Reviewerly/exHarmony huggingface dataset:
Cumulative unsupervised multi-domain adaptation for Holstein cattle re-identification

In dairy farming, ensuring the health of each cow and minimizing economic losses requires individual monitoring, achieved through cow Re-Identification (Re-ID). Computer vision-based Re-ID relies on visually distinguishing features, such as the distinctive coat patterns of breeds like Holstein. However, annotating every cow in each farm is cost-prohibitive. Our objective is to develop Re-ID methods applicable to both labeled and unlabeled farms, accommodating new individuals and diverse environments. Unsupervised Domain Adaptation (UDA) techniques bridge this gap, transferring knowledge from labeled source domains to unlabeled target domains, but have only been mainly designed for pedestrian and vehicle Re-ID applications. Our work introduces Cumulative Unsupervised Multi-Domain Adaptation (CUMDA) to address challenges of limited identity diversity and diverse farm appearances. CUMDA accumulates knowledge from all domains, enhancing specialization in known domains and improving generalization to unseen domains. Our contributions include a CUMDA method adapting to multiple unlabeled target domains while preserving source domain performance, along with extensive cross-dataset experiments on three cattle Re-ID datasets. These experiments demonstrate significant enhancements in source preservation, target domain specialization, and generalization to unseen domains.
High-Resolution Aerial Photo Categorization Model by Cross-Resolution Perceptual Experiences Transfer

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the categories of a rich variety of high-resolution aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a cross-resolution perceptual experiences transfer framework for categorizing high-resolution aerial photos, focusing on leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones. More specifically, we first construct gaze shifting path to mimic human visual perception to both low-resolution and high-resolution aerial photos, wherein the corresponding deep gaze shifting path features are engineered. Afterward, a kernel-induced feature selection algorithm is formulated to obtain a succinct set of deep gaze shifting path features discriminative across low- and high-resolution aerial photos. Based on the selected features, low- and high-resolution aerial photos’ labels are collaboratively utilized to train a linear classifier for categorizing high-resolution ones. Extensive comparative studies have validated the superiority of our method.
Construction of extremal $ \mathbb{Z}_{4} $-codes using a neighborhood search algorithm

In this paper, we present a method for constructing extremal $ \mathbb{Z}_{4} $-codes based on random neighborhood search. This method is used to find new extremal Type Ⅰ and Type Ⅱ $ \mathbb{Z}_{4} $-codes of lengths 32 and 40. For the length 32, at least 182 new Type Ⅱ extremal $ \mathbb{Z}_{4} $-codes of types $ 4^{k}2^{32-2k} $, $ k\in\left\{9,10,12,13,14,15,16\right\} $ are constructed. In addition, we obtained at least 762 new extremal Type Ⅰ $ \mathbb{Z}_{4} $-codes of types $ 4^{k}2^{32-2k} $, $ k\in\left\{7,9,10,12,13,14,15,16\right\} $. For the length 40, constructed extremal $ \mathbb{Z}_{4} $-codes are of types $ 4^{k}2^{40-2k} $, $ k\in\left\{7,10,11,15,16\right\} $. There are at least 40 new Type Ⅱ extremal $ \mathbb{Z}_{4} $-codes, and at least 4144 new Type Ⅰ extremal $ \mathbb{Z}_{4} $-codes.
A people-item relation extraction method based on multiple kernel support vector machine model

In this research, we present a novel approach for relation extraction using the multiple kernel support vector machine model. The aim is to improve the comprehension of Chinese Instructions by family service robots. Our approach focuses on extracting the people-item relation from Chinese instructions. We start by defining four categories of people-item relations: sequential, belong to, equivalent, and direction. Next, we construct a feature combination for the entity using lexical, phrase, order, and property features. We then generate multiple kernel functions using a weighted sum method and selected foundation kernel functions (lexical, syntactic, and property). The multiple kernel support vector machine model is constructed using a simple multiple kernel learning technique. The experimental findings validate that our proposed approach outperforms current methodologies in terms of accuracy, retrieval rate, and F-measure.
Automatic wellbore data recognition method based on Levenshtein distance similarity and TF-IDF

In the research of petroleum exploration and development, original wellbore files collected by researchers are characterized by the massive data volume, diverse file types, and inconsistent file naming methods, which leads to time-consuming data format rearrangement for researchers. This paper proposed an automatic recognition method of wellbore data based on the Levenshtein distance similarity and TF-IDF (Term Frequency Inverse Document Frequency), which can automatically identify and process data of the wellhead, well trajectory, well interval division, mud logging lithology, and well logs of various wellbore file types and convert them into a unified standard format for storage. Compared with manual data sorting, the proposed methods deliver a reduction of data processing time of about 60% and greatly improve the data processing efficiency, also laying a foundation for subsequent data management.
Power equipment fault entity recognition based on BERT-CRF model

With the ongoing development of the national power grid, fault data from power equipment exhibits various characteristics, including a wide range of entity types, weak interrelationships, and high complexity. Consequently, traditional methods fail to extract adequate entity information and struggle with situations involving the multiple meanings of terminology in different contexts. To tackle these challenges, this paper proposes a method for recognizing power equipment fault entities based on the BERT-CRF model. The method initially employs web crawlers to retrieve literature related to power equipment faults. It then inductively designs five distinct types of power equipment fault entities and annotates a dataset accordingly. Subsequently, the BERT model is combined with the CRF model to recognize power equipment fault entities. The experimental results are then compared with those of the BiLSTM-CRF model and other domain datasets to evaluate the impact of power equipment fault entity types and label quantities. The experimental results confirm the feasibility and effectiveness of the BERT-CRF model, enabling the extraction of a wider range of power equipment fault entities from a smaller corpus of data.
A semantic segmentation method for UAV image based on improved BiseNetv2

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for “Human”, the IoU of T-BiseNetv2 is about 4.3% higher than that of the popular UNetFormer.
Students’ preference on cognitive behavior in automotive marketing practice based on eye tracking analysis technology from neuroscience

In neuroscience, eye-tracking technology offers a novel approach to analyze the processes of the human brain by collecting participants’ eye movement data. This study employs eye-tracking equipment to delve deeply into the impact of brand preference on students’ cognitive behavior within the context of automobile marketing practices. Drawing upon prior research, the study’s framework is formulated. The hypothesis posits that preference intensity significantly influences the cognitive decision-making process, which is further subdivided into effects on information gathering behavior and the subsequent cognitive processing of that information. To facilitate this research, a questionnaire—developed by synthesizing, adapting, and refining previous studies—was administered. Its reliability and validity were evaluated through preliminary research. The study’s experiment was designed to combine eye-tracking data with questionnaire responses, aiming to capture and statistically analyze the cognitive behavior of relevant college students. The data was then used to validate the proposed hypothesis. Leveraging the eye tracker for experimental design, as opposed to conventional subjective evaluation methods, promises a more objective analysis of college students’ cognitive behaviors.
Trajectory classification-based car loan collection scoring model using a hierarchical CNN-LSTM method

To avoid economic losses on loan borrowers who are in default, financial institutions and companies employ plenty of debt collection measures like making phone calls, sending messages and even resorting to legal action. Since these measures also incur non-negligible costs and differ significantly in expenses, debt collection scoring models are proposed to assess the likelihood of a client’s payment delinquency thus helping companies in determining appropriate collection measures and improving effectiveness on allocating resources. Traditional methods in this area utilize clients’ personal information and are highly dependent on the accuracy of data which in practice can’t be guaranteed. In this work, we formulate collection scoring as a trajectory classification method and build a discriminative network that mines clients’ personal car trajectories which is an informative and stably accessible data resource in the car loan business scenario. We first propose a novel preprocessing method to extract features and transform raw trajectories into sequences of fixed-shape tensors. Then a convolutional auto-encoder is employed to condense extracted tensors into a low-dimensional representation. Finally, a Bi-LSTM is adopted to capture latent characteristics in the feature sequences and makes a prediction. Experiments on a real-world collected dataset confirm the effectiveness of our model.
A fast Apriori algorithm for telecom fraud

Telecom fraud has become a pressing issue, with most research targeting effective countermeasures. However, extracting valuable rules from fraud data to improve case handling efficiency remains under-explored. The Apriori algorithm, commonly used for association rule mining, faces challenges due to its low efficiency and scalability arising from numerous frequent and candidate item sets. To address this problem, we propose a fast Apriori algorithm. The main idea is as follows: First, we establish a similarity measurement model based on information entropy, and combine similar items to significantly reduce the number of frequent item sets and candidate item sets. Second, we optimize the process of generating frequent item sets and candidate item sets and reduce the number of database scans. Third, we apply the improved algorithm to mine the data set of telecom fraud cases in a city, and obtain some meaningful association rules that reflect the relationships among the duration of the crime, the reporting time, and the amount of fraud. These rules provide a new perspective and idea for investigating telecom fraud cases.
A compositional model for cross-project effort-aware defect prediction

The Cross-Project Effort-Aware Defect Prediction (CPEADP) model can effectively use detection resources and the data from different projects to build models. One factor affecting the performance of CPEADP is the problem of data distribution differences in cross-project settings. The classification performance also greatly impacts the predictive capabilities of the models. Therefore, we propose the BDA-DF model and conduct experiments on 11 cross-project datasets from the PROMISE repository. Compared to traditional data filtering and transfer learning methods, our approach exhibits significant improvements across five effort-aware metrics, including Precision@20%, Recall@20%, F1@20%, PofB@20%, and IFA. To explore the optimal classifier for CPEADP, we embed seven different classifiers into BDA. The experimental results on the BDA embedded with different classifiers reveal that DF exhibits the best overall performance.
A low-latency FPGA-based method for edge extraction of hot-rolled strip

This paper proposes a low-latency method, based on FPGA, for edge extraction of hot-rolled strip. The method uses a global shutter camera module to capture strip images and detects the strip edge position by analyzing the intensity profile and the gradient of image gray values. The method has the advantages of low latency, strong robustness, and accurate results. The feasibility and effectiveness of the method are verified by FPGA simulation experiments, and a foundation is laid for the subsequent strip deviation automatic control algorithm.
Research on augmented reality registration method based on SA-PSO algorithm

In order to address the high cost and limited robustness of continuous lens pose calculation in AR application systems, this paper proposes an AR registration method based on the SA-PSO algorithm. This method mainly combines the advantages of SA and PSO algorithms to fit the camera’s position and posture in a top-down manner. By continuously optimizing multiple particles in the feature space, the computational complexity of the AR registration process is reduced, and the effectiveness of registration time and robustness in practical application scenarios is ensured. The experimental results show that the proposed SA-PSO registration method can effectively achieve AR registration fusion in both manual marking and texture feature modes, and has a certain degree of robustness against occlusion.
A personalized test paper generation model for English examination based on knowledge tracking

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses.
Color image encryption algorithm with DNA coding and Zig-Zag transform based on fractional-order memristive circuit system

This paper proposes a color image encryption strategy based on the Zig-Zag transform, DNA coding technology and fractional-order memristive hyper-chaotic circuit system (FOMHCC). First, the chaotic sequences are generated from FOMHCC using control parameters and initial values. Next, a modified Zig-Zag method is used to confuse the tricolour of the colour image, and then DNA encoding is performed. The DNA coded matrix is then obfuscated and diffused through the chaotic sequences. Finally, DNA decoding is performed to get an encrypted image. The security analysis by simulation confirm that the algorithm is able to withstand multiple attacks and provides excellent image encryption performance.
A subgraph interpretation generative model for knowledge graph link prediction based on uni-relation transformation

In recent years, Graph Neural Networks (GNNs) have witnessed rapid development. Their strengths in capturing topological information of graph data contribute to significant performance improvements in tasks such as knowledge graph (KG) link prediction. To understand the reason for this performance improvement, it is necessary to extract the subgraph patterns learned by GNNs from KGs. Nevertheless, the accuracy of existing GNN interpreters has not been validated in explaining multi-relation graph data, such as KGs, and related tools have not been implemented yet, leading to difficulties in extracting explanation subgraphs. To address this problem, this paper proposes a KG link prediction model that converts multi-relation KGs into uni-relation graphs. This model combines entities in the KG into new nodes, and treats relations as features of the new nodes, thereby creating a graph with only a single relation. A denoising autoencoder is then trained on the new graph for link prediction, and a GNN interpreter is used to generate subgraph explanations. Experiments on three benchmark datasets show that the proposed model based on uni-relation graph transformation significantly enhances the relative AUC, as compared to GraIL without transformation. Finally, an explanation subgraph extraction experiment is performed on the FB15K-237 dataset, demonstrating the effectiveness of the model in directly extracting link predictions for explanation.
RTGAC: RoBERTa-TCN-GRU-CRF combined attention mechanism for grammar error detection in spoken Chinese

Grammar automatic error detection can help language learners identify whether there are errors in their own written text. For the grammatical error detection task of spoken Chinese in this paper, one of the most obvious shortcomings of this task is that the data set is small. In this case, there may be some sparse features in the training data set. To solve this problem, we propose a Chinese grammar error detection method combining Temporal Convolutional Network (TCN) and Threshold Recurrent Unit (GRU). To verify the effectiveness of the model framework proposed in this paper, we test our method on the Chinese spoken grammar dataset and Chinese text writing dataset respectively. By comparing the prediction effects of various deep learning models, the proposed model has a high F1 value in Chinese spoken grammar error detection and Chinese text writing grammar error detection.
PPM: prompt-free prompt-tuning for multi-task learning

The cost of fine-tuning has increased significantly in recent years as the size of language model parameters has increased. Prompt-tuning and adapters have made it possible to train models with a small number of parameters to obtain results similar to those of fine-tuning methods. However, most of the current prompt-tuning methods require the help of hand-crafted templates and verbalizers to achieve outstanding results in few-shot learning. In this work, we propose PPM, Prompt-free prompt-tuning for multi-task learning. First, we insert the task-specific adapter into the pre-trained language model to replace the hand-designed external template. Then, we train each adapter separately on different tasks and adjust the parameters of each adapter layer. Next, we combine the different adapters and draw on their valid knowledge by tuning the parameters of the fusion part to get the smallest loss function in the process of extracting knowledge from different adapters. To boost the training speed, we use Post-LN to replace Pre-LN, which switched the position of the Laynorm layer in the model from after the two Addition layers to before the FFN layer and the Multi-head Attention layer. Experimental results on different NLP tasks show that our model has better synergistic effects on diverse types of downstream tasks.
Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

Abstract With the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane‐changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane‐changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.
A new CAPTCHA based on facial expression recognition

Computers and humans differ in their logic and approach when it comes to recognizing emotions, especially when it comes to micro expressions. Using this difference, we make a new CAPTCHA based on a new facial expression recognition algorithm. This new CAPTCHA requires the user to find a similar expression to the given picture and rotate the picture to fit the background. After that, we counter-attacked the CAPTCHA, and the probability of being successfully attacked was less than 1%. We also conducted user tests based on this. CAPTCHA is superior to others in terms of recognition success rate and recognition time and has a good application significance.