Reviewerly/exHarmony huggingface dataset:
The use of generative adversarial networks for multi-site one-class follicular lymphoma classification

Abstract Recent advances in digital technologies have lowered the costs and improved the quality of digital pathology Whole Slide Images (WSI), opening the door to apply Machine Learning (ML) techniques to assist in cancer diagnosis. ML, including Deep Learning (DL), has produced impressive results in diverse image classification tasks in pathology, such as predicting clinical outcomes in lung cancer and inferring regional gene expression signatures. Despite these promising results, the uptake of ML as a common diagnostic tool in pathology remains limited. A major obstacle is the insufficient labelled data for training neural networks and other classifiers, especially for new sites where models have not been established yet. Recently, image synthesis from small, labelled datasets using Generative Adversarial Networks (GAN) has been used successfully to create high-performing classification models. Considering the domain shift and complexity in annotating data, we investigated an approach based on GAN that minimized the differences in WSI between large public data archive sites and a much smaller data archives at the new sites. The proposed approach allows the tuning of a deep learning classification model for the class of interest to be improved using a small training set available at the new sites. This paper utilizes GAN with the one-class classification concept to model the class of interest data. This approach minimizes the need for large amounts of labelled data from the new site to train the network. The GAN generates synthesized one-class WSI images to jointly train the classifier with WSIs available from the new sites. We tested the proposed approach for follicular lymphoma data of a new site by utilizing the data archives from different sites. The synthetic images for the one-class data generated from the data obtained from different sites with minimum amount of data from the new site have resulted in a significant improvement of 15% for the Area Under the curve (AUC) for the new site that we want to establish a new follicular lymphoma classifier. The test results have shown that the classifier can perform well without the need to obtain more training data from the test site, by utilizing GAN to generate the synthetic data from all existing data in the archives from all the sites.
A fault diagnosis method based on hybrid sampling algorithm with energy entropy under unbalanced conditions

Abstract For the degraded performance of the fault diagnosis model caused by massive normal samples and scarce fault samples under unbalanced conditions, a new fault diagnosis method based on a hybrid sampling algorithm and energy entropy, namely HSEEFD is proposed in this paper. In the proposed method, Empirical Modal Decomposition is employed to decompose the vibration signals into Intrinsic Mode Functions (IMFs), and the energy entropy feature of each IMF component is extracted to construct a feature vector matrix. Then, a new hybrid sampling algorithm using Tomek’s Links algorithm, Euclidean distance, K-means algorithm, and synthetic minority over-sampling technique (SMOTE), namely TSHSA is designed to balance the extracted features. Tomek’s Links algorithm is used to identify and remove the confusable majority class samples at the boundary. Euclidean distance is applied to find the suspected noise points in minority class samples and remove them. The k -means algorithm is employed to cluster the minority class samples and SMOTE is used to deal with each cluster according to the density of the clusters to synthesize new features. Finally, the support vector machine is applied to classify faults and realize fault diagnosis. The experiment results on the actual imbalanced data show that the proposed HSEEFD method can effectively improve the accuracy (AUC) of the fault diagnosis under unbalanced conditions by increasing the AUC value by more than 2.1%, and the AUC and G-mean by more than 0.7%, 2.1%, respectively.
MDI quantum protocol for anonymous countable veto voting

Abstract In order to construct unconditionally secure anonymous countable veto protocol, we proposed a measurement-device-independent quantum secure multiparty authenticated summation protocol. This protocol adopts perfect quantum encryption and one-time pad to achieve unconditional security. In addition, the protocol uses the weak coherent pulses of the BB84 state as the quantum resource, and only needs to perform Bell state identification to complete the calculation. Combined with the idea of grouping, it is applied to the quantum anonymous veto voting scenario, which ensures the anonymity of the protocol. Under the semi-honest model, the security of the designed protocol is proved. It can satisfy the correctness, security and relatively complete voting attributes. Finally, IBM Qiskit is used to carry out simulation experiments, and the experimental results verify the correctness and feasibility of the protocol.
Robots in Geriatric Care: A Future with No Return?

The worldwide increase of the life expectancy puts the healthcare systems in certain countries under growing pressure due to more common functional impairments, chronic and degenerative diseases typically related to senescence. As a response to older people’s needs, robotic systems can offer versatile solutions in geriatric healthcare. Above all, robots can be used as rehabilitative and assistive tools for people with motor impairments. Furthermore, homes are already starting to be inhabited by assistive robotic agents that can offer specific services to stimulate the mental processes, the mood, and the social activity of older adults as well as improve their safety and autonomy. In particular, telepresence robots can actually extend the environment and the social contacts of the older person beyond the domestic boundaries. Finally, indoor sensorized environments can connect different devices to obtain an assistive robotic ecosystem like the EDITH (Embodied Distributed Intelligent Technologies for Healthcare) Setup. This kind of connected health solution can highly improve the quality of life of older adults and, overall, of people with fragilities.
Towards a Multidimensional Ontology Model for DIH-Based Organisations

Digital innovation hubs are of utmost importance when they sustain cooperation in innovative technological domains like the cyber-physical system. In this paper, the authors introduce, in the first part, a DIH ontology that they extend (1) with network and (2) with CPS entities. For this, they remind the questions that the ontology must answer, deploy the methodology proposed by Noy and McGuinness to identify the classes and the associations between classes, and integrate the new CPS ontological extensions. In the second part, they implement the full innovative DIH4CPS ontology within Protégé, and they instantiate it to a real company.
How are websites used during development and what are the implications for the coding process?

Websites are frequently used to support the development process. This paper investigates how websites are used when writing code and programmers' perceptions of the potential impact of this on their behaviour and the quality of the resulting software. We interviewed 18 programmers (13 students enrolled in undergraduate computer science courses, and 5 experienced professionals), and analysed the data thematically. The findings were used to develop a survey, which was distributed to 276 programmers (251 students, 25 experienced professionals). The results indicate that use of websites, especially Stack Overflow, is viewed as an essential part of programming by both students completing coursework and professionals developing code in industry. We also found that developers have experience of encountering a diverse set of problematic code snippets online, that copying code from websites without checking its quality or understanding how it worked is common, and that using online resources in this way had a potentially counter-productive effect on learning. Based on these findings, we make a number of recommendations, including better consideration of online code reuse in taught programmes, co-development and code-reuse practices in professional settings, and software licensing training for professional developers. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Development of a hybrid word recognition system and dataset for the Azerbaijani Sign Language dactyl alphabet

The paper introduces a real-time fingerspelling-to-text translation system for the Azerbaijani Sign Language (AzSL), targeted to the clarification of the words with no available or ambiguous signs. The system consists of both statistical and probabilistic models, used in the sign recognition and sequence generation phases. Linguistic, technical, and human–computer interaction-related challenges, which are usually not considered in publicly available sign-based recognition application programming interfaces and tools, are addressed in this study. The specifics of the AzSL are reviewed, feature selection strategies are evaluated, and a robust model for the translation of hand signs is suggested. The two-stage recognition model exhibits high accuracy during real-time inference. Considering the lack of a publicly available dataset with the benchmark, a new, comprehensive AzSL dataset consisting of 13,444 samples collected by 221 volunteers is described and made publicly available for the sign language recognition community. To extend the dataset and make the model robust to changes, augmentation methods and their effect on the performance are analyzed. A lexicon-based validation method used for the probabilistic analysis and candidate word selection enhances the probability of the recognized phrases. Experiments delivered 94% accuracy on the test dataset, which was close to the real-time user experience. The dataset and implemented software are shared in a public repository for review and further research (CeDAR, 2021; Alishzade et al., 2022). The work has been presented at TeknoFest 2022 and ranked as the first in the category of social-oriented technologies.
Learning Motion-Perceive Siamese network for robust visual object tracking

Siamese networks enable end-to-end training for visual tracking, achieving excellent performance in recent years. However, classical Siamese-based formulation relies on an offline-trained appearance model to perform tracking for each frame, ignoring the temporal variation at the online stage. As been verified that temporal motion is crucial for robust and accurate tracking, we propose a novel Motion-Perceive Siamese network (SiamMP) that explicitly predicts motion patterns, providing complementary clues for the appearance-only formulation. Specifically, successive historical frames are collected, with their appearance and potential trajectory being employed to predict the next state, achieving motion awareness correspondingly. Besides, an adaptive fusion module is dedicated to performing a decision-level negotiation between the tracking evidence of the appearance and the motion models. To verify the effectiveness and merit of our SiamMP, extensive experiments are conducted on several challenging benchmarks, including LaSOT, OTB100, GOT-10k, TC128, and DTB70. The comparison and analysis of the obtained results demonstrate the necessity and validity of involving the motion-perceive design in the Siamese framework.
Perceptual Contrastive Generative Adversarial Network based on image warping for unsupervised image-to-image translation

This paper proposes an unsupervised image-to-image (UI2I) translation model, called Perceptual Contrastive Generative Adversarial Network (PCGAN), which can mitigate the distortion problem to enhance performance of the traditional UI2I methods. The PCGAN is designed with a two-stage UI2I model. In the first stage of the PCGAN, it leverages a novel image warping to transform shapes of objects in input (source) images. In the second stage of the PCGAN, the residual prediction is devised in refinements of the outputs of the first stage of the PCGAN. To promote performance of the image warping, a loss function, called Perceptual Patch-Wise InfoNCE, is developed in the PCGAN to effectively memorize the visual correspondences between warped images and refined images. Experimental results on quantitative evaluation and visualization comparison for UI2I benchmarks show that the PCGAN is superior to other existing methods considered here.
Backward smoothing adaptive SVDCKF integrated navigation algorithm

Aiming at the problem that the Cubature Kalman filter (CKF) algorithm has a non-positive definite of the covariance matrix in GNSS/SINS integrated navigation, which leads to the failure of Cholesky decomposition and the inability to estimate GNSS measurement noise, a backward smoothing adaptive CKF integrated navigation algorithm based on Singular Value Decomposition (SVD) is proposed. Backward smoothing adaptive CKF based on Singular Value Decomposition (BS-A-SVDCKF) algorithm replaces the Cholesky decomposition in standard CKF with SVD, constructs the test threshold for backward smoothing by using the relatively stable noise of the SINS system, and adaptively estimates based on different measurement characteristics when the GNSS measurement noise changes abruptly. Experimental results show that compared with adaptive SVDCKF (A-SVDCKF), backward smoothing SVDCKF (BS-SVDCKF) and SVDCKF, the average positioning accuracy of the proposed algorithm is improved by 32.12%, 57.07% and 68.66%, the average operating efficiency is reduced by 2.19% compared with SVDCKF, and 0.57% and 3.60% higher than that of BS-SVDCKF and A-SVDCKF, which improves the positioning accuracy of the integrated navigation system and ensures real-time.
Using DeepL translator in learning English as an applied foreign language – An empirical pilot study

With the advent of new emerging technologies, machine translation, especially natural machine translation (NMT) and its tools, is being increasingly applied in second language (L2) acquisition. The aim of this study is to investigate the usefulness of machine translation, specifically DeepL Translator, in the second language acquisition process, since it has a great potential to transform foreign language education. The present empirical pilot study describes an experiment dealing with the use of neural machine translation in the process of formal writing (i.e., writing a summary) in a foreign language. Altogether 16 university students learning English as an applied foreign language with C1 level of English proficiency participated in the experiment. The results show differences between pre-test and post-test, and a significant improvement in students' language skills due to the use of DeepL Translator. The questionnaire survey, among other things, reveals positive perceptions of this tool and awareness of improved language skills by the research participants. The findings indicate that purposefully guided working with a NMT tool can contribute to the perceived usefulness of its use in learning English as an applied foreign language.
Fine-grained imbalanced leukocyte classification with global-local attention transformer

Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global-Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available athttps://github.com/ywj1/WBC-GLAformer
Improving Semantic Segmentation Performance of Deep Networks for Autonomous Driving through Loss Functions

Semantic segmentation of images for autonomous driving is a dense pixel-level prediction task. Present-day algorithms make extensive use of deep learning models for parsing the image. A large number of classes and class imbalance in the autonomous driving dataset make accurate labeling of pixels challenging. Cross- Entropy(Ce)loss function is mainly used to optimize the network parameters during training. The overall performance gain resulting from CE loss comes from large object classes. In contrast, the segmentation accuracy of smaller classes and object boundaries remains inferior, even with increased overall accuracy. To address this limitation, task- specific custom loss functions have been developed and used for various applications like biomedical image processing. However, they have rarely been used for road scene segmentation in an autonomous driving context. To fill this gap, we develop a composite loss function by linearly combining the CE loss and boundary/region-based loss to improve the existing state- of-the-art model. We give empirical evidence that our custom loss function improves the segmentation scores on smaller object categories and object boundaries. Through extensive experiments on the Cityscapes dataset, we show that Deep networks trained with our loss function see a gain of more than 2 % on the overall <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> IoU score. In some smaller classes, these scores are increased by more than 20%.
PROSODY-VC: Voice style transfer with speech prosody

With the increasing demand for personalized applications of voice-conversion (VC), research has developed a lot from just content and speaker information swap. The study has shifted towards a more realistic voice style transfer with prosody. In this study, mutual information (MI) within the prosody components is measured to update the model parameters to rate the disentanglement of the components. An adaptive boosted learning rate strategy is used to boost the training and enhance feature learning in the proposed framework. The proposed framework achieved better results than the baseline architecture when evaluated on voice conversion Challenge (VCC) 2018 and Librispeech corpus evaluation data. PROSODY-VC achieved a 13.05 word error rate (WER) score and a better speaker similarity score which explains that our converted output sounds natural and has more similarity with the target speaker. The proposed model achieves a 5.5 mel-cepstral distortion (MCD) score for unseen speakers.
A Neural Network based Approach to Compute Text Readability Using Visual Features

Text elements are an important part of any interface. Readability of text elements present in an interface can be determined using language properties (text length, average word lengths etc.). Except these, the visual properties (color, font size etc.) of textual elements may have an effect on readability. In this work, we proposed a Neural Network based computational model for text readability using six visual features of text elements - background color (Red, Green and Blue), text color (Red, Green, Blue), font size, font style, line spacing, and word spacing. By varying these features, thirty-five samples were designed, and an empirical study was conducted by thirty-five participants. Finally, empirical data were used to develop our proposed model. In order to develop the proposed model, we considered a sequential neural network-based approach. Five dense layers with Relu activation functions were used to construct our model. Experimental results suggest a loss of only 0.0092 during the training process, while a mean error of 0.0796 was observed during testing.
Distributed Feature Representations for Out-of-domain Detection in Dialogue Systems

In recent times, the increasing application of dialogue systems has drawn research interest toward improving the task of identifying out-of-domain (OOD) input in order to avoid catastrophic failures of the system. OOD inputs are utterances by the end user which do not lie within the scope of the current system. Several machine learning-based methods have been employed for this task. Classification-based and distance-based methods are widely used for this task. However, these approaches have limitations like choosing appropriate OOD samples and distance metric. We overcome the limitations by using a lightweight pipeline that applies a representation model to generate vector embeddings for in-domain (IND) and OOD samples in an unsupervised manner. We employ an LSTM model as a classifier trained only with the IND samples. This removes the challenge of curating a large collection of OOD samples representing the open-world scope of the OOD class. We use a dataset comprising fake OOD samples that are very similar to the IND samples. Our pipeline outperforms the CNN-based state-of-the-art model in the task of OOD detection, suggesting that the representation models can capture the features of IND and OOD distribution well. Furthermore, the LSTM classifier is more robust since it outperforms the baseline by 6% in terms of the AUROC score even when a more challenging set of fake OOD samples are used.
Transformer based multimodal similarity search method for E-Commerce platforms

Multimodal similarity search has been attracting much attention from scholars in recent times. The intensification of research into Multimodal search lies in the motive to understand the context of the multiple modes associated with the data and the interdependency of these modes. Multimodal data can be in the form of audio, video, images, texts, and any combination of them. For relevant extraction of features from them can take too much time and thus it may cause the whole process of learning the features and searching for them time-consuming. In this work, a deep neural network uses Convolutional neural network and Bidirectional Encoder Representations from Transformers to learn features from text and image data. A K nearest neighbor <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> long with KD Tree has been used to search for similar image-text pairs. Evaluations based on several image-text multimodal retrieval procedures show the effectiveness of the proposed multimodal search method and its higher speed performance compared to existing algorithms.