Reviewerly/exHarmony huggingface dataset:
THAN: Multimodal Transportation Recommendation With Heterogeneous Graph Attention Networks

Multi-modal transportation recommendation plays an important role in navigation applications. It aims to recommend a travel plan with various transport modes, such as bus, metro, taxi, bicycle, and a hybrid. Analysis of real-world large-scale navigation data shows that the correlation between the data can be represented by a graph containing different types of nodes and edges. As an emerging technology, graph neural networks (GNN) have shown powerful capabilities in representing graph data. However, existing solutions based on GNN only consider converting heterogeneous graph data into homogeneous graph data, ignoring the effects of different types of nodes and edges. In addition, those methods usually face the over-smoothing problem, which reduces the accuracy of recommendation. To this end, we propose a multi-modal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>T</i> </b> ransportation recommendation algorithm with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>H</i> </b> eterogeneous graph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>A</i> </b> ttention <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>N</i> </b> etworks (THAN) based on carefully constructed heterogeneous graphs. We first design a novel graph embedding method to represent the correlation between the origin and the destination, as well as the correlation between origin-destination (OD) pairs and users. Next, a heterogeneous graph from large-scale data is built to describe the relationship between users, OD pairs, and transport modes. Then, we design a hierarchical attention mechanism with residual blocks to generate node embedding in terms of homogeneity and heterogeneity. Finally, a fusion neural layer is designed to fuse embeddings from different views and predict the proper transport mode for users. Extensive experimental results on a large-scale real-world dataset demonstrate that the performance of THAN outperforms five baselines.
Pattern Matching Algorithm in Event Graphs

Event-graphs are used for modelling and analyzing discrete data that usually represent activities and so on. In this work we take into analysis this kind of graphs and the problem of identifying correlations among events. We propose a methodology to first identify events that are significantly correlated and then to event patterns. Both are often related to individual entities and their behaviour, so that their specificity can be spotted in a complex event network. In the last part of the paper we describe our experimental results.
MontageGAN: Generation and Assembly of Multiple Components by GANs

A multi-layer image is more valuable than a single-layer image from a graphic designer’s perspective. However, most of the proposed image generation methods so far focus on single-layer images. In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our method utilized a two-step approach consisting of local GANs and global GAN. Each local GAN learns to generate a specific image layer, and the global GAN learns the placement of each generated image layer . Through our experiments, we show the ability of our method to generate multi-layer images and estimate the placement of the generated image layers.
Residual-Based Fault Detection and Exclusion With Enhanced Localization Integrity

In this paper, we give an integrity framework for vehicle localization in urban environments based on fusion of GPS and dead reckoning. To guarantee localization integrity, a novel Kalman filter-based fault detection and exclusion method is presented to reject faulty measurements via a chi-square test with iterative residuals. For a tight upper-bound for position error, we introduce a protection level calculation method with minimum detectable bias and the integrity budget allocation among all failure modes have been improved to enhance the accuracy of positioning. The algorithm provides integrity assessment and reliable estimations of vehicle position information in an iterative manner. Numerical simulations and experiments show that the proposed method can effectively deal with and recover from abrupt faults with a low rate of false alarm and a tight integrity risk bound.
Sim2Real Instance-Level Style Transfer for 6D Pose Estimation

In recent years, synthetic data has been widely used in the training of 6D pose estimation networks, in part because it automatically provides perfect annotation at low cost. However, there are still non-trivial domain gaps, such as differences in textures/materials, between synthetic and real data. These gaps have a measurable impact on performance. To solve this problem, we introduce a simulation to reality (sim2real) instance-level style transfer for 6D pose estimation network training. Our approach transfers the style of target objects individually, from synthetic to real, without human intervention. This improves the quality of synthetic data for training pose estimation networks. We also propose a complete pipeline from data collection to the training of a pose estimation network and conduct extensive evaluation on a real-world robotic platform. Our evaluation shows significant improvement achieved by our method in both pose estimation performance and the realism of images adapted by the style transfer.
Fall and Normal Activity Classification via Multiple Wearable Sensors

A fall detection and classification system is crucial for reducing the severe consequences of falls, which account for the leading cause of accidents on construction sites. Wearable sensors are one of the technologies used to detect falls. Although much academic work has been dedicated to the study of this class of systems, little attention has been paid to the evaluation of simpler algorithms prior to training on complex ones. This study utilizes the open-source UP Fall Detection Dataset and proposes that effective data processing and simpler baseline models give better results for fall-direction classification. Several data-processing techniques like windowing and filtering are used prior to using simpler baseline models like Neural Network (NN), K-Nearest Neighbor (kNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Discriminant Analysis (DA) Classifiers. It is also investigated how to minimize multisensor cost while achieving acceptable detection accuracy. Based on this robustness analysis, fine kNN and wide NN yield 99.5% accuracy for all five wearable sensors. In comparison, using the best of these sensors (belt and pocket) results in 99% accuracy, with accuracy of all 11 individual activities exceeding 93%. The findings of this study bode well for the development of real-world fall-prediction systems as they enable accurate fall direction identification.
Vertical Split Learning - an exploration of predictive performance in medical and other use cases

In healthcare and other fields, data of an individual is often vertically partitioned across multiple organizations.Creating a centralized data store for AI algorithm development is cumbersome in such cases because of concerns like privacy and data ownership.Methods of distributed learning over vertically partitioned data could offer a solution here.While several studies have evaluated the feasibility, privacy and efficiency of such methods, an extensive evaluation of their impact on predictive performance compared to a centralized approach is missing.Vertical Split Learning (VSL) aims to provide vertical distributed learning through distributed neural network architectures.Our study adapts and applies VSL to 8 datasets, both in medicine and beyond, evaluating the impact of different network and (vertical) feature distributions on predictive performance.In most configurations VSL yields comparable predictive performance to its centralized counterparts.However, certain data and network distributions give an unexpected and severe loss of performance.Based on our findings we give some initial recommendations under which conditions VSL can be applied as a suitable alternative for data centralization.
Mastery: Shifted-Code-Aware Structured Merging

Three-way merging is an essential infrastructure in version control systems. While the traditional line-based textual methods are efficient, syntax-based structured approaches have shown advantages in enhancing merge accuracy. Prior structured merging approaches visit abstract syntax trees in a top-down manner, which is hard to detect and merge shifted code in the general sense. This paper presents a novel methodology combining a top-down and a bottom-up visit of abstract syntax trees, which manipulates shifted code effectively and elegantly. This merge algorithm is order-preserving and linear-time. Compared with four representative merge tools in 40,533 real-world merge scenarios, our approach achieves the highest merge accuracy and 2.4 $$\times $$ as fast as a state-of-the-art structured merge tool.
Development of a Three-Dimensional Simulator for Integrated Testing of Path-Planners and Controllers for Autonomous Underwater Vehicles

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications ranging from marine biology exploration and search for plane crash sites to ship-hull inspection and border patrol. This paper reports on the development of a modeling and simulation platform that supports the design and testing of path planning and control algorithms in a synthetic AUV, representing a simulated version of a physical AUV. The dynamical behavior of the AUV is modeled using the equations of motion that incorporate the effects of external forces (e.g., buoyancy, gravity, hydrodynamic drag, centripetal force, Coriolis force, etc.), thrust forces, and inertial forces acting on the AUV. The equations of motion are translated into a state space formulation and the S-function feature of the Simulink and MATLAB scripts are used to evolve the state trajectories from initial conditions. Experimental validation is carried out by performing integrated waypoint planner (using the A* algorithm) and PD controller implementations that allow the synthetic AUV to track dynamically feasible trajectories in two-and three-dimensional spaces. An underwater pipe-line inspection task carried out by the AUV is demonstrated in a simulated environment.
Selecting Approaches for Enabling Enterprise Data Search: NASA's Science Mission Directorate (SMD) Catalog

NASA's Science Mission Directorate (SMD) is working to build an open-source science infrastructure to accelerate open, collaborative and interdisciplinary science. One key component in the open-source science infrastructure is the SMD data catalog. In this paper, we present our process for selecting a technical approach to building a NASA SMD enterprise-wide integrated search capability for science users across multiple science disciplines to support discovery and access to complex scientific data.
E-Matdaan: A Blockchain based Decentralized E-Voting System

In current times, electronic voting systems are used for conducting elections but E-voting system has many problems like transparency, credibility, security functionality and reliability and also the complete process is quite slow. Most of these drawbacks can be removed by using Blockchain based E-voting system. Blockchain is a shared, immutable record that allows for the recording of transactions and the tracking of benefits in a network. Blockchain is an immutable and shared record that provides the method of recording transactions and tracking benefits in a network. In this paper, we propose and implement a blockchain based E-voting system using proof of work as the consensus algorithm. We feel this field has a lot of potential and scope of improvement in near future.
Development for geometric image analysis programs for forensic engineering examination

The importance of image analysis in the forensic science field has been increasing rapidly alongside the increased usage of security cameras. Specifically, the analysis of geometric information, such as position, length, and shape (i.e., vehicle speed measurement, height measurement, and gait analysis, respectively), is in high demand. In this paper, we aim to understand the mathematical principles of geometric image analysis and to expand its range of applications by using data-driven analysis. We created a set of image geometric analysis programs for legal engineering appraisals related to the following two topics: 1) lens distortion correction, and 2) analysis of the perspective projection image (s).
Acute Lymphoblastic Leukemia Image Classification Performance with Transfer Learning Using CNN Architecture

Leukemia is diagnosed by observing two indicators, bone marrow smear and peripheral blood smear with laboratory skills using a microscope for diagnosing cancer. All diagnostics tests require advanced laboratory tests and another limitations like time and pricing. With all limitations, this study compares deep learning architectures from image augmentation from HSV images for diagnosis and classification for four label outputs using Adam optimizer. As a result of this study, VGG16 achieved better evaluation results than another architecture which attained an accuracy, sensitivity, specificity, and validation accuracy of 97.50%, 99.96%, 100%, and 98.44%, respectively. For its development in real cases, the modeling can be applied directly to the relevant in the future or using a new novel method architecture.
A Risk Predictive Model for Primary Tumor using Machine Learning with Initial Missing Values

The biological term primary tumor, is growing at the anatomical place where tumor growth began and progressed to produce a malignant mass. The further stage of the primary tumor can lead to cancer. Machine learning assists researchers in identifying and classifying tumors based on growth features, size, speed of spread, and other factors, as well as grouping them based on a comparable set of predicting outcomes. But Missing values in medical data can lead to biased study conclusions and makes it difficult to predict and analyze data with high performance. Therefore, using python KNN imputation was implemented, which sorts multiple complete samples with the nearest measurements using Euclidean distance in the primary tumor missing dataset to find the optimal value of K. After imputing inconsistent data and performing several simulations, the overall performance increased. Hence, this approach may be used to diagnose diseases using more intricate clinical data.
Confidence-Aware Multiscale Learning for Online Modeling of Distributed Parameter Systems With Application to Curing Process

In industrial applications, the modeling of distributed parameter systems (DPSs) is of significance for process control and monitoring. Due to infinite dimension, spatiotemporal coupled dynamics, nonlinearity, and model uncertainties, however, modeling and online applications of DPSs are very difficult. To address these issues, an online spatiotemporal modeling method is proposed based on confidence-aware multiscale learning. From the spacial-scale perspective, an evolutionary learning-based spatial basis function is designed by learning from two dimensionality reduction methods, including Karhunen–Lo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\grave{e}$</tex-math></inline-formula> ve and diffusion maps. From the temporal-scale perspective, an efficient broad learning system is developed as reduced-order model to online address temporal dynamics of DPSs. As for the spatiotemporal-scale learning, Gaussian process regression is proposed as confidence-aware estimator to compensate for model generalization errors caused by spatiotemporal coupled dynamics. Through integration with the three-scale learning, the proposed method enables online confidence-aware prediction for DPSs. Experiments based on the curing process in snap curing oven demonstrate the effectiveness of proposed method.
Motion and Appearance Adaptation for Cross-domain Motion Transfer

Motion transfer aims to transfer the motion of a driving video to a source image. When there are considerable differences between object in the driving video and that in the source image, traditional single domain motion transfer approaches often produce notable artifacts; for example, the synthesized image may fail to preserve the human shape of the source image (cf. Fig. 1 (a)). To address this issue, in this work, we propose a Motion and Appearance Adaptation (MAA) approach for cross-domain motion transfer, in which we regularize the object in the synthesized image to capture the motion of the object in the driving frame, while still preserving the shape and appearance of the object in the source image. On one hand, considering the object shapes of the synthesized image and the driving frame might be different, we design a shape-invariant motion adaptation module that enforces the consistency of the angles of object parts in two images to capture the motion information. On the other hand, we introduce a structure-guided appearance consistency module designed to regularize the similarity between the corresponding patches of the synthesized image and the source image without affecting the learned motion in the synthesized image. Our proposed MAA model can be trained in an end-to-end manner with a cyclic reconstruction loss, and ultimately produces a satisfactory motion transfer result (cf. Fig. 1 (b)). We conduct extensive experiments on human dancing dataset Mixamo-Video to Fashion-Video and human face dataset Vox-Celeb to Cufs; on both of these, our MAA model outperforms existing methods both quantitatively and qualitatively.
NormToRaw: A Style Transfer Based Self-supervised Learning Approach for Nuclei Segmentation

Nuclei segmentation is valuable in histopathological image analysis, but labeling nuclei is costly. Different organs, pa-tients and diseases will lead to high variability in the morphology of nuclei, the structure of tissues, etc., which is difficult to elim-inate. Inconsistent staining operations and scanning operations will cause variability in histopathological image style. Relying on a small amount of labeled data, it is hard for the model to adapt to the high variability among histopathological images. Therefore, it is necessary to exploit the value in the massive unlabeled data. However, because the existing pretext tasks in self-supervised learning do not well consider the characteristics of histopathological images and segmentation task, the same for the existing data augmentation approaches in contrastive learning, they are not suitable for nuclei segmentation. In this paper, the proposed method, named NormToRaw, takes into consideration the characteristics of nuclei segmentation, which can learn semantic information from different stains by style transfer. A generative adversarial network is used to transfer the normalized image to the raw image. Pre-trained on more than 8,000 unlabeled images and trained on 16 labeled images, the experimental results of 5 pre-trained models showed that the proposed method is effective for improving the performance of nuclei segmentation.
Effects of Practice on Software Training Video for College Students

Instructional videos for software training have gradually replaced traditional teaching, becoming the first choice of software operation courses in higher education. An effective measure that can support learning is adding practice. However, when arranging practice could bring the best learning effect has rarely been empirically investigated. This study compared the difference between learning experience and learning performance when practicing before or after the DBT video. Participants were 39 undergraduates. The results showed that the video-practice group felt higher motivation than the practice-video group, but their engagement was at a similar level. Besides, the video-practice group also achieved better learning effect in the software operation test of retention and transfer. But this difference did not exist in knowledge test. Further analysis showed that autonomous learning ability was an important factor in the use of DBT video.
Unsupervised Visual Representation Learning by Online Constrained K-Means

Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering since prevalent clustering methods (e.g., k-means) have to run in a batch mode. Besides, there can be a trivial solution consisting of a dominating cluster. To address these challenges, we first investigate the objective of clustering-based representation learning. Based on this, we propose a novel clustering-based pretext task with online Constrained K-means (CoKe). Compared with the balanced clustering that each cluster has exactly the same size, we only constrain the minimal size of each cluster to flexibly capture the inherent data structure. More importantly, our online assignment method has a theoretical guarantee to approach the global optimum. By decoupling clustering and discrimination, CoKe can achieve competitive performance when optimizing with only a single view from each instance. Extensive experiments on ImageNet and other benchmark data sets verify both the efficacy and efficiency of our proposal.
CMT: Context-Matching-Guided Transformer for 3D Tracking in Point Clouds

How to effectively match the target template features with the search area is the core problem in point-cloud-based 3D single object tracking. However, in the literature, most of the methods focus on devising sophisticated matching modules at point-level, while overlooking the rich spatial context information of points. To this end, we propose Context-Matching-Guided Transformer (CMT), a Siamese tracking paradigm for 3D single object tracking. In this work, we first leverage the local distribution of points to construct a horizontally rotation-invariant contextual descriptor for both the template and the search area. Then, a novel matching strategy based on shifted windows is designed for such descriptors to effectively measure the template-search contextual similarity. Furthermore, we introduce a target-specific transformer and a spatial-aware orientation encoder to exploit the target-aware information in the most contextually relevant template points, thereby enhancing the search feature for a better target proposal. We conduct extensive experiments to verify the merits of our proposed CMT and report a series of new state-of-the-art records on three widely-adopted datasets.