ppxscal/embedding_network huggingface dataset:
The Effective Usage of the Results of Self-Check Quizzes
Authors: Yasuko Namatame, Maomi Ueno
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

E-learners typically check their understanding by taking self-check quizzes, usually as often as they like. However, the benefits of doing this are not well understood. In this research, an index was defined that represents the consistency between a series of answers from the repeated taking of a quiz and used to classify the learners into groups. ANOVA analysis of the scores for each group revealed significant differences between the groups. Moreover the difference in the structure of the acquired knowledge for each group was clarified using Bayesian networks. Learners who required additional individual counseling could then be objectively identified. Use of this method enables learners to receive timely feedback at the end of each study unit. The teacher can guide learners appropriately and individually by checking the knowledge acquired by each learner, as represented on an ideal Bayesian network. In addition, because this method does not use subjective information about the learners, its use does not increase the workload of the learners or teacher.
Information model of intelligence and memorizing in early childhood
Authors: Vinko Vidučic, Damir Boras, Ljiljana Viducic
Venue: WSEAS Transactions on Information Science and Applications (2009)

The object of analysis of this paper is global model of intelligence and memorizing of information from conception until a child starts to walk for the period from 2009 until 2020. In order to define main characteristics and determinants, the model of growth was used. Numerous adequate scientific methods were used, the most important among them: analysis and synthesis method, inductive and deductive method, descriptive method, comparative method, statistical and mathematical method, modelling method (matrix of growth), proving and disproving method. As model variables are chosen key factors for strengthening of intelligence, and therefore of memorising of information. Quantitative analysis is than used in order to determine importance of individual variables as well as their interdependence.
Learning and inference in weighted logic with application to natural language processing
Authors: Andrew Mccallum
Venue:  (2008)

Over the past two decades, statistical machine learning approaches to natural language processing have largely replaced earlier logic-based systems. These probabilistic methods have proven to be well-suited to the ambiguity inherent in human communication. However, the shift to statistical modeling has mostly abandoned the representational advantages of logic-based approaches. For example, many language processing problems can be more meaningfully expressed in first-order logic rather than propositional logic. Unfortunately, most machine learning algorithms have been developed for propositional knowledge representations. In recent years, there have been a number of attempts to combine logical and probabilistic approaches to artificial intelligence. However, their impact on real-world applications has been limited because of serious scalability issues that arise when algorithms designed for propositional representations are applied to first-order logic representations. In this thesis, we explore approximate learning and inference algorithms that are tailored for higher-order representations, and demonstrate that this synthesis of probability and logic can significantly improve the accuracy of several language processing systems.
Applying machine learning methods to suggest network involvement and functionality of genes in saccharomyces cerevisiae
Authors: Arvin Agah, Costas Tsatsoulis
Venue:  (2008)

Elucidating genetic networks provides the foundation for the development of new treatments or cures for diseased pathways, and determining novel gene functionality is critical for bringing a better understanding on how an organism functions as a whole. In this dissertation, I developed a methodology that correctly locates genes that may be involved in genetic networks with a given gene based on its location over 50% of the time or based on its description over 43% of the time. I also developed a methodology that makes it easier to predict how a gene product behaves in a cellular context by suggesting the correct Gene Ontology term over 80% of the time. The designed software provides researchers with a way to focus their search for coregulated genes which will lead to better microarray chip design and limits the list of possible functions of a gene product. This ultimately saves the researcher time and money.
On the extreme inequalities of infinite group problems
Authors: Santanu S. Dey, Jean-Philippe P. Richard, Yanjun Li, Lisa A. Miller
Venue: Mathematical Programming: Series A and B (2009)

Infinite group relaxations of integer programs (IP) were introduced by Gomory and Johnson (Math Program 3:23&#x2013;85, 1972) to generate cutting planes for general IPs. These valid inequalities correspond to real-valued functions defined over an appropriate infinite group. Among all the valid inequalities of the infinite group relaxation, extreme inequalities are most important since they are the strongest cutting planes that can be obtained within the group-theoretic framework. However, very few properties of extreme inequalities of infinite group relaxations are known. In particular, it is not known if all extreme inequalities are continuous and what their relations are to extreme inequalities of finite group problems. In this paper, we describe new properties of extreme functions of infinite group problems. In particular, we study the behavior of the pointwise limit of a converging sequence of extreme functions as well as the relations between extreme functions of finite and infinite group problems. Using these results, we prove for the first time that a large class of discontinuous functions is extreme for infinite group problems. This class of extreme functions is the generalization of the functions given by Letchford and Lodi (Oper Res Lett 30(2):74&#x2013;82, 2002), Dash and G&#x00fc;nl&#x00fc;k (Proceedings 10th conference on integer programming and combinatorial optimization. Springer, Heidelberg, pp 33&#x2013;45 (2004), Math Program 106:29&#x2013;53, 2006) and Richard et al. (Math Program 2008, to appear). We also present several other new classes of discontinuous extreme functions. Surprisingly, we prove that the functions defining extreme inequalities for infinite group relaxations of mixed integer programs are continuous.
Watermark-based authentication and decentralized recovery techniques for multimedia content preservation
Authors: Ashfaq Khokhar, Rashid Ansari
Venue:  (2008)

The information age has brought unprecedented benefits to society, but at the same time, it has brought enormous technical challenges. The preservation of online digital content is one such formidable challenge. Preservation refers to safeguarding and providing continual access to document contents in their authentic original form, and document authentication procedures are an essential aid in ensuring preservation. Recently several commercial forums and government consortiums have been established to identify requirements for the preservation of online digital contents and the establishment of related standards. Most of the existing research efforts related to online content preservation have been aimed at preserving textual documents. Such techniques when applied to multimedia contents (video, image, audio) fail to address the issues peculiar to multimedia. Moreover, these efforts assume a managed networked environment where a consortium of trusted publishers or users collaborate together to preserve and authenticate the originality of a document. This dissertation investigates the design and development of a preservation system that specifically caters to the needs and structure of multimedia data. It exploits the perceptual nature of multimedia content, and proposes an authentication scheme that is based on watermarking the contents using content-based signatures. The major contributions of this dissertation include: (i) an efficient hashing based procedure to generate signatures that capture local and global document information, (ii) multiple Compensated Signature Embedding (CSE) schemes to robustly embed the signatures in the media as a watermark, such that perturbations introduced in the media due to embedding are compensated, and (iii) a highly scalable decentralized recovery procedure to recover the tampered/corrupted media. While technically a highly challenging endeavor, the proposed preservation system alleviates the explicit need for a third party authentication agent. For CSE schemes, both iterative and closed-form solutions have been devised. The iterative schemes employ set theoretic approach of Projections Onto Convex Sets (POCS) and additive compensation, and the closed-form scheme incorporates the Lagrange-multiplier based optimization. The proposed data recovery procedure exploits embedded signature information to identify the corrupted or tampered portions of the media and polls its peers in the network to recover the media, reflecting its applicability to preservation of multimedia contents in decentralized P2P networks.
Dynamic Authoring in on-line Adaptive Learning Environments
Authors: A. Alvarez, I. Fernández-Castro, M. Urretavizcaya
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

This paper is centred on the issue of adaptivity in the context of distributed learning systems. It presents an approach to improve the adaptivity aspect through a multi-session learning process. It is based on the concept of dynamic generation of the tutoring knowledge according to the changing characteristic emerging for each session. This approach has produced the MAGADI platform. It is inspired in the structure of IRIS[2] and the architecture of the generated tutors (INTZIRI) has evolved to a distributed multi-agent system. Here its architectural development and authoring aspects are described.
Model-level simulation for COLA
Authors: Markus Herrmannsdoerfer, Wolfgang Haberl, Uwe Baumgarten
Venue: Proceedings of the 2009 ICSE Workshop on Modeling in Software Engineering (2009)

Model-driven development has become the standard approach for the development of avionic and automotive embedded systems. When a semantically founded modeling language is employed, the implemented systems can be checked and further processed in an automated manner. To this end, the Component Language (COLA) was invented. It allows for the definition of all information needed during system development. For a better understanding, and also debugging, of the systems modeled therewith, simulation at the level of the model is a welcome complement. In this paper we present a model-level simulator for COLA. It follows the language's semantics closely, thus guaranteeing the same behavior as specified in the model and implemented by other tools based on COLA. Furthermore, its modular nature allows for the use of different sources for input data. We will demonstrate the architecture and abilities of our simulator, using parts of a recent case study throughout the paper.
Wavelet and Eigen-Space Feature Extraction for Classification of Metallography Images
Authors: Pavel Praks, Marcin Grzegorzek, Rudolf Moravec, Ladislav Válek, Ebroul Izquierdo
Venue: Proceeding of the 2008 conference on Information Modelling and Knowledge Bases XIX (2008)

In this contribution a comparison of two approaches for classification of metallography images from the steel plant of Mittal Steel Ostrava plc (Ostrava, Czech Republic) is presented. The aim of the classification is to monitor the process quality in the steel plant. The first classifier represents images by feature vectors extracted using the wavelet transformation, while the feature computation in the second approach is based on the eigen-space analysis. Experiments made for real metallography data indicate feasibility of both methods for automatic image classification in hard industry environment.
Predicting cross-country results using feature selection and evolutionary computation
Authors: Caio Soares, Juan E. Gilbert
Venue: The Fifth Richard Tapia Celebration of Diversity in Computing Conference: Intellect, Initiatives, Insight, and Innovations (2009)

Although some work has been done to better predict the outcome of sporting events, it has focused on mainstream sports such as football and has typically employed forecasting or machine learning techniques. This work focuses on the sport of cross-country, and uses feature selection and evolutionary computation to better predict National Meet results. Feature Selection is utilized to find the most optimal feature set and a Particle Swarm Optimizer (PSO) to find the most optimal weight set. The best results are attained using the PSO, with an improvement over the current system of 2.5% for Women and 0.3% for Men.
Efficient dynamic services for native runtime environments
Authors: Jonathan E. Cook
Venue:  (2008)

Native runtime environments have long been expected to be lean, efficient platforms for running binary applications at the maximum performance possible. Thus, research and practice in creating more feature-rich environments has progressed mostly in interpreted environments, unfortunately leaving native environments overlooked. This work proposes a new model for native runtime environments that is based on the dynamic services they provide to running application programs. The underlying thesis of this work is that under this new model advanced dynamic services can be efficiently provided to running application programs. An environment that conforms to such a model is called a Service-Based Run-Time (SBRT) environment. A prototype SBRT environment, called pSBRT, is implemented based on the present runtime environment represented by the current dynamic linker, and is comprised of four core components: the dynamic linker, the repository, the event manager, and the dynamic instrumentor. Example key dynamic services from active research areas such as metadata, runtime program monitoring, dynamic program composition, and security, are built and deployed into pSBRT . The thesis is validated with experimental results showing that pSBRT meets the performance goals set to demonstrate that an SBRT environment can be acceptably efficient when compared with current native environments. This work can pave the way for new thinking about native runtime environments, and for the building and deployment of more extensive services in them.
Design of optimized fuzzy cascade controllers by means of Hierarchical Fair Competition-based Genetic Algorithms
Authors: Sung-Kwun Oh, Seung-Hyun Jung, Witold Pedrycz
Venue: Expert Systems with Applications: An International Journal (2009)

In this study, we present a design of an optimized fuzzy cascade controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for a rotary inverted pendulum system. In this system, one controls the movement of a pendulum through the adjustment of a rotating arm. The objective is to control the position of the rotating arm and to make the pendulum maintain the unstable equilibrium point at vertical position. To control the system, we design a fuzzy cascade controller scheme which consists of two fuzzy controllers arrange in a cascaded topology. The parameters of the controller are optimized by means of the HFCGA algorithm. The fuzzy cascade scheme comprises two controllers located in two loops. An inner loop controller governs the position of the rotating arm while an outer controller modifies a set point of the inner controller implied by the changes of the angle of pendulum. The HFCGA being a computationally effective scheme of the Parallel Genetic Algorithm (PGA) has been developed to eliminate an effect of premature convergence encountered in Serial Genetic Algorithms (SGA). It has emerged as an effective optimization vehicle to deal with very large search spaces. A comparative analysis involving computing simulations and practical experiment demonstrates that the proposed HFCGA based fuzzy cascade controller comes with superb performance in comparison with the conventional Linear Quadratic Regulator (LQR) controller as well as HFCGA-based PD cascade controller.
Functioning the intelligent programming to find minimum dispersion wavelengths
Authors: Maan M. Shaker, Mahmood Sh. Majeed, Raid W. Daoud
Venue: WSEAS TRANSACTIONS on COMMUNICATIONS (2009)

A new method based on genetic concepts is suggested to analyze and determine the transmission (propagation and dispersion) characteristics for conventional fibers as well as photonic crystal fibers. The power and the advantage of this work, lie in the fact that it could be applied to any optical fibers type with any refractive index profile and the method computes its proper and accurate propagation constant which leads to investigate the desired dispersion and minimizes it to the least possible value.
Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets
Authors: Silvio Valenti, Dario Rossi, Michela Meo, Marco Mellia, Paola Bermolen
Venue: Proceedings of the First International Workshop on Traffic Monitoring and Analysis (2009)

We present a novel methodology to accurately classify the traffic generated by P2P-TV applications, relying only on the count of packets they exchange with other peers during small time-windows. The rationale is that even a raw count of exchanged packets conveys a wealth of useful information concerning several implementation aspects of a P2P-TV application --- such as network discovery and signaling activities, video content distribution and chunk size, etc. By validating our framework, which makes use of Support Vector Machines, on a large set of P2P-TV testbed traces, we show that it is actually possible to reliably discriminate among different applications by simply counting packets.
Experimental Study of Non-oblivious Greedy and Randomized Rounding Algorithms for Hypergraph b-Matching
Authors: Lasse Kliemann, Anand Srivastav
Venue: Proceedings of the 8th International Symposium on Experimental Algorithms (2009)

We consider the b-matching problem in a hypergraph on n vertices and edge cardinality bounded by ¿. Oblivious greedy algorithms achieve approximations of $(\sqrt{n}+1)^{-1}$ and (¿ + 1)¿ 1 independently of b (Krysta 2005). Randomized rounding achieves constant-factor approximations of 1 ¿ ¿ for large b, namely b = ¿(¿ ¿ 2, ln n), (Srivastav and Stangier 1997). Hardness of approximation results exist for b = 1 (Gonen and Lehmann 2000; Hazan, Safra, and Schwartz 2006). In the range of 1 < b ¿ ln n, no close-to-one, or even constant-factor, polynomial-time approximations are known. The aim of this paper is to overcome this algorithmic stagnation by proposing new algorithms along with the first experimental study of the b-matching problem in hypergraphs, and to provide a first theoretical analysis of these algorithms to some extent. We propose a non-oblivious greedy algorithm and a hybrid algorithm combining randomized rounding and non-oblivious greedy. Experiments on random and real-world instances suggest that the hybrid can, in terms of approximation, outperform the known techniques. The non-oblivious greedy also shows a better approximation in many cases than the oblivious one and is accessible to theoretic analysis.
Partner selection schemes for cooperative multi-hop relay networks
Authors: Li-Chun Wang, Hao Chen
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

In this paper we propose two throughput-oriented relay selection rules for multi-hop relay networks. Our rules can achieve higher throughput while maintaining reliability. We also adopt the total relay transmit power constraint to utilize transmit power more efficiently.
Development program for heat balance analysis fuel to steam efficiency boiler and data wireless transfer
Authors: Nattapong Phanthuna, Warunee Srisongkram, Sunya Pasuk, Thaweesak Trongtirakul
Venue: Proceedings of the 8th WSEAS international conference on Electronics, hardware, wireless and optical communication (2009)

This research aim to improve a combustion system of boiler within increase combustion efficiency and use all out of the energy. The large boilers were used in the industrial factories which consume a lot of energy for production. By oil and gas fuel will be increasing costs everyday cause many factories interested in energy saving with any method technical engineering, specifically for production costs and environment effect decreasing. This researching was installed and program was invented in the industrial factory. This industry factory consumed cogeneration energy for fabric dying. The efficiency before installing the software is measured about 65.85 - 71.98% which heat in exhaust gas about 20% of overall energy is filled in the system. After installing heat loss in the system has been fallen until remain about 5 - 12% and efficiency of heat in system has been reached a peak of 80 - 85%.
Simultaneous Synchronization of Text and Speech for Broadcast News Subtitling
Authors: Jie Gao, Qingwei Zhao, Ta Li, Yonghong Yan
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

In this paper, we present our initial effort in automatic generation of subtitle for live broadcast news programs, utilizing the fact that nearly perfect transcriptions are available. Instead of using the former error-prone automatic-speech-recognition (ASR)-based method, we propose to formulate the subtitling problem as synchronization of text and speech, which is further simplified into an anchor points estimation problem. The Viterbi algorithm for hidden Markov model (HMM) is augmented with new criterions for the online anchor points estimation. Experiments indicate that our proposed methods show satisfying performance for the simultaneous subtitling application. We also present a brief introduction into our whole subtitling system under further development.
Moses: open source toolkit for statistical machine translation
Authors: Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, Evan Herbst
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.