Difficulties In Recognition Research Articles

Poor face recognition predicts social anxiety in autism: A short report.

Research has shown that some autistic people have severe difficulties in recognising other people's faces. However, little is understood about how these difficulties impact the daily life and the mental well-being of autistic people. In this study, we asked 60 autistic adults with varying degrees of face recognition ability to complete two tests of face recognition, a questionnaire about social anxiety and a bespoke survey which asked participants about their experiences of face recognition and social interaction. We found that participants who had poor face recognition reported experiencing higher levels of social anxiety compared to those with average or better face recognition skills. More than half felt that their face recognition difficulties affected their social interactions, and over a third believed it hindered their ability to make friends. These findings suggest that face recognition difficulties may contribute to social anxiety among autistic individuals.

A semantic segmentation network based on CNN and Swin transformer for landslide detection

Abstract To address the issues of poor object localization, difficulty in object recognition, and inadequate segmentation performance of CNN-based semantic segmentation methods in landslide detection, a semantic segmentation model based on the Swin transformer and ResUNet architectures for landslide detection in remote sensing images is proposed in this paper. This method combines the global feature capabilities of the Swin transformer with the local feature extraction abilities of CNN and integrates our proposed RCCT module for landslide segmentation in remote sensing images. We apply this model to the identification and extraction of landslide disasters in Menyuan County and the Sanjiangyuan region of Qinghai Province, China, achieving a promising F1 Score of 81.91. Furthermore, we conducted a comprehensive evaluation of our proposed model on the BiJie Landslide dataset in comparison to four recently developed methods for landslide recognition. The experimental findings indicate the superior performance and effectiveness of our model over the comparative approaches.

Visual Target Interaction Modeling with Multichannel Data Fusion

Facing the demand for accurate, fast, and natural human–computer interaction in the multi-screen control environment, the traditional exchange method of frequent switching control of multi-screen through mouse, keyboard, and other manual methods has problems of low efficiency, weak flexibility, and poor experience. In this paper, we research multi-screen precision control technology based on eye movement to break through the difficulties of precise recognition of human vision under complex environments and poor stability of vision estimation under frequent switching control of multi-screen. This study explores the technology for controlling multiple screens with high precision by tracking eye movements. It overcomes the challenges associated with reliably discerning human visual focus in intricate settings and the instability of visual assessments during rapid transitions between multiple screens. The experimental results of 1143 eye-movement capture tasks are studied, including the accurate recognition technology of human head posture and pupil gaze direction under complex backgrounds, illumination, and different visual field environments, and the theoretical modeling method of multi-screen precise target interaction based on eye-movement is explored. The expression data will be processed using convolutional neural networks, and the dataset will be trained through Python programming and tested on test samples collected by simulated flight crews in the laboratory with an accuracy rate of more than 85%.

A new test for evaluation of marginal cognitive function deficits in idiopathic normal pressure hydrocephalus through expressing texture recognition by sound symbolic words.

The number of dementia patients is increasing with population aging. Preclinical detection of dementia in patients is essential for access to adequate treatment. In previous studies, dementia patients showed texture recognition difficulties. Onomatopoeia or sound symbolic words (SSW) are intuitively associated with texture impressions and are less likely to be affected by aphasia and description of material perception can be easily obtained. In this study, we aimed to create a test of texture recognition ability expressed by SSW to detect the presence of mild cognitive disorders. The sound symbolic words texture recognition test (SSWTRT) is constructed from 12 close-up photos of various materials and participants were to choose the best SSW out of 8 choices to describe surface texture in the images in Japanese. All 102 participants seen in Juntendo University Hospital from January to August 2023 had a diagnosis of possible iNPH (age mean 77.9, SD 6.7). The answers were scored on a comprehensive scale of 0 to 1. Neuropsychological assessments included MMSE, FAB, and the Rey Auditory Verbal Learning Test (RAVLT), Pegboard Test, and Stroop Test from the EU-iNPH Grading Scale (GS). In study 1 the correlation between SSWTRT and the neuropsychological tests were analyzed. In study 2, participants were divided into two groups: the Normal Cognition group (Group A, n = 37) with MMSE scores of 28 points or above, and the Mild Cognitive Impairment group (Group B, n = 50) with scores ranging from 22 to 27 points, and its predictability were analyzed. In study 1, the total SSWTRT score had a moderate correlation with the neuropsychological test results. In study 2, there were significant differences in the SSWTRT scores between groups A and B. ROC analysis results showed that the SSWTR test was able to predict the difference between the normal and mildly impaired cognition groups. The developed SSWTRT reflects the assessment results of neuropsychological tests in cognitive deterioration and was able to detect early cognitive deficits. This test not only relates to visual perception but is likely to have an association with verbal fluency and memory ability, which are frontal lobe functions.

Feature balanced re-enhanced network with multi-factor margin loss for long-tailed visual recognition

Real-world data often exhibits a long-tailed distribution, where the number of training samples for head classes far exceeds that of tail classes. This class imbalance phenomenon poses significant challenges for training deep neural networks. Existing class-aware loss methods typically focus only on the numerical relationship between class samples, blindly favoring the optimization of tail classes during the process, while neglecting the difficulty of samples and the similarity between the current class and other classes. To this end, relying only on the number relationship can easily lead to over-fitting of tail classes, thereby failing to fully utilize the potential information in the data. Therefore, we propose the Multi-Factor Margin Loss (MFMLoss), which consists of positive margin loss and negative margin loss. MFMLoss comprehensively considers three factors at three levels: overall, class, and sample: (1) quantitative relationships, (2) inter-class similarity relationships, and (3) sample recognition difficulty. The combined consideration of these three factors enables the model to pay more attention to confusing classes and difficult samples during the training process, rather than solely on tail classes, thus achieving optimization from coarse-grained to fine-grained. To further mitigate the negative impact of the imbalance between head and tail classes on feature learning, we design a new network architecture, called F-BREN. F-BREN consists of two components: the feature balancing network and the feature re-enhancement network. The former is trained with negative margin loss, which reduces the recognizability of easy samples. The latter is trained with positive margin loss, using positive margin to give more attention to hard samples, thus balancing the model’s attention to all samples. We conducted extensive experiments on four long-tailed benchmark datasets: CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist 2018, comparing the recognition accuracy of our method with eight state-of-the-art methods. The experimental results demonstrate that our proposed method outperforms the eight compared methods.

Use of a radiology tool for the diagnosis of pulmonary fibrosis

ObjectiveThe purpose of this paper was to perform an exploratory reader study to assess the utility of a web-based application in assisting non-chest radiologist in correctly diagnosing the radiographic pattern of pulmonary fibrosis. MethodsThree non-chest radiologists with 5 to 20 years of experience individually reviewed 3 rounds of randomly chosen chest CT scans (round 1: 100 scans, round 2: 50 scans, round 3: 25 scans) from a list of patients with established diagnosis of pulmonary fibrosis. In round 1, radiologists were asked to directly record their diagnosis for the pattern of fibrosis. In round 2 and 3 they were asked to review for features provided in a web-based application and provide diagnosis based on the most likely predicted diagnosis from the application. There was an approximate 1-month interval and relevant tutorials were provided between each round. Diagnosis accuracy is reported by readers at each round. ResultsThe overall accuracy increased from 63 % (n = 188/299) in round 1 to 74 % in round 3 (n = 52/70) (p = 0.0265). Difficulty in recognition of mosaic attenuation and homogeneous has led to misdiagnosis. Refining the definition for feature homogeneous increased the diagnosis accuracy of NSIP from 42 % (n = 20/48) in round 2 to 65 % (n = 24/37) in round 3(p = 0.0179). The Fleiss Kappa across readers varied from Round 1 to Round 3 with values 0.36 to 0.42. ConclusionsUsing the web-based application with refined definition for feature homogeneous helps to improve the non-subspecialty radiologist's accuracy in diagnosing different types of fibrosis.

Citrus recognition in orchard scene based on modified HSV-morphology method

The orchard has always been an important scene for citrus pickers, and the existence of factors such as leaf occlusion and color similarity always lead to the difficulty of robot recognition. Based on a citrus image dataset collected from actual orchard scenes, we extract image features and build a mathematical model to identify and count the number of oranges in each image, and show the distribution of apples in the entire dataset. Firstly, this paper establishes a model based on HSV method, focusing on recognizing yellow citrus. Secondly, with the further analysis of the data set, it is found that the cyan citrus exists. Therefore, this paper defines the empirical HSV range of two kinds of citrus at the same time and introduces contour processing to optimize the model. In order to accurately distinguish the immature citrus from the leaves with similar colors and the citrus occluded by leaves, this paper uses more detailed color threshold setting and morphological operation, and makes the eccentricity of the contour less than 0.85. At the same time, considering the size and overlap of citrus, morphological operations such as dilation and erosion and contour detection techniques are applied to further improve the recognition ability of the model. Finally, the experimental results show that the average accuracy of the established model reaches 92.1%, which has a good recognition effect on citrus.

Cross-Hopping Graph Networks for Hyperspectral–High Spatial Resolution (H2) Image Classification

As we take stock of the contemporary issue, remote sensing images are gradually advancing towards hyperspectral–high spatial resolution (H2) double-high images. However, high resolution produces serious spatial heterogeneity and spectral variability while improving image resolution, which increases the difficulty of feature recognition. So as to make the best of spectral and spatial features under an insufficient number of marking samples, we would like to achieve effective recognition and accurate classification of features in H2 images. In this paper, a cross-hop graph network for H2 image classification(H2-CHGN) is proposed. It is a two-branch network for deep feature extraction geared towards H2 images, consisting of a cross-hop graph attention network (CGAT) and a multiscale convolutional neural network (MCNN): the CGAT branch utilizes the superpixel information of H2 images to filter samples with high spatial relevance and designate them as the samples to be classified, then utilizes the cross-hop graph and attention mechanism to broaden the range of graph convolution to obtain more representative global features. As another branch, the MCNN uses dual convolutional kernels to extract features and fuse them at various scales while attaining pixel-level multi-scale local features by parallel cross connecting. Finally, the dual-channel attention mechanism is utilized for fusion to make image elements more prominent. This experiment on the classical dataset (Pavia University) and double-high (H2) datasets (WHU-Hi-LongKou and WHU-Hi-HongHu) shows that the H2-CHGN can be efficiently and competently used in H2 image classification. In detail, experimental results showcase superior performance, outpacing state-of-the-art methods by 0.75–2.16% in overall accuracy.

The Ediacara Biota of the Wood Canyon formation: Latest Precambrian macrofossils and sedimentary structures from the southern Great Basin

Fossils of the Ediacara Biota preserve the oldest indisputable evidence of macroscopic animal life. Although new finds continue to expand the near global distribution of such organisms, relatively abundant and diverse populations of these primarily soft-bodied taxa are known from just a handful of localities. More commonly, reports of late Precambrian fossils consist of comparably lower diversity assemblages (less than ∼10 genera), with a limited number of total specimens (less than ∼1000 reported macrofossils over a broad geographic area). Determining the factors responsible for such differences can help to fully appreciate preserved patterns of Ediacaran biodiversity.The lower member of the Wood Canyon Formation in the Death Valley region of the southern Great Basin, USA contains fossils of the Ediacara Biota. Here we redescribe previously recovered specimens based on new data from 3D surface scans and report several new finds from this unit. Although this allows the addition of two Ediacara taxa (Pteridinium and Charnia?) to the known diversity in the region, most potential macrofauna consist of amorphous, irregular forms with minimal preserved structures wherein an abiotic origin cannot be ruled out. A combination of factors – including variable taphonomic conditions, difficulty of identification and an original depauperate community – contribute to the observed diversity of this assemblage. Many biases that impact this record are either common (e.g., age, preservation requirements, difficulty of recognition) or comparable (e.g., paleoenvironmental and taphonomic controls) to known occurrences of Ediacara macrofauana. Thus, we argue this fossil record at least partially reflects the original community composition, suggesting that fossils of the lower Wood Canyon record a signal of relatively low diversity of the Ediacara Biota in the lead up to the Ediacaran-Cambrian boundary.

An Improved Medical Image Classification Algorithm Based on Adam Optimizer

Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate according to the characteristics of the sinusoidal function, accelerating the convergence while reducing the drastic parameter fluctuations, improving the efficiency and stability of training. The experimental results show that compared to the classic Adam algorithm, this algorithm can demonstrate better classification accuracy, the GSL algorithm achieves an accuracy of 78% and 75.2% on the MobileNetV2 network and ShuffleNetV2 network under the Gastroenterology dataset; and on the MobileNetV2 network and ShuffleNetV2 network under the Glaucoma dataset, an accuracy of 84.72% and 83.12%. The GSL optimizer achieved significant performance improvement on various neural network structures and datasets, proving its effectiveness and practicality in the field of deep learning, and also providing new ideas and methods for solving the difficulties in medical image recognition.

EXPRESS: Prosopagnosia is highly comorbid in individuals with probable developmental coordination disorder (DCD).

Developmental co-ordination disorder (DCD) is characterised by difficulties in motor control and coordination from early childhood. While problems processing facial identity are often associated with neurodevelopmental conditions, such issues have never been directly tested in adults with DCD. We tested this possibility through a range of tasks, and assessed the prevalence of developmental prosopagnosia (i.e., lifelong difficulties with faces), in a group comprising individuals who self-reported a diagnosis of, or suspected that they had, DCD. Strikingly, we found 53% of this probable DCD group met recently recommended criteria for a diagnosis of prosopagnosia, with 22% acquiring a diagnosis using traditional cognitive task-based methods. Moreover, their problems with faces were apparent on both unfamiliar and familiar face memory tests, as well as on a facial perception task (i.e., could they tell faces apart). Positive correlations were found between self-report measures assessing movement and coordination problems, and objective difficulties on experimental face identity processing tasks, suggesting widespread neurocognitive disruption in DCD. Importantly, issues in identity processing in our probable DCD group remained even after excluding participants with comorbid conditions traditionally associated with difficulties in face recognition, i.e., autism and dyslexia. We recommend that any diagnostic test for DCD should include an assessment for prosopagnosia. Given the high prevalence of prosopagnosia in our probable DCD group, and the positive correlations between DCD and prosopagnosia symptoms, there may be a stronger link between movement and facial identity abilities than previously thought.

Speech Recognition and Spatial Hearing in Young Adults With Down Syndrome: Relationships With Hearing Thresholds and Auditory Working Memory.

Individuals with Down syndrome (DS) have a higher incidence of hearing loss (HL) compared with their peers without developmental disabilities. Little is known about the associations between HL and functional hearing for individuals with DS. This study investigated two aspects of auditory functions, "what" (understanding the content of sound) and "where" (localizing the source of sound), in young adults with DS. Speech reception thresholds in quiet and in the presence of interferers provided insight into speech recognition, that is, the "what" aspect of auditory maturation. Insights into "where" aspect of auditory maturation were gained from evaluating speech reception thresholds in colocated versus separated conditions (quantifying spatial release from masking) as well as right versus left discrimination and sound location identification. Auditory functions in the "where" domain develop during earlier stages of cognitive development in contrast with the later developing "what" functions. We hypothesized that young adults with DS would exhibit stronger "where" than "what" auditory functioning, albeit with the potential impact of HL. Considering the importance of auditory working memory and receptive vocabulary for speech recognition, we hypothesized that better speech recognition in young adults with DS, in quiet and with speech interferers, would be associated with better auditory working memory ability and receptive vocabulary. Nineteen young adults with DS (aged 19 to 24 years) participated in the study and completed assessments on pure-tone audiometry, right versus left discrimination, sound location identification, and speech recognition in quiet and with speech interferers that were colocated or spatially separated. Results were compared with published data from children and adults without DS and HL, tested using similar protocols and stimuli. Digit Span tests assessed auditory working memory. Receptive vocabulary was examined using the Peabody Picture Vocabulary Test Fifth Edition. Seven participants (37%) had HL in at least 1 ear; 4 individuals had mild HL, and 3 had moderate HL or worse. Participants with mild or no HL had ≥75% correct at 5° separation on the discrimination task and sound localization root mean square errors (mean ± SD: 8.73° ± 2.63°) within the range of adults in the comparison group. Speech reception thresholds in young adults with DS were higher than all comparison groups. However, spatial release from masking did not differ between young adults with DS and comparison groups. Better (lower) speech reception thresholds were associated with better hearing and better auditory working memory ability. Receptive vocabulary did not predict speech recognition. In the absence of HL, young adults with DS exhibited higher accuracy during spatial hearing tasks as compared with speech recognition tasks. Thus, auditory processes associated with the "where" pathways appear to be a relative strength than those associated with "what" pathways in young adults with DS. Further, both HL and auditory working memory impairments contributed to difficulties in speech recognition in the presence of speech interferers. Future larger-sized samples are needed to replicate and extend our findings.

Autistic adults have insight into their relative face recognition ability

The PI20 is a self-report questionnaire that assesses the presence of lifelong face recognition difficulties. The items on this scale ask respondents to assess their face recognition ability relative to the rest of the population, either explicitly or implicitly. Recent reports suggest that the PI20 scores of autistic participants exhibit little or no correlation with their performance on the Cambridge Face Memory Test—a key measure of face recognition ability. These reports are suggestive of a meta-cognitive deficit whereby autistic individuals are unable to infer whether their face recognition is impaired relative to the wider population. In the present study, however, we observed significant correlations between the PI20 scores of 77 autistic adults and their performance on two variants of the Cambridge Face Memory Test. These findings indicate that autistic individuals can infer whether their face recognition ability is impaired. Consistent with previous research, we observed a wide spread of face recognition abilities within our autistic sample. While some individuals approached ceiling levels of performance, others met the prevailing diagnostic criteria for developmental prosopagnosia. This variability showed little or no association with non-verbal intelligence, autism severity, or the presence of co-occurring alexithymia or ADHD.

The Challenges Facing China's International Commercial Court System Under the "Belt and Road" Vision and China's Response

In order to provide all-round judicial guarantee for the high-quality development of the "Belt and Road", China established the China International Commercial Court in 2018 and achieved remarkable results after six years of construction. In recent years, with the increasing number of commercial disputes related to the Belt and Road Initiative, such as international trade, international logistics and international project contracting, the construction of China's international commercial courts is faced with difficulties related to insufficient international vision, backward jurisdiction rules, lack of appeal relief channels, difficulties in cross-border recognition and enforcement of judgments, and inflexible convergence of multiple dispute resolution methods. In view of this, the China International Commercial Court should be based on an international perspective, expand its jurisdiction to improve the jurisdiction rules, implement "two final trials" to reconstruct the appeal relief system, transform "factual reciprocity" to "constructive reciprocity" to ensure the cross-border recognition and enforcement of judgments, and constantly optimize the interface mechanism of litigation, arbitration and mediation has become an important way to solve the problem. It will help build a new situation of international commercial court.

Evidence for different visual processing strategy for non-face stimuli in developmental prosopagnosia

ABSTRACT We assembled a test battery to investigate developmental prosopagnosia (DP), a neurodevelopmental syndrome resulting in severe face recognition difficulties. To screen for general cognitive deficits that could explain poor face test performance, participants completed a fluid reasoning task using abstract shapes. This initial screening showed that DPs (n = 21) were more accurate than neurotypical controls (n = 90) but significantly slower, suggesting speed-accuracy trade-off. To address this, we calculated the Balanced Integration Score and found no group differences, highlighting that DPs clearly adopted a different strategy from controls. DPs’ longer response times (RT) on face tasks vs controls are commonly interpreted as evidence of impairment and of lengthy, atypical featural face processing. Our data suggest this interpretation may be unreliable since clear RT differences were also observed in two non-face tasks where DPs showed no accuracy impairment. Instead, slower RTs appear to reflect a strategy shift in DPs.

Experiences of healthcare staff caring for hospitalised people with delirium: a qualitative systematic review.

Inpatient delirium is common and associated with poor outcomes. Although most organisations have evidence-based guidelines to improve delirium prevention and management, delirium rates and outcomes have remained relatively unchanged over time. A lack of understanding of healthcare providers' experience of caring for people with delirium and its integration into existing guidance may explain some of the slow progress in improving delirium care. To review and synthesise existing qualitative evidence on healthcare providers' experience of caring for inpatients with delirium within and across disciplines. We systematically searched OVID Medline, CINAHL, Embase, Emcare, PsychINFO, AMED and Web of Science databases for articles published between January 1990 and November 2022. Article inclusion and study quality were assessed by two independent reviewers. Both thematic synthesis and content analysis were then conducted to synthesise findings from included studies. Within the 25 included studies, the experience of nurses was the most commonly studied perspective, followed by medical and allied health. Nursing, medical and allied health staff all reported that their experience of caring for people with delirium was challenging, highlighting difficulties in delirium recognition and that they felt unsupported at organisational and local levels. Attitudes towards older people and the importance of delirium influenced identification and prioritisation. Healthcare providers often find caring for hospitalised patients with delirium challenging and complex. Although good communication within multidisciplinary teams was deemed helpful, more work is required to understand how to achieve this, recognising the unique perspectives of individual disciplines.

The Impact of Canada’s Multiculturalism Policy on the Employment Rates of High-Skilled Immigrants

This study examines the impact of Canada’s multiculturalism policy on the employment rates of high-skilled immigrants in STEM fields from 2000 to 2020. Utilizing a quantitative research design, the study integrates cross-sectional and longitudinal data from Statistics Canada’s Labour Force Survey (LFS) and the Longitudinal Immigration Database (IMDB) to provide a comprehensive analysis. The findings indicate that multiculturalism policies have had a generally positive effect on the employment outcomes of high-skilled immigrants, particularly through initiatives supporting credential recognition, language training, and inclusive hiring practices. However, significant challenges remain, including difficulties in foreign credential recognition, language barriers, and systemic biases in hiring practices. By comparing employment trends and analyzing factors influencing employment outcomes, the study highlights the areas where policy and support measures need to be strengthened to enhance the integration of high-skilled immigrants into Canada’s STEM workforce. The study concludes with recommendations for policymakers and employers to further improve the effectiveness of multiculturalism policies and support the successful employment of high-skilled immigrants.

Clinical, cognitive, and functional characteristics of recent-onset psychosis with autistic features: A 2-year longitudinal study

Though categorized as separate illnesses, schizophrenia and autism are known to exhibit shared characteristics. This study explored the distinctions in clinical, cognitive, and functional characteristics among individuals with recent-onset psychosis, considering the severity of their autistic symptoms, involving longitudinal examinations. We analyzed 671 patients with recent-onset psychosis from Korean Early Psychosis Cohort Study (KEPS), and used the PANSS Autism Severity Score (PAUSS) to categorize patient into ‘autistic’, ‘moderate’, and ‘non-autistic’ groups. The autistic group had the highest rate of schizophrenia diagnosis, and the lowest incidence of comorbid psychiatric disorders. Schizophrenia diagnosis predicted membership of the autistic group. More severe autistic symptoms correlated with worse overall symptoms and functional outcomes, which significantly predicted membership of the autistic group. Cognitive impairments and emotional recognition difficulties increased with the severity of autistic symptoms. 2-year longitudinal assessments demonstrated that group differences in autistic features and overall symptoms, and functional outcomes remained consistent, and membership of the autistic group significantly predicted symptomatic remission and functional recovery. In conclusion, the presence of autistic symptoms has a significant impact on the overall symptomatology and functional capabilities. They are enduring attributes rather than temporary state variables, and serve as a significant predictor for both symptomatic and functional recovery.

Object–Environment Fusion of Visual System for Automatic Pear Picking

A visual system is a key tool for automatic fruit harvesting. It faces the challenges of varied occlusion and illumination in the field, resulting in difficulties in fruit recognition and picking. At present, there are many studies that lack a comprehensive analysis of the impact of the environment on harvesting. This study proposes an object–environment fusion visual system. It comprises modules for object perception, environment perception, and picking pose estimation. The object perception module aims to identify and locate pears. The environment perception module is deployed to analyze the three-dimensional (3D) information of objects and obstacles. Finally, the picking pose estimation module fuses the information of the objects and environment to calculate the collision-free picking position and orientation. Additionally, specific implementations are employed for each module. It compares three networks to identify pears for object perception. Voxel-based representation is used to simplify point clouds for environmental perception. A sampler and evaluator are applied for picking pose estimation. The S1 and S2 datasets were acquired in a laboratory pear tree model and the orchard of Zhejiang Academy of Agricultural Sciences, respectively, to evaluate the proposed method. In the S2 dataset, the success rate for picking pose estimation reached 87.11% within the distance range of 30∼50 cm. All of the results demonstrate that the proposed method can be applied to visual perception for automatic pear harvesting.

Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the “no-relation” class. Since the annotation process typically focuses only on known relation classes, there are usually no training samples for the “no-relation” class. This poses a zero-shot classification problem, where during the classification process, there is a tendency to classify into a class with a relationship. Zero-shot classification tasks are particularly challenging in this context. Moreover, most relation classification models currently need to traverse all relations to calculate the class with the highest probability. Therefore, the problem of “computational redundancy” is another challenge faced. Thus, how to accurately and efficiently acquire cyberspace knowledge from heterogeneous data sources and address the challenges such as sample scarcity, zero-shot recognition, and computational redundancy is the main focus of this chapter. To address these problems, this chapter designs a multi-relation extraction model based on ontology rule-enhanced prompt learning, which is a parameter-sharing-based multi-task model. By introducing prompt learning, which has shown significant effectiveness in the few-shot domain, this chapter designs prompt templates combining discrete and continuous tokens and uses rule injection in prompt learning to solve the difficulties in zero-shot recognition of “no-relation” and computational redundancy issues, achieving efficient and accurate multi-relation extraction. Specifically, by constructing sub-prompts to achieve an efficient combination of templates, a parameter-sharing structure is used to implement knowledge extraction step by step: The first step constructs entity prompt templates combining discrete and continuous tokens, identifying the classes of two entities based on prompt learning. The second step involves rule injection, identifying whether it belongs to the “no-relation” class based on the combination of sub-prompts; if there is no connection between the classes of two entities, it is classified as “no relation”; if a connection exists, the candidate relation set is filtered out. The third step uses the pre-trained model and vectors from the first step, utilizing prompt learning and rule judgment to determine the relation class from the candidate relation set. Finally, the effectiveness of our model is validated on the general datasets TACRED, ReTACRED, and the cybersecurity dataset constructed in this paper.