Reviewerly/exHarmony huggingface dataset:
Towards the Bicentric Architecture of Cybernetic Models of Sustainable Development of Large-Scale Systems

The paper presents an approach to studying the key laws (as regularities) of the development of the global computer environment based on a universal model of seamless-programmable and cybersecurity distributed computing in a mathematically homogeneous algorithmic space of arbitrarily large computer networks. The proposed approach made it possible to identify the fundamental factors of the joint informational influence of human and computer environments on the processes of digital transformation of large-scale sociotechnical systems. The basis of this approach is the general bicentric model and the corresponding computer-network architectures of its implementation, which open up opportunities for the agreed functioning and sustainable development of large-scale systems of new generations in the conditions of global information strongly-connectedness.
Layout Guide for Design of a Novel Wheelchairs with Circumferential Linkage using Microsoft Word

Abstract In recent decades, the aging of the world’s population has intensified, and the number of people with disabilities caused by various disasters and diseases has gradually increased. Most of the elderly, frail and disabled people will choose wheelchairs as their means of transportation. However, ordinary wheelchairs are unable to climb stairs, especially in cities, which seriously limits the range of activities of wheelchair users and affects their daily life. Hence, it is of great significance and value to design an intelligent building climbing wheelchair with appropriate price, stability and safety. This paper designs a multi-functional planetary height adjustable building climbing wheelchair with simple structure and low price. Firstly, a multi-functional building climbing wheelchair based on planetary gear train is designed by using modular design idea, including ground walking mechanism, in place steering mechanism, center of gravity adjustment mechanism and Seat leveling mechanism. The working principle and design characteristics of each mechanism are analyzed in detail. Then, through the analysis of the mathematical model of the climbing mechanism, the dimensional parameters of the three-star wheel set are determined. According to the power demand of wheelchair, the power system is selected and analyzed. It provided a new possibility for the wheelchair application and also for those in need.
The Conceptual Framework of Megalithic Cultural Information Visualization

Improper method of data storage, lack of complete and up-to-date information sources, weakness in information sharing and decimation, and poor visual representation have attributed to loss of megalithic cultural heritage. Thus, the study aims to design a web-based application that visualises megalithic culture information in Malaysia, specifically in Negeri Sembilan. This study used the Rapid Application Development (RAD) research methodology, which consists of four phases: requirements planning, design development, testing, and implementation. The RAD model was selected to ensure that the design and development processes of information visualisation were conducted systematically. This study has discussed each phase in detail, highlighting specific activities involved. These phases were optimised using the Gestalt principles and Visualisation techniques, the main component of the conceptual research model. The Gestalt principles play an important role in ensuring the application development layout and web interfaces by considering the psychological aspects and experience of the users. Furthermore, visualisation techniques, including photos, maps, charts, and drawing illustrations, were applied to enhance the delivery of information for easier understanding and interpretation by the users. The conceptual framework was designed to reflect the overview of the application development and highlight the components and elements involved. In this light, Negeri Sembilan megalithic culture information should be highlighted to the public as it is a unique treasure and culture practised by previous generations. Thus, the web applications produced in this study are expected to encourage the digital preservation of Megalithic culture in line with current technological developments.
Open Polar

Data from the Polar Regions are of critical importance to modern research and decision makers. Regardless of their disciplinary and institutional affiliations, researchers rely heavily on the comparison of existing data with new data sets to assess changes that are taking effect. However, in a recent survey of 113 major polar data providers, we found that an estimated 60% of the existing polar research data is unfindable through common search engines and can only be accessed through institutional webpages. This raises an awareness sign of the need of the scientific community to harvest different metadata related to the Polar Regions and collect it in a homogenous, seamless database and making this database available to researchers, students and publics through one search platform.&#x0D; This contribution describes the progress in an ongoing project, Open Polar, started in 2019 at UiT The Arctic University of Norway. The project aims to collect metadata about all the open-access research data, articles and other scholarly documents related to the Polar Regions in a homogenous and seamless database. During the first six months of the project, the beta version of the user-interface was established, with a search by map and an advanced search function. An extensive geo-database that includes thousands of polar locations and their geographic information was collected from different sources. The geo-database together with a list of keywords (i.e. on sources, indigenous peoples, languages and other polar-related keywords) will be used in the filtration process.&#x0D; A Reference Board was formed, and the first board meeting took place in April 2020. The geographic definition of “Polar Regions” was defined in order to include most of the current geographic definitions of “Arctic”. The project is still facing some challenges that include for example integration with non-standard data sources who do not use Dublin Core Metadata schema, or are not harvestable through the Open Access Initiative’s standard protocol for harvesting (OAI-PMH).&#x0D;
Distractor-aware Visible and Infrared Tracking based on Multi-feature Fusion

It is still challenging for tracking the object of interest in complex environments. Combinations of different features can extract more discriminative information for the tracking problems. Fusion of the visible and infrared features is a feasible way since they are complementary. In this paper, a distractor-aware visible and infrared tracking method is presented. The multi-feature distractor-aware object model is designed by combining the information of the object, the distractors, the color feature of the visible image, and the intensity feature of the infrared image. Two probability map fusion methods are presented including the fixed weights and the adaptive weights based on the peak to sidelobe ratio (PSR). A scale updating strategy based on feature selection is also designed. Experiments on the public Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS) datasets are conducted. The results demonstrate that the proposed tracking methods perform well in terms of accuracy and robustness in real surveillance scenes and show better results compared to several state-of-the-art methods.
AI and Military Operations’ Planning

The key to the success of a military operation rests in its planning. NATO’s planning activities are conducted in accordance with the operations planning process (OPP) and implemented by so called operations planning groups (OPG). The pace of technological progress has a dramatic impact on the way the planning process in the military is conducted. In this context, advances in sensor systems and artificial intelligence (AI) play a major role. The leading militaries already use AI-driven tools assisting different steps and phases of planning. The same militaries have also already allocated significant budgets to advance AI research and to develop AI assets, such as automated weapon systems and automated planning assistants. However, the real explosion of AI research and AI assets is yet to pragmatize. From a different perspective, planning-wise AI is frequently viewed as a potential tool to assist human operators. Notably, advances in AI might lead to the introduction of personal planning assistants, endowed with capacities unmatched with those of human operators. These developments weigh on the OPP. The objective of this chapter is to examine how and to what extent.
An Error Compensation Method for Improving the Properties of a Digital Henon Map Based on the Generalized Mean Value Theorem of Differentiation

Continuous chaos may collapse in the digital world. This study proposes a method of error compensation for a two-dimensional digital system based on the generalized mean value theorem of differentiation that can restore the fundamental performance of chaotic systems. Different from other methods, the compensation sequence of our method comes from the chaotic system itself and can be applied to higher-dimensional digital chaotic systems. The experimental results show that the improved system is highly consistent with the real chaotic system, and it has excellent chaotic characteristics such as high complexity, randomness, and ergodicity.
When External Knowledge Does Not Aggregate in Named Entity Recognition

In the different areas of knowledge, textual data are important sources of information. This way, Information Extraction methods have been developed to identify and structure information present in textual documents. In particular there is the Named Entity Recognition (NER) task, which consists of using methods to identify Named Entities, such as Person, Place, among others, in texts, using techniques from Natural Language Processing and Machine Learning. Recent works explored the use of external sources of knowledge to boost the Machine Learning models with sets of domain specific relevant information for the NER task. This work aims to evaluate the aggregation of external knowledge, in the form of Gazetter and Knowledge Graphs, for NER task. Our approach is composed of two steps: i) generation of embeddings, ii) definition and training of the Machine Learning methods. The experiments were conducted on four English datasets, and their results show that the applied strategies for external knowledge integration did not bring great gains to the models, as expressed by F1-Score metric. In the performed experiments, there was an F1-score increase in 17 of the 32 cases where external knowledge was used, but in most cases the gains were lesser than 0.5% in F1-score. In some scenarios the aggregated external knowledge does not capture relevant content, thus not being necessarily beneficial to the methodology.
A New Hybrid filtering technique for Despeckling of Ultrasound Images

Medical diagnostics can benefit from ultrasound (US) imaging. Because it is non-invasive and inexpensive, the US is recommended over other medical diagnosis techniques. The inclusion of speckle noise in US images reduces the utility of the images. Correct diagnosis can be aided by a technology that eliminates speckle noise in US images. While reducing the speckle noise, our approach should preserve the critical structural figures cutting-edge US images. The method used for this work can reduce speckle noise in denoised image while still preserving structural information. The suggested method successfully denoises synthetic and real ultrasound pictures, as evidenced by the consequences of numerous measurable tests and optical review. A comparison of existing speckle reduction strategies was carried out in this study. We compared anistropic diffusion, Frost diffusion, Lee, and the Hmedian filter. The approaches are initially tested on simulated photos to compare the results. Exponential thresholding outperforms the other strategies when the noise variance is bigger. Furthermore, the hybrid filter is found to have equivalent enactment and hence functions well concluded a wide series of noise variation.
Localization in quantum walks with periodically arranged coin matrices

There is a property called localization, which is essential for applications of quantum walks. From a mathematical point of view, the occurrence of localization is known to be equivalent to the existence of eigenvalues of the time evolution operators, which are defined by coin matrices. A previous study proposed an approach to the eigenvalue problem for space-inhomogeneous models using transfer matrices. However, the approach was restricted to models whose coin matrices are the same in positions sufficiently far to the left and right, respectively. This study shows that the method can be applied to extended models with periodically arranged coin matrices. Moreover, we investigate localization by performing the eigenvalue analysis and deriving their time-averaged limit distribution.
Using a Data-Driven Context Model to Support the Elicitation of Context-Aware Functionalities – A Controlled Experiment

Background: Context modeling to support the elicitation of context-aware functionalities has been overlooked due to its high complexity. To help overcome this, we have implemented a data-driven process that analyzes contextual data and generates data-driven context models. Objective: We aim at investigating to which extent a data-driven context model supports the identification of more complex contexts (i.e., contexts that combine several contextual elements) and unexpected context-aware functionalities. Method: We used a one factor with two treatments randomized design with 13 experienced software engineers. Given a specific system-supported user task, the participants were asked to come up with requirements that describe context-aware functionalities to improve the user task. Results: Use of the data-driven context model increased the average number of contextual elements used to describe requirements from 1.77 to 4.23. No participant from the control group was able to identify by themselves any of the contexts included in the model. All comparisons between groups had sufficient effect size and power. The participants regarded the data-driven context model as a useful tool to support the elicitation of context-aware functionalities. Conclusion: The data-driven context model has shown potential to support the identification of relevant contexts for given user tasks.
Smart Metering Systems

Blockchain technology is one of the most vital and exciting technologies available today. It can transform the business world and the organizations' functions. It offers several possibilities to flourish existing businesses, grow entirely new ones, and severely disrupt traditional organizations. This chapter aims to study the applications of blockchain in procurement, to improve at the same time effectiveness and sustainability, and to define its critical success factors. The chapter presents a model and then describes its application in a real business case. It highlights the critical challenges in this blockchain implementation and the potentials of blockchain in managing sustainable procurement in support of utilities. This blockchain application is for smart metering. The blockchain solution contributes to the three bottom lines: economic, social, and sustainable. It can help support sustainable procurement, reduce errors, improve organizational functions and procedures, and prevent fraud. The chapter includes some challenges and their possible remediations.
A Mobile Robot Tracking Controller Design and Implementation on ROS-MATLAB based Experiment System

The aim of this paper is solving the problem of an autonomous mobile robot indoor trajectory tracking based on Simultaneous Location And Mapping (SLAM) technique. Faced with the high-cost experiment equipment, this paper introduces a solution via lower-cost hardware. In addition, the system improves mobile robot tracking rate of accuracy by an adaptive controller. Therefore, it can help to rapid development for self-driving industry regarding cost-effectiveness. Similar with a human driver, the adaptive controller optimizes the mobile robot movement based on Lyapunov direct method. In addition, the controller implementation task was finished on a practical low-cost mobile robot, Turtlebot2. In future, the research will focus on a path-planning algorithm when the mobile robot moving on the way and facing with coming obstacles. This technology can be applied to the self-driving car in industry, and improves the feasibility of outdoor automatic logistics.
Radar Echo Image Prediction Algorithm Based on Multi-scale Encoding-Decoding Network

The traditional technology of radar echo image extrapolation for rainfall nowcasting faces such problems as insufficiently high accuracy, the incomplete analysis of the data on radar echo images, and the image blurring from the stacked LSTM (Long Short-Term Memory). In order to more accurately and clearly predict the radar echo image at a future moment, an adversarial prediction network based on multi-scale U-shaped encoder-decoder is proposed. To overcome the problem of insufficient details of the predicted image, the generator of the network adopts a U-shaped encoder-decoder structure with jump-layer connection. At the same time, in order to capture the echo movement at different scales, multi-scale convolution kernels is introduced to the encoder-decoder units. Then the conventional discriminator structure is improved and stacked ConvLSTM(Convolutional Long Short-Term Memory) layers were proposed to classify sequence. Based on the prediction of next ten frames from the given ten frames of images, this paper tests the network on the SRAD(Standardized Radar Dataset), and compares the prediction results of different networks. The test results show that the proposed model reduces image blurring, enhances the prediction accuracy while retaining sufficient prediction details .
Netiquette concept at the assessment of college student social competence with TOPSIS method

Social competence of students college is important during the process of implementing the practicum, from social competence we can find out capable the students are in several ways such as the ability to collaborate, speak in forums, reduce social conflicts in the environment, make a network and much more. In this study, it provides a model solution for assessing social competence in students college when carrying out practicum. The purpose of the study was to develop an assessment model by measuring students' social competence during the implementation of the practicum. The number of correspondents in the study was 78 students taken from the number of practicum students in Aerospace Engineering. The data analysis technique uses the TOPSIS method, where the results of this study will obtain the best ranking of 78 KP students who have the highest social competence value. The model of the assessment can be used by lecturers of the Aerospace Engineering, Adisutjipto Aerospace Technology Institute during the practicum process or after. With this assessment model, the advantages of the Adisutjipto Aerospace Technology Institute can synergize in creating graduates with a global and humanistic character.
Car Image Matting

At present, matting technology has achieved excellent performance under laboratory conditions, but they still fail to meet the needs of some practical businesses. In this paper, according to the practical problem that the contour of the matting object is not smooth when applying matting technology to car data set, we add the smoothing loss, location information, and detailed information based on the original algorithm. The experimental results show that our improvement has achieved nice results.
Towards Efficient Aerial Object Detection: Techniques, Datasets and Challenges

In the past decade, object detection has gained significant attention in computer vision due to variety of its applications including picture retrieval, object tracking, self-driving cars, security, activity recognition, traffic surveillance and many more. Latest research in computer vision is making remarkable progress in multiple directions; aerial object detection is one of the foremost. Object detection in natural scenes has been extensively researched however, challenges remain in aerial object detection due to factors such as variation in scale and shape of objects, occlusion, monotonic appearance, and arbitrary orientation of objects in a scene. Literature highlights deep learning models surpass all others in terms of accuracy and efficiency for aerial object detection and recognition. It is well-known that datasets directly impact deep learning models as deep learning itself is a data driven approach. It is therefore crucial to analyze not only the object detection algorithms in aerial imagery, but also the remote sensing datasets that are available. In this paper, we analyze multiple state-of-the-art models that are used in the field of object detection specific to aerial images. We also present a detailed timeline of the prominent aerial imagery datasets; analyze them in detail and present their strengths and weaknesses. At the end, we discuss various challenges present and future research directions in this field. This research work will further pave way for future advancement in the field of aerial object detection and recognition.
Research on Neural Networks for Landmark Detection in Robotic Vehicle Navigation Task

The article discuss the problem of visual landmarks detection on video images received by a fish-eye camera using neural networks. The problem is important in the framework of navigation of the autonomous vehicle according to the image sequence from the camera using a minimum amount of sensory information. A fish-eye camera lens allow system to view and detect objects in a much wider range of angles, increases the time of tracking an object and using it to clarify the position. To choose the most relevant solution, neural network methods for object detection of different architectures and creation time were considered. To perform an experimental research, a dataset was formed for landmarks detection, containing images of various sizes from cameras with different sensors and types of wide-angle lenses. The collected data set was used to train and test selected neural network architectures. Based on the analysis of the results, a conclusion was made about which of the presented architectures can be used in the navigation task when object detection process performed on embedded and desktop PC.
Evaluating Deep Neural Nets and Optimized Hyperparameters Random Forest for Decision Support Systems

The use of telemarketing by business enterprises can be intrusive to customers and time consuming for the businesses. A way to mitigate it, is to use an intelligent decision support system (DSS) that will effectively target potential customers likely to buy into the business. Therefore, there is need for a targeted, effective, and trained predictive Decision Support Systems DSS. This research work uses deep learning and optimized hyperparameters Random Forest to build predictive DSS and compared the performance accuracy and ROC Area to that of Support Vector Machines (SVM). The research uses a dataset from a Portuguese bank collected over a period of 2008 to 2013 at the onset of a financially difficult time for banks. The experimental results show that there are no significant differences between the performance accuracy of the different deep learning algorithms. However, the deep learning algorithms performance accuracy and ROC Area are better than that of SVM (which was used in a previous work on the same dataset). The optimized hyperparameters Random Forest has the best accuracy of 99.74% and ROC Area of 100% compared to the best deep learning algorithm with 90.87% (for RNN and Seq2Seq), and ROC Area of 92.64.