ppxscal/embedding_network huggingface dataset:
Exploring the Receptor-Based Virtual Screening Study of the Aromatic Substituents for the Hepatitis C Virus NS5B Polymerase by Docking and Scoring
Authors: Po-Yuan Chen, Wei-Tse Hsu, Mien-De Jhuo, Tzu-Hurng Cheng
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

HCV (Hepatitis C virus) that the NS3protease and NS5B RNA-dependent RNApolymerase (RbRp) which the enzymes for virtualreplication. HCV plays an important role that tocause the chronic and liver diseases. The computeraided drug design (CADD) that is the new methodto design the new molecules as like the drugs fromthe potent compounds. We took the program ofDiscovery Studio 2.0 and the scoring function in thisthat the Dock Score, -PLP1, -PLP2, -PMF, and the Jain score in the program. The compound 26a thathave the highest biological activity (IC50), but thecompound didn’t have the highest score in thestudy. In the scoring function of Dock Score, thecompound 13a has the highest score value. Thecompound 19a has the highest scoring value in theboth score functions of –PMF and Jain. Thecompound 20 showed the highest value in thescoring functions that the –PLP1 and –PLP2. So thede novo evolution in the program that we want todesign the HCV NS5B inhibitor that has higherdocking scoring value than the potent compound in the future.
Restart schedules for ensembles of problem instances
Authors: Matthew Streeter, Daniel Golovin, Stephen F. Smith
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

The mean running time of a Las Vegas algorithm can often be dramatically reduced by periodically restarting it with a fresh random seed. The optimal restart schedule depends on the Las Vegas algorithm's run length distribution, which in general is not known in advance and may differ across problem instances. We consider the problem of selecting a single restart schedule to use in solving each instance in a set of instances. We present offline algorithms for computing an (approximately) optimal restart schedule given knowledge of each instance's run length distribution, generalization bounds for learning a restart schedule from training data, and online algorithms for selecting a restart schedule adaptively as new problem instances are encountered.
Efficient Certificate-Based and Randomized Signature from Pairings
Authors: Xin Xiangjun, Xing Peixu
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

Most of pairing-based schemes need two or more operations of pairings. To find an efficient randomized certificate-based signature from pairings, by constructing a fraction such that a random element of the gap Diffie-Hellman group is included in the numerator and the sum of the master key and the output of the hash function is included in the denominator, a new signature scheme from pairings is proposed, in which only one operation of pairings is used. Under the hardness assumption of CDHP, the scheme can be proved to be secure in random oracle. The new signature is an efficient pairing-based one, since there is only one operation of pairings in it. The new randomized signature is short. On the other hand, the security proof has a tight reduction.
Short Segment Frequency Equalization: A Simple and Effective Alternative Treatment of Background Models in Motif Discovery
Authors: Kazuhito Shida
Venue: Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics (2009)

One of the most important pattern recognition problems in bioinformatics is the de novo motif discovery. In particular, there is a large room of improvement in motif discovery from eukaryotic genome, where the sequences have complicated background noise. The short segment frequency equalization (SSFE) is a novel treatment method to incorporate Markov background models into de novo motif discovery algorithms, namely Gibbs sampling. Despite its apparent simplicity, SSFE shows a large performance improvement over the current method (Q/P scheme) when tested on artificial DNA datasets with Markov background of human and mouse. Furthermore, SSFE shows a better performance than other methods including much more complicated and sophisticated method, Weeder 1.3, when tested with several biological datasets from human promoters.
A Benefit-Cost Based Method for Cost-Sensitive Decision Trees
Authors: Xingyi Liu
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

Cost-sensitive learning is popular during the process of classification. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken the trade-off between cost and benefit into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between classification ability and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform the existed methods in terms of the decrease of misclassification cost.
Prosodic Characteristics and Emotional Meanings of Slovak Hot-Spot Words
Authors: Štefan Beňuš, Milan Rusko
Venue: Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions: COST Action 2102 International Conference Prague, Czech Republic, October 15-18, 2008 Revised Selected and Invited Papers (2009)

In this paper, we investigate emotionally charged hot-spot jVj-words from a corpus that is based on recordings of puppet plays in Slovak. The potential of these hot-spot words for detecting emotion in larger utterances was tested. More specifically, we tested the effect of prosodic and voice quality characteristics and the presence or absence of lexical context on the perception of emotions that the jVj-words convey. We found that the lexical cues present in the context are better predictors for the perception of emotions than the prosodic and voice quality features in the jVj-words themselves. Nevertheless, both prosodic as well as voice quality features are useful and complementary in detecting emotion of individual words from the speech signal as well as of larger utterances. Finally, we argue that a corpus based on recordings of puppet plays presents a novel and advantageous approach to the collection of data for emotional speech research.
Application of Dual-RAM in Numerical Control System
Authors: YanXia Liu, ShuFen Li, LiTing Cao
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Computer Numerical Control(CNC) system is the basis of advanced manufacture technology and develop quite rapidly now. All kinds of movement controller come out, which plug in the main board of computer. The traditional movement controller transmit data between controller board and PC by ISA bus, which effect the transmission efficiency at some extend. This paper present a new resolution of this problem by adopted PCI bus and dual-RAM. Recently, with the rapidly development of technology, PCI( Peripheral Component Interconnect) bus is put forward and applied more and more widely. The G Code produced by CAD(computer aided design) is sent to Dual-RAM of movement controller by PCI bus. At the same time, the movement controller read data from dual-RAM to control the track of movement. Using dual-RAM communication will greatly improve the rate of data transmission and ensure the quality of machining.
Sentiment retrieval using generative models
Authors: Koji Eguchi, Victor Lavrenko
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

Ranking documents or sentences according to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polarities of the targeted text are not explicitly given, as is often the case on the web. In this paper, we propose several sentiment information retrieval models in the framework of probabilistic language models, assuming that a user both inputs query terms expressing a certain topic and also specifies a sentiment polarity of interest in some manner. We combine sentiment relevance models and topic relevance models with model parameters estimated from training data, considering the topic dependence of the sentiment. Our experiments prove that our models are effective.
Automated Certification of Non-Interference in Rewriting Logic
Authors: Mauricio Alba-Castro, María Alpuente, Santiago Escobar
Venue: Formal Methods for Industrial Critical Systems: 13th International Workshop, FMICS 2008, L'Aquila, Italy, September 15-16, 2008, Revised Selected Papers (2009)

In this paper we propose a certification technique for non-interference of Java programs based on rewriting logic, a very general logical and semantic framework efficiently implemented in the high-level programming language Maude. Non---interference is a semantic program property that prevents illicit information flow to happen. Starting from a basic specification of the semantics of Java written in Maude, we develop an information---flow extension of this operational Java semantics which allows us to observe non-interference of Java programs. Then we develop in Maude an abstract, finite-state version of the information-flow operational semantics which supports finite program verification. As a by---product of the verification, a certificate of non-interference is delivered which consists of a set of (abstract) rewriting proofs that can be easily checked by the code consumer using a standard rewriting logic engine.
A semi-supervised approach to space carving
Authors: Surya Prakash, Antonio Robles-Kelly
Venue: Pattern Recognition (2010)

In this paper, we present a semi-supervised approach to space carving by casting the recovery of volumetric data from multiple views into an evidence combining setting. The method presented here is statistical in nature and employs, as a starting point, a manually obtained contour. By making use of this user-provided information, we obtain probabilistic silhouettes of all successive images. These silhouettes provide a prior distribution that is then used to compute the probability of a voxel being carved. This evidence combining setting allows us to make use of background pixel information. As a result, our method combines the advantages of shape-from-silhouette techniques and statistical space carving approaches. For the carving process, we propose a new voxelated space. The proposed space is a projective one that provides a colour mapping for the object voxels which is consistent in terms of pixel coverage with their projection onto the image planes for the imagery under consideration. We provide quantitative results and illustrate the utility of the method on real-world imagery.
Using AI Approach to Solve a Deteriorating Inventory Model Considering Multiple JIT Delivery and Price
Authors: Jonas Chao-pen Yu, Wei-Min Wee, Kung-Jeng Wang, Yu-Siang Lin
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

This paper develops a deteriorating inventory model for the product experiencing continuous decrease in unit price. We proposes an inventory model with efficient replenishment policy for a single supplier-buyer partnership in which the buyer considers JIT through a realization of multiple deliveries considering the buyer's EOQ ordering strategy. The market possesses characters that the selling price exponentially decreases over time until approaching its bottom line, while the customer demand will increases linearly with the selling price down; besides, considering short life cycle, only a single production-setup of the supplier is allowed and the objective of this model is to maximize the supplier's profit during a finite planning horizon and then to derive an optimal product life cycle. A genetic algorithm (GA) is developed to obtain the optimal lifecycle time. Numerical examples are given to validate the analysis of the models
A spectrum of symbolic on-line diagnosis approaches
Authors: Anika Schumann, Yannick Pencolé, Sylvie Thiébaux
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

This paper deals with the monitoring and diagnosis of large discrete-event systems. The problem is to determine, on-line, all faults and states that explain the flow of observations. Model-based diagnosis approaches that first compile the diagnosis information off-line suffer from space explosion, and those that operate on-line without any prior compilation have poor time performance. Our contribution is a broader spectrum of approaches that suits applications with diverse time and space requirements. Approaches on this spectrum differ in the amount of reasoning and compilation performed off-line and therefore in the way they resolve the tradeoff between the space occupied by the compiled information and the time taken to produce a diagnosis. We tackle the space and time complexity of diagnosis by encoding all approaches in a symbolic framework based on binary decision diagrams. This allows for the compact representation of the compiled diagnosis information, and for its handling across many states at once rather than for each state individually. Our experiments demonstrate the diversity and scalability of our symbolic methods spectrum, as well as its superiority over the corresponding enumerative implementations.
Smooth Multi-Manifold Embedding for Robust Identity-Independent Head Pose Estimation
Authors: Xiangyang Liu, Hongtao Lu, Heng Luo
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

In this paper, we propose a supervised Smooth Multi-Manifold Embedding (SMME) method for robust identity-independent head pose estimation. In order to handle the appearance variations caused by identity, we consider the pose data space as multiple manifolds in which each manifold characterizes the underlying subspace of subjects with similar appearance. We then propose a novel embedding criterion to learn each manifold from the exemplar-centered local structure of subjects. The experiment results on the standard databases demonstrates that the SMME is robust to variations of identities and achieves high pose estimation accuracy. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Research and Application of PSO-Based BP Neural Network in the Project Estimate of Government Investment
Authors: Wenhui Liu, Zhifeng Chi
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

In order to increase the accuracy of the cost estimates in government investment, a method based on the PSO trained neural network to estimate the cost is proposed. First the neural network model of a project cost estimate is created, and then PSO is introduced to optimize the weight and threshold of the neural network, at last the neural network trained is used to estimate cost of the project. The results show that this method not only overcome the defects of traditional neural network such as learning for a long time and shock, but also increase the accuracy of thecost of the project to estimate
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
Authors: Abhinav Gupta, Aniruddha Kembhavi, Larry S. Davis
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene/event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape/appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.
Maximum entropy based rule selection model for syntax-based statistical machine translation
Authors: Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual information around rules and information of sub-trees covered by variables in rules. Therefore, our model allows the decoder to perform context-dependent rule selection during decoding. We incorporate the MERS model into a state-of-the-art linguistically syntax-based SMT model, the tree-to-string alignment template model. Experiments show that our approach achieves significant improvements over the baseline system.
The Internet and the Next Legal Paradigm: A Web-Assisted Regulatory Approach toward e-Harassment
Authors: Chi-Shing Chen
Venue: Proceedings of the 2009 First International Conference on Advances in Future Internet (2009)

How the Internet can facilitate a paradigm shift in law is the focus of this article. I first analyze the dilemma plaguing law today. I then discuss the idea of governance that is needed to pull law out of its current hardships. Finally, I propose a Web-assisted e-harassment regulatory approach in order to draw attention to, and invite further comments on, what institutional design can best realize the idea of web-assisted governance.
Modified Local Discriminant Bases and Its Application in Audio Feature Extraction
Authors: Zheng Jiming, Wei Guohua, Yang Chunde
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03 (2009)

One of the major challenges in classification problems based on signal decomposition approach is to identify the right basis function and its derivatives that can provide optimal features to distinguish the classes. Local Discriminant Bases (LDB) algorithm is one such algorithm, which efficiently selects a set of significant basis functions from the library of orthonormal bases based on certain defined dissimilarity measure. In this paper, we modified the LDB algorithm and used the fisher criterion for feature selection. Finally, support vector machines is used as a classifier to identify music, pure speech and speech with music.
A General Framework: Combining Statistical Models and Network Structure for Deep Content Mining
Authors: Chengli Zhao, Xue Zhang, Dongyun Yi
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

In this paper, we propose a general framework for content mining, which combines statistical model and network structure to leverages the power of both statistical topic models and network method. This method is a novel view to both text oriented method and network oriented method. The proposed framework is general, it can be applied to any text collections with an associated network structure.
Self-regulation in ACT: a case study in peer-assessment activities
Authors: Agoritsa Gogoulou, Evangelia Gouli, Christos Tsakostas, Maria Grigoriadou
Venue: Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1 (2009)

Having as an objective to support students in collaborating/communicating fruitfully with respect to the underlying collaborative learning setting, we developed ACT, a synchronous communication tool which supports mechanisms for students' self-regulation as well as for the adaptation and personalization of the communication. The selfregulation mechanism enables the diagnosis and the evaluation of students' collaborative behavior both at the cognitive and social level and provides feedback at awareness, metacognitive and guiding level. The use of ACT in the context of a peer-assessment activity showed that the tool can help students in self-regulation and improve their collaboration behavior.
Mining Class Contrast Functions by Gene Expression Programming
Authors: Lei Duan, Changjie Tang, Liang Tang, Tianqing Zhang, Jie Zuo
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As Gene Expression Programming (GEP) can discover essential relations from data and express them mathematically, it is desirable to apply GEP to mining such class contrast functions from data. The main contributions of this paper include: (1) proposing a new data mining task --- class contrast function mining, (2) designing a GEP based method to find class contrast functions, (3) presenting several strategies for finding multiple class contrast functions in data, (4) giving an extensive performance study on both synthetic and real world datasets. The experimental results show that the proposed methods are effective. Several class contrast functions are discovered from the real world datasets. Some potential works on class contrast function mining are discussed based on the experimental results.
A machine learning approach to sentiment analysis in multilingual Web texts
Authors: Erik Boiy, Marie-Francine Moens
Venue: Information Retrieval (2009)

Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.