Miracle-dz/UniFAR-FTUs huggingface dataset:
Defining and delineating urban neighbourhoods: a case study of housing areas in Brent, North London

There is no geographical definition of the neighbourhood despite it being the chosen spatial scale for U.K. government policy tackling social injustice and rebuilding democracy, and the setting for the majority of life’s experiences where an individual’s lifelong welfare is largely determined. Consequently, resources are targeted sub-optimally with environmental and social dynamics largely undiagnosed. This has fuelled an urgent demand for revealing the nature of neighbourhoods, and how they can be identified on the ground and delineated on maps. Implementing a positive methodology I build upon the foremost theoretically-supported pedestrian-street network neighbourhood model, harnessing established theory, and present an integrative geographical theory of the neighbourhood and its practical manifestation to address the research problem. Using Grannis's empirical work as a benchmark I test the transferability of his methodology to the UK and the explanatory power of my housing area neighbourhood model, using correlation analysis, in two London case study areas, with positive results. Housing areas arise from the physical and built environments and are shown to explain social distributions better than other spatial units tested, and compare favourably with applied surrogate neighbourhoods. I then identify the datasets required to roll out the methodology for developing a practical, meaningful and bounded neighbourhood geography. Further analysis confirms the role of wealth as the great social and spatial segregator. The neighbourhood is re-conceptualised as an holistic and commonly understood entity, whilst the spatial precision introduced facilitates measurement and assessment for optimal service and resource provision, as well as monitoring and intervention. Presenting structural and social homophily as the geneses of social interaction patterns and the explanation for how space is transformed into place is a paradigm shift in our understanding of this fundamental geographical concept which promises to stimulate additional theoretical substantiation and development whilst providing a framework for phenomenological and ethnographical approaches.
Plant Disease Detection and Classification Using Transfer Learning Inception Technique

To avoid productivity and quantity losses in agricultural products, it is essential to identify plant diseases. Examining visually discernible patterns on plants is a key component of plant disease research. For agriculture to be sustainable, disease detection and plant health monitoring are essential. Plant disease monitoring by hand is fairly difficult. It demands a sizable quantity of labour, expertise in plant diseases, and protracted processing times. In order to identify plant diseases, image processing is used. In this study, the method for identifying plant diseases using photographs of leaves is discussed. Transfer Learning Inception v3 is used in the suggested method to identify diseases in examples of plant leaf imagery. The model has also been assessed in terms of accuracy and loss. The performance study was carried out using the Plant Village Kaggle database, which is the standard dataset for plant illnesses and problems in terms of intensity variations, color changes, and discrepancies observed in the shapes and sizes of leaves. According to both qualitative and quantitative studies, the proposed method is more proficient and reliable than other existing methods for recognizing and categorizing plant diseases.
Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications

Secure software engineering is crucial but can be time-consuming; therefore, methods that could expedite the identification of software weaknesses without reducing the process’ efficacy would benefit the software engineering industry and thus benefit modern life. This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. The research uses the CWE repository and PROMISE_exp dataset for training. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naïve Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested, with SVM and neural network producing reliable results. The research's unique contribution lies in the mapping technique and algorithm selection. It serves as a valuable reference for the secure software engineering community seeking to expedite the development lifecycle without compromising efficacy. Future work involves testing more algorithms, optimizing existing ones, and improving the training sets’ accuracy.
EPIDEMIOLOGICAL STUDY ON FLATFOOT IN CHILDREN: A LARGE SURVEY INCLUDING THOUSANDS OF SUBJECTS FROM DIFFERENT ITALIAN REGIONS

The significance of flat-foot in childhood and adolescence in Italy has never been studied on a statistically significant sample. In our survey, thousands of subjects from different regions of northern, central and southern Italy (Calabria, Lazio, Umbria, Lombardia) were studied in order to determine the prevalence of flat-foot and search for possible genetic or environmental factors that may influence the normal development of the longitudinal arch, starting from birth. Materials and methods: In order to obtain the footprints of children (aged 6–11, attending primary school), we used a polarized light podoscope; we used this instrument, instead of more advanced ones such as baropodometric platforms, because flat-foot diagnosis is basically static and also because the majority of existing studies use the same methodology and have established simple, reliable and reproducible diagnostic criteria, making a more accurate data comparison possible. Podoscopy was integrated with anamnesis, physical examination and measurement of weight and height. Footprint morphology has been related with variables such as age, sex, geographical area of origin and BMI (Body Mass Index).
How to explain the Leaky Pipeline?

In most European universities, today the overall share of female bachelor’s degrees is more than 50 percent but drops at the level of full professors to about 25 percent. This phenomenon is called Leaky Pipeline. Most explanations refer to gender norms, motherhood, implicit or explicit sexism, and “tokenism”. We take a novel approach, comparing the Leaky Pipeline across various study fields in the two biggest Swiss Universities. We start with the popular Token hypothesis of Rosabeth Moss Kanter, that women suffer from their minority position. According to this hypothesis, it is expected that the higher the women ́s share in a study field the less pronounced the Leaky Pipeline. In contrast, the Self-Selection hypothesis and the Status Group hypothesis lead to different expectations: The higher the women ́s share in a study field the more pronounced the Leaky Pipeline. Our data clearly reject the Token hypothesis. To test the second and the third hypotheses, we conduct a representative survey at the two Swiss Universities. We find strong evidence for Self-Selection effects, but no discrimination according to a Status Group effect. We show that men and women in different fields of study have different career motivations, family aspirations, and resources, which shape their career and family dynamics and thus the observed Leaky Pipeline differently across disciplines. These dynamics are reinforced by partner choice as women in female-dominated fields tend to match with men in male-dominated fields, and vice versa. Our findings may explain why many of the current measures to mitigate the Leaky Pipeline – in particular quota are not effective.
Enhancing Prostate Cancer Diagnosis: Leveraging XGBoost for Accurate Classification

This research introduces a new method for classifying prostate cancer using a powerful machine learning algorithm called XGBoost. The leading cause of cancer-related fatalities among males globally is prostate cancer. Accurately categorizing prostate cancer is important for deciding the right treatments and improving patient outcomes. The study aimed to create a strong classification model using XGBoost, which can handle complex and extensive data. The model used a comprehensive dataset containing clinical and histopathological information from prostate cancer patients. By conducting thorough experiments and adjusting the model's settings, the XGBoost-based approach achieved impressive results, showing high accuracy in identifying prostate cancer cases. These findings highlight the potential of XGBoost as a valuable tool for classifying prostate cancer, enabling more precise and personalized diagnoses and ultimately enhancing patient care and treatment strategies.
Providing Psychological Support After Miscarriage Through a Customizable Virtual Reality Experience

A miscarriage, an unintentional pregnancy loss before the 20th week of gestation, can be a traumatic experience. The psychological impact that miscarriage often has on parents, especially on women, is substantial. Nevertheless, social and professional support often fall short for those who go through it. Here, we describe the development process of AViR (Adaptive Virtual Reality), an immersive Virtual Reality (VR) system for delivering psychological support after miscarriage. AViR merges standard protocols of psychological support with procedural VR scenarios and user-generated content to deliver customized restorative experiences. This innovative approach to using VR for this target population expands research on a substantially understudied topic. The impact AViR could have is significant and it has the potential to make a difference in the lives of many women.
Project Management Software Tools – One Step Closer to UN Sustainable Development Goals

The purpose of this research was to explore the contribution of different engineering applications, more precisely software for project management, to the overall success of projects that help achievement of the United Nations sustainable development goals. The research was conducted in several phases, the first of which was a literature review on the topic of project success, in order to define a comprehensive list of dimensions, factors and measures of success. The second step was the implementation of a questionnaire, on the use of project management software and the success of implemented projects in the Republic of Serbia, and their contribution to the achievement of UN's sustainable development goal. The research was designed to investigate the impact of software for project management on the success of projects in relation to the United Nations sustainable development goals. The results show that the use of engineering applications contributes to project success, particularly in achieving SDG goals 4, 8, and 9. The analysis also reveals a positive and statistically significant correlation between critical success factors and the dimensions of project success. These findings demonstrate that using software for project management is beneficial for achieving success in all three dimensions of project success and is justified for managing time, resources, costs, changes, and risks on both single and multi-project management. Furthermore, the use of software can improve the chances of meeting stakeholder requirements and completing the project within defined constraints.
Toward Fairness Across Skin Tones in Dermatological Image Processing

Skin cancer is a prevalent and concerning form of cancer, with an annual incidence rate estimated to be more than 3 million cases in the US. In recent years, the field of medical image processing has made a remarkable progress in the domain of skin cancer detection, surpassing the diagnostic capabilities of dermatologists in certain settings. However, it has been reported that the performance of these deep learning detection models varies significantly across different skin tones (e.g., light versus dark), motivating the need for fair and unbiased classification results. Here, we evaluate DeepDerm [10], a state-of-the-art skin cancer detection model, specifically focusing on its performance across skin types classified by the Fitzpatrick Skin Tones (FST). By analyzing the model's accuracy and fairness, we observe notable discrepancies in its performance across different FST categories. We propose a novel architecture that leverages fine-tuning, an ensemble architecture, and fairness-based resampling for supporting high accuracy and fairness in skin cancer detection. The proposed framework demonstrates promising outcomes, marking a significant stride toward achieving fairness and accuracy in dermatological image processing.
Clinicopathological and Ultrasound Features as Risk Strati�cation Predictors of Clinical and Pathological Nodal Status in Papillary Thyroid Carcinoma: A Study of 748 Patients

Background: Papillary thyroid carcinoma (PTC) is the most common histological type of thyroid malignancy that tends to metastasize to cervical lymph nodes. In the present study, we aimed to investigate which clinicopathologic and ultrasound features of PTC are associated with clinical lymph node metastasis (LNM) and numbers of pathological LNM. Methods: From January 2016 to December 2018, we identi�ed a cohort of patients with PTC who underwent cervical ultrasonography and were diagnosed through operation and pathology. Clinical N1(cN1) and >5 pathologic N1(pN1) was performed in accordance with a standardized protocol. A model to determine the intermediate risk strati�cation of the 2015 ATA was established using the outcomes of univariate and multivariate analyses. Results: we collected 748 PTC patients in the �nal inclusion criteria. From the analyses, primary tumor size >21mm, capsule contact, extrathyroidal extensions (ETE), postsurgical radioiodine (RAI) treatment and central LNM remained independent risk factors for cN1 in PTC patients. Multifocality, primary tumor size >21mm, capsule contact, ETE and RAI treatment are signi�cant independent risk factors for >5 pN1. Primary tumor size >21mm, capsule contact, ETE and RAI treatment were independent risk factors for both cN1 and >5 pN1. Conclusions: We conclude that primary tumor size >21mm, capsule contact, ETE and RAI treatment were independent predictors of for intermediate risk of recurrence in patients with PTC. Ultrasonography is a good technique for the preoperative lymph node staging of PTC and is helpful for detecting LNM. A few studies have attempted to explore the association of LNM with clinicopathologic and ultrasound features of the primary tumor(11, 12). Thus, it would be useful to examine preoperative cN1 and pN1 data to predict intermediate risk in PTC patients. The results showed that size greater than 2cm, capsule contact and ETE were independent risk factors for both cN1 and >5 pN1 in PTC patients. This study shows that primary tumor size >2cm, capsule contact, ETE and central LNM independent affected cN1. cN1 were identi�ed by preoperative physical examination, US, CT imaging and/or intraoperative inspection. In the clinic, thyroid disease and LNs are commonly detected during preoperative evaluation by US. US can directly and accurately detect lateral cervical LNM and obvious central LNM. If central metastatic nodes are masked by surrounding structures in the central neck, clavicle and sternum, US can determine the status of the thyroid nodule. Although US results cannot replace pathological results, most studies have found that ultrasound results are consistent with pathological results(13). When US results show primary tumor size >2cm, capsule contact or ETE, they may indicate LNM and an intermediate risk of recurrence.
Deep Learning-Based Image Retrieval: Addressing the Semantic Gap for Accurate Content-Based Image Retrieval

The paper proposes a deep learning-based Content-Based Image Retrieval (CBIR) system that retrieves images based on feature sets and displays the resultant images with minimum vector distance. The proposed model utilizes convolutional neural networks (CNNs) for the extraction of high-level and abstract visual data, which can learn hierarchical representations that encapsulate complicated semantics and increase retrieval accuracy by utilizing deep neural networks. The paper highlights the advantages of content-based image retrieval over textual indexation and its less time-consuming nature. The proposed model is compared with other models, and the efficiency and correctness of the suggested process for extracting visually related photos from a huge database are established through rigorous experimentation and review. The paper also discusses the use of multimodal techniques that mix textual information with visual characteristics to further enhance semantic understanding. Overall, the paper provides a comprehensive discussion on the use of deep learning-based techniques for content-based image retrieval and is informative for individuals interested in the field of image processing and computer science.
Pre-Trained Deep Learning-Based Approaches for Eye Disease Detection

This study primarily examines how well four pretrained deep learning models perform in identifying eye disorders using four metrics we developed: recall, precision, accuracy, and F1 Score. With the help of universal custom layers, the models are adjusted, and the outcomes are examined. The study then suggests an ensemble method that uses majority voting to combine the probabilistic outputs of the top-performing models. The suggested methodology outperforms state-of-the-art algorithms in experiments using a publicly available dataset, with average values for Recall, Precision, Accuracy, and F1 Score of 81.25%, 83.68%, 95.17%, and 79.12%, respectively. The work shows how well-trained deep learning models can identify eye illnesses and have the potential to improve public health, especially in mass screening programs.
Multi-Feature Embedding and Deep Classification for Elderly Activity Recognition

Human Activity Recognition (HAR) has many potential applications in the real world, including biometric user identification, elderly health monitoring, and governmental surveillance, and as a result, it is currently the subject of intensive study. HAR has emerged as a major focus in the fields of mobile and omnipresent computing because to the increasing prevalence of wearable sensors and the Internet of Things. In recent years, deep learning has been the dominant method for making inferences and solving problems in the HAR system. However, significant difficulties remain in applying HAR to issues in biometric user identification, whereby a wide range of human behaviors may be considered biometric features and utilized to distinguish individuals. Using deep learning models to recognize human behavior as its foundation, this study presents an original framework for multi-class wearable identification of users. Sensory data from the wearable devices' tri-axial gyroscopic and tri-axial accelerometers are used to learn more about users while they engage in a variety of activities. Further, the usefulness of the suggested framework was shown by a series of tests. According to the results, the Convolutional Neural Network's model had the most overall accuracy (91.77%) across all users, while the Long Short-Term Memory machine learning model achieved the highest overall accuracy (92.43%). Regarding biometric user identification, both of them are good enough.
Triple Attention Vision Transformers for Remote Sensing Image Classification

Remote sensing image classification is gradually growing into an attractive research topic and is widely used in various tasks. But unlike natural general images, remote sensing images are usually high-resolution, which are consist of complex backgrounds and various randomly arranged targets. Remote sensing images usually have the characteristics of large intra-class differences and huge inter-class similarity, which make it difficult for the network to focus on the main objects in the scene, resulting in unsatisfactory classification performance. In this paper, we propose the Triple Attention Vision Transformers (TaViT) network to solve this problem. The backbone of TaViT is mainly composed of two branches, namely Dual Attention Vision Transformers (DaViT) and Branch Attention Module (BA Module). DAViT uses the self-attention mechanism of “spatial tokens” and “channel tokens” to capture the features of the global context. The BA Module aggregates the features of each channel along the horizontal and vertical directions, respectively, and continuously provides rich local details for the network. Experimental results on three publicly available remote sensing datasets demonstrate that our TaViT achieves state-of-the-art performance.
Identification of Individuals Using Multimodal Data and LSTM Neural Networks

Currently, biometric systems are widely used in the process of identifying individuals. These systems can be classified as unimodal or multimodal, depending on the number of biometric characteristics or modalities used. Multimodal systems tend to provide authentication with higher hit rates and greater robustness against fraud. This paper proposes a multimodal biometric identification system based on face and voice features using long-short-term-memory recurrent networks and a fusion method at feature level. The face features were extracted using an autoencoder neural network, while for voice, the features are the Mel Frequency Cepstral Coefficients. Data from 50 individuals (37 males and 13 females), taken from the MOBIO database, were used. The best results obtained for each system were: accuracy of the unimodal face system = 97.79%; accuracy of the unimodal voice system = 79.98%; accuracy of the multimodal face and voice system = 99.47%.
Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL).
A Survey on Design Concepts and Optimization in Dual Band Patch Antenna for 5G Applications

Mobile communication has become in demand as consumers of mobile devices multiply quickly. Mobile consumers want additional features on their phones, such as fast data speeds, effective communication, less traffic, the ease of using a variety of applications, etc. The requirements of mobile customers must be met, and network operators must achieve this with the aid of 5G technology. High bandwidth, low latency, improved quality of service, maximum capacity, and wide-band spectrum accessibility are all features of 5G technology. In order to offer a high frequency spectrum with a significant amount of bandwidth, 5G is operated in the millimeter wave band. This essay is based on research on the best antenna layout for mobile data transmission using 5G technology. To determine various design which is appropriate for 5G mobile communication, measurements of various antenna design characteristics are also investigated in this paper. This review article summarizes the many methods, designs, and development strategies employed by researchers in the multi-band antennas that make up 5G wireless networks and seeks to give a survey of the current research trends. The researchers' examination and analysis of current design approaches, processes, and obstacles is going to be aided by this research.