verify the usefulness of the proposed method, we show an example of generating user's experience to story with user context in mobile environment. Comparison of user's report and generated story confirms the validity of the automatic story generation.
A Workflow-Based Approach for Creating Complex Web Wrappers
Authors: Paula Montoto, Alberto Pan, Juan Raposo, José Losada, Fernando Bellas, Javier López
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

In order to let software programs access and use the information and services provided by web sources, wrapper programs must be built to provide a "machine-readable" view over them. Although research literature on web wrappers is vast, the problem of how to specify the internal logic of complex wrappers in a graphical and simple way remains mainly ignored. In this paper, we propose a new language for addressing this task. Our approach leverages on the existing work on intelligent web data extraction and automatic web navigation as building blocks, and uses a workflow-based approach to specify the wrapper control logic. The features included in the language have been decided from the results of a study of a wide range of real web automation applications from different business areas. In this paper, we also present the most salient results of the study.
Transferred Dimensionality Reduction
Authors: Zheng Wang, Yangqiu Song, Changshui Zhang
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Dimensionality reduction is one of the widely used techniques for data analysis. However, it is often hard to get a demanded low-dimensional representation with only the unlabeled data, especially for the discriminative task. In this paper, we put forward a novel problem of Transferred Dimensionality Reduction, which is to do unsupervised discriminative dimensionality reduction with the help of related prior knowledge from other classes in the same type of concept. We propose an algorithm named Transferred Discriminative Analysis to tackle this problem. It uses clustering to generate class labels for the target unlabeled data, and use dimensionality reduction for them joint with prior labeled data to do subspace selection. This two steps run adaptively to find a better discriminative subspace, and get better clustering results simultaneously. The experimental results on both constrained and unconstrained face recognition demonstrate significant improvements of our algorithm over the state-of-the-art methods.
Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks
Authors: Changhe Yuan, Marek J. Druzdzel
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

Importance sampling-based algorithms are a popular alternative when Bayesian network models are too large or too complex for exact algorithms. However, importance sampling is sensitive to the quality of the importance function. A bad importance function often leads to much oscillation in the sample weights, and, hence, poor estimation of the posterior probability distribution. To address this problem, we propose the adaptive split-rejection control technique to adjust the samples with extremely large or extremely small weights, which contribute most to the variance of an importance sampling estimator. Our results show that when we adopt this technique in the EPIS-BN algorithm[14], adaptive split-rejection control helps to achieve significantly better results.
UMPTEEN: Named and Anonymous Learner Model Access for Instructors and Peers
Authors: Susan Bull, Andrew Mabbott, Abdallatif S. Abu Issa
Venue: International Journal of Artificial Intelligence in Education (2007)

Recently, opening the learner model to the learner it represents has become more common in adaptive learning environments. There have also been systems that allow instructors access to the learner models of their students, and a few examples of systems which open the learner model to the student's peers. However, little investigation has taken place into students' attitudes to viewing the learner models of others, and their likelihood of making their own models available for inspection by others. The UMPTEEN approach was developed in order to investigate students' views about releasing their learner model more widely, and to observe how they use their own and peer models. This paper presents the first results from a lab-based study of an open learner model that can be optionally opened to others, in named and/or anonymous form. Results suggest that there are sufficient students interested in viewing peer models, and sufficient students willing to open their learner models to instructors and peers, to make further studies worthwhile.
A Control Lyapunov Approach to Predictive Control of Hybrid Systems
Authors: S. Cairano, M. Lazar, A. Bemporad, W. P. Heemels
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

In this paper we consider the stabilization of hybrid systems with both continuous and discrete dynamics via predictive control. To deal with the presence of discrete dynamics we adopt a "hybrid" control Lyapunov function approach, which consists of using two different functions. A Lyapunov-like function is designed to ensure finite-time convergence of the discrete state to a target value, while asymptotic stability of the continuous state is guaranteed via a classical local control Lyapunov function. We show that by combining these two functions in a proper manner it is no longer necessary that the control Lyapunov function for the continuous dynamics decreases at each time step. This leads to a significant reduction of conservativeness in contrast with classical Lyapunov based predictive control. Furthermore, the proposed approach also leads to a reduction of the horizon length needed for recursive feasibility with respect to standard predictive control approaches.
TagNSearch: Searching and Navigating Geo-referenced Collections of Photographs
Authors: Quang Minh Nguyen, Thi Nhu Kim, Dion Hoe-Lian Goh, Yin-Leng Theng, Ee-Peng Lim, Aixin Sun, Chew Hung Chang, Kalyani Chatterjea
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

TagNSearch is a map-based tool for searching and browsing geo-tagged photographs based on their associated tags. Using Flickr as the dataset, TagNSearch returns, for a given query, photographs clustered by locations, and summarizes each cluster of photographs by cluster-specific tags. A map-based interface is also provided to help users better search, navigate and browse photographs and their clusters. A qualitative evaluation comparing TagNSearch and an existing tag search support in Flickr was also conducted. The task involved finding locations associated with a set of photographs. Participants were found to perform this task better using TagNSearch than Flickr.
Multivariate Statistical Analysis of Whole Brain Structural Networks Obtained Using Probabilistic Tractography
Authors: Emma C. Robinson, Michel Valstar, Alexander Hammers, Anders Ericsson, A. David Edwards, Daniel Rueckert
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

This paper presents a new framework for the analysis of anatomical connectivity derived from diffusion tensor MRI. The framework has been applied to estimate whole brain structural networks using diffusion data from 174 adult subjects. In the proposed approach, each brain is first segmented into 83 anatomical regions via label propagation of multiple atlases and subsequent decision fusion. For each pair of anatomical regions the probability of connection and its strength is then estimated using a modified version of probabilistic tractography. The resulting brain networks have been classified according to age and gender using non-linear support vector machines with GentleBoost feature extraction. Classification performance was tested using a leave-one-out approach and the mean accuracy obtained was 85.4%.