Reviewerly/exHarmony huggingface dataset:
A Combined Learning-Based Bagging Method for Classification Improvement

Classification is one of the most popular and important data mining tasks. It has been resolved by many various machine learning approaches. Combining different learning approaches has been considered in many existing works to take advantages of each learning approach and to suit a particular application domain. At this moment, their methods have been mainly defined with unsupervised learning and supervised learning. However, they are quite complex with data space exploration and parameter settings. As a result, they are non-trivial to be brought to various application domains. Different from the existing works, in this paper, we propose a combined learning-based bagging method for classification improvement in a simple but effective manner. In our method, partitional clustering and k-Nearest Neighbors (k-NN) are combined with each other to form a base classifier. This base classifier is then a component of a final ensemble model designed in a bagging mechanism. Three new design points that make our method effective are: (1). tackling the difficulties of k-NN by means of cluster-based nearest neighbors, (2). defining a new distance that can take into account both nearest neighbors and density of neighborhoods, and (3). conducting a bagging mechanism to form a better classifier from many ordinary classifiers based on individual combinations. Via an empirical evaluation on several datasets in education and medicine, our method outperforms its variants and other baseline ones with better Accuracy and F-measure results. The method can thus be applied in practical applications for more effectiveness.
Building natural language responses from natural language questions in the spatio-temporal context

With the evolving research in geographic information system (GIS) owing to its ability to support decision makers in different fields, there is a strong need to enabling all users; specialists and non-specialists to profit from this technology. Although, the key impediment to non-specialists is the language to interact with the GIS and especially its embedded geographic database (GDB) which require SQL skills. In this paper we explore a new approach which alleviates nomad GIS users from any formatting effort by only using the natural language as a GDB communication mean. The process is generally two-fold: 1) formatting the natural language user query to be processed by the GDB engine; 2) translating the GDB retrieved answer to a text easily interpreted by all GIS users. The resulting implemented system was integrated to the OpenJump GIS and has been evaluated to give satisfactory results.
Teaching Architecture Design using the “Smart Decisions” Game

Architecture design is notoriously difficult to teach and to learn. Most competent architects in industry have deep knowledge won from long years of experience. But if we want architecture design to be methodical and repeatable, we need better methods for teaching it. Simply waiting for an aspiring architect to accumulate 10 or 20 years of experience is not acceptable if we believe that software engineering is a true engineering discipline that can be taught and learned in a reasonable time-frame.
Monitoring of Production Systems Using Artificial Intelligence Tools

Monitoring of industrial systems is necessary to ensure reliability and long-term availability of equipments. The overall purpose of this study is to identify and evaluate the use of the artificial intelligence (AI) tools in monitoring complex industrial systems and more precisely the artificial neural networks (ANN) or connectionist systems that stand out from the rest by their capacity of learning and solving problems in the same way that a human brain would. This paper is a state of the art of monitoring techniques and neural architectures the most used in static and dynamic monitoring. This work is divided into two main parts: in the first one we will present a comparative study of the different architectures of neuron networks that are most used in static monitoring such as RBF and MLP. This comparison led to the choice of RBF. Since dynamic monitoring (dynamic detection, prognosis) requires taking into account the temporal aspect; we devoted the second part to dynamic neural networks (temporal). The tests done using a dynamic detection benchmark and a prognostic benchmark allowed us to choose RRBF as the optimal neural configuration for dynamic monitoring.
Treasure maps, sustainable development, and the billion-year stability of cratonic lithosphere

&amp;lt;p&amp;gt;Sustainable development and transition to a clean-energy economy is placing ever-increasing demand on global supplies of base metals (copper, lead, zinc and nickel). Consumption over the next ~25 years is set to exceed the total produced in human history to date, and it is a growing concern that the rate of exploitation of existing reserves is outstripping discovery of new deposits. Therefore, improvements in the effectiveness of exploration are required to reverse this worrying trend and maintain growth in global living standards.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Approximately 70% of known lead, 55% of zinc and 20% of copper has been deposited between 2 Ga and recent by low temperature hydrothermal circulation in shallow sedimentary basins. These basins are formed by extension and rifting, which are key manifestations of the plate-mode of tectonics. Despite 150 years of research, the relationship between deposit locations and local geological structure is enigmatic and there remains no accurate technique for predicting their distribution at continental scales.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we show that modern surface wave tomography and recent parameterisations for anelasticity at seismic frequencies can be used to map lithospheric structure, and that sediment-hosted base metal deposits occur exclusively along the edges of thick lithosphere. Approximately 90% of the world's sediment-hosted copper, lead and zinc resources lie within 200 km of these boundaries, including all giant deposits (&amp;gt;10 megatonnes of metal). Incorporation of higher resolution regional seismic studies into global lithospheric thickness models further enhances the robustness of this relationship.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This observation implies that lithospheric architecture imparted by the plate-mode of tectonics is stable over billion-year timescales, and that there is a genetic link between lithospheric scale &amp;amp;#160;processes and near-surface hydrothermal mineral systems. Our new maps provide an unprecedented global means to identify fertile regions for targeted mineral exploration, and provide a clear economic justification for funding targeted seismic arrays, theoretical advances in imaging techniques, and geodynamic studies that improve our understanding of deep-time plate tectonics.&amp;lt;/p&amp;gt;
Stable Computation of Hahn Moments for Large Size 1D Signal Analysis

Hahn’s discrete orthogonal moments are powerful tools for image and signal analysis. Several methods have been proposed to implement Hahn’s moments, but their implementation remains limited for high orders by the problem of the propagation of numerical errors. This problem is due to rounding errors during recursive computations of Hahn’s polynomials under machine (using Matlab, C++, Java…). In this paper, we propose a stable computation of Hahn polynomials at high orders based on the modified Gram-Schmidt orthonormalization process. the proposed method significantly reduces the propagation of numerical errors and therefore preserves the orthonormality property of Hahn polynomials. The results obtained show the stability of Hahn moments obtained by the proposed method for the reconstruction of large 1D signals.
Extended Kalman filter steady gain scheduling using k-means clustering

This paper studies the gain scheduling problem for extended Kalman filter (EKF). To save throughput, the steady gain is usually used for Kalman Filter. In the context of EKF, there is no universal steady gain. In this paper, we propose a methodology to schedule the steady gain for EKF. The idea is to offline linearise the nonlinear model at various operating points, and for each of the linearised systems, to compute the steady gain corresponding to conventional Kalman filter by solving the corresponding algebraic Riccati equation. The operating space is then divided into multiple zones, through k-means clustering algorithm, so that within zone, the steady state gains are close to each other. For real time filtering, the centroid of each zone is used as gain for correction step, instead of computing the time-varying gain online, hence saving throughput. We demonstrate the proposed methodology in a two-state nonlinear system.
PENGARUH MODEL PEMBELAJARAN MULTIMEDIA INTERAKTIF DENGAN PENDEKATAN KONTRUKTIF PADA MATA KULIAH TEKNIK DIGITAL

With multimedia learning, students' understanding of digital engineering courses will be easier because the source of information is no longer focused on text originating from books but more broadly. The results of interviews conducted by researchers with lecturers supporting digital engineering courses at STEKOM, say that learning what is done in class only uses powerpoint in the delivery of lecture material even with the teaching material module, as a result students feel bored when the learning process takes place and the lecturer has not been able to provide a refresher in delivering the material. Therefore the importance of the role of lecturers following the development of technology in the world of globalization in making interactive learning media using multimedia Keywords:Teknik Digital, Learning, Multimedia Interaktif
Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience

Stereoscopic 3D (S3D) technologies have gained significant attention due to their wide applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D Quality of Experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a data set of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.
Extracting Highlights from a Badminton Video Combine Transfer Learning with Players’ Velocity

We present a novel method for extracting highlights from a badminton video. Firstly, we classify the different views of badminton videos for video segmentation through building classification model based on transfer learning, and achieve high-precision with real-time segmentation. Secondly, based on object detection by the object detecting model YOLOv3, we locate players in a video segment and calculate the players’ average velocity to extract highlights from a badminton video. Video segments with higher players’ average velocity reflect the intense scenes of a badminton game, so we can regard them as highlights in a way. We extract highlights by sorting badminton video segments with higher players’ average velocity, which make users save their time to enjoy the highlights of an entire video. We laterally evaluate the proposed method through verifying whether a segment has admitted objective details such as exciting response from audiences and positive evaluation from narrators.
HUMAN CAPITAL IN THE EDUCATIONAL PROCESS

The appeal to the category of «human capital», its conceptual development becomes particularly topical in the late 20th century. The conditions and requirements of the digital economy, which are actively stated by society, significantly increase the requirements for training graduates of educational institutions at all levels. Special attention is paid to the system of higher professional education. Intensive development of a person’s productive abilities and subsequent formation of its readiness (within the limits of competences) is possible only with significant investment in specific (primarily educational) processes of activity. Thus, investments in education, upbringing, and the formation of a healthy physical body are included in the first phase of creating human capital. The article discusses some aspects of the formation of human capital in the learning process. Human capital as an integral procedural component of the educational process is characterised by the level of intellectual and spiritual development, innovative abilities of students. In the context of higher education, the most clearly considered elements are manifested through a network of innovation parks. What we describe in the article, is the pedagogic innovation Park functioning at Petrozavodsk State University in its activities is subject to the tasks of increasing the availability and quality of education at all levels that meet the requirements of advanced innovative development of the regional economy. The success of innopark’s practical activities is shown in the creation and testing of a large number of methods in the work of the University, which include blocks and elements of forming the human capital of each student.
RETRACTED: Optimization Strategy of Computer Automatic Control System Based on Artificial Intelligence

Abstract The computer automatic control system of artificial intelligence is an important technology which has developed with the development of economy and technology in recent years, and this technology has shown a very important position in all walks of life in our country, especially in the development of the industry, the computer automatic control system of artificial intelligence can not only help to improve the operating efficiency of equipment in the enterprise, but also help to protect the safety of the relevant staff. In recent years, computer control system has become a very important part of modern information system on the support of artificial intelligence technology, its optimization and progress for China’s social development is very important. This paper briefly analyzes the optimization strategy of computer automatic control system based on artificial intelligence, aiming to provide help and reference for relevant personnel in China. [1]
Joint Depth Estimation and Semantic Segmentation with Adversarial Multi-task Network

Some exiting works have explored the promotion between depth estimation and semantic segmentation. These works are usually based on convolutional neural networks, which extract compact features and map the relationship between input and output according to specific tasks. In this paper, we introduce a novel adversarial training strategy, that is, generator produces the semantic segmentation map and depth map, and then the discriminator can judge the authenticity of the synthesized color image, thereby supervising the output of the network during back propagation. We use the adversarial loss combined with the reconstruction loss function to supervise the model, and find that the adversarial loss function which is seen as a global supervision can further optimize the output. We use 200 color images from Kitti dataset with semantic segmentation ground truth as the training set, and train the network in an end-to-end manner. The experimental results show that the adversarial training method is well applied to the multi-task training combining semantic segmentation and depth estimation, and can further improve the quantitative performance of depth estimation.
Mobile Cyber-Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Communications

This research focuses on secure software development of mobile applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP). OWASP maintains best practices on the current industry top ten security threats to mobile and web applications. We develop knowledge graphs based on the two most recent top ten OWASP threat reports. We, then, show how the knowledge graph relationships can be discovered in mobile application source code, specifically Android. From the developed knowledge graph, we analyse 200+ healthcare applications posted on GitHub to gain insights into the cyber-assurance of these mobile software. We specifically examine the source code for one of the OWASP top ten mobile threats, the threat of insecure communications. We find that many of the analysed applications are communicating with potential personal identifying information employing insecure methodologies leaving users exposed to higher risks.
GREY SYSTEM THEORY SEBAGAI ALTERNATIF ANALISIS STATISTIK TRADISIONAL

This Community Service Implementation aims to socialize data processing techniques based on the gray system theory to colleagues in universities. This technique is very helpful in solving the problem of analyzing a fact from phenomena in various scientific fields in real terms which often have uncertainties, related to incomplete data and inaccurate information. This socialization is carried out from a technical perspective, its application and potential problems and challenges in the future, in the context of providing services to the community. Specific targets to be achieved are understanding, knowledge and ability to apply gray system theory. The method used is in the form of counseling with practical delivery through knowledge transfer and the presentation of the roles and functions of the gray system theory so that it is truly acceptable and adds to the essence of knowledge of colleague lecturers and researchers in the university environment. Â  Keyword: Grey system, statistic,
Microphones that predate modern electroacoustics

Our modern understanding of Electroacoustics includes acoustic sensors (microphones) and projectors (loudspeakers) and the electronic amplifiers that form a complete electroacoustic system. The first electronic amplifiers were made with vacuum tubes and became available about a century ago. Thus, we may consider electroacoustics as a technology whose history began at that time. The first microphones were invented before then, but most microphone technologies needed electronic amplification for practical employment. On the other hand, the telephone communication industry, which began in the 1870s, was reasonably mature by the 1920s using a design that did not include vacuum tubes. This presentation will describe the devices used in the early telephone system and how that system could exist without electronic amplification.
Blockchain application in remote sensing big data management and production

The rise of blockchain technology provides new ideas for remote sensing big data management and production and overcomes challenges to data tampering detection, process recording, process accuracy evaluation, and private data protection. The introduction of blockchain technology into remote sensing big data management and production will surely give full play to the value of remote sensing data sources and provide increasingly credible decision -making knowledge for global change research. Therefore, we carried out a prospective study on blockchain-based strategies for remote sensing big data management and product generation. Specifically, we proposed remote sensing big data management rules for metadata entering chain, image files remaining in the original storage, and watermarking to maintain consistency on -chain and off -chain. In addition, we established the UMNI to solve the problem of heterogeneous metadata in multisource data integration and constructed a data block structure containing metadata identification, image encryption paths, and other information. We proposed technical solutions for multisource remote sensing data integration, chain entry, and sharing and established the specific steps and technical details of the blockchain-based product production. Finally, we constructed a logical blockchain-based system serving remote sensing big data management and product generation under a multi -satellite data center scenario.
A Modified I2A Agent for Learning in a Stochastic Environment

The paper proposes and analyses the evolution of a deep reinforcement learning agent in a stochastic environment that represents a simple game. We investigate the use of an embedded planning loop in the training of a model free agent, using a learned model in the style of I2A (Imagination-Augmented Agent), to solve a stochastic grid environment. The performance of the proposed agent architecture is compared against a baseline A2C (Advantage Actor Critic) agent.
Customer Sentiment Analysis Using Cloud App and Machine Learning Model

The customer sentiments are very important to any business, as positive or negative feedback can affect the sales and adoption of the product in the market and subsequently define the product’s success. The monthly active usage of major social media platform such as Facebook is 2.32 billion monthly active users (MAU) and of Twitter is 126 million; hence, the market for understanding the customer sentiment through social media can be a game changer for a company and can help define the success of the company in the future. If the sentiments of the users are not captured correctly, it could lead to catastrophic failure of the product and hamper company’s reputation. Existing systems require a lot of manual tasks such as customer surveys, aggregating the sentiments then generating excel reports which are not very interactive and require a lot of time to gather results. These reports also do not show real-time data. People express their opinion on social media. Companies can use such platforms to capture honest and transparent opinions of the consumers. The cognitive service evaluates tweet texts and returns a sentiment score for each text, ranging from 0 (negative) to 1 (positive). This capability is useful for detecting positive and negative sentiments in social media such as Facebook, Twitter, customer reviews, and discussion forums. The machine learning model used by the cognitive service helps determine sentiment using data provided by the user. This feedback can allow the company to know the acceptance of the product in prototype stages and can use the same to modify the product as per the customer feedback before making the product generally available. The implementation environment uses Azure services (Logic apps, Cognitive Services, SQL Database, App Services) with Power BI used to generate real-time business intelligent reports to capture customer sentiment, and a Windows 10 workstation can be used to access all these services.