ppxscal/embedding_network huggingface dataset:
Effect of copying and restoration on color barcode payload density
Authors: Steven J. Simske, Margaret Sturgill, Jason S. Aronoff
Venue: Proceedings of the 9th ACM symposium on Document engineering (2009)

2D barcodes are taking on increasing significance as the ubiquity of high-resolution cameras, combined with the availability of variable data printing, drives increasing amounts of "click and connect" applications. Barcodes therefore serve as an increasingly significant connection between physical and electronic portions, or versions, of documents. The use of color provides many additional advantages, including increased payload density and security. In this paper, we consider four factors affecting the readable payload in a color barcode: (1) number of print-scan (PS), or copy, cycles, (2) image restoration to offset PS-induced degradation, (3) the authentication algorithm used, and (4) the use of spectral pre-compensation (SPC) to optimize the color settings for the color barcodes. The PS cycle was shown to consistently reduce payload density by approximately 55% under all tested conditions. SPC nearly doubled the payload density, and selecting the better authentication algorithm increased payload density by roughly 50% in the mean. Restoration, however, was found to increase payload density less substantially (~30%), and only when combined with the optimized settings for SPC. These results are also discussed in light of optimizing payload density for the generation of document security deterrents.
How to Solve Key Escrow Problem in Proxy Re-encryption from CBE to IBE
Authors: Ke Niu, Xu an Wang, Minqing Zhang
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

In 1998, Blaze, Bleumer, and Strauss proposed a kind of cryptographic primitive called proxy re-encryption. In proxy re-encryption, a proxy can transform a ciphertext computed under Alice's public key into one that can be opened under Bob's decryption key. In 2007, Matsuo proposed a new type of re-encryption scheme which can re-encrypt the ciphertext in the certificate based encryption(CBE) setting to one that can be decrypted in identity based setting (IBE). Now this scheme is being standardized by IEEEP1363.3 working group. In this paper, we further extend their research. One feature of their scheme is that it inherits the key escrow problem from IBE, that is, KGC can decrypt every re-encrypted ciphertext for IBE users.We ask question like this: can the malicious KGC not decrypt the re-encryption ciphertext? Surprisingly, the answer is affirmative. We construct such a scheme and prove its security in the standard model. So we give the conclusion that key escrow problem is not unavoidable in re-encryption from CBE to IBE.
HIT: web based scoring method for English lexical substitution
Authors: Shiqi Zhao, Lin Zhao, Yu Zhang, Ting Liu, Sheng Li
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

This paper describes the HIT system and its participation in SemEval-2007 English Lexical Substitution Task. Two main steps are included in our method: candidate substitute extraction and candidate scoring. In the first step, candidate substitutes for each target word in a given sentence are extracted from WordNet. In the second step, the extracted candidates are scored and ranked using a web-based scoring method. The substitute ranked first is selected as the best substitute. For the multiword subtask, a simple WordNet-based approach is employed.
An adaptive automated method for identity verification with performance guarantees
Authors: Hanan Luss, Arnold L. Neidhardt, K. R. Krishnan
Venue: Electronic Commerce Research (2009)

Businesses often rely on customers that access an automated resource to receive information, service or products. We present an adaptive method for automated authentication with performance guarantee of probabilistic error bounds, referred to as identity verification. A database includes a record of identifiers for each identity. The identifiers are partitioned into groups, where identifiers in the same group are correlated while identifiers in different groups are independent. A claimant requesting access into the system is probed with a sequence of identifiers. The response to an identifier can be a match, no-match, or ambiguous. Each identifier is characterized by prior response probabilities for a legitimate claimant and for an impostor. Impostor's response probabilities are updated during a session. The method guarantees that the probabilities of accepting an impostor or rejecting a legitimate claimant do not exceed specified parameters. Once a given number of identifiers have been probed and the claimant has been neither accepted nor rejected, the session terminates with an inconclusive decision and the claimant is referred to further manual interrogation. The method computes various performance characteristics such as the probability that a session of a legitimate claimant terminates inconclusively. These characteristics are valuable in the design of an effective record of identifiers for each of the claimants.
Finite Element Analysis of Dynamic Charateristic for the XK717 CNC Milling Machine
Authors: Weiwei Wang
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

In this paper, dynamic characteristics of XK717 CNC milling machine are analyzed by using the finite element method. Vibration resistance is the very important to evaluate the dynamic characteristic as well as the natural vibration frequency and mode shape. The defective parts of the machine are found by means of analyzing three indexes. The improving suggestion for this milling machine is put forward accordingly.
Independent rule in classification of multivariate binary data
Authors: Junyong Park
Venue: Journal of Multivariate Analysis (2009)

We consider the performance of the independent rule in classification of multivariate binary data. In this article, broad studies are presented including the performance of the independent rule when the number of variables, d, is fixed or increased with the sample size, n. The latter situation includes the case of d=O(n^@t) for @t>0 which cover ''the small sample and the large dimension'', namely d@?n when @t>1. Park and Ghosh [J. Park, J.K. Ghosh, Persistence of plug-in rule in classification of high dimensional binary data, Journal of Statistical Planning and Inference 137 (2007) 3687-3707] studied the independent rule in terms of the consistency of misclassification error rate which is called persistence under growing numbers of dimensions, but they did not investigate the convergence rate. We present asymptotic results in view of the convergence rate under some structured parameter space and highlight that variable selection is necessary to improve the performance of the independent rule. We also extend the applications of the independent rule to the case of correlated binary data such as the Bahadur representation and the logit model. It is emphasized that variable selection is also needed in correlated binary data for the improvement of the performance of the independent rule.
Research and Application of an Intelligent Integrated Planning and Scheduling System
Authors: Lv Zhimin, Xu Jinwu, Zhang Xuejun, Fan Haitao
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

An intelligent integrated planning and scheduling system for direct hot charge rolling process was introduced in this paper. The intelligent system frame and function, the integrated planning and scheduling models and the optimization method etc. have been described in detail. The actual application showed that the solution for integrated planning and scheduling problem of direct hot charge rolling was feasible and effective.
User-centered design of a social game to tag music
Authors: Luke Barrington, Damien O'Malley, Douglas Turnbull, Gert Lanckriet
Venue: Proceedings of the ACM SIGKDD Workshop on Human Computation (2009)

We present "Herd It", a competitive, online, multi-player game that has the implicit benefit of collecting tags for music. We describe Herd It's user-centered design process and demonstrate that the game can collect both musical and social data. This data can be used to build machine learning models that automatically associate music with tags. Herd It differs from previous "games with a purpose" in that it is designed to be social: the game runs on the Facebook online social network and scoring is based on consensus between a large group of listeners - "the Herd". By presenting music in a social context, Herd It adds demographic context to the semantic music descriptions that it collects.
Evaluation of Classroom Teaching Quality in Universities Based on Artificial Neural Network
Authors: Yan-ming Qi, Chang-long Wang, Kai Yang
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

It is of great importance to evaluate scientifically teachers’ teaching quality in the classroom. The methods of artificial neural network were applied on evaluation of classroom teaching quality. By analyzing constituent elements of teaching quality, proposed with teaching attitude, teaching method, teaching content and teaching effectiveness as input layer, quality of classroom teaching quality as output layer, establish artificial neural network model, and actual data were used for training and practice of the network. The practice showed that the model has well recognizes the precision. Finally obtains digitized the evaluation result,can be accurate, direct-viewing reflection the classroom teaching quality was the fit or unfit, thus the wide prospect of artificial neural network in the evaluation of the classroom teaching quality were presented.
Preference-Based Recommendations for OLAP Analysis
Authors: Houssem Jerbi, Franck Ravat, Olivier Teste, Gilles Zurfluh
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

This paper presents a framework for integrating OLAP and recommendations. We focus on the anticipatory recommendation process that assists the user during his OLAP analysis by proposing to him the forthcoming analysis step. We present a context-aware preference model that matches decision-makers intuition, and we discuss a preference-based approach for generating personalized recommendations.
Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
Authors: Krzysztof Dembczyński, Wojciech Kotłowski., Roman Słowiński
Venue: Fundamenta Informaticae (2009)

Ordinal classification problems with monotonicity constraints (also referred to as multicriteria classification problems) often appear in real-life applications, however, they are considered relatively less frequently in theoretical studies than regular classification problems. We introduce a rule induction algorithm based on the statistical learning approach that is tailored for this type of problems. The algorithm first monotonizes the dataset (excludes strongly inconsistent objects), using Stochastic Dominance-based Rough Set Approach, and then uses forward stagewise additive modeling framework for generating a monotone rule ensemble. Experimental results indicate that taking into account knowledge about order andmonotonicity constraints in the classifier can improve the prediction accuracy.
Low Relative Humidity Layer Observation with Micro Pulse Lidar in Qingdao
Authors: Bin Zhang, Jinjia Guo
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

A low backscattering intensity layer with clear boundary was found with an eye-safe, compact Micro Pulse Lidar (MPL) system in Qingdao (120.33, 36.07). This layer was also found from the energy monitor channel signal of the High Resolution Spectral Lidar (HRSL) system. From radiosonde data we found this layer had low relative humidity (RH) and the RH kept as a constant. Also the radiosonde data showed there was an obvious temperature inversion in low atmosphere which blocked aerosol transport. Jinan(117.55, 36.70) in Shandong province, China and west coast of Korea were two low RH centers. Similar phenomena were also found in many other days, especially in autumn. But the reasons caused to the low RH area need further research.
Social Network Model Based on Keyword Categorization
Authors: Prantik Bhattacharyya, Ankush Garg, S. Felix Wu
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

A user profile on an online social network is characterized by its profile entries (keywords). In this paper, we study the relationship between semantic similarity of user keywords and the social network topology. First, we present a `forest' model to categorize keywords and define the notion of distance between keywords across multiple categorization trees (i.e., a forest). Second, we use the keyword distance to define similarity functions between a pair of users and show how social network topology can be modeled accordingly. Third, we validate our social network topology model, using a simulated social graph, against a real life social graph dataset.
Automated Generation of Emotive Virtual Humans
Authors: Joon Hao Chuah, Brent Rossen, Benjamin Lok
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

Emotive virtual humans (VHs) are important for affective interactions with embodied conversation agents [1]. However, emotive VHs require significant resources and time. As an example, the VHs in movies and video games require teams of animators and months of work. VHs can also be imbued with emotion using appraisal theory methods that use psychology based models to generate emotions by using the VH's goals and beliefs to evaluate external events. These external events require manual tagging or natural language understanding [2]. As an alternative approach, we propose tagging VH responses with emotions using textual affect sensing methods. The method developed by Neviarouskaya et al. [3] uses syntactic parses and a database of words and associated emotion intensities.We use this database, and because these emotions are associated with specific words, we can combine the emotions with audio timing information to generate lip-synched facial expressions. Our approach, AutoEmotion, allows us to automatically add basic emotions to VHs without the need for manual animation or tagging or natural language understanding.
REQUIREMENTS MODELING WITH UML
Authors: Cynthia Phillips
Venue:  (2009)

The Unified Modeling Language (UML) provides an environment for modeling complex systems. It supports a variety of diagrams for analyzing, designing, and implementing software systems. During the requirements phase, developers abstract concepts from the application domain and describe what the system is intended to do, not how it will do it. UML was adopted as a standard for OO modeling by the Object Management Group in 1997 and has found use in various software development projects. However, the continued success of any new technology depends a great deal on its usability. To predict the future success of a language like UML it is important to address the issue of usability from the perspective of the users of the language, the software developers. This publication reports on the results of an empirical study aimed at assessing the usability of UML for developing software requirements. It addresses the dimensions of ease of use, usefulness, and usefulness for communicating requirements to various project stakeholders. 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
Collocated AAR: Augmenting After Action Review with Mixed Reality
Authors: John Quarles, Samsun Lampotang, Ira Fischler, Paul Fishwick, Benjamin Lok
Venue: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality (2008)

This paper proposes collocated After Action Review (AAR) of training experiences. Through Mixed Reality (MR), collocated AAR allows users to review past training experiences in situ with the user’s current, real-world experience. MR enables a user-controlled egocentric viewpoint, a visual overlay of virtual information, and playback of recorded training experiences collocated with the user’s current experience. Collocated AAR presents novel challenges for MR, such as collocating time, interactions, and visualizations of previous and current experiences. We created a collocated AAR system for anesthesia education, the Augmented Anesthesia Machine Visualization and Interactive Debriefing system (AAMVID). The system was evaluated in two studies by students (n=19) and educators (n=3). The results demonstrate how collocated AAR systems such as AAMVID can: (1) effectively direct student attention and interaction during AAR and (2) provide novel visualizations of aggregate student performance and insight into student understanding for educators.
Contextual classification of image patches with latent aspect models
Authors: Florent Monay, Pedro Quelhas, Jean-Marc Odobez, Daniel Gatica-Perez
Venue: Journal on Image and Video Processing (2009)

We present a novel approach for contextual classification of image patches in complex visual scenes, based on the use of histograms of quantized features and probabilistic aspect models. Our approach uses context in two ways: (1) by using the fact that specific learned aspects correlate with the semantic classes, which resolves some cases of visual polysemy often present in patch-based representations, and (2) by formalizing the notion that scene context is image-specific--what an individual patch represents depends on what the rest of the patches in the same image are. We demonstrate the validity of our approach on aman-made versus natural patch classification problem. Experiments on an image collection of complex scenes show that the proposed approach improves region discrimination, producing satisfactory results and outperforming two noncontextual methods. Furthermore, we also show that co-occurrence and traditional (Markov random field) spatial contextual information can be conveniently integrated for further improved patch classification.
Contextual Behaviors and Internal Representations Acquired by Reinforcement Learning with a Recurrent Neural Network in a Continuous State and Action Space Task
Authors: Hiroki Utsunomiya, Katsunari Shibata
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

For the progress in developing human-like intelligence in robots, autonomous and purposive learning of adaptive memory function is significant. The combination of reinforcement learning (RL) and recurrent neural network (RNN) seems promising for it. However, it has not been applied to a continuous state-action space task, nor has its internal representations been analyzed in depth. In this paper, in a continuous state-action space task, it is shown that a robot learned to memorize necessary information and to behave appropriately according to it even though no special technique other than RL and RNN was utilized. Three types of hidden neurons that seemed to contribute to remembering the necessary information were observed. Furthermore, by manipulate them, the robot changed its behavior as if the memorized information was forgotten or swapped. That makes us feel a potential towards the emergence of higher functions in this very simple learning system.
State estimation using particle filters in wildfire spread simulation
Authors: Feng Gu, Xuefeng Yan, Xiaolin Hu
Venue: Proceedings of the 2009 Spring Simulation Multiconference (2009)

A fundamental issue in data assimilation of wildfire simulation is to estimate the dynamically changing states, e.g., the fire front position of wildfire, based on observation data of fire sensors. This is a challenging task because of the dynamic and non-linear behavior of fire spread. In this paper, we apply particles filters, also called sequential Monte Carlo methods, to data assimilation in wildfire simulation for estimating the dynamically evolving fire front of a spreading fire. The framework of applying particle filters to the DEVS-FIRE simulation model is presented. Preliminary experiment results show that the particle filtering algorithm was able to track the dynamically changing fire front based on fire sensor data, and thus to provide more accurate predictions of wildfire spread.
Clustering Based on Context Similarity
Authors: László Kovacs, Tibor Repasi, Erika Baksa-Varga, Péter Barabas
Venue: Proceedings of the 2008 First International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (2008)

The discovery of word categories is an important step in statistical grammar induction systems. Word categories can be considered as clusters containing words with similar grammatical or semantic behavior. Having a metric space of words, the clustering algorithm will place similar words into the same cluster, whereas dissimilar ones are clustered into different groups. In this paper we propose an approximate word clustering method based on context similarity. The context of a word is defined here as the set of sentences containing the word. The similarity of two words is measured with the similarity of the corresponding context sets. For the calculation of the context-based distance of two words, a hierarchical agglomerative clustering algorithm has been developed, and is presented here.
A Tool for Estimating Memory Usage
Authors: Shengyi Wang, Zongyan Qiu
Venue: Proceedings of the 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering (2009)

We introduce a tool under development for assembly-level programs, which captures memory requirement of each method in terms of symbolic values of its parameters. It exploits fix-point analysis for loops and recursions. Up now the tool can handle most common structures in programs except some rare situations.
Total or Selective Decompression of a Compressed Plane Triangulation
Authors: Miguel Guardiola Marin, Raquel Viaña Fernandez
Venue: Proceedings of the 2009 Second International Conference in Visualisation (2009)

Efficient techniques to compress/decompress triangulations are commonly used when dealing with huge size meshes. Optimal compression methods which allow to decompress just a part of a triangulation are nevertheless rather unpopular in practice. In this paper we present a simple technique which allows encoding plane triangulations without knowing a priori whether the whole mesh or just a part of it will have to be decompressed. In a pre-processing step the triangulation is encoded following a lossless approach. The compressed representation of the triangulation consists on a string of three types of symbols, and requires at most 5 bits per vertex. Provided the whole mesh has to be decoded, the method finishes here. If, on the contrary, just a query zone has to be extracted, the encoding sequence will be slightly modified, and a new sequence will be obtained from which just a query zone of the triangulation can be decompressed in optimal time. The method is suitable for those applications requiring to manage small parts of huge plane triangulations or to send via web the whole triangular mesh.