Reviewerly/exHarmony huggingface dataset:
Dynamic Transfer for Multi-Source Domain Adaptation

Recent works of multi-source domain adaptation focus on learning a domain-agnostic model, of which the parameters are static. However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain. In this paper, we present dynamic transfer to address domain conflicts, where the model parameters are adapted to samples. The key insight is that adapting model across domains is achieved via adapting model across samples. Thus, it breaks down source domain barriers and turns multi-source domains into a single-source domain. This also simplifies the alignment between source and target domains, as it only requires the target domain to be aligned with any part of the union of source domains. Furthermore, we find dynamic transfer can be simply modeled by aggregating residual matrices and a static convolution matrix. Experimental results show that, without using domain labels, our dynamic transfer outperforms the state-of-the-art method by more than 3% on the large multi-source domain adaptation datasets – DomainNet. Source code is at https://github.com/liyunsheng13/DRT.
Breast Cancer Segmentation Methods: Current Status and Future Potentials

Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
Detecting White Supremacist Hate Speech Using Domain Specific Word Embedding With Deep Learning and BERT

White supremacist hate speech is one of the most recently observed harmful content on social media.The critical influence of these radical groups is no longer limited to social media and can negatively affect society by promoting racial hatred and violence.Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information and the implicit nature of hate speech.Therefore, it is necessary to detect such speech automatically and in a timely manner.This research investigates the feasibility of automatically detecting white supremacist hate speech on Twitter using deep learning and natural language processing techniques.Two deep learning models are investigated in this research.The first approach utilizes a bidirectional Long Short-Term Memory (BiLSTM) model along with domain-specific word embeddings extracted from white supremacist corpus to capture the semantic of white supremacist slangs and coded words.The second approach utilizes one of the most recent language models, which is Bidirectional Encoder Representations from Transformers (BERT).The BiLSTM model achieved 0.75 F1-score and BERT reached a 0.80 F1-score.Both models are tested on a balanced dataset combined from Twitter and a Stormfront dataset compiled from white supremacist forum.
Trends in human activity recognition using smartphones

Abstract Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.
A sound based method for fault detection with statistical feature extraction in UAV motors

The motors of the Unmanned Aerial Vehicle are critical parts, especially when used in applications such as military and defense systems. The fact that the brushless DC (BLDC) motors used in UAVs operate at high speed causes malfunctions. In this study, propeller, eccentric and bearing failures, which are frequently seen in UAV motors, were created. Then the fault diagnosis was made by applying the recommended method on the sound data received from the motors. Signal pre-processing, feature extraction, and machine learning methods were applied to the obtained sound dataset. Decision tree (DT), Support Vector Machines (SVM), and k Nearest Neighbor (KNN) algorithms are used for machine learning. The results have been obtained using three different UAV motors of 1400 KV, 2200 KV, and 2700 KV. For the 2200 KV motor, the accuracy of 99.16%, 99.75%, and 99.75% was calculated in DT, SVM, and KNN algorithms, respectively. The high accuracy of the proposed method indicates that the study will contribute to the studies in the relevant field. Another advantage is that the method is fast and able to work in real-time on embedded systems.
Pretrained Language Model for Text Generation: A Survey

Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs). In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation. As the preliminaries, we present the general task definition and briefly describe the mainstream architectures of PLMs for text generation. As the core content, we discuss how to adapt existing PLMs to model different input data and satisfy special properties in the generated text. We further summarize several important fine-tuning strategies for text generation. Finally, we present several future directions and conclude this paper. Our survey aims to provide text generation researchers a synthesis and pointer to related research.
Achieving Privacy-Preserving DSSE for Intelligent IoT Healthcare System

As the product of combining Internet of Things (IoT), cloud computing and traditional healthcare, Intelligent IoT Healthcare (IIoTH) brings us a lot of convenience, meanwhile security and privacy issues have attracted great attention.Dynamic searchable symmetric encryption (DSSE) technique can make the user search the dynamic healthcare information from IIoTH system under the condition that the privacy is protected.In this paper, a novel privacy-preserving DSSE scheme for IIoTH system is proposed.It is the first DSSE scheme designed for PHR files database with forward security.We construct the secure index based on hash chain and realize trapdoor updates for resisting file injection attacks.In addition, we realize fine-grained search over encrypted PHR files database of attribute-value type.When the user executes search operations, he/she gets only a matched attribute value instead of the whole file.As a result, the communication cost is reduced and the disclosure of patient's privacy is minimized.The proposed scheme also achieves attribute access control, which allows users have different access authorities to attribute values.The specific security analysis and experiments show the security and the efficiency of the proposed scheme.
From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 3D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 3D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/djiajunustc/H-23D_R-CNN</uri> .
A Consortium Blockchain-Based Energy Trading for Demand Response Management in Vehicle-to-Grid

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals.
Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution

Recently, the state-of-the-art performance in various sensor-based human activity recognition (HAR) tasks has been acquired by deep learning, which can extract automatically features from raw data. In standard convolutional neural networks (CNNs), there is usually the same receptive field (RF) size of artificial neurons within each feature layer. It is well known that the RF size of neurons is able to change adaptively according to the stimulus, which has rarely been exploited in HAR. In this article, a new multibranch CNN is introduced, which utilizes a selective kernel mechanism for HAR. To the best of our knowledge, it is for the first time to adopt an attention idea to perform kernel selection among multiple branches with different RFs in the HAR scenario. We perform extensive experiments on several benchmark HAR datasets, namely, UCI-HAR, UNIMIB SHAR, WISDM, PAMAP2, and OPPORTUNITY, as well as weakly labeled datasets. Ablation experiments show that the selective kernel convolution can adaptively choose an appropriate RF size among multiple branches for classifying numerous human activities. As a result, it can achieve a higher recognition accuracy under a similar computing budget.
A comprehensive review on blockchain and Internet of Things in healthcare

Abstract Blockchain Technology, the fundamental technology behind Bitcoin, has drawn considerable recognition ever since its origin. Its potential has gained significant interest in various applications, varying from the music industry, financial services, Internet‐of‐Things (IoT), smart grid, edge computing, cybersecurity, and the healthcare industry. However, the information is divided amongst several intermediaries involved with adverse impacts on data quality in the healthcare domain. In the near future, blockchain technology can reshape the way the healthcare industry works by providing personalized and reliable patient data management, reforming the traditional healthcare practices, secure mechanisms for data sharing, efficient pharmaceutical supply chain management, and drug traceability and many more. In this study, an extensive literature review has been provided that includes the different prospects of using blockchain technology in healthcare. The review investigates the work done to enable the amalgamation of IoT and Blockchain in the health ecosystem. Significant blockchain‐based healthcare use cases such as data storage, data sharing, drug traceability, clinical trials, and remote patient monitoring are investigated. Further, the Internet of Things and blockchain technology‐based SWOT (Strength, Weakness, Opportunity, and Threat) analysis and the challenges linked in the healthcare domain because of the enactment of IoT and blockchain technology are discussed to support advanced studies in this domain.
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement

Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction. The classical image matching paradigm detects keypoints per-image once and for all, which can yield poorly-localized features and propagate large errors to the final geometry. In this paper, we refine two key steps of structure-from-motion by a direct alignment of low-level image information from multiple views: we first adjust the initial keypoint locations prior to any geometric estimation, and subsequently refine points and camera poses as a post-processing. This refinement is robust to large detection noise and appearance changes, as it optimizes a featuremetric error based on dense features predicted by a neural network. This significantly improves the accuracy of camera poses and scene geometry for a wide range of keypoint detectors, challenging viewing conditions, and off-the-shelf deep features. Our system easily scales to large image collections, enabling pixel-perfect crowd-sourced localization at scale. Our code is publicly available at github.com/cvg/pixel-perfect-sfm as an add-on to the popular SfM software COLMAP.
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and depth features, ignoring their intrinsic differences. In fact, the RGB values capture the photometric appearance properties in the projected image space, while the depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. Compared with the base, the shape probably is more inherent and has a stronger connection to the semantics, and thus is more critical for segmentation accuracy. Inspired by this observation, we introduce a Shape-aware Convolutional layer (ShapeConv) for processing the depth feature, where the depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. ShapeConv is model-agnostic and can be easily integrated into most CNNs to replace vanilla convolutional layers for semantic segmentation. Extensive experiments on three challenging indoor RGB-D semantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID, demonstrate the effectiveness of our ShapeConv when employing it over five popular architectures. Moreover, the performance of CNNs with ShapeConv is boosted without introducing any computation and memory increase in the inference phase. The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.
Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning With Deep Unfolding

The year 2019 witnessed the rollout of the 5G standard, which promises to offer significant data rate improvement over 4G. While 5G is still in its infancy, there has been an increased shift in the research community for communication technologies beyond 5G. The recent emergence of machine learning approaches for enhancing wireless communications and empowering them with much-desired intelligence holds immense potential for redefining wireless communication for 6G. The evolving communication systems will be bottlenecked in terms of latency, throughput, and reliability by the underlying signal processing at the physical layer. In this position letter, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks. To this end, we begin by presenting the service requirements and the key challenges posed by the envisioned 6G communication architecture. We outline the deficiencies of the traditional algorithmic principles and data-hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep unfolded signal processing is presented by sketching the interplay between domain knowledge and DL. The deep unfolded approaches reviewed in this letter are positioned explicitly in the context of the requirements imposed by the next generation of cellular networks. Finally, this letter motivates open research challenges to truly realize hardware-efficient edge intelligence for future 6G networks. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—In this letter, we discuss why the infusion of domain knowledge into machine learning frameworks holds the key to future embedded intelligent communication systems. Applying traditional signal processing and deep learning approaches independently entails significant computational and memory constraints. This becomes challenging in the context of future communication networks, such as 6G with significant communication demands where dense deployments of embedded Internet of Things (IoT) devices are envisioned. Hence, we put forth deep unfolded approaches as the potential enabling technology for 6G artificial intelligence (AI) radio to mitigate the computational and memory demands as well as to fulfill the future 6G latency, reliability, and throughput requirements. To this end, we present a general deep unfolding methodology that can be applied to iterative signal processing algorithms. Thereafter, we survey some initial steps taken in this direction and more importantly discuss the potential it has in overcoming challenges in the context of 6G requirements. This letter concludes by providing future research directions in this promising realm.