Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Assessment of Calibration Motion Profiles in Robotic-assisted Ultrasound System

3D ultrasound(US) reconstruction has grown quickly in preoperative disease diagnosis, intraoperative surgical navigation and postoperative treatment assessment. US image calibration is a crucial step to produce accurate 3D representations from 2D images. Several image calibration methods have been developed for freehand and robotic systems. During calibration, the scanning motions can vary a lot and typically have a large impact on the resulting calibration and subsequent reconstruction quality. However, the adopted calibration motions are not detailed extensively in previous studies. This complicates reproduction of the obtained results. Moreover, the influence of the different scanning motions on the calibration accuracy is hardly investigated. Therefore, it is difficult to devise optimal scanning motion profiles for automatic robotic ultrasound calibration. This paper studies calibration and reconstruction results with different motion profiles employed in robotic US image calibration. Then, the calibration procedure was performed with a sphere phantom. The performance of the calibration was validated by assessing the reconstruction quality on a 3D printed mock-up model with two quantitative measurements for the geometric representation error and the 3D localization error. The geometric representation error of the reconstruction is within 1 mm by using the different combination of motion profiles. However, the 3D localization error changes with the various motion profiles. By using the proposed motion profile, the RMSE of the reconstructed model could be reduced to 2.18 mm.
Influence of different Machine learning algorithms on Prediction Model of Fuel Consumption of Inland Ships

An accurate prediction of ship fuel consumption is of great significance to the economy of ship operations. To study the influence of different machine learning algorithms on the prediction of ship fuel consumption, an inland river cruise ship has been considered as the research object. Based on the actual operation data obtained by the energy efficiency data acquisition system of the real ship, a prediction model of the ship's main engine fuel consumption was established using the Multi-Layer Perceptron Regression, Decision Tree Regression, Random Forest Regression, K-Nearest Neighbor, Extra Trees Regression and Support Vector Regression algorithms. Mean absolute error, mean square error, root mean square error, and coefficient of determination were used to evaluate the accuracy of the model, and the effect of different machine learning algorithms on the inland ship fuel consumption prediction model was investigated under different data volumes. The results show that the Extra Trees Regression and Random Forest Regression models perform best, and the amount of modelling data has a significant impact on the training time of the Random Forest Regression and Support Vector Regression models.
Naming the Pain in Requirements Engineering - NaPiRE-Report 2013

Context: For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, those studies either concentrate on validating specific methods or on single companies or countries. Therefore, they allow only for limited empirical generalisations. Objective: To lay an empirical and generalisable foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method: We designed a globally distributed family of surveys in joint collaborations with different researchers from different countries. The instrument is based on an initial theory inferred from available studies. As a long-term goal, the survey will be regularly replicated to manifest a clear understanding on the status quo and practical needs in RE. This report presents the design of the family of surveys and the results from the survey start in Germany. Results: Our first results contain responses from 58 German companies. The results are not yet generalisable, but already indicate several trends and problems. For instance, a commonly stated problem respondents see in their company standards are artefacts being underrepresented, and important problems they experience in their projects are incomplete and inconsistent requirements. Conclusion: The results suggest that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice.
Optimal Energy Management of V2B with RES and ESS for Peak Load Minimization

In order to decrease fuel consumption and greenhouse gas emissions, electric vehicles (EVs) are being widely adopted as a future transportation system. Accordingly, increasing the number of EVs will mean battery charging will have a significant impact on the power grid. In order to manage EV charging, an intelligent charging strategy is required to prevent the power grid from overloading. Therefore, we propose an optimal energy management algorithm (OEMA) to minimize peak load on a university campus consisting of an educational building with laboratories, a smart parking lot, EVs, photovoltaic (PV) panels and an energy storage system (ESS). Communication networks are used to connect all the system components to a university energy management system (UEMS). The proposed OEMA algorithm coordinates EV charging/discharging so as to reduce the peak load of the building’s power consumption by considering the real-time price (RTP). We also develop a priority determination method for the time allocation of the optimal charging algorithm. Priority is determined by arrival time, departure time, state-of-charge (SOC), battery capacity and trip distance. The performance of the proposed algorithm is evaluated in terms of charging cost and peak load under the real environment of the university engineering building.
Technical Debt Prioritization: A Developer's Perspective

Background: The prioritization of technical debt is an essen-tial task in managing software projects because, with current analysis tools, it is possible to find thousands of technical debt items in the software that would take months or even years to be fully paid. Aims: In this study, we aim to under-stand which criteria software developers use to prioritize code technical debt in real software projects. Methods: We performed a survey to collect data from open-source soft-ware projects in order to reach a large and diverse set of ex-periences. We analyzed the data using Straussian Grounded Theory techniques: open coding, axial coding, and selective coding. Results: We grouped the criteria into 15 categories and divided them into 2 super-categories related to paying off the technical debt and 3 related to not paying it. Conclusions: When participants decided to pay off technical debt, they wanted to do it soon. However, when they decided not to pay it, it is often because the debt occurred intentionally due to a project decision. Also, participants using similar criteria for their decisions tended to choose similar priority levels for those decisions. Finally, we observed that each software project needs to tailor the rules used to identify code technical debt to their project context.
An overview of robust maritime situation awareness methods

Maritime situation awareness is the core of ship navigation and determines the development direction of smart ships. Visual perception becomes an active yet important research task in maritime traffic. To aim that, this paper introduces three situation awareness methods based on visual images. The first method uses kernelized correlation filtering algorithms and Kalman filter models to solve ship occlusion in the image and achieve accurate ship tracking tasks; The second method uses kernelized correlation filtering and logarithmic polar coordinate transformation to solve the imaging size changes during ship navigation; The third method uses yolo algorithms to obtain the high-quality distance information from port videos. The experimental results and quantitative indicators show that each method can effectively achieve reliable maritime situational awareness tasks. The comprehensive realization of maritime situation awareness can better reduce risks and ensure the safety of ships.
PILOT: Synergy between Text Processing and Neural Networks to Detect Self-Admitted Technical Debt

During the development phase, software programmers usually introduce code that contains issues intentionally left for additional treatment. To allow for future fixing, they mark such code using textual comments, resulting in Self-Admitted Technical Debt (SATD). Detecting SATD contained in source code has become crucial in the development cycle since it helps program-mers locate issues that need to be solved, thus improving code quality. We introduce PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques. First, the semantic among SATD comments is captured using feature extraction steps. Then, neural network algorithms are applied to classify comments, represented as vectors. We built a PILOT prototype with a feed-forward neural network and evaluated it using real-world datasets as proof of concept. The empirical evaluation shows that PILOT obtains an encouraging performance and outperforms a well-established baseline. We anticipate that our tool will come in handy, as once being embedded in the IDE, it can help developers recognize SATD manifested in their code, allowing them to conveniently identify and fix issues.
Integrated Power Delivery Methodology for 3D ICs

Efficient power delivery is a critical enabler for the future of three-dimensional integrated circuits (3D ICs). To this end, on-chip power demand, impedance of power delivery paths (on- and off-chip), and heat dissipation, need to be considered simultaneously. Given the high off-chip parasitics, power conversion within the 3D stack (on-chip) is a promising solution to overcome key power delivery challenges in 3D ICs. Recent developments in fabrication of high-density on-chip passive components have paved the way for the implementation of fully integrated voltage regulators (FIVRs). This work builds on the FIVR approach to propose an efficient integrated power delivery methodology for 3D ICs.In the proposed methodology, depending on the number of layers and the power characteristics of each layer, one or more layers of the 3D structure are dedicated to power conversion, regulation, and management. A case study of a five-layer 3D IC is considered where the proposed methodology is compared with conventional and FIVR-based approaches under, both, static and transient conditions. The proposed methodology exhibits a reduction in power loss and voltage drop of, respectively, 5X and 24X, while occupying a significantly smaller horizontal area, as compared to the other approaches.
Maritime Traffic Network Extraction and Application Based on AIS Data

The paper presents a data-driven approach for maritime traffic network extraction based on Automatic Identification System (AIS) data. A maritime traffic network, consisting of nodes that represent waypoint areas and navigational legs, is constructed to represent the maritime traffic at a larger scale. The proposed maritime traffic network extraction approach consists of three phases: 1) extraction of maritime traffic motion patterns based on historical Automatic Identification System data; 2) enrich the ship trajectories with semantic information and each ship trajectory is abstracted as an “origin-waypoint-destination” itinerary object; 3) ship itinerary objects are further merged into nodes and edges of a directed maritime traffic network. Based on the maritime traffic network, a hierarchical reasoning approach is proposed to associate a partially observed ship trajectory to the derived compatible ship routes and ship abnormal behaviour can be detected. The presented method can assist maritime authorities to improve the efficiency of maritime traffic surveillance and to develop strategies to improve navigation safety.
Evaluation of blind source separation algorithms applied to music signals

We present an evaluation of single-channel blind audio source separation (BASS) algorithms for different musical instrument combinations, based on statistical approach. So-called primitive BASS algorithms were chosen, because of their low computational latency and similarity to human hearing principles. The most common musical instruments (drums, guitars, vocals) were combined with each other, and segregate subsequently by 4 different blind audio source separation techniques. 25-second samples from 30 songs of various genres were used to preserve natural diversity. The main difference in our approach lies in the evaluation of average deviations from ground truth, instead of evaluating absolute values of signal-to-distortion ratio.
Electromagnetic Force and Vibration Study on Axial Flux Permanent Magnet Synchronous Machines With Dual Three-Phase Windings

This paper presents a detailed analysis of the electromagnetic force and vibration behavior of an axial flux permanent magnet synchronous machine (AFPMSM). First, the AFPMSM configuration and two types of dual three-phase winding are introduced. Subsequently, the electromagnetic force of the double-stator inner-rotor AFPMSM is investigated. Its spatial and temporal characteristics are derived analytically and validated through two-dimensional Fourier decomposition. A multiphysics model is established to calculate the vibration of the AFPMSM, which includes control, electromagnetic, structural, and vibration model. The accuracy of this multiphysics model is verified by the modal and vibration test. Furthermore, the vibration of the AFPMSM with three winding types considering current harmonics are calculated based on the multiphysics model. The vibration performance of the AFPMSM with three different winding types are compared and the mechanism of vibration is discussed. It was concluded that the novel detached winding could mitigate the vibration of dual three-phase AFPMSMs. Finally, the vibration test is carried out to validate the theoretical analysis and the experimental results agree well-with the simulation results. This paper provides an effective method for double-stator permanent magnet synchronous machine (PMSMs) to reduce vibration.
Characteristics of Learning Outcomes for Integrating Work-Based Learning into the Business Information Technology Study Program

Work-based learning is a general approach that allows students to gain practical knowledge and bridge the gap between university and industry (or company) needs. Work-based learning is often informal, but the formalized learning outcomes could help evaluate the relevance of industry (or company) projects to the university curricula and specify the objectives a student can achieve through the Work-based project. This work aims to analyze the learning outcomes and the characteristics of learning outcomes that can be achieved and assessed in the working environment. For a two-year Business Information Technology MSc program, we determined what learning outcomes could not be earned via workplace learning. In addition, the aim was to analyze whether non-achieved learning outcomes were something that the company could provide in different circumstances, or whether they cannot possibly be achieved in workplace learning, or whether the learning outcome has been wrongly formulated.
Sonarlizer Xplorer: a tool to mine Github projects and identify technical debt items using SonarQube

The advancement of artificial intelligence and the imple-mentation of machine learning capabilities in programming languages such as Python, along with cloud services, allow researchers to apply methods to cluster and predict behav-iors and patterns in software engineering data. On the other hand, these methods need a large amount of data in order to work with high accuracy in different contexts. This paper introduces Sonarlizer Xplorer: a tool that captures a large number of technical debt items and code metrics from pub-lic GitHub projects. Sonarlizer Xplorer is composed of two sub-tools. The first is Github Xplorer, responsible for mining public Github repositories from an initial project. The second is Sonarlizer, responsible for taking projects and analyzing them using SonarQube. We used the tool over four months, collecting technical debt items and code metrics on almost 46,000 public Java projects. In addition, we mined over 57 million repositories and 4 million users.
Proposição de um sistema de classificação das propriedades de forma de agregados caracterizados com o uso do processamento digital de imagens a partir de materiais oriundos do Brasil

Os agregados minerais constituem cerca de 90% do volume das misturas asfálticas. O comportamento destas, com relação aos principais defeitos encontrados nos pavimentos (deformação permanente, trincamento por fadiga e trincamento térmico), está diretamente ligado às características de seus agregados constituintes. O Aggregate Imaging Measurement System 2 (AIMS2) está estabelecido como uma das principais técnicas de Processamento Digital de Imagens (PDI) para a caracterização das propriedades de forma de agregados utilizadas em Infraestrutura de Transportes, por usar métodos diretos e eficientes. Diante da importância de se avaliar essas características, esta pesquisa teve como objetivo principal propor um sistema de classificação das propriedades de forma baseado nos resultados do AIMS2, a partir de um banco de dados composto por agregados de mineralogias distintas, oriundos de diferentes localizações no Brasil. Para gerar os valores limites da classificação proposta, foi utilizado o método estatístico de cluster. Por fim, comparou-se a classificação proposta com outras existentes e os resultados mostraram que um agregado pode ser classificado de maneira diferente, dependendo do sistema adotado. Essa constatação é importante, pois a classe em que um material é categorizado pode ser determinante para a sua seleção, no âmbito de projeto, a depender das propriedades requeridas para sua aplicação.
Damage identification of RDT non-metal motor sheath based on helical guided wave

In order to obtain the healthy state of the motor sheath of the Rim-Driven Thruster (RDT) in service, a method for identifying damage to the non-metal sheath of the RDT motor based on helical guided waves is proposed. Damage detection is performed on a non-metal hollow cylinder (110 mm of outer diameter and 3.2 mm of wall thickness). A pair of MFC sensors are arranged on the surface of the cylinder to excite and receive the helical guided waves, and the non-metal sheath of the RDT motor damage monitoring model is established. The space expansion method is used to complete the tracking of the helical guided wave packet and determine the propagation order of the helical guided wave. U sing the short-time wavelet entropy (STWE) algorithm to complete the calculation of the energy entropy of the damage signal, and compare the energy entropy in the non-destructive state to finish the damage identification. The experimental results show that by arranging the sensor in a certain area outside the cylindrical structure to be detected to excite the spiral guided wave, the damage identification of the difficult-to-detect area and the small damage can be realized.
Abnormal Behavior Detection of Vessels Based on Deep Learning Algorithm: Case Study

Abnormal behavior detection for vessels is vital to satisfy the reliability and safety requirements of maritime transportation. The current detection method is manual analysis, which has many defects such as high labor costs, weak stability, poor efficiency, low accuracy, so this passive mode cannot meet the needs of maritime transportation. Using deep learning algorithms to extract valuable information from various types of data and accurately perceive the vessel's behavior is the current research hot spot. This paper firstly preprocessed the data from the automatic identification system (AIS). And then the variable recurrent neural network algorithm was applied to establish the normal model. Next, according to the geographical and seasonal characteristics, several case studies were conducted to show the model's performance. Finally, the research and technological challenges in abnormal behavior detection using deep learning algorithms were summarized.
Anomaly detection of web traffic between IoT Devices

There are challenges in anomaly detection and monitoring the data traffic on Internet of Things (IOT) networks which are the results of the state of devices/sensors that's connected to IOT networks. Moreover. , protecting and determining the data traffic of devices requires anonymity. , another challenge represented by the diversity and heterogeneity of the devices/sensors. Thus. , this study proposes a comparison between specific modules which support heterogeneous devices and detect the anomalies data. In this paper. , three different ML algorithms (K-Nearest Neighbors., random forest., XGB Classifier) are applied for solving issues related to data traffic management and network monitoring. It is shown that even with the poor data traffic. , accuracy between 80% and 89% is achieved for effectively detecting anomality in an IoT network.
Recent and Future Outlooks for Nuisance Flooding Impacts on Roadways on the U.S. East Coast

Tidal floods (i.e., “nuisance” flooding) are occurring more often during seasonal high tides or minor wind events, and the frequency is expected to increase dramatically in the coming decades. During these flood events, coastal communities’ roads are often impassable or difficult to pass, thus impacting routine transport needs. This study identifies vulnerable roads and quantifies the risk from nuisance flooding in the Eastern United States by combining public road information from the Federal Highway Administration’s Highway Performance Monitoring System with flood frequency maps, tidal gauge historic observations, and future projections of annual minor tidal flood frequencies and durations. The results indicate that tidal nuisance flooding across the East Coast threatens 7508 miles (12,083 km) of roadways including over 400 miles (644 km) of interstate roadways. From 1996–2005 to 2006–2015, there was a 90% average increase in nuisance floods. With sea level rise, nuisance-flood frequency is projected to grow at all locations assessed. The total induced vehicle-hours of delay due to nuisance flooding currently exceed 100 million hours annually. Nearly 160 million vehicle-hours of delay across the East Coast by 2020 (85% increase from 2010); 1.2 billion vehicle-hours by 2060 (126% increase from 2010); and 3.4 billion vehicle-hours by 2100 (392% increase from 2010) are projected under an intermediate low sea-level-rise scenario. By 2056–2065, nuisance flooding could occur almost daily at sites in Connecticut, New Jersey, Maryland, the District of Columbia, North Carolina, and Florida under an intermediate sea-level-rise scenario.