Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedded eye-gaze tracking on mobile devices

The eyes are one of the most expressive non-verbal tools a person has and they are able to communicate a great deal to the outside world about the intentions of that person. Being able to decipher these communications through robust and non-intrusive gaze tracking techniques is increasingly important as we look toward improving Human-Computer Interaction (HCI). Traditionally, devices which are able to determine a user's gaze are large, expensive and often restrictive. This work investigates the prospect of using common mobile devices such as tablets and phones as an alternative means for obtaining a user's gaze. Mobile devices now often contain high resolution cameras, and their ever increasing computational power allows increasingly complex algorithms to be performed in real time. A mobile solution allows us to turn that device into a dedicated portable gazetracking device for use in a wide variety of situations. This work speci cally looks at where the challenges lie in transitioning current state-of-the-art gaze methodologies to mobile devices and suggests novel solutions to counteract the speci c challenges of the medium. In particular, when the mobile device is held in the hands fast changes in position and orientation of the user can occur. In addition, since these devices lack the technologies typically ubiquitous to gaze estimation such as infra-red lighting, novel alternatives are required that work under common everyday conditions. A person's gaze can be determined through both their head pose as well as the orientation of the eye relative to the head. To meet the challenges outlined a geometric approach is taken where a new model for each is introduced that by design are completely synchronised through a common origin. First, a novel 3D head-pose estimation model called the 2.5D Constrained Local Model (2.5D CLM) is introduced that directly and reliably obtains the head-pose from a monocular camera. Then, a new model for gaze-estimation is introduced the
IEEE Transactions on Automation Science and Engineering

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING is published by the IEEE Robotics and Automation Society. All members of the IEEE are eligible for membership in the Society and will receive this TRANSACTIONS upon payment of the annual Society membership fee of $9.00 plus an annual subscription fee of $50.00. For information on joining, write to the lEEE Service Center at the address below. Member copies of Transactions/Journals are for personal use only.
Survey of Classification Techniques in Data Mining

Classification is a data mining technique based on machine learning which is used to classify each item in a set of data into a set of predefined classes or groups. Classification methods make use of mathematical and statistical techniques such as decision trees, linear programming, neural network and support vector machines. Classification is process of generalizing the data according to different instances. Several major kinds of classification algorithms including C4.5, k-nearest neighbour classifier, Naive Bayes, SVM, and IB3.This paper provide an inclusive survey of different classification algorithms and their advantages and disadvantages.
ANN-Based Hand Gesture Recognition Using Self co-articulated Set of Features

ABSTRACT Hand gesture recognition system can be a particularly challenging task because of the presence of several spatio-temporal variations like hand trembling, self co-articulation within a gesture, etc. during the segmentation stage. In this paper, we focus our attention on self co-articulation problem which has not been addressed before. With the addition of a new feature, i.e. removing the hand movement during pause state along with the velocity features within the gestures, we were able to perform the gesture spotting more smoothly for the isolated gestures. Moreover, a new set of novel features was added in the feature extraction stage: (1) number of self co-articulated stroke; (2) orientation of the self co-articulated strokes; (3) position of the hand; (4) distance between the start and end points of a gesture; and (5) ratio between the longest and shortest distances from centre. These features were fed to neural network classifier. The results of experimentation were carried out with 40 gestures and it showed an average true positive rate of 91.25%.
How science responds to technology push from new research tools. Translational medical research as a case of â€œnew scienceâ€

Introduction of powerful new Research Technologies (RTs) implies a technology push on science by expanding the creation, manipulation and representation of data at a rate far exceeding that of theoretical development. As classical reductionism turns inadequate, what new principles emerge for organizing scientific knowledge accumulation? Drawing on theory of modularization we argue that a particular role is played in the accumulation of knowledge by those contributions which establish new interfaces and linkages between empirical phenomena. Without reduction to fundamental theory, these new interfaces offer knowledge integration in the form of isomorphism, analogous relationships etc. As a case of RT-driven science, we study translational medical research in a specific oncological field. Using bibliometric data, we assess the impact of translational studies benchmarked against simultaneous standard clinical research in the exact same field. Whereas reductionism sets its mark within both approaches, we find that a number differentiating traits appearing for RT-driven research: impact of translational articles depends on how broadly it sources its intellectual
FOUR PHASE INTERLEAVED BOOST CONVERTER - ANALYSIS AND VERIFICATION

This paper deals with an analysis of four phase interleaved DC-DC converter for higher power application in continuous conduction mode. The interleaved topology is widely used due to their advantage as lower input current ripple which means volume reduction of input capacitor. The current ripple equation of an input current for the boost operation mode and the ripple current in individual phases of the interleaved converter using uncoupled inductor are shown. The theoretical equations are supplemented by the simulation results using Spice simulator and by measurement on the interleaved converter.
A concurrent object-oriented programming language system for highly parallel data-driven computers and its applications

With recent developments of highly parallel computers, practical methods for developing programs on such machines become urgent. In this study, we regard our practical concurrent object-oriented programming language as a basic language for highly parallel machines and pursue necessary software approaches to achieving high performance by implementing the language system and developing application programs/parallel algorithms. As for the language system, we propose a language design suitable for parallel execution and implementation schemes that achieve high performance. Our implementation target is a ne-grained data-driven parallel computer, EM-4, developed at Electrotechnical Laboratories. We present and discuss the design of our runtime architecture and optimizing compiler, including reactive representations of objects, e cient management of message bu ers with freelists, interruptible pipelined message sends, plan-do style intermediate code, an e cient implementation scheme for table look-up, and advanced register/storage allocation. As for application programs/parallel algorithms, we present concurrent objectoriented algorithms for highly parallel computers, (e.g., N-body problem) and evaluate them. We have developed a prototype compiler and a runtime system which run the N-body problem program on the real machine, and measured its performance. Performance measurements on EM-4 show that the cost of each remote object-creation and remote message-passing can be, in order, comparable to that of a sequential procedure call and the N -body algorithm based on the concurrent object-oriented models can be executed e ciently for a wide range of N . Our results indicate that the object-oriented concurrent computation models and languages are extremely suitable for realizing practical parallel systems. ANY OTHER IDENTIFYING INFORMATION OF THIS REPORT This report is the author's thesis submitted to the graduate school of the University of Tokyo in partial ful llment of the requirements for the degree of doctor of science in information science. DISTRIBUTION STATEMENT This technical report is available ONLY via anonymous FTP from ftp.is. s.u-tokyo.ac. jp (directory /pub/tech-reports). SUPPLEMENTARY NOTES REPORT DATE April 5, 1994 TOTAL NO. OF PAGES 124 WRITTEN LANGUAGE English NO. OF REFERENCES 37 DEPARTMENT OF INFORMATION SCIENCE Faculty of Science, University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo, 113 Japan A CONCURRENT OBJECT-ORIENTED PROGRAMMING
New Approach for Detecting and Tracking a Moving Object

This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.
Network of TAMCNS: Identifying Influence Regions Within the GCSS-MC Database

TheGlobal Combat Support System-MarineCorps (GCSS-MC) system enables the logistics and supply chain management system for the United States Marine Corps. This system allows leaders, operators, maintainers, and suppliers to work together on a common platform and provides transparency and situational awareness in the logistics and supply cycle. The data associated with the GCSS-MC system is stored in a database. As the size of the data increases, challenges arise in obtaining insights into large data sets and its impact on network infrastructure. Network science identifies relationships between objects and provides tools to quantitatively determine objects whose influence impacts other objects or the system as a whole. This thesis applies network science techniques to determine important Table of Authorized Material Control Numbers (TAMCNs) in the GCSS-MC database, according to their impact on other TAMCNs in the database based on degree, eigenvector, betweenness, and closeness centrality. Additionally, this thesis develops a formula to rank components from most to least important. We further develop a process to identify pertinent tables within the database and export the information to create a complex network containing multiple layers that analyze various attributes associated with GCSS-MC. We find that the methodology identifies the most important TAMCN and provides a list of TAMCNs in order of importance. We also analyze the community and core structure of the GCSS-MC complex network and identify influential TAMCN regions in the database.
Visual Analysis of Clustering Algorithms A Methodology and a Case Study

Clustering is probably one of the most frequently used ap- proaches when facing a scaling problem in large collections of documents. In many situations, however, the choice of the most appropriate algo- rithm for clustering can turn into a real dilemma. Numerical criteria have been proposed to evaluate the quality of the results of clustering algorithms. However, so many different criteria have been proposed that the dilemma even worsens. Most criteria reveal different aspects of the quality of the results and hide others. The aim of this paper is to help with the understanding of clustering and to facilitate the comparison and the choice of clustering algorithm for a given purpose. Our proposal consists in studying both quality evaluation criteria and clustering algo- rithms. We start by discussing a selected set of representative criteria, and further conduct a case study on a large set of real data, measuring not only the quality of different representative clustering algorithms but also the impact of each criterion on the ranking of the algorithms. By providing empirical results on large scale corpus of either documents or lexical networks useful to digital library, we hope to clarify the field and facilitate designers' choices.
A Survey on Secured Approach of External Cloud without Avoiding the Data Integrity

Cloud Computing settled state of mind has included a New York minute inmany organizations. Cloud Computing offers numerous advantages in uncertainty of delicate cost and administration of information. Guaranteeing the guarantee of exceed processing assumes a leisure activity part in the leave in the shade registering, as clients regularly five and dime shop imperative data by all of obscuration automated data suppliers in any case these suppliers make out be hazardous. Clients are pondering generally assaults on the arrangement of significant worth and the accessibility of their story in the diminish from terrible insiders and untouchables, and from accepting nobody guarantee worth of diminish administrations. These issues are strongly critical yet there are still around space for stake investigating in leave in the shade processing. Managing by the entire of "single cloud" suppliers is anticipated to wind up noticeably slight well known commonly clients appropriate to dangers of engagement in movement application accessibility blemish and the incident of wretched insiders in the fair hit cloud. An arrangement towards "multi-mists", or in particular words, "inter clouds" or "billow of-Cloud" has expanded as of late. The thought process of this freebee is to skim late research over each other to hit and multi-mists cash in the bank and to give conceivable arrangements. It is rely on that the examination into the demonstration with respect to of multi-cloud suppliers to sponsor security has gotten rare consideration from the exploration swarm than has the oversee of fair hit mists. This exchange tries to protect the consider of multi-mists guerdon to its adaptability to influenced a long story to short security chances that push the distributed computing buyer.
3D-model reconstruction with use of monocular RGB camera

Every year the edge between the real and digital worlds is becoming more and more blurred. Augmented and virtual reality rapid development creates new opportunities for more productive work and entertainment, revolution in 3D printing technologies begets boost in multiple DIY communities appearance and sharing economy growth. All these factors require new technologies that allow making 3D models from real world objects, but most of these solutions are either very expensive or require complex technical knowledge that most ordinary people do not have. This paper provides a review and comparison of modern methods for 3D models of physical objects real time reconstruction that can be used in present-day mobile solutions.
Service composition using dynamic programming and concept service (CS) network

The explosion of ecommerce and a need to automate internet based services has necessitated research into automated service composition. This research addresses the optimal selection of services in order to satisfy customer requests. In this paper, we develop a dynamic programming and concept service (CS) matrix based model to solve service composition problems under uncertainty (e.g., changes in customer queries and service provider availability) and derive an analytical optimal policy.
Emotional Evaluation of Frisky Robot Based on SD and Biosignal Method

This paper describes the emotional evaluation of a frisky robot that weaves around its human owner; the evaluation is based on the SD (Semantic Differential) method and the RRV(R-R wave variance) method using electrocardiograms (ECG) as the biosignal source. The SD method is effective to measure emotion, and the RRV method is suitable for investigating the physiological state from the heartbeat. The SD method represents that humans show a significant response only when the robot weaves, not when it simply shadows the human. Furthermore, it is confirmed that these methods are correlated and the RRV method is effective in real time evaluation.
Enhancing human-computer interaction in medical segmentation

Medical image acquisition devices are becoming increasingly multidimensional, and predictions assume 5000 images per patient study within the next decade. Therefore, new paradigms for computerized medical image analysis and visualization are of fundamental importance to make possibly full use of the information buried in the enormous flood of image data. The aim of the presented project is the optimal cooperation between computer-based image analysis algorithms and human operators using new closed-loop segmentation systems for improved information flow. This paper describes an enhanced, visuo-haptic interaction tool we have developed for medical segmentation. Evaluation studies with the system, which confirm the value of adding haptic feedback, are also presented.
Integrating Tagging into the Web of Data: Overview and Combination of Existing Tag Ontologies

As the number of social websites offering tagging facilities increases, tagging has become not only a common basis for user participation, but also an important aspect of social content. Tagging is primarily based on the user's participation and interaction, including the sharing and the exchange of their interests. However, even though users can collect and share tagging data with conventional technologies, this data is fragmentary and remains disconnected. Therefore, discovering content across independent sites remains a challenge. On the other hand, technologies of the Web of Data, and especially ontologies, help towards this goal of integrating tagged data. In this paper, we investigate the core features of existing tag ontologies that attempt to represent a common conceptualization of tagging and that provide well-defined semantics for tagging data, allowing for the sharing and interlinking of tagging data across independent sites or communities.
AUTOMATIC SUMMARIZATION OF CLINICAL ABSTRACTS FOR EVIDENCE-BASED MEDICINE

The practice of evidence-based medicine (EBM) encourages health professionals to make informed treatment decisions based on a careful analysis of current research. However, after caring for their patients, medical practitioners have little time to spend reading even a small fraction of the rapidly growing body of medical research literature. As a result, physicians must often rely on potentially outdated knowledge acquired in medical school. Systematic reviews of the literature exist for specific clinical questions, but these must be manually created and updated as new research is published. Abstracts from well-written clinical research papers contain key information regarding the design and results of clinical trials. Unfortunately, the free text nature of abstracts makes it difficult for computer systems to use and time consuming for humans to read. I present a software system that reads abstracts from randomized controlled trials, extracts key clinical entities, computes the effectiveness of the proposed interventions and compiles this information into machine readable and human readable summaries.s from well-written clinical research papers contain key information regarding the design and results of clinical trials. Unfortunately, the free text nature of abstracts makes it difficult for computer systems to use and time consuming for humans to read. I present a software system that reads abstracts from randomized controlled trials, extracts key clinical entities, computes the effectiveness of the proposed interventions and compiles this information into machine readable and human readable summaries. This system uses machine learning and natural language processing techniques to extract the key clinical information describing the trial and its results. It extracts the names and sizes of treatment groups, population demographics, outcome measured in the trial and outcome results for each treatment group. Using the extracted outcome measurements, the system calculates key summary measures used by physicians when evaluating the effectiveness of treatments. It computes absolute risk reduction (ARR) and number needed to treat (NNT) values complete with confidence intervals. The extracted information and computed statistics are automatically compiled into XML and HTML summaries that describe the details and results of the trial. xiii Extracting the necessary information needed to calculate these measures is not trivial. While there have been various approaches to generating summaries of medical research, this work has mostly focused on extracting trial characteristics (e.g. population demographics, intervention/outcome information). No one has attempted to extract all of the information needed, nor has anyone attempted to solve many of the tasks needed to reliably calculate the summary statistics.
Reasoning with fractions: Supporting pre-service teachers’ learning

We report on a mathematics education course for primary pre-service teachers (PSTs) in which both mathematical and pedagogical learning is essential. Part of this course is devoted to supporting PSTs in (a) strengthening conceptual understanding of fractions and (b) coming to imagine ways in which fractions can be effectively introduced in primary classrooms. In collaborative group work on fraction activities, PSTs generated solutions that were very different from those of year 3-5 pupils. This was unexpected and led to new insights and learning for the instructors. We draw implications about supporting the PSTs’ development of deeper mathematical reasoning in circumstances where prior mathematical learning limits their problem-solving skills and creativity. This work contributes to a larger effort of developing resources that could enhance adults’ learning across STEM disciplines.
Finite state machine control for aircraft electrical distribution system

This study deals with the development of new control logic for more electric aircraft (MEA) electrical power systems (EPSs). A key aspect of the MEA concept is that traditional pneumatic and hydraulic loads are replaced by electrical equivalents. These new electrical systems are more reliable, highly efficient, and easier to replace or maintain. However, as the number of on-board electrical loads increases the electrical distribution systems on-board are becoming more and more complex. As a result, the control system needs to be adapted and improved in order to allow better manage the overall electrical energy flow, provide faster computational operations, and ensure operation of safety-critical loads under all fault scenarios. This study first gives a brief analysis of the different electrical system topologies before outlining potential control strategies which may be applicable to future MEA EPSs. A new control concept for MEA EPSs is then investigated and considered as a potential substitute of the classical control systems. Finally, a model of the newly proposed logic is implemented and simulated showing how it is able to select and apply a correct reconfiguration of the electrical system under different operating conditions.