Recent years have seen remarkable progress on both advanced multimedia data processing and intelligent network informationsystems. The objective of this book is to contribute to the development of multimedia processi...
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ISBN:
(数字)9783319439822
ISBN:
(纸本)9783319439815
Recent years have seen remarkable progress on both advanced multimedia data processing and intelligent network informationsystems. The objective of this book is to contribute to the development of multimedia processing and the intelligent informationsystems and to provide the researches with the essentials of current knowledge, experience and know-how. Although many aspects of such systems have already been under investigation, but there are many new that wait to be discovered and defined.;The book contains a selection of 36 papers based on original research presented during the 10th International Conference on Multimedia & Network informationsystems (MISSI 2016) held on 14–16 September 2016 in Wrocław, Poland. The papers provide an overview the achievements of researches from several countries in three continents.;The volume is divided into five parts: (a) Images and Videos - Virtual and Augmented Reality, (b) Voice Interactions in Multimedia systems, (c) Tools and Applications, (d) Natural Language in informationsystems, and (e) Internet and Network Technologies.;The book is an excellent resource for researchers, those working in multimedia, Internet, and Natural Language technologies, as well as for students interested in computer science and other related fields.
This paper presents a novel application of data mining techniques to guide academic programs design and assessment. More specifically, it propose using association rule mining techniques to discover a set of rules tha...
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This paper presents a novel application of data mining techniques to guide academic programs design and assessment. More specifically, it propose using association rule mining techniques to discover a set of rules that govern the relationship between two core components of an academic program, program educational objectives (PEOs) and students outcomes(SOs). As a case study, this paper demonstrates how association rule mining techniques are applied to mine mapping rules between the PEOs and a predefined set of SOs adopted by the American Board for Engineering and Technology-Engineering Accreditation Commission (ABET-EAC) for engineering programs. To this end, a set of 152 ABET-EAC accredited engineering programs’ self-study reports have been collected and the mapping data between their PEOs and ABET-EAC SOs have been extracted. This dataset has been pre-processed and transformed into a representation suitable for applying association rule mining techniques. This involves identifying a set of PEOs labels, annotating data instances with PEOs labels, and projecting each multi-label data instance into a set of single-label instances. Apriopi algorithm is then applied to discover the rules that govern the mapping between PEOs and ABET-EAC SOs. The discovered rules are of particular importance for guiding the design and assessment of engineering academic programs. Besides that, the discovered rules unveil of a number of interesting correlations between PEOs and ABET-EAC SOs that need further investigation by pedagogists.
Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermosco...
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Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic *** recent advances in deep learning(DL)models find useful to examine the medical image and make proper *** this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is *** goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of *** ADL-MDC technique performs contrast enhancement and data augmentation at the initial ***,the k-means clustering technique is applied for the image segmentation *** addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction ***,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification *** exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several *** simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.
We present technologies that underpin the OBDA system Ontop and take full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop, including the tree-witness query rewriting,...
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We present technologies that underpin the OBDA system Ontop and take full advantage of storing data in relational databases. We discuss the theoretical foundations of Ontop, including the tree-witness query rewriting, τ-mappings and optimisations based on database integrity constraints and SQL features.
Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and ...
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In a faceless business transaction such as in e-commerce, trust plays a major influence on a customer's decision making behavior. Trust on e-commerce is very much dependent on the information obtained from the ven...
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In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an ...
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In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.
Fine-grained image classification (FGIC) is a challenging task due to small visual differences among inter-subcategories, but large intra-class variations. In this paper, we propose a fusion approach to address FGIC b...
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Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as hea...
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A study to assess the effect of programming language on student comprehension of source code is presented, comparing the languages of C++ and Python in two task categories: overview and find bug tasks. Eye gazes are t...
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ISBN:
(纸本)9781450327510
A study to assess the effect of programming language on student comprehension of source code is presented, comparing the languages of C++ and Python in two task categories: overview and find bug tasks. Eye gazes are tracked while thirty-eight students complete tasks and answer questions. Results indicate no significant difference in accuracy or time, however there is a significant difference reported on the rate at which students look at buggy lines of code. These results start to provide some direction as to the effect programming language might have in introductory programming classes.
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