Textual Descriptions Research Articles

Digital mapping of resected cancer specimens: The visual pathology report

BackgroundThe current standard-of-care pathology report relies only on lengthy written text descriptions without a visual representation of the resected cancer specimen. This study demonstrates the feasibility of incorporating virtual, three-dimensional (3D) visual pathology reports to improve communication of final pathology reporting. Materials and methodsSurgical specimens are 3D scanned and virtually mapped alongside the pathology team to replicate grossing. The 3D specimen maps are incorporated into a hybrid visual pathology report which displays the resected specimen and sampled margins alongside gross measurements, tumor characteristics, and microscopic diagnoses. ResultsVisual pathology reports were created for 10 head and neck cancer cases. Each report concisely communicated information from the final pathology report in a single page and contained significantly fewer words (293.4 words) than standard written pathology reports (850.1 words, p < 0.01). ConclusionsWe establish the feasibility of a novel visual pathology report that includes an annotated visual model of the resected cancer specimen in place of lengthy written text of standard of care head and neck cancer pathology reports.

Reversing accidents involving pedestrians: an epidemiological investigation and cluster analysis using a novel database derived from Swedish National Register data

Objectives In contemporary urban environments, the intersection between Vulnerable Road Users, specifically unprotected pedestrians, and motor vehicles poses a persistent challenge to road safety. Reversing accidents, characterized by vehicles moving backward, often result in severe consequences for pedestrians due to limited visibility and inherent blind spots for the driver. This paper aims to provide a thorough examination of reversing accidents involving pedestrians in Sweden between 2000–2021; shedding light on the magnitude of the problem, contributing factors, consequences, and potential mitigation strategies. Method Using a national database, reversing accidents were compiled by classifying free text descriptions of traffic accidents reported to the Swedish Traffic Accident Data Acquisition. The database was then used to describe reversing accidents and their consequences for struck pedestrians using descriptive epidemiology and cluster analysis. Results The results show that reversing accidents accounted for 12% of all pedestrians injured in a collision with a motor vehicle in Sweden during the studied period. In terms of personal characteristics, the struck pedestrians were more often female and of old or young age, whilst the drivers of the reversing vehicles were more often between 18–54 years and men. Most accidents were non-fatal, with only roughly 2% resulting in fatalities. Through a cluster analysis, four distinct accident types were identified. Two of these were identified as particularly important to prioritize in future preventative work: accidents that are characterized by occur during daylight hours in the urban environment (often low speeds) and often result in fatality or serious injury. Conclusions Due to the low speeds, reversing accidents are rarely fatal. However, they account for a large proportion of accidents with pedestrians and can be grouped into clearly distinguishable accident types that can function as templates in road safety development in Sweden.

Text-Driven Video Prediction

Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption separately. The key to addressing the TVP task depends on fully exploring the underlying motion information in text descriptions, thus facilitating plausible video generation. In fact, this task is intrinsically a cause-and-effect problem, as the text content directly influences the motion changes of frames. To investigate the capability of text in causal inference for progressive motion information, our TVP framework contains a Text Inference Module (TIM), producing step-wise embeddings to regulate motion inference for subsequent frames. In particular, a refinement mechanism incorporating global motion semantics guarantees coherent generation. Extensive experiments are conducted on Something-Something V2 and Single Moving MNIST datasets. Experimental results demonstrate that our model achieves better results over other baselines, verifying the effectiveness of the proposed framework.

ExTraCT - Explainable trajectory corrections for language-based human-robot interaction using textual feature descriptions.

In human-robot interaction (HRI), understanding human intent is crucial for robots to perform tasks that align with user preferences. Traditional methods that aim to modify robot trajectories based on language corrections often require extensive training to generalize across diverse objects, initial trajectories, and scenarios. This work presents ExTraCT, a modular framework designed to modify robot trajectories (and behaviour) using natural language input. Unlike traditional end-to-end learning approaches, ExTraCT separates language understanding from trajectory modification, allowing robots to adapt language corrections to new tasks-including those with complex motions like scooping-as well as various initial trajectories and object configurations without additional end-to-end training. ExTraCT leverages Large Language Models (LLMs) to semantically match language corrections to predefined trajectory modification functions, allowing the robot to make necessary adjustments to its path. This modular approach overcomes the limitations of pre-trained datasets and offers versatility across various applications. Comprehensive user studies conducted in simulation and with a physical robot arm demonstrated that ExTraCT's trajectory corrections are more accurate and preferred by users in 80% of cases compared to the baseline. ExTraCT offers a more explainable approach to understanding language corrections, which could facilitate learning human preferences. We also demonstrated the adaptability and effectiveness of ExTraCT in a complex scenarios like assistive feeding, presenting it as a versatile solution across various HRI applications.

Cultural synergy: Integrating Lamin Dayak Kenyah in English language learning – A need analysis study

This research aims to identify students' preferences and interests in learning English through Lamin Dayak Kenyah architectural buildings. This research is a mixed research that combines quantitative and qualitative data. The participants were 271 students and 33 English lecturers at universities. The methods used to collect data were classroom observations, surveys, and interviews to identify specific needs faced by students and lecturers when incorporating traditional houses into learning materials. The four fundamental English language skills—listening, reading, writing, speaking—are included in the results. In listening skills, students and lecturers prefer to learn and teach English by watching videos and listening to dialogues or conversations. In reading ability, students and lecturers want to learn with intensive reading, active reading, and critical reading. Regarding writing skills, students and lecturers prefer assignments such as writing descriptive texts and opinion articles. In speaking skills, students and lecturers want to learn through discussion and describing pictures orally. In choosing topics, students and lecturers are interested in the history of local architectural buildings, traditional house design, and the symbolism, color, and meaning of the carvings. Project-based learning (PBL) is the preferred learning method, and learning resources are drawn from videos and interactive learning tools. Future research can utilize these findings to design more targeted instructional materials, leveraging the results of the needs analysis that has been conducted in this study.

Few-Shot Image Classification of Crop Diseases Based on Vision-Language Models.

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision-Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.

A Multimodal Sentiment Analysis Model for Graphic Texts Based on Deep Feature Interaction Networks

Due to the widespread adoption of social networks, image-text comments have become a prevalent mode of emotional expression compared to traditional text descriptions. However, there are currently two major challenges. The first is the question of how to extract rich representations effectively from both text and images, and the second is the question of how to extract cross-modal shared emotion features. This study proposes a multimodal sentiment analysis method based on a deep feature interaction network (DFINet). It leverages word-to-word graphs and deep attention interaction networks (DAIN) to learn text representations effectively from multiple subspaces. Additionally, it introduces a cross-modal attention interaction network to extract cross-modal shared emotion features efficiently. This approach helps alleviate the difficulties associated with acquiring image-text features and representing cross-modal shared emotion features. Experimental results on the Yelp dataset demonstrate the effectiveness of the DFINet method.

DEVELOPMENT OF ENGLISH POP-UP BOOK MEDIA TO TEACH WRITING SKILL AT JUNIOR HIGH SCHOOL

This study aims to ensure that the use of Pop-Up Book Media affects the writing abilities of junior high school students. This media focuses with Descriptive Text material to teach writing skill to class VII students. This research use ADDIE model. The sample for this research was class VIIA MTs Sunan Ampel Kediri, totaling 14 students. Data collection was carried out by observation, unstructured interviews with teacher and questionnaires. Data were analyzed using a manual percentage formula. These findings show that the validation percentage of media experts reached 98%, that means "very valid", then the percentage of material validation reached 96%, with means "very valid". The total percentage of student satisfaction questionnaires reached 83.9% which is classified as "Very Satisfied". In conclusion, the use of Pop-Up Book media in learning can increase students' motivation in learning English and increase the creativity of students' in making paragraphs this is immediately effective for developing students' writing skills and the Pop-Up Book learning media is ready to be used by teachers as a learning media.

Animating Text Descriptions into Characters: A Comparative Review of Generative Models

Animating Text Descriptions into Characters: A Comparative Review of Generative Models

Zero-shot prompt-based video encoder for surgical gesture recognition.

In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called zero-shot capability). In this paper we investigate the feasibility of latter option. Leveraging the bridge-prompt framework, we prompt-tune a pre-trained vision-text model (CLIP) for gesture recognition in surgical videos. This can utilize extensive outside video data such as text, but also make use of label meta-data and weakly supervised contrastive losses. Our experiments show that prompt-based video encoder outperforms standard encoders in surgical gesture recognition tasks. Notably, it displays strong performance in zero-shot scenarios, where gestures/tasks that were not provided during the encoder training phase are included in the prediction phase. Additionally, we measure the benefit of inclusion text descriptions in the feature extractor training schema. Bridge-prompt and similar pre-trained+prompt-tuned video encoder models present significant visual representation for surgical robotics, especially in gesture recognition tasks. Given the diverse range of surgical tasks (gestures), the ability of these models to zero-shot transfer without the need for any task (gesture) specific retraining makes them invaluable.

Language Model-Assisted Machine Learning, Photoelectrochemical, and First-Principles Investigation of Compatible Solvents for a CH3NH3PbI3 Film in Water.

Machine learning and data-driven methods have attracted a significant amount of attention for the acceleration of the design of molecules and materials. In this study, a material design protocol based on multimode modeling that combines literature modeling, numerical data collection, textual descriptor design, genetic modeling, experimental validation, first-principles calculation, and theoretical efficiency calculation is proposed, with a case study on designing compatible complex solvent molecules for a halide perovskite film, which is notorious for optoelectronic deactivation under hostile conditions, especially in water. In the multimode modeling design process, the textual descriptors play the central role and store rich literature scientific knowledge, which starts from the construction of a high-dimension literature model based on scientific articles and is realized by a genetic algorithm for materials predictions. The prediction is substantiated by follow-up experiments and first-principles calculations, leading to the successful identification of effective molecular combinations delivering an unprecedented large aqueous photocurrent (increasing by 3 orders of magnitude compared with that of CH3NH3PbI3) and remarkable aqueous stability (improving from 36% to 89% after immersion in water) under the hostile condition. This study provides a practical route via multimode modeling for accelerating the design of molecule-modified and solution-processed materials in a real scenario.

FICE: Text-conditioned fashion-image editing with guided GAN inversion

Fashion-image editing is a challenging computer-vision task where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model called FICE (Fashion Image CLIP Editing) that is capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically, with FICE, we extend the common GAN-inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the text-provided semantics, due to its impressive image–text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate the FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art, text-conditioned, image-editing approaches. Experimental results demonstrate that the FICE generates very realistic fashion images and leads to better editing than existing, competing approaches. The source code is publicly available from: https://github.com/MartinPernus/FICE.

The Enhancement of Statistical Literacy: A Cross-Institutional Study Using Data Analysis and Text Mining to Identify Statistical Issues in the Transition to University Education

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) provides support for various mathematics-related subjects, with statistics students being the second-largest cohort of visitors. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support; the categorization of statistical concepts has been made with the statistics module description for each statistics subject at the Universidad Rey Juan Carlos (URJC). First, we conducted a categorization of statistical concepts taught in university (based on URJC’s catergorization); after that, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. The statistical issues presented were categorized as descriptive statistics (22.8%), probability (44%), statistical inference (29.2%), and statistical software (4%). Students struggled with material that was introduced at university level rather than material seen at secondary level. Our findings on students’ main statistical issues contribute to the development of a suite of evidence-based educational applications and games to support undergraduate students internationally in first- and second-year statistical modules.

Peningkatan Hasil Belajar Peserta Didik Kelas VII SMP Negeri 10 Palembang Pada Materi Teks Deskripsi Dengan Pendekatan Culturally Responsive Teaching dan Model Problem Based Learning

This research aims to apply the Problem Based Learning (PBL) learning model with a Culturally Responsive Teaching (CRT) approach assisted by animated videos to improve student learning outcomes in descriptive text material in class VII.2 of SMP Negeri 10 Palembang. This research uses the classroom action research (PTK) method which is carried out in two cycles. Each cycle involves planning, implementation, observation, and reflection. Trial research subjects in class VII.2 research at SMP Negeri 10 Palembang with a total of 35 students. Collection of observation data, learning results tests, and documentation. Based on the results of research that has been carried out, it can improve the learning outcomes of class VII.2 students at SMP Negeri 10 Palembang. This can be seen from the increase in the percentage of student learning outcomes in cycle I by 71% and in cycle II by 88%. The average value of students' abilities in cycle I was 75.14 and in cycle II 84.57. So it can be concluded that applying the Problem Based Learning (PBL) learning model with a Culturally Responsive Teaching (CRT) approach assisted by animated videos in descriptive text material is able to improve student learning outcomes in class VII.2 of SMP Negeri 10 Palembang.

MPNTEXT: An Interactive Platform for Automatically Extracting Metal-Polyphenol Networks and Their Applications from Scientific Literature.

In recent years, metal-polyphenol networks (MPNs) have gained significant attention due to their unique properties and broad applications across various fields. However, the burgeoning volume of MPN literature necessitates the automation of chemical information extraction from the extensive corpus of unstructured data, including scientific publications. To address this challenge, we proposed a platform named MPNTEXT, which utilized natural language processing techniques and machine learning algorithms to efficiently identify and extract pertinent information, thereby assisting users in comprehending complex MPNs and their textual descriptions of applications. Users can enter keywords, such as "Fe", "drug delivery", or "tannic acid", to retrieve relevant information, which is then presented in a structured format. This study aims to provide a user-friendly tool for collecting and retrieving MPN data and promotes data-driven material design. The platform offers researchers a more convenient and efficient way to design versatile MPNs and explore their applications.

Exploring Text Classification Systems for Automatically Coding Historical Occupations and Causes of Death

ObjectivesText classification models can be used to automatically categorize occupations and causes of death within historical documents. It is important to classify/code these categories as different words or textual descriptions could refer to the same occupation or cause of death. Given the many historical documents that are becoming available for research, accurate classification systems can be valuable resources. ApproachWe explore different text classification techniques, from traditional machine learning to deep learning, and investigate methodologies that transform occupations and causes of death into a vectorial space and use these representations as features to train text classification systems. Our data come from IPUMS USA/International, and SCADR. ResultsHistorians have coded occupations and causes of death for some census collections (e.g., US, Canada), but not yet for others (e.g., Scotland). We train and evaluate our classification systems using data from the US and Canada and then deploy it on data from Scotland. We quantitatively measure the performance of the classification systems for historical documents that have codes available. Additionally, once we deploy the model to data that does not yet have codes, we qualitatively evaluate our results by engaging with historians working on those data. We report and discuss these results to understand where the models are performing well and where the models are underperforming. ConclusionsResults suggest that there is value in building and deploying these classification models. We recommend the use of such models in conjunction with engaging with domain experts.

Multi-modal transformer with language modality distillation for early pedestrian action anticipation

Language-vision integration has become an increasingly popular research direction within the computer vision field. In recent years, there has been a growing recognition of the importance of incorporating linguistic information into visual tasks, particularly in domains such as action anticipation. This integration allows anticipation models to leverage textual descriptions to gain deeper contextual understanding, leading to more accurate predictions. In this work, we focus on pedestrian action anticipation, where the objective is the early prediction of pedestrians’ future actions in urban environments. Our method relies on a multi-modal transformer model that encodes past observations and produces predictions at different anticipation times, employing a learned mask technique to filter out redundancy in the observed frames. Instead of relying solely on visual cues extracted from images or videos, we explore the impact of integrating textual information in enriching the input modalities of our pedestrian action anticipation model. We investigate various techniques for generating descriptive captions corresponding to input images, aiming to enhance the anticipation performance. Evaluation results on available public benchmarks demonstrate the effectiveness of our method in improving the prediction performance at different anticipation times compared to previous works. Additionally, incorporating the language modality in our anticipation model proved significant improvement, reaching a 29.5% increase in the F1 score at 1-second anticipation and a 16.66% increase at 4-second anticipation. These results underscore the potential of language-vision integration in advancing pedestrian action anticipation in complex urban environments.

Progress and developments towards making historic administrative data research ready

ObjectivesTo make research ready data from transcribed historic civil registration records: birth, marriage and death (from mid-19th century onwards). To use historic records effectively for large-scale research they must not only be made machine-readable, but also coded in a suitable format – along with classification of transcribed information. Included on the digitised historic records are textual descriptions of occupations and causes of death. Thus, to code transcribed occupations to HISCO and causes of death to ICD. ApproachIt is impractical to hand-code the records manually (34 million occupations and 8 million causes of death), especially for deaths where more than one cause can be given. As such, coding is viewed as a text classification task and the process is automated. To facilitate auto-coding, a proportion of records were hand-coded as training data (90,000 occupations and 102,000 deaths). Ahead of the auto-coding, initial pre-processing, cleaning and standardising is done on both the occupations and deaths. ResultsPreliminary experiments undertaken obtained reasonable results from a combination of exact matching and statistical classification. Experiments using a larger pilot uncovered that since some occupations are very common, the training data set covers a very large proportion of the records (ie exact match). This proportion is not as high for deaths given the different ways causes are written. ConclusionThis is work in progress to create the research ready data, and the poster will include the results from experiments using the full training data (90,000 occupations and 102,000 deaths).

Making Country Proud: The Role of Athletics in the Development of Eldoret City, Kenya

This study examined the impact of athletics on the development of Eldoret City from 1920-2020. The study was informed by the need to discuss in detail and document the development of athletics among the Kalenjin community in Kenya. To realize the above, the study utilized Social Constructivism and Self-efficacy theories. The study was guided by one objective: the development and impact of athletics among the Kalenjin community in Kenya from 1920 through 2020. The literature reviewed, global, regional, and local, helped identify the knowledge gap, hence the study’s justification. The philosophical paradigm that anchored this study is constructivist philosophy. The study is qualitative and has an applied historical research design. The sampling procedure entailed using purposive and snowball sampling to arrive at knowledgeable informants. The sample size of 20 informants was determined by saturation. Data collected was from primary and secondary sources. Primary sources entailed archival materials such as artefacts and photographs, while unstructured interview schedules were used to collect oral data. The secondary sources were the books, theses, journals, magazines, and newspapers. Using the qualitative method, the study analyzed data thematically and presented the findings through narration, direct quotes and descriptive texts. Photographs were also used to present the findings. According to the research findings, the Kalenjin community had traditional sports activities, primarily athletics. The thrust of the athletics was reminiscent of their cultural aspirations. Further, the study established that the development of athletics grew gradually in phases marked by the arrival of the European colonialists, the internationalization of sports in the 1950s and the emergence of Kenya as a world athletics player. The study additionally found that scholarship, employment opportunities, and acquisition of wealth through diverse investments are evident in Eldoret City. The town has recently grown into the 5th Kenyan City following a significant economic contribution from the athletics arena. The rise of the Kalenjin diaspora and the attraction of multinationals to local athletics constitute substantial socioeconomic and political impacts on athletics among the Kalenjin. The study thus recommends that the government improve funding for sports and fully implement the existing laws and policies

Impact of consumer familiarity on acceptability and purchase intent of a novel amaranth‐based coffee creamer

SummaryThe objective of the study was to identify the impact of consumer familiarity on liking, acceptability, purchase intention and textual description of a novel cream substitute formulated with popped amaranth flour. Four formulations were evaluated in different proportions of amaranth vs. a control. A total of 400 consumers from two regions participated (Veracruz: familiar with coffee and Tlaxcala: familiar with amaranth). Consumers from both regions had significant differences in liking the attributes of the different formulations. Liking across regions was mainly a function of product aroma. Regarding the acceptance of the product, it was found that the attributes flavour, colour and overall liking were significant (P &lt; 0.05) for consumers from Veracruz, whereas appearance and mouthfeel were significant (P &lt; 0.05) for consumers from Tlaxcala. In purchase intent, overall liking was significant for both regions (P &lt; 0.05). In relation to the impact of amaranth on purchase intention, Tlaxcala showed higher purchase willingness. The words used for sensory characterisation and that are important for both regions were the amaranth aroma, and the creamy flavour in the formulation with the highest amaranth content. The findings of this study allowed for understanding the potential of amaranth to formulate new creamers in a cross‐cultural context, considering consumers from two different levels of familiarity with specific crops.