Reviewerly/exHarmony huggingface dataset:
Prompt-GAN–Customisable Hate Speech and Extremist Datasets via Radicalised Neural Language Models

Online hate speech and violent extremism knows no borders, no political boundaries, no remorse. Researchers face an uphill battle to collect hate speech data in volumes and topical diversity suitable for training state-of-the-art content-moderation systems. Neural language models ushered in a new era of synthetic data generation in use across various businesses, all despite calls for research to protect against unintended toxic output. We present a method for radicalising pre-trained neural language models to identify real hate speech and highlight the risks of AI which could undermine our trust in social media. We present Prompt-GAN, a prompt-tuning adversarial approach with three achievements. Namely, we demonstrate prompt-tuning's ability to generate realistic types of hate and non-hate speech which mimics political extremist discourse. Prompt-GAN's architecture offers a twofold reduction in memory and runtime requirements compared to fine-tuning. Prompt-GAN improves hate speech classification F1-scores by up to 10.1% and sets a new record in neural language simulation compared to the current state-of-the-art across three benchmark social media datasets.
Remote Sensing Image Object Detection Algorithm Combining Attention

To address the issue of the complex background being difficult to differentiate and the object percentage, rotation angle, and position affecting the identification accuracy, a better object detection algorithm for remote sensing images is given. This algorithm uses the improved frequency channel attention to make the network take more notice of the foreground information, in order to achieve the effect of suppressing the complex background information. It also introduces a multiple attention mechanism in the baseline, which is conducive to alleviating the issue of increasing the detection difficulty due to the different proportion of objects in remote sensing images, arbitrary rotation angle, and position. Experiments on the DIOR, NWPUVHR-10, and RSOD datasets, respectively, are conducted to confirm the efficacy of the proposed algorithm. The suggested method's average accuracy on the DIOR dataset is 1.46% higher than that of the single-stage ATSS algorithm, and it has also produced results that are competitive on the NWPUVHR-10 and RSOD datasets.
Cross-validation for local-linear regression by observations from mixture

We consider a generalization of local-linear regression for estimation of compnents' regression functions by observations from mixture with varying concentrations. A cross-validation technique is developed for the bahdwidth selection. Performance of the obtained estimator is compared with the modified Nadaraya-Watson estimator performance by simulations.
Simultaneous Coherent Detection With Baseband Enhancement in Chaotic Random Bit Generation by an Optically Injected Laser

Optical injection into a semiconductor laser invokes chaos that is coherently detected without direct detection for fast random bit generation (RBG). Originating from the injection without any feedback, the chaos contains no undesirable time-delay signature. Although the injection dynamics only supports a low dimensionality of three, simultaneous coherent detection uses the two dimensions of intensity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> and phase <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> in yielding two signals <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{H}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{B}}$ </tex-math></inline-formula> , which are from heterodyning and balanced delayed homodyning, respectively. Compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> from direct detection, the two coherently detected signals <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{H}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{B}}$ </tex-math></inline-formula> are baseband-enhanced for effectively utilizing the low-frequency responses of the detectors. Experimentally, on a laser with a relaxation resonance of 5.2 GHz, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{H}}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I_{\mathrm{B}}$ </tex-math></inline-formula> are baseband-enhanced by 8 dB and 12 dB, respectively. Through a basic postprocessing by discarding bits, they are digitized for RBG with an output bit rate reaching 280 Gbps. Through an extensive postprocessing by involving pseudo-random contributions, a boosted output bit rate of 1.28 Tbps is possible even when the detection bandwidth is reduced to 3 GHz. Both postprocessings satisfy a set of standardized randomness tests from the National Institute of Standards and Technology. Based on the simultaneous coherent detection, the potential of baseband enhancement is illustrated for the low-dimensional dynamics from injection.
Transfer Learning Using Cluster Centers for Surface Defect Identification of Piston Rods in Pneumatic Cylinders

Surface defect identification is essential for piston rods to ensure leakproofness, non-pollution, and control precision of pneumatic cylinders. However, due to environmental conditions, the distribution discrepancy between surface images is unavoidable. Transfer Learning (TL) methods can be adopted to reduce this discrepancy, but they utilized the class center to represent samples from one class for measuring the distribution discrepancy, which can not reflect the dispersion of samples. To address this problem, this paper considers samples within one class dispersing into multiple clusters, and then proposes Transfer Learning using Cluster Centers (TLCC). First, to determine cluster centers within each class, TLCC applies the K-means algorithm to divide samples within each class into K clusters. Second, noticing that using all cluster centers to represent samples from one class can effectively reflect the dispersion of samples, TLCC establishes Cluster Centers Discrepancy (CCD) for measuring the distribution discrepancy between the source and target domains. CDD is designed on the distance between each pair of cluster centers within each class. Next, to reduce the distribution discrepancy, the optimization objective of TLCC is established on minimizing CCD. Finally, sufficient results demonstrate that TLCC outperforms the state-of-the-art TL methods, where the average accuracies of TLCC on the benchmark dataset (CMU-PIE) and our SIPRPC dataset are 85.3% and 98.8%.
Prototypes Sampling Mechanism for Class Incremental Learning

Incremental learning aims to alleviate the catastrophic forgetting problem of deep neural networks during learning sequential data stream. This problem is even more challenging when old data is unavailable, since learning system can only be trained under the supervision of current data. To address this problem, we proposed a prototype sampling mechanism based on K-means clustering method. On the one hand, we proposed to use K-means clustering to pick out class-representative prototypes for each old class. During incremental stages, prototypes and deep features from current data are trained together to maintain the distinction and balance between old and new classes. On the other hand, we proposed to attach a mask to the loss function based on the cosine similarity between the prototypes and the current data. Which further enhances the discrimination between old and new classes compared to naive knowledge distillation schemes. Extensive experiments conducted on three benchmark datasets including CIFAR100, Tiny-ImageNet and vggface2 verified the effectiveness and advantages of our proposed method. Specifically, we improved class incremental performance by 1.6%, 1.2% and 1.7% on three datasets respectively.
Application of PNN-HMM Model Based on Emotion-Speech Combination in Broadcast Intelligent Communication Analysis

The emotive manifestation of a news anchor ought not to be arbitrary, but rather meticulously crafted and refined to elicit a more coherent emotional response.Hence, the identification of the appropriate emotional disposition during news broadcasts constitutes a meritorious domain of inquiry.To address concerns related to imprecise extraction of speech features and intricate detection of emotional states in broadcasting, this study presents an innovative Chinese pronunciation system predicated on speech recognition.The GA-SVM algorithm is employed to ascertain the endpoint of input Chinese speech signals and extract emotional feature parameters.For the recognition of the emotional temperament in broadcast speech, a PNN model is utilized to execute the decoding process of Viterbi in HMM.Experimental findings evince that the SVM optimized by GA attains a robust classification effect across diverse test samples.Moreover, the PNN-HMM model exhibits a noteworthy capacity to withstand noise during Chinese speech extraction, thereby enabling accurate discernment of the emotional characteristics of the speech under examination.Additionally, this research furnishes a valuable point of reference for the application of intelligent classification technology to audio information.
Research on Blockchain-Based FinTech Trust Evaluation Mechanism

The financial technology (FinTech) has promoted the wide application of FinTech with the help of artificial intelligence, blockchain, cloud computing, data science and other new technologies. Trust evaluation has become a forward-looking issue for the rapid development of FinTech. The existing trust evaluation methods of FinTech do not consider the impact of the timeliness, reliability and non-invasive factors of trust on trust evaluation, which results in low accuracy of trust evaluation and incapability of effectively identifying malicious behaviors of users. Firstly, this paper introduces the blockchain technology to construct a four-layer architecture structure and multiple trust evaluation indicators based on blockchain service data. Secondly, the paper proposes a blockchain based FinTech trust evaluation mechanism (BFTEM), which utilizes blockchain to record relevant data and multiple trust parameters during the transmission process of each block, and verify the trust degree issued by the trust holder through the comprehensive trust value of the user. Finally, the block generation time, throughput, delayed response time and comprehensive trust value are experimentally studied through simulation experiments. Simulation experiments show that BFTEM mechanism can better improve the security and reliability of FinTech data, and has advantages in improving the accuracy of trust evaluation and expanding potential applications.
A New Cyborg Rat Auto Navigation System Based on Finite State Machine

Due to its high environmental adaptation and locomotor abilities, the cyborg rat, a novel form of robot created since brain–computer interface technology, has demonstrated great potential in search and rescue operations following natural disasters such as earthquakes. However, there is currently no overarching framework for building an autonomous navigation system for cyborg animal. In this article, we propose an auto navigation system for cyborg rat based on an inertial sensor and an infrared thermal sensor. The navigation system utilizes an inertial sensor to detect and analyze the rat’s movement, an infrared thermal sensor to identify infrared target objects, and lastly feeds the sensor data into a finite state machine, which outputs an optimized stimulus pattern to lead and control the cyborg rat to finish the corresponding movement. Finally, our navigation model can guide the cyborg rat to achieve autonomous search for target objects in the open field, and it exhibits strong exploratory capabilities in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> m complex field.
Self-Supervised Boundary Point Prediction Task for Point Cloud Domain Adaptation

Unsupervised domain adaptation (UDA) could significantly improve the cross-domain performance of current supervised 3D deep learning methods and have a widespread application prospect. However, the domain gap between source domain and target domain renders the UDA problem highly challenging. In this letter, we present a novel UDA method for point clouds from the perspective of multi-strategy. First, we explore the effectiveness of state-of-the-art data augmentation methods to point cloud domain adaptation, and introduce a data augmentation procedure to two widely-existed scenarios, i.e., sim-to-sim and sim-to-real. And then, we explore a mask deformation procedure to simulate the missing parts with respect to the real-world point clouds. On one hand, the masked point clouds push network to pay more attention to local features rather than global features; on other hand, we employ a prediction-consistency contrastive loss to improve the prediction robustness of network based on the mask deformation. Moreover, we propose a self-supervised learning task by predicting the boundary points of masked region. Specifically, the network could effectively perceive the occlusion and capture fine-grained features by automatically labeling and predicting the boundary points of the marked region. Extensive experiments conducted on both PointDA-10 and PointSegDA benchmarks for point cloud classification and segmentation, respectively, demonstrate the effectiveness of the proposed method.
The Naive Bayes Classifier

The Naive Bayes Classifier makes a so-called conditional independence assumption that is almost always wrong. This incorrect assumption earns the classifier the designation “naive.” The assumption greatly simplifies calculations; the naive Bayes classifier is very fast. The assumption trades off increased bias with reduced variance making the classifier surprisingly successful. The Naive Bayes classifier often benefits from smoothing. We discuss Laplace smoothing and the m-estimator. Somewhat cheekily, we use the Naive Bayes classifier to determine whether the movie “Shakespeare in Love” would be classified as a history, tragedy, or comedy, had the movie been written by Shakespeare. Our case study is about an open-ended survey question where respondents give advice to “Patient Joe” in a hypothetical situation. We classify the text answers into one of four classes.
Gamified DAOs as Blockchain-Based Catalysts for Prosocial and Environmentally Oriented Cities

This paper explores the role of gamification in decentralized autonomous organizations (DAOs) that are run through rules encoded as computer programs called smart contracts rather than being managed by people. DAOs operate on a decentralized blockchain network and allow for secure and transparent decision-making processes without the need for intermediaries. Token holders typically govern them with voting rights to make decisions and allocate resources within the organization. In the context of ever-growing cities, previous research has shown that prosocial behavior and a positive mental attitude toward the environment are not always related. People without motivation tend not to take meaningful and valuable actions for their societies. Over the past decade, gamification has offered new strategies and tactics for encouraging and sustaining positive change. More recently, advances in blockchain technology have enabled the rise of DAOs. Therefore, this research aims to review and discuss the potential of gamification to support DAOs aiming at prosocial and pro-environmental transformations in cities, as well as the role of blockchain as a technology that fosters such societal improvements and enables organizations to better align with sustainability goals.
Reinforcement Learning for a Continuous DC Motor Controller

Electric motors control is a very knows topic in research and most of the activities are drawn towards classic PI or model predictive control methods. Implementing reinforcement learning techniques in the field of motor control depends on fidelity environments, types of considered motors and modeled power electronics used for control. Training an RL speed controller means finding an optimal control policy by offline training using an environment, before implementing it in a real-world scenario. Different environments and techniques have been developed for training RL controllers, most of them being extensions of Open AI gym environments. This paper presents a trained RL speed controller, developed through Reinforcement learning techniques, specifically TD3 RL algorithm, applied to permanently excited dc motors. In this work, the open-source Python package gym-electric-motor (GEM) [1] is used for environment setup, and pytorch framework for developing the controller.
Deep Multi-Representation Model for Click-Through Rate Prediction

Click-Through Rate prediction (CTR) is a crucial task for online advertising and recommender systems. Therefore, it has gained considerable attention in the past few years as it highly affects the revenue of several commercial platforms and online systems. The primary purpose of recent research emphasizes obtaining meaningful and powerful representations through mining low and high-feature interactions using various components such as Deep Neural Networks (DNN), CrossNets, or transformer blocks. However, models utilizing one representation for the input fields in each instance restrict the model's predictive power. Other models tend to be overly complicated to reach high input data expressiveness and predictive power. In this work, we propose a simple yet effective Deep Multi-Representation model (DeepMR) that is capable of learning informative representations by jointly training a mixture of two powerful feature representation learning components, namely DNNs and multi-head self-attentions. Furthermore, DeepMR integrates the novel residual with zero initialization (ReZero) connections to the DNN and the multi-head self-attention components for learning superior input representations. Experiments on three real-world datasets show that the proposed model significantly outperforms all state-of-the-art models with a relative improvement of up to 16.6% in the task of click-through rate prediction. Our implementation code and datasets are available here https://github.com/Shereen-Elsayed/DeepMR.
NeuronsMAE: A Novel Multi-Agent Reinforcement Learning Environment for Cooperative and Competitive Multi-Robot Tasks

Multi-agent reinforcement learning (MARL) has achieved remarkable success in various challenging problems. Meanwhile, more and more benchmarks have emerged and provided some standards to evaluate the algorithms in different fields. On the one hand, the virtual MARL environments lack knowledge of real-world tasks and actuator abilities. On the other hand, the current task-specified multi-robot platform has poor support for the universality of multi-agent reinforcement learning algorithms and lacks support for transferring from simulation to the real environment. Bridging the gap between the virtual MARL environments and the real multi-robot platform becomes the key to promoting the practicability of MARL algorithms. This paper proposes a novel MARL environment for real multi-robot tasks named NeuronsMAE (Neurons Multi-Agent Environment). This environment supports cooperative and competitive multi-robot tasks and is configured with rich parameter interfaces to study the multi-agent policy transfer from simulation to reality. With this platform, we evaluate various popular MARL algorithms and build a new MARL benchmark for multi-robot tasks. We hope that this platform will facilitate the research and application of MARL algorithms for real robot tasks. Information about the benchmark and the open-source code are released at https://github.com/DRL-CASIA/NeuronsMAE.
The Effect of Training Data Quantity on Monte Carlo Dropout Uncertainty Quantification in Deep Learning

When deploying deep neural networks, quantification of a model's uncertainty is necessary to provide confidence in its predictions by distinguishing between accurate predictions and coincidentally correct guesses. While it is known that the accuracy of predictions is dependent on the data on which the model was trained, to date, limited work has examined the relationship between training data quantity and uncertainty quantification. In this paper, we propose two metrics to assess the ‘quality’ of uncertainty quantification, and investigate the relationship between training data quantity and Monte Carlo Dropout uncertainty quantification in supervised and semi-supervised learning across various text-based datasets. We found that in supervised learning, uncertainty quan-tification quality (across both metrics) initially increased for larger quantities of training data, but interestingly, after a certain threshold, began to gradually decline. In semi-supervised learning, uncertainty quantification was enhanced by both a greater number of training samples and greater proportion of pre-labelled data. These results suggest that for supervised learning, data scientists generally ought not to invest resources into acquiring more training data solely for superior uncertainty quantification. However, if semi-supervised learning is necessary, then there is a marked benefit in obtaining more data.
An Enhanced Transferable Adversarial Attack Against Object Detection

In this work, we propose an enhanced adversarial attack based on DAG, to improve the transferability of adversarial example to fool CNN-based object detectors. DAG achieves excellent performance under white-box settings via attacking the detection head, but has poor transferability under black-box cases. We jointly attack the feature map of backbone and the detection head to improve the transferability. Because, we think the attack against high-level feature of backbone can transfer to disturb other homogeneous CNN-based backbones. To this end, we optimize the combination of feature loss of backbone and classification loss of region proposals to generate adversarial examples. Extensive experiments on PASCAL VOC and COCO datasets demonstrate that our attack can transfer to attack the detector with different backbone or different pipelines even under defense situations, and has achieved superior transferability than state-of-the-arts.