Reviewerly/exHarmony huggingface dataset:
NLP based on prompt-based learning and prompting methods: a survey

In this survey paper, we attempted to summarize the recent work of a paradigm shift in the natural processing language field that we call "Prompt-based learning". In recent years, the rapid development and stability of pre-trained language models have driven the advancement of this novel approach. Prompt-based learning leverages language models for clue-driven learning and has made significant strides in the field of natural language processing. To make predictions, the process begins with the design of text prompts based on the nature of the task and input-output format. Subsequently, user input data is combined with these text prompts to create a new text sequence, which is then fed into a pre-trained language model. Following this, information required for the task is extracted or interpreted from the generated text, and ultimately, the interpreted results are presented to the user or used for further processing, depending on the task requirements. However, there is currently no standardized framework for prompt-based learning. Existing code libraries for prompt-based learning are typically unregulated and provide limited functionality tailored to specific scenarios. Therefore, this paper aims to elucidate the theoretical foundations, various mathematical approaches, and existing research methodologies, as well as key aspects such as model selection, prompt optimization processes, and practical applications of prompt-based learning.
Enhancing multiple information extraction with comprehensive augmentation strategies leveraging a text generation model

Information extraction is a vital component of natural language processing, involved in extracting structured data from unstructured text. Considerable efforts have been dedicated to developing advanced approaches aimed at enhancing the performance of multiple information extraction tasks, particularly focusing on scenarios where multiple groups of information must be extracted from a single paragraph. One primary challenge is the scarcity of annotated training data, particularly across diverse domains. To comprehensively address this challenge, this paper proposes three robust data augmentation strategies that significantly enhances both the diversity and quantity of the training data. The methodology combines Numeric Variation, Sentence-Level Augmentation, and Token-Level Augmentation techniques, all integrated into a text generation model. Extensive experiments are conducted on a purposefully designed real-world dataset that accurately reflects the challenges associated with extracting multiple groups of information from a single paragraph. The results exhibit a significant enhancement in performance across diverse information extraction domains.
Multimodal LaF-CMA-ES algorithm based on homotopic convex transformation

Multimodal optimization is a difficult problem in the field of evolutionary computation due to multiple attraction basins. The balance of exploration and exploitation is important to the performance of the algorithm in multimodal problems. In this paper, a novel algorithm, named KbP-LaF-CMAES, is proposed based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to cope with the multimodal optimization problem. Two strategies, the leader and follower (LaF) mechanism and the homotopic convex transformation mechanism, are introduced in the algorithm. In the first stage of the proposed algorithm, the LaF strategy is conducted to explore the search space in a vast domain. Two co-evolutionary populations, leaders and followers evolve synergistically. When the solutions are stalled in the first stage, the KbP strategy is performed to improve the ability of exploration. In the second stage of the proposed KbP-LaF-CMAES, two variants of the CMA-ES algorithm are adopted to exploit the specific domain-containing local optimum domain, which has a potential global optimum of the problem. Finally, the experimental results on the CEC benchmark show significant improvements in the performance of KbP-LaF-CMAES than other CMA-ES variants and SPSRDEMMS. The performance of the proposed KbP-LaF-CMAES is comparable with the EBO with CMAR.
Real-time binocular depth estimation for autonomous driving based on convolutional neural network

The relative distance to other vehicles on the road is one of the key data points that an autonomous driving camera system needs to acquire. Most of the related research has primarily focused on estimating depth distances using image sequences from stereo cameras to generate depth maps for entire images. To reduce the computational complexity of depth estimation and improve real-time capabilities, this study proposes a strategy that utilizes convolutional neural network (CNN) technology in conjunction with triangulation methods for estimating the depth distance of the measured object. Initially, the CNN network structure is tailored through cropping and trained to serve as a vehicle detection model. Subsequently, this model is applied to multiple frames from the stereo camera. Following this, relative distance between the object of interest and the host vehicle is estimated using histogram similarity estimation and triangulation calculations. To validate the effectiveness of this approach, extensive simulation test was conducted on an autonomous driving platform. Experimental results demonstrate that this method exhibits a certain degree of effectiveness, with depth estimation errors being kept within an acceptable range.
Reconstruction-based time series anomaly detection on contaminated data

Time series anomaly detection(TSAD) aims to find abnormal segments in sequences. There have been many methods are proposed for time series anomaly detection, including reconstruction-based and prediction-based methods. Previous reconstruction-based time series anomaly detection models are implicitly trained to perform poorly on anomalies, so that anomalies will stand out in residuals. But some unexpected fitting issues may allow the models to still reconstruct anomalies well in time series. For example, the default no-anomaly assumption of the training set in previous researches may make the models fit the hidden anomalies, causing a degraded detection sensitivity. Toward a more rigorous detection, we innovatively introduce the Drop-Loss to unsupervised eliminate possible anomalous losses during training to block the backpropagation of abnormal gradients, thereby avoiding unexpected fitting. The proposed method is validated on four benchmarks with extensive experiments and achieve state-of-the-art results.
Method for detection of traffic signal lights based on improved YOLOV5

Aiming at the problems of difficult detection and low accuracy of traditional traffic light detection algorithms in practical applications, a traffic light detection algorithm based on improved YOLOv5 is proposed. This method integrates Transformer and CBAM modules into the YOLOv5 framework to further improve the detection performance of traffic lights. The Transformer module helps the model to enhance the attention to the target, especially the recognition of traffic lights in complex environments. CBAM helps to enhance the model&apos;s response to different signal light colors, thereby improving the recognition accuracy of traffic signal light colors. The improved algorithm introduces the backbone part of the YOLOv5 model into the encoder structure of the Transformer, replaces the self-attention module with the convolution layer of the Transformer, and adds the CBAM module after each stage. By increasing the attention mechanism, the feature extraction ability of the environment is enhanced. The experimental results on the BDD dataset and the self-built dataset show that the improved algorithm improves the accuracy rate by 8.8%, compared with YOLOv5 s. In this study, ablation experiments and comparative experiments of different algorithms were carried out. The results show that the improved algorithm has a significant improvement in detection speed and accuracy, and basically meets the requirements of complex traffic light detection in practical applications.
Based on atrous spatial convolutional and attention mechanism cycle-consistent generative adversarial defogging network

In order to solve the problem of poor de-fogging effect caused by incomplete prior knowledge of atmospheric scattering model and lack of actual paired image data set in image de-fogging method, a cycle-consistent generative adversarial network based on convolution and attention modules of void space is proposed for image defogging. The unsupervised defogging of unpaired images is completed by using the constrained transfer learning ability and cyclic structure of cycle-consistent generative adversarial network. Considering the serious detail loss of the target object and the complexity of fog distribution in actual imaging, combined with the real visual features, the common convolution is replaced by the empty space convolution ASPP module, the attention module CBAM is integrated into the network to realize the non-homogenization of different features and different regions. The experimental results show that the proposed method has better results on both the synthetic image data set and the real fog data set.
Attention-YOLOX: Improvement in On-Road Object Detection by Introducing Attention Mechanisms to YOLOX

Deep learning-based Object detection methods have been widely used in autonomous driving in recent years. An object detection algorithm having high detection accuracy and speed is crucial to the safety of autonomous driving vehicles. In this paper, we propose Attention-YOLOX, which embeds an attention mechanism in the object detection algorithm YOLOX to improve the detection accuracy. We propose four models that provide attention mechanisms to capture the inter-channel relationship, the relationship between channel-wise and spatial features, and the long- distance dependency of features, which assist the model to accurately predict the class and location of the target object. We evaluated the effectiveness of the proposed methods on the KITTI, BDDIOOK, and SODAIOM datasets containing images of various scenes and visual conditions. We confirmed that our methods have better detection accuracy than YOLOX with no attention mechanism while maintaining a high processing speed.
Digital Forensics Challenges in Cyberspace: Overcoming Legitimacy and Privacy Issues Through Modularisation

The significance of the cloud environment is growing in the current digital world. It provides several advantages, such as reduced expenses, the ability to adjust to different needs, adaptability and enhanced cooperation. The field of digital forensic investigations has encountered substantial difficulties in reconciling the requirement for efficient data analysis with the increasing apprehensions regarding privacy in recent times. As investigators analyse digital evidence to unearth crucial information, they must also traverse an intricate network of privacy rules and regulations. Given the increasing prevalence of remote work and the necessity for businesses to be adaptable and quick to react to shifting market circumstances, the cloud infrastructure has become a crucial asset for organisations of various scales. Although the cloud offers benefits such as scalability, flexibility and enhanced collaboration, it presents difficulties in digital forensic investigations regarding data protection, ownership and jurisdictional boundaries. These concerns are becoming increasingly significant as more data is kept in the cloud. In this paper, we present three major challenges that are faced during cloud-based forensics investigation. We analyse the extent to which different data formats increase complexity in forensics investigations in cyberspace. This paper analyses three core challenges facing digital forensics in the cloud environment: legitimacy, complexity and an increase in data volume, looking at the implications these have on data liable for legal issues in court. These challenges contribute to the backlog in digital forensics investigations due to a lack of modularisation of the procedures. To address these concerns, modularisation model is proposed to offer a way to integrate traditional processing functions while ensuring strict adherence to privacy protocols. To overcome these challenges, we propose modularisation as a strategy for improving the future of digital forensic research's operational efficiency, overcoming the identified challenges faced during cloud-based investigations and demonstrating how organisations can mitigate potential risks associated with storing sensitive information in the cloud.
High Precision Carry-Look-Ahead Logic for Negation, Absolute Value, and Two’s Complement

This paper presents a novel paradigm, based on carry-look-ahead principles, for finding the negation of a high-precision binary integer represented in two’s complement, and also for converting a two’s complement number to sign-and-magnitude (i.e., absolute value), and vice versa. Theoretical analysis of the proposed algorithm yields a logarithmic bound on delay with increasing input size n. Implementation results of the proposed hardware show linear growth in complexity. The linear growth for lookup tables is also small: around 1.1n for the hardware alone and less than 2n for the hardware with a serial transceiver after n=512, due to overhead. Flip-flop usage grows similarly. The proposed hardware has a delay of 2.254 ns or less for all three aforementioned operations.
Quaternion Neural Networks for Multidimensional Applications: An Overview

Although the concept of quaternion neural networks (QNNs) is not entirely new, interest in this type of network has increased recently. The possibilities they offer for effectively processing datasets characterised by inherent multidimensionality make them an interesting object of study and investigation. This contribution aims at highlighting the relevant key principles underlying these adaptive architectures and at providing an essential overview of the different classes and application areas available, serving as a tutorial introduction to the world of hypercomplex neural networks.
Ensemble Transfer Learning for Time Series Forecasting: A Comparative Sensitivity Analysis of Two Shallow Networks

Given the success that transfer learning has recorded in various domains, the interest in its application to time series forecasting is growing. This paper contributes to tackling the need for empirical studies by providing practical guidelines for TL methods and approaches for time series forecasting. To that effect, using a set of four novel TL metrics, we conduct a comparative sensitivity analysis on two shallow networks for transfer learning, namely the MLP and the LSTM. While the empirical results show the impact of the width of the neural network on transfer learning performance, an interesting question is raised concerning the dimensioning of the neural network for optimal performance when designing the transfer learning process.
Transformers in Time Series Forecasting: A brief Analysis of the Autoformer Transfer Learning Performance

For the task of long-term time-series forecasting, transformer models have shown great potential in achieving high prediction accuracy. For this reason, numerous transformer architectures designed for time series forecasting have been introduced in the literature. Nevertheless, the issue of a limited amount of training data in certain domains is a real challenge in deep learning. Transfer learning promises to be the solution. However, it is essential to develop transformer-based transfer learning techniques that can lead to the design of Transformer-based Time Series Pre-Trained Models (TSPTMs) that can be used in this situation. This is why, in this paper, we discuss a brief performance analysis of transfer learning techniques applied to the Autoformer, a transformer model designed for time series forecasting, to draw attention to this area. Initial experimental results show potential transfer learning gain. However, given the complexity of transformer models, various transfer learning techniques need to be developed to advance research in this area.
Maneuvering Target Tracking with Turn-Rate Learning Using Dirichlet Processes

Accurately tracking maneuvering targets is a challenging task due to the inherent difficulty in precisely modeling their dynamics in advance. To achieve good tracking performance, it is crucial for an estimator that the predicted dynamics closely matches the actual motion during maneuvers. Typically, constant turn models with preset turn-rates are incorporated into the interacting multiple model (IMM) estimator to address maneuvering target tracking. However, the finite number of models in the model set, determined by prior knowledge, fails to cover all possible maneuvers, which leads to tracking performance degradation when unexpected maneuvers occur. To overcome this limitation, we propose a turn-rate learning scheme for maneuvering target tracking based on Dirichlet processes (DP). By employing DP to model the evolution behavior of the turn-rate, our algorithm exhibits the ability to learn various turn-rates and adapt the model set more effectively. Finally, simulation results demonstrate the superior tracking accuracy of our proposed algorithm compared with the IMM algorithm.
ConvmixFormer-Unet: A New Approach for Medical Image Segmentation based on Convmixer and Transformer

The research community has shown a great interest in transformer-based structures because of their capacity to understand contextual data and dependencies over long distances, even though they demand substantial computational resources. To overcome this challenge, the MLP-mixer approach offers an alternative to attention token-mix by employing MLP and transpose operations and the results are promising. This approach also showed advantages over the CNN-based models that utilize local information but often neglect global contexts. In the current study, we introduce a novel segmentation model called ConvmixFormer Unet, which leverages the strengths of the Former structure and a CNN-based network. Our model incorporates the ConvmixFormer block in the encoder stage, where the Former structure is utilized by substituting the token-mix layer with a Multi-scale ConvMixer token-mix block while retaining the channel-mix layer. The Convmix Transpose blocks are also proposed in the decoder stage. We assess the effectiveness of our proposed method by employing three distinct datasets, namely GlaS, Data Science Bowl 2018, and ISIC 2018. Additionally, we evaluate our approach against the most advanced models. The findings demonstrate that our proposed model achieves a significant improvement in performance while reducing remarkably the overall number of model parameters.
A Regularization Approach to Maximize Common Sub-Expressions in Neural Network Weights

This paper modifies the training of artificial neural networks in order to derive weights the binary expression of which is composed of a limited set of sub-expressions only, placed in specified positions. The benefit of the proposed technique is the substantial complexity reduction achieved. The proposed method can be applied as a post-processing step on pre-trained models, further expanding its impact. The main concept is the use of an introduced regularization function that promotes specific sub-expressions which, in turn, are shown to improve the performance of common sub-expression sharing techniques, reducing area, time and power requirements for inference. Synthesis results reported here, quantify the impact of the proposed method onto hardware implementations and demonstrate substantial area, delay and power improvements, over prior art. In certain cases a $\times4$ area reduction is achieved, combined with corresponding reduction in delay and power dissipation.
Target Tracking Algorithm Using Two-Stage Cubature Kalman Filter

This study presents the two-stage cubature Kalman filter (TSCKF), which is a sophisticated technique designed to address the issue of variations in system models in real-life scenarios, and utilises nonlinear two-stage transformations to reorganise covariance matrices into a block-diagonal structure, effectively overcoming the limitations of conventional augmented methods. This technique effectively eliminates the need to calculate the cross-covariance between state variables and biases. This leads to a substantial reduction in computational load and facilitates seamless operation of the filter. The TSCKF design is underpinned by a robust theoretical framework, which ensures optimal computational efficiency while also ensuring precise estimations. This work demonstrates the mathematical equivalence between the TSCKF and the standard cubature Kalman filter (CKF) by utilising updated information equivalent transformations, and empirically verifies the equivalence through trajectory tracking experiments conducted on two-wheeled robotic systems subjected to random perturbations, thus affirming the greater accuracy and dependability of the TSCKF in tracking scenarios. Moreover, comparison evaluations offer further proof of the same performance between both methodologies. This study introduces a highly efficient approach in the domain of nonlinear systems and provides a dependable remedy for scenarios where traditional filtering procedures may be inadequate due to deficiencies in the system model.
Innovative technologies drive the modernization of higher education

The article shows the need to modernize higher education and its entire system, which consists of improving the efficiency of the educational process in higher education through the implementation and design of innovative modern educational technologies. The main types of innovative approaches in the educational process of a higher school are disclosed. The classification of educational technologies according to the factor of psychological development, orientation on personal structures, according the nature of the content and structure, is considered. Methodological approaches that are important in the modernization of higher education through the introduction of innovative technologies are analyzed. Taking into account modern methodological approaches, it is shown that the content of innovative technologies should be determined using the basic principles discussed in the article. The research work was aimed at proving the need to modernize higher education and its entire system, which consists of improving the efficiency of the educational process in higher education through the implementation and design of innovative modern educational technologies. In conclusion, the study demonstrates that the modernization of higher education through the implementation of innovative educational technologies can improve the efficiency of the educational process and prepare students for the challenges of today's world.