Abstract: In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy.
Abstract: Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.
Abstract: The purpose of this study is to compare the conventional crop monitoring system with the satellite based crop monitoring system in Pakistan. This study is conducted for SUPARCO (Space and Upper Atmosphere Research Commission). The study focused on the wheat crop, as it is the main cash crop of Pakistan and province of Punjab. This study will answer the following: Which system is better in terms of cost, time and man power? The man power calculated for Punjab CRS is: 1,418 personnel and for SUPARCO: 26 personnel. The total cost calculated for SUPARCO is almost 13.35 million and CRS is 47.705 million. The man hours calculated for CRS (Crop Reporting Service) are 1,543,200 hrs (136 days) and man hours for SUPARCO are 8, 320hrs (40 days). It means that SUPARCO workers finish their work 96 days earlier than CRS workers. The results show that the satellite based crop monitoring system is efficient in terms of manpower, cost and time as compared to the conventional system, and also generates early crop forecasts and estimations. The research instruments used included: Interviews, physical visits, group discussions, questionnaires, study of reports and work flows. A total of 93 employees were selected using Yamane’s formula for data collection, which is done with the help questionnaires and interviews. Comparative graphing is used for the analysis of data to formulate the results of the research. The research findings also demonstrate that although conventional methods have a strong impact still in Pakistan (for crop monitoring) but it is the time to bring a change through technology, so that our agriculture will also be developed along modern lines.
Abstract: A practical and simple self-indexing data structure, Partitioned Elias-Fano (PEF) - Compressed Suffix Arrays (CSA), is built in linear time for the CSA based on PEF indexes. Moreover, the PEF-CSA is compared with two classical compressed indexing methods, Ferragina and Manzini implementation (FMI) and Sad-CSA on different type and size files in Pizza & Chili. The PEF-CSA performs better on the existing data in terms of the compression ratio, count, and locates time except for the evenly distributed data such as proteins data. The observations of the experiments are that the distribution of the φ is more important than the alphabet size on the compression ratio. Unevenly distributed data φ makes better compression effect, and the larger the size of the hit counts, the longer the count and locate time.
Abstract: Emerging medical devices are highly relying on embedded software that runs on the specific platform in real time. The development of embedded software is different from ordinary software development due to the hardware-software dependency. MDevSPICE® has been developed to provide guidance to support such development. To increase the flexibility of this framework agile practices have been introduced. This paper outlines the challenges for embedded medical device software development and the structure of MDevSPICE® and suggests a suitable combination of agile practices that will help to add flexibility and address corresponding challenges of embedded medical device software development.
Abstract: We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment.
Abstract: The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.
Abstract: This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.
Abstract: Convolutional Neural Networks (CNN) have
demonstrated their effectiveness in synthesizing 3D views of object
instances at various viewpoints. Given the problem where one
have limited viewpoints of a particular object for classification, we
present a pose normalization architecture to transform the object to
existing viewpoints in the training dataset before classification to
yield better classification performance. We have demonstrated that
this Pose Normalization Network (PNN) can capture the style of
the target object and is able to re-render it to a desired viewpoint.
Moreover, we have shown that the PNN improves the classification
result for the 3D chairs dataset and ShapeNet airplanes dataset
when given only images at limited viewpoint, as compared to a
CNN baseline.
Abstract: Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.
Abstract: This paper addresses the shortcomings of architectural computation tools in representing human behavior in built environments, prior to construction and occupancy of those environments. Evaluating whether a design fits the needs of its future users is currently done solely post construction, or is based on the knowledge and intuition of the designer. This issue is of high importance when designing complex buildings such as hospitals, where the quality of treatment as well as patient and staff satisfaction are of major concern. Existing computational pre-occupancy human behavior evaluation methods are geared mainly to test ergonomic issues, such as wheelchair accessibility, emergency egress, etc. As such, they rely on Agent Based Modeling (ABM) techniques, which emphasize the individual user. Yet we know that most human activities are social, and involve a number of actors working together, which ABM methods cannot handle. Therefore, we present an event-based model that manages the interaction between multiple Actors, Spaces, and Activities, to describe dynamically how people use spaces. This approach requires expanding the computational representation of Actors beyond their physical description, to include psychological, social, cultural, and other parameters. The model presented in this paper includes cognitive abilities and rules that describe the response of actors to their physical and social surroundings, based on the actors’ internal status. The model has been applied in a simulation of hospital wards, and showed adaptability to a wide variety of situated behaviors and interactions.
Abstract: Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.
Abstract: In response to a changing world and the fast growth of the Internet, more and more enterprises are replacing web-based services with cloud-based ones. Multi-tenancy technology is becoming more important especially with Software as a Service (SaaS). This in turn leads to a greater focus on the application of Identity and Access Management (IAM). Conventional Near-Field Communication (NFC) based verification relies on a computer browser and a card reader to access an NFC tag. This type of verification does not support mobile device login and user-based access management functions. This study designs an NFC-based third-party cloud identity and access management scheme (NFC-IAM) addressing this shortcoming. Data from simulation tests analyzed with Key Performance Indicators (KPIs) suggest that the NFC-IAM not only takes less time in identity identification but also cuts time by 80% in terms of two-factor authentication and improves verification accuracy to 99.9% or better. In functional performance analyses, NFC-IAM performed better in salability and portability. The NFC-IAM App (Application Software) and back-end system to be developed and deployed in mobile device are to support IAM features and also offers users a more user-friendly experience and stronger security protection. In the future, our NFC-IAM can be employed to different environments including identification for mobile payment systems, permission management for remote equipment monitoring, among other applications.
Abstract: The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.
Abstract: Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The correct extraction of the component for posterior analysis is a critical step of the AOI process. Nowadays, the Pattern Matching Algorithm is commonly used, although this algorithm requires extensive calculations and is time consuming. This paper will present an improved algorithm for the component localization process, with the capability of implementation in a parallel execution system.
Abstract: Recently, collectable manufacturing data are rapidly
increasing. On the other hand, mega recall is getting serious as
a social problem. Under such circumstances, there are increasing
needs for preventing mega recalls by defect analysis such as
root cause analysis and abnormal detection utilizing manufacturing
data. However, the time to classify strings in manufacturing data
by traditional method is too long to meet requirement of quick
defect analysis. Therefore, we present String Length Distribution
Classification method (SLDC) to correctly classify strings in a short
time. This method learns character features, especially string length
distribution from Product ID, Machine ID in BOM and asset list.
By applying the proposal to strings in actual manufacturing data, we
verified that the classification time of strings can be reduced by 80%.
As a result, it can be estimated that the requirement of quick defect
analysis can be fulfilled.
Abstract: Grid of computing nodes has emerged as a
representative means of connecting distributed computers or
resources scattered all over the world for the purpose of computing
and distributed storage. Since fault tolerance becomes complex due
to the availability of resources in decentralized grid environment,
it can be used in connection with replication in data grids. The
objective of our work is to present fault tolerance in data grids
with data replication-driven model based on clustering. The
performance of the protocol is evaluated with Omnet++ simulator.
The computational results show the efficiency of our protocol in
terms of recovery time and the number of process in rollbacks.
Abstract: The objective of this paper is to study the existence and
uniqueness of Mild solutions for a complex fuzzy evolution equation
with nonlocal conditions that accommodates the notion of fuzzy sets
defined by complex-valued membership functions. We first propose
definition of complex fuzzy strongly continuous semigroups. We then
give existence and uniqueness result relevant to the complex fuzzy
evolution equation.
Abstract: Nowadays, the vigorous popularity of video processing techniques has resulted in an explosive growth of multimedia data illegal use. So, watermarking security has received much more attention. The purpose of this paper is to explore some watermarking techniques in order to observe their specificities and select the finest methods to apply in digital cinema domain against movie piracy by creating an invisible watermark that includes the date, time and the place where the hacking was done. We have studied three principal watermarking techniques in the frequency domain: Spread spectrum, Wavelet transform domain and finally the digital cinema watermarking transform domain. In this paper, a detailed technique is presented where embedding is performed using direct sequence spread spectrum technique in DWT transform domain. Experiment results shows that the algorithm provides high robustness and good imperceptibility.
Abstract: Biometric authentication is an essential task for any
kind of real-life applications. In this paper, we contribute two
primary paradigms to Iris recognition such as Robust Eyelash
Detection (RED) using pathway kernels and hair curve fitting
synthesized model. Based on these two paradigms, rotation invariant
iris recognition is enhanced. In addition, the presented framework
is tested with real-life iris data to provide the authentication for
LRC (Learning Resource Center) users. Recognition performance
is significantly improved based on the contributed schemes by
evaluating real-life irises. Furthermore, the framework has been
implemented using Java programming language. Experiments are
performed based on 1250 diverse subjects in different angles of
variations on the authentication process. The results revealed that the
methodology can deploy in the process on LRC management system
and other security required applications.