Reviewerly/exHarmony huggingface dataset:
AI Enhancements for Linguistic E-Learning System

E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original system, the first enhancement is a disordered speech classification module; this module is driven by the MFCC-CNN model, which aims to distinguish disordered speech and nondisordered speech. The accuracy of the classification is about 83%. The second enhancement is a grapheme-to-phoneme converter. This converter is based on the transformer model and designed for teachers to better generate IPA words from the regular written text. Compared with other G2P models, our transformer-based G2P model provides outstanding PER and WER performance. The last part of this paper focuses on a Tacotron2-based IPA-to-speech synthesis system, this deep learning-based TTS system can help teacher generate high-quality speech sounds from IPA characters which significantly improve the functionality of our original system. All of these three enhancements are related to the phonetic transcription process. and this work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.
“Like the Growth Rings of a Tree”

Abstract This chapter situates the development of Okinawan musical culture in a socio-ecological systems (SES) framework. Its focus is the sanshin, a three-string lute likely introduced to the Ryūkyū archipelago in the late fourteenth century. As of 2022, the sanshin is among the best-recognized symbols of Ryūkyūan/Okinawan culture worldwide. From the viewpoint of some luthiers and musicians, however, sanshin culture is trapped in a “downward spiral” precipitated by the rise of offshore production and unsustainable tonewood sourcing. This chapter employs the concept of socio-ecological thresholds to track sanshin production networks through five historical inflection points to their present state of crisis. It then delves into local visions for the instrument’s future, focusing on the Kuruchi Island Network, an initiative to sustainably increase the population of Ryūkyūan ebony or kuruchi, a tonewood used to make sanshin necks. For the Kuruchi Island Network and its partners, kuruchi reforestation is part of an integrated strategy designed to push sanshin production into a more sustainable regime by cultivating positive feedback across the sociocultural, economic, and ecological domains. After identifying intersections between the Kuruchi Island Network’s strategy and the SES-based paradigm of adaptive co-management, the chapter proposes directions for future research on coupled musical and socio-ecological change.
Breast Cancer Prediction Based on Differential Privacy and Logistic Regression Optimization Model

In order to improve the classification effect of the logistic regression (LR) model for breast cancer prediction, a new hybrid feature selection method is proposed to process the data, using the Pearson correlation test and the iterative random forest algorithm based on out-of-bag estimation (RF-OOB) to screen the optimal 17 features as inputs to the model. Secondly, the LR is optimized using the batch gradient descent (BGD-LR) algorithm to train the loss function of the model to minimize the loss. In order to protect the privacy of breast cancer patients, a differential privacy protection technology is added to the BGD-LR model, and an LR optimization model based on differential privacy with batch gradient descent (BDP-LR) is constructed. Finally, experiments are carried out on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, accuracy, precision, recall, and F1-score are selected as the four main evaluation indicators. Moreover, the hyperparameters of each model are determined by the grid search method and the cross-validation method. The experimental results show that after hybrid feature selection, the optimal results of the four main evaluation indicators of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, in which the accuracy, recall, and F1-scores are increased by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget ε is taken as 0.8, the classification performance and privacy protection effect of the model reach an effective balance. At the same time, the four main evaluation indicators of the model are 0.9721, 0.9975, 0.9664, and 0.9816, which are improved by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. Comparative analysis shows that the models of BGD-LR and BDP-LR constructed in this paper perform better than other classification models.
Natural differential privacy—a perspective on protection guarantees

We introduce "Natural" differential privacy (NDP)-which utilizes features of existing hardware architecture to implement differentially private computations. We show that NDP both guarantees strong bounds on privacy loss and constitutes a practical exception to no-free-lunch theorems on privacy. We describe how NDP can be efficiently implemented and how it aligns with recognized privacy principles and frameworks. We discuss the importance of formal protection guarantees and the relationship between formal and substantive protections.
Becoming Anonymous

Abstract Although anonymity is a central feature of democratic participation—for example in the secret ballot—democratic theory has been surprisingly quiet on this matter. This chapter explores anonymity in voting, campaign funding, masked protest, pamphleteering, graffiti, and online debate. It generates the first concept of anonymity rooted in democratic theory. Anonymity rearranges democratic space by interrupting the established order of things through interfaces such as masks or computer screens. This spatial rearrangement generates a channel from private into public space. But anonymity does not always have democratic effects. Rather, anonymity affords three sets of contradictory freedoms: inclusion and exclusion, subversion and submission, honesty and deception. Anonymity is always liberating—freeing the subject to express its multiple self. This freedom is, however, used in both constructive and destructive ways. Anonymity appears as inherently ambivalent. The chapter explores the effects of anonymity in various empirical examples including the hacktivist collective Anonymous, the Ku Klux Klan, Pussy Riot, the US constitutional debate, and bathroom graffiti. Anonymity’s ambivalent effects are highly context dependent. They are shaped by the communicative infrastructure, the power relations between participants, and the configuration of identity knowledge.
Improved transfer learning using textural features conflation and dynamically fine-tuned layers

Transfer learning involves using previously learnt knowledge of a model task in addressing another task. However, this process works well when the tasks are closely related. It is, therefore, important to select data points that are closely relevant to the previous task and fine-tune the suitable pre-trained model's layers for effective transfer. This work utilises the least divergent textural features of the target datasets and pre-trained model's layers, minimising the lost knowledge during the transfer learning process. This study extends previous works on selecting data points with good textural features and dynamically selected layers using divergence measures by combining them into one model pipeline. Five pre-trained models are used: ResNet50, DenseNet169, InceptionV3, VGG16 and MobileNetV2 on nine datasets: CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Stanford Dogs, Caltech 256, ISIC 2016, ChestX-ray8 and MIT Indoor Scenes. Experimental results show that data points with lower textural feature divergence and layers with more positive weights give better accuracy than other data points and layers. The data points with lower divergence give an average improvement of 3.54% to 6.75%, while the layers improve by 2.42% to 13.04% for the CIFAR-100 dataset. Combining the two methods gives an extra accuracy improvement of 1.56%. This combined approach shows that data points with lower divergence from the source dataset samples can lead to a better adaptation for the target task. The results also demonstrate that selecting layers with more positive weights reduces instances of trial and error in selecting fine-tuning layers for pre-trained models.
Lattice-Based Homomorphic Encryption For Privacy-Preserving Smart Meter Data Analytics

Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.
Diagnosis of Thyroid Nodule Based on Multi-Scale Se-Segnet and Resnet50

Thyroid nodule is a common chronic endocrine system disease, which may deteriorate into thyroid cancer without treatment. However, due to the distribution of many blood vessels, nerves and organs near the thyroid, the clarity of ultrasonic image is poor, and human eye recognition has great limitations.We propose a method for thyroid nodule diagnosis based on multi-scale Se-SegNet network and ResNet50. We improved and optimized the SegNet as the main backbone, and constructed a multi-scale Se-SegNet network to segment thyroid ultrasonography. Then we used ResNet50 to classify the segmented images and get the diagnosis results. The accuracy of the multi-scale Se-SegNet image segmentation model is 0.959.The accuracy of classification results was 0.967. This paper proposes a new diagnostic scheme for thyroid nodule, which can diagnose thyroid nodule quickly and accurately. The experimental results show that it has higher diagnostic accuracy than the traditional scheme..
Blockchain transaction fee and Ethereum Merge

We study the determinants of transaction fees in the Bitcoin and Ethereum blockchains, particularly focusing on the recent Merge – Ethereum's transition from a Proof-of-Work to a Proof-of-Stake consensus mechanism. Using multivariate regression analysis, we find that blockchain fees in USD increase with network congestion, measured by the mempool count, in a non-linear manner as hypothesized by Huberman, Leshno, and Moallemi (2021). Additionally, the fees are positively influenced by the respective native currency's returns over the past six months. The Merge resulted in a fee reduction for both Bitcoin and Ethereum blockchains.
Constructing negative samples via entity prediction for multi-task knowledge representation learning

Knowledge representation learning (KRL) aims at embedding the entities and relations in knowledge graphs (KGs) into vectors by learning. In recent years, several multi-task learning (MTL) frameworks were proposed to KRL and achieved promising results. Nevertheless, most of these models only share the initial embeddings of entities and relations among sub-tasks. We construct hard negative samples for relation prediction (RP) task and triple classification (TC) task by using the similarity matrix from the entity prediction (EP) task. We also share the features in hidden layers of EP task to TC. The proposed framework is applicable to CNN based models for KRL. Experimental results on WN18RR and FB15k-237 datasets show that the proposed framework promotes the performance of EP, RP and TC tasks evidently comparing with the single task models and obtains better results in RP and TC than the state-of-the-art MTL-based models/frameworks. Code of NS-KRL is available online.1
Weakly supervised semantic segmentation via self-supervised destruction learning

Currently, weakly supervised semantic segmentation approaches adopt the Class Activation Map (CAM) to generate the initial attention maps from the standard classification backbone network, with only image-level class labels as training supervision. In this paper, we propose a novel “destruction learning” method via self-supervised manner, producing the CAM attention maps better covering the whole object rather than only the most discriminative regions as previous approaches. Region destruction mechanism is proposed to deliberately “destruct” the global structure in both Mid-Level and Low-Level feature learning following jigsaw puzzle operation, for better local feature extraction of the classification network. Specifically, a Mid-Level Distribution-and-Collection Module is proposed to firstly independently process local patches in parallel and then aggregate them, achieving enhanced local part sensitivity. For low-level “destruction learning”, a Low-Level Destruction Module is proposed to partition the original image into patches and shuffle them randomly to obtain a destructed image, which enforces the network to find weak information from the scattered discriminative small parts. The two destruction modules can be conveniently embedded into arbitrary backbone networks and trained in an end-to-end manner, without any extra annotations. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset (e.g., with 69.1% mIoU on the testing set), which surpasses existing state-of-the-art weakly supervised semantic segmentation methods.
Token-level disentanglement for unsupervised text style transfer

Text style transfer is the task of altering the style of a source text to a desired style while preserving the style-independent content. A common approach involves disentangling a given sentence into a style-agnostic content representation within the latent space and then generating the text in the desired style, guided by a separate style embedding. However, previous methods have a limitation in that they assume the input sentence is encoded by a fix-sized latent vector at the sentence level, making it challenging to capture rich semantic information at the token level, especially when dealing with longer texts. Consequently, this leads to suboptimal preservation of non-stylistic semantic content. In this paper, we address this challenge, and propose TED, a fine-grained model for disentangling content and style representation at the token level to enhance content preservation. Specifically, TED use tf–idfs to estimate the pivot tokens for different styles and incorporates multi-task and adversarial objectives to disentangle the content and style information of each token within the latent space. Experimental results on two popular text style transfer datasets show that our proposed model significantly outperforms state-of-the-art baselines, particularly in terms of content preservation. Moreover, the quantitative and qualitative experiments demonstrate the effectiveness of our model in achieving token-level disentanglement within the latent space.
A Bottleneck Network with Light Attention for Multimodal Clustering

Multimodal Clustering (MMC) aims to boost clustering performance by utilizing complementary information from multimodal data. However, the heterogeneous gap between various modalities and the interference of superfluous information in multimodal data makes it challenging to comprehend the complex dependencies between them. To address these issues, we present a Bottleneck Network with Light Attention for Multimodal Clustering (LABMC) to understand the dependencies between different modalities and explore complementary information while maximally eliminating superfluous information. Specifically, Light Attention (LA) is proposed as a concise multimodal interaction mechanism that introduces a convolutional high-level semantic layer to understand the common high-level semantics among different modalities and explores related complementary information to obtain a multimodal fusion representation. It can efficiently comprehend the latent dependencies between diverse modalities to bridge the heterogeneous gap. Subsequently, we construct a Superfluity Elimination (SE) function to erase superfluous information from the modalities. We divide the mutual information between the fusion representation and multimodal data into superfluous information and complementary information. Under the premise of preserving complementary information with an approximation, we utilize a variational optimization method to minimize the mutual information to safely remove superfluity. The combination of LA and SE naturally forms a bottleneck, which enables the extraction of complementary information while reducing superfluous information. Finally, we measure the quality of the modalities to dynamically assign the optimal weights to SE function, which significantly enhances the rationality of SE. Extensive experiment results on five real-world datasets show that LABMC outperforms the state-of-the-art MMC methods.
A self-training algorithm based on the two-stage data editing method with mass-based dissimilarity

A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self-training algorithms consider only the geometric distance between data while ignoring the data distribution when calculating the similarity between samples. In addition, misclassified samples can severely affect the performance of a self-training algorithm. To address the above two problems, this paper proposes a self-training algorithm based on data editing with mass-based dissimilarity (STDEMB). First, the mass matrix with the mass-based dissimilarity is obtained, and then the mass-based local density of each sample is determined based on its k nearest neighbors. Inspired by density peak clustering (DPC), this study designs a prototype tree based on the prototype concept. In addition, an efficient two-stage data editing algorithm is developed to edit misclassified samples and efficiently select high-confidence samples during the self-training process. The proposed STDEMB algorithm is verified by experiments using accuracy and F-score as evaluation metrics. The experimental results on 18 benchmark datasets demonstrate the effectiveness of the proposed STDEMB algorithm.
Exploiting spatial relationships for visual tracking

Transformer has been widely applied to visual tracking tasks, and the performance of object tracking keeps getting better since the attention mechanism excels in capturing long-range dependencies. However, conventional attention mechanisms can only capture feature dependencies from a single dimension, limiting their ability to fully leverage the spatial position information that is critical for accurate target localization. In this work, we propose a novel axial attention mechanism that is specifically designed for object tracking tasks that require precise target positioning. The axial attention mechanism utilizes both row attention and column attention, enabling it to capture global feature dependencies from both dimensions. Additionally, we propose a low-complexity yet highly effective feature fusion network that employs a customized dual attention mixing strategy. Our CRTrack outperforms all state-of-the-art trackers on GOT-10k, LaSOT, TrackingNet, and UAV123, demonstrating the superiority of the framework. Notably, the MACs and Params of our proposed feature fusion network are only 40% and 24% of those of the Transformer-based feature fusion network, respectively.
An improved binary grey wolf optimizer for constrained engineering design problems

Abstract An Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified ‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is .
Explaining URL Phishing Detection by Glass Box Models

Phishing is one of the successful cyberattack that targets unsuspecting users to make them divulge personal sensitive information. Uniform Resource Locator (URL) phishing particularly involves distributing address strings of phishing websites via various vectors like email, websites and phone SMSes. Feature engineering of these URLs reveals that extracting meaningful statistical information and training Machine Learning (ML)based models for identifying whether a URL is legitimate is an effective automated means of protecting users from these attacks. In this work, a case of Explainable AI (XAI) has been made to provide insights into how algorithms make predictions, enabling humans to understand and evaluate why the URL under question is not secure to visit. Various examples have been illustrated to visualize the explanations. The work can act as a basis for generating counterfactuals to minimize the unintentional composition of a URL as phishing.