Reviewerly/exHarmony huggingface dataset:
Specific Emitter Identification Using Regression Analysis between Individual Features and Physical Parameters

In this paper, a semi-physical simulation platform and ADS are used to acquire the signals of three types of specific emitters, then high-order spectral analysis and variational modal decomposition are used to extract features of the signals. The phase noise of the oscillator and the bias voltage of the power amplifier are used as independent variables to study their influence on the features, based on which the correlation analysis is carried out. Regression fitting is performed on the variables with significant correlation to obtain a regression function, then a feature-weighted support vector machine is constructed for classification. The results show that the accuracy of the proposed identification algorithm using regression analysis is more than 10 percent higher than that of the single-kernel support vector machine under the same signal-to-noise ratio.
Improved Harris Eagle Optimization Algorithm Based on Adaptive Water Wave

Aiming at the shortcomings of Harris Eagle optimization algorithm such as weak ability to jump out of local optimum and slow convergence speed, a Harris Eagle optimization algorithm based on dynamic adaptive water waved was proposed. Firstly, Tent chaotic sequence and refraction reverse learning are introduced to increase the population diversity of initialized individuals and improve the population quality; Secondly, the dynamic adaptive factor of water waved are introduced into the position updating formula for the global exploration stage to enhance its global optimization ability; Then, the adaptive search operator is introduced to the local development stage. With the increase in the number of iterations, the weight of prey position is adjusted adaptively, and the Cauchy mutation is used to make it still have strong search ability in the late iteration. Finally, the simulation results in 2 benchmark functions shows that IHHO has better global exploration and local development ability, shortens the average time of the original algorithm, and improves the performance of the algorithm.
BCCMS, Authorization Through an Approach for a Blockchain Based Electronic Cards Management System

Electronic cards are utilized by different businesses for customer identification. Such cards are issued to targeted customers in a one-to-one or a one-to-many relationship according to business needs. Card issuers provide such services to businesses to provide electronic cards to their customers. Issuers keep records related to the issued cards while integrating businesses to link each card to the targeted customer. Such cards are then used to gain access to products or services provided by the business. The integration of different card issuers and businesses creates a costly and complex environment in terms of operation as the integration protocol differs from one card processor to the other. In this research, a blockchain based card management system is proposed that may assist businesses to issue cards to their customers with reduced cost and less complex process. Such a platform will also introduce a unified integration protocol that will create a unified and straightforward integration cycle. A blockchain based card issuance system will also provide a comprehensive, secure network of authorization where transactions become immutable unlike others.
Database Management for Data Streaming in Traditional Long-Boat Racing in Thailand

In new distribution of information era, Thai traditional long-boat racing is propagated to the world rapidly by the online streaming. To make the audiences to easy to enjoy this sport, information technology is let to manage and promote this sport also. This paper presents the first database of the traditional Thai longboat racing in Thailand, supporting the data online for live streaming. ER-Diagrams and data dictionaries were constructed with attributes, data types, and relationships between the data of 113 boats and 11 racetracks, using MySQL, collecting and retrieving by our web pages. Microsoft Power BI created the mapping areas showing the racing boats, residence, historical details of tournaments, teams, and the involving oarsmen. The efficiency is measured by the satisfaction of the 6 organizers, 6 broadcasters, and 13 audiences. Most said “very satisfied” (4–5 from 5). They are pleasure to use in practical for racing report and promoting the audience broadcasting. Hence, this work has a lot benefit in purposes of information supporting for the team management, tournament organization, and racing broadcasting for the new target audiences in online world.
Assisted Reality as an enabler for resilient remote learning during COVID-19

During the COVID'19 pandemic the vital practical aspect of Technical Vocational Educational Training (TVET) almost halted completely., as most providers were insufficiently prepared to respond to the containment measures applied to stop the spread of the virus. In this paper., we report on our preliminary work towards developing a resilient maritime TVET ecosystem by enabling remote learning and examination using Assisted Reality (AR) technologies. We present a novel framework for remote maritime TVET., incorporating AR devices., and evaluate our approach in real world conditions., in a class of more than 28 students of the Rotterdam Mainport Institute. We attempt to qualitatively and quantitatively measure the effect such systems have on the students learning process and report on the results.
The adoption of the Scaled Agile Framework (SAFe) in non-agile organizations

The high project failure rate has been recognized as one of the challenges within the project management space and agile methodologies have been proposed to solve this challenge. In the last several years, many organizations have been using traditional project management methodologies, but they are now in the process of choosing an appropriate agile methodology as a substitute for the control-oriented, rigid, and inflexible traditional methodologies. Some are thinking about the move. The Scaled Agile Framework (SAFe) is an agile approach that brings into line collaboration between various teams in agile software development. It is not clear how non-agile organizations can implement the SAFe to derive organizational benefit and achieve transformation. The purpose of this study is to develop best practices to help in guiding agile adoption in non-agile organizations and determine the benefits, and challenges inherent to organizations implementing SAFe. The study outlines a 10-step implementation roadmap to be followed by non-agile organizations and identifies additional benefits experienced by the project team which includes defect reduction, skills sharing, and improved quality and productivity. The top three steps include (1) Train Lean-agile change agents, (2) Train executive manager leaders, and (3) Identifying value streams & ARTs.
Scrum's Methodology adaptations to meet the teleworking needs experienced by Software Development Teams

Among the different approaches to implementing Agility, the Scrum, created in the late 1980s, has stood out as the most used tool by the software development industry. Understand how the concepts underlying this approach, such as ceremonies and time boxes, have been adapted to meet the situation of telework imposed by social distancing to prevent the spread of the COVID-19 virus, experienced by the elements of the software development teams, presents an opportunity to learn what are the most efficient ways to use its process. It was observed how Scrum was applied in practice by software development teams during the mandatory telework period and from there, it will be look for better ways to do it, either by developing new technological tools, or using existing tools, to support the ceremonies, or, by creating new processes to support such evolution on methodology's process.
Dual-Level Structural Information Learning Neural Network for Monocular 2D Pose Estimation

In this paper, a dual-level structural information learning neural network is proposed for human pose estimation referring to the human body structure. Human poses are highly structured in 2D space; thus, the structural information among human body parts is meaningful for human pose estimation. However, most existing methods do not incorporate structural information, which limits the achievable accuracy. A bidirectional memory-augmented RNN (biLSTM) based dual-level structural information learning module is proposed to compose a structural information learning neural network in which human body parts are taken as sequential information. Keypoint heatmaps and bone heat-maps are predicted through an end-to-end network, and structural information is captured at both the joint and the bone levels by exploiting the coarse keypoint heatmaps and bone heatmaps as mutual priors. Experimental results on two challenging datasets demonstrate that the proposed method achieves a PCK detection rate improvement of 0.3% on LSP dataset compared with a state-of-the-art baseline, and a PCKh detection rate improvement of 0.2% on MPII dataset over the sate-of-the-art baseline work.
An Improved Harris Corner Points Detection for Low-Light Scenes Based on Contrast Limited Adaptive Histogram Equalization

Corner points are commonly defined as the intersection of two edges, and the Harris algorithm, which performs corner point detection based on the grey value variation between a patch and its neighborhood, is commonly used in various computer vision tasks. For low-light images, Harris algorithm is affected because the details of the image become blurred by low contrast. This paper proposes an improved Harris algorithm, which is inspired by the Contrast Limited Adaptive Histogram Equalization (CLAHE). After extracting a patch of the target image, the gray value of the patch is adjusted based on the cumulative distribution function (CDF). As a result, the gray value of the patch becomes evenly distributed, and the variation of the gray value of the patch becomes sharper. The improved Harris algorithm has been compared with the original Harris algorithm on different images of low-light scenes. Experimental results show that the proposed algorithm can effectively detect corner points in low contrast regions, and the repeatability of corner points matching in the low-light regions is significantly improved.
Smart Campus: Concepts, development and future

With the advent of the sanitary crisis there was a shift to the online courses that radically changed the education practices. Although most of the universities succeeded been resilient to ensure education continuity. Today, most universities are trying to implement solutions to make their campuses smart. The objective of this document is to show through a literature review, the future development direction of smart university campus and their associated concepts. The stages of development, the associated technologies and a proposal for a smart campus model from a Living-Lab approach are presented.
Human-in-the-loop: Explainable or accurate artificial intelligence by exploiting human bias?

Artificial intelligence (AI) is a major contributor in industry 4.0 and there exists a strong push for AI adoption across fields for both research and practice. However, AI has quite well elaborated risks for both business and general society. Hence, paying attention to avoiding hurried adoption of counter-productive practices is important. For both managerial and general social issues, the same solution is sometimes proposed: human-in-the-loop (HITL). However, HITL literature is contradictory: HITL is proposed to promote fairness, accountability, and transparency of AI, which are sometimes assumed to come at the cost of AI accuracy. Yet, HITL is also considered a way to improve accuracy. To make sense of the convoluted literature, we begin to explore qualitatively how explainability is constructed in a HITL process, and how method accuracy is affected as its function. To do this, we study qualitatively and quantitatively a multi-class classification task with multiple machine learning algorithms. We find that HITL can increase both accuracy and explainability, but not without deliberate effort to do so. The effort required to achieve both increased accuracy and explainability, requires an iterative HITL in which accuracy improvements are not continuous, but disrupted by unique and varying human biases shedding additional perspectives on the task at hand.
Agilization of technical development processes for medical devices

Agile methods have long been proven in software development, but not in all branches. Unfortunately, not all sectors benefit from the advantages of agile development, yet. Especially highly regulated areas, such as medical devices, often fall back on traditional process models due to regulatory and normative requirements because of perceived conformity. Using targeted placement of agile modules in combination with regulatory requirements, a process model for medical device manufacturers was created in the flexible, innovative process model ADmed, which demonstrates on the one hand the added value of agile development for the company and on the other hand enables conformity with important process-oriented regu-lations and standards for medical devices. By conducting expert interviews, the model was successfully evaluated and aspects for further optimization of the model could be identified.
Influence of Project Parameters on the Decision of an Agile or Plan-based Approach

Since its introduction two decades ago, agile project management has established itself as an alternative to plan-based process models like the classical waterfall model. The selection of an appropriate process model is a critical factor for the success of the project. Many studies were conducted to identify parameters that should be considered in taking this decision. While these parameters are generally known, even down to specific fields like software development, they are usually only identified but not ranked in a specific order. However, not all parameters are of same importance. In order to determine the relevance of the parameters, a Delphi-based expert study was conducted. The result is an ordered list of parameters which help decision makers when selecting an appropriate agile or plan-based process model for project management.
How can machine learning improve waste-to-energy plant operation

This paper deals with the integration and the role of waste- to-energy plants. The hygienic and safe disposal of waste is a central aspect of human infrastructure. It is a prerequisite for preventing the spread of disease in society and is a very important issue today as it has been in the past. While serving as waste disposal in the past, today, waste incineration has changed to waste-to-energy plants, in addition to keeping the first goal of waste reducing and sanitation. Therefore, the highest energy harvesting is not the primary goal of the plant. The plant operation is difficult to control related to the stochastical variation of the properties of municipal waste due to its heterogeneous nature. Hence, it is expected that the optimization of waste-to-energy plants will benefit significantly if any applied method may handle the stochastic properties well. The work presented here aims to provide insights into a novel approach to develop a new method for enhancing the performance of the waste-to-energy plant related to blast cleanings to prevent build-up of particles. This also ensures that the overall performance of the plant improves. Artificial Intelligence was applied to sensor emission data directly from an operating real incinerator and with machine learning the data shows the effects of the blast cleanings related to typical plant properties. The results of this paper give first indications that forecasting of the incineration process is possible. With these results, the plant operator could handle the plant efficiently with the least performance reduction.
On the Use of Low-Code and No-Code Tools for Teaching Data Science in Applied Industrial and University Settings

The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules ('tools') for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector. The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code /low-code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.
Security Inspection Framework and its Application to Use Cases

There have been several threats targeting supply chains. These threats can compromise security of supply chain and can affect quality and/or safety of the product. A framework for mitigating supply chain threats has been proposed where all organisations in the supply chain self-assess their compliance to regulations/requirements. The results of the assessment will be shared to other organisations. In this paper, based on the existing framework, we consider the extension of the conformance validation and the format of the certificate in order to support wide range of the use cases. Then we apply the framework to three use cases and discuss how the framework can improve the security of entire supply chain.
A dense obstacle avoidance algorithm for UAVs based on safe flight corridor

Aiming at the problem of autonomous obstacle avoidance of fixed-wing UAVs in a complex, dense and multi-obstacle environment, a path planning algorithm for fixed-wing UAVs based on a safe flight corridor is proposed. The difficulty of avoiding dense obstacles lies in the choice of obstacle circumvention and traversal: although circumvention is safer, the flight cost is greater; although the traversal cost is lower, the safety threat is higher. How to quickly solve the optimal path is the core issue. This paper firstly defines a safe flight corridor innovatively based on the maneuvering characteristics of fixed-wing UAVs and the Dubins curves. By comprehensively considering UAV flight safety and flight costs, an obstacle threat evaluation function is constructed. Secondly, in view of the computational complexity caused by the dense obstacles, an obstacle clustering algorithm based on obstacle density is proposed, and the nonlinear evaluation function in a high dynamic environment is quickly approximated by Monte Carlo sampling method. Finally, simulations verify the effectiveness of the proposed algorithm in solving dense obstacle avoidance for fixed-wing UAVs.
Research on Application of micro assistant combined with PACS based virtual imaging system in teaching imaging internship

Objective: To investigate the effect of micro assistant combined with image storage and transmission system based virtual imaging system in medical imaging internship teaching. Methods: a total of 61 imaging professional students who entered the Department of imaging, the Second Affiliated Hospital of Qiqihar Medical College, from August 1st 2019, were randomly divided into test group and control group in the Department of trial. Results: The students in the test group were significantly better than the students in the control group in attendance examination performance, the difference was statistically significant, in the satisfaction survey of teaching methods, the micro - Assistant combined with PACS based virtual imaging system teaching methods were significantly higher in student satisfaction than the traditional teaching methods. Conclusions: the combination of micro assistant and PACS based virtual imaging system in medical imaging internship teaching can improve students' knowledge of the imaging characteristics of diseases.
KD-PatchMatch: A Self-Supervised Training Learning-Based PatchMatch

Traditional learning-based multi-view stereo (MVS) methods usually need to find the correct depth value from a large number of depth candidates, which leads to huge memory consumption and slow inference. To address these problems, we propose a probabilistic depth sampling in the learning-based PatchMatch framework, i.e., sampling a small number of depth candidates from a single-view probability distribution, which achieves the purpose of saving computational resources. Furthermore, to overcome the difficulty of obtaining ground-truth depth for outdoor large-scale scenes, we also propose a self-supervised training pipeline based on knowledge distillation, which involves self-supervised teacher training and student training based on knowledge distillation. Extensive experiments show that our approach outperforms other recent learning-based MVS methods on DTU, Tanks and Temples, and ETH3D datasets.
Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach

Academic societies and funding bodies that conduct peer reviews need to select the best reviewers in each field to ensure publication quality. Conventional approaches for reviewer selection focus on evaluating expertise based on research relevance by subject or discipline. An improved perceiving conflict of interest (CoI) reviewer recommendation process that combines the five expertise indices and graph analysis techniques is proposed in this paper. This approach collects metadata from the academic database and extracts candidates based on research field similarities utilizing text mining; then, the candidate scores are calculated and ranked through a professionalism index-based analysis. The highly connected subgraphs (HCS) algorithm is used to cluster similar researchers based on their association or intimacy in the researcher network. The proposed method is evaluated using root mean square error (RMSE) indicators for matching the field of publication and research fields of the recommended experts using keywords of papers published in Korean journals over the past five years. The results show that the system configures a group of Top-K reviewers with an RMSE 0.76. The proposed method can be applied to the academic society and national research management system to realize fair and efficient screening and management.
Shap-PreBiNT: A Sentiment Analysis Model Based on Optimized Transformer

Confront the problems of the semantic representation, classification accuracy and efficiency of text sentiment analysis methods need to be optimized, this paper proposes an optimized text sentiment classification model (Shap-PreBiNT) based on Transformer. In the text embedding stage, a Shap-Word model is devised to improve the ability of text semantic representation. The Shapley-value method is introduced to calculate the contribution weight of words in sentences, which fused with Word2Vec word vector. In the feature extraction stage, a bidirectional normalization layer is designed to regulate the feature distribution from multi-dimension. In the stage of network structure optimization, a pre-normalization structure is adopted to stabilize the gradient norm and accelerate the convergence rate. The experimental results demonstrate that the proposed model has a better performance by comparing with other related models. On the IMDB English dataset, the classification accuracy and F1-score reach 94.87% and 94.83%, which are 1.48% and 1.47% higher than Transformer. On the ChnSentiCorp Chinese dataset, our model achieves the highest accuracy and F1-Score, which are 91.82% and 91.66%, respectively, and increased by 2.43% and 2.51%.