Reviewerly/exHarmony huggingface dataset:
A new clustering mining algorithm for multi-source imbalanced location data

In the era of big data, clustering based on multi-source data fusion has become a hot topic in data mining field. Existing studies mainly focus on fusion models and algorithms of data sets in the same domain, but few studies consider imbalanced data sets from different domains. Furthermore, studies on imbalanced data sets mostly focus on classification and less on clustering problems. Therefore, we propose a novel clustering algorithm for mining fused location data. This algorithm can deal with imbalanced data sets with large density differences, find clusters generated by the minority class data, and reduce the time complexity of the clustering process. Since current evaluation indices are not suitable for evaluating clustering results of imbalanced data sets, we present a new comprehensive evaluation metric used in the clustering validity judgment. Urban hotspots mining is used as an example, and the effectiveness of the proposed method is validated using GPS trajectory data from the transport domain and check-in data from the social network. The experimental results demonstrate that the performance of the proposed algorithm outperforms that of the state-of-the-art clustering algorithms, and it can simultaneously discover urban hotspots formed by the majority and minority class data.
Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection

Regression loss function in object detection model plays a important factor during training procedure. The IoU based loss functions, such as CIOU loss, achieve remarkable performance, but still have some inherent shortages that may cause slow convergence speed. The paper proposes a Scale-Sensitive IOU(SIOU) loss for the object detection in multi-scale targets, especially the remote sensing images to solve the problem where the gradients of current loss functions tend to be smooth and cannot distinguish some special bounding boxes during training procedure in multi-scale object detection, which may cause unreasonable loss value calculation and impact the convergence speed. A new geometric factor affecting the loss value calculation, namely area difference, is introduced to extend the existing three factors in CIOU loss; By introducing an area regulatory factor γ to the loss function, it could adjust the loss values of the bounding boxes and distinguish different boxes quantitatively. Furthermore, we also apply our SIOU loss to the oriented bounding box detection and get better optimization. Through extensive experiments, the detection accuracies of YOLOv4, Faster R-CNN and SSD with SIOU loss improve much more than the previous loss functions on two horizontal bounding box datasets, i.e, NWPU VHR-10 and DIOR, and on the oriented bounding box dataset, DOTA, which are all remote sensing datasets. Therefore, the proposed loss function has the state-of-the-art performance on multi-scale object detection.
One-Stage Incomplete Multi-view Clustering via Late Fusion

As a representative of multi-view clustering (MVC), late fusion MVC (LF-MVC) algorithm has attracted intensive attention due to its superior clustering accuracy and high computational efficiency. One common assumption adopted by existing LF-MVC algorithms is that all views of each sample are available. However, it is widely observed that there are incomplete views for partial samples in practice. In this paper, we propose One-Stage Late Fusion Incomplete Multi-view Clustering (OS-LF-IMVC) to address this issue. Specifically, we propose to unify the imputation of incomplete views and the clustering task into a single optimization procedure, so that the learning of the consensus partition matrix can directly assist the final clustering task. To optimize the resultant optimization problem, we develop a five-step alternate strategy with theoretically proved convergence. Comprehensive experiments on multiple benchmark datasets are conducted to demonstrate the efficiency and effectiveness of the proposed OS-LF-IMVC algorithm.
Long Short-term Convolutional Transformer for No-Reference Video Quality Assessment

No-reference video quality assessment has not been widely benefited from deep learning, mainly due to the complexity, diversity and particularity of modelling spatial and temporal characteristics in quality assessment scenario. Image quality assessment (IQA) performed on video frames plays a key role in NR-VQA. A perceptual hierarchical network (PHIQNet) with an integrated attention module is first proposed that can appropriately simulate the visual mechanisms of contrast sensitivity and selective attention in IQA. Subsequently, perceptual quality features of video frames derived from PHIQNet are fed into a long short-term convolutional Transformer (LSCT) architecture to predict the perceived video quality. LSCT consists of CNN formulating quality features in video frames within short-term units that are then fed into Transformer to capture the long-range dependence and attention allocation over temporal units. Such architecture is in line with the intrinsic properties of VQA. Experimental results on publicly available video quality databases have demonstrated that the LSCT architecture based on PHIQNet significantly outperforms state-of-the-art video quality models.
Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability

Spacecraft is a complex system integrating a large number of electronic components and payloads. During the in-orbit operation, abnormal events often occur due to the influences of space environment, performance degradation and other factors. These anomalies affect the operational reliability of spacecraft system in orbit. The telemetry data of spacecraft is the main basis to determine its in-orbit state. Data-driven telemetry data anomaly detection method can timely detect the abnormal state of spacecraft system, which provide reference for ground maintenance and ensure the safety and reliability of operation as well as the spacecraft itself. This paper proposes an improved deep learning based anomaly detection method for the anomaly detection of spacecraft telemetry data. Especially, the highly nonlinear modeling and predicting ability of Long Short-Term Memory (LSTM) networks are combined with multi-scale anomaly detection strategy to increase the detection performance. The effectiveness of the proposed method is verified using the NASA benchmark spacecraft data and the hydrogen clock data of the Beidou Navigation Satellite.
Dense Semantic Contrast for Self-Supervised Visual Representation Learning

Self-supervised representation learning for visual pre-training has achieved remarkable success with sample (instance or pixel) discrimination and semantics discovery of instance, whereas there still exists a non-negligible gap between pre-trained model and downstream dense prediction tasks. Concretely, these downstream tasks require more accurate representation, in other words, the pixels from the same object must belong to a shared semantic category, which is lacking in the previous methods. In this work, we present Dense Semantic Contrast (DSC) for modeling semantic category decision boundaries at a dense level to meet the requirement of these tasks. Furthermore, we propose a dense cross-image semantic contrastive learning framework for multi-granularity representation learning. Specially, we explicitly explore the semantic structure of the dataset by mining relations among pixels from different perspectives. For intra-image relation modeling, we discover pixel neighbors from multiple views. And for inter-image relations, we enforce pixel representation from the same semantic class to be more similar than the representation from different classes in one mini-batch. Experimental results show that our DSC model outperforms state-of-the-art methods when transferring to downstream dense prediction tasks, including object detection, semantic segmentation, and instance segmentation. Code will be made available.
VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis

In this paper, we propose VISinger, a complete end-to-end high-quality singing voice synthesis (SVS) system that directly generates singing audio from lyrics and musical score. Our approach is inspired by VITS [1], an end-to-end speech generation model which adopts VAE-based posterior encoder augmented with normalizing flow based prior encoder and adversarial decoder. VISinger follows the main architecture of VITS, but makes substantial improvements to the prior encoder according to the characteristics of singing. First, instead of using phoneme-level mean and variance of acoustic features, we introduce a length regulator and a frame prior network to get the frame-level mean and variance on acoustic features, modeling the rich acoustic variation in singing. Second, we further introduce an F0 predictor to guide the frame prior network, leading to stabler singing performance. Finally, to improve the singing rhythm, we modify the duration predictor to specifically predict the phoneme to note duration ratio, helped with singing note normalization. Experiments on a professional Mandarin singing corpus show that VISinger significantly outperforms FastSpeech+Neural-Vocoder two-stage approach and the oracle VITS; ablation study demonstrates the effectiveness of different contributions.
Learned Block-Based Hybrid Image Compression

Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. First, parallel acceleration of the autoregressive entropy model cannot be achieved due to serial decoding. Second, full-resolution inference often causes the out-of-memory (OOM) problem with limited GPU resources, especially for high-resolution images. Block partition is a good choice to handle the above issues, but it brings about new challenges in reducing the redundancy between blocks and eliminating block effects. To tackle the above challenges, this paper provides a learned block-based hybrid image compression (LBHIC) framework. Specifically, we introduce explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks. Superior to context modeling by linear weighting of neighbor pixels in traditional codecs, we propose a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction. Moreover, to alleviate blocking artifacts, we further propose a boundary-aware postprocessing module (BPM) with the edge importance taken into account. Extensive experiments demonstrate that the proposed LBHIC codec outperforms the VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by approximately 86.7% compared with that of state-of-the-art learned image compression methods.
Deep Federated Learning for Autonomous Driving

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user’s privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server. Our source code can be found at: https://github.com/aioz-ai/FADNet
Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control

We present a novel adaptive deep joint source-channel coding (JSCC) scheme for wireless image transmission. The proposed scheme supports multiple rates using a single deep neural network (DNN) model and learns to dynamically control the rate based on the channel condition and image contents. Specifically, a policy network is introduced to exploit the tradeoff space between the rate and signal quality. To train the policy network, the Gumbel-Softmax trick is adopted to make the policy network differentiable and hence the whole JSCC scheme can be trained end-to-end. To the best of our knowledge, this is the first deep JSCC scheme that can automatically adjust its rate using a single network model. Experiments show that our scheme successfully learns a reasonable policy that decreases channel bandwidth utilization for high SNR scenarios or simple image contents. For an arbitrary target rate, our rate-adaptive scheme using a single model achieves similar performance compared to an optimized model specifically trained for that fixed target rate. To reproduce our results, we make the source code publicly available at https://github.com/mingyuyng/Dynamic_JSCC.
A Novel Ultra-Compact FPGA-Compatible TRNG Architecture Exploiting Latched Ring Oscillators

In this brief we present a novel, ultra-compact, True Random Number Generator (TRNG) architecture and its FPGA implementation. The proposed Latched Ring Oscillator (LRO) TRNG allows the generation of a TRNG bit from a single FPGA Slice. Despite its very compact structure, the proposed LRO-TRNG relies on both meta-stability and accumulated jitter as entropy sources, and exhibits very good results in terms of unpredictability and randomness. The proposed architecture has been implemented on Xilinx Spartan-6 devices and the TRNG performances have been extensively validated under supply voltage and temperature variations. Measurements results have shown that the LRO-TRNG exhibits an estimated entropy of about 7.99834 per bit (according to T8 test of the AIS-31) and a throughput of 0.76 Mbits/s with a 50MHz clock. A comparison against the state of the art shows that the proposed LRO-TRNG outperforms most of the previously published TRNGs, in terms of the ratio between throughput and FPGA resources usage.
Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device

The classification of simple and complex sequences of operations is made easier according to the use of heterogeneous sensors from a wearable device. Sensor-based human activity recognition (HAR) is being used in smartphone platforms for elderly healthcare monitoring, fall detection, and inappropriate behavior prevention, such as smoking habit, unhealthy eating, and lack of exercise. Common machine learning and deep learning techniques have recently been presented to tackle the HAR issue, with a focus on everyday activities, particularly general human activities including moving, sitting, and standing. However, there is an intriguing and challenging HAR research subjects involving more complicated psychological activities in various environments, including smoking, eating, and drinking. The use of heterogeneous sensor data to enhance recognition performance over sensor-based deep learning networks is considered in this work. We demonstrate that using a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA). Furthermore, we describe the impact of five window sizes (5s - 40s) on a publicly accessible benchmark dataset and how increasing window size effects to the classification performance of CHA deep learning networks.
Compound Figure Separation of Biomedical Images with Side Loss

Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i\(^\circledR \) search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
DeFi-ning DeFi: Challenges &amp; Pathway

The decentralized and trustless nature of cryptocurrencies and blockchain technology leads to a shift in the digital world. The possibility to execute small programs, called smart contracts, on cryptocurrencies like Ethereum opened doors to countless new applications. One particular exciting use case is decentralized finance (DeFi), which aims to revolutionize traditional financial services by founding them on a decentralized infrastructure. We show the potential of DeFi by analyzing its advantages compared to traditional finance. Additionally, we survey the state-of-the-art of DeFi products and categorize existing services. Since DeFi is still in its infancy, there are countless hurdles for mass adoption. We discuss the most prominent challenges and point out possible solutions. Finally, we analyze the economics behind DeFi products. By carefully analyzing the state-of-the-art and discussing current challenges, we give a perspective on how the DeFi space might develop in the near future.
Unsupervised Cluster Guided Object Detection in Aerial Images

Object detection from high-resolution aerial images have received increasing attention during the last few years.It is a common practice to downsize images before feeding them into a network.In real life, there are lots of scenes where many objects gather together in certain areas, such as crossroads, parking lots, and playgrounds.The downsizing operation significantly limits the detection ability in these scenes.In this paper, we proposed an unsupervised cluster guided detection framework (UCGNet) to address these issues by guiding the detector focus on the object-densely distributed area.In particular, a local location module is first applied to predict a binary map presenting how objects distribute in terms of the pixel of the map.Then, an unsupervised cluster method is used to produce dense regions.Each adjusted dense region is fed into the detector for object detection.Finally, a global merge module generates the final predict results.Experiments were conducted on two popular aerial image datasets including VisDrone2019 and UAVDT.In both datasets, our proposed method outperforms the existing baseline methods with achieving 32.8% mAP and 19.1% mAP, respectively.
Enabling Sustainable Clouds

Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuses on improving energy-efficiency and purchasing carbon offsets. These approaches have limits: many cloud data centers already operate near peak efficiency, and carbon offsets cannot scale to near zero carbon where there is little carbon left to offset. Instead, enabling sustainable clouds will require applications to adapt to when and where unreliable low-carbon energy is available. Applications cannot do this today because their energy use and carbon emissions are not visible to them, as the energy system provides the rigid abstraction of a continuous, reliable energy supply. This vision paper instead advocates for a "carbon first" approach to cloud design that elevates carbon-efficiency to a firs--class metric. To do so, we argue that cloud platforms should virtualize the energy system by exposing visibility into, and software-defined control of, it to applications, enabling them to define their own abstractions for managing energy and carbon emissions based on their own requirements.
Blockchain-based solutions for cloud computing: A survey

Blockchain is a disruptive technology applied by academia and industry in a broad spectrum of applications ranging from cryptocurrency, financial services, IoT, 5G, risk management to public and social services. On the other hand, cloud computing is spearheading the Internet revolution and rapidly becomes the hottest topic in IT. However, cloud computing inherently suffers from centralization, lack of transparency and trust. Blockchain, with its innovative features like decentralization and immutability, adds trustworthiness and transparency to cloud computing. This paper presents a comprehensive survey of Blockchain-based cloud services literature published over the period 2008–21. This survey focuses on the technical fusion of Blockchain and cloud computing and investigates the recent advances in the field of Blockchain-based cloud computing. In particular, we classify the literature into three categories: (1) Blockchain-based Infrastructure-as-a-Service (2) Blockchain-based Platform-as-a-Service and (3) Blockchain-based Software-as-a-Service. We start by identifying a set of characteristics of Blockchain and their potential benefits to cloud computing. Then, we present our research methodology and a brief introduction of essential backgrounds. Later, we present state-of-the-art works in Blockchain-based storage-as-a-service, resource management, computation-as-a-service, data aggregation-as-a-service, microservice-as-a-service and VNF-as-a-service. We conclude the paper with the current challenges in Blockchain-based cloud computing, their potential challenges and future research directions. We believe our survey will provide a comprehensive view of the state-of-the-art Blockchain-based cloud services and facilitate reengineering the cloud data centres in the future.