Reviewerly/exHarmony huggingface dataset:
Application of hybrid SFLA-ACO algorithm and CAM softwares for optimization of drilling tool path problems

In drilling process almost seventy percent time is spent in tool switching and moving the spindle from one hole to the other. This time travel is non productive as it does not take part in actual drilling process. Therefore, this non productive time needs to be optimized. Different metaheuristic algorithms have been applied to minimize this non productive tool travel time. In this study, two metaheuristic approaches, shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) have been hybridized. In industry, the CAM softwares are employed for minimization of non productive tool travel time and it is considered that the path obtained by using the CAM softwares is the optimized path. However this is not the case in all problems. In order to show the contribution of the SFLA-ACO algorithm and to prove that results achieved through CAM softwares are not always optimized, hybrid SFLA-ACO algorithm has been applied to two drilling problems as case studies with the main objective of minimization of non productive tool travel time. The drilling problems which are taken from the manufacturing industry include ventilator manifold problem and lift axle mounting bracket problem. The results of hybrid SFLA-ACO algorithm have been compared with the results of commercially available computer aided manufacturing (CAM) software. For comparison purpose, the CAM softwares used are Creo 6.0, Pro E, Siemens NX and Solidworks. The comparison shows that the results of proposed hybrid SFLA-ACO algorithm are better than commercially available CAM softwares in both real world manufacturing problems.Different optimization techniques are being used for optimization of drilling tool path problems. In this paper two techniques SFLA and ACO has been combined to form a hybrid SFLA-ACO algorithm and has been applied to the real world industrial problems.Two real world problems have been taken from the local manufacturing industries. In both the problems the objective is to optimize the tool traveling time through hybrid SFLA-ACO and compare it with CAM software.Four CAM softwares have been used for comparison purpose. The problems undertaken are solved through these CAM software and compared with the results of hybrid SFLA-ACO results. As result of comparison it is found that in both the problems the performance of hybrid SFLA-ACO algorithm remains outclass. This signifies that results of CAM software in case of optimization of drilling tool path are not always optimal and these can be improved by using different optimization techniques.
Random Orthogonal Search with Triangular and Quadratic Distributions (TROS and QROS): Parameterless Algorithms for Global Optimization

In this paper, the behavior and performance of Pure Random Orthogonal Search (PROS), a parameter-free evolutionary algorithm (EA) that outperforms many existing EAs on the well-known benchmark functions with finite-time budget, are analyzed. The sufficient conditions to converge to the global optimum are also determined. In addition, we propose two modifications to PROS, namely Triangular-Distributed Random Orthogonal Search (TROS) and Quadratic-Distributed Random Orthogonal Search (QROS). With our local search mechanism, both modified algorithms improve the convergence rates and the errors of the obtained solutions significantly on the benchmark functions while preserving the advantages of PROS: parameterless, excellent computational efficiency, ease of applying to all kinds of applications, and high performance with finite-time search budget. The experimental results show that both TROS and QROS are competitive in comparison to several classic metaheuristic optimization algorithms.
Pseudo-spectrum-based multi-sensor multi-frame detection in mixed coordinates

Multi-sensor track-before-detect (MS-TBD) can greatly enhance the weak target detection ability due to utilizing multi-frame (MF) data from multiple sensors. However, in the existing MS-TBD methods, when the resolutions of the MS are different, the target echoes obtained by the MS are arranged to the measurement space with unified resolution. This process may remove some valuable information of raw sensor data or introduce additional noise, which may lead to inadequate integration of the echo energy and degradation of the output envelope. For the sake of solving the issues mentioned above, a pseudo-spectrum (PS) method in mixed coordinates is investigated for TBD in MS systems. For each sensor, the conversion of each measurement cell's position from sensor coordinates to Cartesian coordinates is carried out and the prediction of that is conducted with an assumed velocity in Cartesian framework. Then the predicted position is converted back to global sensor coordinates, which takes one of the sensors as the origin. A PS is designed around the predicted global polar position and its samples are added to the integration frame for MF accumulation. This avoids additional spatial alignment and facilitates well-maintained target envelope, leading to effective energy integration and the improvement of estimation accuracy. The procedures of coordinate transformation and MF accumulation in multiple sensors are derived and the analysis of theoretical output SNR is provided. The effectiveness and the superiority of the proposed method is demonstrated by several simulations.
Flood Vulnerability Analysis using Random Forest Method in Gresik Regency, Indonesia

Abstract Recently, machine learning models have been used for flood vulnerability mapping. The purpose of this study is analyze the level of flood vulnerability in Gresik Regency using machine learning Random Forest. Data processing is done by dividing the flood occurrence dataset that contains parameter values into two datasets, training data as input data for random forest and testing data to evaluate the output model. The results of the flood vulnerability level are divided into 3 classes of flood vulnerability, which are low, medium and high levels vulnerability. The results of the importance degree index show that the most influential parameter of flood vulnerability is the river with a value of 41.025%. Furthermore, The performance of the random forest model shows an overall accuracy and kappa coefficient of 0.859 and 0.718, respectively while the evaluation using the ROC curve shows an AUC value of 0.97 which indicates the classification results are included in the very good category.
Visual Three-Dimensional Reconstruction Based on Spatiotemporal Analysis Method

To accurately reconstruct the three-dimensional (3D) surface of dynamic objects, we proposed a wrapped phase extraction method for spatiotemporal analysis based on 3D wavelet transform (WT). Our proposed method uses a 2D spatial fringe image combined with the time dimension and forms a 3D image sequence. The encoded fringe image sequence’s wrapped phase information was extracted by 3D WT and complex Morlet wavelet, and we improved the wrapped phase extraction’s accuracy by using the characteristics of spatiotemporal analysis and a multi-scale analysis of 3D WT, then we reconstructed the measured object by wrapped phase unwrapping and phase height transformation. Our simulation experiment results show that our proposed method can further filter the noise in the time dimension, and its accuracy is better than that of the one- (1D) and two-dimensional (2D) WT wrapped phase extraction method and the 3D Fourier transform wrapped phase extraction method because the reconstructed spherical crown’s RMSE value does not exceed 0.25 and the PVE value is less than 0.95. Our results show that the proposed method can be applied to the dynamic 3D reconstruction of a real human thoracic and abdominal surface, which fluctuates slowly with respiration movement, further verifying its effectiveness.
Determinants of student performance with mobile‐based assessment systems for <scp>English</scp> as a foreign language courses

Abstract Background Mobile‐based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile‐based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives This study aims to identify the determinants of student performance during students' use of a mobile‐based assessment application in a formative assessment activity as part of English as a Foreign Language (EFL) courses in higher education. Methods A structural model based on hypotheses was validated using PLS‐SEM with data from the interaction of 127 students of eight EFL courses from the A1 and A2 levels of English that used a mobile‐based assessment system for a period of 4 weeks. Automatic data collection in the application and self‐reported instruments were applied. Results and Conclusions Use of scaffolding mechanisms, time on‐task and reported effort are strong predictors of students' learning outcomes. The use of scaffolding strategies predicts students' time on‐task. The provision of corrective feedback is not a predictor of students' learning performance but predicts other constructs such as perceived usefulness and the behavioural intention to use. Implications Mobile‐based assessment systems should include scaffolding mechanisms and integrate strategies to increase the perceived relevance of the formative assessment activity to increase the student learning performance. Scaffolding mechanisms are also useful to increase the student time on‐task in the formative assessment activity. In mobile‐based formative assessment activities more elaborated forms of feedback other than corrective feedback are needed to increase student performance.
Quality-aware pattern diffusion for video object segmentation

Recently, great progresses have been achieved under the support of memory mechanism when dealing with video object segmentation (VOS) problem. Despite its achievements, existing VOS approaches still suffer from abnormal samples, which derive from intrinsic video artifacts such as occlusion and motion blur. To mitigate the above issue, in this paper, we propose a quality-aware pattern diffusion (QPD) framework to boost the VOS performance. To achieve quality-aware pattern diffusion, a quality alignment mechanism is proposed, and it aims to promote the contributions of those normal samples while suppressing those abnormal ones during the feature propagation/diffusion processes. With our proposed quality alignment mechanism, the diffused instance features could be kept staying in the normal feature space, keeping from feature contamination caused by those low-quality samples. We first introduce a learnable quality evaluator to assess the sample qualities in both the temporal domain (i.e., across the historical frames), as well as the spatial domain (i.e., within the current frame). To achieve adaptive historical feature propagation into the current instance, a quality-aware long-term context propagation module is proposed, with which more stable instance representations could be achieved through the established quality-aware feature propagation process. A quality-aware pattern diffusion module is further introduced to address the spatial-domain abnormal samples, resulting in effective decoder feature refinement through building the quality-aware correspondence weights. Extensive experiments have demonstrated that our proposed quality alignment mechanism could boost the performance by a great margin over a strong baseline while achieving state-of-the-art performances on public VOS benchmarks.
Mobile and internet usage, institutions and the trade balance: Evidence from African countries

Abstract This study examines the influences of institutions, the Internet and mobile usage on the trade balance of African countries between 2003 and 2017. Our empirical results have been estimated with a panel‐corrected standard error method (PSCE) and they have been confirmed by several alternative techniques. First, the increase of internet usage and mobile usage has a significant negative effect on total and inter‐continental trade balances while these factors improve the intra‐African trade balances. Second, better institutions appear to have a negative impact on the total‐, inter‐, and intra‐African trade balances – in other words, better institutions appear to stimulate imports rather than exports. This observation explains the decreasing trends in the current account balances of African countries. Third, the combined effect of the three factors (institutions, internet, and mobile use together) has a significant positive impact on all trade balances: total‐, inter‐, and intra‐continental. Our study shows that an improvement in institutional quality acts as a mitigating factor for any negative impact internet\mobile development might cause on the trade balances of African countries. Further, our analysis examines the influence of institutions, internet usage, and mobile usage on the two parts of the trade: exports and imports. We observe that internet and mobile can influence negatively and differently impact the two wings of the balance trade. However, all improvements in institutions and their associations with internet usage and mobile usage have a significant positive impact on the trade balance especially on exporting activities of African countries.
A New Type of Classical Logic Circuit with Exponential Speedup

Compared with classical algorithms, quantum algorithms can show the advantage of exponential speedup in solving some problems. Solving the NAND‐Tree problem is a typical example, which can be speed up exponentially by quantum walk. This scheme is groundbreaking due to the universality of the negative‐AND (NAND) gate. In fact, the NAND gate is also universal for classical computation, making it an important target for development. Herein, the quantum NAND‐Tree onto the classical circuit network is mapped and a new type of classical logic circuit with the NAND gate that can perform the quantum algorithms is designed. These classical logic gates that are designed have exponential speedup functions compared with conventional ones. Because classical circuit networks possess good scalability and stability, the present scheme is expected to have potential applications for information processing in the era of big data.
Boosting unsupervised domain adaptation: A Fourier approach

By using unsupervised domain adaptation (UDA), knowledge is transferred from a label-rich source domain to a target domain that contains relevant information but has no labels. Most existing UDA algorithms primarily align domain-invariant features are primarily aligned during training, whereas target-specific information is ignored when learning the domain-invariant features. To address this issue, we attempted to boost the performance of unsupervised domain adaptation using a Fourier approach (FUDA). Specifically. FUDA is inspired by the fact that the amplitude of the Fourier spectrum mainly primarily preserves low-level statistics. Thus, the source domain can be augmented in FUDA to effectively equip with some low-level information in a target domain by fusing the amplitude of the two domains in the Fourier domain. Meanwhile, we propose Fourier transform channel attention, which represents the weight of Fourier transform to capture feature diversity. On the basis of Fourier analysis, we further show that the conventional attention that is built upon global average pooling is a special case of our proposed attention. Our method is evaluated by using four domain adaptation benchmarks, such as Office-31, Office-Home, VisDA-2017 and DomainNet, demonstrating the effectiveness of our FUDA.
Trajectory Planning and Control of Bathy-drone: A Drone Towing a Boat equipped with Sonar for Bathymetry Mapping

This paper considers a trajectory planning and control problem for a drone that tows an unpowered boat via a tether. The system, named Bathy-drone, is tasked with underwater bathymetry mapping. The boat equipped with a side-scan sonar sensor that can scan seafloor and obtain side-scan sonar imagery. Because the sonar vessel is unpowered, this platform is able to scan shallow water environment that other boats with propulsion system cannot navigate. However, because the sensing unit is dragged through a tether, which can be taut or loose, relative configurations and the delayed dynamics of the vessel must be incorporated in the planning and control algorithm because the vessel trajectory does not coincide with the drone trajectory. This paper proposes a trajectory planning and control approach for a drone that can maneuver a tethered vessel such that the sensor field-of-view scans the entire given region of interest considering the delayed dynamics of a tethered system by applying a concept of rapidly-exploring random trees (RRT). The proposed trajectory planning and control approach was be developed by a Gazebo Simulator and demonstrated by experiments on a retention pond in Citra, FL
A Comprehensive Assessment to the Potential Of Reinforcement Learning In Dynamic Soaring

The ability of an albatross to travel very long distances without any source of power, has drawn researchers' attention. Albatross exploits an inspiring environmental phenomenon named by Dynamic Soaring (DS), through which it extracts energy from the wind -- particularly, when the wind is changing with altitude causing what is known as "wind shear". This paper provides the DS problem setup to be solved as an optimal control problem as typically done in literature. Moreover, We investigate a novel deep reinforcement learning (RL) approach in continuous space to find the optimal control signals corresponding to optimal DS trajectories of bio-mimicking UAVs performing DS and flying in dynamic shear wind conditions. In addition, we also compare the performance of different deep RL algorithms for trajectory optimization in terms of total average reward to identify some candidate approaches. Additionally, we compare the RL results with two numerical optimizers: GPOPS2, which can be applied using Matlab; and a direct collocation based algorithm, which can be applied using Python. We provide discussions and assessments following said comparisons between the newly developed RL framework and the two numerical optimizers above-mentioned.
Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.
INS Aiding by Combining Radar Altimeter and Seeker Measurements

INS is aided by GPS for most of the missile applications. However, GPS blockage can occur, especially in combat areas. Without aid, INS errors will diverge. This paper proposes a novel methodology to improve the INS solution by using two extra measurements from a seeker and radar altimeter. Seekers are usually used at the final stage of the flight to measure the line-of-sight (LOS) angle and/or LOS rates. The radar altimeter provides the distance between the missile and the ground. In this study, seeker’s measurements, during the midcourse, and radar altimeter measurements are fused with the INS by using Extended Kalman Filter. The main advantage of the proposed methodology is that the position of the landmark is not required if the ground over which the missile flying is in the same level. Simulation results demonstrate the effectiveness of the proposed methodology.
DLUNet: Semi-supervised Learning Based Dual-Light UNet for Multi-organ Segmentation

The manual ground truth of abdominal multi-organ is labor-intensive. In order to make full use of CT data, we developed a semi-supervised learning based dual-light UNet. In the training phase, it consists of two light UNets, which make full use of label and unlabeled data simultaneously by using consistent-based learning. Moreover, separable convolution and residual concatenation was introduced light UNet to reduce the computational cost. Further, a robust segmentation loss was applied to improve the performance. In the inference phase, only a light UNet is used, which required low time cost and less GPU memory utilization. The average DSC of this method in the validation set is 0.8718. The code is available in https://github.com/laihaoran/Semi-Supervised- nnUNet .
Combining Self-training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation

Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manually annotating organs from CT scans is time-consuming and labor-intensive. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we follow the self-training strategy and employ a high-performance hybrid architecture (PHTrans) consisting of CNN and Swin Transformer for the teacher model to generate precise pseudo labels for unlabeled data. Afterward, we introduce them with labeled data together into a two-stage segmentation framework with lightweight PHTrans for training to improve the performance and generalization ability of the model while remaining efficient. Experiments on the validation set of FLARE2022 demonstrate that our method achieves excellent segmentation performance as well as fast and low-resource model inference. The average DSC and NSD are 0.8956 and 0.9316, respectively. Under our development environments, the average inference time is 18.62 s, the average maximum GPU memory is 1995.04 MB, and the area under the GPU memory-time curve and the average area under the CPU utilization-time curve are 23196.84 and 319.67. The code is available at https://github.com/lseventeen/FLARE22-TwoStagePHTrans .
A Pseudo-labeling Approach to Semi-supervised Organ Segmentation

In this paper, we adopt a "pseudo-labeling" approach to semi-supervised learning based on 50 labeled images and 2000 unlabeled images. This approach yields a model with 0.7496 mean DSC on the validation set, outperforming the 0.6903 mean DSC of the model with only 50 labeled images.