Reviewerly/exHarmony huggingface dataset:
Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis

This paper presents the functional subspace variational autoencoder, a technique addressing challenges in sensor data analysis in transportation systems, notably the misalignment of time series data and a lack of labeled data. Our technique converts vectorial data into functional data, which captures continuous temporal dynamics instead of discrete data that consist of separate observations. This conversion reduces data dimensions for machine learning tasks in fault diagnosis and facilitates the efficient removal of misalignment. The variational autoencoder identifies trends and anomalies in the data and employs a domain adaptation method to associate learned representations between labeled and unlabeled datasets. We validate the technique’s effectiveness using synthetic and real-world transportation data, providing valuable insights for transportation infrastructure reliability monitoring.
U-Net_dc: A Novel U-Net-Based Model for Endometrial Cancer Cell Image Segmentation

Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions, and improving the efficiency of treatment and the useful effect of drugs. However, traditional image segmentation models are not ideal solutions for cancer cell image segmentation due to the fact that cancer cells are highly dense and vary in shape and size. To tackle this problem, this paper proposes a novel U-Net-based image segmentation model, named U-Net_dc, which expands twice the original U-Net encoder and decoder and, in addition, uses a skip connection operation between them, for better extraction of the image features. In addition, the feature maps of the last few U-Net layers are upsampled to the same size and then concatenated together for producing the final output, which allows the final feature map to retain many deep-level features. Moreover, dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) modules are introduced between the encoder and decoder, which helps the model obtain receptive fields of different sizes, better extract rich feature expression, detect objects of different sizes, and better obtain context information. According to the results obtained from experiments conducted on the Tsinghua University’s private dataset of endometrial cancer cells and the publicly available Data Science Bowl 2018 (DSB2018) dataset, the proposed U-Net_dc model outperforms all state-of-the-art models included in the performance comparison study, based on all evaluation metrics used.
FedSpy: A Secure Collaborative Speech Steganalysis Framework Based on Federated Learning

Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, in this paper, we present an effective framework, named FedSpy, using federated learning, which enables multiple agencies to securely and jointly train the speech steganalysis models without sharing their speech samples. FedSpy is a flexible and extensible framework that can work effectively in conjunction with various deep-learning-based speech steganalysis methods. We evaluate the performance of FedSpy by detecting the most widely used Quantization Index Modulation-based speech steganography with three state-of-the-art deep-learning-based steganalysis methods representatively. The results show that FedSpy significantly outperforms the local steganalyzers and achieves good detection accuracy comparable to the centralized steganalyzer.
Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC

The problem of information security and copyright protection of video is becoming increasingly prominent. The current video watermarking algorithm does not have strong anti-compression, which has a significant impact on the visual effect of video. To solve this problem, this paper proposes a video watermarking algorithm based on H.264/AVC. The algorithm combines the non-zero quantization coefficient and the energy factor to select the appropriate chroma subblock, and then an optimized modulation is designed to embed the watermark into its DCT quantization coefficients in order to minimize the number of modifications of the subblocks. The invisibility and robustness experiments of the algorithm are conducted in the paper, and the Structural Similarity Indexes are above 0.99, and the False Bit Rates are all below 0.03. The results show that the algorithm has good invisibility, anti-compression performance and obvious advantages compared with other similar methods.
Fuzzy Hardware Tool: An Adaptable Tool to Facilitate the Implementation of Fuzzy Inference Systems in Hardware

A Fuzzy Inference System (FIS) is a system that represents human reasoning based on fuzzy if-then rules to extract valid results from imprecise or uncertain information. These systems have been applied to various engineering fields, such as control and image recognition, and have encouraged the search for development tools that provide facilities for the design, simulation, and implementation of FISs. Most of the available software tools for the design of FISs have been developed by the scientific community, and very few are commercial tools. According to the state of the technology, the fuzzy systems implemented in hardware have shown higher performance than software implementations. For this reason, different strategies for automatic hardware synthesis of FISs have been proposed in the literature. The available tools for FIS implementation, including MATLAB, Simulink, and Xfuzzy environment, require advanced hardware knowledge, and it is necessary to synthesize and implement the program on FPGA each time a change is made to the code. This paper presents a novel approach for the implementation of fuzzy systems on hardware devices that enables the direct implementation of FISs through a graphical user interface. The VHDL code is pre-synthesized and integrated with the interface, allowing users to experiment with different input parameters, such as membership functions and fuzzy rules, without resynthesizing the code on an FPGA. To verify the effectiveness of the proposed approach, experiments were conducted on two similar System-on-Chip (SoC) devices, VEEK-MT and VEEK-MT2. The results showed that the proposed solution represents a significant advancement in the study of fuzzy systems on hardware devices, providing a flexible and user-friendly approach to their design and implementation.
Application of Data-Driven Semantic Map Modeling in International Education of Chinese

To better reveal the patterns of negative interlingual transfers made by international learners of Chinese, large-scale cross-linguistic data are required to be compared.In presenting and analyzing big set of data from 71 languages of the world, the study employs digital technologies of data analysis and map modeling to present a Semantic Map Model (SMM) related to the notion of "universal quantification".Besides, a Communicative and Controlled Graph (CCG) based on parameters of Probability Entailment, which is also known as Weighted Map with Least Edges by frequencies of occurrences, has also verified the validity of the connective pattern of the original SMM.By comparing the detailed SMMs of Chinese and Hindi, the study instantiates how data-driven map modeling techniques shed light on predicting and correcting the possible semantic errors made by international Chinese learners.
An Empirical Analysis on the Information Literacy of College Students in Guizhou Province

In today's era of intelligence, personal information literacy is becoming more and more important, and the university stage is an important period for the development of information literacy.Research on how to improve college students' information literacy is very necessary.This study takes college students from a university in Guizhou Province as the research object, investigates their network information literacy through a questionnaire survey.The collected data were analyzed by ANOVA and T test, and results indicate that: (1) There are significant differences in information understanding and information creation among college students of different genders; (2) The information literacy of college students increases with the increase of grades.(3) There are also differences in the information literacy of college students in different disciplines, and (4) there is no significant difference in information literacy whether students study information courses or not.On this basis, corresponding suggestions are put forward, which have certain reference significance for the education of information literacy of college students in other universities in Guizhou Province.
FedCLS: Class-Aware Federated Learning in a Heterogeneous Environment

Federated learning is an approach of training the global model on the server by utilizing the personal data of the end users while data privacy is preserved. The users, referred to as clients, are responsible for performing local training using their respective datasets. Once trained, the clients forward their local models to the server, where the models are aggregated to update the global model. Practically, the datasets of clients have different classes of labels regardless of the number of samples. In other words, the data is non-independent and identically distributed (non-iid) among clients in terms of classes of labels, which creates heterogeneity among them. Hence, the local model weights updated by clients result in a broad variation due to heterogeneity among their local datasets. Thus, the process of aggregating the diversified local models of clients has a valuable impact on the performance of global training. When the server aggregates the local models by calculating the weighted average based solely on the number of samples available at the clients, the aggregation process may misguide the global training process. To address this issue, our paper proposes a novel reweighting method called FedCLS that performs based on the volume and variance of local datasets among clients. By taking into account the heterogeneity of data for aggregation in federated learning, the proposed method aims to achieve the minimum global point. The simulation results show that the proposed method achieves 28% performance improvement compared to the conventional federated learning methods.
Menstrual Tracking, Fitness Tracking and Body Work: Digital Tracking Tools and Their Use in Optimising Health, Beauty, Wellness and the Aesthetic Self

Digital self-tracking tools can be part of body work to measure, monitor, and optimise progress towards idealised versions of the self. Fitness and calorie trackers are obvious examples but menstrual tracking apps, which can track a large range of bodily ‘symptoms’, can also be part of body work. In this article, I present accounts of young people’s menstrual and fitness tracking experiences from existing literature, illustrating how both types of trackers can function as part of their users’ body work. I interweave these stories with an autoethnographic account of my own embodied experiences with and through menstrual and fitness tracking apps. I explore the ways in which health, beauty, and wellness can become enmeshed in self-tracking practices; how emotions, stress, and sleep can become personal problems to solve; how tracking tools can make body work feel more ‘real’; and the mutual but asymmetrical shaping of digital tracking tools and their users. Though there is an existing body of work on young people’s experiences of digital self-tracking tools more generally (particularly health and fitness tracking tools), young people’s use of menstrual tracking apps is a relatively understudied phenomenon to date. The themes identified in this article point towards possible avenues for future research.
A Hybrid Feature Selection Method Using an Improved Binary Butterfly Optimization Algorithm and Adaptive β–Hill Climbing

The Butterfly Optimization Algorithm (BOA) is a recently proposed nature-inspired metaheuristic algorithm mimicking the food-foraging behavior of butterflies. Its abilities include simplicity, good convergence rate towards local optima, and avoiding the local optima stagnation problem to some extent. In earlier studies, the performance of Binary BOA (BBOA) is superior to various state-of-the-art methods in different optimization issues, such as search space reduction and solving classical engineering problems. Here, BBOA expands the original search space with all possibilities (Exploration) and seeks to determine the best one from all the produced solutions (Exploitation). Generally, the global performance of BBOA depends on the tradeoff between the Exploration and Exploitation phase and produces quality solutions when a suitable tradeoff is maintained. This study introduces an improved and computationally effective variant of conventional BBOA by improving the local search ability of the Butterfly Optimization Algorithm. Initially, twelve binary variants were produced using three different transfer functions (S, U, V-shaped), and solution quality is evaluated in terms of respective fitness function scores. Next, we explored the local search ability of BOA by another recently developed optimization technique, namely, Adaptive β –Hill Climbing, to compute quality solutions. This optimization process employed two stochastic operators: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N -operator</i> (Neighborhood operator) and β -operator (Mutation operator) to generate improved offspring compared to parent solutions. This phase is iteratively implemented until the desired level of binary pattern with suitable classification accuracy is obtained. We validated the proposed approach on twenty datasets with eleven state-of-the-art feature selection algorithms. The overall results suggest that the proposed improvements increase the classification accuracy with fewer features on most datasets. In addition, the proposed approach’s time complexity was significantly reduced on eighteenth out of twenty datasets. Moreover, the proposed method balances space exploration and solution exploitation in feature selection problems.
A DIC method for thermal micro-deformation measurement of satellite high stability composite structure in simulated space environment

In order to ensure the stability and reliability of the entire satellite and onboard equipment indicators, the satellite highly stable structure can only produce small deformation or “near zero deformation” under the space on-orbit (thermal/vacuum) environment. The effective measurement and verification of its dimensional stability indicators directly determine the success or failure of the development of highly stable satellites. This work proposes a method for measuring the thermal deformation of highly stable satellite structures based on digital image correlation, (DIC), which designs a combined methodology of the digital image correlation measurement scheme, a measurement instrument protection method, a highly uniform temperature measurement and control system in the simulated space on-orbit thermal/vacuum environment. The methodology proposed in the work makes the digital image correlation measurement system suitable for space on-orbit measurement, and verifies that the measurement system can maintain high measurement accuracy. The research results indicate that this method can effectively solve the problems of measurement field interference, high uniform temperature control, speckle measurement camera, and measurement baseline protection. The thermal stability measurement experiment of a highly stable structure of a certain satellite has been successfully completed, and it has been verified that this method can meet the effectiveness and measurement accuracy of simulating thermal deformation measurement of structures in the simulated space on-orbit thermal environment.
Vision Transformer with hierarchical structure and windows shifting for person re-identification

Extracting rich feature representations is a key challenge in person re-identification (Re-ID) tasks. However, traditional Convolutional Neural Networks (CNN) based methods could ignore a part of information when processing local regions of person images, which leads to incomplete feature extraction. To this end, this paper proposes a person Re-ID method based on vision Transformer with hierarchical structure and window shifting. When extracting person image features, the hierarchical Transformer model is constructed by introducing the hierarchical construction method commonly used in CNN. Then, considering the importance of local information of person images for complete feature extraction, the self-attention calculation is performed by shifting within the window region. Finally, experiments on three standard datasets demonstrate the effectiveness and superiority of the proposed method.
BREE-HD: A Transformer-Based Model to Identify Threats on Twitter

With the world transitioning to an online reality and a surge in social media users, detecting online harassment and threats has become more pressing than ever. Gendered cyber-hate causes women significant social, psychological, reputational, economic, and political harm. To tackle this problem, we develop a dataset and propose a transformer-based model to classify tweets into threats or non-threats that are either sexist or non-sexist. We have developed a model to identify sexist and non-sexist threats from a collection of sexist, non-sexist tweets. BREE-HD performs extraordinarily well with an accuracy of 97% when trained on the dataset we developed to detect threats from a collection of derogatory tweets. To provide insight into how BREE-HD makes classifications, we apply explainable A.I. (XAI) concepts to provide a detailed qualitative analysis of our proposed methodology. As an extension of our work, BREE-HD could be used as a part of a system that could detect threats targeting people specifically tailored to classify them in real-time adequately.
Four Secular Ruke Dongba Notes Generated during Fieldwork: An Innovative Language Documentation Approach

This paper documents and analyzes several Dongba script notes collected during my interviews with the Ruke Dongba priests. It provides an overview of Ruke Dongba glyphs in processing daily vocabulary and the International Phonetic Alphabet. It compares these Dongba pictographs with the Geba script, the syllabic writing system of Naxi people, as well as the various types of scripts used by some other ethnic groups, which show a general tendency of Romanization in the generation of alphabetic writing systems and provides a pattern inherent in the historical evolution of writing. IPA symbols, a fundamental tool of field linguists, facilitate learners in grasping a language and nonalphabetic scripts. In addition to analyzing the Ruke Dongba pictographs attested in the secular manuscripts in the context of history of writing, this study contributes to the linguistic description of Ruke Dongba culture and the diversity of Dongba tradition.
Context Conditioning via Surrounding Predictions for Non-Recurrent CTC Models

Connectionist Temporal Classification (CTC) loss has become widely used in sequence modeling tasks such as Automatic Speech Recognition (ASR) and Handwritten Text Recognition (HTR) due to its ease of use. Recent sequence models that incorporate CTC loss have been focusing on speed by removing recurrent structures, hence losing important context information. This paper presents extensive studies of Contextualized Connectionist Temporal Classification (CCTC) framework, which induces prediction dependencies in non-recurrent and non-autoregressive neural networks for sequence modeling. CCTC allows the model to implicitly learn the language model by predicting neighboring labels via multi-task learning. Experiments on ASR and HTR tasks in two different languages show that CCTC models offer improvements over CTC models by 2.2-8.4% relative without incurring extra inference costs. We have also found that higher order of context information can potentially help the model produce better predictions.
Estimation of Abnormal Cell Growth and MCG-Based Discriminative Feature Analysis of Histopathological Breast Images

The accurate prediction of cancer from microscopic biopsy images has always been a major challenge for medical practitioners and pathologists who manually observe the shape and structure of the cells from tissues under a microscope. Mathematical modelling of cell proliferation helps to predict tumour sizes and optimizes the treatment procedure. This paper introduces a cell growth estimation function that uncovers the growth behaviour of benign and malignant cells. To analyse the cellular level information from tissue images, we propose a minimized cellular graph (MCG) development method. The method extracts cells and produces different features that are useful in classifying benign and malignant tissues. The method’s graphical features enable a precise and timely exploration of huge amounts of data and can help in making predictions and informed decisions. This paper introduces an algorithm for constructing a minimized cellular graph which reduces the computational complexity. A comparative study is performed based on the state-of-the-art classifiers, SVM, decision tree, random forest, nearest neighbor, LDA, Naive Bayes, and ANN. The experimental data are obtained from the BreakHis dataset, which contains 2480 benign and 5429 malignant histopathological images. The proposed technique achieves a 97.7% classification accuracy which is 7% higher than that of the other graph feature-based classification methods. A comparative study reveals a performance improvement for breast cancer classification compared to the state-of-the-art techniques.
Investigating the Prediction of Breast Cancer Diagnosis by Use of Support Vector Machines

This study examines the use of support vector machine (SVM) learning algorithms in predictive analytics models for the detection of breast cancer. This study uses the breast cancer Wisconsin dataset and evaluates the model's performance using measures including accuracy, F1-score, precision, and recall. Comparisons are made between the SVM model's performance and those of alternative classification techniques including logistic regression, decision trees, and random forests. The findings demonstrate the usefulness of utilising predictive analytics models, notably the SVM algorithm, for the detection of breast cancer. The SVM model demonstrated significant predictive effectiveness and accuracy, making it a viable choice of tool for clinicians in the early identification and diagnosis of breast cancer.