Miracle-dz/UniFAR-FTUs huggingface dataset:
Imageability- and Length-controllable Image Captioning

Image captioning can show great performance for generating captions for general purposes, but it remains difficult to adjust the generated captions for different applications. In this paper, we propose an image captioning method which can generate both imageability- and length-controllable captions. The imageability parameter adjusts the level of visual descriptiveness of the caption, making it either more abstract or more concrete. In contrast, the length parameter only adjusts the length of the caption while keeping the visual descriptiveness on a similar degree. Based on a transformer architecture, our model is trained using an augmented dataset with diversified captions across different degrees of descriptiveness. The resulting model can control both imageability and length, making it possible to tailor output towards various applications. Experiments show that we can maintain a captioning performance similar to comparison methods, while being able to control the visual descriptiveness and the length of the generated captions. A subjective evaluation with human participants also shows a significant correlation of the target imageability in terms of human expectations. Thus, we confirmed that the proposed method provides a promising step towards tailoring image captions closer to certain applications.
Q-learning with Long-term Action-space Shaping to Model Complex Behavior for Autonomous Lane Changes

In autonomous driving applications, reinforcement learning agents often have to perform complex behavior, which can translate into optimizing multiple objectives while following certain rules. Encoding traffic rules and desires such as safety and comfort via classical methods based on reward shaping (i.e. a weighted combination of different objectives in the reward signal) or Lagrangian methods (including auxiliary losses in the optimization) can be very hard and cumbersome. In this work, we propose to instead shape the action-space at the maximization step of Q-learning. We further introduce a formulation for fixed-horizon estimation of auxiliary costs under the current target-policy based on truncated value- functions to encode the desire of comfortable driving ensuring interpretable behavior. We compare our algorithm to reward shaping and Lagrangian methods in the application of high- level decision making in autonomous driving, considering rules for safety, keeping right and comfort. We train and evaluate our agent in the open-source simulator SUMO on a variety of scenarios with different driver types and traffic situations. Additionally, we apply our method on the real HighD data set, showing the real-world applicability and simplicity of Q- learning with Action-space Shaping.
Autonomous Scanning Target Localization for Robotic Lung Ultrasound Imaging

Under the ceaseless global COVID-19 pandemic, lung ultrasound (LUS) is the emerging way for effective diagnosis and severeness evaluation of respiratory diseases. However, close physical contact is unavoidable in conventional clinical ultrasound, increasing the infection risk for health-care workers. Hence, a scanning approach involving minimal physical contact between an operator and a patient is vital to maximize the safety of clinical ultrasound procedures. A robotic ultrasound platform can satisfy this need by remotely manipulating the ultrasound probe with a robotic arm. This paper proposes a robotic LUS system that incorporates the automatic identification and execution of the ultrasound probe placement pose without manual input. An RGB-D camera is utilized to recognize the scanning targets on the patient through a learning-based human pose estimation algorithm and solve for the landing pose to attach the probe vertically to the tissue surface; A position/force controller is designed to handle intraoperative probe pose adjustment for maintaining the contact force. We evaluated the scanning area localization accuracy, motion execution accuracy, and ultrasound image acquisition capability using an upper torso mannequin and a realistic lung ultrasound phantom with healthy and COVID-19-infected lung anatomy. Results demonstrated the overall scanning target localization accuracy of 19.67 ± 4.92 mm and the probe landing pose estimation accuracy of 6.92 ± 2.75 mm in translation, 10.35 ± 2.97 deg in rotation. The contact force-controlled robotic scanning allowed the successful ultrasound image collection, capturing pathological landmarks.
Using Redundancy in a Sensor Network to Compensate Sensor Failures

Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
RF-LIO: Removal-First Tightly-coupled Lidar Inertial Odometry in High Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability for intelligent vehicles and mobile robots. However, most of the current lidar SLAM approaches are based on the assumption of a static environment. Hence the localization in a dynamic environment with multiple moving objects is actually unreliable. The paper proposes a dynamic SLAM framework RF-LIO, building on LIO-SAM, which adds adaptive multi-resolution range images and uses tightly-coupled lidar inertial odometry to first remove moving objects, and then match lidar scan to the submap. Thus, it can obtain accurate poses even in high dynamic environments. The proposed RF-LIO is evaluated on both self-collected datasets and open Urbanloco datasets. The experimental results in high dynamic environments demonstrate that, compared with LOAM and LIO-SAM, the absolute trajectory accuracy of the proposed RF-LIO can be improved by 90% and 70%, respectively. RF-LIO is one of the state-of-the-art SLAM systems in high dynamic environments.
The Role of Digit Arrangement in Soft Robotic In-Hand Manipulation

The need for robotic hands capable of gentle in-hand manipulation is growing rapidly as robots enter the real world. In this work, we show that the arrangement of digits in a soft robotic hand has a strong effect on in-hand manipulation capabilities. Introducing task-based performance metrics which quantify the range of motion, repeatability, and accuracy of in-hand manipulation tasks, we investigate hand designs with finger arrangements ranging from axisymmetric-circular to anthropomorphic. Using an open-source soft robot simulator, the effect of object size and aspect ratio on the in-hand manipulation performance is studied for a variety of finger arrangements, and findings are validated using a physical hardware platform. We found that the ideal finger arrangement is task-dependent; anthropomorphic arrangements excel at lateral translations, and axisymmetric arrangements are best suited for rotations. The aspect ratio of the object also has a strong effect on in-hand manipulation, with anthropomorphic designs performing best on objects of high aspect ratio, and axisymmetric arrangements doing well on objects of low aspect ratio. These findings are further confirmed in a real-world task with delicate pastries, where gentle in-hand manipulation is critical. Overall, our results suggest that active control of digit arrangement is necessary for soft robotic hands to maximize in-hand manipulation capabilities with arbitrary objects.
Resistive and CTAT Temperature Sensors in a Silicon Carbide CMOS Technology

Accurately sensing the temperature in silicon carbide (power) devices is of great importance to their reliable operation. Here, temperature sensors by resistive and CMOS structures are fabricated and characterized in an open silicon carbide CMOS technology. Over a range of 25-200°C, doped design layers have negative temperature coefficients of resistance, with a maximum change of 79%. Secondly, CMOS devices are used to implement a CTAT, which achieves a maximum sensitivity of 7.5mV/K in a temperature range of 25-165°C. The integration of readout electronics and sensors that are capable of operation in higher temperature than silicon, opens application in harsher environments.
Competency Assessment and Human Resource Management Performance of County Extension Chairs

The purpose of this descriptive and correlational study was to examine perceptions of Ohio State University Extension county chairs regarding their human resource management competencies and performance of human resource management activities. The study also sought to describe the relationship between human resource management competencies and performance of human resource management activities of county chairs. County chairs were selected for study because they represent the first line of management most clientele and employees face. They also represent a group of Extension managers least likely to have formal education or specialized training in human resource management. The highest human resource management competencies perceived by county chairs were written comprehension, oral comprehension, written expression, information gathering, inductive reasoning, and problem sensitivity. The human resource management activities for which county chairs indicated the highest means were: developing and maintaining positive work environment, administering wages and benefits, ensuring safety and health at worksites, and selecting and hiring employees. The correlation between the summated competency and activity score was significant with a very strong relationship between the variables, r(86)=. 71, p<. 05.
Parâmetros solo-máquina em função de doses de resíduos vegetais e profundidades de deposição de adubo em semeadura direta

O trafego continuo e inadequado de maquinas em solos sob semeadura direta tem provocado alteracoes dos atributos fisicos e mecânicos dos solos, influenciando, dessa forma, na produtividade das culturas. O experimento foi conduzido na Estacao Experimental Agronomica da Universidade Federal do Rio Grande do Sul, no municipio de Eldorado do Sul - RS, num Argissolo Vermelho distrofico tipico, com o objetivo de quantificar a forca de tracao e obter informacoes sobre os atributos fisicos do solo, em semeadura direta sobre residuos de aveia-preta e ervilhaca parcialmente decomposta. O delineamento experimental utilizado foi em blocos casualizados, com parcelas subsubdivididas, sendo, nas parcelas principais, doses de residuos da cultura de inverno (0; 1,3; 2,6; 3,2; 3,8 e 5,1 Mg ha-1) de palha de aveia-preta consorciada com ervilhaca, nas subparcelas profundidades de atuacao da haste sulcadora de adubo (0,06 e 0,12 m) e nas subsubparcelas trafegos dos rodados do trator e colhedora. Os atributos fisicos do solo foram afetados pelo trafego dos rodados da maquina. A forca de tracao foi influenciada pela profundidade de atuacao das hastes sulcadoras e o trafego dos rodados.
COVID-19 Classification Using Cough Sounds

Coronavirus disease 2019 (COVID-19) is the currently happening pandemic. Up until mid-2021, the total cases of COVID-19 have reached 171 million worldwide. The virus is mainly transmitted through droplets generated when an infected person coughs, sneezes, or exhales. The most common occurring symptoms are fever, cough, and fatigue. The current diagnosis method is done through Reverse-Transcription Polymer Chain Reaction (RT-PCR) testing. Even though this is the current gold standard, this method has several downsides. The RT-PCR is costly, time-consuming, and can lead to another infection if done improperly. In this paper we try to utilize AI to classify COVID-19 using cough sound. This method can work as a triaging tool to help prioritize a person to get future-diagnosis.In this research, our contribution is trying several feature extractions, imbalance handling and modelling techniques to classify COVID-19 using cough sound. We obtained the best result using the combination of NMF-Spectrogram feature, undersampling method, and SVM. It gives the sensitivity of 90.9%, specificity of 55.6% and overall AUC-ROC of 73.3%. We also discovered that the NMF-Spectrogram feature works better than MFCC-based features.
Finding Robust 2D-to-3D Correspondence with LSTM Score Estimation for Camera Localization

2D-to-3D correspondence estimation is the key step of 3D model-based image localization, and most of the existing research in this field focuses on improving the feature matching performance. Even with the best feature matching method, there are still some outliers, and thus, almost all the methods simply apply the RANSAC algorithm to select the inliers and estimate the camera pose afterwards. However, the reliability of RANSAC depends considerably on the inlier ratio. Once the inlier ratio decreases, for example a challenging scenario occurs, it will be unable to select the inliers well and lead to a worse camera pose. In this study, we attempted to build a neural network to learn the geometric relationship between 2D images and the 3D model to select the correct correspondence from the initial 2D-to-3D matching results to improve the performance of camera localization. Because the number of inputs, i.e., the number of 2D-to-3D correspondences, is unknown and different for each image, we propose a PointNet-based Geometric Consistency Network (GCC-Net) for the correct correspondence estimation and an LSTM-based Hypothesis Rating Network (HR-Net) to enhance GCC-Net with the camera localization loss. Experimental results showed that the proposed method outperforms RANSAC considerably on the camera pose estimation, particularly when the inlier ratio of the initial correspondence was low.
Market-based Multi-robot coordination with HTN planning

We propose a decentralized approach that simultaneously allocates and decomposes high level tasks among various robots. The approach exploits HTN structures and algorithms, that are used within an auction-based allocation scheme, and aims at dealing with complex tasks with causal or temporal relations. The paper formalizes the approach, and depicts how HTN planning processes are used to estimate bids and distribute tasks. Results on a statistical series of coverage problems are presented and their performance is assessed through a comparison with a state of the art algorithm.
Superline: A Robust Line Segment Feature for Visual SLAM

Along with point features, line features play an important role in achieving robust Simultaneous Localization and Mapping (SLAM) under complex environments. This paper proposes a fast and effective method, namely Superline, to simultaneously detect line segments and generate robust descriptors for matching. The entire model is composed of a convolutional backbone and two task heads, i.e., detection head and description head respectively. A line selecting mechanism and a spatial pyramid Line-of-Interest (LOI) pooling module is specially designed in the description head to aggregate multi-scale information into line feature descriptors. The entire model is implemented end-to-end and can be trained on a dataset with only line annotations and without the need of providing ground truth matching. Comparative experimental results on Wireframe and York Urban datasets as well as applying the Superline features on SLAM applications demonstrate the superior performance of our method.
A New Hybrid Excitation Machine with Dual-Stator Single-Rotor Axial-Flux Topology for Electric Vehicle Traction Application

For electric vehicles (EV s), a new type of axial-flux hybrid-excitation machine (AFHEM) is proposed and developed in this paper. Its dual stators sandwiched a consequent-pole rotor in order to settle the problems of poor flux-weakening capacity and relatively low overall efficiency of traditional axial-flux permanent magnet (AFPM) machines with surface mounted PMs. The unique external magnetic ring and flux-adjusting principle of the proposed AFHEM are discussed and compared with a dual-rotor single-stator AFHEM. The finite-element analysis (FEA) for electromagnetic design and performance simulation are performed extensively. The proposed topology is also compared with the corresponding structure of a traditional AFPM machine, AFHEM has a better torque output capability when the key parameters are consistent. Furthermore, temperature rises are also discussed to quantify the influence of two DC excitation windings. The effectiveness and advantage of the new AFHEM topology are verified for EV application.
Determinants of energy-saving behavioral intention among residents in Beijing: Extending the theory of planned behavior

Given the rapid increase of residential energy consumption in Beijing, the question of how to promote residential energy-saving behavior is an emerging topic that is increasingly engaging the attention of scholars. To address this issue, this paper provides an empirical analysis that identifies and explores the determinants of an energy-saving behavioral intention among residents from the perspective of the theory of planned behavior (TPB). A theoretical model was constructed by refining and extending the classic TPB model according to the scope and requirements of this study and the existing situation in China. Survey data collected from 276 residents in Beijing were analyzed, and hypothesized relationships in the model were then verified using a structural equation model. The results show that subjective norms, environmental attitudes, information publicity, lifestyles, and perceived behavioral control significantly influence residents' energy-saving behavior, while demographic factors, such as educational background, household income, and age, had no obvious effects on behavioral intentions. Although knowledge regarding energy did not exert a direct influence on residents' energy-saving behavioral intentions, it did exert an indirect influence via environmental attitudes. Our results indicate that the role of households in saving energy expenditure should be emphasized, and financial incentives could be adopted to help promote environmental awareness among Beijing's residents. In addition, environment-friendly and energy-saving habits should also be inculcated.
ArtBeat – Deep Convolutional Networks for Emotional Inference to Enhance Art with Music

Paintings and music are two universal forms of art that are present across all cultures and times in human history. In this paper, we present ArtBeat, a machine learning application to connect the two. Not only are these two art forms universal, but they are also deeply emotionally charged. This emotional factor is what we use as a bridge between the mediums. Using a Convolutional Neural Network (CNN), we aimed to create a model that can classify the emotions evoked by a painting, and use the predicted values to pair it with a piece of music to complement the viewing experience. Our system uses a pre-trained Wide ResNet model as a base, which we then fine-tuned. In this paper, we describe the design and implementation of this model as well as report its results and analyze its behaviour.
Development of hysteresis-free and linear knitted strain sensors for smart textile applications

Smart textiles have been attracting considerable interest in imparting a wide range of functions to traditional clothing ranging from sensing, actuation, data processing, and energy storage. In the case of textile-based strain sensors, most of the studies proved that they can work in principle, however, producing strain sensors with desirable properties such as stable sensitivity, small hysteresis, large enough working range, and good repeatability still remains a challenge necessitating the developments of novel technologies for soft sensors. This paper conducts a systematic approach to investigate the electromechanical properties of the knitted strain sensors to find out the optimum process parameters. We found a repeatable and robust method to produce knitted strain sensors with low hysteresis at a working range of at least 40%.
Work production using the two-way shape memory effect in NiTi and a Ni-rich NiTiHf high-temperature shape memory alloy

The work output capacity of the two-way shape memory effect (TWSME) in a Ni50.3Ti29. 7Hf20 (at%) high-temperature shape memory alloy (HTSMA) was investigated and compared to that of binary Ni49.9Ti50. 1 (at%). TWSME was induced through a training procedure of 100 thermomechanical cycles under different tensile stresses. It was observed that TWSME in as-extruded and trained Ni50.3Ti29. 7Hf20 could produce 0.7% strain against a compressive stress of 100 MPa, corresponding to a maximum work output of 0.08 J g−1, compared to a maximum value of 0.06 J g−1 for binary NiTi. A peak aging heat treatment of 3 h at 550 °C, which previously has been shown to result in near-perfect functional stability in Ni50.3Ti29. 7Hf20 during isobaric thermal cycling, did not improve the TWSME and actually resulted in a decrease in the magnitude and stability of the TWSME and its work output capacity. Nevertheless, the magnitude of TWSM behavior of Ni50.3Ti29. 7Hf20, in the absence of an aging heat treatment, renders it an attractive candidate for high-temperature TWSM actuation.
Modelling of suspension components in a rail vehicle dynamics context

Suspension components play key roles in the running behaviour of rail vehicles, and therefore, mathematical models of suspension components are essential ingredients of railway vehicle multi-body models. The aims of this paper are to review existing models for railway vehicle suspension components and their use for railway vehicle dynamics multi-body simulations, to describe how model parameters can be defined and to discuss the required level of detail of component models in view of the accuracy expected from the overall simulation model. This paper also addresses track models in use for railway vehicle dynamics simulations, recognising their relevance as an indispensable component of the system simulation model. Finally, this paper reviews methods presently in use for the checking and validation of the simulation model.