Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability & Transparency Analysis of Teleoperation Systems

This thesis presents novel methods for the stability and transparency analysis of haptic teleoperation systems. The main contribution is a novel stability analysis method for linear time-invariant time-delay systems with parametric uncertainties which determines stable parameters in the parameter space. This stability analysis method is complemented by an intuitive parameter-space transparency analysis method. By applying both methods to a number of different teleoperation architectures the effect of time delay on stability and transparency is evaluated.
A New Model for Image Fusion Using Bi-dimensional Empirical Mode Decomposition with Directional Information ⋆

Image fusion is a procedure in which two or more images of one scene captured by different sensors are combined into one image. The target of image fusion is to produce images which are more suitable for human visual perception and further machine-processing. Empirical Mode Decomposition (EMD) is a new tool for signal (especially non-stationary signal) analysis. Comparing to other multi-scale transforms, EMD is more suitable for detecting information of images. Therefore, we propose a new model for image fusion based on Bi-dimension EMD (BEMD). In addition, considering that the existing BEMD can hardly capture the directional information which is important for image fusion, directional filter bank will be employed to run on each level coefficients in the step of image decomposition. The proposed method is tested on medical images and compared with some newly-proposed image fusion models. The experimental results show that the proposed method outperforms others in terms of several fusion evaluation metrics.
Assisting Bulgarian special educators with competency development online

Peterson, R. , Herrington, J. Deslea, K. , Tzvetkova-Arsova, M. , & Stefanov, K. (2008). Assisting Bulgarian special educators with competency development online. The international journal of information and learning technology: Campus-Wide Information Systems, special issue on "ePortfolio, employability and lifelong learning in the knowledge society", 25(4), 197-208. http://www.emeraldinsight. com/10.1108/10650740810900649 [See also http://hdl.handle. net/1820/1679 ]
Perancangan Android Package Mobile Web pada Sistem Penilaian di Perguruan Tinggi

Sistem Penilaian Plus adalah sebuah sistem yang telah ada di Perguruan Tinggi Raharja untuk membantu dosen dalam menginput nilai dan juga mahasiswa untuk mengakses nilai kelas. Namun metode akses yang digunakan hanya melalui sebuah PC. Dan itu membatasi pengguna jika ingin menggunakan penilaian plus di luar berbagai mobilitas yang dirasakan. Maka Android Packaging di perlukan untuk membangun sebuah metode akses baru, yaitu di sebuah mobile device dengan webview. Hasilnya sebuah Android Packaging penilaian plus yang sudah ada, akan di kombinasikan dengan Google Play. Dengan tujuan pengguna dapat menemukan penilaian plus dengan mudah. Dan hasilnya, feedback yang di dapatkan mencapai 576 Downloader dan 352 Rating Review. Dapat di simpulkan bahwa sebuah sistem yang di build sebagai Android Packaging dengan hasil webview cukup membantu pengguna dalam mengoperasikan sistem dengan mobile device.
Inducting students into academic integrity: Conceptual design of an online learning module

Academic Integrity (AI) is one of the central facets of student learning and can impact significantly on students‘ post-education life. Educational institutions have a central responsibility for developing a positive academic culture through policies and mechanisms that address and foster AI in their institutions. However, in practice, even where institutions have adopted formal AI mechanisms, some students still develop a poor understanding of the essential facets of AI, leading to unintentional AI breaches. Moreover, some students find it hard to contextualise the university-wide AI policies as part of their discipline. In addressing this issue, some universities have developed online, discipline-specific AI modules to enhance students‘ AI understanding. This paper discusses a discipline-specific online Academic Integrity Module (AIM) developed for built environment students at the University of Newcastle (UoN). The module was designed within the Blackboard environment to provide an online and interactive form of AI learning. Whilst the AIM draws heavily on AI policies at UoN, it contextualises this information as part of the built environment discipline and enables students to engage in self-paced AI learning and self-assessment.
Programme in Menschengestalt: Digitale Menschmodelle für CAx- und PLM-Systeme • digitale Menschmodellerechnergestützte Konstruktion (CAD, CAx) • Produktlebenszyklus- management (PLM) • ProduktgestaltungProzessgestaltung

digitale Menschmodelle fur CAxund PLM-Systeme Jens Muhlstedt (e-Mail: jens.muehlstedt@mb.tu-chemnitz.de, Tel./Fax: 0371 531-36464/-836464) Hans Kausler (e-mail: hans.Kaussler@siemens.com, Tel. 089 636-49647) Birgit Spanner-Ulmer (e-Mail: birgit.spanner-ulmer@mb.tu-chemnitz.de, Tel. 0371 531-23210/-823210) Professur Arbeitswissenschaft, Institut fur Betriebswissenschaften und Fabriksysteme, Technische Universitat Chemnitz Erfenschlager Str. 73, 09125 Chemnitz Siemens AG, Corporate Technology Otto-Hahn-Ring 6, Munchen
Agility of Project Manager in Global is Project

The agility of the global IS project manager is considered to be important for the success of global IS projects. Yet, there has not been much research on the agility of the global IS project manager from a knowledge perspective. The objective of this research is to investigate the knowledge sets that influence the agility of a global IS project manager, and the mediation effect of the agility on project success. A sample of global IS project managers were surveyed to assess the knowledge that affect the agility of the project manager. System knowledge, project management knowledge, cultural knowledge and interpersonal knowledge had an impact on a project manager's agility. We have also found the mediation effect of the agility on global IS project success. The results have important practical implications as to what areas of knowledge organizations should focus to train their potential project managers for the project success.
Designing efficient communication subsystems for distributed shared memory (dsm) systems

Programmability has proved to be the biggest obstacle to ubiquitous use of scal­ able high performance computing systems. The emerging distributed shared memory (DSM) systems, broadly classified into hardware DSM systems and software DSM systems, provide good programmability and scalability. The key to materializing the potential of DSM systems is to ensure low latency for various remote meraorj'^ and syn­ chronization operations. In this thesis we make four important contributions towards building efficient communication subsystems for DSM systems. First, we categorize various types of network contention and evaluate their impact on the performance of DSM systems. We show tha t network contention can affect DSM system performance significantly. Next, we develop a parameterized analytical model for estimating the performance of a DSM system by characterizing its key components and their interac­ tions. This model provides system architects with a fast and economical method for identifying the bottlenecks in existing or future DSM systems. Based on this model and detailed simulations, a set of network design guidelines are established. Third, we propose two new designs to improve the efficiency of node-network interfaces (NNI) : (i) a pipelined NNI supporting cut-through delivery and partial cache-filling and (ii) a novel block correlated FIFO strategy and its implementation exploiting multiple paths in interconnects. These designs can significantly reduce remote memory access latencies and the complexity of NNI. Finally, we propose two kinds of architectural
Optimization of Regular Path Queries in Graph Databases

Regular path queries offer a powerful navigational mechanism in graph databases. Recently, there has been renewed interest in such queries in the context of the Semantic Web. The extension of SPARQL in version 1.1 with property paths offers a type of regular path query for RDF graph databases. While eminently useful, such queries are difficult to optimize and evaluate efficiently, however. We design and implement a cost-based optimizer we call Waveguide for SPARQL queries with property paths. Waveguide builds a query plan—which we call a waveplan (WP)—which guides the query evaluation. There are numerous choices in the construction of a plan, and a number of optimization methods, so the space of plans for a query can be quite large. Execution costs of plans for the same query can vary by orders of magnitude with the best plan often offering excellent performance. A WP’s costs can be estimated, which opens the way to cost-based optimization. We demonstrate that Waveguide properly subsumes existing techniques and that the new plans it adds are relevant. We analyze the effective plan space which is enabled
Complete Analysis of the Characteristics of Propagation into Photonic Crystal Fibers, by the Finite Element Method

Abstract Using the finite element method, we propose an accurate vector analysis of the lowest electromagnetic modes of photonic crystal fibers. The likeness of their propagation characteristics to those of step index fibers is emphasized. Within the determined limits of a domain of validity, an equivalent step index fiber is defined. In addition, the single-mode broadband behavior of photonic crystal fibers is analyzed. The chromatic dispersion at any wavelength is reliably predicted from the geometric parameters of these fibers. With suitable geometrical parameters, the zero dispersion wavelength can be shifted, for example, toward values close to 1 μm.
Brain-Computer Interface: The Mindwave Mobile Interactive Device for Brainwave Monitoring

The idea of using brain activity as a path to communicate with external devices quickly evolved in the last decades. Brain-reading technologies are starting to be used in different life areas, since we have companies that launched such innovative tools on the market. In this matter, the most common method for measuring and observing the brain activity is the electroencephalogram (EEG), used mainly in the medical field with important research related to disability problems. In the enthusiastic attempt to discover the complexity of the brain, researchers sought for a way to reproduce all its intricated functionsas accurately as the possibilities of the latest tehnology can. A Brain-Computer Interface (BCI) represents a direct communication path between the active brain and an external device, a graphic result of it’s monitoring process in real time. This article presents a type of device of this kind, the MindWave Mobile by NeuroSky, through which we can observe and analyse the electrical activity of the brain. The different levels of frequency of the brainwave signal are related to cognitive function like attention, and meditation. Based on NeuroSky’s own developed eSense algorithms, MindWave Mobile can detect even the very subtle levels of attention and meditation, each of them being calculated and updated every second, as seen on the monitor of the device that’s paired with. This paper also presents a related experiment using the EEG device regarding the effect of music on different people, in terms of the concentration and relaxation levels.
A Survey on Text Mining using Genetic Algorithm

Text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting interesting and non-trivial patterns or knowledge from text documents. Regarded by many as the next wave of knowledge discovery, text mining has very high commercial values. Text Mining can be achieved directly by applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. The various researches show the performance is improved due to the nature of Genetic Algorithm. Genetic Algorithm has been used to tackle extensive variety of optimization problems. In this paper text mining concept is discussed and how genetic algorithm is applied on it and its usage in text mining.
Outlier Detection using Cluster-Based Approach

Data mining has the crucial task of Outlier detection which aims to detect an outlier from given data set. The data is said to be an outlier which appears to have inconsistent observation with the remaining data. Outliers are generated because of improper measurements, data entry errors or data arriving from various sources than remaining data. Outlier detection is the technique which discovers such type of data from the given data set. Several techniques of outlier detection have been introduced which requires input parameter from the user such as distance threshold, density threshold, etc. The goal of this proposed work is to partition the input data set into the number of clusters using Unsupervised Extreme Learning Machine algorithm. Then the clusters are given as an input to the outlier detection methods namely cluster based outlier detection algorithm and outlier detection algorithm. The methods detects an outlier from each cluster. This work aims at studying cluster based outlier detection algorithm and outlier detection algorithm with different data sets. Also analyzing the performance of each method based on outlier detection accuracy.
Improving Teacher Performance using Data Mining

This study examines the factors associated with the assessment of teacher's performance. To improve the teacher performance, good prediction of training course that will be obtained by teacher in one way to reach the highest level of quality in Teacher performance, but there is no certainty if there are accurately determine Teacher advantage and increase its efficiency through this session. In this case the real data is collected for teachers from the Ministry of Education and Higher Education in Gaza City. It contains data from the academic qualifications for teachers as well as their experience and courses. The data includes three years and questionnaire contains many questions about the course and length of service in the ministry. We propose a model to evaluate their performance through the use of techniques of data mining like association, classification rules (Decision Tree, Rule Induction, K-NN, Naive Bayesian (Kernel)) to determine ways that can help them to better serve the educational process and hopefully improve their performance and thus reflect it on the performance of teachers in the classroom. In each tasks, we present the extracted knowledge and describe its importance in teacher performance domain.
A multidisciplinary approach to learning human-robot interaction (HRI) through real-world problem solving

This article examines a cross-disciplinary approach to learning human-robot interaction (HRI) through real-world problem solving. The problem originated from the need of archaeologists at the University of California, Berkeley, and Ryerson University to safely explore archaeologically significant areas disturbed by heavy looting activities at the ancient site of el-Hibeh, Egypt. The learning objectives were developed through interdisciplinary collaboration of three departments at Ryerson University. The deliverable was an HRI final examination---known as the "BUSA Dig"---in which students teleoperated a robot of their own design and manufacture that explored and mapped a simulated archaeological site. The students participated in the examination through their membership in one of six mixed groups composed of undergraduate computer science and graduate digital media students. At the end of the exam, students were expected to understand and explain HRI principles, paradigms, and metrics, construct appropriate robots that could survive and function in a defined environment, and employ mobile and teleoperated robots that solved problems.
Interactive Visual Analysis of Streaming Data

Interactive Visual Analysis (IVA) has proven to be a robust set of methods for visually exploring complex data sets and generating hypotheses from data. Datasets and techniques where the temporal aspect is central has been an important area of study, both for the visualization field in general and for research on IVA. However, the challenge of handling streaming data sources for the purposes of decision support and analysis in real time, has been given comparatively little attention. This thesis presents a summary of the visualization literature addressing time-oriented and streaming data, with emphasis on Interactive Visual Analysis and its related techniques. We then explain the contemporary distinction between real-time data monitoring and retrospective data analysis, explore challenges that occur when a human user attempts to visually analyze data in real time, and use these observations to extend the scope of IVA such that it can be used to analyze streaming data in real time.
An efficient iterative graph data processing framework based on bulk synchronous parallel model

Graph data processing has been widely applied in a variety of domains such as industry, science, social network, and so on. It therefore has stimulated many efforts devoted to this area. To embrace the fast development trend of big graph data, graph data processing based on Pregel‐like systems has been regarded as one of the most promising ways and has widely attracted the attention of researchers. However, it still remains in its early stage and there still exist many challenges. In Pregel, the superstep synchronization is time consuming as the graph data iteration operation requires multiple synchronizations. Furthermore, the graph data partition strategy adopted by Pregel fails to support load balancing, therefore causing the increase of network I/O overhead as the scale of graph data grows. To address these issues, this paper presents an efficient computational framework for graph data processing based on the bulk synchronous parallel model. The global synchronization control mechanism is improved by determining the start time of the next round of superstep through counting the number of global message files. Furthermore, an improved graph data partition mechanism based on a balanced hash method is proposed to reduce the communication overhead between different partitions of sub‐graph computational tasks. We also re‐design the PageRank algorithm to verify the effectiveness of the proposed framework. Experimental results on different real‐world datasets verify the efficiency of our proposed framework as it outperforms Giraph (an open source Pregel‐like system) by 58%−69%, and achieves 10×−17× performance improvement over Hadoop.
Chinese Text Classification Based on Ant Colony Optimization

It's significance for us to study Chinese Text Classification, when we face so much dynamic information. The development of Text Classification has a close connection with Pattern Recognition. However, some peculiarity of Chinese Text Classification, such as it has many classes, much noise, and excessive samples, make Pattern Recognition difficult to classify texts. Recently, Artificial Intelligence provides a new intellectualized method to Text Classification. This paper tentatively leads Ant Colony Optimization, a ripe algorithm of Swarm Intelligence, into Text Classification. We construct a Text ACO-Miner Classification Model based on Ant Colony Optimization, and test it. The result shows the model can accurately be used to classify Chinese texts.
DESIGN AND APPLICATION OF THE KENTUKCY MICROARRAY ANALYSIS SUITE

In recent years, microarrays have become the most widely used standard in the study of gene expression. The biggest problem in microarray data analysis is the dimensionality of the data, compared to other more traditional biomedical research methods. The inherent nature of the data, and the problems associated with the microarray data analysis, has led to the development of many methods for microarray data analysis. Microarray data analysis methods are commonly classified into Class Discovery methods e.g. clustering, Class Comparison methods e.g. predicting differentially expressed genes, and Class Prediction methods e.g. classification. In this thesis, a new microarray analysis tool called Kentucky Microarray Analysis Suite that has all the three major microarray analysis methods is introduced. As a proof of concept Affymetrix array data related to aging in C. elegans is analyzed with the Kentucky Microarray Analysis Suite and the results are presented.
A robust optimization approach to online problems

In this thesis, we consider online optimization problems that are characterized by incrementally revealed input data and sequential irrevocable decisions that must be made without complete knowledge of the future. We employ a combination of mixed integer optimization (MIO) and robust optimization (RO) methodologies in order to design new efficient online algorithms that outperform state-of-the-art methods for many important practical applications. We empirically demonstrate that RO-based algorithms are computationally tractable for instances of practical size, generate more cost-effective decisions and can simultaneously model a large class of similar online problems due to exceptional modeling power of MIO. In Part I, we consider the well-known K-server problem from the perspective of robust adaptive optimization. We propose a new tractable mixed integer linear formulation of the K-server problem that incorporates both information from the past and uncertainty about the future. By combining ideas from classical online algorithms developed in the computer science literature and robust and adaptive optimization developed in the operations research literature we propose a new method that (a) is computationally tractable, (b) almost always outperforms all other methods in numerical experiments, and (c) is stable with respect to potential errors in the assumptions about the future. In Part II, we consider several extensions of the asset-based weapon-to-target assignment problem whose objective is to protect ships in a fleet from incoming threats. We demonstrate that the new highly nonlinear MIO formulation (a) can be combined with lazy constraints techniques allowing the system designer to find optimal solutions in real time, (b) can be extended to the multiperiod setting, and (c) admits a decentralized solution with limited loss of optimality. In Part III, we present a novel covariate-adaptive optimization algorithm for online allocation in clinical trials. The new approach leveraging MIO and RO techniques (a) guarantees a better between-group covariate balance in comparison with stateof-the-art methods, (b) yields statistical power at least as high as, and sometimes 3 significantly higher than, randomization-based algorithms, and (c) is well protected against selection, investigator and accidental bias. Thesis Supervisor: Patrick Jaillet Title: Dugald C. Jackson Professor Department of Electrical Engineering and Computer Science Co-Director, Operations Research Center Thesis Supervisor: Dimitris Bertsimas Title: Boeing Leaders for Global Operations Professor Sloan School of Management Co-Director, Operations Research Center
Applications of Wireless Sensor Networks in Fall Detection for Senior People

Due to the advance of modern medical technology, the average age of global population increases continuously in recent years. In 1993, the society in Taiwan had already reached the criterion of aging population, bringingthe country an importantissue of looking after old people which cou ld not be ignored by its society. In this study, a wireless sensor device is developed to monitor the activities of o ld people at home and report fall events in real time. It can decrease tmental and physical injuries caused by fall events, and reduce the cost of looking after the injured old people for a long time. Another objective of this study is to enable the society to realizethe problem of looking afterold people and pay more attention to the senior citizens living alone as well.