Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of Web Search Methods Using Ontology

An enormous amount of information is present on the Internet. If any user wants to access some data, they would tend to enter their query into a search engine. This is known as Web Searching. Web Searching has increasingly become a complicated task over the years. To add to this, it often provides inaccurate results. Thus there is a dire need for a more accurate and fast search result providing system. This problem may be solved by the Semantic Web. One of the pillars of the Semantic Web is Ontology. Usage of ontologies during web search has provided some promising results. This paper discusses some of the proposed methods of incorporating ontologies in web search. After a brief description is provided for each of the methods, they are compared on the basis of the ontology technologies supported by them and the methods utilized for implementation.
On estimation of uniform distribution if data are measured with additive error

The paper considers the problem of estimating the edges of the symmetric uniform distribution on the line when data are measured with normal additive error. The main purpose is to compare the efficiency of the maximum likelihood estimator and the moment method estimator. It is proven that the model is regular and that the maximum likelihood estimator is more efficient then the moment method estimator. There is also given a sufficient condition for existence of both estimators.
Reliable Media Communications in the IoT by Advanced JPEG and Blind Watermarking

This paper includes the consequences which lies in the IoT environment, specifically Secure mean of communication and user authentication are performed in biomedical images. Exchange of results and data takes place in a smart healthcare environment. A secure camera along with AJPEG Compression technique is proposed here. The AJPEG architecture includes both encryption and watermarking which overcome all security related issues. The Encryption along with secure blind watermarking technique is used to provide high security with authentic data communication . The resulting data shows that the new BPG compression technique presents high first-rate photograph with authentic information as compare to JPEG approach. The performance of BPG Compression technique has been reached by placing the signature with encryption at middle location of the image and by introducing DCT technique of length 8*8 pixel used by frequency domain blind watermarking technique.
Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.
Proceedings of the 12th Working Conference on Mining Software Repositories

Welcome to MSR 2015, the Twelfth International Working Conference on Mining Software Repositories, held on May 16 - 17 in Florence, Italy, and co-located with the 37th ACM/IEEE International Conference on Software Engineering (ICSE 2015). We are happy to report that the community of MSR researchers continues to grow, while at the same time the research being performed is maturing. This year marks the 12th edition of MSR. Since 2004 MSR has grown from a one-day workshop with 38 submissions to a two-day conference with 182 submissions combined across all submission categories. To account for the growing industrial interest in smart data and data science, the technical track contains an additional submission category of practice papers, which report experiences of applying mining repository algorithms in an industry/open source organization context.
A pedestrian detection algorithm based on deep deconvolution networks in complex scenes

Pedestrian detection is an important application in computer vision. Due to uneven illumination, serious obstacles, low quality images, abnormal posture and other factors, pedestrian detection faces the problem of low detection accuracy in complex scenes. In this paper, pedestrian detection algorithm based on deep convolution neural network is studied. Since shorter connections between the input and output layers can help to build deeper and more efficient network in CNN, a densely connected convolution structure is introduced in this paper to optimize the Deconvolutional Single Shot Detector and improve the feature utilization and reduce the network parameters. Meanwhile, by augmenting the context information, the detection performance for small size pedestrians is improved. The initial experimental results show that the proposed algorithm improves the detection accuracy to 87.84% at the speed of 12.3fps on low-resolution (64x128) pedestrian dataset, which outperforms the reference algorithms.
Neuro-scond Order Sliding Mode Field Oriented Command for DFIG Based Wind Turbine

Wind energy has many advantages because it does not pollute and is an inexhaustible source of energy. The Double Fed Induction Generator (DFIG) is one of the most important generators used for Horizontal Axis Wind Turbine (HAWT). The DFIG stator is linked directly to the grid, whilst the DFIG rotor is coupled via an electronic converter. In this article, a discrete-time Neural Scond Order Sliding Mode (NSOSM) Field Oriented Control (FOC) for DFIG stator active and reactive powers command is proposed. The adaptive nature of the proposed regulator improves the transient response as compared with discrete-time Conventional Second Order Sliding Mode Control (SOSMC) for DFIG parameters variations and disturbances. The proposed regulator is based on a Neural Network (NN), using fuzzy space vector modulation (FSVM) strategy. The NN works as an identifier to obtain an accurate model, which is robust against disturbances and parameter variations. A comparison between SOSMC with pulse width modulation (PWM) and NSOSMC with FSVM is included. The validity and the effectiveness of the proposed regulator are tested using Matlab/Simulink simulation results.
A MORF-Vision Method for Strategic Creation of IoT Solution Opportunities

ABSTRACT With the rise of Internet of Things (IoT), companies are now facing new challenges such as diverse consumer demands and shorter lifecycle of products and services. Even in big companies, they may have to “extract” new products every six months or less. Nonetheless, shorter lifecycle is never a waiver for poor user satisfaction. Common to the companies that survived such disruptive changes is that they emphasized the user experience (UX) as one of the most important elements of their strategy. This explains why most product innovation processes such as user-centered design or design thinking emphasize so much on “empathizing. ” Developing a new product or service from scratch, however, is challenging especially when the underlying technology is new to people. The aim of this study is to develop a systematic process for creating opportunities from the UX perspective. This study suggests opportunity-creation process by adopting an “abductive” design thinking approach: (1) future envisioning and (2) opportunity identification and analysis. A new method called MORF-Vision (Morphological Future Envisioning) was developed to generate prospective scenarios that can be used in phase (2) to identify potential opportunities. An experiment showed that the MORF-Vision method is helpful to create opportunities in various business domains.
Design of a symmetric open slot antenna for UWB applications

In this paper, an ultra-wideband (UWB) antenna with symmetric open slots is proposed. The symmetric open slots and the U-shaped feeding line are introduced for obtaining the symmetrical radiation property and the lower cross-polarization level. By using the U-shaped feeding line to feed the symmetric open slots in parallel, four resonances are excited by the different effective resonant length of the open slots and form a wideband, which locates at 3.1–10.8GHz. The measured results show that the stable radiation patterns and the low cross-polarization level are obtained in the operating band, which shows that the proposed antenna is a good candidate for UWB applications.
Developing Teachers' Computational Thinking Beliefs and Engineering Practices through Game Design and Robotics.

This research report presents the final year results of a three-year research project on computational thinking (CT). The project, funded by the National Science Foundation, involved training teachers in grades four through six to implement Scalable Game Design and LEGO® EV3 robotics during afterschool clubs. Thirty teachers and 531 students took part in the Year-3 study that blended game design and robotics. Eight of these teachers and 98 students participated in a large urban city in Pennsylvania, while the remaining 22 teachers and 433 students participated in rural Wyoming. This paper reports on the results as it pertained to teacher outcomes, specifically, teachers’ development of CT beliefs and engineering practices.
Development of the Korean Handwriting Assessment for Children Using Digital Image Processing

The efficiency and accuracy of handwriting measurement could be improved by adopting digital image processing. This study developed a computer-based Korean Handwriting Assessment tool. Second graders participated in this study by performing writing tasks of consonants, vowels, words, and sentences. We extracted boundary parameters for each letter using digital image processing and calculated the variables of size, size coefficient of variation (CV), misalignment, inter-letter space, inter-word space, and ratio of inter-letter space to inter-word space. Children were also administered traditional handwriting and visuomotor tests. Digital variables from image processing were correlated with these previous tests. Using these correlations, we established a three-point scoring system that computed test scores for each variable. We analyzed inter-rater reliability between the computer rater and human rater and test-retest reliability between the first and second performances. The validity was examined by analyzing the relationship between the Korean Handwriting Assessment and previous handwriting and visuomotor tests. We suggested the Korean Handwriting Assessment to measure size, size consistency, misalignment, inter-letter space, inter-word space, and space ratio using digital image processing. This Korean Handwriting Assessment tool proved to have reliability and validity. It is expected to be useful for assessing children’s handwriting.
Dual-Polarized Spatial Media-Based Modulation

A novel scheme called dual-polarized spatial media-based modulation (DP-SMBM), which combines judiciously the media-based modulation (MBM), spatial modulation (SM), and dual-polarized (DP) antennas, is proposed in this paper. The DP-SMBM introduces a new DP domain to convey additional information without occupying extra physical space, effectively enhancing the transmission rate and alleviating the finite space issue. The bit error rate (BER) and capacity over uncorrelated/correlated Rayleigh and Rician fading channels are analyzed. A tight upper bound on the BER of DP-SMBM is further derived in closed form. Computer simulations are conducted to investigate the performance of DP-SMBM and comparisons with the existing SMBM are made in two different cases: 1) equal number of antennas and 2) equivalent channel matrix. Simulation results show that for the first case, the BER and capacity performance of DP-SMBM significantly outperforms that of SMBM, and for the second case, DP-SMBM is superior to SMBM in terms of BER and space utilization efficiency under highly correlated channels, revealing the potential of DP-SMBM in combating spatial correlation and the impact of line-of-sight.
Application of Single-Server Queue System in Performance Analysis of Shuttle Bus Operation: A Case Study of Federal University of Technology Akure

This study has examined the performance of University transport bus shuttle based on utilization using a Single-server queue system which occur if arrival and service rate is Poisson distributed (single queue) (M/M/1) queue. In the methodology, Single-server queue system was modelled based on Poisson Process with the introduction of Laplace Transform. Also, PASTA was introduced in queuing systems with Poisson arrivals. It is concluded that the performance of University transport bus shuttle is 93 percent which indicates a very good performance such that the supply of shuttle bus in FUTA is capable of meeting the demand. This study can be improved upon by examining the peak and off-peak period of traffic in the two major corridors (North gate and South gate) of FUTA, the economic cost of operating bus shuttle services can also be examined.
USING COHERENT SCATTERERS IN TIME SERIES OF HIGH RESOLUTION SAR IMAGES FOR THE MONITORING OF CONSTRUCTION ACTIVITY

Abstract. In this paper, we present a method for detecting and quantifying changes due to construction activity in time series of high resolution SAR images. We propose to detect these changes by the appearance or disappearance of a large number of coherent scatterers, rather than by looking at changes in SAR amplitude or coherence. These coherent scatterers, which appear mainly in buildings and other man-made structures, will be detected in each individual image by analysing their stability across multiple frequency sub-bands. This implies that long time series are not required, unlike for other similar approaches based in persistent scatterer interferometry, and that this approach can be applied for the monitoring of fast changing areas such as temporary settlements. The presented approach will be applied for monitoring the construction activity taking place during the preparations for two large festivals in Munich, Germany.
An investigation of network countermeasure against fast self-propagating malware

A self-propagating malware is a malicious software program that spreads itself across the Internet by exploiting flaws in software systems and therefore capable of launching attack against vulnerable Internet hosts. Fast self-propagating malware poses a security threat to hosts that are connected to the Internet because the speed of their propagation is very high and causes disruption of services across the Internet. Thus it becomes crucial to effectively detect and contain the propagation of fast self-propagating malware on the Internet. This thesis presents a mechanism for the detection and containment of fast self-propagating malware. The thesis initially presents an overview of self-propagating malware and the need for a solution to counter the propagation of this class of malware. The thesis also presents a comprehensive literature survey to identify research gaps and limitations of previously reported worm detection and containment systems. Based on the identified limitations and shortcomings, an improved detection and containment scheme has been developed to counter the spread of fast self-propagating malware. The developed scheme, termed NEDAC, uses a cross-layer architecture to provide a combined countermeasure solution against fast self-propagating malware, i.e., a detection technique at network layer and a containment technique at data link layer. Furthermore, an improved testing environment, termed V-Network, has been developed for high fidelity malware experimentation and testing of countermeasure systems. An evaluation framework has been developed and used to test the NEDAC scheme along with other previously reported countermeasure systems using known and contemporary self-propagating malware. The NEDAC scheme demonstrated a better performance than the previously reported countermeasure systems.
Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.
The MediaMill TRECVID 2009 Semantic Video Search Engine

In this paper we describe our TRECVID 2009 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive search. Starting point for the MediaMill concept detection approach is our top-performing bag-of-words system of last year, which uses multiple color descriptors, codebooks with soft-assignment, and kernel-based supervised learning. We improve upon this baseline system by exploring two novel research directions. Firstly, we study a multi-modal extension by the inclusion of 20 audio concepts and fusing using two novel multi-kernel supervised learning methods. Secondly, with the help of recently proposed algorithmic refinements of bag-of-words, a bag-of-words GPU implementation, and compute clusters, we scale-up the amount of visual information analyzed by an order of magnitude, to a total of 1,000,000 i-frames. Our experiments evaluate the merit of these new components, ultimately leading to 64 robust concept detectors for video retrieval. For retrieval, a robust but limited set of concept detectors necessitates the need to rely on as many auxiliary information channels as possible. For automatic search we therefore explore how we can learn to rank various information channels simultaneously to maximize video search results for a given topic. To improve the video retrieval results further, our interactive search experiments investigate the roles of visualizing preview results for a certain browse-dimension and relevance feedback mechanisms that learn to solve complex search topics by analysis from user browsing behavior. The 2009 edition of the TRECVID benchmark has again been a fruitful participation for the MediaMill team, resulting in the top ranking for both concept detection and interactive search.
Designing Secure Lightweight Blockchain-Enabled RFID-Based Authentication Protocol for Supply Chains in 5G Mobile Edge Computing Environment

Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.
Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network

In human-machine interaction, facial emotion recognition plays an important role in recognizing the psychological state of humans. In this study, we propose a novel emotion recognition framework based on using a knowledge transfer approach to capture features and employ an improved deep forest model to determine the final emotion types. The structure of a very deep convolutional network is learned from ImageNet and is utilized to extract face and emotion features from other data sets, solving the problem of insufficiently labeled samples. Then, these features are input into a classifier called multi-composition deep forest, which consists of 16 types of forests for facial emotion recognition, to enhance the diversity of the framework. The proposed method does not need require to train a network with a complex structure, and the decision tree-based classifier can achieve accurate results with very few parameters, making it easier to implement, train, and apply in practice. Moreover, the classifier can adaptively decide its model complexity without iteratively updating parameters. The experimental results for two emotion recognition problems demonstrate the superiority of the proposed method over several well-known methods in facial emotion recognition.