Miracle-dz/UniFAR-FTUs huggingface dataset:
On KANSEI facial image processing for computerized facial caricaturing system PICASSO

The PICASSO system for facial caricaturing is a typical KANSEI vision system in the following senses: it can process 2D, 3D and motion facial images to generate a facial sketch of line drawings; it can extract individuality features by introducing a "mean face" for enforcing the visual KANSEI impressions by which the face looks more likely to be so; and it can evaluate the generated facial caricature to be moderate for the individual visual KANSEI characteristics, based on the model of visual illusion. Image processing methods for generating a facial sketch based on the Hough transform are presented. Principles to generate a facial caricature and to evaluate the generated works are explained precisely, together with demonstrations of experimental results. In order to equip the PICASSO system for the feedback loop from the observer, we have introduced the eye camera system. This equipment is expected to provide a new method to extract the simultaneous individual eye-mark patterns which show where in the face the observer is watching. These preliminary experiments are also presented to show the possibility to extract more tightly individual KANSEI visual information.
Hierarchical storage management: leveraging new capabilities

E-commerce has created new problems in data management as mission-critical data swells a business' database into the terabytes. IT shops must efficiently manage data and still provide a rock-solid system with guaranteed 24/7 access. Finding a way to effectively fulfil this mission requires rethinking traditional data management methods. Among the many tools already available, hierarchical storage management (HSM) is an effective data management solution for this demanding new environment. HSM enables centralized data management across a variety of storage platforms, such as servers, RAID (redundant arrays of inexpensive disks), magneto-optical jukeboxes, and tape libraries. It maintains data through a three-level management system. Level one is online data maintained in an online server volume or RAID. Level two is infrequently accessed data; so HSM maintains it in slower storage platforms. Level three is offline data stored in a tape library or on shelved media. The business rarely accesses such data, basically keeping it to comply with record retention rules.
Continuous learning with a quiz for stroke nurses

A continuous learning solution was sought which allows stroke nurses to keep the vast body of theoretical knowledge fresh, stay up-to-date with new knowledge, and relate theoretical knowledge to practical experience. Based on the theoretical background of learning in the medical domain, reflective and game-based learning, we carried out a user-oriented design process that involved a focus group and a design workshop. In this process, a quiz that includes both content-based and reflection questions was identified as a viable means of transportation for theoretical knowledge. In this paper we present the result of trialling a quiz with both content-based and metacognitive reflective questions in two settings: In one trial the quiz was used by nurses as part of a qualification programme for stroke nurses, in the second trial by nurses outside such a formal continuous learning setting. Both trials were successful in terms of user acceptance, user satisfaction and learning. Beyond this success report, we discuss barriers to integrating a quiz into work processes within an emergency ward such as a stroke unit.
Parameterization of a metapopulation model: an empirical comparison of several different genetic algorithms, simulated annealing and tabu search

Analysis of metapopulation dynamics is currently of great interest in population biology and in conservation biology. In this study a metapopulation model is augmented with external environmental factors, which are modelled by a group of polynomials. The parameter estimation of the extended model is attempted with three methods of global optimization, simulated annealing (SA), tabu search (TS) and genetic algorithms (GA). GA variants tested include a binary coded implementation, several floating point implementations such as the breeder GA (BGA), and a simulated diffusion model parallel implementation. The BGA produced the most consistent convergence whereas SA eventually produced the lowest value of the objective function.
Combat Simulation Trajectory Management.

Abstract : This project tested the feasibility of explicitly tracking multiple outcomes for random events in combat simulations. A simple force on force combat simulation resembling Eagle was built that creates new states and trajectories when selected random events occur, in order to maximize coverage of the simulation outcome space. This multitrajectory simulation concept was implemented in C++ using a class structure that hides the complexity of spawning new trajectories, so that the functional code programmer's task is nearly the same as for stochastic simulation. The challenge is that a random choice method called once must return multiple times, once for each trajectory, if an event is resolved in a multi-trajectory fashion. The prototype simulation was tested for 4 unit and 40 unit scenarios, with a variety of management strategies selected to manage (and ration) trajectory proliferation. The results showed superior information gained for a given commitment of computational resources, compared to stochastic simulation. There remain some software issues in the use of local variables. The project was unable to address scaling issues to larger sized scenarios, which will require more sophisticated management heuristics.
Comparison of methods for modeling ALAN's power plane structure

This paper compares three methods for modeling power plane structures. The power plane structure that was analyzed was given by Dr. Albert Ruehli and Prof. Andreas Cangellaris, hence the acronym ALAN. The methods have been compared in terms of accuracy and computation time. The advantages of each method have also been discussed.
Example-based Machine Translation from English to Farsi with the help of WordNet Ontology

Machine translation is a process of automatic translation from one language to another. In the present study, architecture was proposed for the system of example-based machine translation from English to Farsi with the help of WordNet ontology. This system uses a knowledge base containing a set of examples, a bilingual dictionary and WordNet. An effective factor in quality of translation is ambiguity in selecting one of the different meanings that a word can have while being translated from one language to another. In the proposed method of the present study, WorldNet is utilized in the formation phase of example set and the adaptation phase so that ambiguity in translation can be reduced and selection of the correct meaning from among different meanings will be possible.
On the Understanding of Object and Class

This paper presents the results from a phenomenographic study of rst year university students' understandings of the concepts object and class. The students had just nished their rst programming course using Java as the programming language. The analyses of the study display qualitatively di erent ways to understand the concepts. These aspects of the understandings are formulated as categories of description. Each aspect is important and relevant when solving a programming task, and a good understanding includes all aspects of the concepts. The categories show understandings of di erent complexity. There are students who can only express an understanding of the concepts as code and syntax rules. Other students can also express the role the objects play for the programmer and the performance of the task given, and classes as abstract data types. The students who express the richest understanding includes the understandings already mentioned, but they can also express that classes and objects depicts the real world. Learning to understand a phenomenon means, in a phenomenographic perspective, to discern new aspects of that phenomenon. This discernment is only possible if there is a variation in a dimension that corresponds to the aspect, critical for the speci c understanding. The question of variation gives interesting implications for teaching discussed in the paper. The discernment requires the students to have a mindful kind of learning, re ective learning. This implies for the teachers that explicitness in the teaching is of great importance. Explicitness in the teaching is de ned at several levels, not only that the di erent aspects of the understandings found in the study should explicitly be mentioned, but also explicitness in the explanation and variation of these aspects. The importance of o ering a broad context for object oriented programming is also emphasized. This can include explaining not only the object oriented paradigm, but also to discuss some other programming paradigms, and the historical background that lead to these paradigms. It can also include giving the students the opportunity to follow a whole programming task with analysis of the problem, and to design, implement and test the program even at an early stage of their education. The paper also includes some examples how the results from the study can be implemented in the teaching to help the students in their learning
A relationship between external noise and the ocean clutter models for bistatic operation of a pulsed high-frequency radar

Bistatic models of the ocean clutter in the context of pulsed high-frequency ground wave radar (HFGWR) operation have previously been developed. Several new features, distinct from earlier monostatic developments, appear as products of the analysis. The question that must be addressed is "will the characteristics of the theoretical clutter models be visible in real-life data collected from the ocean surface? " One of the major considerations in answering this is the development of an appropriate noise model. Such a model for the noise spectral density as it relates to the pulsed HF radar operation is considered here. The analysis proceeds on the assumption of an ideal, externally noise limited, system. The aliasing due to noise undersampling is seen to be an integral part of the model. Statistical stationarity is assumed throughout.
LANGUAGE LEARNING VIA AN ANDROID AUGMENTED REALITY SYSTEM

Augmented Reality (AR) can be described as one of possible steps between real world and fully virtual reality. Into this mixed reality we can make an overlay with virtual objects onto the real world typically by capturing camera images in real-time to produce a new layer to the environment with which we can interact. Mobile Augmented Reality (MAR) is a term used when equipment through which we achieve AR is small in size and typically easy to carry e.g. a smartphone or a tablet. The concept of using AR in facilitating learning and improving its quality seems to attract more attention in the academic world in recent years. One of the areas that receive much attention is AR language learning. In this thesis an experiment on a group of 20 people was conducted to answer the question: “Is MAR language learning system a viable solution for language learning? ” For the purpose of the experiment an AR Language Learning Tool was designed for Android smartphones. This AR Language Learning Tool facilitated vocabulary learning by displaying 3D objects along with their spelling and providing audio of pronunciation. Participants were divided into an equal control group and test group. The control group learned new vocabulary through classic flashcards while the test group used the previously designed AR Language Learning Tool. The Vocabulary Knowledge Scale questionnaires were provided for both groups right after learning and one week later. By performing statistical analysis with Student’s t-test on gathered data it was discovered that there is a positive improvement in long term recall rate in the AR Language Learning Tool group when compared with the flashcards learning group. No difference was found in short term recall rate between both groups. Participants also provided feedback about their quality of experience and enthusiasm for new learning methods. Their answers were very positive and provided proof that mobile AR is a viable method of learning vocabulary.
Serving Up Information at the QandAcafè

Abstract Since 2001, increasing numbers of libraries have implemented real-time online reference services. QandAcafe is one of the first live reference programs to involve both public and academic libraries. The authors outline the genesis and development of this consortial virtual reference desk, describing the participants, funding, training program, and outcomes. Reference priorities of public libraries (quick, accurate question answering) and those of academic libraries (instruction in online research) are not mutually exclusive; both can be addressed when working toward the goal of serving remote users. In summary, the authors discuss lessons learned from the project and provide recommendations for multitype library groups establishing real-time reference services.
Large vocabulary off-line handwritten word recognition

Considerable progress has been made in handwriting recognition technology over the last few years. Thus far, handwriting recognition systems have been limited to small-scale and very constrained applications where the number of different words that a system can recognize is the key point for its performance. The capability of dealing with large vocabularies, however, opens up many more applications. In order to translate the gains made by research into large and very-large vocabulary handwriting recognition, it is necessary to further improve the computational efficiency and the accuracy of the current recognition strategies and algorithms. In this thesis we focus on efficient and accurate large vocabulary handwriting recognition. The main challenge is to speedup the recognition process and to improve the recognition accuracy. However, these two aspects are in mutual conflict. It is relatively easy to improve recognition speed while trading away some accuracy. But it is much harder to improve the recognition speed while preserving the accuracy. First, several strategies have been investigated for improving the performance of a baseline recognition system in terms of recognition speed to deal with large and very-large vocabularies. Next, we improve the performance in terms of recognition accuracy while preserving all the original characteristics of the baseline recognition system: omniwriter, unconstrained handwriting, and dynamic lexicons. The main contributions of this thesis are novel search strategies and a novel verification approach that allow us to achieve a 120 speedup and 10% accuracy improvement over a state-of-art baseline recognition system for a very-large vocabulary recognition task (80,000 words). The improvements in speed are obtained by the following techniques: lexical tree search, standard and constrained lexicon-driven level building algorithms, fast two-level decoding algorithm, and a distributed recognition scheme. The recognition accuracy is improved by post-processing the list of the candidate N-best-scoring word hypotheses generated by the baseline recognition system. The list also contains the segmentation of such word hypotheses into characters. A verification module based on a neural network classifier is used to generate a score for each segmented character and in the end, the scores from the baseline recognition system and the verification module are combined to optimize performance. A rejection mechanism is introduced over the combination of the baseline recognition system with the verification module to improve significantly the word recognition rate to about 95% while rejecting 30% of the word hypotheses.
Adaptive reference model tracking control of electrode systems of arc furnace

Proposes an adaptive reference model tracking control system design approach for the electrode regulator system of an arc furnace. First the general description of this system is presented, in which the nonlinear part of the reference model is replaced by the identification result of the controlled system, then the existing conditions of the tracking control scheme is discussed. Based on that, the design of a feedforward control matrix and the iterative regulative algorithm of the feedback control matrix are put forward, which may lead the system to meet a good tracking performance. The simulation result has verified the effectiveness of the proposed scheme for a nonlinear system.
Recognizing API Features for Malware Detection Using Static Analysis

Android is one of the mobile operating system. Rapidly increasing of devices which used android as platform, make the Android is the best target to cybercriminal. Besides, Android offers many applications and these applications can get from other than Google Play. This make the cybercriminal easier develop malware and easily spread the malware to user. Malware can be made by injecting the malicious code into benign applications. Therefore, this research will use static analysis technique. Static analysis technique will extracted the benign and malware application to get their source code. The source code in benign and malware will be compared and categorized into API and manager classes. Then the most frequent API and manager class used in malware will be detected.
Exploring customization in higher education: an experiment in leveraging computer spreadsheet technology to deliver highly individualized online instruction to undergraduate business students

This dissertation describes a research study designed to explore whether customization of online instruction results in improved learning in a college business statistics course. The study involved utilizing computer spreadsheet technology to develop an intelligent tutoring system (ITS) designed to: a) collect and monitor individual real-time performance feedback during the delivery of the instruction, b) use logical algorithms to intelligently analyze individual strengths and weaknesses, and c) individualize learning experiences by altering the subsequent instructional content and sequencing according to individual needs. The ADDIE model of instructional design was used to develop the customized ITS. From an instructional design standpoint, this study examines whether the ADDIE model aligns with the development of customized online instructional content. The study shows the ADDIE model is an effective framework for developing customized online instruction. The study also discusses the importance of incorporating an analysis of student errors into the ADDIE model to facilitate the design of customized instruction. The effectiveness of customized instruction was evaluated by comparing the ITS with the current online instructional module, which is neither customized nor spreadsheet-based. A third, spreadsheet-based instructional module without customization was also developed to explore the incremental effects of customization on student achievement, after controlling for the spreadsheet format of instructional delivery. Other relevant factors were also controlled in the study, including previous enrollment in a statistics course, SAT or ACT math percentile, and prerequisite GPA. A posttest assessment was administered to measure the effects of customization on student achievement. The results of the study showed customization was not significantly related to student achievement after controlling for spreadsheet delivery format and relevant learner characteristics. Furthermore, customization did not result in significant differences in the proportion of students who obtained a criterion score of 70% or higher on the posttest. However, using spreadsheet technology to deliver instruction had a significant effect on both student achievement and the proportion of students obtaining the criterion score. These results indicate that although customization did not have an effect on student scores, the use of spreadsheet technology to deliver online instruction did result in significant improvement in learner achievement.
Adaptive equalization for wireless ATM

The performance of various adaptive equalization strategies for broadband fixed radio systems to be used in the last mile is compared. Results are presented comparing linear equalizers with decision feedback equalizers and comparing the coefficient adjustments according to the zero forcing criterion with the mean squared error criterion. The performance measure is the resulting signal to noise and interference ratio at the decision device. The influence of the number of equalizer taps is shown as well as the effect of varying the rolloff factor of the transmit and receive filters. The performance of the LMS algorithm and of the RLS algorithm in terms of convergence speed is shown. The paper closes with a discussion of various fast least squares algorithms.
Brain computer interface: the use of low resolution surface Laplacian and linear classifiers for the recognition of imagined hand movements

An EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous or surface Laplacian (SL) transformed EEG signals. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). In this paper, recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and those obtained with the SL computed from 26 electrodes (full resolution SL). Recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%).
Mutable data representations for lightweight publishing and curation

Web site authors frequently exhibit collections of structured data that visitors can contribute to, using specialized interfaces. Website authors also desire powerful tools for managing their website's data, so they can integrate visitor suggestions, search past versions of their data, and perform edits. However, content management frameworks have too steep a learning cure for casual users, and require programming to customize how a site's data is generated, viewed, and edited. Incorporating a curation process into lightweight data publishing frameworks can lessen the burden of data submission and management for web site visitors and authors alike. The proposed thesis investigates a data submission process that is easy, customizable, and portable. It also explores the characteristics of successful data management interfaces as applied to evolving sets of data. Thesis Supervisor: David R. Karger Title: Professor, Department of Electrical Engineering and Computer Science