Reviewerly/exHarmony huggingface dataset:
Enhancing Contrast of Dark Satellite Images Based on Fuzzy Semi-Supervised Clustering and an Enhancement Operator

Contrast enhancement of images is a crucial topic in image processing that improves the quality of images. The methods of image enhancement are classified into three types, including the histogram method, the fuzzy logic method, and the optimal method. Studies on image enhancement are often based on the rules: if it is bright, then it is brighter; if it is dark, then it is darker, using a global approach. Thus, it is hard to enhance objects in all dark and light areas, as in satellite images. This study presents a novel algorithm for improving satellite images, called remote sensing image enhancement based on cluster enhancement (RSIECE). First, the input image is clustered by the algorithm of fuzzy semi-supervised clustering. Then, the upper bound and lower bound are estimated according to the cluster. Next, a sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels because after clustering, pixels belong to clusters with the corresponding membership values. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. The test results demonstrate that the suggested algorithm is superior to several recently developed approaches.
A Proposed Model for Drugs' Review Analysis and Adverse Drug Reaction Discovery

The medical social platforms enable users to exchange their reviews that reflect their experience over the drugs. The classification of patients' reviews on drugs into positive or negative ratings and the discovery of the drugs that provoke an Adverse Drug Reaction (ADR) can guide the medical sector and the pharmaceutical sector to understand the patients' discomfort or the patients' satisfaction with specific drugs. This paper presents a model to predict the rating of drugs based on review analysis using natural language processing techniques and machine learning algorithms. The model enables drugs discovery which provokes ADRs via the sentiment extraction of the reviews using the syuzhet package in R-program. The proposed model was applied to the Drugs.com dataset and evaluated using another dataset (drugs.lib) that is not used during the training of the machine learning algorithms. The proposed model achieves an accuracy of 87% and 78% for the Drugs.com and DrugsLib.com testing. Furthermore, the model was applied on Drugs.com dataset to discover the ADRs. It classifies the side effects grading of each review based on its sentiment analysis result. The proposed model discovers 44534 patient reviews out of 213865 indicate that the patients suffer from moderate side effects. Also, it discovers 66 reviews out of 213865 indicate that the patients suffer from severe side effects. Both of the moderate and severe side effects are considered as a trigger of ADRs occurrence. In other words, 21.03% of drugs reviews indicate that the patient suffer from ADRs due the drug consumption.
Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power

Conventional power grids dominated by synchronous generators gradually shift to variable renewable-energy-integrated grids. With the growth of building-level photovoltaic (PV) panels and other inverter-based resource (IBR) deployments in recent years, market retailers and distribution operators have had to deal with the additional operational and management challenges posed by unobserved energy flow if its behind-the-meter (BTM) configuration. Also, the intermittent and stochastic nature of IBR-based solar power introduces uncertainty into the net load forecasting of electric distribution systems. Hence, management and control with high uncertainty in load prediction due to unobservable PV is a technical challenge. This article presents deep-learning-based algorithms for BTM PV power generation using a limited number of sensors in a given distribution system and extending to adjacent geographical areas. The proposed BTM PV forecasting method is based on geometric deep learning—a spatiotemporal graph neural network by processing the relationship between data in a non-Euclidean graph structure. The predictions of short-term BTM PV are aggregated with loss estimation at the data aggregation point with the net load forecasting to compute the true load forecasting with superior performance. The developed BTM PV forecasting method significantly improved true load forecasting results, which were validated and analyzed using a collection of actual BTM PV and load measurements in a test distribution feeder.
Coexistence of Quantum and 1.6 Tbit/s Classical Data Over Fibre-Wireless-Fibre Terminals

In this article a fibre free-space fibre link that allows a quantum key distribution (QKD) signal and high-speed data to be transmitted in a single optical beam is presented. This setup has the potential to provide ultra-high data rate secure wireless connectivity within indoor spaces. Both quantum and classical channels were transmitted using wavelengths in the C band, over a range of 2.5 m in free space and in the presence of a 200 lx background illumination. Raman noise caused by the light propagating in the optical fibres which provide connections to the free-space segment, together with geometric loss of the free-space link are the major impairments. Results yield secret key rates of 2.343 kbit/s when the QKD signal is sent without high-speed data (and associated Raman noise), and rates of 1.305–1.996 kbit/s when high-speed data is also transmitted (depending on spectral separation of QKD and classical information). Further, it is shown that the secret key rate can be optimised by increasing loss to attenuate the Raman noise by misalignment of the link. Improvement of the secret key rate from 1.305 kbit/s to 1.887 kbit/s was shown using this method.
IRWoZ: Constructing an Industrial Robot Wizard-of-OZ Dialoguing Dataset

Enabling a flexible and natural human-robot interaction (HRI) for industrial robots is a critical yet challenging task that can be facilitated by the use of conversational artificial intelligence (AI). Prior research has concentrated on strengthening interactions through the deployment of social robots, while disregarding the capabilities required to boost the flexibility and user experience associated with human-robot collaboration (HRC) on manufacturing tasks. One of the main challenges is the lack of publicly available industrial-oriented dialogue datasets for the training of conversational AI. In this work, we present an industrial robot wizard-of-Oz dialoguing dataste (IRWoZ) focused on enabling HRC in manufacturing tasks. The dataset covers four domains: assembly, transportation, position, and relocation. It is created using the Wizard-of-Oz technique to be less noisy. We manually constructed, annotated and validated dialogue segments (e.g., intentions, slots, annotations), as well as the responses. Building upon the proposed dataset, we benchmark it on the state-of-the-art (SoTA) language models, generative pre-trained (GPT-2) models, on dialogue state tracking and response generation tasks. We expect that the IRWoZ dataset will facilitate exciting ongoing dialogue research and we provide it freely accessible at https://github.com/lcroy/ToD4IR/tree/main/dataset.
Quality Prediction of View Synthesis Based on Curriculum-Style Structure Generation

Existing quality metrics of view synthesis usually perform on synthesized images, which are produced based on a computationally expensive depth-image-based rendering (DIBR) process. Moreover, current metrics quantify quality by extracting hand-crafted features, which may fail to fully capture the complex distortion characteristics. With the success of deep learning on numerous computer vision tasks, it has become possible to utilize convolutional neural networks to predict the quality of DIBR-synthesized images. In this letter, we propose a deep model to predict the quality of view synthesis based on Curriculum-style Structure Generation without conducting the DIBR process. Specifically, considering that the distortion of view synthesis is mainly manifested in the destruction of image structure, a structure generation network is first built to learn the structure of the new view from the original one by curriculum-style training. Then, we transfer the prior knowledge learned from the last phase into the quality prediction network for measuring the structure distortion, based on which a regressor is introduced to produce the quality score. Experimental results prove the advantages of the proposed model.
CNTS: Cooperative Network for Time Series

The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly detection methods have gained popularity due to their intuitive assumptions and low computational requirements. However, these methods are often susceptible to outliers and do not effectively model anomalies, leading to suboptimal results. This paper presents a novel approach for unsupervised anomaly detection, called the Cooperative Network Time Series (CNTS) approach. The CNTS system consists of two components: a detector and a reconstructor. The detector is responsible for directly detecting anomalies, while the reconstructor provides reconstruction information to the detector and updates its learning based on anomalous information received from the detector. The central aspect of CNTS is a multi-objective optimization problem, which is solved through a cooperative solution strategy. Experiments on three real-world datasets demonstrate the state-of-the-art performance of CNTS and confirm the effectiveness of the detector and reconstructor. The source code for this study is publicly available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> .
Visible and Infrared Object Tracking via Convolution-Transformer Network With Joint Multimodal Feature Learning

The existing Transformer-based RGBT tracker mainly focus on the enhancement of features extracted by Convolutional Neural Network (CNN). The potential of Transformer in representation learning remains under-explored. In this paper, we propose a Convolution-Transformer network with joint multimodal feature learning, in which both representation learning and feature fusion leverage Transformer. Specifically, we use the multi-branch Convolution-Transformer feature extraction network to process the extraction task of local modality-independent features and global modality-shared features respectively. Several simplified Transformer encoder layers form the Transformer backbone network, which is more suitable for the real-time object tracking. Besides, we found that inter-modality correlation is an important factor for modality interactions and mutual exploitation. Therefore, we propose a Joint Multimodal Feature Learning (JMFL) module, which uses cross-attention to capture the dependencies of cross-modal and enhance multimodal fusion by bidirectional guidance of multimodal information. The proposed method is fully experimented on two large benchmark datasets and compared with some current well-performing methods. The experimental results show that the proposed method performs well in terms of tracking accuracy and speed.
Global and Local Structure Network for Image Classification

Principal Component Analysis Network (PCANet) is a feature learning algorithm which is widely used in face recognition and object classification. However, original PCANet still has some shortages. One is that PCA algorithm only extracts features by considering the global structure. The other lies in that the original PCANet only employs one particular single layer convolutional results, which loses the information of other convolutional layers. In this paper, we propose a new simple and efficient convolutional neural network called global and local structure network (GLSNet) to address the problems. The network extracts the features both from the global structure and the local structure of the original data space. Specifically, a principal component analysis (PCA) convolutional layer which learns the filters by PCA algorithm is used to remove the noises and redundant information at the first stage. Then at the second stage, another PCA convolution is added to extract features by considering the global structure. As for the local structure, we use the neighborhood preserving embedding (NPE) algorithm to learn the convolutional filters. At the output stage, the global structure feature extracted by PCA convolution and the local structure feature extracted by NPE convolution is concatenated as a united feature. Furthermore, the first layer convolutional feature is also taken into consideration to obtain shallow-level information. Finally, these features are concatenated as a united feature, and a spatial pyramid pooling layer is followed to pool above the united features. To test the effectiveness of the proposed algorithm, the experiments on some image datasets, including three types: human face dataset, object dataset, and handprinted dataset, proceeded. And it performs better than the original PCANet and some improvement algorithms of PCANet, such as PLDANet, and MMPCANet.
Learned Scale-arbitrary Image Downscaling for Non-learnable Upscaling

In this paper, we propose a learned scale-arbitrary image downscaling method to downscale high-resolution (HR) images to a target low-resolution (LR) where it could be well upscaled by traditional simple upscaling methods. Specifically, the scale information is first fused into the feature extraction process of the input HR image by employing our proposed scale-adaptive feature enhancement module (FEM). Then the proposed scale-arbitrary feature resampler (SAFR) adaptively generates sampling weights which are applied to the resampled candidate feature maps to produce the downscaled LR image. Experimental results demonstrate that when compared to the existing learned image downscaling method for non-learnable upscaling, the reconstructed HR images upscaled by our proposed method receive better quality. The code is publicly available on: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/crhuang321/SAID</uri> https://github.com/crhuang321/SAID .
1-b Delta-Sigma ADC-Based Power Side-Channel Attack Detection Sensor

Countermeasures against power side-channel attack (SCA) range from algorithmic modifications to modifying the power consumption characteristics of the device by the inclusion of noise sources or suppressing the current signature. However, these techniques are, in most cases, passive protection techniques and come with the unacceptable area and power overheads for resource-constrained devices. In this letter, we propose a power SCA detection sensor based on a 1-b delta-sigma analog-to-digital converter, which allows for detection of an ongoing attack within as fast as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6.4\,\mu \text{s}$</tex-math></inline-formula> , with immunity to power supply noise, at an area overhead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq 8\%$</tex-math></inline-formula> for advanced encryption standard (AES)-256, while reducing the power overhead by 66% compared to the current state-of-the-art.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search

Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users’ click behaviors has become one of the effective methods to construct click models. In this paper, We propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. The model uses Transformer as the backbone network of feature extraction and add filter layer innovatively. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.
MSAGAN: A New Super-Resolution Algorithm for Multispectral Remote Sensing Image Based on a Multiscale Attention GAN Network

In the absence of high-resolution sensors, super-resolution( SR) algorithms for remote sensing imagery improve the spatial resolution of the images. Currently, most of the SR algorithms are based on deep learning methods e.g., convolutional neural networks(CNN). Particularly, the generative adversarial networks(GANs) have demonstrated accepted performances in image super-resolution owing to their powerful generative capabilities. However, remote sensing images have complex feature types, which largely limits the performances of GAN-based SR methods for real satellite images. To address this issue, an attention mechanism and a multi-scale structure are introduced into the generator of the GAN network, and a multi-scale attention GAN(MSAGAN) is constructed in this study. We sequentially arrange the channel attention module and the spatial attention module after the multi-scale structure to emphasize important information, suppress unimportant information details, improve the model’s performance. Furthermore, we add residual connections and dense blocks to further enhance the performance of the generative network by increasing its depth. We compared to other existing deep learning-based SR methods, our proposed MSAGAN algorithm performed better in generating high spatial satellite images.
Cross-Frame Resource Allocation With Context-Aware QoE Estimation for 360° Video Streaming in Wireless Virtual Reality

Wireless virtual reality (VR), aiming to provide an untethered immersive experience through 360° videos, could be facilitated by viewport-guided streaming with the help of viewport prediction. Although many recent viewport predictors can output a series of predictions over upcoming frames, most existing work on video streaming does not fully utilize the capability of these predictors. In this paper, we investigate the problem of 360° video streaming by incorporating the complete series of viewport predictions for maximizing the quality of experience (QoE) through cross-frame resource allocation. To address the problem of viewport prediction errors that could result in erroneous estimation of QoE contribution of tiles in upcoming frames, we develop a novel approach based on contextual multi-armed bandit (CMAB) to “learn” online the viewing behavior of the user and the capability of the predictor such that resource can be preferentially allocated to tiles with significant QoE contribution. Further, to address the problem of transmission failures during wireless streaming, we formulate a constrained Markov decision process (CMDP) and apply model predictive control (MPC) to account for resource competition among reactive and proactive transmissions as well as retransmissions of tiles. The performance of the proposed streaming system is evaluated using a real-world VR dataset, state-of-the-art viewport predictors, and realistic mmWave channel models. An improvement of 5.68% in QoE and a reduction of 23.0% in resource waste are achieved across various videos, users, and predictors. Simulation results substantiate that the context-aware QoE learned by the proposed CMAB effectively addresses prediction errors for tiles with different temporal and spatial contexts, and the proposed CMDP can achieve the desired performance even under high viewport prediction error and channel error.
Cryptographic Algorithms to Secure IoT Devices: A Survey

IoT has been around for a few years now and it’s only gaining more momentum as technology evolves. There’s no doubt that the internet has played a big role in its growth. Nowadays, a lot of inanimate objects can communicate with each other sans human intervention. For example, there are smart devices like smartphones, home appliances, vehicles, and even flight management systems. IoT devices are often unsecured, leaving them open to attack by unauthorized persons. These individuals can intercept data or manipulate the devices for their own malicious purposes. This paper reviews the security threats that face the Internet of Things. It does this by reviewing several related surveys on the security of IoT. In doing this, the paper will discuss the Cryptographic Schemes in IoT. It will also discuss the various light weigh Cryptography algorithms and the design differences for normal block ciphers.
A Literature Review of Bitcoin Network Infrastructure, Methodology, and Challenges<sup>*</sup>

Cryptocurrency has become a significant business stream in recent years for investors worldwide. Through a giant decentralized bitcoin network, cryptocurrency solutions have been extended to world investors over the internet. Understanding the underlying infrastructure of the bitcoin network is essential for internet users to realize the business environment for their investment and for researchers to improve the provisioning of cryptocurrency solutions. This paper provides a literature review to illustrate the primary methodology of bitcoin networks, underlying infrastructure, and the significant technical challenges and security issues. We describe the blockchain network and its components and structure. We also discuss the bitcoin transaction handling over the blockchain networks and the cryptographic techniques for securing the blockchain networks and cryptocurrency solutions.
Path Planning of Unmanned Aerial Vehicle Based on Supervised Learning

Traditional modular-based path planning methods have certain limitations, they have high latency and computational complexity, and cannot work in unknown environments. Current learning-based path planning methods show significant advantages in solving different planning problems in high-dimensional spaces and complex environments. Learning path planning from demonstrations is a hot research topic. We propose an end-to-end framework based on supervised learning. The end-to-end mapping of unmanned aerial vehicle (UAV) from perception to collision-free trajectories is achieved by learning demonstrations generated based on global path planning methods in a simulated environment. Replacing the traditional modular approach with neural networks can reduce computational latency and prevent error accumulation. Experimental results show that our method has a better performance than traditional path planner.