Reviewerly/exHarmony huggingface dataset:
SPV-SSD: An Anchor-Free 3D Single-Stage Detector with Supervised-PointRendering and Visibility Representation

Recently, 3D object detection based on multi-modal sensor fusion has been increasingly adopted in automated driving and robotics. For example, the semantic information provided by cameras and the geometric information provided by light detection and ranging (LiDAR) are fused to perceive 3D objects, as single modal sensors are unable to capture enough information from the environment. Many state-of-the-art methods fuse the signals sequentially for simplicity. By sequentially, we mean using the image semantic signals as auxiliary input for LiDAR-based object detectors would make the overall performance heavily rely on the semantic signals. Moreover, the error introduced by these signals may lead to detection errors. To remedy this dilemma, we propose an approach coined supervised-PointRendering to correct the potential errors in the image semantic segmentation results by training auxiliary tasks with fused features of the laser point geometry feature, the image semantic feature and a novel laser visibility feature. The laser visibility feature is obtained through the raycasting algorithm and is adopted to constrain the spatial distribution of fore- and background objects. Furthermore, we build an efficient anchor-free Single Stage Detector (SSD) powered by an advanced global-optimal label assignment to achieve a better time–accuracy balance. The new detection framework is evaluated on the extensively used KITTI and nuScenes datasets, manifesting the highest inference speed and at the same time outperforming most of the existing single-stage detectors with respect to the average precision.
Extended Graph of Fuzzy Topographic Topological Mapping Model: G04(FTTMn4)

Fuzzy topological topographic mapping (FTTM) is a mathematical model that consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. The key to the model is its topological structure that can accommodate electrical or magnetic recorded brain signal. A sequence of FTTM, FTTMn, is an extension of FTTM whereby its form can be arranged in a symmetrical form, i.e., polygon. The special characteristic of FTTM, namely, the homeomorphisms between its components, allows the generation of new FTTM. The generated FTTMs can be represented as pseudo graphs. A pseudo-graph consists of vertices that signify the generated FTTM and edges that connect their incidence components. A graph of pseudo degree zero, G0(FTTMnk ), however, is a special type of graph where each of the FTTM components differs from its adjacent. A researcher posted a conjecture on G03(FTTMn3) in 2014, and it was finally proven in 2021 by researchers who used their novel grid-based method. In this paper, the extended G03(FTTMn3), namely, the conjecture on G04(FTTMn4) that was posed in 2018, is narrated and proven using simple mathematical induction.
An Information System Supporting Insurance Use Cases by Automated Anomaly Detection

The increasing availability of vast quantities of data from various sources significantly impacts the insurance industry, although this industry has always been data driven. It accelerates manual processes and enables new products or business models. On the other hand, it also burdens insurance analysts and other users that need to cope with this development parallel to other global changes. A novel information system (IS) for artificial intelligence (AI)-supported big data analysis, introduced within this paper, shall help to overcome user overload and to empower human data analysts in the insurance industry. The IS research’s focus lies neither in novel algorithms nor datasets but in concepts that combine AI and big data analysis for synergies, such as usability enhancements. For this purpose, this paper systematically designs and implements an AI2VIS4BigData reference model to help information systems conform to automatically detect anomalies and increase its users’ confidence and efficiency. Practical relevance is assured by an interview with an insurance analyst to verify the demand for the developed system and derive all requirements from two insurance industry user stories. A core contribution is the introduction of the IS. Another significant contribution is an extension of the AI2VIS4BigData service-based architecture and user interface (UI) concept on AI and machine learning (ML)-based user empowerment and data transformation. The implemented prototype was applied to synthetic data to enable the evaluation of the system. The quantitative and qualitative evaluations confirm the system’s usability and applicability to the insurance domain yet reveal the need for improvements toward bigger quantities of data and further evaluations with a more extensive user group.
Improving Healthcare Question Answering System by Identifying Suitable Answers

Healthcare question answering is an important task in the field of Natural Language Processing. As the era of the internet has already begun, textual data related to the healthcare field has also increased rapidly. There is a huge opportunity to utilize the full potential of deep learning-based methods to achieve better accuracy in healthcare question answering. Unfortunately, the open-domain pre-trained models do not perform well in healthcare question answering. As the terminologies used in the healthcare field are unique, the concept of understanding them is hard. To resolve this issue, in this project, the BioBERT model is used to answer questions related to the healthcare field. BioBERT is based on BERT (Bidirectional Encoder Representations from Transformers) and it is pre-trained on large biomedical corpora which gives it the ability to understand healthcare-related terminologies. As the BERT model has already proved its efficiency as a bidirectional language model, the BioBERT model here is trained with the SQUAD question-answering dataset. The model is able to answer questions related to the healthcare field with an F1 score of 88.
A Continual Few-shot Learning Method via Meta-learning for Intrusion Detection

Most of the existing intrusion detection systems use supervised machine learning models, which can detect attacks well by using a large amount of sample data. However, with the rapid changes in the network environment and the emergence of various attack methods, the ability to respond quickly is required, which requires the intrusion detection system to continuously learn new knowledge from a few shots of new attack samples. In this paper, we describe this problem as continuous few-shot learning for intrusion detection and propose a metric-based first-order meta-learning framework, which enables intrusion detection models to be trained through multiple tasks to maximize the model’s generalization ability to handle different tasks. In the face of increasing attack classes, the trained model can quickly adapt to new attacks with a few shots of samples. In addition, to avoid forgetting the previous knowledge, we save a small number of representative old-class attack samples. With extensive experiments, the results show that the model has good plasticity, and can detect new attacks well using a few shots of samples.
Deep COVID-19 Face Mask Detection in Public Using Transfer Learning

Ahstract- The face mask is necessary for crowded places to control the pernicious effect of Corona Virus (COVID-19). The government officials of various countries have mandated the usage of face masks in public places. However, inspecting unmasked people in crowded areas is very tough. To solve this issue, the research demonstrates the automatic detection of masked faces from the images using transfer learning. In the proposed works, the pre-trained models ResNet34 and ResNet50 have been used on the MAFA data set to analyze the accuracy of face mask detection. Experimental testing evaluated 91.74% accuracy for ResNet34 whereas ResNet50 outperformed and achieved 92.3% accuracy. However, the training loss is found to be minimum in Resnet50 as compared to Resnet34.
Boosting Query Efficiency of Meta Attack With Dynamic Fine-Tuning

In black-box attack, excessive queries to target model may cause suspicion and expose attacker's identity. Equipped with advanced meta learning technique, Meta Attack simulates the target model with a surrogate model, significantly reducing the queries. However, it queries for ZOO-gradients to correct the estimated meta-gradients with a fixed frequency, thereby still leading to massive unnecessary queries. To overcome this limitation, this letter takes the dynamic changes of the accuracy of the estimated gradients as a starting point, and develops a Dynamic Meta Attack (DMA). At the beginning of each fine-tuning round, DMA computes the distance between the above two types of gradients. Such distance metric can reflect the accuracy of the meta-gradients, and guide the dynamic adjustment of query frequency for the ZOO-gradients. Moreover, the working flow of the dynamic fine-tuning process can be controlled by a set of parameters, which are of physical significance and easy to be tuned. By this means, DMA merely launches queries at critical moments, greatly saving query resource. Experiments conducted on MNIST and CIFAR10 show that the proposed DMA requires far fewer queries than existing methods while maintaining a satisfying attack success rate and distortion.
Membership Inference Attacks Against Robust Graph Neural Network

With the rapid development of neural network technologies in machine learning, neural networks are widely used in artificial intelligence tasks. Due to the widespread existence of graph data, graph neural networks, a kind of neural network specializing in processing graph data, has become a research hotspot. This paper firstly studies the relationship between adversarial attacks and privacy attacks on graphs, i.e., whether a robust model trained on graph adversarial can improve the attack effect of graph membership inference attacks. We also find the different performance of the robust model’s loss function on the training set and the test set is a critical reason for the increasing membership inference attack success rate. Extensive experimental evaluations on Cora, Cora-ml, Citeseer, Polblogs and Pubmed demonstrate that the robust model obtained by adversarial training can significantly improve the attack success rate of membership inference attacks.
An Asynchronous Federated Learning Optimization Scheme Based on Model Partition

Federated learning based on edge computing environment has great potential to facilitate the implementation of artificial intelligence at the edge of the network. However, because of the limited resource at the edge, place the complete Deep Neural Networks (DNN) model on the edge for training may not a good choice. In this paper, we study the time optimization for asynchronous federated learning based on model partition. That is, the DNN model is divided into two parts and deployed separately on the device and the edge server for the model training. First, we give the metric of the relationship between learning accuracy and iteration frequency, and then we build a mathematical model based on this. Because the solution space of mathematical model is too large to be solved directly, we propose an algorithm to minimize the total time by dynamically adjusting the model partition point and bandwidth allocation. Simulation results show that our algorithm can reduce the time by 32% to 60% compared with the other three methods.
Virtual, Augmented, and Mixed Reality Interventions for Physical Activity: A Systematic Review

The emergence of Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies have made it possible to actualize many things in virtual space that may have cost more to explore in real-life settings. Such technologies have significantly contributed to human health and well-being over the years and they recently gained research attention in promoting physical activities across all age groups. This paper presents a systematic review of VR/AR/MR applications for promoting physical activity (PA). We reviewed 39 papers from 5 databases that met the inclusion criteria. The review results show that (1) VR-driven interventions were more common than AR and MR interventions, (2) more interventions were targeted toward the general public, (3) most interventions adopted exergames as the approach for designing their systems, (4) the self-monitoring strategy emerged as the most used persuasive strategy while tailoring was the least used strategy, and (5) the majority of the interventions were perceived to be effective in promoting PA. We propose that future AR/VR/MR interventions should investigate skill-building goals through systems that provide dynamic feedback to the target audience. This will allow participants to gain experience and skills to promote their physical health.
Building a Unified Ontology for Behavior Driven Development Scenarios

Behavior Driven Development (BDD) offers a way to write scenarios in structured natural language on how to successfully fulfill a requirement. We fail to find documentation on how to use existing BDD templates. A set of templates with a clear definition of the keywords to use would provide guidance. This paper empirically explores the keywords found in the different dimensions of BDD scenarios to build a reference set of non-redundant concepts.
Cross-Modal Generation and Pair Correlation Alignment Hashing

Cross-modal hashing is an effective cross-modal retrieval approach because of its low storage and high efficiency. However, most existing methods mainly utilize pre-trained networks to extract modality-specific features, while ignore the position information and lack information interaction between different modalities. To address those problems, in this paper, we propose a novel approach, named cross-modal generation and pair correlation alignment hashing (CMGCAH), which introduces transformer to exploit position information and utilizes cross-modal generative adversarial networks (GAN) to boost cross-modal information interaction. Concretely, a cross-modal interaction network based on conditional generative adversarial network and pair correlation alignment networks are proposed to generate cross-modal common representations. On the other hand, a transformer-based feature extraction network (TFEN) is designed to exploit position information, which can be propagated to text modality and enforce the common representation to be semantically consistent. Experiments are performed on widely used datasets with text-image modalities, and results show that the proposed method achieved competitive performance compared with many existing methods.
Unsupervised Acoustic Anomaly Detection Systems Based on Gaussian Mixture Density Neural Network

Failure of critical industrial equipment leads to significant costs due to downtime and unplanned maintenance interventions, which in turn drives the demand for Acoustic Anomaly Detection (AAD). Previous approaches based on a deep Auto-Encoder (AE) followed by Gaussian Mixture Models (GMMs) have made significant progress. However, these tandem approaches suffer from a major weakness in that the dimensionality reduction of the feature stage and the density estimation stage are optimised separately. This paper proposed an unsupervised Gaussian Mixture Density Neural Network (GMDNN). In our method, instead of using a tandem two-stage AAD system, GMDNN jointly optimizes the dimension reduction network that generates a low-dimensional representation, the reconstruction contrast network that produces contrastive features, and the density estimation network with improved end-to-end anomaly detection performance. Experimental results on MIMII DUE industrial datasets show that GMDNN significantly outperforms previous approaches and improves up to 14.7% based on the mean of area under the curve (AUC) score.
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation

For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5 $$\times $$ without sacrificing the accuracy. Our code will be publicly available at https://github.com/Wenxuan-1119/Med-DANet .
Blockchain-Based Smart Home Gateway using Secure Chaotic Hash Function

There are a number of issues that existing authentication systems face in a smart community. These include insufficient authentication efficiency, an inflexible authentication methodology, and an unsecured data exchange server. The dependability and security of the proposed blockchain-based smart home strategy were extensively tested for confidentiality, integrity, and privacy, as well as other factors. In terms of the parameters of classic centralized architecture, blockchain technology is being used in the data storage and exchange blocks. To ensure data integrity both inside and outside of the smart home network, it is necessary to ensure that all users present in the network are authenticated. The network under consideration is built on the Ethereum platform. The performance measures to be considered were accuracy, security, and response time. The experimental results reveal that our proposed technique outperforms currently available techniques in terms of overall performance. In particular, when compared to other state-of-the-art systems, our proposed system has the highest accuracy of 96% and generates a stronger output in the second stage when compared to centralized architecture with an accuracy of 71%, representing a 25% difference in terms of improving the accuracy of smart homes.
Strong Robustness Watermarking Algorithm Based on Lifting Wavelet Transform and Hessenberg Decomposition

Watermark imperceptibility and robustness in the present watermarking algorithm based on discrete wavelet transform (DWT) could be weakened due to data truncation. To solve this problem, a strong robustness watermarking algorithm based on the lifting wavelet transform is proposed. First, the color channels of the original image are separated, and the selected channels are processed through lifting wavelet transform to obtain low-frequency information. The information is then split into blocks, with Hesseneberg decomposition performed on each block. Arnold algorithm is used to scramble the watermark image, and the scrambled watermark is transformed into a binary sequence that is then embedded into the maximum element of Hessenberg decomposed matrix by quantization modulation. The experimental results exhibit a good robustness of this new algorithm in defending against a wide variety of conventional attacks.
Voronoi-Based Collision Avoidance Using Localization Uncertainty Region

Multi-robot systems have been applied to numerous professional areas in industry, consisting of applications from warehousing logistics to drone swarm for military. Under realistic working conditions, errors of localizations may lead to collisions between robots although the multi-robot system is theoretically collision-free. This paper introduces a comprehensive control algorithm for collision-free multi-robot systems based on buffered Voronoi diagram enhanced by localization uncertainty region. Compared to previous related works, this paper combines the features of the Voronoi diagram, two-dimensional Gaussian distribution and the deadlock avoidance algorithm together to derive a control algorithm endowing the multi-robot system with high robustness and practical reliability.
Visualization Of Furniture Model Using Augmented Reality

AR furniture aims at providing a platform to visualize a furniture model using augmented reality (AR) so that it will be easy to choose the furniture according to color combinations and combinations of furniture. This application provides a real representation of furniture as a 3D model interpreting in a real world. This Application let user choose the choice of furniture from a range of furniture and let them place 3D object of the same furniture on empty area so that they can look at the way it will feel at that area. You can view multiple objects at the same time. It will help in decorating empty room. By looking at multiple object user will be able to choose object that he/she desires. The project is developed on Unity Software and Visual Studio
Global and Part Feature Fusion for Cross-Modality Person Re-Identification

Visible-Infrared person re-identification (VI Re-ID) is a challenging but practical task that aims at matching pedestrian images between the visible(daytime) modality and the infrared(nighttime) modality, playing an important role in criminal investigation and intelligent video surveillance applications. Numerous previous studies focused on alleviating the modality discrepancy and obtaining discriminating features by devising complex networks for VI Re-ID, but a cumbersome network structure is not suitable for practical industrial applications. In this paper, we propose a novel fusion method of global and part features to extract distinguishing features and alleviate cross-modality differences, named Global and Part Feature Fusion network(GPFF), which has not been well studied in the current literature. Specifically, we first adopt a dual-stream ResNet50 as a backbone network to alleviate the modality discrepancy. Then, We explore how to fuse global and local features to obtain discriminative features. Finally, we apply a heterogeneous center triplet loss(hetero-center triplet loss) instead of traditional triplet loss to guide sample center learning. Our proposed approach is simple but effective, and can remarkably boost the performance of VI Re-ID. The results of experiments on two public datasets(SYSU-MM01 and RegDB) demonstrate that our approach is superior to the state-of-the-art methods. Through experiments, we find that the effective fusion of global features and local features plays an important role in extracting discriminative features.
Variance Reduction in Stochastic Methods for Large-Scale Regularized Least-Squares Problems

Large dimensional least-squares and regularized least-squares problems are expensive to solve. There exist many approximate techniques, some deterministic (like conjugate gradient), some stochastic (like stochastic gradient descent). Among the latter, a new class of techniques uses Determinantal Point Processes (DPPs) to produce unbiased estimators of the solution. In particular, they can be used to perform Tikhonov regularization on graphs using random spanning forests, a specific DPP. While the unbiasedness of these algorithms is attractive, their variance can be high. We show here that variance can be reduced by combining the stochastic estimator with a deterministic gradient-descent step, while keeping the property of unbiasedness. We apply this technique to Tikhonov regularization on graphs, where the reduction in variance is found to be substantial at very small extra cost.