Reviewerly/exHarmony huggingface dataset:
Correct orchestration of Federated Learning generic algorithms: formalisation and verification in CSP

Federated learning (FL) is a machine learning setting where clients keep the training data decentralised and collaboratively train a model either under the coordination of a central server (centralised FL) or in a peer-to-peer network (decentralised FL). Correct orchestration is one of the main challenges. In this paper, we formally verify the correctness of two generic FL algorithms, a centralised and a decentralised one, using the CSP process calculus and the PAT model checker. The CSP models consist of CSP processes corresponding to generic FL algorithm instances. PAT automatically proves the correctness of the two generic FL algorithms by proving their deadlock freeness (safety property) and successful termination (liveness property). The CSP models are constructed bottom-up by hand as a faithful representation of the real Python code and is automatically checked top-down by PAT.
An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.
Quantum squeezing cannot beat the standard quantum limit

Quantum entanglement between particles is expected to allow one to perform tasks that would otherwise be impossible. In quantum sensing and metrology, entanglement is often claimed to enable a precision that cannot be attained with the same number of particles and time, forgoing entanglement. Two distinct approaches exist: creation of entangled states that either i) respond quicker to the signal, or ii) are associated with lower noise and uncertainty. The second class of states are generally called squeezed states. Here we show that if our definition of success is -- a precision that is impossible to achieve using the same resources but without entanglement -- then the second approach cannot succeed. In doing so we show that a single non-separable squeezed state provides fundamentally no better precision, per unit time, than a single particle.
Cross Architecture Distillation for Face Recognition

Transformers have emerged as the superior choice for face recognition tasks, but their insufficient platform acceleration hinders their application on mobile devices. In contrast, Convolutional Neural Networks (CNNs) capitalize on hardware-compatible acceleration libraries. Consequently, it has become indispensable to preserve the distillation efficacy when transferring knowledge from a Transformer-based teacher model to a CNN-based student model, known as Cross-Architecture Knowledge Distillation (CAKD). Despite its potential, the deployment of CAKD in face recognition encounters two challenges: 1) the teacher and student share disparate spatial information for each pixel, obstructing the alignment of feature space, and 2) the teacher network is not trained in the role of a teacher, lacking proficiency in handling distillation-specific knowledge. To surmount these two constraints, 1) we first introduce a Unified Receptive Fields Mapping module (URFM) that maps pixel features of the teacher and student into local features with unified receptive fields, thereby synchronizing the pixel-wise spatial information of teacher and student. Subsequently, 2) we develop an Adaptable Prompting Teacher network (APT) that integrates prompts into the teacher, enabling it to manage distillation-specific knowledge while preserving the model's discriminative capacity. Extensive experiments on popular face benchmarks and two large-scale verification sets demonstrate the superiority of our method.
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling

Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing mechanisms among multiple entities (e.g., hospitals) can accelerate model training and yield more accurate predictions. Recently, approaches such as Federated Learning (FL) and Split Learning (SL) have facilitated collaboration without the need to exchange private data. In this work, we propose a framework for medical imaging classification tasks called Federated Split learning of Vision transformer with Block Sampling (FeSViBS). The FeSViBS framework builds upon the existing federated split vision transformer and introduces a block sampling module, which leverages intermediate features extracted by the Vision Transformer (ViT) at the server. This is achieved by sampling features (patch tokens) from an intermediate transformer block and distilling their information content into a pseudo class token before passing them back to the client. These pseudo class tokens serve as an effective feature augmentation strategy and enhances the generalizability of the learned model. We demonstrate the utility of our proposed method compared to other SL and FL approaches on three publicly available medical imaging datasets: HAM1000, BloodMNIST, and Fed-ISIC2019, under both IID and non-IID settings. Code: https://github.com/faresmalik/FeSViBS
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models

Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis. In this paper, we present DiffSketcher, an innovative algorithm that creates \textit{vectorized} free-hand sketches using natural language input. DiffSketcher is developed based on a pre-trained text-to-image diffusion model. It performs the task by directly optimizing a set of B\'ezier curves with an extended version of the score distillation sampling (SDS) loss, which allows us to use a raster-level diffusion model as a prior for optimizing a parametric vectorized sketch generator. Furthermore, we explore attention maps embedded in the diffusion model for effective stroke initialization to speed up the generation process. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual details of the subject drawn. Our experiments show that DiffSketcher achieves greater quality than prior work. The code and demo of DiffSketcher can be found at https://ximinng.github.io/DiffSketcher-project/.
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression

The accurate predictions and principled uncertainty measures provided by GP regression incur O(n^3) cost which is prohibitive for modern-day large-scale applications. This has motivated extensive work on computationally efficient approximations. We introduce a new perspective by exploring robustness properties and limiting behaviour of GP nearest-neighbour (GPnn) prediction. We demonstrate through theory and simulation that as the data-size n increases, accuracy of estimated parameters and GP model assumptions become increasingly irrelevant to GPnn predictive accuracy. Consequently, it is sufficient to spend small amounts of work on parameter estimation in order to achieve high MSE accuracy, even in the presence of gross misspecification. In contrast, as n tends to infinity, uncertainty calibration and NLL are shown to remain sensitive to just one parameter, the additive noise-variance; but we show that this source of inaccuracy can be corrected for, thereby achieving both well-calibrated uncertainty measures and accurate predictions at remarkably low computational cost. We exhibit a very simple GPnn regression algorithm with stand-out performance compared to other state-of-the-art GP approximations as measured on large UCI datasets. It operates at a small fraction of those other methods' training costs, for example on a basic laptop taking about 30 seconds to train on a dataset of size n = 1.6 x 10^6.
Parameter-Level Soft-Masking for Continual Learning

Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i.e., as more tasks are learned, the performance deteriorates. The goal of this paper is threefold: (1) overcoming CF, (2) encouraging KT, and (3) tackling the capacity problem. A novel technique (called SPG) is proposed that soft-masks (partially blocks) parameter updating in training based on the importance of each parameter to old tasks. Each task still uses the full network, i.e., no monopoly of any part of the network by any task, which enables maximum KT and reduction in capacity usage. To our knowledge, this is the first work that soft-masks a model at the parameter-level for continual learning. Extensive experiments demonstrate the effectiveness of SPG in achieving all three objectives. More notably, it attains significant transfer of knowledge not only among similar tasks (with shared knowledge) but also among dissimilar tasks (with little shared knowledge) while mitigating CF.
Performance Analysis and Evaluation of Post Quantum Secure Blockchained Federated Learning

Post-quantum security is critical in the quantum era. Quantum computers, along with quantum algorithms, make the standard cryptography based on RSA or ECDSA over FL or Blockchain vulnerable. The implementation of post-quantum cryptography (PQC) over such systems is poorly understood as PQC is still in its standardization phase. In this work, we propose a hybrid approach to employ PQC over blockchain-based FL (BFL), where we combine a stateless signature scheme like Dilithium (or Falcon) with a stateful hash-based signature scheme like the extended Merkle Signature Scheme (XMSS). We propose a linear-based formulaic approach to device role selection mechanisms based on multiple factors to address the performance aspect. Our holistic approach of utilizing a verifiable random function (VRF) to assist in the blockchain consensus mechanism shows the practicality of the proposed approaches. The proposed method and extensive experimental results contribute to enhancing the security and performance aspects of BFL systems.
Restart Sampling for Improving Generative Processes

Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plateau in performance while SDE-based samplers deliver higher sample quality at the cost of increased sampling time. We attribute this difference to sampling errors: ODE-samplers involve smaller discretization errors while stochasticity in SDE contracts accumulated errors. Based on these findings, we propose a novel sampling algorithm called Restart in order to better balance discretization errors and contraction. The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE. Empirically, Restart sampler surpasses previous SDE and ODE samplers in both speed and accuracy. Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$. In addition, it attains significantly better sample quality than ODE samplers within comparable sampling times. Moreover, Restart better balances text-image alignment/visual quality versus diversity than previous samplers in the large-scale text-to-image Stable Diffusion model pre-trained on LAION $512 \times 512$. Code is available at https://github.com/Newbeeer/diffusion_restart_sampling
A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images

White blood cells, also known as leukocytes are group of heterogeneously nucleated cells which act as salient immune system cells. These are originated in the bone marrow and are found in blood, plasma, and lymph tissues. Leukocytes kill the bacteria, virus and other kind of pathogens which invade human body through phagocytosis that in turn results immunity. Detection of a white blood cell count can reveal camouflaged infections and warn doctors about chronic medical conditions such as autoimmune diseases, immune deficiencies, and blood disorders. Segmentation plays an important role in identification of white blood cells (WBC) from microscopic image analysis. The goal of segmentation in a microscopic image is to divide the image into different distinct regions. In our paper, we tried to propose a novel instance segmentation method for segmenting the WBCs containing both the nucleus and the cytoplasm, from bone marrow images.
Distributive Pre-Training of Generative Modeling Using Matrix-Product States

Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models are gradient descent methods. In this work, we consider an alternative training scheme utilizing basic tensor network operations, e.g., summation and compression. The training algorithm is based on compressing the superposition state constructed from all the training data in product state representation. The algorithm could be parallelized easily and only iterates through the dataset once. Hence, it serves as a pre-training algorithm. We benchmark the algorithm on the MNIST dataset and show reasonable results for generating new images and classification tasks. Furthermore, we provide an interpretation of the algorithm as a compressed quantum kernel density estimation for the probability amplitude of input data.
Evolutionary approaches to explainable machine learning

Machine learning models are increasingly being used in critical sectors, but their black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) or explainable machine learning (XML) has emerged in response to the need for human understanding of these models. Evolutionary computing, as a family of powerful optimization and learning tools, has significant potential to contribute to XAI/XML. In this chapter, we provide a brief introduction to XAI/XML and review various techniques in current use for explaining machine learning models. We then focus on how evolutionary computing can be used in XAI/XML, and review some approaches which incorporate EC techniques. We also discuss some open challenges in XAI/XML and opportunities for future research in this field using EC. Our aim is to demonstrate that evolutionary computing is well-suited for addressing current problems in explainability, and to encourage further exploration of these methods to contribute to the development of more transparent, trustworthy and accountable machine learning models.
Data-driven plasma modelling: Surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders

We have developed a deep generative model that can produce accurate optical emission spectra and colour images of an ICP plasma using only the applied coil power, electrode power, pressure and gas flows as inputs -- essentially an empirical surrogate collisional radiative model. An autoencoder was trained on a dataset of 812,500 image/spectra pairs in argon, oxygen, Ar/O\textsubscript{2}, CF\textsubscript{4}/O\textsubscript{2} and SF\textsubscript{6}/O\textsubscript{2} plasmas in an industrial plasma etch tool, taken across the entire operating space of the tool. The autoencoder learns to encode the input data into a compressed latent representation and then decode it back to a reconstruction of the data. We learn to map the plasma tool's inputs to the latent space and use the decoder to create a generative model. The model is very fast, taking just over 10 s to generate 10,000 measurements on a single GPU. This type of model can become a building block for a wide range of experiments and simulations. To aid this, we have released the underlying dataset of 812,500 image/spectra pairs used to train the model, the trained models and the model code for the community to accelerate the development and use of this exciting area of deep learning. Anyone can try the model, for free, on Google Colab.
Impact of Female Menstrual Cycle on Voice Production with Special Reference to Wavelet Transform

The main objective of this review is to discuss the impact of female hormonal changes on voice production throughout the whole life cycle of female starting from the puberty continued in reproductive year and then declined at menopause.For this purpose we analyse the voice signals by using the very effective and robust method of wavelet analysis called wavelet transform.
Sistem E-Learning Berbasis Web Pada SMA Negeri 1 Merangin

Teaching and learning activities at SMAN 1 Merangin have several problems, namely the teaching and learning process in the classroom is lacking due to time constraints and also the media used to deliver the subject matter. Submission of subject matter that is less understood by all students. For example, when the teacher has to deliver material to students, it makes it difficult for students to get the subject matter in full and also students have difficulty getting subject matter when students are not in class and also communication between teachers and students is difficult to realize when outside the classroom due to lack of facilities. interaction and communication between teachers and students. Based on these problems, the authors propose an e-learning system design as a solution to the problems faced. This study uses a tool that is UML in system modeling. The result of this research is the design of an e-learning system prototype that can be implemented in the form of an application at SMAN 1 Merangin as a solution to existing problems.&#x0D;
Sistem Informasi Pendaftaran Peserta KB Berbasis Web Pada Kantor Dinas Pengendalian Penduduk Dan Keluarga Berencana Kabupaten Muaro Jambi

Family planning is one of the efforts to realize family happiness and prosperity. The main goal of family planning is to further improve the well-being of mothers and children. By arranging the birth of a wife or pregnant woman so that many have the opportunity to pay attention and educate children in addition to having sufficient time to carry out activities as housewives. Children will get enough attention from parents which later can have a positive impact on the growth and development of the child's personality. Usually the registration of family planning participants is still recorded on the registration sheet so that in an effort to process the registration data for family planning participants it still takes a relatively long time. The design of a web-based family planning participant registration information system using a prototype method creates a more effective and efficient performance for people who want to register for the installation of contraceptives.
Folding Fan Cropping and Splicing (FFCS) Data Augmentation

Convolutional neural networks (CNNs) are widely used to tackling complex tasks, which are prone to overfitting if the datasets are noisy. Therefore, we propose folding fan cropping and splicing (FFCS) regularization strategy to enhance representation abilities of CNNs. In particular, we propose two different methods considering the effect of different segmentation numbers on classification results. One is the random folding fan method, and the other is the fixed folding fan method. Experimental results showed that FFCS reduced the classification errors both with the value of 0.88% on CIFAR-10 dataset and 1.86% on ImageNet dataset. Moreover, FFCS consistently outperformed Mixup and Random Erasing approaches on classification tasks. Therefore, FFCS effectively prevents overfitting and reduces the impact of background noises on classification tasks.
Retarding Basin Design As Flood Countermeasures at Cicendo Bandung City

The current solution Bandung City Government do to avoiding flood was building the retarding basin. The purpose of this research was to plan the retention basin’s discharge, volume and area. This research was conducted on Bima Street, Cicendo which is drained by Citepus River. The research data includes rainfall data from five rain posts, maps and existing river conditions. The analysis contains catchment area made from ArcGis program which result are 40 km2. Hydrological analysis produces the planned rain discharge for 100 years return period which is 20,22658 m3/s. Hydraulics analysis which is finding the discharge river existing. Basin discharge acquired from differences between rain discharge and river discharge then plan for inlet and outlet doors. Total capacity of retarding basin was 4360,5 m3. Inlet and outlet doors are planned each two doors with 2 meters width and 1 meter height.
Wounds Care on Patients During The Covid-19 Pandemic Era

Introduction: Wound care is part of nursing management. During Covid-19, the practice of wound care decreased under normal conditions. Nursing management knowledge is needed on how to practice wound care during COVID-19 to provide a wound healing process. This systematic review aims to identify wound care nursing care management during COVID-19. Methods: Article searched from PubMed; ProQuest; and EBSCO; selected based on the following inclusion criteria: study focus on nurses practicing chronic wound care during Covid-19, focused nursing management, case studies, articles published in the 2019-2021 range, full text, articles in English. Results: In the final stage, 5 articles were found. The results of the study show that nursing care management uses telehealth and telemedicine services for wound care patients. Conclusion: The findings of this systematic review indicate that telehealth and telemedicine services can provide opportunities to improve wound care to patients.