Miracle-dz/UniFAR-FTUs huggingface dataset:
Goal-based Decision Strategies

We present a process model of decision making mediated by goals and relationships. The model is implemented in the VOTE computer program which simulates Congressional roll call voting. In this paper, we focus on VOTE's decision strategies , which are based on the need not only to arrive at a vote, but also to produce an explanation for each decision. We describe several typical strategies, as well as an indirect strategy, Deeper Analysis, that is invoked when the normal strategies fail to arrive at a decision. Decision making is a complex cognitive process. A realistic model of decision making must account for a multitude of goals and limited resources. Many problems will not admit to perfect solutions. Practical decisions often involve trade-oos. We have developed a goal-based model of decision making in which the relative priorities of goals drive the decision process. This model serves as the basis for the VOTE program which simulates Congressional roll call voting decisions on thousands of votes, based on the relative priorities of goals and constituent relationships , and the need to provide an explanation for the decision. Alternative models of decision making have used prescriptive decision analysis Raiia, 1968], which uses estimates of payoos and uncertainty of outcomes to arrive at an optimal result. The assumptions of decision analysis are inappropriate in many domains for which our goal-based, descriptive model may apply. Our model is an extension of the theory of goals developed by Schank and Abelson (1977), Wilensky (1978) and Carbonell (1979), and is compatible with the use of goals in case-based reasoning systems (Simpson 1985, Hammond 1986). We deene the priority or importance of a goal to be proportional to the resources that an agent may be Author's address as of fall 1991: Table 1: VOTE Summary Statistics willing to allocate in service to that goal. Resources are viewed quite broadly to include not only time or money, but also cognitive resources, such as attention and memory. For example, an agent should think more about important matters than about trivial ones. We extend the model of goals to include interper-sonal relationships (Slade 1990). An agent adopts the goal agenda of another agent with a priority proportional to the importance of the relationship. For example , an agent is more likely to help a friend than a stranger. This extension permits the processing of adopted goals in a manner uniform with individual …
Correctness of Java Card Tokenisation

We present a formalisation of the bytecode optimisation of Sun's Java Card language from the class file to CAP file format as a set of constraints between the two formats and define and prove its correctness. Java Card bytecode is formalised as an abstract operational semantics, which can then be instantiated into the two formats. The optimisation is given as a logical relation such that the instantiated semantics are observably equal.
Multimodal Biometric Authentication Methods: A COTS Approach | NIST

We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometrics on a population approaching 1000 individuals. Prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations approximately 10% of this size. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using already highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce novel methods of fusion and normalization that improve accuracy still further through population analysis.
A comparison of pixel-based and object-oriented classification using SPOT5 imagery

Land cover maps can assist decision-makers in both the scientific and business activities, and remote sensing techniques provide effective and efficient methods to create such maps. However, some image processing approaches may be more appropriate than others in distinguishing land-cover categories, particularly when classifying high spatial resolution imagery. Here both the pixel-based classification method based on the knowledge rule and the object-oriented fuzzy classification method have been studied systematically and deeply in this paper with SPOT5 high spatial resolution imagery. The Digital elevation model (DEM) data and the texture feature are integrated into the two knowledge-based classifications in this paper. And the result of accuracy assessment indicates that the two knowledge-based classifications can catch very good classification precision. Besides, the shape and context information can be used fully to distinguish the roads from the houses with the objected-oriented fuzzy classification method, which is hard to accomplish in pixel-based classification. Furthermore, the "Salt and Pepper" effect is eliminated very well with the objected-oriented fuzzy classification method, which always exists in pixel-based classification.
An Implicit-Feedback Based Ranking Methodology for Web Search Engines

The World Wide Web (WWW) is a fast growing network of information covering nearly every possible topic. With the input of a few keywords a search engine can return a list of relevant web pages by querying its index. However, it is quite common to witness irrelevant results being presented to the user. One way to improve the ordering of the search results is by incorporating user feedback in ranking the documents for relevancy. We present a model for search engine enhancement by using implicit feedback in the form of ClickThrough data from the users. The order of the links returned as the query result is re-arranged for the future queries based on the choices made by the majority of the users. An algorithm, with its implementation, is presented and then evaluated to demonstrate its capability as an add-on component for enhancement of the current ranking algorithms.
An Analytical Study of the Qualities of Style and Rhetoric Found in English Compositions

The compositions used in this study were taken from eleven well-known standardized scales and represented general-merit ratings ranging in quality from 30 to 70, inclusive. The data were obtained from two sources: (1) graduate students in English, who pointed out the elements, principles, and qualities of merit in compositions; and (2) experienced teachers of English, who rated specific compositions on the basis of each of the elements, principles, and qualities and gave reasons to support their judgments.
Classification of Digital Modulation Schemes Using Linear and Nonlinear Classifiers

Abstract : The potential benefits of automated detection of digital modulation types have made it a continuing topic of research for many years. Commercial systems could be made more interoperable and military sensors could send demodulated products for analysis, to name just two. Noisy channels and multipath fading environments continue to make this a challenging problem. This thesis applies classification algorithms that have been used in other applications. Nine different digital modulation schemes are considered. The criteria for selecting higher-ordered moments and cumulants as features for discrimination are discussed. An overview of the classification algorithms considered is provided, as well as the statistical models for noisy channels. Results show that the scheme proposed here works well in AWGN channels and in moderate fading conditions.
Dynamic cloth interaction including fast self-collision detection

This paper describe for interactive simulation of cloth which can be used in certain applications such as games, animation, virtual reality, medical and Computer-Aided Design (CAD). In order to create high quality of cloth simulation, we use a mass-spring system, which is the universal model in cloth simulation. For the animation of the particles, we considered internal and external forces to create cloth to look more realistic. As cloth simulators, to handle the cloth self collision is more complicated. So, several methods have been suggested by numerous researchers. Furthermore, we discuss numerous methods for an efficient avoidance of self collision detection. So that the efficiency animation of cloth can be improves much.
Artificial Intelligence: Concepts, Techniques, and Applications

This book examines the fundamental concepts and various techniques involved in real-world applications of artifical intelligence, such as computer representation of problems, searching and inference mechanisms, intelligent languages, and computer representation and utilization of knowledge. It explores applications in theorem-proving, games, robotics, natural language processing, knowledge engineering, and more.
Face detection: A comparison between histogram thresholding and neural networks

Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this thesis, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. Multi-layer perceptron (MLP) neural networks and histogram thresholding techniques have been used for skin and lips detection. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web. Only the skin and the lips colour in the normalised RGB colour scheme were used for the skin and lips detection respectively. A new method for obtaining the r, g, and b components of the normalised RGB systems from the R, G, and B components of the RGB system was proposed. It was found out that the proposed method, called maximum intensity normalisation, gives higher percentage of correct skin detection than the conventional rgb colour scheme regardless of the database used or the skin detection method. Two methods were used to find the number of neurons in the hidden layer of the MLP. The first method use binary search between a minimum and a maximum values while the second method use sequential search with a stopping criteria. The effect of scale factor, facial expressions and minor occlusions with glasses on skin, lips and face detection was investigated. It was found out that, as the scale factor increases the percentage skin and lips detection error decreases. However, the percentage decrease in skin and lips detection errors depends on the intensity normalisation, the detection method and the chrominance component used. But the scale factor did not have any effect on the face detection. In general, the facial expression did not have any significant effect on skin detection. However, for lips detection, the laughing expression did give the highest lips detection error followed by smiling expression. Furthermore, the percentage increase in lips detection error as a result of the facial expression depends on the intensity normalisation, the detection method and the chrominance component used. As for face detection, the facial expression has a negative effect on the correct face detection especially at scale factor of 3. Although, the minor occlusion increases the skin detection error it has no significant effect on the performance of face detection.
Myriad: scalable VR via peer-to-peer connectivity, PC clustering, and transient inconsistency: Research Articles

Distributed scene graphs are important in virtual reality, both in collaborative virtual environments and in cluster rendering. Modern scalable visualization systems have high local throughput, but collaborative virtual environments (VEs) over a wide-area network (WAN) share data at much lower rates. This complicates the use of one scene graph across the whole application. Myriad is an extension of the Syzygy VR toolkit in which individual scene graphs form a peer-to-peer network. Myriad connections filter scene graph updates and create flexible relationships between nodes of the scene graph. Myriad's sharing is fine-grained: the properties of individual scene graph nodes to share are dynamically specified (in C++ or Python). Myriad permits transient inconsistency, relaxing resource requirements in collaborative VEs. A test application, WorldWideCrowd, demonstrates collaborative prototyping of a 300-avatar crowd animation viewed on two PC-cluster displays and edited on low-powered laptops, desktops, and over a WAN. We have further used our framework to facilitate collaborative educational experiences and as a vehicle for undergraduates to experiment with shared virtual worlds. Copyright © 2006 John Wiley & Sons, Ltd.
Assessing the efficacy of the trim and fill method in adjusting for publication bias in meta analysis

One of the most common critics leveled against meta-analysis pertains to the issue of publication bias. A publication bias may be encountered if a meta analysis is restricted to the combination of results obtained from studies which have been published. The bias occur when unpublished studies are associated with their outcomes, namely, studies which produced small effects or negative results are more likely not to get published. The most common method used to adjust for publication bias is the Duvall and Tweedie’s Trim and Fill method. The method relies on the scrutiny of one side of a funnel plot for asymmetry which is assumed to be due to publication bias. In most of the studies investigating the performance of this method, binary meta analysis data were used, utilizing the effect size measures such as the odds ratio, or risk relative. The main aim of this study is to evaluate the efficacy of the Trim and Fill method in correcting the publication bias in meta analysis of continuous data with standardized mean difference as the measure of effects. The data used in this study were simulated using R. The characteristics and assigned values of the simulated meta-analysis, namely the size of treatment effects, study variances and the number of primary studies used, were based on a meta-analyses from medical research. Three levels of primary studies ,N, associated with small, medium and large meta analysis are N =10, 30 and 50/100, respectively, and six levels of percentage of missing publications ,x%, were induced, corresponds to high, medium and low degree of bias at 70%/50%, 30%/20% and 10%/5%, respectively. Each combination of meta-analysis generated was replicated 10,000 times. The inverse-variance weighted random effects estimate were computed for both unadjusted and adjusted meta analysis. The assessment of the effect estimates were evaluated in terms of statistical bias and the standard errors for point estimates, and in terms of confidence band coverage for the interval estimates.
Time Varying Behavior of Programs

Modern architecture research relies heavily on detailed pipeline simulation. Furthermore, programs often times exhibit interesting and important time varying behavior on an extremely large scale. Very little analysis has been conducted to classify the time varying behavior of popular benchmarks using detailed simulation for important architecture features. In this paper we classify the behavior of the SPEC95 benchmark suite over their course of execution correlating the behavior between IPC, branch prediction, value prediction, address prediction, cache performance, and reorder buffer occupancy. Branch prediction, cache performance, value prediction, and address prediction are currently some of the most influential architecture features driving microprocessor research, and we show important interactions and relationships between these features. In addition, we show that many programs have wildly different behavior during different parts of their execution, which makes the section of the program simulated of great importance to the relevance and correctness of a study. We show that the large scale behavior of the programs is cyclic in nature, point out the length of cyclic behavior for these programs, and suggest where to simulate to achieve results representative of the program as a whole.
Polymorphic Type Inference in Scheme

This paper presents a type-inference system for Scheme that is designed to be used by students in an introductory programming course. The major goal of the work is to present a simple type inference system that can be used by beginning students, yet is powerful enough to express the ideas of types, polymorphism, abstract data types (ADTs), and higher-order procedures. The system also performs some rudimentary syntax checking. The system uses subtyping, but only in a primitive fashion. It has a type datum which is a supertype of all types, and a type poof which is a subtype of all types. It uses and-types (intersection types) to control the use of datum and to generate accurate but simple types.
Is a closed-loop discovery system feasible?

In order to construct scientifically reasoning artifacts we not only have to close the loop between hypothesis generation and evaluation but also to make the system embodied. To genuinely understand scientific insights, “robot scientists” need to represent scientific knowledge within their own representational structure rather than in terms of a priori defined logical propositions. Two main features of such systems are identified: projective constructivism that reverses the flow of information processing, and cognitive canalization that reduces computational requirements.
Comparaison de SMLLR et de SMAP pour une adaptation au locuteur en utilisant des modèles acoustiques markoviens

In this paper, two adaptation schemes are presented: SMAP and SMLLR. Both methods update the parameters of the acoustic models of a speaker independent system in order to improve its performances for a new speaker. We experimented SMAP and SMLLR to HMMs of the ESPERE engine in the batch mode and in the unsupervised incremental mode. The HMMs were learned on the Resource Management corpus. Results of the batch adaptation show a greatest efficienty of SMAP. For the unsupervised incremental adaptation, SMLLR is more powerful than SMAP, according to the incremental scheme we choose.
A Sociohistorical Analysis of the Hawaiian Language Immersion Program.

Papahana Kaiapuni is a K-12 indigenous language immersion program in selected public schools in the State of Hawaii. Instruction in Kaiapuni classrooms is conducted in the Hawaiian language. Program goals include students' development of a high level of proficiency in both Hawaiian and English. For nearly a century, policy banning the Hawaiian language from the public schools and other government activities contributed to the decline of the language's use. Papahana Kaiapuni is an effort to revive the Hawaiian language, now the second official language of the state. Supporters of the program believe that the program may also serve to promote both the cultural identity and the academic achievement of the Native Hawaiians. Social and historical influences on the program's development are outlined, drawing from literature about the program, interviews with individuals involved in early program development, and an analysis of selected board of education and federal documents. Interview questions are appended. (Contains 49 references.) (Author/MSE) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ********************************************************************************
Applications in Neurorobotics

The field of neurorobotics is still in its infancy; however, its intersecting motivations are not. On the one hand, theories of neuroscience that require immersion in the real-world can be embedded in mobile agents creating complex patterns of activity believed to be a requirement for understanding higher-order neural function. On the other, the cognitive capabilities of humans remain unparalleled by artificial agents. Emulating biology is one strategy for creating more capable artificial intelligence. Despite these strong motivations for creating neurorobotic entities technological hurdles still remain at all levels. This thesis presents two different contributions to the field of neurorobotics. The first is aimed at reducing the complexity of coupling spiking neural models with virtual agents. This is accomplished through a set of tools that act to abstract the neuroscience details from roboticists and the mechanical details away from the neuroscientists. The second contribution provides an example of how higher-level cognitive theories of speech processing can be integrated into the neurorobotics paradigm. Extracting the emotional content of a speaker, independent of what is being spoken, is a daily act for most people. The neural basis for this ability remains illusive, however cognitive models have been realized. This class of models can be integrated with the biologically realistic neural simulations in a complementary way to expand the capabilities of a neurorobotic system.
Communications between Deaf and Hearing Children Using Statistical Machine Translation

Communication with hearing people society is an important problem for deaf people. They have not learned the valid rules of spoken language that hearing people use them. We therefore prepare an efficient corpus and apply it to Moses Machine Translation to simplify these communications. We choose communications between children because they use e-communications more than adults. All systems that automatically process sign language corpus rely on appropriate data. So our corpus with a limited set of words and with specific subject is the first Persian corpus containing Persian language, PL, and Persian sign language, PSL, based on the domain of children's conversations. At the first step raw data are pre-processed which provides necessary information for translation. These data are statistic information extracted of sentences. After getting important data from initial sentences, the corpus is applied for training of Moses machine translation. In addition to the main goal of this system, we can educate deaf people the valid Persian grammar that is a problem for deaf people in school and society. In this paper, we compare our results with the results taken from the Moses decoder in other spoken languages that indicate our purpose is applicable in the real world.