Reviewerly/exHarmony huggingface dataset:
A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm

Quantum ML is a very fast-growing area of research with much theoretical variety & applications. For picture identification, machine learning algorithms learn a desired input–output relationship. Researchers looked into quantum computing technologies in the past few of years to see whether they may help improve traditional ML algorithms. Quantum computing explores the existing approaches of quantum ML. In this article the SVM in a quantum environment gave results accurate and in a speedup manner. The major observations are that the machine recognized the handwritten letters/characters across whole instances. This comparative study offers the comparative analysis as well as the technical scopes in a unified manner and addresses the future of quantum learning theory.
Text sentiment analysis with CNN &amp; GRU model using GloVe

The sentiment analysis is crucial to understanding people’s position, attitude, and opinion about a given event, which has many applications, such as movie review, advertising, electoral prediction, and evaluation of products. There are several techniques for sentiment analysis, but recently most researchers used word embedding methods in the sentiment classification tasks, word2vec, genism and Global Vector (GloVe) are presently among the best usable and accurate word embedding methods which can transform words on a meaningful vector. In this paper, we propose to use GloVe as a word embedding and introduce a developed classification using convolutional neural network (CNN), Gated Recurrent Unit (GRU), and a hybrid model of GRU and CNN applied on IMDB consist of 50k movie review, then we used Adadelta and Adam optimizer. Experimental results show that the CNN_GRU model with the Adadelta optimizer function achieved good classification results with 86.34% on training accuracy value.
Scale attentive network for scene recognition

Scene recognition aims at classifying a scene image to one of the predefined scene categories by comprehending the entire image. The complex composition of scenery images makes scene recognition a challenging task. However, most state-of-the-art visual recognition methods are developed on general-purpose datasets and omit the uniqueness of scene data. In this work, we propose an efficient Scale Attentive (SA) Module to address the predicament of scene recognition, which streamlines the scale-aware attention learning pipeline to assist the feature re-calibration and refinement process. By integrating SA Module into ResNet-50, we obtain a boost of Top-1 accuracy by 1.83% on the benchmark scene dataset with only 0.12% additional parameters and 0.24% additional FLOPs. Moreover, comprehensive experiments show that our method achieves better performance compared with the state-of-the-art attention and multi-scale methods in a computationally efficient manner.
DeepLabV3+/Efficientnet Hybrid Network-Based Scene Area Judgment for the Mars Unmanned Vehicle System

Due to the complexity and danger of Mars's environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.
Exploring Event-Driven Dynamic Context for Accident Scene Segmentation

The robustness of semantic segmentation on edge cases of traffic scene is a vital factor for the safety of intelligent transportation. However, most of the critical scenes of traffic accidents are extremely dynamic and previously unseen, which seriously harm the performance of semantic segmentation methods. In addition, the delay of the traditional camera during high-speed driving will further reduce the contextual information in the time dimension. Therefore, we propose to extract dynamic context from event-based data with a higher temporal resolution to enhance static RGB images, even for those from traffic accidents with motion blur, collisions, deformations, overturns, <i>etc.</i> Moreover, in order to evaluate the segmentation performance in traffic accidents, we provide a pixel-wise annotated accident dataset, namely DADA-seg, which contains a variety of critical scenarios from traffic accidents. Our experiments indicate that event-based data can provide complementary information to stabilize semantic segmentation under adverse conditions by preserving fine-grained motion of fast-moving foreground (crash objects) in accidents. Our approach achieves &#x002B;8.2&#x0025; performance gain on the proposed accident dataset, exceeding more than 20 state-of-the-art semantic segmentation methods. The proposal has been demonstrated to be consistently effective for models learned on multiple source databases including Cityscapes, KITTI-360, BDD, and ApolloScape.
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks

The generative adversarial network (GAN) is successfully applied to study the perceptual single image super-resolution (SISR). However, since the GAN is data-driven, it has a fundamental limitation on restoring real high frequency information for an unknown instance (or image) during test. On the other hand, the conventional model-based methods have a superiority to achieve instance adaptation as they operate by considering the statistics of each instance (or image) only. Motivated by this, we propose a novel model-based algorithm, which can extract the detail layer of an image efficiently. The detail layer represents the high frequency information of image and it is constituted of image edges and fine textures. It is seamlessly incorporated into the GAN and serves as a prior knowledge to assist the GAN in generating more realistic details. The proposed method, named DSRGAN, takes advantages from both the model-based conventional algorithm and the data-driven deep learning network. Experimental results demonstrate that the DSRGAN outperforms the state-of-the-art SISR methods on perceptual metrics, meanwhile achieving comparable results in terms of fidelity metrics. Following the DSRGAN, it is feasible to incorporate other conventional image processing algorithms into a deep learning network to form a model-based deep SISR.
MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series

Detecting anomalies in time-varying multivariate data is crucial in various industries for the predictive maintenance of equipment. Numerous machine learning (ML) algorithms have been proposed to support automated anomaly identification. However, a significant amount of human knowledge is still required to interpret, analyze, and calibrate the results of automated analysis. This paper investigates current practices used to detect and investigate anomalies in time series data in industrial contexts and identifies corresponding needs. Through iterative design and working with nine experts from two industry domains (aerospace and energy), we characterize six design elements required for a successful visualization system that supports effective detection, investigation, and annotation of time series anomalies. We summarize an ideal human-AI collaboration workflow that streamlines the process and supports efficient and collaborative analysis. We introduce MTV (MultivariateTime SeriesVisualization), a visual analytics system to support such workflow. The system incorporates a set of novel visualization and interaction designs to support multi-faceted time series exploration, efficient in-situ anomaly annotation, and insight communication. Two user studies, one with 6 spacecraft experts (with routine anomaly analysis tasks) and one with 25 general end-users (without such tasks), are conducted to demonstrate the effectiveness and usefulness of MTV.
Learning Semantic-Aligned Feature Representation for Text-Based Person Search

Text-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve the feature alignment across modalities. In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features. First, we introduce two Transformer-based backbones to encode robust feature representations of the images and texts. Second, we design a semantic-aligned feature aggregation network to adaptively select and aggregate features with the same semantics into part-aware features, which is achieved by a multi-head attention module constrained by a cross-modality part alignment loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K datasets show that our method achieves state-of-the-art performances.
Cross-Modal Knowledge Distillation For Vision-To-Sensor Action Recognition

Human activity recognition (HAR) based on multi-modal approach has been recently shown to improve the accuracy performance of HAR. However, restricted computational resources associated with wearable devices, i.e., smartwatch, failed to directly support such advanced methods. To tackle this issue, this study introduces an end-to-end Vision-to-Sensor Knowledge Distillation (VSKD) framework. In this VSKD framework, only time-series data, i.e., accelerometer data, is needed from wearable devices during the testing phase. Therefore, this framework will not only reduce the computational demands on wearable devices, but also produce a learning model that closely matches the performance of the computational expensive multi-modal approach. In order to retain the local temporal relationship and facilitate visual deep learning models, we first convert time-series data to two-dimensional images by applying the Gramian Angular Field (GAF) based encoding method. We adopted multi-scale TRN with BN-Inception and ResNet18 as the teacher and student network in this study, respectively. A novel loss function, named Distance and Angle-wised Semantic Knowledge loss (DASK), is proposed to mitigate the modality variations between the vision and the sensor domain. Extensive experimental results on UTD-MHAD, MMAct, and Berkeley-MHAD datasets demonstrate the competitiveness of the proposed VSKD model which can be deployed on wearable devices.
Better Visual Interpretation for Remote Sensing Scene Classification

Deep learning-based methods have been widely applied in remote sensing scene classification tasks. Recently, researchers focus more on clarifying the basis of a decision. For example, class activation mapping (CAM) can provide us the evidence by highlighting the related area in an image. However, the interpretability of remote sensing scene classification is more challenging than natural images, since remote sensing images usually contain more complicated objects. As a result, the CAM visual interpretation with traditional convolutional neural networks cannot accurately locate all target objects, which leads to some important objects are ignored. In this letter, we propose a novel model, named encoder-classifier-reconstruction CAM (ECR-CAM) neural network, to provide a more precise visual explanation. Specifically, ECR-CAM consists of four modules: an encoder module, a classifier module, a reconstruction module, and a CAM module. Encoder module is utilized to extract image features, and classifier module accounts for generating predictions. The reconstruction module is the key to locate more target objects. It employs the extracted features to reconstruct the input images, which is a pixel-level process. The reconstruction process allows the features to retain important information about all objects, which cannot be achieved by the classification task alone. Finally, the CAM module can show more target objects with more informative features. Experimental results show that our model not only improves the classification performance but also can locate the target objects more accurately.
Brain Tumor Detection and Classification using Convolution Neural Network Models

Brain tissue is classified into patients such as Glioma Tumor, Meningioma Tumor, Pituiary Tumor, etc. Appropriate treatment and diagnosis should be made to prolong the life span of the affected people. The most effective way to identify brain tissue is to think of Magnetic Resonance. Large data images show scanning. Radiologist examines these images. The degree of difficulty involved in the structures of the brain adjacent to their structures is an error that occurs through manual testing. This paper proposes the detection and classification of Brain Tumors using in-depth learning algorithms using CNN, ANN and TL. MRI images are classified using ANN and CNN &#x2018;Deep Learning Models&#x2019; methods. These types have permissions and combinations of different &#x2018;Parameter Network&#x2019;. After creating all three types to combine these types we use the middle measurement method.
Multiscale feature fusion for surveillance video diagnosis

Recently, surveillance video diagnosis has attracted increasing interest for generating real-time alarms related to camera failure in video surveillance systems. The existing surveillance video diagnosis methods do not have sufficient ability to detect multiple types of anomalies. Therefore, this paper proposes a surveillance video diagnosis method based on deep learning to detect multiple types of anomalies. A multiscale feature fusion residual network is designed to detect and classify camera anomalies. The experimental results show that the classification accuracy of the proposed method is more than 98%.
Privacy-Preserving Location-Based Data Queries in Fog-Enhanced Sensor Networks

Fog computing has emerged as a promising framework with the rapid growth of the Internet of Things (IoT). In fog computing, the new entity, named fog device, can help the cloud process the large amount of data generated by IoT devices. Along with this trend, a location-based query scheme that collects IoT devices’ data from specific areas is an important application, especially in fog-enhanced sensor networks. However, in this application, the cloud and fog devices require the user’s query, sensors’ locations, and sensor data so that it raises critical privacy and security concerns. In this article, we devise a privacy-preserving-location-based data query scheme in fog-enhanced sensor networks, which allows the cloud and fog devices to collect sensor data from a query area without learning the three kinds of information. Specifically, we resort to a cryptographic primitive, named somewhat homomorphic encryption (SHE), with ciphertext packing to encrypt query, locations, and sensor data and efficiently calculate the distances between the user’s query and sensors. Then, we show how to build a hardware-assisted data query scheme to extract the matched data based on the distances. We formally analyze the security strengths and implement the system prototype. In order to implement secure processing within software guard extension (SGX), we make an effort to adapt the existing mathematical libraries to the advanced SGX trusted environment. Evaluation results demonstrate that our proposed design is secure and efficient.
On Elapsed Time Consensus Protocols

Proof of Elapsed Time (PoET) is a Nakamoto-style consensus algorithm where proof of work is replaced by a wait time randomly generated by a trusted execution environment (TEE). PoET was originally developed by Intel engineers and contributed to Hyperledger Sawtooth, but has never been formally defined or analyzed. In particular, PoET enables consensus on a bitcoin-like scale without having to resort to mining. Proof of Luck (PoL), designed by Milutinovic et al., is a similar (but not identical) protocol that also builds a Nakamoto-style consensus algorithm using a TEE. Like PoET, it also lacks a formal proof.
REVE-CE: Remote Embodied Visual Referring Expression in Continuous Environment

Ithas always been a great challenge for the robot to navigate in the visual world following natural language instructions. Recently, several tasks such as the Vision-and-Language Navigation (VLN) and Remote Embodied Visual Referring Expression in Real Indoor Environments (REVERIE) are proposed trying to solve this challenge. And the most significant difference between VLN and REVERIE tasks is that REVERIE uses a higher guidance level instruction. However, the navigation process of REVERIE is implemented in a discrete environment, which is unrealistic in real world scenarios. To make the REVERIE task more consistent with the real physical world, we develop a new task of Remote Embodied Visual Referring Expression in Continuous Environment, namely REVE-CE, in which the agent executes a much longer sequence of low-level actions given language instructions. Furthermore, we propose a multi-branch cross modal attention (MBCMA) framework to solve the proposed REVE-CE task. Extensive experiments are conducted demonstrating that the proposed framework greatly outperforms the state-of-the-art VLN baselines and a new benchmark for the proposed REVE-CE task is built.
Improved hole repairing algorithm for livestock point clouds based on cubic B-spline for region defining

Because of the occlusion of the railings, the collected animal point clouds become fragmentary and have the large holes. The incomplete point clouds have a great impact on body size measurements of pigs. This paper presents an improved hole repairing algorithm based on cubic B-spline curve for region defining. Meanwhile, by applying approximation fitting and interpolation based on cubic B-spline curve, we can repair the holes of the pig point clouds after slicing longitudinally in the mid-axis plane of the pig body point clouds. In the experiment, comparisons were made among polar coordinate transformation algorithm, surface fitting algorithm, triangularization algorithm, and the algorithm proposed by us to verify hole repairing effect and estimation accuracy of abdominal circumference. The results show that we have proposed an algorithm with a better repairing effect and a higher accuracy in the abdominal circumference measurement of pigs.
A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference

While recent advances in deep neural networks (DNNs) enabled remarkable performance on various computer vision tasks, it is challenging for edge devices to perform real-time inference of complex DNN models due to their stringent resource constraint. To enhance the inference throughput, recent studies proposed collaborative intelligence (CI) that splits DNN computation into edge and cloud platforms, mostly for simple tasks such as image classification. However, for general DNN-based object detectors with a branching architecture, CI is highly restricted because of a significant feature transmission overhead. To solve this issue, this paper proposes a splittable object detector that enables edge-cloud collaborative real-time video inference. The proposed architecture includes a feature reconstruction network that can generate multiple features required for detection using a small-sized feature from the edge-side extractor. Asymmetric scaling on the feature extractor and reconstructor further reduces the transmitted feature size and edge inference latency, while maintaining detection accuracy. The performance evaluation using Yolov5 shows that the proposed model achieves 28 fps (2.45X and 1.56X higher than edge-only and cloud-only inference, respectively), on the NVIDIA Jetson TX2 platform in WiFi environment.
Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation1

The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.
Hypergraph matching via game-theoretic hypergraph clustering

Feature matching is used to build correspondences between features in the model and test images. As the extension of graph matching, hypergraph matching is able to encode rich invariance between feature tuples and improve matching accuracy. Different from many existing algorithms based on maximizing the matching score between correspondences, our approach formulates hypergraph matching as a non-cooperative multi-player game and obtains matches by extracting the evolutionary stable strategies (ESS). While this approach generates a high matching accuracy, the number of matches is usually small and it involves a large computation load to obtain more matches. To solve this problem, we extract multiple ESS clusters instead of one single ESS group, thereby transforming hypergraph matching of features to hypergraph clustering of candidate matches. By extracting an appropriate number of clusters, we increase the number of matches efficiently, and improve the matching accuracy by imposing the one-to-one constraint. In experiments with three real datasets, our algorithm is shown to generate a large number of matches efficiently. It also shows significant advantage in matching accuracy in comparison with some other hypergraph matching algorithms.