Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensible Interest Management for Scalable Persistent Distributed Virtual Environments

Abstract : Eventually there will exist virtual environments inhabited by millions, but as virtual environments grow in size and number of entities, many problems emerge. Because of these problems, increasing attention is being brought to the issue of filtering data that is not of interest to a given client. Such filtering is known as interest management. This dissertation outlines a Three Tiered approach to interest management. The first tier breaks the world into manageable pieces. The second tier uses the data from the first to create a protocol independent perfect match between a client's interests and the environment. The third tier, building on the second, adds protocol dependence allowing the client to receive only the data from the protocol it needs. At the same time, separating out the protocol from the core interest management can allow multiple protocols to simultaneously exist within the same environment, while using the same underlying filtering mechanism. Results from this work have shown that it is possible to create an interest management software architecture that allows bandwidth, packets per second, and CPU time to scale dependent only on the number of entities a given client is interested in at any one time.
Events in the Semantics of English: A Study in Subatomic Semantics

This extended investigation of the semantics of event (and state) sentences in their various forms is a major contribution to the semantics of natural language, simultaneously encompassing important issues in linguistics, philosophy, and logic. It develops the view that the logical forms of simple English sentences typically contain quantification over events or states and shows how this view can account for a wide variety of semantic phenomena.Focusing on the structure of meaning in English sentences at a "subatomic" level - that is, a level below the one most theories accept as basic or "atomic" - Parsons asserts that the semantics of simple English sentences require logical forms somewhat more complex than is normally assumed in natural language semantics. His articulation of underlying event theory explains a wide variety of apparently diverse semantic characteristics of natural language, and his development of the theory shows the importance of seeing the distinction between events and states.Parsons demonstrates that verbs, also, indicate kinds of actions rather than specific, individual actions. Verb phrases, too, he argues, depend on modifiers to make their function and meaning in a sentence specific. An appendix gives many of the details needed to formalize the theory discussed in the body of the text and provides a series of templates that permit the generation of atomic formulas of English.Terence Parsons is Professor of Philosophy and Dean of Humanities at the University of California, Irvine.
Sample size determination for training cancer classifiers from microarray and RNA-seq data

The objective of many high-dimensional microarray and RNA-seq studies is to develop a classifier of cancer patients based on characteristics of their disease. The germinal center B-cell (GCB) classifier study in lymphoma and the National Cancer Institute's Director's Challenge lung (DC-lung) study are two examples. In recent years, such classifiers are often developed using regularized regression, such as the lasso. A critical question is whether a better classifier can be developed from a larger training set size and, if so, how large the training set should be. This paper examines these two questions using an existing sample size method and a novel sample size method developed here specifically for lasso logistic regression. Both methods are based on pilot data. We reexamine the lymphoma and lung cancer data sets to evaluate the sample sizes, and use resampling to assess the estimation methods. We also study application to an RNA-seq data set. We find that it is feasible to estimate sample size for regularized logistic regression if an adequate pilot data set exists. The GCB and the DC-lung data sets appear adequate, under specific assumptions. Existing human RNA-seq data sets are by and large inadequate, and cannot be used as pilot data. Pilot RNA-seq data can be simulated, and the methods in this paper can be used for sample size estimation. A MATLAB program is made available.
Video browsing using clustering and scene transitions on compressed sequences

This paper describes a new technique for extracting a hierarchical decomposition of a complex video selection for browsing purposes. The technique combines visual and temporal information to capture the important relations within a scene and between scenes in a video, thus allowing the analysis of the underlying story structure with no a priori knowledge of the content. We define a general model of hierarchical scene transition graph, and apply this model in an implementation for browsing. Video shots are first identified and a collection of key frames is used to represent each video segment. These collections are then classified according to gross visual information. A platform is built on which the video is presented as directed graphs to the user, with each category of video shots represented by a node and each edge denotes a temporal relationship between categories. The analysis and processing of video is carried out directly on the compressed videos. Preliminary tests show that the narrative structure of a video selection can be effectively captured using this technique.
Developing a multimedia instrument for technical vocabulary learning: a case of EFL undergraduate physics education

The aim of the present study is to investigate the process of constructing a Multimedia Assisted Vocabulary Learning (MAVL) instrument at a university in Rwanda in 2009. The instrument is used in a one-computer classroom where students were taught in a foreign language and had little access to books. It consists of video clips featuring images, sound and text intended to facilitate learning and retention of technical vocabulary in a first-year electricity course. The construction takes theories of social constructivism and learning with multimedia as its point of departure. In response to the need for knowledge of scientific vocabulary, the researcher collaborated with a group of 82 students and their two teachers to select and encode 81 video clips of technical terminology. The gains from the process of constructing the instrument enabled collaborative and interdisciplinary learning and showed the potential to facilitate technical vocabulary acquisition.
Proceedings of the Third Workshop on Annotation of Corpora for Research in the Humanities (ACRH-3)

The huge digitization step that archives, publishers, and libraries are currently undertaking enables access to a vast amount of information for historians. Yet, this does not necessarily make life easier for historians, as the main problem remains how to find relevant sources in this sea of information. We present a case study demonstrating how automatic text analysis can aid historians in finding relevant primary sources. We focus on strike events in the 1980s in the USA. In earlier work on strikes, researchers did not have at their disposal a full and comprehensive list of major strike events. Existing databases of this kind (e.g [19, 22]) are the result of intensive manual work and took years to build. Natural language processing (NLP) tools allow for faster assembly of datasets of this kind on the basis of collections of free texts that contain the information that should be in the database. We aim to construct a database of events using a digital newspaper archive and unsupervised NLP methods such as Latent Dirichlet Allocation (LDA) and clustering techniques to group together newspaper articles that describe the same strike. We study the effect of different feature representations, such as simple bagof-words features, named entities, and time stamp information. We evaluate our results on a manually labeled sample of news articles describing a small set of strikes.
Porting the OPATM-BFM Application to a Grid e-Infrastructure - Optimization of Communication and I/O Patterns

OPATM-BFM is an off-line three-dimensional coupled eco-hydrodynamic simulation model used for biogeochemical and ecosystem-level predictions. This paper presents the first results of research activities devoted to the adaptation of the parallel OPATM- BFM application for an efficient usage in modern Grid-based e-Infrastructures. For the application performance on standard Grid archi- tectures providing generic clusters of workstations, such results are important. We propose a message-passing analysis technique for communication-intensive parallel applications based on a preliminary application run analysis. This technique was successfully used for the OPATM-BFM application and allowed us to identify several optimization proposals for the current realization of the communication pattern. As the suggested improvements are quite generic, they can be potentially useful for other parallel scientific applications.
The Theory and Practice of Translation

The Theory and Practice of Translation, first published in 1982 and a companion work to Toward a Science of Translating (Brill, 1964), analyses and describes the set of processes involved in translating. Bible translating, the focus of this work, offers a unique subject for such a study, as it has an exceptionally long history, involves more than 2,000 languages, a vast range of cultures and a broader range of literary structures than any other type of translating. Not only of interest to Biblical scholars, therefore, this work explores issues of textual meanings and the procedures for communicating these meanings into other languages and cultures.
An Introduction to the Special Volume on "Political Methodology"

This special volume of the Journal of Statistical Software on political methodology includes 14 papers, with wide-ranging software contributions of political scientists to their own field, and more generally to statistical data analysis in the the social sciences and beyond. Special emphasis is given to software that is written in or can cooperate with the R system for statistical computing.
Fast and Rigid 3D Shape Deformation Based on Moving Least Squares

ABSTRACT We present a fast 3D shape deformation method that achieves smoothly deformed result by approximating a rigid transformation based on moving least squares (MLS). Our modified MLS formulation reduces the computation cost for computing the optimal transformation of each point and still keeps the rigidity of the deformed results. Even complex geometric shapes are easily, intuitively, and interactively deformed by manipulating point and ellipsoidal handles.Keywords: Shape Deformation, Rigid Transformation, Moving Least Squares 1. Introduction 1) Shape deformation has been an active research topic in computer graphics. 3D shapes can be deformed generally by defining some handles, such as control points and line segments, displacing these handles, and transforming the target surfaces/polygons appropriately according to the displacement of the handles. There are two important issues in shape deformation. One is how to maintain the rigidity between the original and the transformed one and the other is the computation overhead for computing the optimal position of the transformed shape.As-rigid-as-possible deformation methods are suggested to minimize the distortion in a deformed shape. One
Content-based Image Retrieval by Information Theoretic Measure

Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset. Defence Science Journal, 2011, 61(5), pp.415-430 , DOI:http://dx.doi. org/10.14429/dsj.61. 1177
Recomendação de conteúdo baseada em interações multimodais

Providing products, information and services from user profiles has made the recommendation systems to be increasingly present, increasing the ease of selection and retention of users in Web services. However, there are optimizations to be made in these systems mainly with respect to modeling the user profile. Generally, the preferences are modeled superficially, due to the scarcity of information collected, as notes or comments, or because of inductive information that is susceptible to errors. This work proposes a recommendation tool based on multimodal interactions that combines users’ interactions, wich are processed individually by traditional recommendation algorithms. In this tool developed four combination of techniques in order to provide recommendation systems subsidies to improve the quality of predictions. Keyword: User Profiles, Recommendation Systems, User Interactions, Ensemble Tecniques.
Neural Network Applications In Hand-Written Symbol Understanding

Several issues in neural networks are investigated in the context of hand-written symbol recognition and a new hierarchical network structure is proposed. There have been many successful demonstrations of hand-printed character or cursive word recognition using neural network algorithms with a limited data set. However, the required network size increases as the variations allowed in the training and test patterns increase. Two highly stylized data sets (American Bankers Association's E-13B font and ANSI standard OCR-A font) are compared with hand-printed block characters in terms of required network size, mean squared error per pattern at fixed training sweeps, and number of sweeps required to reach a certain confidence level of recognition. At each training stage, the connection strengths are stored and these stored connection strengths are loaded into the net later to test generalization against similar test patterns. When the test data is presented to the net, the performance is not very good, but the network adapts to the new data quite quickly when the training and test data are very regular and/or the number of different classes for which the network was trained is small. To enhance the generalization and adaptation performance, a new hierarchical structure is proposed where the training patterns are grouped into cascade of three class problems. This structure is verified by experiments.
Challenges to automated attack traceback

Ideally, a traceback system would identify the human responsible for the attack. In this discussion, however, we limit traceback to determining the computer host that is the source of an attack. Once a traceback system identifies an attacker host, investigators can apply traditional methods of crime investigation or intelligence gathering to assign responsibility to an individual. To analyze the traceback problem, we must understand how attackers hide their identity. Most often, they compromise the two components of identity on the Internet.
Improved Sample and Hold Circuit using MOSFET

The focus of this paper is the design of low distortion sample and hold circuit. The main idea behind avoiding distortion is to make gate-to-source voltage of the MOSFET independent of input voltage. The preferred circuit of the present paper is capable of performing at high speed since it does not need an operational amplifier in its operation [9]. This feature also reduces power consumption. Another advantage is that in some aspects transistor device S1 is in closed matched to the switching device, which helps in minimizing distortion. Also the circuit does not have any problem with drain induced barrier lowering (DIBL). Sample and hold circuits find use in linear systems. In some types of analog-to-digital converters, the input voltage is compared to a voltage produced internally from a digital-to-analog converter (DAC). The circuit takes a series of values, and conversion stops once the voltages are equal, within a defined error margin. If the value of the input were allowed to change during this comparison, the outcome of the conversion would be skewed and might be completely unrelated to the actual input value [2].
Robot motion planning with many degrees of freedom and dynamic constraints

A hydraulically powered, cam controlled means for actuating the turret slide of an automatic turret lathe including a valve actuator, which translates any input motion received from the cam into endwise motion of the control valve spool regardless of the direction from which this motion is received, thereby making unlimited cam steepness possible, so that it becomes now possible to obtain any desired speed for the rapid advance, rapid return as well as for the feed motion of the turret slide, all positively controlled by the cam.
A Novel and Hybrid Optimization Mechanism For Denoising And Classification Of Medical Images using DTCWPT And Neuro-Fuzzy Classifiers

Computed tomography (CT) images are usually corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To attain the best possible diagnosis it is very vital that medical images be clear, sharp, and free of noise and artifacts. In this research paper, we propose a robust technique to denoise, detect and classify the tumour part from CT medical images. Our proposed approach consists of four phases, such as denoising, region segmentation, feature extraction and classification. In the denoising phase Dual Tree Complex Wavelet Packets and Empirical Mode Decomposition are used for removing noise. The performance of the proposed technique is assessed on the five CT images taking into consideration, Gaussian and salt & pepper noise for the parameters, PSNR and SDME. In the segmentation process K-means clustering technique is employed. For the feature extraction, the parameters contrast, energy and gain are extracted. In classification, a modified technique called Cuckoo-Neuro Fuzzy (CNF) algorithm is developed and applied for detection of the tumour region. The cuckoo search algorithm is employed for training the neural network and the fuzzy rules are generated for classification, according to the weights of the training sets. From the obtained outcomes, we can conclude that the proposed denoising technique have shown better values for the SDME of 69.9798, PSNR of 29.8413 for salt & pepper noise and an accuracy of 96.3% which is very superior compared to existing methods
Spoken word recognition based on top-down phoneme segmentation

This paper describes a new spoken word recognition approach based on the top-down phoneme segmentation. Fourteen phoneme recognition functions are introduced to deal with various coarticulations. This new approach has two advantages. First, a precise phoneme recognition can be achieved because of the phoneme level top-down verification. Second, only phoneme symbol sequences are required for the vocabulary knowledge source, because the coarticulation knowledge is included in phoneme level knowledge sources. Experimental recognition results for 100 city names uttered by 50 speakers indicate that the phoneme concatenations showing strong coarticulation must be segmented as a unit to achieve a high recognition rate.
An analysis of formal errors in a corpus of L2 English produced by Chinese students

This paper describes the investigation of a small corpus of writing of English for academic purposes produced by L1 speakers of Mandarin. The investigation involved the development of a tagset for the identification of formal errors in the corpus, and the subsequent analysis of these errors with a view to creating remedial grammar materials for Chinese students studying in the medium of English. Some past approaches to error analysis are discussed, the process of developing the tagging system is described, and error types are identified, categorised, quantified, described and (as far as possible) explained.
A NON-REPUDIABLE BIASED BITSTRING KEY-AGREEMENT PROTOCOL WITHROOT PROBLEM IN NON-ABELIAN GROUP

The Key exchange problems are of central interest in security world. The basic aim is that two people who can only communicate via an insecure channel want to find a common secret key without any attack. In this paper, we elaborated the process for well secured and assured for sanctity of correctness about the sender’s and receiver’s identity, as non-repudiable biased bitstring key agreement protocol using root problem in non-abelian group (NKR-NAG).
Transmitting television as clusters of frame-to-frame differences

A number of redundancy reduction techniques are used in a coder that is about eight times more efficient than simple PCM. The coder is capable of transmitting Picturephone® signals at an average rate of one bit per picture-element (2 Megabits per second). When there is movement in the scene, most transmission time is devoted to the parts of the picture that change significantly. The data are generated irregularly but the data flow is smoothed prior to transmission in a buffer that holds about one frame of data. The redundancy reduction techniques used and the behavior of the coder are discussed both from an intuitive and from a statistical viewpoint. The positions of elements that change are signaled by addressing the first element of a run of changes and marking the end of the run with a special code word. The changes of luminance are transmitted as frame-to-frame differences using variable-length code words. When rapid motion makes the buffer more than a quarter full, only differences for every second element are transmitted, the values of the intervening changed elements being set equal to the average of their neighbors. If the buffer continues to fill, the threshold that determines which changes are significant is raised from 4/256 to 7/256 of the maximum signal value. When violent motion causes the buffer to fill completely, replenishment is stopped for about one frame while the buffer empties. Subsampling and raising the threshold are not objectionable because viewers rarely detect the small impairments introduced in moving images. Observers are critical, however, of small impairments in stationary scenes. Thus, to maintain high quality in stationary areas, the entire picture is forcibly updated every three seconds by transmitting 8-bit luminance values for three lines of every frame. A record of the coder's behaviour is available as a 16-milimeter movie film.