Reviewerly/exHarmony huggingface dataset:
Super-resolution of license-plates using frames of low-resolution video

In this paper, we consider the problem of multi-frame super-resolution for license plate images given low-resolution frames. Optical Character Recognition (OCR) systems may not work well when the resolution of a license plate image is low. Using high-resolution cameras is an expensive solution to this problem. However, super-resolution methods can reconstruct a high-resolution image from a set of low-resolution frames. In this paper, we propose a new interpolation method that enhances the resolution of the license plate, using consecutive frames of a video. The images obtained by this method are suitable for a license plate recognition system. Experimental results show that applying the proposed method on low-resolution frames, improves the quality of the license plate significantly, and produces better results when fed into an OCR system.
A perspective on categorizing Personal and Sensitive Data and the analysis of practical protection regulations

The modern era is marked with users' comprehensive manners in interaction and data mining. This phenomenon is an extraordinary aspect of modern digital age. The digital path of a user associated with surfing the webpages on internet is unconditionally interconnected with a small dialogue box asking about permission to use cookies from the website. Afterwards, the relevant user's web-findings of items or information that had been browsed before is recommended automatically from the searching engine again. It is clearly acknowledged that after the announcement of the EU General Data Protection Regulation (GDPR), which was enforced in 2018, the worldwide impact has been evolving on the awareness and specification of the data protection. Therefore, data protection at the present era focusing on the area of fintech services is the research object. The objective of this study is to classify categories and definitions of users' sensitive data. Moreover, it analyses methods of defining sensitive data of the user and their realized activities especially as on-line transactions such as Zhifubao-the biggest "third-party electronic payment service platform". In the meantime, we evaluate the GDPR, compare to the Data Protection Act (DPA), and discuss whether and to what extent it marked a milestone progress above the current legislation of processing data standards, or whether some obstacles have occurred in law implementation process.
Conditional random field tracking model based on a visual long short term memory network

In dense pedestrian tracking, frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories. In this study, a conditional random field tracking model is established by using a visual long short term memory network in the three dimensional space and the motion estimations jointly performed on object trajectory segments. Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation. To address the uncertainty of the length and interval of trajectory segments, a multimode long short term memory network is proposed for the object motion estimation. The tracking performance is evaluated using the PETS2009 dataset. The experimental results show that the proposed method achieves better performance than the tracking methods based on independent motion estimation.
Prediction and Classification Of Vector-Borne and Communicable Diseases through Artificial Neural Networks

It is easy enough to be infected with communicable and vector-borne diseases, which have very similar symptoms, most of which occur after days. Nowadays technology can help in the correct diagnosis of these diseases. Early diagnosis is necessary to ensure that appropriate treatments and medications are administered, which requires the need for an automated system to predict possible infections. This requires a system that allows the patient to distinguish between these conditions and diagnose the possible disease based on symptoms. After having diagnosed the disease, the goal is to provide appropriate treatment based on the type of disease expected. The implementation of this medical diagnosis system is carried out with the help of Artificial Neural Networks that use backpropagation algorithm for training. With the implementation of Artificial Neural Networks in medical diagnosis, the accuracy of the system improves with respect to the rule-based model and with the use of the backpropagation algorithm together with the gradient optimization technique, the results are more precise.
Enrichment of Semantic Sensor Network Ontology: Description Logics based approach

Description Logics (DLs) is the formalism of representing knowledge in Web Ontology Language (OWL). The Semantic Sensor Network Incubator Group (SSN-XG) developed the Semantic Sensor Network (SSN) ontology. However, there is less attention paid to develop any rigorous formal approach for it. This paper aims to enhance the semantics of SSN ontology by developing suitable DLs. Various concepts of such ontology are elaborated logically and with the support of suitable case study. The novelty of the proposed research work is to understand and represent the inherent knowledge of SSN ontology using DLs. Thus, it facilitates mathematical proof system.
Agent data merging

The present article deals with data collection in a given field using the agent-based technologies from various information sources of the Internet with the aim to ob-tain reliable and up-to-date data. The agent-based approach is illustrated by the data collection on the nuclear power plants operating all over the world. Three open information sources have been selected for data extraction. The information sources concerned have been analyzed and the features of data provision structure identified. In the course of the present work the following tools for the develop-ment of the software agents have been described: the browser control for human behavior simulation, HTML markup analysis using the XPath query language and data extraction from PDF-documents using regular expressions. Above all, the article considers the software architecture and the database scheme. In the re-sult of the software operation, data regarding 789 nuclear power plants has been obtained.
A Novel technique for test case minimization in object oriented testing

Software maintenance is the costliest phase of software life cycle and it consumes almost 70 percent of resources of development process. Software testing involves the examining the built software with the intension to find the defects in it. Exhaustive testing of the software for all possible test cases is not feasible as may take infinitely large amount of time and may consume large number of other resources. Researchers in the field of the software testing are exploring the different possibilities to reduce the required number of test cases to test given software. In case of an object oriented (OO) software, the complexities like inheritance, coupling and cohesion, make the software modules more prone to the faults. This problem gets augmented in case of very large software systems. Many researchers have solved the problem of test case minimization from the different perspectives like requirements coverage; statement coverage and risk coverage. But negligible research has been done of the basis of security metrics coverage. In this paper, a technique has been presented for minimization of test cases for the OO systems. The research reported in the paper has considered security as a perspective for test cases evaluation and minimization. Publically available data sets pertaining to open source software Camel 1.6.1 have been used for the evaluation of proposed methodology. Linear Regression (LR) model for the bugs present in the data and various object oriented metrics for security have been developed. The proposed model dissects the given metrics sets into effective and non-effective metrics. Effective metrics are then utilized for giving weights to the test cases, further with the help of weights obtained the test suite is minimized. The performance results of proposed approach are encouraging.
Professional Preparation of Teachers for New Models of Student Practical Training

In the professional work of practical training teachers, the role of a mentor is becoming increasingly important as one of the factors to support the more complex skills that students need to achieve in the twenty-first century. In this sense, the professional and pedagogical competence of their teachers plays a significant role in the realization of professional practice of students. However, many vocational teacher training initiatives are not effective in supporting changes in practice and student training. In order to identify the characteristics of effective professional development, this paper addresses the problems of methodologically organized training that would show a positive correlation between professional development of teachers, practical training and students training outcomes. The features of these approaches have been identified, and descriptions of some models described in order to inform those who want to understand how to stimulate successful strategies for realization of practical training.
Video Forgery Detection based on Metadata Analysis and Double Compression

With the advancement of technology nowadays integrity of digital media has been questioned in many ways. With the help of technology, the integrity of digital video can be disturbed in many ways. Integrity of a video cannot be seen with naked eyes. Its somehow can be determined whether a video is tampered with using Double Compression method. Digital Video can be analyses frame by frame to check any frame insertion, deletion or fade in or out throughout the whole video. Input video will be separated into several frames and converted into grey-scale to improve the efficiency of processing each frame and then each frame will be compared with one before and after frame to check the amount of changes frame to detect any tampering occurs throughout the video flow. To check the integrity of a video double compression method is used and it shows significant result different when check with video which tampered and not tampered. Several tests have been taken throughout the project. The method was tested with white noise as well data gather from other sources for testing purpose. With the use of this method, we can ensure the integrity of the digital video that when it plays as important role as an evidence or proof in certain circumstances.
Comparative study of SVM &amp; KNN for signature verification

Signature is a unique identification of an individual and is a traditional means of human identification. It is very useful in legal activities in the physical absence of the person. Signature Verification is mostly used in forensic document analysis. In this paper, a comparative study is done on Support Vector Machine (SVM) & k-nearest neighbor algorithm (KNN) to find the accuracy of the algorithms. Genuine and Forged signature is taken to create training and testing dataset. Signature pixel values are stored in the dataset to train the algorithm. In this paper we are implementing a study between both the algorithm to find the advantages and disadvantages. Confusion matrix and ROC Curve is used to compare both algorithms accuracy and performance.
Performance Assessment of Bug Fixing Process in Open Source Repositories

Software bugs are inevitable. Large software projects often use bug tracking systems in order to collect, organize and keep track of reported bugs. The primary objective of using these bug tracking systems is to expedite the bug fix process which further improves the overall quality of software product. However, managing bug tracking systems incurs additional cost and efforts. Hence, it is important to monitor the performance of the bug fix process which is followed in bug tracking systems. The aim of this paper is to identify the performance characteristics of the bug fixing process in open source bug repositories. Various performance metrics have been investigated which measures the bug fix process quality. For experimental evaluation, bug reports are collected from Bugzilla repository of Mozilla Firefox, Eclipse, NetBeans and Open Office projects. It has been found that overall 45.67% of bug reports are closed with fixed resolution. Rest 54.33% bug reports keep lying in bug repository unresolved thereby creating an extra backlog of work for software developers. Further, the root causes have been determined which keeps these large portions of bug reports lying in bug repositories opened and found that Non-Reproducible bugs are the most deteriorating factor in bug fix process and affects the fix process performance negatively.
Machine Learning: A Contribution to Operational Research

In this work, we integrate computational techniques based on machine learning (ML) and computational intelligence (CI) to conventional methodologies used in the Operational Research (OR) degree course for Engineers. That synergy between those techniques and methods allows students to deal with decision-making complex problems. The primary goals of this research work are to present potential interactions between the two computational fields and show some examples of them. This is a contribution to engineering education research where we present how ML techniques, such as neural networks, fuzzy logic, and reinforcement learning are integrated through applications in an OR course, being able to increase the approach of more complex problems in a simpler way compared to traditional OR methods. The current paper is a different proposal for OR courses that uses the symbiosis between mathematical models employing computer simulations, CI and different hybrid models.
Digital Watermarking Techniques and its Application towards Digital Halal Certificate: A Survey

The ease of alterations, distributions and duplications of a digital data makes the multimedia security to be crucial. Digital image watermarking is used in the multimedia security field in order to secure the ownership of a multimedia content from being forged by the illegal distribution and duplication. This survey paper upholds the techniques and algorithms that is used in digital image watermarking. In brief, digital image watermarking will secure the secret information by embedding it in the original data in order to defend the copyrights and also the ownership of the original multimedia data. Spatial domain or on the medium of the wavelets are types of techniques in digital image watermarking. The spatial domains works on the pixels of the image while the frequency domains process the transform coefficient of multimedia data. Each types of techniques represents different types of implementation. In this survey paper, the advantages and disadvantages of both techniques will be explained further as it is the most crucial part for this title. The watermarking architecture and the types of attacks towards the watermarked media is also further explained in this paper. This paper also includes the previous study regarding the digital watermarking towards the Malaysian Halal Certificate using a different methods and algorithms such as Spread Spectrum. Several previous papers has been referred to in the completion of this review paper to ensure the stability of this research.
Evaluation of Human-Computer-Interaction Design in Production and Logistics by Using Experimental Investigations

HCI, as the cooperation of humans and machines in socio-technical systems, is playing an increasingly important role in production technology and logistics applications. This cooperation will lead to a higher occurrence of interaction among these actors in production and logistics work. One of the central challenges in this context is the development of interfaces between humans and machines. In this paper, the evaluation of HCI systems is examined based on three use cases. The participation of users in the evaluation of HCI system development is investigated by means of the design of human work in Cyber-physical production systems, the use of Augmented Reality as a promising new technology in production scenarios, as well as on the example of a serious game-based decision support in urban logistics. The implementation of user-based evaluation studies in the use cases shows that such studies can increase user acceptance of HCI systems in the context of production and logistics. Especially in connection with new technologies, this can be achieved by the early development of prototypes and the participation of the user over the entire development process.
Machine Learning Algorithms for building Recommender Systems

Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social- network, health, education, travel, food or tourism. Till date several recommendation approaches have been introduced, the most popular being Content-based filtering, Collaborative filtering, Hybrid and Knowledge based systems. Hybrid systems combine multiple recommendation techniques to enhance the performance of a single recommendation approach and to do so, they follow several hybrid models. This article presents an overview of the state-of-the-art Recommender systems with the prime focus on hybrid recommender systems. Further, different categories of hybridization models are studied, and the existing work is classified categorically based on the hybrid model they follow, and the Machine learning algorithm used.
Character Recognition Algorithm Based on Fusion Probability Model and Deep Learning

Abstract The authors proposed a character recognition algorithm to improve the recognition efficiency and recognition accuracy in character recognition. The algorithm is based on a deep belief network classifier. In the character recognition, a comprehensive feature model is established firstly by combining the histogram Gabor feature, grid level feature and gray level co-occurrence matrix. Subsequently, the deep belief network trains the feature model. Finally, the probability model is used to judge the recognition symbols. The algorithm is tested with 74 k data set and is compared and analyzed from three indexes: false acceptance rate, false rejection rate and accuracy. The data set simulation and comparison with other algorithms indicate that the recognition system based on the probability model and depth learning has higher accuracy and better performance.
Realistic Face Image Generation Based on Generative Adversarial Network

Using a computer to generate images with realistic images is a new direction in current computer vision research. This paper designs an image generation model based on the Generative Adversarial Network (GAN). This paper creates a model - a discriminator network and a generator network by eliminating the fully connected layer in the traditional network and applying batch normalization and deconvolution operations. This paper also uses a hyper-parameter to measure the diversity and quality of the generated image. The experimental results of the model on the CelebA dataset show that the model has excellent performance in face image generation.
Knowledge and Human Development Authority in Dubai (KHDA) Open Data: What Do Researchers Want?

Government open data is a booster for government performance, transparency, and innovation. The purpose of this study to investigate how researchers use Dubai open data on Dubai Pulse platform, Using Dubai open data for Knowledge and Human Development Authority in Dubai (KHDA) shows was not covered by academic researchers. To find the reasons behind that, a systematic review were conducted for all published papers and academic researchs in the period of 2015 until 2019. 68 articles were identified, but only 38 articels passed the including/excluding criteria, and considered within this study. The majority of researchers focus mainly on detailed students' datasets. on the other hand the published KHDA open dataset provide many usefull datasets but it's not including detailed students datasets; a gap between the required datasets by researchers and what KHDA open dataset provide is found. Therefore, to bridge the gap and improve researches in educational domain, KHDA needs to provide more students' details that supports machine-readable format to become useful and usable for researchers.
Smaller Compressed Suffix Arrays†

Abstract An alternative to compressed suffix arrays is introduced, based on representing a sequence of integers using Fibonacci encodings, thereby reducing the space requirements of state-of-the-art implementations of the suffix array, while retaining the searching functionalities. Empirical tests support the theoretical space complexity improvements and show that there is no deterioration in the processing times.
Investigation on Different Color Spaces on Faster RCNN for Night-Time Human Occupancy Modelling

Convolutional Neural Network (CNN) has been shown to have worse performance for images with low illumination. This means that object detection methods using CNN, such as Faster RCNN, will have problem detecting humans during low lighting conditions. This will lower the accuracy for human occupancy modelling during night-time. In general, most methods that has been employed to improve the performance of Faster RCNN for night-time human detection depends on RGB color space. Currently, there are few papers investigating the effect of using various color spaces including RGB color space to perform Faster RCNN night-time human detection. The objective this paper is to investigate the effect of using various color space on the performance of Faster RCNN night-time human detection. This paper will be investigating the effect of using RGB, CIEXYZ, and CIELAB color space on night-time human detection. KAIST multispectral pedestrian dataset will be used for training and evaluation. Faster RCNN will use Resnet101 base network and using ImageNet pretrained weight trained using RGB images. Using log-average miss rate as evaluation metric, night-time human detection using CIEXYZ achieves 54.93% while RGB achieves 54.42%. The results of this paper show that CIEXYZ have similar performance to RGB for night-time human detection.