Search Results (39)

There is an established association between neurodivergence and gender variance, with growing documentation of the challenges and inequities faced by those who exist at this intersection. This paper contributes a critical analysis of interviews with 24 autistic adults in the U.S. about their gender experience; yielding three themes: “gender divergence?”, “gender socialization on crip time”, and “either/or: whose intolerance for ambiguity?”. Results suggest that gender variance—if it is best understood as such—among those on the spectrum emerges through a complex set of relationships between participants’ bodyminds (e.g., sensory and cognitive styles); dominant cultural concepts of gender; and ableist and heterocissexist social relations. Neuronormative ways of knowing gender, institutionalized through biomedical research, healthcare, and social policy, emerge as a normalizing discourse contributing to the oppression and marginalization of participants as neurodivergent people. Justice implications of accounting for the epistemology of the neurodivergent bodymind and decentering neuronormative ways of knowing are discussed. Full article

While drill music is often talked about in relation to crime, it is often overlooked as an art form and cultural practice. Consequently, its artists are rarely heard from. To address this societal and academic gap, we have conducted in-depth interviews with ten UK artists from this genre and subculture. This article presents the shared meanings these UK drill artists attach to the motivation to make their own music, the music subgenre and its culture, and its ongoing criminalisation. We do so by conceptualising these meanings as counter-narratives. The article departs from the observation that these counter-narratives present themselves in drill, as a form of expression, on two dimensions: drill as the outcome of intra-group expression of emotions and social relations, and as the platform to engage with social injustice on the inter-group level. An interdisciplinary theoretical framework, combining psychological insights on needs, philosophical cues on (mis)recognition, and the lens intersectionality, allows us to study and bridge these two dimensions. We identify twelve counter-narratives that were validated by a majority of respondents. The study, besides analysing these in-depth counter-narratives, also foregrounds UK drill artists’ agency generally absent from both societal and academic discourse. Full article

Peter S. Beagle’s decision to feminize the formerly masculine figure of the unicorn in his influential 1968 fantasy novel The Last Unicorn represents a key moment in the evolution of this now ubiquitous image, one embraced today as a symbol of pride by LGBTQ+ communities. The novel and its 1982 animated film adaptation have themselves remained popular among queer and especially trans audiences, who have often found the narrative resonant with their own experiences. This essay provides a preliminary overview of the queer history of the unicorn symbol and continues into a trans reading of the novel, arguing that these responses to Beagle’s work by contemporary readers reflect dimensions of the narrative congruent with concerns about gender performance and misrecognition; gender dysphoria; and queer temporalities. The nature of the fantasy form itself, we maintain throughout, can also particularly enable reparative readings by queer and trans audiences. Full article

To address the complex challenge of recognizing mixed handwritten text in practical scenarios such as examination papers and to overcome the limitations of existing methods that typically focus on a single category, this paper proposes MHTR, a Multimodal Handwritten Text Adaptive Recognition algorithm. The framework comprises two key components, a Handwritten Character Classification Module and a Handwritten Text Adaptive Recognition Module, which work in conjunction. The classification module performs fine-grained analysis of the input image, identifying different types of handwritten content such as Chinese characters, digits, and mathematical formula. Based on these results, the recognition module dynamically selects specialized sub-networks tailored to each category, thereby enhancing recognition accuracy. To further reduce errors caused by similar character shapes and diverse handwriting styles, a Context-aware Recognition Optimization Module is introduced. This module captures local semantic and structural information, improving the model’s understanding of character sequences and boosting recognition performance. Recognizing the limitations of existing public handwriting datasets, particularly their lack of diversity in character categories and writing styles, this study constructs a heterogeneous, integrated handwritten text dataset. The dataset combines samples from multiple sources, including Chinese characters, numerals, and mathematical symbols, and features high structural complexity and stylistic variation to better reflect real-world application needs. Experimental results show that MHTR achieves a recognition accuracy of 86.63% on the constructed dataset, significantly outperforming existing methods. Furthermore, the context-aware optimization module demonstrates strong adaptive correction capabilities in various misrecognition scenarios, confirming the effectiveness and practicality of the proposed approach for complex, multi-category handwritten text recognition tasks. Full article

Kumite is a karate sparring competition in which two players fight each other using various techniques. In kumite matches, it is essential to reduce a preliminary action (hereinafter referred to as “pre-action”), such as pulling the arms and lowering the shoulders just before performing an attack technique. This is because pre-actions reveal the timing of the attack to the opponent. However, players often find it difficult to recognize their own pre-actions, and accurately estimating their presence or absence is challenging with conventional motion analysis methods, as pre-actions are subtle compared to major techniques like punching or kicking. Previously, we proposed a method for detecting pre-actions during single punches performed in a static state using inertial sensors. While this method was effective in controlled situations, it failed to detect pre-actions in punches during actual kumite matches. The main reason is that players generally perform footwork during matches, and this footwork is often misrecognized as pre-action via conventional detection methods. To address misrecognition caused by footwork, we propose a new method that combines preprocessing designed to detect and smooth footwork segments in the inertial data with the conventional pre-action detection method, thereby enabling pre-action detection during kumite matches. In the preprocessing, we apply an autocorrelation function to assess the constancy of footwork and accurately separate the footwork segment from the kumite technique segment. Only the footwork segment is then smoothed to suppress its influence on the detection process. Our experimental results show that the proposed method can estimate the presence or absence of pre-action in the punch of an actual kumite match with an accuracy of 0.875. Full article

Efficient monitoring of concrete pouring operations is critical for ensuring compliance with construction regulations and maintaining structural quality. However, traditional monitoring methods face limitations such as overlapping objects, environmental similarities, and detection errors caused by ambiguous boundaries. This study proposes an Ambient Detection-based Monitoring Framework that enhances object detection by incorporating contextual relationships between objects in complex construction environments. The framework employs the You Only Look Once version 11 (YOLOv11) algorithm, addressing issues of boundary ambiguity and misrecognition through relational analysis. Key components including Distance Relationship (${\mathit{D}}_{\mathit{R}}$), Attribute Relationship (${\mathit{A}}_{\mathit{R}}$), and Spatial Relationship (${\mathit{S}}_{\mathit{R}}$) allow the system to quantitatively evaluate contextual associations and improve detection accuracy. Experimental validation using 232 test images demonstrated a 12.07% improvement in detection accuracy and a 71% reduction in false positives compared with baseline YOLOv11. By automating the monitoring process, the proposed framework not only improves efficiency but also enhances construction quality, demonstrating its adaptability to diverse construction scenarios. Full article

High-speed trains are some of the most important transportation vehicles requiring human–computer collaboration. This study investigated the effects of different types of icons on recognition performance and cognitive load during frequent observation and sudden takeover tasks in high-speed trains. The results of this study can be used to improve the efficiency of human–computer collaboration tasks and driving safety. In this study, 48 participants were selected for a simulated driving experiment on a high-speed train. The recognition reaction time, operation completion time, number of recognition errors, number of operation errors, SUS scale, and NASA-TLX questionnaire for the icons were all analyzed using analysis of variance (ANOVA) and the nonparametric Mann–Whitney U test. The results show that anthropomorphic icons can reduce the drivers’ visual fatigue and mental load in frequent observation tasks due to the anthropomorphic facial features attracting driver attention through simple lines and improving visual search efficiency. However, for the sudden takeover human–computer collaboration task, the facial features of the anthropomorphic icons were not recognized in a short period of time. Additionally, due to the positive emotions produced by the facial features, the drivers did not perceive the suddenness and danger of the sudden takeover human–computer collaboration task, resulting in the traditional icons being more capable of arousing the drivers’ alertness and helping them take over the task quickly. At the same time, neither type of icon triggered misrecognition or operation for sufficiently skilled drivers. These research results can provide guidance for the design of icons in human–computer collaborative interfaces for different types of driving tasks in high-speed trains, which can help improve the recognition speed, reaction speed, and safety of drivers. Full article

At the centenary of the death of Franz Kafka (1883–1924), this paper explores the complexities of Artificial Intelligence (AI) through the lens of Kafka’s literary and professional work, especially those relating to the dynamics of recognition and misrecognition. Through Kafkan eyes, both philosophical and technological hankerings after recognition and its connection with the notion of the ‘true self’ are thrown into sharp relief, whether this ‘truth’ is related to authenticity or to accuracy. This encourages us to challenge some of the core assumptions of our relationship with systems and tools, including (1) the taken-for-granted formula of recognition being good, misrecognition being bad; (2) the suggestion that aligning AI with human values will make it, and therefore us, safer and more secure; and (3) the assumption that the masters are in charge in the master/slave dialectic that is often used to express the relationship between humans and technologies. The paper references three of Kafka’s most famous works, The Trial, The Castle and In the Penal Colony, in ways that are accessible to those new to Kafka. More seasoned Kafka enthusiasts will be able to see and contextualise the paper’s themes and provocations within these works, and extrapolate to his other writings. Full article

There are calls for the democratisation of higher education in line with the principles of social justice. Collaboration with students offers the potential for creating a more inclusive higher education environment, and open textbook development initiatives can be a vehicle for change. This paper focuses on the experiences of students as co-creators in open textbook initiatives at the University of Cape Town, South Africa. Drawing on interviews with 11 open textbook collaborators, this paper utilises Nancy Fraser’s social justice framework to explore students’ perspectives on injustices, challenges of collaboration and co-creation, and power dynamics in student–staff partnerships. The study shows that students experience and navigate various injustices in their classroom contexts related to economic maldistribution, cultural misrecognition and political misrepresentation. It reveals a complex interrelationship between student voice, power dynamics in the classroom, and the power of student–staff partnerships to build confidence and flatten hierarchies in open textbook co-creation. The student views presented here provide powerful evidence of a range of benefits they experience when the traditional hierarchies between student and lecturer are levelled through collaborative open textbook development processes. Results indicate that co-creation activities enabled them to have a voice through the power of publication and own their academic journeys. Full article

As an important part of intelligent traffic, vehicle recognition plays an irreplaceable role in traffic management. Due to the complexity and occlusion of various objects in the traffic scene, the accuracy of vehicle target recognition is poor. Therefore, based on the distribution features of vehicle components, this paper proposes a two-stage VSRS-VCFM net occlusion vehicle target recognition method. Based on the U-net codec structure, combining multi-scale detection and double constraints loss to improve the visual region segmentation under complex background (VSRS) performance. At the same time, to establish the vehicle component feature mask (VCFM) module, based on the Swin Transformer backbone unit, combined with the component perception enhancement unit and the efficient attention unit, the extraction of the low-contrast component area of the vehicle target and the filtering of the irrelevant area are realized. Then, the component mask recognition unit is introduced to remove the occlusion component feature area and realize the accurate recognition of the occluded vehicle. By labeling the public data set and the collected data set, six types of vehicle component data sets are constructed for training, as well as design ablation experiments and comparison experiments to verify the trained network, which prove the superiority of the recognition algorithm. The experimental results show that the proposed recognition method effectively solves the problem of misrecognition and missing recognition caused by interference and occlusion in vehicle recognition. Full article

Background/Objectives: The non-canonical GC-5′ splice sites (5′ss) are the most common exception (~1%) to the classical GT/AG splicing rule. They constitute weak 5′ss and can be regulated by splicing factors, so they are especially sensitive to genetic variations inducing the misrecognition of their respective exons. We aimed to investigate the GC-5′ss of the breast/ovarian cancer susceptibility genes, ATM (exon 50), BRIP1 (exon 1), and PALB2 (exon 12), and their dysregulation induced by DNA variants. Methods: Splicing assays of the minigenes, mgATM_49-52, mgBRIP1_1-2, and mgPALB2_5-12, were conducted to study the regulation of the indicated GC-5′ss. Results: A functional map of the splicing regulatory elements (SRE) formed by overlapping exonic microdeletions revealed three essential intervals, ATM c.7335_7344del, PALB2 c.3229_3258del, and c.3293_3322del, which are likely targets for spliceogenic SRE-variants. We then selected 14 ATM and 9 PALB2 variants (Hexplorer score < −40) located at these intervals that were assayed in MCF-7 cells. Nine ATM and three PALB2 variants affected splicing, impairing the recognition of exons 50 and 12, respectively. Therefore, these variants likely disrupt the active SREs involved in the inclusion of both exons in the mature mRNA. DeepCLIP predictions suggested the participation of several splicing factors in exon recognition, including SRSF1, SRSF2, and SRSF7, involved in the recognition of other GC sites. The ATM spliceogenic variants c.7336G>T (p.(Glu2446Ter)) and c.7340T>A (p.(Leu2447Ter)) produced significant amounts of full-length transcripts (55–59%), which include premature termination stop codons, so they would inactivate ATM through both splicing disruption and protein truncation mechanisms. Conclusions: ATM exon 50 and PALB2 exon 12 require specific sequences for efficient recognition by the splicing machinery. The mapping of SRE-rich intervals in minigenes is a valuable approach for the identification of spliceogenic variants that outperforms any prediction software. Indeed, 12 spliceogenic SRE-variants were identified in the critical intervals. Full article

This paper explores the intersection of poverty and recognition theory, grounded in Axel Honneth’s framework, to offer a novel perspective on poverty as a multifaceted social phenomenon. It argues that poverty should be understood not only as a lack of material resources but also as a significant deficit in social recognition, encompassing respect and social esteem. By situating poverty within the institutionalized order of recognition, the paper highlights how poverty both stems from and contributes to a lack of recognition, leading to social exclusion, shame, and stigmatization. The theoretical approach is complemented by selected empirical studies that illustrate the lived experiences of poverty, emphasizing the emotional and psychological impacts that extend beyond material deprivation. While the paper advances the theoretical understanding of poverty, it also identifies gaps in the current research, particularly the need for more empirical studies to substantiate these claims. Future research could expand upon these insights through cross-cultural studies and empirical investigations that further explore the connection between recognition and poverty. This work lays the groundwork for a deeper exploration of poverty as a social phenomenon that transcends economic metrics, advocating for a more holistic approach to poverty research. Full article

Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing to errors of unrecognition and misrecognition. Research in named entity recognition predominantly focuses on English, which poses challenges for non-English languages. By specifying procedures for the development of Korean-based tag sets, data collection, and preprocessing, we formulated directions on the application of entity recognition research to non-English languages. Such research could significantly benefit artificial intelligence (AI)-based natural language processing globally. We developed a personal information tag set comprising 33 items and established guidelines for dataset creation, later converting it into JSON format for AI learning. State-of-the-art AI models, BERT and ELECTRA, were employed to implement and evaluate the named entity recognition (NER) model, which achieved an 0.943 F1-score and outperformed conventional recognizers in detecting personal information. This advancement suggests that the proposed NER model can effectively prevent personal information leakage in systems processing interactive text data, marking a significant stride in safeguarding privacy across digital platforms. Full article

Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees. Full article

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results. Full article