Reviewerly/exHarmony huggingface dataset:
Computational Speed and Qualitative Assessment of Real-Time Image Stitching Algorithm

In remote sensing and environmental mapping, Unmanned Aerial Vehicle (UAV) has been used extensively to capture images. For many years, digital maps are generated by using a method called image stitching. It is a method of combining multiple images to produce a segmented panorama. Since this method has been commonly used, many users produce an accurate map by using commercial software. However, a downside of this commercial software is a long computational time which is not appropriate for immediate mapping activities at chaos areas in particularly during rescue missions. This paper proposes a method to speed up the process of map generation by using a revised real-time image stitching algorithm including the qualitative assessments. In this research, the images are extracted from a video taken by a drone that flies in a dedicated flight path. These videos are then immediately transmitted to a ground station for further image processing and computation. The overlapping images are stitched and later undergoes features extraction process to identify the common features between the images. These common features are used to compute homography matrix which beneficial for image wrapping. The finding of this study suggests that ORB and AKAZE are the most suitable descriptors to be used in real-time image stitching because of their fast computational speed at adequate level quality. For instance, at 5 number of skip frame, ORB is at least 2-fold faster than AKAZE and goes up to 10-fold faster than BRISK.
Assessment of the Viability of a Biometric Characteristic in the Context of Biometric Authentication on Mobile Devices

The issue of safe utilization of mobile devices is becoming an increasingly important problem, among others due to the widespread use of such devices to access sensitive data (such as electronic documents or banking data). In our work we analyze the use of biometric techniques in order to secure a mobile device, with particular emphasis on the viability of selected biometric characteristics. For this purpose, we investigate the possibility of applying machine learning models to assess the authenticity of a biometric characteristic. Results of our tests have shown that the most effective method of assessing the viability of a biometric characteristic involves blink and smile detection.
An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures

We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm. An extremely challenging, yet simple to understand problem landscape is adopted where the probability of randomly finding a solution is approximately one in a trillion. A number of learning mechanisms operating on variable-length structures are implemented and their performance analysed. The social learning setup, which combines forms of both social and asocial learning in combination with evolution is found to be most performant, while the setups exclusively adopting evolution are incapable of finding solutions.
Enhancing Integrity Modeling for Emotional Conversation Generation

Establishing an open-domain emotional conversation generation with content and emotional dependence is a key step toward intelligent interactions. However, the researchers only dedicated to adding the thematic content or emotional words and handling both factors are not yet properly solved. In this article, we aim to address the issue of integrity in emotional content which indicates the response has a dependency relationship between theme and emotion. We propose a thematic–emotional conversation model, which consists of parallel channels and joint encoding, to maximize integrity in conversation generation. We first use the encoding of the parallel channel to strengthen the decoding weight of the theme and emotion. The joint encoding of the dual attention mechanism and the thematic emotional tradeoff strategy are involved in the encoding to ensure that the theme complements the emotional content during the response generation. After that, the bidirectional training interactions between post and response are calculated to derive the new response inference vectors. Experiment results suggest that the proposed model is effective not only in content integrity but also in emotion.
The Resource Databases Management System of New Fundamental Surveying Based on Integrating Multi-Source Information

As the conception of new fundamental surveying is introduced, our country's provinces and cities have also put forward new requirements for the construction and management of fundamental surveying resource databases. Due to the multi-source and heterogeneous characteristics of the new fundamental surveying data, how to effectively store and manage the data has become a difficult problem. Taking Zhejiang Province as an example, this paper studies the structure and storage mode of multi-source fundamental surveying resource data, analyses the requirements of the resource databases management system of new fundamental surveying, introduces the function and application of the system. Finally, this paper analyzes and summarizes the development prospects of the system.
Using a case study as a learning vehicle in improving the learning outcomes of a course in SoSE Research Methods

In our master programs for industrial economy and systems engineering, we prepare students for their master's project with a course in research methods. We concluded in 2020 that the course was too abstract without a running case. Following up on previous research, we report how we improved the intended learning outcomes by using a case study as a learning vehicle. The selected case study was on Technical leadership. In this paper, we document and discuss how well we achieved the intended learning outcomes for the course. Main conclusion is that the running case needs more preparation to ensure that students can collect sufficient data for use in the course.
Serials and Continuing Resources

Chapter 4 of the book Assessment Strategies in Technical Services, edited by Kimberly A. Edwards and Michelle Leonard. This chapter focuses on the main area of assessment for continuing resources: conducting a serials review. The majority of the chapter is given over to describing the process for undertaking large-scale collection review projects. The focus on serials includes journals, periodicals, standing orders, newspapers, and microfilm subscriptions; it omits databases, streaming resources, and other aggregated online research content. Special consideration is given to the review of large packages of e-journals—typically referred to as “Big Deals.”
Predictive Modeling for Classification of Breast Cancer Dataset Using Feature Selection Techniques

Predictive modeling or predict analysis is the process of trying to predict the outcome from data using machine learning models. The quality of the output predominantly depends on the quality of the data that is provided to the model. The process of selecting the best choice of input to a machine learning model depends on a variety of criteria and is referred to as feature engineering. The work is conducted to classify the breast cancer patients into either the recurrence or non-recurrence category. A categorical breast cancer dataset is used in this work from which the best set of features is selected to make accurate predictions. Two feature selection techniques, namely the chi-squared technique and the mutual information technique, have been used. The selected features were then used by the logistic regression model to make the final prediction. It was identified that the mutual information technique proved to be more efficient and produced higher accuracy in the predictions.
Design of a CAVE Immersive Visualization System for Teaching and Learning: A Case Study of Air Cargo Logistics

The increasing availability of immersive virtual reality (VR) systems offers new opportunities and challenges in teaching and learning with the sensory experience of sight, touch, and interaction. To enrich learning experience and student interest in learning, an effective instructional design is crucial in converting the real environment into the virtual world in immersive VR systems. Therefore, in this study, a framework for designing a CAVE-based immersive visualization system is presented. By investigating critical instructional design strategies, guidelines on content design and development in the immersive VR-based learning environment are emphasized in order to achieve the desirable learning outcomes. With the aim of developing students’ problem-solving and learning processes, a case study in air cargo logistics is discussed to demonstrate how to facilitate instructional design in an immersive VR system. It is expected that insights into interactive teaching and learning can be provided through the immersive VR system enhancement.
Protected JPEG Flows Reversible Data Hiding

Among different techniques for reversible information stowing away (REVERSIBLE DATA HIDING) in JPEG pictures, just rate-mutilation, for example the picture quality with given payload, is contemplated during calculation planning. Notwithstanding, record size development is another significant assessment metric for JPEG REVERSIBLE DATA HIDING techniques.
A Blockchain-Based COVID-19 Protection Framework

Diseases like COVID-19, SARS-CoV2 and Ebola have led to loss of lives, economic breakdown and mass hysteria. Governments and healthcare workers are on an incessant strive to isolate and curb the community spread of the virus. Several countries have employed mobile-based applications for tracing contacts, distributing COVID protection guidelines, helping people self-diagnose and forming infection patterns and control groups. These existing strategies, however, do not provide end-to-end privacy in the collection and handling of critical patient data. Moreover, they are vulnerable to linkage attacks. In this paper, we introduce a privacy-preserving COVID-19 protection strategy resting on a decentralized, blockchain-based framework, that aids in contact tracing and social distancing. Our simulation validates that our proposed system maintains the confidentiality of the user.
On the Robustness of 3D Human Pose Estimation

It is widely shown that Convolutional Neural Networks (CNNs) are vulnerable to adversarial examples on most recognition tasks, such as image classification and segmentation. However, few work studies the more complicated task - 3D human pose estimation. This task often requires large-scale datasets, specialized network architectures, and it can be solved either from single-view RGB images or from multi-view RGB images. In this paper, we make the first attempt to investigate the robustness of current state-of-the-art 3D human pose estimation methods. To this end, we build four representative baseline models, where most of the current methods can be generally classified as one of them. Furthermore, we design targeted adversarial attacks to detect whether 3D pose estimators are robust to different camera parameters. For different types of methods, we present a comprehensive study of their robustness on the large-scale Human3.6M benchmark. Our work shows that different methods vary significantly in their resistance to adversarial attacks. Through extensive experiments, we show that multi-view 3D pose estimators can be more vulnerable to adversarial examples. We believe that our efforts can shed light on future works to design more robust 3D human pose estimators.
Eye-Interface System Using Convolutional Neural Networks for People with Physical Disabilities

In recent years, lack of effective communication method is one of the important issues that the people with physical disabilities, such as ALS (amyotrophic lateral sclerosis) face in social life. In this research we constructed an eye-gaze input system based on an image processing method using a PC and a web camera that are inexpensive and easy to install under natural light environment without the risk of illness due to the use of infrared radiation. In our proposed system, convolutional neural networks (CNN) is applied to improve the accuracy for practical use. The CNN in this study estimates the gaze position on the monitor screen from the image acquired from the web camera, and aims to obtain higher accuracy than the conventional system by learning for specific individuals.
Mr Peter Cantor standing in front of a solar mirror

Research School of Physical Sciences, Solar Energy Research Project - Dr. R. E. Barwick, Mr. Peter Cantor & others
Arduino Base Ultrasonic Map -Maker

This work is a supportive one for driverless vehicles which are going to be a boon for upcoming generation. This project is one such work that paves the way to avoid accidents in driverless vehicles. This work uses RADAR based Ultrasonic sensors (HC-SR04) and Servo Motor for the detection of obstacles at desired distance. This also enables the options of sending SMS to the concerned person about the distance of the object, camera to view the live video of the object, also can be equipped with Wi-Fi connection between android and Arduino for monitoring. The other important domains are the defense systems, and this can also be used in the dark sites for navigations
Comparison of fall detection methods and the prospect of model-based: pattern recognition on green computing implementation by using smartphone

Abstract Portable healthcare solution to detect fall is needed for special care people and elderly, especially if they want to live independently. If this solution needs special made device by the factory, it requires huge electric consumption, lots of materials, and can contribute to the environmental pollution. Smartphone is one of the most promising device to be used as fall detection because it has lot of potentials: easy to find, compact, has sensors, and can even be accepted by elderly. There are two main approaches in fall detection: threshold-based method and model-based: pattern recognition method. Threshold-based usually been used in real life because it can be developed quite quickly and use minimal computation process to provide movement classification result (fall or non-fall). Model-based: pattern recognition method (using machine learning) has relatively better accuracy result but requires a long training time, a lot of resources to process, and more difficult to be developed. The researchers present the comparison results of threshold-based and model-based: pattern recognition; and optimizing the model in machine learning so the model has the prospect to be implemented in applications to support green computing.
Multiobjective Parameters Optimization of Extreme Learning Machine Based on MOEA/D

In recent years, Extreme learning machine (ELM) is widely used in data mining analysis and optimization problems. However, most existing ELMs are based on single-objective optimization, which may reduce the generalization performance of the model. To overcome this problem, a multiobjective parameter optimization strategy is proposed for ELM. This strategy simultaneously optimizes model error and generalization performance, which are two conflict objectives. Furthermore, the improved algorithm is called MOEA/D-ELM, which is used for supervised classification problems. The proposed algorithm in this paper has been tested on the UCI dataset. The experimental results show that MOEA/D-ELM is effective.
Convolve4D: A Novelty Approach to Improve Convolutional Process

Abstract Convolutional neural networks (ConvNet or CNN) are deep learning algorithms that can process input images, assign meaning to various aspects or objects in the image (biases and learnable weight) and recognize one image from another. The bigger kernel size will take more time to process the input.We present a novelty way to use a 4D rank tensor to improve a convolutional process. At the early stage of the Convolve4D development, the edge detection with 3×3 kernel and The Laplacian of Gaussian (LoG) with 5×5 kernel size was used to demonstrate the convolutional process improvement. The Convolve4D needs more elaboration to be used into a CNN algorithm. The advantage of convolve4D is only need 9 loops to calculate 81 outputs, whereas convolve2D need 9 × 9 × 3 × 1 × 7 × 7 = 11.907 loops. The result is 18.5% shorter when using a 5×5 kernel; it reduces from 0.54 seconds to 0.44 seconds for the edge detection convolution process.
Research on Normalization Detection Method of Academic Papers based on Document Rule Tree

Academic papers is an important means to test students' mastery of basic knowledge and professional ability. The quality of academic papers, on the one hand, is relected by the innovation and prefessionalism of content. On the other hand, the normalization of documents is an important index. In order to solve the problem of low efficiency that the paper standardization inspection is basically completed by manual work. This paper proposes an automatic detection framework based on document rule tree (DRT) and can solve the problem well. The practical application results show that good results have been achieved in terms of accuracy and query efficiency.
An Efficient FNN Model with Chaotic Oppositional Based SCA to Solve Classification Problem

In recent years, many studies have been used in feed-forward neural network (FNN) to develop decision-making systems. The primary objective is to get the least error by finding the best combination of control parameters. It has been observed that FNNs using meta-heuristics techniques always converges very quickly towards the optimal positions but suffers from slow searching speeds at later stages of generation. Due to slow convergence, it is a prevalent phenomenon that traditional optimization does not ensure to find global optima. As a result, it falls under local optima. Recently, another meta-heuristic optimization-based algorithm called sine cosine algorithms (SCA) was introduced to solve the aforementioned issues. The algorithm is fundamentally predicated on two trigonometric functions, one being sine and the other being cosine. However, like other traditional approaches, SCA has a tendency to be stuck in sub-optimal regions due to poor exploration and exploitation capabilities. This paper proposes an improved version of SCA named chaotic oppositional SCA (COSCA) by integrating with chaos theory and oppositional based learning into the SCA optimization process. It is an incipient training method employed to train an FNN. Three benchmark problems are used to examine the precision and performance of FNNs equipped with COSCA, COPSO, OSCA, SCA, PSO, and backpropagation. The experimental results showed that, relative to other meta-heuristic optimization techniques, the COSCA technique is able to improve performance.
HSim - A scalable tool for health-data simulation

Remote patient monitoring (RPM) is commonly employed in home environments to monitor a patient's health in compliance with their healthcare plan. Simulating real-life scenarios for testing the health applications is a complex and challenging task, particularly in the RPM scenario, for testing issues related to scalability, which can raise problems that could not be identified during development. Several tools have been proposed to support tests using clinical and biological data of patients, but there exist several gaps, mainly related to scalability and the data sources used for simulation. This work presents HSim, a simulator for the evaluation of health applications in RPM. HSim aims to simplify the simulation of biomedical patient data to provide support for the assessment of infrastructures and services for healthcare applications. System validation tests focused on scalability and the use of real databases demonstrated the potential use of HSim to validate RPM applications.
Engendering Heaven for Scholarly Community: Weaving Platform for Open Access Books through Virtual Domain

The paper is based on a website www.openaccessbooks.webs.com which is a collection of web resources facilitating access of eBooks and other publications including monographs and research reports. The paper visualizes various open access web portals maintained by publishers, international agencies, non-government organizations and governmental institutions. The paper is witnessed a number of databases which host editorially enhanced eBooks and provide free of cost access for the benefit of the societal and educational development around the world. The paper is very much relevant for libraries have less funding assistance for procurement of electronic books for their clients.
Evaluation Research on Online and Offline Blended Teaching of Organic Chemistry Based on Logistic Model

The MOOC-based online and offline blended teaching model has gradually adapted to the needs for educational environment in the new era, and provided conditions for students' individualized learning. Student performance in organic chemistry learning was taken as the dependent variable to investigate the influence of online and offline blended teaching means on the teaching effect of organic chemistry based on the Logistics model. The results show that the online and offline blended teaching model makes it possible for teachers to acquire the teaching feedbacks timely and comprehensively, and enhances the teacher-student interaction and student-student interaction. Moreover, it obviously motivates students' learning initiatives and significantly elevates their attainment level in the organic chemistry course. Meanwhile, this research has also provided an effective teaching model reference for the first-class online and offline blended course construction with respect to organic chemistry.
IG-CEE: An Embedded Information Gain approach to Genetic Algorithms

The classification task is among the most used in Data Mining and it is widely researched nowadays. Some works have already been developed using Genetic Algorithms (GAs) for classification tasks, building simple and small IF-THEN classification rules with good classification results. These methods produce understandable outputs (IF-THEN rules), unlike some of the traditional classifiers, e.g. SVM and Artificial Neural Networks, known as black-box type. In this paper, we proposed a new method called Information Gain-based Computational Evolutionary Environment (IG-CEE). The proposed method IG-CEE extends the GA proposed by Amaral and Hruschka named Computational Evolutionary Environment (CEE). The IG-CEE uses the Information Gain approach to select a better subset of attributes to compose the IF-THEN rules, improving classification accuracy and convergence rates. The IG-CEE was compared with CEE and four (4) traditional classifiers: J48, IBK, Naive Bayes, and SVM. To compare all methods, we built three (3) synthetic datasets, generated by GeNIe software. To realize a fair comparison, all methods were executed, with distinct random seeds, 100 times. For each method, the final result was calculated using a confidence interval with 95% of confidence. The proposed method IG-CEE showed better classification rates in several comparisons.