Reviewerly/exHarmony huggingface dataset:
Federated Learning for Medical Imaging Segmentation via Dynamic Aggregation on Non-IID Data Silos

A large number of mobile devices, smart wearable devices, and medical and health sensors continue to generate massive amounts of data, making edge devices’ data explode and making it possible to implement data-driven artificial intelligence. However, the “data silos” and other issues still exist and need to be solved. Fortunately, federated learning (FL) can deal with “data silos” in the medical field, facilitating collaborative learning across multiple institutions without sharing local data and avoiding user concerns about data privacy. However, it encounters two main challenges in the medical field. One is statistical heterogeneity, also known as non-IID (non-independent and identically distributed) data, i.e., data being non-IID between clients, which leads to model drift. The second is limited labeling because labels are hard to obtain due to the high cost and expertise requirement. Most existing federated learning algorithms only allow for supervised training settings. In this work, we proposed a novel federated learning framework, MixFedGAN, to tackle the above issues in federated networks with dynamic aggregation and knowledge distillation. A dynamic aggregation scheme was designed to reduce the impact of current low-performing clients and improve stability. Knowledge distillation was introduced into the local generator model with a new distillation regularization loss function to prevent essential parameters of the global generator model from significantly changing. In addition, we considered two scenarios under this framework: complete annotated data and limited labeled data. An experimental analysis on four heterogeneous COVID-19 infection segmentation datasets and three heterogeneous prostate MRI segmentation datasets verified the effectiveness of the proposed federated learning method.
Multimodal Pretraining from Monolingual to Multilingual

Multimodal pretraining has made convincing achievements in various downstream tasks in recent years. However, since the majority of the existing works construct models based on English, their applications are limited by language. In this work, we address this issue by developing models with multimodal and multilingual capabilities. We explore two types of methods to extend multimodal pretraining model from monolingual to multilingual. Specifically, we propose a pretraining-based model named multilingual multimodal pretraining (MLMM), and two generalization-based models named multilingual CLIP (M-CLIP) and multilingual acquisition (MLA). In addition, we further extend the generalization-based models to incorporate the audio modality and develop the multilingual CLIP for vision, language, and audio (CLIP4VLA). Our models achieve state-of-the-art performances on multilingual vision-text retrieval, visual question answering, and image captioning benchmarks. Based on the experimental results, we discuss the pros and cons of the two types of models and their potential practical applications.
Monocular spatial geometrical measurement method based on local geometric elements associated with out-of-view datum

In the field of industrial inspection, visual non-contact measurement of the radial dimension of the extension end of the long pole structure is a difficult problem. Utilizing the principle of telecentric vision measurement, we propose a monocular spatial geometrical measurement method based on local geometric elements associated with out-of-view datum, focusing on key techniques such as visual calibration and global unification of separate dimensions. Firstly, the designed associated target is used to establish the conversion relationship between the measurement reference space and the camera FOV space. Singular Value Decomposition (SVD) is used to obtain the initial values R0 and T0 of the rotation matrix and translation vector during the three-dimensional coordinate system conversion. Then the initial values are linearized based on the Rodrigues iterative transformation to obtain the optimized R and T, thereby achieving the high-precision conversion of the coordinate system, realizing the global unification of the coordinates of the separated size elements. In order to verify the effectiveness of the method, an experimental measurement device was constructed. The experimental results show that the measurement uncertainty (95% confidence level) of the space radial dimension is ±10.37 µm, and the %R&R index is 12.91%. This method is suitable for applications such as rapid and precise measurement of narrow and long workpieces such as partial visible elements in vitro or cross-scale measurement.
Learning contextual representations with copula function for medical image segmentation

Long-range contextual information modeling plays an important role in medical image segmentation. Existing approaches often ignore local continuous information or the attention missing. In this paper, we propose a novel approach named CouplaNet for efficiently modeling dense 3D contextual dependencies. CouplaNet consists of two core modules, namely Relationship Modelling (RM) and Contextual Aggregation (CA). RM leverages the GCN to model the relationships of elements and yields the long-range contextual representation for each dimension individually. Then CA is introduced to perform the aggregation for the ensemble of the representation of each dimension according to the coupla property in the Sklar's theorem. We conducted extensive experiments across six medical image segmentation tasks, including nodule segmentation, skin lesion segmentation, liver tumor segmentation, optic disc and cup segmentation, thyroid nodule segmentation, and automated cardiac diagnosis. Through these experiments, we were able to thoroughly evaluate our contributions and demonstrate the effectiveness of our approach.
Fake Colorized Image Detection Based on Special Image Representation and Transfer Learning

Nowadays, images have become one of the most popular forms of communication as image editing tools have evolved. Image manipulation, particularly image colorization, has become easier, making it harder to differentiate between fake colorized images and actual images. Furthermore, the RGB space is no longer considered to be the best option for color-based detection techniques due to the high correlation between channels and its blending of luminance and chrominance information. This paper proposes a new approach for fake colorized image detection based on a novel image representation created by combining color information from three separate color spaces (HSV, Lab, and Ycbcr) and selecting the most different channels from each color space to reconstruct the image. Features from the proposed image representation are extracted based on transfer learning using the pre-trained CNNs ResNet50 model. The Support Vector Machine (SVM) approach has been used for classification purposes due to its high ability for generalization. Our experiments indicate that our proposed models outperform other state-of-the-art fake colorized image detection methods in several aspects.
Hybrid Granularities Transformer for Fine-Grained Image Recognition

Many current approaches for image classification concentrate solely on the most prominent features within an image, but in fine-grained image recognition, even subtle features can play a significant role in model classification. In addition, the large variations in the same class and small differences between different categories that are unique to fine-grained image recognition pose a great challenge for the model to extract discriminative features between different categories. Therefore, we aim to present two lightweight modules to help the network discover more detailed information in this paper. (1) Patches Hidden Integrator (PHI) module randomly selects patches from images and replaces them with patches from other images of the same class. It allows the network to glean diverse discriminative region information and prevent over-reliance on a single feature, which can lead to misclassification. Additionally, it does not increase the training time. (2) Consistency Feature Learning (CFL) aggregates patch tokens from the last layer, mining local feature information and fusing it with the class token for classification. CFL also utilizes inconsistency loss to force the network to learn common features in both tokens, thereby guiding the network to focus on salient regions. We conducted experiments on three datasets, CUB-200-2011, Stanford Dogs, and Oxford 102 Flowers. We achieved experimental results of 91.6%, 92.7%, and 99.5%, respectively, achieving a competitive performance compared to other works.
Il matrimonio e la famiglia nel Codice civile italiano: un'analisi contrastiva dei termini e delle espressioni del C.C. del 1942 e del C.C. aggiornato nel 2022

The purpose of this article is to analyse the image of the family in the Italian Civil Code. This study is carried out from a linguistic point of view. Passages on the family will be analysed in order to present the roles of the man and woman in marriage. The terminological evolution will be shown by comparing the 1942 Code and the 2022 Code. The aim of the study is to point out how the analysis of a legal text can contribute to a better understanding of a country's culture.
Sensing and Actuation as a Service for Health Data Integration

In the last few years, many advances in sensing usage have been developed, and their application on healthcare is substantially growing due to many factors, but the most important one is the patient's physical parameters data gathering for remote monitorization and intervention. Sensing and actuation as a service using clouds as basis may provide a platform to directly connect the patient's sensors to the health care providers and fast monitor or intervene in case of alarm, especially on elderly people. In Portugal, all patient's data is collected to a personal patient data register (health portal), that can be seen by any medical and technical health staff working on public institutions. This study aims to design a framework using sensing and actuation as a service on clouds able to provide data integration into the Portuguese National Patient Data Register and alarm triggering for fast medical intervention.
Interkulturowość w wybranych podręcznikach do nauki włoskiego i angielskiego języka biznesu i ekonomii. Studium porównawcze

This article attempts to characterize three textbooks for learning the language of business and economics in terms of the presence of elements developing intercultural competences. Two textbooks for learning Italian and one for learning English were analyzed: (1) Italiano per economisti, (2) L'italiano in azienda, (3) Market Leader. Intermediate business English. The above-mentioned titles were published in the years 2002-2003, which allowed to eliminate possible differences resulting from the changing over time approach to the phenomenon of intercultural language education. The aim of the study is to identify the elements developing intercultural competences, their qualitative and quantitative characteristics, to discuss the differences in the approach in Italian and English-language texts and to indicate the possibility of using selected solutions in the new teaching materials. This is important as research shows that contacts with foreign cultures alone do not naturally lead to the development of intercultural competence (Sobkowiak, 2015: 64, Budzanowska-Drzewiecka and Motyl-Adamczyk, 2016: 56). The analysis covered non-verbal communication (gestures), stereotypes, proverbs and sayings, rules of linguistic politeness. From the glottodidactic point of view, the proposed types of exercises and the connection of (inter)cultural competences with other communicative competences will be analyzed. The main hypothesis verified during the analyzes assumes that the concept of intercultural language education, although it does not appear as a notion on the pages of textbooks, is an important component of the didactic process in materials for learning the business language, and the approach to interculturality may differ depending on the target culture and status of the foreign language taught (Italian vs English, modern lingua franca).
Real-time airplane detection using multi-dimensional attention and feature fusion

The remote sensing image airplane object detection tasks remain a challenge such as missed detection and misdetection, and that is due to the low resolution occupied by airplane objects and large background noise. To address the problems above, we propose an AE-YOLO (Accurate and Efficient Yolov4-tiny) algorithm and thus obtain higher detection precision for airplane detection in remote sensing images. A multi-dimensional channel and spatial attention module is designed to filter out background noise information, and we also adopt a local cross-channel interaction strategy without dimensionality reduction so as to reduce the loss of local information caused by the scaling of the fully connected layer. The weighted two-way feature pyramid operation is used to fuse features and the correlation between different channels is learned to improve the utilization of features. A lightweight convolution module is exploited to reconstruct the network, which effectively reduce the parameters and computations while improving the accuracy of the detection model. Extensive experiments validate that the proposed algorithm is more lightweight and efficient for airplane detection. Moreover, experimental results on the airplane dataset show that the proposed algorithm meets real-time requirements, and its detection accuracy is 7.76% higher than the original algorithm.
Unicity distance of the Zodiac-340 cipher

In December 2020, David Oranchak, Jarl Van Eycke, and Sam Blake solved a 51-year old mystery: the Zodiac cipher of 340 symbols. The correctness of their solution has not been seriously doubted, and here we give a further argument in its favor: the unicity distance of the cipher’s system is at most 153.
Multimodal face shape detection based on human temperament with hybrid feature fusion and Inception V3 extraction model

In this research, the multi model (image and video) based face shape detection with human temperament is developed. Here, the video is captured by webcam via live recording session and the proposed model undergoes three major stages namely, pre-processing, extracted feature fusion and temperament detection. Extraction of facial landmark and facial boundary takes place in the pre-processing stage. In the feature extraction stage, the handcrafted features from image are extracted (i.e. face, forehead, eyes, cheeks, nose and mouth). From the video frames, the intrinsic features related to face region are extracted using pretrained Inception V3 model. Then robust principal component analysis (RPCA) is introduced to reduce the dimension of extracted features. Further, the feature fusion process is performed using discriminant correlation analysis (DCA) and canonical correlation analysis (CCA) at hybrid phase. Finally, the gated recurrent unit (GRU) classifier model is applied to identify the human temperaments based on face shapes. In the experimental scenario, the performance measures of accuracy (98.51%, 98.86%), precision (96.14%, 97.89%), recall (96.34%, 97.95%), F-measures (96.24%, 97.94%), etc are evaluated and compared with state-of-the-art methods under two datasets. In addition to this, the statistical test is also conducted to validate the efficacy of the proposed model.
New metric formulas that include measurement errors in machine learning for natural sciences

The application of machine learning to physics problems is widely found in the scientific literature. Both regression and classification problems are addressed by a large array of techniques that involve learning algorithms. Unfortunately, the measurement errors of the data used to train machine learning models are almost always neglected. This leads to estimations of the performance of the models (and thus their generalization power) that is too optimistic since it is always assumed that the target variables (what one wants to predict) are correct. In physics, this is a dramatic deficiency as it can lead to the belief that theories or patterns exist where, in reality, they do not. This paper addresses this deficiency by deriving formulas for commonly used metrics (both for regression and classification problems) that take into account measurement errors of target variables. The new formulas give an estimation of the metrics which is always more pessimistic than what is obtained with the classical ones, not taking into account measurement errors. The formulas given here are of general validity, completely model-independent, and can be applied without limitations. Thus, with statistical confidence, one can analyse the existence of relationships when dealing with measurements with errors of any kind. The formulas have wide applicability outside physics and can be used in all problems where measurement errors are relevant to the conclusions of studies.
N-Gram Tabanlı Tahmin Modeli ile XSS Saldırısı Algılama

The increment developments in technology has empowered the web applications. Meanwhile, the existence of Cross-Site Scripting (XSS) vulnerabilities in web applications has become a concern for users. In spite of the numerous current detection approaches, attackers have been exploiting XSS vulnerabilities for years, causing harm to the internet users. In this paper, a text-mining based approach to detect XSS attacks in web applications is introduced. This approach is built to extract a set of features from a publicly available source code files, which are then used to build a prediction model. The findings include few comparisons between Word Tokenization and N-Gram in accuracy, time spend to build the model and AUC-ROC curve. The results show that N-Gram tokenization outperforms the Word Tokenization.
Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.
Pairwise learning for the partial label ranking problem

The partial label ranking problem is a particular preference learning scenario that focuses on learning preference models from data, such that they predict a complete ranking with ties defined over the values of the class variable for a given input instance. This work proposes to transform the rankings into preference relations among pairs of class labels and to learn a standard classifier for each of them. This classifier is then used to estimate the probability of each event from the preference relation between the two compared class labels. Finally, the probabilities obtained for each preference comparison are used to compute a preference matrix utilized to solve the corresponding rank aggregation problem and so obtain the ranking among all the class labels. The experimental evaluation shows that the proposed method is ranked ahead of competing algorithms in accuracy while obtaining similar CPU time results.
A Novel Channel Pruning Compression Algorithm Combined with an Attention Mechanism

To solve the problem of complex network models with a large number of redundant parameters, a pruning algorithm combined with an attention mechanism is proposed. Firstly, the basic training is performed once, and the network model is then re-trained with the attention mechanism for the baseline. The obtained model is pruned based on channel correlation, and finally a simplified model is obtained via continuous cyclic iteration while the accuracy rate is kept as close as possible to that of the baseline model. The algorithm was experimentally validated on ResNet based on different datasets, and the results showed that the algorithm provided strong adaptability to different datasets and different network structures. For the CIFAR-100 dataset, ResNet50 was pruned to reduce the amount of model parameters by 80.3% and the amount of computation by 69.4%, while maintaining accuracy. For the ImageNet dataset, the ResNet50 parameter volume was compressed by 2.49 times and the computational volume was compressed by 3.01 times. The ResNet101 parameter volume was reduced by 61.2%, and the computational volume was reduced by 68.5%. Compared with the traditional fixed threshold, the model achieves better results in terms of detection accuracy, compression effect, and inference speed.