Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing E-Learning Applications with Students: The Case of the We!Design Methodology

In years past, many methodological approaches, methods and techniques have been implemented based on the belief that users can and should be involved in the design process of technology products that affect them. Inspired by the findings of research involving users and particularly students at diverse levels and phases of the design process of technology products, we have developed the We! Design methodology, a student-centered participatory design methodology that assigns students a primary role in the design process. We present the various phases of the We! Design methodology, examine the results of its application in four different design projects (a web-based e-assessment application for tertiary education, a course website, an e-Portfolio application, and a Tablet-PC-based e-assessment application for secondary education) and elaborate upon our overall experiences with the methodology during the past 3 years. Participant evaluations indicated that the We! Design methodology was an adequate means for successful elicitation of students’ needs and their application in educational software design.
Model-based analysis of user behaviors in medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., nonpersonalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Insup Lee Subject Categories Computer Sciences This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1652 MODEL-BASED ANALYSIS OF USER BEHAVIORS IN MEDICAL CYBER-PHYSICAL SYSTEMS
Generation of Cyber-security Reinforcement Strategies for Smart Grid Based on the Attribute-based Attack Graph

A smart grid is a kind of energy cyber-physical system (ECPS) with the interdependency of information and physicality. A cyber-attack gravely threatens the safe and stable operation of a physical power grid. Cyber-security reinforcement of smart grid has become a research issue. However, the information network scale of a smart grid is massive, and the generation of security reinforcement strategies has become a problem. Therefore, a generation method of security reinforcement strategies based on an attribute-based attack graph was proposed in this study. The method defined a smart grid based on premise and consequence attributes to form an attribute-based attack graph. With this graph, the method for the generation of security reinforcement strategies was transferred to the minimum dominating set of the attribute-based attack graph and solved to realize space reduction in the security reinforcement strategies. An algorithm for the generation of security reinforcement strategies was designed based on the greedy algorithm, and strategies for large-scale cyber security reinforcement of the smart grid were determined to eliminate the complexity and difficulty of this problem effectively. Through a simulation analysis of a large-scale node network, the efficiency of the generation method of reinforcement strategies based on the attributebased attack graph and minimum dominating set was verified. Results show that the proposed method can be used for security reinforcement of large-scale complicated networks of a smart grid.
Network-based connected digit recognition using vector quantization

This paper describes a network-based approach to speaker-independent connected digit recognition. The digits are modeled by a pronunciation network whose arcs represent classes of acoustic-phonetic segments. Each arc is associated with a matcher for rating an input speech interval as an example of the corresponding segment class. The matchers are based on vector quantization of LPC spectra and the use of gross acoustic features for pruning. Recognition involves finding a minimum quantization distortion path through the network by dynamic programming. The system has been evaluated using a portion of a large multi-dialect database developed by Texas Instruments (TI). Using a baseline network of concatenated independent digit models, string and digit accuracies of 86% and 97% respectively have been obtained.
New measure of contrast: the dynamics

On a grey level image, extrema correspond to zones of uniform altitude whose neighboring pixels have a strictly lower (maxima) or strictly higher (minima) altitude. If an image is seen as a relief, minima are located at the bottom of valleys or pits (structures darker than the background) and maxima are located at the top of plateaus, pics, or domes (structures lighter than the background). The detection of extrema is often used as the first step of a segmentation process when we just need to roughly located objects of interest. Unfortunately, the noise sensitivity of this transformation makes it uneasy to use. We propose in this paper a transformation which valuates the extrema on a contrast criterion: the dynamics. The selection of the minima of interest is easier with this contrast information. The dynamics is a measure at the scale of the structure. It does not characterize the extremum itself or its catchment basin but the structure containing the extremum. An original aspect of the dynamics is that this transformation does not consider the size and the shape of the structures. We do not need to know a priori the size of the structures to evaluate their contrast. That is not the case for contrast feature extraction like the top-hat transformation. The computation of the dynamics is not straight forward from its definition. We propose a technique of computation based on flooding simulations. Using last algorithmical developments, this implementation is particularly efficient. That will help to develop the use of the promising transformations based on the dynamics.
PENGEMBANGAN SISTEM PENJADWALAN KULIAH MENGGUNAKAN ALGORITMA STEEPEST ASCENT HILL CLIMBING

University of Technology Yogyakarta is a private university in Yogyakarta, it has several purposes, one of them is utilizing the maximum potential of technology to improve the effectiveness and efficiency of learning and dissemination of science and technology. One of the factors that can improve academic services are scheduling courses. Scheduling proses isn’t easy because they have to consider the possibility in scheduling, include subjects, times, lecturers, and lecture halls. Now, subject scheduling is still using Ms. Excel, the process is to input lecturer’s name one by one, to teach to the prescribed schedule and also to check whether the data is conflicting or not, so it takes long time. Therefore, we need a system that is able to perform scheduling more quickly, effectively and optimally. Subject scheduling system can be done by using the optimization system. For the case of optimization can be done using the steepest ascent hill climbing algorithm. Steepest ascent hill climbing algorithm is a search algorithm (heuristic), it is able to solve the problems of optimization with estimates to be produced in accordance with the desired criteria or rules. Subject scheduling process produces a schedule that takes less time compared to a system that is currently running. Although it is able to minimize the level of clashing schedules, its application needs to be re-checked on the level of conflicting schedules.
An improved AODV routing protocol with network coding in wireless Ad Hoc network

This paper introduces a practical scheme in order to improve multi-hop throughput as well as to minimize packet's average end to end delay by incorporating network coding, which is at present a widely discussed technique in multicast, with AODV in wireless ad hoc networks. This scheme is simple and easily implemented with modest overhead, and the simulation results show that this scheme can achieve higher throughput and much smaller packet's average end to end delay in certain network topology.
Oracle Database 12c The Complete Reference

Master the Cutting-Edge Features of Oracle Database 12c Maintain a scalable, highly available enterprise platform and reduce complexity by leveraging the powerful new tools and cloud enhancements of Oracle Database 12c. This authoritative Oracle Press guide offers complete coverage of installation, configuration, tuning, and administration. Find out how to build and populate Oracle databases, perform effective queries, design applications, and secure your enterprise data. Oracle Database 12c: The Complete Reference also contains a comprehensive appendix covering commands, keywords, features, and functions. Set up Oracle Database 12c or upgrade from an earlier version Design Oracle databases and plan for application implementation Construct SQL and SQL*Plus statements and execute powerful queries Secure data with roles, privileges, virtualization, and encryption Move data with SQL*Loader and Oracle Data Pump Restore databases using flashback and the Oracle Database Automatic Undo Management feature Build and deploy PL/SQL triggers, procedures, and packages Work with Oracle pluggable and container databases Develop database applications using Java, JDBC, and XML Optimize performance with Oracle Real Application Clusters
Information Retrieval Under Distributed Network Based on LDAP and Multi-agent

With the development of Internet technology,the information retrieval based on distributed heterogeneous information resources becomes a focus of studying in the sphere of learning at present.After analyzing several typical technologies which are relevant to this studying field and according to the applied requirement of E-government platform,this paper proposes a distributed network information search model,which is based on lightweight directory access protocol(LDAP) and multi-agent technology.It also presents the key techniques,such as the information organization based on directory service technology and communication among multi-agent.Finally,it provides the systematic model based on J2EE platform to realize.
Subject Independent Facial Emotion Classification Using Geometric Based Features

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles and others relative geometric features for recognition give accuracy about 95.73% when the seven emotion classes are tested and 97.23% when the 6 classes (except normal class) are only tested. These rates are considered high when compared with the results of other newly published works.
The European Wind Atlas

The aim of the European Wind Atlas is to establish the meteorological basis for the assessment of wind energy resources. The main objective is to provide suitable data for evaluating the potential wind energy output from large electricity producing wind turbine installations. In addition, the Wind Atlas provides data and guidelines for the meteorological aspects of the detailed siting of large and small wind turbines. The EUROPEAN WIND ATLAS IS: A DATA BANK of European Wind climate. It contains comprehensive statistics from more than 200 meteorological stations covering the intire EC - plus colour maps of the wind resources of each country in the EC. The statistical data are given also on a diskette for use with IBM compatible PC's. A HANDBOOK for regional wind resource assessment, and the local siting of wind turbines - including guidelines with computational procedures for the effects of shelter from buildings and other obstacles, the effects of varying surface roughnesses, and for the influence of hills and mountains on the wind climate and on the local wind power resources. A METHODOLOGY description. The computational procedures employed for the analysis of the meteorological data are described in full. These methods have applications for all parts of the world. The computer codes are available separately in the form of the Wind Atlas analysis and Application (PC-) Programme (WAsP). (author).
Evolution of SSR diversity from wild types to U.S. advanced cultivars in the Andean and Mesoamerican domestications of common bean (Phaseolus vulgaris)

Progress in common bean breeding requires the exploitation of genetic variation among market classes, races and gene pools. The present study was conducted to determine the amount of genetic variation and the degree of relatedness among 192 selected common bean advanced cultivars using 58 simple-sequence-repeat markers (SSR) evenly distributed along the 11 linkage groups of the Phaseolus reference map. All the lines belonged to commercial seed type classes that are widely grown in the USA and include both dry bean and snap beans for the fresh and processing markets. Through population structure, principal components analyses, cluster analysis, and discriminant analysis of principal components (DAPC), Andean and Mesoamerican genotypes as well as most American commercial type classes could be distinguished. The genetic relationship among the commercial cultivars revealed by the SSR markers was generally in agreement with known pedigree data. The Mesoamerican cultivars were separated into three major groups—black, small white, and navy accessions clustered together in a distinct group, while great northern and pinto clustered in another group, showing mixed origin. The Andean cultivars were distributed in two different groups. The kidney market classes formed a single group, while the green bean accessions were distributed between the Andean and Mesoamerican groups, showing inter-gene pool genetic admixture. For a subset of 24 SSR markers, we compared and contrasted the genetic diversity of the commercial cultivars with those of wild and domesticated landrace accessions of common bean. An overall reduction in genetic diversity was observed in both gene pools, Andean and Mesoamerican, from wild to landraces to advanced cultivars. The limited diversity in the commercial cultivars suggests that an important goal of bean breeding programs should be to broaden the cultivated gene pool, particularly the genetic diversity of specific commercial classes, using the genetic variability present in common bean landraces.
Plasmonic properties of suspended nanodisc structures for enhancement of the electric field distributions

Metallic nanostructures possess many advantages for utilization in various applications including sensing applications. However, achieving an easy to fabricate platform with high sensitivity performance is considered the main challenge in designing such nanostructures. Two factors should be considered when designing a wavelength based nanostructured sensor; the field distribution around the nanostructures, and the full width at half maximum (FWHM) of the sensor spectral response. In this paper, we study suspended nanodisc structures as a candidate for enhancing the electric field distribution in-plane and out of plane axes of the nanodiscs, and hence enhancing the probe depth of the nanosensor. Another advantage of the suspended nanodisc structure is that it offers a 100% surface coverage. The Finite Difference Time Domain (FDTD) method is used for the study of optical properties of the structure. The resonance location depends on the dimensions of the nanodiscs as well as the polymer base. Higher order modes can also be supported by nanodiscs with larger dimensions. The local electric field is enhanced as it is distributed in both perpendicular and horizontal planes with respect to the plane of gold nanodiscs without altering the FWHM relative to the regular nanodisc structure. This is considered as an advantage in sensing applications. Another advantage of this structure is that it can be readily fabricated by nanoimprint lithography and gold deposition.
Modeling and Evaluation for Product Usability Based on Opinion Mining

A novel product usability modeling and evaluation method based on opinion mining is proposed in this paper.The data is the comments from the Web.Opinion mining is used to process the unstructured product comment to structured comment database.This paper chooses the feature related to product usability and uses factor analysis method to extract common factors which affect product usability.A case study on handset is conducted to demonstrate the usefulness of the proposed methodology.Experimental result shows this method can find the defect existed in product usability from user aspect and provide the reference for product design.
Moving object detection algorithm based on Gaussian mixture model

An improved algorithm based on adaptive Gaussian mixture model is proposed to overcome the deficiency in the object detection phase of ITS(intelligent transportation system). At the beginning of background model the interval frame differencing method is put forward to make a judgment,which can rapidly detect the movement area.The sensitivity of the algorithm is improved and the adaptability for slow vehicles is enhanced.In order to recover background covered by paused objects when they start to move again,the different update rates in different areas are given,which can eliminate shadow that moving vehicles may leave.Lastly by combining gray and canny edge features background subtraction,moving target outline similar with the background's gray is effectively retained.Experimental results confirm the good effort of the detection algorithm.
Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.
Arc-smooth functions on closed sets

By an influential theorem of Boman, a function $f$ on an open set $U$ in $\mathbb{R}^{d}$ is smooth ( ${\mathcal{C}}^{\infty }$ ) if and only if it is arc-smooth, that is, $f\,\circ \,c$ is smooth for every smooth curve $c:\mathbb{R}\rightarrow U$ . In this paper we investigate the validity of this result on closed sets. Our main focus is on sets which are the closure of their interior, so-called fat sets. We obtain an analogue of Boman’s theorem on fat closed sets with Hölder boundary and on fat closed subanalytic sets with the property that every boundary point has a basis of neighborhoods each of which intersects the interior in a connected set. If $X\subseteq \mathbb{R}^{d}$ is any such set and $f:X\rightarrow \mathbb{R}$ is arc-smooth, then $f$ extends to a smooth function defined on $\mathbb{R}^{d}$ . We also get a version of the Bochnak–Siciak theorem on all closed fat subanalytic sets and all closed sets with Hölder boundary: if $f:X\rightarrow \mathbb{R}$ is the restriction of a smooth function on $\mathbb{R}^{d}$ which is real analytic along all real analytic curves in $X$ , then $f$ extends to a holomorphic function on a neighborhood of $X$ in $\mathbb{C}^{d}$ . Similar results hold for non-quasianalytic Denjoy–Carleman classes (of Roumieu type). We will also discuss sharpness and applications of these results.
Filtering of Malicious Traffic with Secret Sharing

-Nowadays on the Internet, there is a huge and growing expanse of unsought traffic like phishing, spam and distributed access denied service attacks. To solve this problem we can generally filter unsought traffic on routers based on the source IP addresses. Since the boundless number of existing filters in routers now are aggregation used in practice: a sole filter and block defines a variety of IP addresses. Consequently blocking of all (unsought and desired) traffic caused from hosts with IP addresses in that range. Here, a set of algorithms is been established that specified a blacklist containing the source IP addresses of unsought traffic and a limitation on the number of sieving, build a set of sieving rules that enhance the interchange between the unsought and valid traffic is blocked and permits furtive distribution amongst a group of members with Multiprotocol label switching(MPLS). Furthermore, to illustrate that precise good when applied to realistic sharing of causes of unsought traffic, that is said to exhibit space and time elements clustering. But it is exclusive to block valid traffic from the prefix filtering. We will show how to choose optimal prefix to filter for different situations, attacking political entrepreneurs.