Reviewerly/exHarmony huggingface dataset:
A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces.

Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs.
Fingerprint Based Voting System

Abstract-We are all familiar with the current electronic voting machines where the user has to press the voting button. But the machines were criticized from the start. This article explains how to build a biometric based voting machine using a fingerprint sensor R307 and Arduino Uno. In this system, a person has to register their fingerprints matchings to the system database which will be stored centrally in Arduino. Voters must put their finger on the fingerprint sensor, and if the finger matches the previously stored information, the voter can vote. To determine the voters, the ID of the candidate will be initiliazed to store fingerprint so that the voter votes. It has simple equipment and is easy to access. It shortens the voting time, is easy to move from ballot boxes to ballot boxes, reduces the number of personnel in the ballot boxes, provides smooth, easy and accurate counting Keywords— Biometric, voting system, Fingerprint, Arduino, identification
Quantum Cryptography Using Untrusted Devices

This book offers an accessible and engaging introduction to quantum cryptography, assuming no prior knowledge in quantum computing. Essential background theory and mathematical techniques are introduced and applied in the analysis and design of quantum cryptographic protocols. The title explores several important applications such as quantum key distribution, quantum money, and delegated quantum computation, while also serving as a self-contained introduction to the field of quantum computing. With frequent illustrations and simple examples relevant to quantum cryptography, this title focuses on building intuition and challenges readers to understand the basis of cryptographic security. Frequent worked examples and mid-chapter exercises allow readers to extend their understanding, and in-text quizzes, end-of-chapter homework problems, and recommended further reading reinforce and broaden understanding. Online resources available to instructors include interactive computational problems in Julia, videos, lecture slides, and a fully worked solutions manual.
Delegated Computation

This book offers an accessible and engaging introduction to quantum cryptography, assuming no prior knowledge in quantum computing. Essential background theory and mathematical techniques are introduced and applied in the analysis and design of quantum cryptographic protocols. The title explores several important applications such as quantum key distribution, quantum money, and delegated quantum computation, while also serving as a self-contained introduction to the field of quantum computing. With frequent illustrations and simple examples relevant to quantum cryptography, this title focuses on building intuition and challenges readers to understand the basis of cryptographic security. Frequent worked examples and mid-chapter exercises allow readers to extend their understanding, and in-text quizzes, end-of-chapter homework problems, and recommended further reading reinforce and broaden understanding. Online resources available to instructors include interactive computational problems in Julia, videos, lecture slides, and a fully worked solutions manual.
Noise Benefits in Complex-Valued Neural Networks

In this paper, we investigate the effectiveness of noise that is injected into the output of complex-valued neural networks. We first establish the equivalence between the complex backpropagation algorithm and the complex expectation maximization algorithm from the perspective of maximum probability likelihood estimation, then based on this equivalence we establish the separation hyperplane to distinguish the beneficial noise from the harmful noise in the sense of speeding up the training. The theoretical findings are validated by numerical simulations on the MNIST and Fashion MNIST data sets.
Quantifying Information

This book offers an accessible and engaging introduction to quantum cryptography, assuming no prior knowledge in quantum computing. Essential background theory and mathematical techniques are introduced and applied in the analysis and design of quantum cryptographic protocols. The title explores several important applications such as quantum key distribution, quantum money, and delegated quantum computation, while also serving as a self-contained introduction to the field of quantum computing. With frequent illustrations and simple examples relevant to quantum cryptography, this title focuses on building intuition and challenges readers to understand the basis of cryptographic security. Frequent worked examples and mid-chapter exercises allow readers to extend their understanding, and in-text quizzes, end-of-chapter homework problems, and recommended further reading reinforce and broaden understanding. Online resources available to instructors include interactive computational problems in Julia, videos, lecture slides, and a fully worked solutions manual.
Coda

The Coda pushes the implications of the book’s argument both backward and forward in time. Love reads Sir Arthur Conan Doyle’s character Sherlock Holmes as a nineteenth-century precursor to the cybernetic impulses of data-collection and data-processing, and points to the growing body of criticism that is probing the historical parameters of cybernetic thinking even further back in time. Love proposes that the emerging sense of a literary–critical cybernetic paradigm is particularly significant to scholars of modernism in light of the field’s recent global turn. Finally, she illustrates how the cybernetic reading of modernist formal experimentation can both illuminate and offer aesthetic alternatives to contemporary technological debates surrounding issues like surveillance and privacy. This conclusion drives home the argument that modernist literature can help us understand the longer and more diverse cultural history of our present-day, information-rich world; it also gestures to new possibilities for research and analysis that might push our awareness of cybernetic thinking back even further into earlier cultural moments, movements, genres, and texts.
Comparison of Receiver Front-end Differences for RF Fingerprint based IoT Device Identification

RF fingerprint (RFF) based wireless device identification is an emerging technique for authentication. In this paper, we investigate RFF based Zigbee device identification with different front-end receivers. 12 CC2530 Zigbee transmitters are employed as identification targets. The USRP and HackRF software defined radio (SDR) platforms are used as high-end and low-end receivers. Zigbee device identification is evaluated from practical measurements and crossing test. Experimental results show that it is essential to obtain the target training features with high-end USRP receiver. Moreover, it is also practical to identify accessing device with low-end HackRF receiver using the trained features from high-end USRP receiver. Finally, experimental results show that both USRP and HackRF receivers could obtain 0 identification error rate in near field line-of-sight (LOS) condition. However, in the case of remote non-line-of-sight (NLOS), low-end HackRF receiver with 8-bits analog to digital convertor (ADC) suffers from serious signal to noise ratio (SNR) degradation compared to high-end USRP receiver.
Correlation Filter Tracking Algorithm Based on Filter Space Constraint and Object Re-Detection

fast Discriminative Scale Space Tracking (fDSST) is an excellent object tracking algorithm with a good balance between tracking precision and running speed. In order to improve its long-term and stable tracking performance, especially its ability to recapture object, the fDSST_SrRed algorithm is proposed in this paper. The improvement details include: in order to reduce the negative impact of boundary effect on fDSST, a spatial constraint method of position filter is proposed; Aiming at the defect that the fDSST cannot relocate the object after the object is occluded and disappeared, an object re-detection method based on zero-filling of position filter was proposed. In order to judge when to start object detection, a tracking confidence evaluation index is proposed. Based on this confidence evaluation index, a dynamic filter update method is designed to make the filter learn more accurate object information and background information. Experiments show that compared with the baseline fDSST, proposed algorithm has a great improvement in the tracking precision of occluded and disappeared scenes. Compared with other correlation filter based long-term tracking algorithms (LCT, MUSTER and FuCoLoT), the proposed algorithm not only has good tracking precision, but also has the fastest running speed. The experimental results show that the improvement is effective.
Design of Microhabitat Monitoring System for Spodoptera Frugiperda Based on Internet of Things Architecture

Temperature, humidity, light, rainfall, wind speed, and wind direction are the main environmental factors influencing the occurrence and spread of the Spodoptera frugiperda. At present, data on the environmental factors of Spodoptera frugiperda life in China are mainly obtained from meteorological data from county-level and higher meteorological stations, and the field microhabitat data collection of Spodoptera frugiperda occurrence is rarely mentioned in the literature. Based on the above problems, this design constructs an IoT architecture and designs a field microhabitat environmental factor data collection and remote transmission system for Spodoptera frugiperda habitats. This design uses temperature and humidity sensors, light sensors, rain sensors, wind speed and wind direction sensors to collect data and send it to a single-chip microcomputer, display each data on the liquid crystal screen in real-time and transmit the data to the Alibaba Cloud service. The researchers were able to remotely monitor the Spodoptera frugiperda’s living environment’s temperature and humidity, light intensity, rainfall, and wind speed and direction way in time. This system can be used to monitor various factors of the mirohabitat environment of Spodoptera frugiperda in real-time, which is vital for further elucidating the suitable environment of Spodoptera frugiperda in my country and developing scientific monitoring and early warning plans as well as prevention and control strategies.
An Autocorrelation Function-Based Method Used for Image Denoising with Accuracy and Efficiency

Noise is always inevitably appeared in images, which directly affects the performance of machine vision applications. Currently, the commonly used denoising methods can be divided into three strategies: the filtering-based, model-based, and deep learning-based methods. However, they are always difficult to get the considerable accuracy and efficiency simultaneously. In this study, a novel denoising method based on autocorrelation function is investigated, which improve the image quality by utilizing the independence of useful periodic information and noise. Simulations and experiments compared with the current denoising methods confirm that the investigated method has a good comprehensive effect on noise reduction and efficiency improvement.
An Autonomous Vehicle Approach Based on Improved Target Detection and Trajectory Tracking

Context perception and path planning and tracking control are important parts of autonomous driving technology, which can determine the optimal path for the vehicle and ensure that the vehicle travels along the path accurately. However, at present, autonomous vehicles mainly face two problems: on the one hand, the traditional object detection algorithm is prone to errors in the detection of dynamic targets, and the matching between image feature points is prone to errors, so the detection accuracy is low. On the other hand, due to the constant changes in the external environment, absolute tracking control under a given desired trajectory can not meet the requirements of safe driving of driverless cars. Based on this, this paper proposes a dynamic target detection method combining ORB-SLAM3 algorithm and YOLOv5s algorithm. In this scheme, Hybid A* algorithm is used for path planning and model predictive control (MPC) controller model is used to realize vehicle trajectory tracking, which not only improves the accuracy of target detection, but also ensures the safety of vehicles in the process of trajectory tracking. Through dataset testing and Simulink/CarSim simulation experiments, the good object detection and path tracking capabilities of this scheme are verified.
Face Recognition Using the Improved SRGAN

Image super-resolution reconstruction is a pivotal issue in the field of computer vision, as it aims to generate high-resolution images from low-resolution ones. To tackle this task, an advanced deep learning model called SRGAN has been widely employed, which has proven effective in improving image quality. However, it still has certain limitations, especially when dealing with face images. In this paper, we propose an improved SRGAN model that includes two new improvements: Self-attention mechanism and U-net structure. To address the problem of face image super-resolution reconstruction, we trained the model on a large-scale face dataset. We introduced a self-attention mechanism to enhance the model's ability to extract detail information. The self-attention mechanism enables the model to adaptively focus on important regions in the input image, thereby better capturing the details of the image. Secondly, we added a U-net structure to the generator of SRGAN to enhance the model's perceptual ability and reduce the probability of generating visual artifacts. As a result, the performance and robustness of the model are effectively improved. We validated our proposed improved SRGAN model on multiple face datasets. Experimental results show that our model performs better in handling face images compared with the original SRGAN model. It can better preserve facial detail and texture information while reducing the generation of visual artifacts, thereby improving the accuracy of face recognition to some extent.
The Staircase Procedure Toolkit: Psychophysical Detection Threshold Experiments Made Easy

We propose a novel open-source software toolkit to support researchers in the domains of human-computer interaction (HCI) and virtual reality (VR) in conducting psychophysical experiments. Our toolkit is designed to work with the widely-used Unity engine and is implemented in C# and Python. With the toolkit, researchers can easily set up, run, and analyze experiments to find perceptual detection thresholds using the adaptive weighted up/down method, also known as the staircase procedure. Besides being straightforward to integrate in Unity projects, the toolkit automatically stores experiment results, features a live plotter that visualizes answers in real time, and offers scripts that help researchers analyze the gathered data using statistical tests.
Combining embodiment and 360 video for teaching protection of civilians to military officers

This demo presents an innovative use of embodiment in combination with 360º video to support teaching the threat-based approach to protection of civilians at a military university. To create a realistic and emotionally appealing XR experience and at the same time save on developing time and costs, scanned 3D objects and avatar were integrated in 360º videos. The app also includes interactions with a virtual perpetrator and collaborative map exercise and received positive feedback from end users.
Parameter Optimization via Reinforcement Learning for the Regulation of Swarms

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space.
Deep Pyramid Network for Low-light Endoscopic Image Enhancement

Endoscopic images captured under low-light enclosed intestinal environment usually have poor visibility (manifested as uneven illumination and noise), affecting the work efficiency of physicians and the accuracy of lesion detection. To improve the image quality, the literature has reported many low-light image enhancement (LIE) methods. However, most methods do not perform well in handling the low-light endoscopic image enhancement (LEIE) task, usually bringing additional artifacts or amplifying noise. In this paper, we propose a novel deep pyramid enhancement network (DPENet) to enhance endoscopic images from both global and local perspectives. Specifically, considering the uneven illumination of endoscopic images, DPENet utilizes an image pyramid framework with three parallel branches to explore and integrate both global and local features at different scales. To suppress noise, DPENet sets multiple scale-space feature extraction blocks (SFEBs) in each branch. SFEB consists of a contextual feature extraction module (CFEM) and a spatial residual attention module (SRAM). CFEM mines contextual information to help the network understand semantic information while suppress the isolated noise. SRAM leverages the spatial attention mechanism to help the network adaptively focus on dim regions. Experimental results on a public dataset and our collected dataset show that DPENet is competent for the LEIE task with promising results, and outperforms 9 state-of-the-art LIE methods in both qualitative and quantitative aspects.
Pulse oximeter implemented with ESP32 and monitored in Blynk and ThingSpeak

Before the COVID-19 pandemic, pulse oximeters were commonly used in clinics, hospitals, medical centers, and homes of individuals requiring a prescription. As a result of the health crisis caused by COVID-19, their use has been normalized and they can now be found in the first aid kit of many homes along with the thermometer. This article shows the design and experimental implementation of a pulse oximeter using an ESP32 microcontroller that allows through its Bluetooth and WiFi modules to perform an IoT (Internet of Things) application in conjunction with the online platform ThingSpeak and the Android application Blynk, generating remote and real-time monitoring of the information generated by a sensor that can measure blood oxygen concentration and heart rate reference MAX30102.
A wearable device for fall detection in elderly

In this study, fall detection was developed for the elderly or patients with balance problems who require monitoring all the time to reduce the impact of a greater fall. The system tracks the movement of the human body, identifies falls from normal daily activities by calculating acceleration and orientation, and then sends requests for help to nurses or their families via. Telegram messages and fall locations using the Blynk application. The results show that the system can distinguish between normal activities and falls. The system can detect falls forward, backward, and sideways to the left and right with an accuracy of 95%, 80%, 100%, and 75%, respectively.
Automation of EEG Data Processing with HPC Community Cloud

We study and further develop an approach based on the computational models formalism to automation of data processing pipelines in a specific application domain-neurophysiology. A computational model is a formal specification of a set of variables (in a mathematical sense) of an application domain and a set of operations with their input and output variables. If values of some variables are given, the operations that have these variables as inputs can compute values of their output variables, these variables serve as inputs for other operations and, thus, some subset of variables can obtain their values. This way, all possible/meaningful computational scenarios in the domain can be defined and algorithms (scenarios) for particular computational problems can be automatically derived by forward chaining. In this work, we apply the approach to building a framework for managing data and computations in neurophysiology. Brain studies produce large amounts of data such as electroencephalograms, functional magnetic tomography images, questionnaires, and results of other medical tests. There is a need for systematic management of these data, specification of data processing pipelines, implementing these pipelines on high performance computing systems, representing the process of computing and the results with a graphic user interface. We build a sample computational model to represent a limited set of scenarios that can be reused and further extended in a regular way by adding more variables and operations, propose a framework for management of data and computations, automation of building graphical user interfaces. A prototype implementation of the framework based on the developed computation model is a practical output of this work.
Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network

Abstract Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.