Reviewerly/exHarmony huggingface dataset:
Chameleon Hash Based Efficiently Updatable Oblivious Key Management

Updatable oblivious key management (UOKM) schemes are considered an important backbone of key management for large storage systems, which allows the key management server to periodically update all its clients’ ciphertexts stored in the remote storage cloud without interacting with the clients. All the encrypted data can only be decrypted by the current key (tag), which ensures security against forward and post compromise. In this paper, we propose two UOKM schemes: the first is public key-based, while the second is identity-based. Compared with Jarecki et al.'s UOKM scheme from oblivious pseudorandom functions, the implementations show that our chameleon hash-based schemes have faster updating processes. Moreover, our identity-based UOKM scheme does require ”heavy” public key certificate management and maintains a secure channel from the key management server to the storage cloud, which greatly enhances security and efficiency. Our proofs show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unconditional oblivious</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">security</i> of our schemes are based on the security of chameleon hash functions.
Pantograph Pose Measurement Based on Keypoint Location and Edge Shape Optimization Under Complex Conditions

The violent swing of the pantograph deteriorates the contact status of the pantograph and catenary system (PCS) and seriously threatens the operation security of high-speed trains. Currently, video surveillance is commonly used for pantograph pose monitoring. Due to complex weather conditions of illumination change, occlusion, and background interference on the roof, achieving effective real-time high-precision attitude measurement remains challenging. Therefore, this study proposes a deep-learning-based 3-D measurement method. First, an accurate end-to-end pantograph keypoint location network (PKLNet) is proposed to position image coordinates of the keypoints. In PKLNet, local and global contexts are integrated by an improved Transformer encoder to solve the common interference and occlusion. Differentiable numerical regression (DNR) is performed to realize an accurate coordinate regression of the keypoints. Second, the keypoint positioning is used to overcome the interference of the complex background to extract the edge, and an edge shape algorithm is employed to optimize the pose. If the edge extraction fails, the perspective- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -point (PnP) of the keypoints is carried out for pose calculation. Evaluation of practical image sequences indicates that the average precision at PCKp@0.03 is 96%. An attitude accuracy experiment further confirms that the optimized attitude root mean squared error (RMSE) is less than 0.35° and the average speed is 36.2 frames/s, fulfilling the strict requirement of pantograph attitude measurement.
Predicting Drug Review Polarity Using the Combination Model of Multi-Sense Word Embedding and Fuzzy Latent Dirichlet Allocation (FLDA)

The massive volume of textual data generated in recent years has led to the development of new computer-based technologies, especially in the field of healthcare area. Sentiment analysis opens a new door in healthcare to improve public health data analysis and efficiently predict diseases. Many words in natural language have multiple meanings or senses. However, traditional algorithms mainly focus on a single meaning but cannot capture the multiple senses of the words, leading to potential inaccuracies in sentiment analysis. Additionally, dealing with vagueness in linguistic terms is a common challenge in natural language processing; particularly, applying simple frequency terms is insufficient to measure the development states of different topics. In this research, we applied two multi-sense word embedding models, Probabilistic Fasttext and Multi-sense Skip-gram, to the sentiment analysis of drug reviews. The proposed models can better represent words with multiple meanings, producing more accurate sentiment analysis results. Additionally, we compared multi-sense word embedding with single embedding models and evaluated the classification methods compared to other classical machine learning technologies. Finally, the Fuzzy system was applied to estimate the topics hidden in the drug review dataset using the Latent Dirichlet Allocation (LDA) model; the Fuzzy rule-based system was applied to explain the classification result of drug review polarity. In particular, both models can have good performances during the classification task. Probabilistic Fasttext achieved an accuracy of 82.1%, and multi-sense skip-gram achieved an accuracy of 79.8%. The work has addressed several critical challenges related to sentiment analysis of healthcare data and has proposed a comprehensive approach to tackle them. The reported results indicate promising performance and the potential future applications in other medical domains beyond drug reviews further highlight the significance of this research.
Breast Cancer Diagnosis Using Lightweight Deep Convolution Neural Network Model

In the past few decades, breast cancer has rapidly increased the death rate among women worldwide.An early diagnosis of such fatal disease is important for the best treatment and death rate reduction.Automatic diagnosis of breast cancer from histopathological images using artificial intelligence (AI) based methods is a top-priority research area in the biomedical field.However, automatic detection is challenging due to high resolution of histopathology images and the tremendous amount of parameters required by deep AI models.Due to high computational complexity and bulky memory usage, deep models suffer from inefficient inference that limits their application in resource-constrained platforms.To address this problem, a fast cancer detection strategy has been proposed to overcome the computational cost issue of deep automatic systems.Instead of directly using input images the wavelet transform (WT) is applied to decompose the images into different frequency bands and then only low frequency bands are subjected to our proposed lightweight deep convolutional neural network (CNN).The lightweight deep model is designed using invertible residual block module.The incorporation of invertible residual block module in the deep CNN model and the use of WT considerably reduces the computational cost of the proposed model, without a noticeable accuracy downgrade Further, the effect of various machine vision classifiers i.e. support vector machine (SVM), softmax, and K nearest neighbor classifier (KNN) on model performance is analyzed.Experiments are performed using three publicly available benchmark histopathology image datasets.The proposed model has shown multi-class classification accuracy of 96.25%, and 99.8% and 72.2%, on the international conference on image analysis and recognition (ICIAR 2018), BreakHis and Bracs datasets, respectively.The reported inference time per image of the proposed model is 0.67s, and 0.21s for ICIAR 2018 and BreakHis and Bracs images, respectively.
Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models

With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the text. This paper presents a hybrid model of transfer deep learning methods for the aspect-oriented sentiment analysis of influencers’ tweets to predict the trend of cryptocurrencies. In the first model, different aspects of tweets are extracted using the Concept Latent Dirichlet Allocation (Concept-LDA). Then, by using the pre-trained RoBERTa network and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) deep learning network and attention layer, sentiments of different aspects of tweets are determined. In the following, the price trend of seven cryptocurrencies, Bitcoin, Ethereum, Binance, Ripple, Dogecoin, Cardano, and Solana, is determined using the historical price and the polarity of tweets with BiGRU combined deep neural network and the attention layer. Also, we used the gridsearch method to select dropout hyper-parameters, learning rate, and the number of GRU units, and the Akaike Information Criterion (AIC) criterion confirmed the results of this proposed combination. The results show that the proposed model in the aspect-based sentiment analysis section has been able to achieve 5.94% accuracy and 9.9% improvement in the f1-score on the SemEval 2015 dataset and 2.61% improvement on the SemEval 2016 dataset in f1-score compared to the state-of-arts. Also, the results of predicting the price trend of cryptocurrencies show that the proposed model has correctly recognized the price trend in the next five days in 77% of cases according to the ROC-AUC criterion.
A Design of High-Efficiency Coherent Sampling Based TRNG With On-Chip Entropy Assurance

True Random Number Generator (TRNG) is indispensable in cryptographic algorithms and protocols, and the quality of randomness directly influences the security of cryptographic applications. Multiple theoretical or offline entropy estimation methods have been proposed to evaluate the security of TRNGs, while their ideal assumptions commonly cannot be satisfied due to the perturbation of operating conditions at runtime, which makes it difficult to achieve sufficient entropy for the output of TRNGs in practice. Moreover, the output bitrate of TRNG is another fundamental concern during TRNG practical applications, while popular elementary oscillator-based structure commonly has relatively low output bitrate due to the inherent low sensitivity of entropy extraction to jitter (source of randomness). In this paper, we aim to design a TRNG satisfying both practical security (i.e., on-chip entropy assurance) and high output bitrate simultaneously. In particular, an improved stochastic model and a measurement method are established to quantify the entropy of coherent sampling based TRNG. Moreover, an on-chip entropy assurance module is provided to realize the robustness of the proposed design under various operating conditions. We implement the proposed TRNG in a simulation platform and ASIC chips (with SMIC 130 nm CMOS technology). Experimental results indicate that the generated data has sufficient entropy ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\geq 0.999$ </tex-math></inline-formula> per bit) under various operating conditions. In addition, all the output can pass the NIST SP800-22 and AIS 31 statistical tests with an output bitrate of 4.2 Mbps, which is equivalent to 2 orders of magnitude faster than that of the elementary oscillator-based TRNG.
Visualization and Synthesis of LCCC Space Based on RF-SVM Optimization

The comprehensive carrying capacity of land is a key indicator for judging the sustainable development of nature and economy.Conducting comprehensive studies on this land capacity can provide the government with valuable information for decision-making.To divide and utilize land in a more reasonable and scientific manner, this study constructed an evaluation model based on the Random Forest and Support Vector Machine algorithms.The objective is to establish a more precise evaluation model and conduct a more comprehensive evaluation of the indicators.Based on the experimental results, it can be concluded that the carrying capacity of all five systems increased between 2014 and 2022.System Dexhibited the fastest increase in carrying capacity, with a growth rate of 58.43%, while system R recorded a growth rate of 46.29%.The difference in data before and after spatial processing in a certain region was analyzed: the average error of the five regions was relatively small, indicating that the processed values can accurately express spatial visualization information.The accuracy of Random forest support vector machine evaluation model is higher than that of support vector machine, which verifies the optimization effect of Random forest algorithm on support vector machine.In the comparative experiment of four models, each error evaluation parameter from the constructed evaluation module was superior to those from other models.These results illustrate the superior applicability of the proposed research model in terms of comprehensive land bearing capacity.The study optimized the evaluation model for comprehensive carrying capacity of land.This model proposes countermeasures and suggestions for the assessment of land carrying capacity and provides decision-making references for the protection and development of land resources.
Quantum Error Correction Via Noise Guessing Decoding

Quantum error correction codes (QECCs) play a central role in both quantum communications and quantum computation. Practical quantum error correction codes, such as stabilizer codes, are generally structured to suit a specific use, and present rigid code lengths and code rates. This paper shows that it is possible to both construct and decode QECCs that can attain the maximum performance of the finite blocklength regime, for any chosen code length when the code rate is sufficiently high. A recently proposed strategy for decoding classical codes called GRAND (guessing random additive noise decoding) opened doors to efficiently decode classical random linear codes (RLCs) performing near the maximum rate of the finite blocklength regime. By using noise statistics, GRAND is a noise-centric efficient universal decoder for classical codes, provided that a simple code membership test exists. These conditions are particularly suitable for quantum systems, and therefore the paper extends these concepts to quantum random linear codes (QRLCs), which were known to be possible to construct but whose decoding was not yet feasible. By combining QRLCs and a newly proposed quantum-GRAND, this work shows it is possible to decode QECCs that are easy to adapt to changing conditions. The paper starts by assessing the minimum number of gates in the coding circuit needed to reach the QRLCs’ asymptotic performance, and subsequently proposes a quantum-GRAND algorithm that makes use of quantum noise statistics, not only to build an adaptive code membership test, but also to efficiently implement syndrome decoding.
Privacy and Transparency in Blockchain-Based Smart Grid Operations

In the past few years, blockchain technology has emerged in numerous smart grid applications, enabling the construction of systems without the need for a trusted third party. Blockchain offers transparency, traceability, and accountability, which lets various energy management system functionalities be executed through smart contracts, such as monitoring, consumption analysis, and intelligent energy adaptation. Nevertheless, revealing sensitive energy consumption information could render users vulnerable to digital and physical assaults. This paper presents a novel method for achieving a dual balance between privacy and transparency, as well as accountability and verifiability. This equilibrium requires the incorporation of cryptographic tools like Secure Multiparty Computation and Verifiable Secret Sharing within the distributed components of a multi-channel blockchain and its associated smart contracts. We corroborate the suggested architecture throughout the entire process of a Demand Response scenario, from the collection of energy data to the ultimate reward. To address our proposal’s constraints, we present countermeasures against accidental crashes and Byzantine behavior while ensuring that the solution remains appropriate for low-performance IoT devices.
V-SKP: Vectorized Kernel-Based Structured Kernel Pruning for Accelerating Deep Convolutional Neural Networks

In recent years, kernel pruning, which offers the advantages of both weight and filter pruning methods, has been actively conducted. Although kernel pruning must be implemented as structured pruning to obtain the actual network acceleration effect on GPUs, most existing methods are limited in that they have been implemented as unstructured pruning. To compensate for this problem, we propose vectorized kernel-based structured kernel pruning (V-SKP), which has a high FLOPs reduction effect with minimal reduction in accuracy while maintaining a 4D weight structure. V-SKP treats the kernel of the convolution layer as a vector and performs pruning by extracting the feature from each filter vector of the convolution layer. Conventional L1/L2 norm-based pruning considers only the size of the vector and removes parameters without considering the direction of the vector, whereas V-SKP removes the kernel by considering both the feature of the filter vector and the size and direction of the vectorized kernel. Moreover, because the kernel-pruned weight cannot be utilized when using the typical convolution, in this study, the kernel-pruned weights and the input channels are matched by compressing and storing the retained kernel index in the kernel index set during the proposed kernel pruning scheme. In addition, a kernel index convolution method is proposed to perform convolution operations by matching the input channels with the kernel-pruned weights on the GPU structure. Experimental results show that V-SKP achieves a significant level of parameter and FLOPs reduction with acceptable accuracy degradation in various networks, including ResNet-50, and facilitates real acceleration effects on the GPUs, unlike conventional kernel pruning techniques.
MHGAN: A Multi-Headed Generative Adversarial Network for Underwater Sonar Image Super-Resolution

Super-Resolution is a technique for recovering image details based on available information, avoiding image quality degradation by increasing an image’s resolution. Despite the great success of deep learning-based SR models, they are rarely applied to underwater sonar scenarios due to the lack of underwater sonar datasets and the difficulty in recovering texture details. To address these challenges, we propose a multi-headed generative adversarial network (MHGAN) for the super-resolution of underwater sonar images. The main body of the generator is designed with the introduced Residual Simple-dense Self-calibrated Block (RSSB), which combines Self-calibrated Attention and Pixel Attention to improve generalization capability. Additionally, the discriminator employs a novel multi-headed U-net structure to acquire detailed texture information at multiple patch sizes of different scales, along with the generated image and High-Resolution(HR) image details. Moreover, we introduce the correction loss, which acts as a pixel-level correction to help improve the quality of the output images, and multi-perceptual loss based on VGG19 to extract multi-scale features for comparison. Meanwhile, we also introduce the Underwater Sonar Dataset for Super-Resolution (USDSR). We perform the visual and quantitative comparison, ablation experiments, and model analysis with state-of-the-art methods on both the public KLSG dataset and the built USDSR dataset. Experimental results demonstrate the effectiveness and superiority of our proposed MHGAN model.
Representing Multi-View Time-Series Graph Structures for Multivariate Long-Term Time-Series Forecasting

Multivariate long-term time-series forecasting tasks are very challenging tasks in many real-world application areas. Recently, researchers focus on designing robust and effective methods, and have made considerable progress. However, there are several issues with existing models that need to be overcome. First, the lack of a relationship structure between multivariate variables needs to be addressed. Second, most models only have a weak ability to capture local dynamic changes across the entire long-term time-series. Third, current models suffer from high computational complexity and unsatisfactory accuracy. To figure out the above issues, we propose an effective and efficient method called Multi-view Time-series Graph Structure Representation (MTGSR). MTGSR uses GCNs to construct topological relationships in the multivariate time-series from three different perspectives: time, dimension, and crossing segments. Variation trends in different dimensions are extracted through a difference operation to construct topological maps that reflect the correlations between different dimensions. To capture the dynamically changing characteristics of fluctuation correlations between adjacent local sequences, MTGSR constructs a cross graph by calculating correlation coefficients between adjacent local sequences. Extensive experiments show that MTGSR reduces errors by 17.41% over the state-of-the-art. Additionally, memory use is decreased by 66.52% and the running time is reduced by 78.09%.
Sentiment Analysis of Covid-19 Vaccination Response

As a result of COVID-19 vaccinations being developed and licenced, people are more optimistic about the eventual end of the pandemic and the return to normalcy. Vaccination hesitancy, which can be prompted by misinformation, is a major barrier to reaching desired vaccination rates. A big obstacle in the field of machine learning is the processing of data to identify emotions using methods that can tell us if people's feelings about a topic are positive or negative. Although social media and microblogging sites can be excellent resources for gathering facts and data, their primary function is the expression of individual viewpoints and values. After distributing the COVID-19 vaccine, we plan to analyze the reactions of Twitter users to the measure. In this study, we present a machine learning-based language model for analyzing the tone of Vaccination Responses in the context of an increase in new cases of COVID19.After the initial stage of data pre-processing and tokenizing, this framework assigns sentiment classes, then uses the technique of feature extraction to convert the text value into a matrix. Last but not least, machine learning methods will be used to identify and categorize people's opinions on immunization. We have implemented and evaluated the performance of three distinct machine learning methods—logistic regression, naive Bayes, and a voting classifier. Performance metrics including accuracy, precision, recall, and f1score were used to evaluate the framework, and the experiments were conducted using a publicly available Twitter dataset on Kaggle. The purpose of this study is to analyze the public's reaction to immunization against COVID-19 as expressed on Twitter. The findings could be used by public health officials in making decisions about vaccination promotion initiatives. Researchers hope that this study may help medical professionals better comprehend the steps involved in administering vaccinations.