Reviewerly/exHarmony huggingface dataset:
A Parallel Processing CNN Accelerator on Embedded Devices Based on Optimized MobileNet

In the field of machine vision and pattern recognition, the convolutional neural network (CNN) is one of the hottest research topics. However, the application of CNN, which requires complicated operations, appears to be exceptionally difficult in resource-constrained embedded devices. In this article, a parallel processing CNN accelerator based on optimized MobileNet is presented. By modifying the fully connected layer of the MobileNet network topology with the convolution process, and postponing the global pooling layer, the model topology is unified, which is conducive to the design of the hardware accelerator. After using the 8-bit quantization strategy of network model parameters, the process of depthwise separable convolution is accelerated by parallel processing between channels and pipelined processing between layers. Thus, the processing speed and throughput of the accelerator can be improved. The designed accelerator classification performance on ImageNet achieved 580.6 frames per second (fps) on a ZYNQ AXZU5EV platform and a system power consumption of only 6.51 W. This result represents a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22.3\times $ </tex-math></inline-formula> speedup compared to CPU and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> speedup compared to graphics processing unit (GPU), while the design has a lower power consumption than CPU and GPU, providing a reference for the application of CNN in embedded devices.
A Random Access Scheme for Federated Learning Over Massive MIMO Systems

In this article, we present a random access (RA) scheme for federated learning (FL) over massive multiple-input–multiple-output (MIMO) systems to tackle the issue of some local devices not being able to compute their local models. This scheme adopts a multichannel model and allows devices to randomly select their uploading channels, and then the base station (BS) aggregates the local models received from channels directly based on the over-the-air computation. We call this scheme as RA-based FL over massive MIMO (RAFL-MIMO). Furthermore, to enable more devices to be involved in the FL process, we propose to utilize an access class barring (ACB) method to select the uploading devices and formulate an optimization problem of the ACB factor. We also derive the expected asymptotic convergence rate of the proposed RAFL-MIMO scheme to analytically show that the proposed RAFL-MIMO scheme can improve the performance of FL. Simulation results based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L2}$ </tex-math></inline-formula> -norm linear regression, and MNIST handwritten digits identification, Cifar-10 photograph classification show that the proposed RAFL-MIMO scheme significantly outperforms the case of the RAFL-MIMO without the ACB factor.
A Benchmarking of Commercial Small Fixed-Wing Electric UAVs and RGB Cameras for Photogrammetry Monitoring in Intertidal Multi-Regions

Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.
<i>T</i> 2 <i>-NER</i>: A <u>T</u>wo-Stage Span-Based Framework for Unified Named Entity Recognition with <u>T</u>emplates

Abstract Named Entity Recognition (NER) has so far evolved from the traditional flat NER to overlapped and discontinuous NER. They have mostly been solved separately, with only several exceptions that concurrently tackle three tasks with a single model. The current best-performing method formalizes the unified NER as word-word relation classification, which barely focuses on mention content learning and fails to detect entity mentions comprising a single word. In this paper, we propose a two-stage span-based framework with templates, namely, T2-NER, to resolve the unified NER task. The first stage is to extract entity spans, where flat and overlapped entities can be recognized. The second stage is to classify over all entity span pairs, where discontinuous entities can be recognized. Finally, multi-task learning is used to jointly train two stages. To improve the efficiency of span-based model, we design grouped templates and typed templates for two stages to realize batch computations. We also apply an adjacent packing strategy and a latter packing strategy to model discriminative boundary information and learn better span (pair) representation. Moreover, we introduce the syntax information to enhance our span representation. We perform extensive experiments on eight benchmark datasets for flat, overlapped, and discontinuous NER, where our model beats all the current competitive baselines, obtaining the best performance of unified NER.
VaR Estimation with Quantum Computing Noise Correction Using Neural Networks

In this paper, we present the development of a quantum computing method for calculating the value at risk (VaR) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the VaR of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the VaR in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1% in the empirical measurements with respect to the parametric model.
Atmosphere: Towards Practical Verified Kernels in Rust

Historically, development of formally-verified operating systems was a challenging, time-consuming undertaking that relied on a narrow formal verification expertise and required many person-years of effort. We argue, however, that the balance of practicality is finally changing with development of automated verification tools that leverage a unique combination of the linear type system of Rust and automated verification based on satisfiability modulo theories (SMT). Our work leverages, Verus, a new SMT-based verifier for Rust, for development of a minimal yet practical microkernel, Atmosphere. Atmosphere is designed as a full-featured microkernel conceptually similar to the line of early L4 microkernels. We develop all code in Rust and prove its functional correctness, i.e., refinement of a high-level specification with Verus. Our experience shows that Verus provides a collection of practical features that significantly lower the burden of a verification effort making it possible to reason about correctness of the low-level systems code, e.g., low-level memory and address space management, recursive data structures like linked lists and page tables, etc. On average our code has proof-to-code ratio of 7.5:1 which is significantly lower than in prior approaches.
The K2 Architecture for Trustworthy Hardware Security Modules

K2 is a new architecture and verification approach for hardware security modules (HSMs). The K2 architecture's rigid separation between I/O, storage, and computation over secret state enables modular proofs and allows for software development and verification independent of hardware development and verification while still providing correctness and security guarantees about the composed system. For a key step of verification, K2 introduces a new tool called Chroniton that automatically proves timing properties of software running on a particular hardware implementation, ensuring the lack of timing side channels at a cycle-accurate level.
Encapsulated Functions: Fortifying Rust's FFI in Embedded Systems

Memory-safe languages like Rust are increasingly popular for systems development. Nonetheless, practical systems must interact with code written in memory-unsafe languages. This is especially true in security and safety-critical embedded systems, where subsystems such as cryptographic implementations are subject to industrial and governmental certification requirements. Direct interactions with such libraries, however, expose memory-safe languages to significant risks: Any single bug in either the foreign code or the cross-language interactions may arbitrarily violate the memory safety of the wrapping language.
Leveraging Rust for Lightweight OS Correctness

This paper reports our experience of providing lightweight correctness guarantees to an open-source Rust-based OS, Theseus. First, we report new developments in intralingual design that leverage Rust's type system to enforce invariants at compile time, trusting the Rust compiler. Second, we develop a hybrid proof approach that combines formal verification, type checking, and informal reasoning. By lessening the strength of correctness guarantees, this hybrid approach substantially lowers the proof burden. We share our experience of applying this approach to the memory subsystem of Theseus, demonstrate its utility, and quantify its reduced proof effort.
Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes.
Siamese Visual Tracking with Spatial-Channel Attention and Ranking Head Network

Trackers based on the Siamese network have received much attention in recent years, owing to its remarkable performance, and the task of object tracking is to predict the location of the target in current frame. However, during the tracking process, distractors with similar appearances affect the judgment of the tracker and lead to tracking failure. In order to solve this problem, we propose a Siamese visual tracker with spatial-channel attention and a ranking head network. Firstly, we propose a Spatial Channel Attention Module, which fuses the features of the template and the search region by capturing both the spatial and the channel information simultaneously, allowing the tracker to recognize the target to be tracked from the background. Secondly, we design a ranking head network. By introducing joint ranking loss terms including classification ranking loss and confidence&amp;IoU ranking loss, classification and regression branches are linked to refine the tracking results. Through the mutual guidance between the classification confidence score and IoU, a better positioning regression box is selected to improve the performance of the tracker. To better demonstrate that our proposed method is effective, we test the proposed tracker on the OTB100, VOT2016, VOT2018, UAV123, and GOT-10k testing datasets. On OTB100, the precision and success rate of our tracker are 0.925 and 0.700, respectively. Considering accuracy and speed, our method, overall, achieves state-of-the-art performance.
Blind Super-Resolution Network with Dual-Channel Attention for Images Captured by Sub-Millimeter-Diameter Fiberscope

A blind super-resolution network with dual-channel attention is proposed for images captured by the 0.37 mm diameter sub-millimeter fiberscope. The fiberscope can used in scenarios where other image acquisition devices cannot be applied based on its flexible, soft, and minimally invasive characteristics. However, the images have black reticulated noise and only 3000 pixels. To improve image quality, the Butterworth band-stop filter is used to reduce the frequency of the reticulated noise. By optimizing the blind super-resolution model, high-quality images can be reconstructed that do not require a lot of synthetic paired fiberscope image data. Perceptual loss is utilized as a loss function, and channel and spatial attention mechanisms are introduced to the model to enhance the high-frequency detail information of the reconstructed image. In the comparative experiment with other methods, our method showed improvements of 2.25 in peak signal-to-noise ratio (PSNR) and 0.09 in structural similarity (SSIM) based on objective evaluation metrics. The learned perceptual image patch similarity (LPIPS) based on learning was reduced by 0.6. Furthermore, four different methods were used to enhance the resolution of the fiberscope images by a factor of four. The results of this paper improve the information entropy and Laplace clarity by 0.44 and 2.54, respectively, compared to the average of other methods. Validation results show that the approach in this paper is more applicable to sub-millimeter-diameter fiberscopes.
Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators

The Poisson equation is a fundamental equation of mathematical physics that describes the potential distribution in static fields. Solving the Poisson equation on a grid is computationally intensive and can be challenging for large grids. In recent years, quantum computing has emerged as a potential approach to solving the Poisson equation more efficiently. This article uses quantum algorithms, particularly the Harrow–Hassidim–Lloyd (HHL) algorithm, to solve the 2D Poisson equation. This algorithm can solve systems of equations faster than classical algorithms when the matrix A is sparse. The main idea is to use a quantum algorithm to transform the state vector encoding the solution of a system of equations into a superposition of states corresponding to the significant components of this solution. This superposition is measured to obtain the solution of the system of equations. The article also presents the materials and methods used to solve the Poisson equation using the HHL algorithm and provides a quantum circuit diagram. The results demonstrate the low error rate of the quantum algorithm when solving the Poisson equation.
Proposal for Mediative Fuzzy Control: From Type-1 to Type-3

This paper presents an initial proposal for the utilization of mediative fuzzy logic in control problems. Mediative fuzzy logic (MFL) was originally proposed with the idea of modeling situations in which there exists contradictory knowledge among several experts in an application domain. In this situation, a mediative solution may be a better choice in this particular decision-making situation. In this paper, we are extending the concept of fuzzy control to the realm of MFL for situations in which we have two or more control experts, and the design of the fuzzy controller has to be based on their knowledge. In this situation, we are taking advantage of the symmetrical nature of membership functions in reducing the complexity of designing the fuzzy controllers. The goal of this study was to improve control results by combining the knowledge of several experts, which MFL is aimed at executing. The initial architecture of mediative fuzzy control for type-1 fuzzy logic is presented, and an illustrative example is used to better comprehend the proposed approach. Later, we extend type-1 MFL to the realms of type-2 and type-3 fuzzy logic, and we also provide a comparative study that exhibits that the type-3 version surpasses the type-2 and type-1 versions of mediative fuzzy control. The idea of utilizing type-2 and type-3 is to improve the capabilities of the fuzzy controller in handling uncertainty coming from noise in the control process.
GLHAD: A Group Lasso-Based Hybrid Attack Detection and Localization Framework for Multistage Manufacturing Systems

Abstract As Industry 4.0 and digitization continue to advance, the reliance on information technology increases, making the world more vulnerable to cyberattacks, especially cyber-physical attacks that can manipulate physical systems and compromise sensor data integrity. Detecting cyberattacks in multistage manufacturing systems (MMS) is crucial due to the growing sophistication of attacks and the complexity of MMS. Attacks can propagate throughout the system, affecting subsequent stages and making detection more challenging than in single-stage systems. Localization is also critical due to the complex interactions in MMS. To address these challenges, a group lasso regression-based framework is proposed to detect and localize attacks in MMS. The proposed algorithm outperforms traditional hypothesis testing-based methods in expected detection delay and localization accuracy, as demonstrated in a simple linear multistage manufacturing system.
Classifying the Main Technology Clusters and Assignees of Home Automation Networks Using Patent Classifications

Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests.
Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods

While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-Newton methods that are able to construct Hessian approximations using only gradient information. In this work, we study the performance of stochastic quasi-Newton algorithms for training deep neural networks. We consider two well-known quasi-Newton updates, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the symmetric rank one (SR1). This study fills a gap concerning the real performance of both updates in the minibatch setting and analyzes whether more efficient training can be obtained when using the more robust BFGS update or the cheaper SR1 formula, which—allowing for indefinite Hessian approximations—can potentially help to better navigate the pathological saddle points present in the non-convex loss functions found in deep learning. We present and discuss the results of an extensive experimental study that includes many aspects affecting performance, like batch normalization, the network architecture, the limited memory parameter or the batch size. Our results show that stochastic quasi-Newton algorithms are efficient and, in some instances, able to outperform the well-known first-order Adam optimizer, run with the optimal combination of its numerous hyperparameters, and the stochastic second-order trust-region STORM algorithm.
Automatic programming (source code generator) based on an ontological model

Source AI is an AI-powered tool that can generate code in any programming language from any human language description. It can also simplify, find errors and fix them and debug your code. Automatic code generation capabilities continue to evolve within programming languages, IDEs and tools that work at compile time. This coding technique has proliferated because it can reduce mundane programming grunt work, and developers have found that it improves turnaround times and accuracy. Auto generated code usually becomes a hindrance for developers who want to tweak it later on Teams should plan to restrict these tools to only certain parts of the SDLC, such as where they can act as facilitators in smaller, less complex situations.
Generalized Net Model of a Collective Work of a DOBOT Magician Robot System with a Centralized Control

Applying the theory of Generalized Nets (GN), proven as successful tool for parallel processes modeling and analysis of complex systems, a GN model of the DOBOT Magician robot system with centralized control is developed. The GN model describes the collective work of five robots (DOBOT Magician serial manipulators) and two conveyor belts which joint operation is controlled by a centralized system. Presented model may be considered as a basis of a more complex GN model aiming to describe a collective work of a group of self-controlled system of robots.