Reviewerly/exHarmony huggingface dataset:
Variational quantum circuits for convolution and window-based image processing applications

Abstract Quantum information processing is gaining popularity in the fields of machine learning and image processing because of its advantages. Quantum convolution is an interesting topic in this field, and studies on this topic can be divided into value-based and angle-based methods. Although quantum convolution studies on angle-based or variational quantum circuits (VQCs) is called convolution, the circuits work differently from classical convolution. In this study, contrary to the literature, the VQC was trained to imitate classical convolution. The differential evolution algorithm (DEA) was used to optimize the VQCs. The proposed method requires as many qubits as the filter size ( N × N ). The generated circuits contain N × N × 4 quantum gates and N × N × 3 trainable parameters. The generated circuits were tested in Python environment using Cirq simulator. The Cifar10 and MNIST datasets are used as examples. For 2 × 2 filters with different weights, the convolution was successfully modeled with a mean squared error of less than 0.001. In general, the proposed method imitates classic convolution within <?CDATA $ \pm 5\% $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>±</mml:mo> <mml:mn>5</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> tolerance. In conclusion, VQCs that imitate classical convolution with fewer qubits and quantum gates than value-based methods were obtained.
Review on Applications of Knowledge Graph in Software Development

Knowledge graph (KG) can represent the domain knowledge in the structural entities and relations and have become a popular AI technique. Software knowledge provides a deep understanding of the software development process. In this paper, we firstly provide a brief review on the related techniques, construction methods and applications of KG in the software development process. Then, we detailedly review and discuss the specific applications of KG in the whole software development process, including the phases of software requirement analysis, software design, software testing, software vulnerability detection, software bug detection and software fault diagnosis, respectively. Moreover, this paper provides some unsolved problems about the KG’s applications in the field of spacecraft control software development to facilitate future research.
Cross-Attention Network for Cross-View Image Geo-Localization

The task of cross-view geo-location is to get a corresponding image from a dataset of Global Positioning System (GPS) labeled aerial-view images, given a ground-view query image with an unknown location. This task presents challenges due to the significant differences in viewpoint and appearance between the two types of images. To overcome these challenges, we have developed a novel attention-based method that leverages a key localization cue. The cross-attention-based Swap Encoder Module (SEM) is proposed, which effectively aligns features by directing the network’s focus towards relevant information. Additionally, we employ an Image Proposal Network (IPN) to ensure consistent inputs of both aerial and ground-view images that correspond, during both training and validation phases. Experimental results show that our proposed network significantly outperforms existing benchmarking CVUSA dataset, with significant improvements for top-1 recall from 61.4% to 71.45%, and for top-10 from 90.49% to 92.30%.
Grouped Grey Wolf Optimizer with differential evolution for global Optimizer and test scheduling for Support vector regression

This paper presents a new approach to optimization, called the grouped grey wolf optimizer (GWO) with differential evolution. The proposed structure divides the grey wolves into two groups: the cooperative hunting and the random scout group. The hunting group is responsible for exploring unfamiliar environments and exploiting deeper search spaces, while the scout group conducts a global search using the differential evolution algorithm to prevent the optimizer from getting stuck in local optima. This approach strikes a balance between exploration and exploitation, resulting in improved convergence. There are three case studies that demonstrate a better global convergence.
A novel secure and distributed architecture for privacy-preserving healthcare system

Patients often visit several hospitals to obtain medication, generating a significant volume of data. Moreover, hospitals use different data analytic techniques to improve healthcare services, leading to patient data privacy. However, no integrated architecture has a trustworthy data repository and secure communication protocols to store and share the data with various parties (e.g., hospitals and data analysts) for different healthcare services. This study proposes an innovative three-tier secure and distributed privacy-preserving healthcare architecture that addresses the aforementioned challenges. The first tier introduces a trustworthy data repository called custodian, where the data owner stores encrypted data and offer real-time privacy-preserving health monitoring service. The second tier provides Elliptic Curve Cryptography-based authentication for secure data exchange between the data owner and the hospital. The third tier utilizes smart contracts and local differential privacy (LDP) for secure machine learning model training. All transactions are recorded on the blockchain and managed by a smart contract. Security analysis shows that the proposed framework ensures privacy, security, and data integrity. Performance analysis is done based on score metrics, scalability metrics, and formal analysis. A transaction takes 0.00121 s in the first tier, and in the second tier, it takes 0.1267 s. The third tier uses ɛ−LDP with a privacy budget of ɛ = 3 on Random Forest and achieves 97%, 83%, and 74% accuracy on breast cancer, heart disease, and diabetic datasets. This accuracy is higher than the previous state-of-the-art methods. Moreover, the proposed healthcare framework ensures privacy, security and outperforms the previous state-of-the-art methods.
ElixirST: A session-based type system for Elixir modules

This paper investigates the adaptation of session types to provide behavioural information about Elixir modules. We devise a type system, called ElixirST, which statically determines whether functions in an Elixir module observe their endpoint specifications, expressed as session types; a corresponding tool automating this typechecking has also been constructed. In this paper we also formally validate this type system. An LTS-based operational semantics for the language fragment supported by the type system is developed, modelling its runtime behaviour when interacting with the module client. This operational semantics is then used to prove a form of session fidelity and progress for ElixirST.
Adaptive Shape Prediction Model (ASPM) for touched and overlapping cell segmentation in cytology images

Despite the recent development in segmentation of cytological images, designing automatic and accurate segmentation model for touched and overlapping cells is still challenging owing to the poor image contrast with unclear cell contours. In this paper, we introduce a contour and shape-aware segmentation model to establish the shape of touched and overlapping cells; named Adaptive Shape Prediction Model (ASPM). This model is fast, simple, adaptable, and it can be easily generalized for all kinds of cytological images as it does not rely on texture or specific shape knowledge. An open-source Matlab implementation of ASPM is available.
Customer Experience Analysis Skincare Products Through Social Media Data Using Topic Modeling and Sentiment Analysis

Currently, skin care products (skincare) are popular among the public. Both men and women are interested in buying skin care products. Moreover, there are many brands of skin care products that are divided into several types of facial and body care, such as moisturizers, toners, cleansers, and masks. Therefore, many consumers take the time to find information, for example, in terms of price, quality, and brand for decision-making. A lot of useful information is in the form of Twitter messages known as tweets which are sent from people who use skin care products because Twitter is one of the online social media where users can share their opinions and experiences. However, consumers still have to spend a lot of time searching, reading, and understanding the comprehensive collection of tweets before buying skin care products.The purpose of this study is to analyze customer experience, analyzing automated tweets about skin care products. Tweets about skin care products will be subjected to a topic modeling process to find out what topics are being discussed. In addition, the topics that have been obtained will be subject to sentiment analysis in the form of positive and negative messages for skin care products. Consumers who are app users don’t waste time reading and analyzing large amounts of data manually and they can decide to buy skin care products more easily.The results of this study obtained 14 topics of discussion related to skincare. Meanwhile, the sentiment analysis results of 14 topics resulted in more positive sentiment class tweets overall. It related the category topic that has the number of tweets to the importance of skincare. In addition, categories related to ingredients for skincare products from nature, namely fruits and spices, are the topics that have the second highest number of tweets. The results of the analysis of tweets related to user experience on Twitter, it was found that users prefer skincare products that use ingredients from nature.
Small, Multilingual, Explainable Transformers for Online Handwriting Decoding

A compact Transformer architecture is shown to provide an effective end-to-end framework for sentence recognition based on online handwritten gestures representing glyph strokes. Despite its reduced size, the encoder exhibits impressive transfer learning capabilities, producing latent representations that can generalise to various target grammars and output vocabularies, while also accurately performing spatio-temporal segmentation tasks. Novel visualization techniques are introduced, generalizable to other architectures and tasks, providing valuable insights and qualitative assessments of the model’s learning. The model is suitable for real-time edge inference and achieves an average Levenshtein accuracy over 96% on all tested languages, even when the encoder is imported frozen from the English language.
On Generating Synthetic Histopathology Images Using Generative Adversarial Networks

Interest has been growing over the past number of years in the application of Artificial Intelligence (AI) to aid in cancer diagnosis. However, the requirement for extensive training data to train these AI models presents a significant challenge. This paper proposes a viable solution to this issue by synthesising images to augment the real data in the training dataset. We investigate the potential of Generative Adversarial Networks (GANs) in generating high-quality tissue images for both colon and prostate. The datasets we use are from the TCGA repository and contain various cancerous diseases for both colon and prostate. We outline how the data was pre-processed, and quality controlled and we also delineate the relevant experiments applied. We apply some of the most recent GAN architectures in ProGAN and StyleGAN to generate synthetic colon tissue images, achieving a literature-high Fréchet Inception Score (FID) score of 6.019 for colon tissue images and an FID score of 3.03 for prostate tissue images. Notably, the synthetic images produced were deemed indistinguishable from real samples when assessed by a practicing consultant histopathologist. By addressing the challenge of limited training data, our approach could ultimately contribute to earlier detection and improved treatment outcomes for cancer patients worldwide.
The value of blockchain technology in supply chains with capital constraints: from the perspective of demand volatility

Abstract Blockchain technology (BCT) has the potential to revolutionize the supply chain financing (SCF) system and mitigate the detrimental effects of demand volatility on SCF. As one of the first to examine the effects of BCT implementation in SCF from the perspective of demand volatility, we focus on the impacts of BCT on equilibrium and whether the retailer has the incentive to adopt BCT. We perform a comparative analysis of the Stackelberg game involving three parties under scenarios with and without BCT adoption. We find that when the demand volatility reduction coefficient is sufficiently large, BCT can facilitate a reduction of the ordering quantity while still sustaining the same service level, thus avoiding excessive inventory, and reducing the associated risk of bankruptcy. Additionally, the decreased bankruptcy risk enabled the bank to reduce interest rates. The adoption of BCT can improve the profit of the retailer when the adoption cost of BCT is sufficiently low. Our findings provide management insights into when the small and medium‐sized enterprises should adopt BCT and the interactive decision‐making of all parties in the SCF business after BCT adoption.
Joint Speech-Text Embeddings with Disentangled Speaker Features

This paper presents a novel model architecture for speech processing that takes advantage of a joint speech-text embedding space and disentangled speaker features. Here unsupervised representation learning extracts latent features from the input without labels which results in task-agnostic, but information-entangled embeddings. On the other hand, a unified embedding space of speech and text aims to leverage acoustics and semantic knowledge from the two modalities, respectively. The model was trained on 4 speakers from the CMU Arctic dataset and evaluated on three downstream tasks: speaker recognition, automatic speech recognition (ASR), and text-to-speech (TTS). Results show 96.87% speaker classification accuracy, 11.57% Word Error Rate, and 8.91 Mel Cepstral Distortion mean on the evaluation set.
A Novel Image Blurring Detection Scheme using Spatial Autocorrelation

Image blurring prediction has a significant role in image restoration, forensics, and computer vision. Because of both the sharpened blurred image and the original image enlarge the image noise differently. To discern this difference, an autocorrelation measurement is suggested. This paper proposes a non-reference image blurring detection scheme based on the specificity of Moran statistics and UM (Unsharp Masking). Unsharp Masking is sensitive to image noise and extends noise in a sharpening process. The Moran’s Z histogram has been successfully used to discern blurriness and sharpness in an image. The blurred image can be distinguished from images using an examination method that uses the Moran’s Z score histogram on a UM pre-processed image. The Z histogram median values for blurred images are all higher than those from the original image, having a value of approximately 9. Based on these effects, image blur judgment factors are constructed. The features found in this work based on Moran statistics can be in the future fed into a support vector machine (SVM) classifier for classification.
Improving Phoneme Recognition with Augmented Autoregressive Predictive Coding

Self-supervised learning (SSL) has improved the performance in various speech processing downstream tasks. Pretraining speech representations with SSL models requires large datasets which can be hard to collect for low resource language scenarios. To address this problem we propose to use audio augmentation to pre-train the SSL model with a smaller dataset but augmented. We illustrate our technique using autoregressive predictive coding (APC) by using augmentation effects directly at an audio level in order to generate synthetic training data. As a result, we aim to increase robustness in trained SSL model. We apply two kinds of audio augmentation, speed perturbation and speed perturbation with pitch change. We then evaluate our pre-trained APC model on a downstream phoneme recognition task. From our results and analysis, we conclude that applying augmentation in a low resource configurations can improve the robustness in the pre-trained model resulting in improved phoneme classification accuracy for a downstream task.
Automated Industry Classification of Chinese Enterprises Based on Business Scope Texts Using Deep Learning Methods

The rapid development of China’s economy has led to a significant increase in the number of enterprises. Industry classification of enterprises based on their business scope text can help analyse industry development and enable efficient management of enterprise information. In this paper, we employ deep learning methods to classify Chinese enterprises by industry using business scope texts. To automate the classification process, we first pre-process business scope texts data based on their characteristics. We then construct several deep learning models including TextCNN, TextRCNN, TextRNN_Att and fastText to classify Chinese enterprises based on their business scope texts. The TextCNN model achieved the best text classification performance with an accuracy of 95.67%. Our experimental results demonstrate that the proposed approach is effective in classifying Chinese enterprises by industry based on their business scope texts.
Privacy-Preserving Collaborative AI for Distributed Deep Learning with Cross-sectional Data

The article discusses the challenges of using deep learning in healthcare due to the lack of extensive medical datasets and concerns about confidentiality and privacy. The article then focuses on the analysis of skin lesion images, which are complex and difficult to classify. The article presents a unique CNN model (skin-net) using a progressively private federated learning architecture to potentially address these issues. Recent advancements in medical image analysis using convolutional neural networks and federated learning techniques have shown promising results (96.19 accuracy, 96.37 sensitivity and 96.53 specificity) in accurately classifying skin diseases while maintaining data privacy. Using these methods offers a potentially effective way to examine human skin while improving the model’s generalization performance through image augmentation.
Cross-Modal Prediction of Superclasses Using Cortex-Inspired Neural Architecture

The concept of stimulus feature tuning is fundamental to neuroscience. Cortical neurons acquire their feature-tuning properties by learning from experience and using proxy signs of tentative features’ potential usefulness that come from the spatial and/or temporal context in which these features occur. According to this idea, local but ultimately behaviorally useful features should be the ones that are predictably related to other such features either preceding them in time or taking place side-by-side with them. Inspired by this idea, in this paper, deep neural networks are combined with Canonical Correlation Analysis (CCA) for feature extraction and the power of the features is demonstrated using unsupervised cross-modal prediction tasks. CCA is a multi-view feature extraction method that finds correlated features across multiple datasets (usually referred to as views or modalities). CCA finds linear transformations of each view such that the extracted principal components, or features, have a maximal mutual correlation. CCA is a linear method, and the features are computed by a weighted sum of each view’s variables. Once the weights are learned, CCA can be applied to new examples and used for cross-modal prediction by inferring the target-view features of an example from its given variables in a source (query) view. To test the proposed method, it was applied to the unstructured CIFAR-100 dataset of 60,000 images categorized into 100 classes, which are further grouped into 20 superclasses and used to demonstrate the mining of image-tag correlations. CCA was performed on the outputs of three pre-trained CNNs: AlexNet, ResNet, and VGG. Taking advantage of the mutually correlated features extracted with CCA, a search for nearest neighbors was performed in the canonical subspace common to both the query and the target views to retrieve the most matching examples in the target view, which successfully predicted the superclass membership of the tested views without any supervised training.
Breast image classification based on ResNet and Random forest multilayer classifier model

In this paper,a more optimal breast cancer detection model based on ResNet and random forest models is developed based on the combination of diagnostic methods of breast cancer pathology slice images and breast cancer detection studies. The proposed model is based on ResNet to classify breast MRI images, and random forest to select some samples and their features to compensate for the overfitting of ResNet in small sample data, and random forest to compensate for the overfitting of ResNet in small sample data.The random forest compensates the disadvantage that ResNet generates a lot of redundancy when the sample features are of high dimension.After the two models complement each other,the efficiency of image processing and the accuracy of image classification of the model are improved.Finally,a statistical analysis of the test results of the breast cancer detection model in this paper concluded that this model is more accurate and efficient in detecting classified breast cancer than a single machine learning model.
Generative Adversarial Networks for Down Syndrome Face Generation

This paper presents a method for generating realistic images of individuals with Down Syndrome using the state-of-the-art Generative Adversarial Network (GAN) architecture StyleGAN2. The goal of this work is to create a set of images that can be used to evaluate the performance of image recognition algorithms on individuals with Down Syndrome. To achieve this goal, we collected a dataset of real-world images of individuals with Down syndrome manually from the internet. The dataset was then preprocessed and used to train and evaluate our GAN model. We experimented with different configurations of StyleGAN2. We trained and evaluated the GAN using different dataset sizes, ranging from 400 to 1000 images, to investigate the effect of dataset size on the performance of the model. The results indicate that using a larger dataset size improves the overall quality and diversity of the generated images. Our experiments with StyleGAN2 demonstrated the ability of the model to generate high-quality, diverse images that can be used to evaluate the performance of image recognition algorithms on individuals with Down Syndrome.
Prediction of Malaria Plasmodium Stage and Type Through Object Detection

The use of image processing, artificial intelligence in general, and objects detection in particular for the diagnosis of malaria is increasingly remarkable. In the present work, we suggested a comparison of two objects detection models which is not only capable of detecting the infected blood smears cells but also it enables to distinguish the plasmodium different species and the parasitic stage of malaria. To create our two models, transfer learning from the Faster-RCNN and YOLO models have been used with the MP-IDB database (Malaria Parasite Image Database for Image Processing and Analysis). Then, we have followed three main steps to develop our approach of objects detection for malaria: the creation of annotated databases, image preprocessing, and fine-tuning the two pre-trained models on our annotated database. The Faster R-CNN-based model produced better results than the Yolo-based one, with a mAP @.50IOU of 0.76 versus 0.3.