Reviewerly/exHarmony huggingface dataset:
Mixed‐Precision Continual Learning Based on Computational Resistance Random Access Memory

Artificial neural networks have acquired remarkable achievements in the field of artificial intelligence. However, it suffers from catastrophic forgetting when dealing with continual learning problems, i.e., the loss of previously learned knowledge upon learning new information. Although several continual learning algorithms have been proposed, it remains a challenge to implement these algorithms efficiently on conventional digital systems due to the physical separation between memory and processing units. Herein, a software–hardware codesigned in‐memory computing paradigm is proposed, where a mixed‐precision continual learning (MPCL) model is deployed on a hybrid analogue–digital hardware system equipped with resistance random access memory chip. Software‐wise, the MPCL effectively alleviates catastrophic forgetting and circumvents the requirement for high‐precision weights. Hardware‐wise, the hybrid analogue–digital system takes advantage of the colocation of memory and processing units, greatly improving energy efficiency. By combining the MPCL with an in situ fine‐tuning method, high classification accuracies of 94.9% and 95.3% (software baseline 97.0% and 97.7%) on the 5‐split‐MNIST and 5‐split‐FashionMNIST are achieved, respectively. The proposed system reduces ≈200 times energy consumption of the multiply‐and‐accumulation operations during the inference phase compared to the conventional digital systems. This work paves the way for future autonomous systems at the edge.
LEVERAGING DYNAMIC OBJECTS FOR RELATIVE LOCALIZATION CORRECTION IN A CONNECTED AUTONOMOUS VEHICLE NETWORK

Abstract. High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further improve road safety by sharing information in a communication network of Connected Autonomous Vehicles (CAV). In this scenario, small localization errors can impose additional difficulty on fusing the information from different CAVs. In this paper, we propose a RANSAC-based (RANdom SAmple Consensus) method to correct the relative localization errors between two CAVs in order to ease the information fusion among the CAVs. Different from previous LiDARbased localization algorithms that only take the static environmental information into consideration, this method also leverages the dynamic objects for localization thanks to the real-time data sharing between CAVs. Specifically, in addition to the static objects like poles, fences, and facades, the object centers of the detected dynamic vehicles are also used as keypoints for the matching of two point sets. The experiments on the synthetic dataset COMAP show that the proposed method can greatly decrease the relative localization error between two CAVs to less than 20cm as far as there are enough vehicles and poles are correctly detected by both CAVs. Besides, our proposed method is also highly efficient in runtime and can be used in real-time scenarios of autonomous driving.
Blockchain-based Direct-sale Transaction with Peak Shaving and Green Certificate Mechanism

Nowadays, the direct-sale of electricity by large users has become an important development trend. Combining the quota system, it can promote the consumption of renewable energy and achieve the goal of carbon neutrality. In order to make the direct transaction of electricity more transparent and reliable, relying on the blockchain technology with the characteristics of encryption and decentralization, a transaction platform for the direct transaction of electricity has been constructed. The first is that it involves blockchain smart contracts to realize the direct power purchase transaction management function and ensure the safety and trustworthiness of the power direct sale transaction path. Secondly, introduce the load peak shaving market, design peak shaving market transactions and green certificate matching transactions to participate in the direct selling market link, and compare the three transaction modes. Finally, run the constructed model on the designed blockchain trading platform to complete the entire process of energy trading. The results show that the participation of the green certificate mechanism and the peak shaving mechanism in the direct purchase market can rationally allocate market resources and guide emission reduction through market means, which is conducive to the further realization of carbon neutrality in electricity market transactions.
Natural Language Processing for Corpus Linguistics by Jonathan Dunn. Cambridge: Cambridge University Press, 2022. ISBN 9781009070447 (PB), ISBN 9781009070447 (OC), vi+88 pages.

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A Feature Importance Analysis Based Solar Irradiance Mapping Model Using Multi-channel Satellite Remote Sensing Data

Solar irradiance is a crucial parameter that impacts the accuracy of photovoltaic (PV) generation prediction. However, due to the equipment deployment limitations and malfunction factors, accurate irradiance data with sufficient historical accumulation and wide spatial distribution are usually unavailable. Therefore, satellite remote sensing shows its unique significance as an additional stable irradiance observation source. This paper develops a feature importance analysis-based solar irradiance mapping model to calculate ground solar irradiance only using satellite data. In this paper, the K-means method is employed to classify the weather condition. The correlation of all channels data of FY4 satellite with the solar radiation of PV site is analyzed using the Pearson correlation coefficient under different sky conditions. The XGBoost feature important algorithm is applied to analyze the importance of different channel features, which optimizes and determines the input of the mapping model. The gradient boost regression model (GBR) is chosen as the mapping model to calculate solar irradiance with the combination channels satellite data obtained according to feature important analysis. The simulation results show that the proposed model performs best compared with other regression methods.
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning

This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR).We propose CREATER, 1 a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews.To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR.To alleviate the low-resource issue, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning.Experiments on industrial datasets show that CREATER significantly outperforms current approaches.It has been deployed online in a leading advertising platform and brings uplift on core online metrics.
Health: Human-Machine Interaction, Medical Robotics, Patient Rehabilitation

This chapter presents a new prototype of an electric wheelchair that considers assistive navigation system and health monitoring enablers within serious games interfaces that teaches the end-user to exploit the advantages of this type of wheelchair. However, other potential areas require further research, for instance, to detect and prevent diseases through Human Machine Interfaces (HMIs). In this regard, biometric signals research has provided assistive technology that could help users manage products using robotic systems through HMIs. Nonetheless, some of those products and HMIs require a certain degree of cognitive skills to use them properly and safely and according to the user’s needs. Thus, games with educational purposes, namely, serious games, into HMIs environments, allow individuals to understand better how to use them. Consequently, this chapter introduces an intelligent electric wheelchair that takes advantage of biometrics signals such as voice commands, head, and eye movements. It then processes them using artificial intelligence algorithms and integrates them into serious games HMIs to produce a smart navigation system. Besides, this proposal can be implemented in any conventional electric wheelchair and provide feedback related to the user-health condition by employing the signals as mentioned earlier.
Chinese Clinical Named Entity Recognition with ALBERT and MHA Mechanism

Traditional clinical named entity recognition methods fail to balance the effectiveness of feature extraction of unstructured text and the complexity of neural network models. We propose a model based on ALBERT and a multihead attention (MHA) mechanism to solve this problem. Structurally, the model first obtains character-level word embeddings through the ALBERT pretraining language model, then inputs the word embeddings into the iterated dilated convolutional neural network model to quickly extract global semantic information, and decodes the predicted labels through conditional random fields to obtain the optimal label sequence. Also, we apply the MHA mechanism to capture intercharacter dependencies from multiple aspects. Furthermore, we use the RAdam optimizer to boost the convergence speed and improve the generalization ability of our model. Experimental results show that our model achieves an F1 score of 85.63% on the CCKS-2019 dataset—an increase of 4.36% compared to the baseline model.
Informative Causality Extraction from Medical Literature via Dependency-Tree–Based Patterns

Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods-with an order of magnitude improvement in the F-score metric over the best of the existing methods. We also build different variants of PatternCausality, which use different phrase extraction methods; all variants are better than the existing methods. PatternCausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.
Solar Power Forecasting Using Long Short-Term Memory Algorithm in Tamil Nadu State

Solar power forecasting entails understanding of the sun’s path, the atmosphere’s condition, the scattering techniques and the traits of a solar plant which makes use of the sun light to generate solar energy. This system converts the solar energy into electrical energy. The output power relies upon at the incoming radiation and at the sun panel traits. Photovoltaic energy manufacturing is growing nowadays. Forecast data are vital for a green use, control of the power grid and for solar power trading. This challenge predicts sun irradiance for numerous hours for the future, primarily based totally on modern sun Irradiance and nearby climate situations by the usage of DL algorithms like LSTM model. Data supply is from TANGEDCO and additionally obtained from internet sources. Date-oriented and month-oriented data are saved in CSV files. Later the same is extracted using Pandas Library function and for the entire system Python software is used. From the ancient irradiance, information’s are collected in the form of data frames. Reading and cleaning of data is made by the support of Pandas Libraries. Pandas Library is a machine-learning library that is used to clear the data and to accept valuable and non-redundant data set. The newly derived data set is saved in the form of pickle format and the same is used for later use. The data preparation is done using Numpy and Pandas Center Library functions. All the prepared information is stored as data frames. From the obtained data frames, different varieties of manipulations are carried out to generate the results in the form of reports with diagrams and charts. The outcomes are related to the real power generated and are concluded with good prediction efficiency.
Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper's goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.
Towards a Quantum Benchmark Suite with Standardized KPIs

Benchmarking quantum computing hardware and related software is being researched very actively at the moment as predicting the real-world performance of quantum processing units for broad application areas in a fair, standardized and vendor-agnostic way is still very challenging. In this paper, we present our research plans for designing a quantum benchmark suite based on measuring a complete set of standardized key performance indicators together with a brief overview of the state-of-the-art of quantum benchmarking. With this in mind we discuss general ideas about designing, assessing and standardizing quantum benchmarks.
Comparison of Different Kernels in a Support Vector Machine to Classify Prostate Cancerous Tissues in T2-Weighted Magnetic Resonance Imaging

Background and objectives: A support vector machine (SVM) is one of the most powerful classifiers in machine learning that can be applied when a data set is introduced in two classes in a high dimensional feature space.The objective of this study is to compare different kernels of SVMs to classify prostate cancerous tissues. Methods:In the present study, a novel algorithm was proposed to classify cancerous prostate tissues.Five features, among 14 Haralick, were chosen as the most significant features: contrast, correlation, homogeneity, energy, and entropy.In addition, 17 features were calculated from each outlined region of interest (ROI) on the images.Then, the dimensionality number of the features set was reduced from 17 to 5 using the principal component analysis (PCA) technique.The reduced features were considered as given inputs to an SVM algorithm for classification. Results:The sensitivity of the SVM was 0.9565 with the radial basis function (RBF) of 0.9097 and 0.9028 were achieved with the Gaussian and the linear kernels, respectively.Moreover, the accuracy of the linear, RBF, and Gaussian functions were 0.9028, 0.8405, and 0.8239, respectively. Conclusions:The RBF is preferable compared with the other examined functions due to the highest sensitivity and the second-largest accuracy.
Cultural Creativity, Industrial Scale, Management Methods, and Their Roles in Rural Revitalization from the Perspective of Big Data

Rural revitalization, as a significant element of the national economy, has become a hot topic at present. The development of digital economies makes big data play become more significant for the national economy. To accelerate the process of rural revitalization, using up big data is vital. To mine the relationship between factors, such as cultural creativity, industrial scale, management methods, and rural revitalization from big data, this study adopts a neural network method. Based on the proposed neural network, a scheme to analyze the relationship between cultural creativity, industrial scale, management methods and the level of rural revitalization is presented. Through case studies, the effectiveness of analyzing the influence of cultural creativity, industrial scale, and management methods on rural revitalization based on big data is demonstrated. Moreover, the results validate that the proposed neural network has good prediction accuracy, which indicates that it is reliable to use neural networks to analyze the relationship between the impacting factors and the rural revitalization level with the assistance of big data.
Transfer learning-based channel estimation in orthogonal frequency division multiplexing systems using data-nulling superimposed pilots

Data-nulling superimposed pilot (DNSP) effectively alleviates the superimposed interference of superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems, while facing the challenges of the estimation accuracy and computational complexity. By developing the promising solutions of deep learning (DL) in the physical layer of wireless communication, we fuse the DNSP and DL to tackle these challenges in this paper. Nevertheless, due to the changes of wireless scenarios, the model mismatch of DL leads to the performance degradation of CE, and thus faces the issue of network retraining. To address this issue, a lightweight transfer learning (TL) network is further proposed for the DL-based DNSP scheme, and thus structures a TL-based CE in OFDM systems. Specifically, based on the linear receiver, the least squares estimation is first employed to extract the initial features of CE. With the extracted features, we develop a convolutional neural network (CNN) to fuse the solutions of DL-based CE and the CE of DNSP. Finally, a lightweight TL network is constructed to address the model mismatch. To this end, a novel CE network for the DNSP scheme in OFDM systems is structured, which improves its estimation accuracy and alleviates the model mismatch. The experimental results show that in all signal-to-noise-ratio (SNR) regions, the proposed method achieves lower normalized mean squared error (NMSE) than the existing DNSP schemes with minimum mean square error (MMSE)-based CE. For example, when the SNR is 0 decibel (dB), the proposed scheme achieves similar NMSE as that of the MMSE-based CE scheme at 20 dB, thereby significantly improving the estimation accuracy of CE. In addition, relative to the existing schemes, the improvement of the proposed scheme presents its robustness against the impacts of parameter variations.
Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor.In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors.The robot's path is marked by a distinct line or track, which the robot follows to move.Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation.Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications.A visual navigation line tracking robot should detect the line with a camera using an image processing technique.The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.
Impact of Gaze Uncertainty on AOIs in Information Visualisations

Gaze-based analysis of areas of interest (AOIs) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that gaze uncertainty frequently and significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are usually designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.
Adaptive Contrastive Learning with Label Consistency for Source Data Free Unsupervised Domain Adaptation

Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy issues and intellectual rights. In this paper, we discuss a more challenging and practical source-free unsupervised domain adaptation, which needs to adapt the source domain model to the target domain without the aid of source domain data. We propose label consistent contrastive learning (LCCL), an adaptive contrastive learning framework for source-free unsupervised domain adaptation, which encourages target domain samples to learn class-level discriminative features. Considering that the data in the source domain are unavailable, we introduce the memory bank to store the samples with the same pseudo label output and the samples obtained by clustering, and the trusted historical samples are involved in contrastive learning. In addition, we demonstrate that LCCL is a general framework that can be applied to unsupervised domain adaptation. Extensive experiments on digit recognition and image classification benchmark datasets demonstrate the effectiveness of the proposed method.