Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing English Noun Groups for Their Conceptual Content.

Abstract : An expectation-based system, Noun Group Processor(NGP), for parsing English noun groups into the Conceptual Dependency representation is described. The system is a part of ELI (English Language Interpreter) which is used as the front end to several natural language understanding systems and is capable of handling a wide range of sentences of considerable complexity. NGP processes the input from left to right, one word at a time, using linguistic and world knowledge to find the meaning of a noun group. Dictionary entries for individual words contain much of the program's knowledge. In addition, a limited ability for the handling of slightly incorrect sentences and unknown words is incorporated.
The application of multicriterion satisfactory optimization in computer networks design

The computer networks design is a nonlinear combinatorial optimization problem with constraint set identical to that of the multiple choice multiconstraint knapsack problem, which is known to be NP-complete. We present a new approach in which a multicriterion satisfactory optimization is used in the computer networks design. The optimal computing model is proposed. The satisfactory rate function of the criteria, which represents the importance of performance specification, and the synthesis satisfactory rate function representing the optimization are designed. An improved genetic algorithm is used for optimization computing. Computational experience shows that this method is efficient and effective.
Video advertisement mining for predicting revenue using random forest

Yuan-Hsin, Huang. M.S., Purdue University, May 2015. Video Advertisement Mining for Predicting Revenue Using Random Forest. Major Professor: John A. Springer. Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers’ needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machinelearning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use.
Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History

This contribution discusses how computer vision combined with art history can analyze the visual codes and artistic representations of embodied communication represented in medieval culture. Our computer-based detection algorithms are able to search directly for gestures in images and thus avoid limitations of retrieval systems that search only textual annotations. As a result, art history is provided not only with a system that enables efficient access to large image datasets, but also a quantitative analysis of the variability and interrelation among gestures or between gestures and other objects. We base our approach on one of four illustrated manuscripts of Eike von Repgow's (ca. 1180–ca. 1235) Sachsenspiegel (Mirror of the Saxons), which reveals a visual grammar that arranges the gestures to a certain extent, but within that framework, the drafter composes freely and with artistic perspective.
An object-oriented framework for feedback control applications

We propose a reusable framework for a known engineering problem: feedback control systems. Feedback systems are commonly described in terms of block diagrams and have different implementations according to the software system designer. The proposed framework is constructed using software design patterns as its building blocks. The Unified Modeling Language (UML) notation is used for specifying the framework class and collaboration diagrams. The framework is represented in the design level to be reused as an initial step in designing feedback control applications. An instantiation of the framework in a sample application is illustrated to show the flexibility and simplicity of using the framework.
Markov chain Monte Carlo methods for parameter identification in systems biology models

Understanding nature is a deep human desire. Therefore, experiments are carried out and measurements are performed to shed light onto what is so far unknown. But experiments only lead to an information gain which is restricted to very limited circumstances and conditions. In order to generalize information, to derive conclusions relevant to a broader scale, and to make well-founded predictions, in short, to actually “learn” something, abstract models have to be established. Model selection, i.e., finding a suitable model class (here: defining the mathematical framework of the model) is a challenging task. A good model has to strike the balance between a sufficiently detailed description of the observations (complexity) and a good generalization performance (simplicity). More precisely, it has to be as accurate as possible without overparametrization and overfitting. Graphical models reduce the complexity by encoding dependencies among random variables, enabling the factorization of the joint probability distribution into a product of simpler “local” distributions. After a suitable model class has been selected, the parameters describing it can be estimated based on the observations (parameter inference). Bayesian methods approach this task by including prior knowledge and maximizing the posterior probability of the parameters, considered as random variables, given the observations. Due to the inherent complexity of the chosen model class, however, this posterior distribution can’t be accessed analytically in many situations. Approximation algorithms such as Markov Chain Monte Carlo sampling solve this problem and avoid complex or even infeasible calculations by drawing representative samples from the distribution of interest. In addition to a simple “best fit”, they provide valuable information on the uniqueness of the solution and the variability of the parameter estimates as a function of the data. Both model selection and parameter inference benefit from the development of increasingly faster computer systems in recent years, which facilitate the exploration of large model spaces. Basically, model selection and parameter inference are subject to three major kinds of error: The model bias arises from the fact that essentially every model class is an abstract and usually compressed version of the true physical processes involved and hence necessarily not correct. Since it is difficult if not practically impossible to identify the best model within a model class, the estimation bias quantifies the difference between the best fit and the estimate that has been obtained from the inference algorithm. The variance represents the error that is due to the stochastic nature of measurements. The extent of these errors can and must be assessed by simulations.
A new data tablet system for handwriting characters and drawing based on the image processing

This paper describes the new input system for data tablet based on the observation of handwriting by CCD camera. The system takes the pen movement on the writing plane with 64 gray level sequential images. As the system needs 0.25 sec. for one frame image, we can observe the slow handwriting by this system. The CCD camera takes the sequential images written on the writing pad with a 50/spl times/50 mm frame. After that, the system can extract the online stroke data, that is, the sequential position coordinates from the sequential input images. We can perform the style of the data tablet without sensor table and wired stylus pen. This system is useful for a new data tablet based human friendly input device.
Design and implementation of a prototype PC based graphical and interactive MILSATCOM requirements database system

Abstract : This thesis develops a prototype PC based Military Satellite Communications MILSATCOM) Requirements Database (MRDB) application for U.S. Space Command, using Microsoft's Access relational database management system (DBMS) for Windows. It demonstrates the advantages of using the proposed database system over the existing one and shows how U.S. Space Command can save both time and money by using a PC based interactive, graphical, and user friendly database system. A rapid prototyping approach in concert with a six phase database design process was used to develop the prototype. The first two chapters of the thesis provide a background of the application and describe database management systems in general and Microsoft Access in particular. The applications of Access - tables, queries, forms, reports, macros, and modules - to the design of the MRDB are then discussed in the succeeding five chapters. The Conclusions describe the advantages and benefits of using the prototype MRDB database system and make recommendations for future improvements. MILSATCOM requirements database, MRDB, Databases, Database design, Microsoft access
Synchronizability of OFDM signals

This paper deals with symbol timing and carrier phase estimation for orthogonal frequency division multiplexed signaling (OFDM) in satellite/terrestrial systems. For transmission over frequency selective channels, a synchronizer at the receiver estimates the phase rotation of each individual carrier; the synchronizer can not distinguish between the phase shift introduced by the channel and by a symbol timing delay. It is shown that for transmission over a nondispersive channel (e.g. fixed satellite link) this algorithm performs suboptimal as compared to a synchronizer which separately estimates the unknown phase and timing. A lower bound on the timing error variance and phase error variance for both synchronizers is calculated. The BER degradation due to imperfect estimation of the timing and phase is given.
Recognizing contextual valence shifters in document-level sentiment classification

Sentiment classification is an emerging research field. Due to the rich opinionated webcontent, people and organizations are interested in knowing others’ opinions, so they need an automated tool for analyzing and summarizing these opinions. One of the major tasks of sentiment classification is to classify a document (i.e. a blog, news article or review) as holding an overall positive or negative sentiment. Machine learning approaches have succeeded in achieving better results than semantic orientation approaches in documentlevel sentiment classification; however, they still need to take linguistic context into account, by making use of the so-called contextual valence shifters. Early research has tried to add sentiment features and contextual valence shifters to the machine learning approach to tackle this problem, but the classifier’s performance was low. In this study, we would like to improve the performance of document-level sentiment classification using the machine learning approach by proposing new feature sets that refine the traditional sentiment feature extraction method and take contextual valence shifters into consideration from a different perspective than the earlier research. These feature sets include: 1) a feature set consisting of 16 features for counting different categories of contextual valence shifters (intensifiers, negators and polarity shifters) as well as the frequency of words grouped according to their final (modified) polarity; and 2) another feature set consisting of the frequency of each sentiment word after modifying its prior polarity. We performed several experiments to: 1) compare our proposed feature sets with the traditional sentiment features that count the frequency of each sentiment word while disregarding its prior polarity; 2) compare our proposed feature sets after combining them with stylistic features and n-grams with traditional sentiment features combined with stylistic features and n-grams; and 3) evaluate the effectiveness of our proposed feature sets against stylistic features and n-
Intelligent motion planning by genetic algorithm with fuzzy critic

A strategy for motion planning in robotics is proposed. The proposed strategy applies a genetic algorithm (GA) to optimize the motion planning. To evaluate the planned motion, the strategy also applies fuzzy logic to a fitness function. The fitness function is referred to as fuzzy critic. The fuzzy critic evaluates plans as populations in the GA with respect to multiple factors. Depending on the goals of the tasks, human operators can easily determine inference rules in the fuzzy critic because of the fuzzy logic. The strategy determines a path for a mobile robot which moves from a starting point to a goal point while avoiding obstacles in a work space and picking up loads on the way. Simulations illustrate the effectiveness of the proposed strategy. <<ETX>>
A Single Image Compression Framework Combined with Sparse Representation-Based Super-Resolution

This paper addresses the problem of single image compression by using sparse representation-based superresolution modeling. The proposed single image compression framework (SICF) is mainly composed of two components, including image encoder and decoder. Given an image, the framework takes down-sampling processing. Then, the downsampling image is fed into JPEG and reconstructed by the sparse representation-based super-resolution, which is using the proposed targeted dictionary based on soft-decision adaptive interpolation (TD-SAI) and targeted residual dictionary (TRD). For further improving the quality of the final decoded image, the feedback is designed to obtain residual assistance image which can be compensated for the loss of high-frequency details in process of super-resolution reconstruction. Supported by the targeted dictionary, our method achieved an improvement in objective quality assessment and competitive performance in visual quality when comparing with other SR algorithms. More importantly, the proposed SICF achieved significant bit-rate saving under the same PSNR compared with JPEG standard. Extensive experiments manifest the viability and efficiency of the proposed image compression framework. Keywords—image compression; targeted dictionary training; super-resolution
3D Audio and Acoustic Environment Modeling

Recently there has been a proliferation of 3D audio technologies intended for desktop computers. Many sound cards, multimedia speakers, video games, audio software, and CD-ROMs are marketed as having some sort of 3D capability. In addition, a new technology called acoustic environment modeling has emerged which combines basic 3D technology with reverberation and other effects in order to simulate natural acoustic scenes.
A comparison of risk assessment techniques from qualitative to quantitative

Risk assessment techniques vary from purely qualitative approaches, through a regime of semi-qualitative to the more traditional quantitative. Constraints such as time, money, manpower, skills, management perceptions, risk result communication to the public, and political pressures all affect the manner in which risk assessments are carried out. This paper surveys some risk matrix techniques, examining the uses and applicability for each. Limitations and problems for each technique are presented and compared to the others. Risk matrix approaches vary from purely qualitative axis descriptions of accident frequency vs consequences, to fully quantitative axis definitions using multi-attribute utility theory to equate different types of risk from the same operation.
Controlling Rigid Bodies with Dynamic Constraints

We present a technique, "Dynamic Constraints," for controlling the positions and orientations of rigid bodies n computer graphics models. The technique addresses the issue of providing control over a physically-based model, in which the bodies'' behavior is determined by simulation of Newton''s Laws of Motion. The "Dynamic Constraints" technique allows the modeler to control the configuration of bodies in a model by specifying constraints which must be met and maintained, as the simulation unfolds. To meet the constraints, we introduce forces into the model; these "constraint forces" forces are applied to the bodies that are constrained. We use an inverse dynamics method to derive a linear "constraint-force equation," whose solution yields the values of the constraint forces. We continually solve the constraint-force equation during the simulation of a model, so that the bodies'' behavior will be consistent with the constraints. This thesis is accompanied by a narrated 10 -minuted videotape, "Caltech Modeling Demos, 1987" that contains several animations demonstrating the "Dynamic Constraints" technique. This thesis additionally includes a glossary of terms relating to physically-based modeling and dynamic constraints.
The multimedia interface using "Paper": Ultra Magic Key

We have developed a new multimedia interface called Ultra Magic Key which uses "Paper". The new interface will allow users to access multimedia information simply by "Turning over a Paper" or "Touching a Paper". The "Paper" sheet has no electric devices or batteries. The system recognizes the "Paper" sheet and the action of a user's finger by image processing. All the information printed on each "Paper" sheet is hyper-linked with each TV channel and AV/IT device.
Creating a test corpus for term extractors through term annotation.

In this paper, we describe a methodology used to create a test corpus for the evaluation of term extractors. This methodology relies on term annotation: terms in a corpus on automotive engineering are selected based on specific criteria pertaining to the terminological setting as well as linguistic and formal properties of terms and term variations. The test corpus accounts for the variety of ways in which terms are realized in running text, and provides a means of automatically evaluating the relevance of term candidate lists produced by term extractors. Due to the XML annotation scheme used, the corpus can be customized, e.g. by filtering out some of the annotated terms based on the type of term or term variation, or frequency. In this paper, we focus on the methodological aspects of this work.
Evaluating Web-latency reducing Protocols in Mobile Environments

User perceived latency is the most prominent performance issue influencing the World Wide Web (www) presently. Hyper-Text Transfer Protocol(HTTP) and Transmission Control Protocol (TCP) have been the backbone of web transport for decades, thus received a lot of attention recently due to end-to-end performance degradation in mobile environments. Inefficiencies of HTTP and TCP strongly affect web response time mainly in resource limited devices. HTTP compression reduces some of the burden imposed by TCP slow start phase. However, compression is still an underutilized feature of the web today [1]. In order to fulfill the end user expectations, we can optimize HTTP to improve Page Load Time (PLT), low memory usage and better network utilization. SPDY, a web latency reducing protocol and HTTP pipelining are a recent proposal to provide faster information exchange over web. Through the course of this work, we present a comprehensive study of new approaches to reduce mobile web latency. At first, we measure the PLT after implementing SPDY, HTTP and HTTP pipelining. Secondly, we also analyze the performance of these protocols after tuning the network parameters like bandwidth and round-trip time (RTT). Finally, we compare the performance of HTTP and other latency reducing protocols. We have conducted all experiments over DummyNet under user-configured network conditions. We critically discuss the challenges of shifting from HTTP to these latency-reducing protocols.
A Depth Video-based Facial Expression Recognition System

Abstract Video cameras are being actively used with a lot of functions in many applications nowadays. In this paper, a depth camera-based new method is proposed to recognize several facial expressions from time-sequential depth images. At first, Independent Component (IC) features are extracted from the time-sequential depth faces that are further improved by Generalized Discriminant Analysis (GDA) to generate more robust features. Finally, Hidden Markov Models (HMMs) are utilized to train and recognize different facial expressions successfully. Performance of the proposed facial expression recognition system is compared against conventional RGB camera-based approaches and obtained superior recognition rate over them.
Computational Modeling and Psvchophysics in Low- and Mid-Level Vision

This thesis addresses a series of topics related to the question of how people find the foreground objects from complex scenes. With both computer vision modeling, as well as psychophysical analyses, we explore the computational principles for low- and mid-level vision. We first explore the computational methods of generating saliency maps from images and image sequences. We propose an extremely fast algorithm called Image Signature that detects the locations in the image that attract human eye gazes. With a series of experimental validations based on human behavioral data collected from various psychophysical experiments, we conclude that the Image Signature and its spatial-temporal extension, the Phase Discrepancy, are among the most accurate algorithms for saliency detection under various conditions. In the second part, we bridge the gap between fixation prediction and salient object segmentation with two efforts. First, we propose a new dataset that contains both fixation and object segmentation information. By simultaneously presenting the two types of human data in the same dataset, we are able to analyze their intrinsic connection, as well as understanding the drawbacks of today’s “standard” but inappropriately labeled salient object segmentation dataset. Second, we also propose an algorithm of salient object segmentation. Based on our novel discoveries on the connections of fixation data and salient object segmentation data, our model significantly outperforms all existing models on all 3 datasets with large margins. In the third part of the thesis, we discuss topics around the human factors of boundary analysis. Closely related to salient object segmentation, boundary analysis focuses on delimiting the local contours of an object. We identify the potential pitfalls of algorithm evaluation for the problem of boundary detection. Our analysis indicates that today’s popular boundary detection datasets contain significant level of noise, which may severely influence the benchmarking results. To give further insights on the labeling process, we propose a model to characterize the principles of the human factors during the labeling process. The analyses reported in this thesis offer new perspectives to a series of interrelating issues in low- and mid-level vision. It gives warning signs to some of today’s “standard” procedures, while proposing new directions to encourage future research.