Reviewerly/exHarmony huggingface dataset:
An improved PDE-constrained optimization fluid registration for image multi-frame super resolution

The main idea of multi-frame super resolution (SR) algorithms is to recover a single high-resolution image from a sequence of low resolution ones of the same object. The success of the SR approaches is often related to a well registration and restoration steps. Therefore, we propose a new approach based on a partial differential equation (PDE)-constrained optimization fluid image registration and we use a fourth order PDE to treat both the registration and restoration steps that guarantee the success of SR algorithms. Since the registration step is usually a variational ill-posed model, a mathematical study is needed to check the existence of the solution to the regularized problem. Thus, we prove the existence and of the well posed fluid image registration and assure also the existence of the used second order PDE in the restoration step. The results show that the proposed method is competitive with the existing methods.
Wildlife 3D multi-object tracking

The study of wildlife populations and species has gained increased relevance due to significant endangerment, loss of habitats and world climate change. Using camera traps for monitoring is a common method to estimate species diversity, occupancy and relative abundance. We present a 3D multi-object tracking method for wildlife camera trapping using RGB-D cameras. We infer precise three-dimensional pose estimations of individual animals (i.e. locations and orientations) and also three-dimensional estimations of their observed movements, including velocity estimations. More precisely, we achieved a scaled Average Multi-Object Tracking Accuracy (sAMOTA) of 88.43%. The code is available athttps://github.com/m-klasen/3D_wildlife_tracking.
Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

With the improvement of computer computing power and storage capacity, the emergence of massive data makes the methods based on human action feature detection and recognition unable to meet people’s needs due to poor generalization ability. Based on the detection and recognition of human action features based on deep learning algorithms, a suitable neural network can be constructed to identify locked human action features from surveillance video and analyze whether it is a specific behavior. In this paper, a deep learning algorithm is proposed to optimize the detection of human action features, and a multiview reobservation fusion action recognition model of 3D pose is designed. Several factors affecting the recognition of human action features are analyzed, and a detailed summary is made from the detection environment. Experiments show that adding one or two layers of feature attention enhancement to the multiview observation fusion network can improve the accuracy by 1% to 3%. In this way, the model can integrate action features from multiple observation angles to judge actions and learn to find observation angles suitable for action recognition, thereby improving the performance of action recognition.
Enhanced Fuzzy State Estimation of Discrete-Time Nonlinear Circuits via Two Relaxed Measures

This brief gives an enhanced result for fuzzy state estimation of discrete-time nonlinear circuits by developing two new technical measures of relaxations. Firstly, a novel balanced matrix method is proposed in which all the involved free matrices are with arbitrary variable orders, and thus a lot of freedom can be introduced into different switching modes for reducing the conservatism caused by existing those free matrices with fixed orders in the recent result. Secondly, an important property is employed in order to further relax the conservatism caused by the mismatch of adjacent normalized fuzzy weighting functions. Therefore, the scope of application of fuzzy state estimation can be enlarged by a large margin and much better estimation performance is obtained than before. Finally, the superiority of the developed approach is tested and verified based on the common benchmark example applied in the literature.
Unmanned Aerial Vehicle Swarm Cooperative Decision-Making for SEAD Mission: A Hierarchical Multiagent Reinforcement Learning Approach

Unmanned aerial vehicle (UAV) swarm cooperative decision-making has attracted increasing attentions because of its low-cost, reusable, and distributed characteristics.However, existing non-learningbased methods rely on small-scale, known scenarios, and cannot solve complex multi-agent cooperation problem in large-scale, uncertain scenarios.This paper proposes a hierarchical multi-agent reinforcement learning (HMARL) method to solve the heterogeneous UAV swarm cooperative decision-making problem for the typical suppression of enemy air defense (SEAD) mission, which is decoupled into two subproblems, i.e., the higher-level target allocation (TA) sub-problem and the lower-level cooperative attacking (CA) sub-problem.We establish a HMARL agent model, consisting of a multi-agent deep Q network (MADQN) based TA agent and multiple independent asynchronous proximal policy optimization (IAPPO) based CA agents.MADQN-TA agent can dynamically adjust the TA schemes according to the relative position.To encourage exploration and promote learning efficiency, the Metropolis criterion and inter-agent information exchange techniques are introduced.IAPPO-CA agent adopts independent learning paradigm, which can easily scale with the number of agents.Comparative simulation results validate the effectiveness, robustness, and scalability of the proposed method.
Efficient CNN Architecture Design Guided by Visualization

Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the number of parameters and the computational complexity. But some inherent characteristics of networks are overlooked. Inspired by visualizing feature maps and N×N(N>1) convolution kernels, several guidelines are introduced in this paper to further improve parameter efficiency and inference speed. Based on these guidelines, our parameter-efficient CNN architecture, called VGNetG, achieves better accuracy and lower latency than previous networks with about 30%~50% parameters reduction. Our VGNetG-1.0MP achieves 67.7% top-1 accuracy with 0.99M parameters and 69.2% top-1 accuracy with 1.14M parameters on ImageNet classification dataset. Furthermore, we demonstrate that edge detectors can replace learnable depthwise convolution layers to mix features by replacing the N×N kernels with fixed edge detection ker-nels. And our VGNetF-1.5MP archives 64.4%(-3.2%) top-1 accuracy and 66.2%(-1.4%) top-1 accuracy with additional Gaussian kernels.
Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Leukemia cancer is the most common type of cancer that occurs in childhood. The most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which affect children and adults, respectively. Several health issues occur due to these cancers. Leukemia affects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. The affected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classifier is developed and proposed. The method detects both types, determines their severity, and creates a message that recommends next steps to patients. This approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. The obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. The margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its effectiveness. This dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. This analysis compares the method used to extract features, the classifier that is utilized, the accuracy, the precision, and the recall. The obtained results indicate that the proposed method outperforms other works and produces better results.
The Integration of a Canonical Workflow Framework with an Informatics System for Disease Area Research

Abstract A recurring pattern of access to existing databases, data analyses, formulation of new hypotheses, use of an experimental design, institutional review board approvals, data collection, curation, and storage within trusted digital repositories is observable during clinical research work. The workflows that support the repeated nature of these activities can be ascribed as a Canonical Workflow Framework for Research (CWFR). Disease area clinical research is protocol specific, and during data collection, the electronic case report forms can use Common Data Elements (CDEs) that have precisely defined questions and are associated with the specified value(s) as responses. The CDE-based CWFR is integrated with a biomedical research informatics computing system, which consists of a complete stack of technical layers including the Protocol and Form Research Management System. The unique data dictionaries associated with the CWFR for Traumatic Brain Injury and Parkinson's Disease resulted in the development of the Federal Interagency Traumatic Brain Injury and Parkinson's Disease Biomarker systems. Due to a canonical workflow, these two systems can use similar tools, applications, and service modules to create findable, accessible, interoperable, and reusable Digital Objects. The Digital Objects for Traumatic Brain Injury and Parkinson's disease contain all relevant information needed from the time data is collected, validated, and maintained within a Storage Repository for future access. All Traumatic Brain Injury and Parkinson's Disease studies can be shared as Research Objects that can be produced by aggregating related resources as information packages and is findable on the Internet by using unique identifiers. Overall, the integration of CWFR with an informatics system has resulted in the reuse of software applications for several National Institutes of Health-supported biomedical research programs.
Interactive Methods of Foreign Language Teaching Relevant for Use in the Educational Process at a Military University

The study aims to demonstrate the results of using the most effective modern interactive methods of teaching a foreign language to cadets in the educational process of a military university. The paper describes the most suitable methods of teaching cadets taking into account the specifics of teaching a military professional foreign language; provides the experience and examples of practical application of interactive methods of teaching cadets in foreign language classes at a military university. The study is novel in that it identifies the features of using modern interactive pedagogical methods in the educational process when teaching the discipline “Foreign Language” (English) at the Far Eastern Higher Combined Arms Command School. As a result of the study, the researchers have expanded the ideas about effective interactive methods of foreign language teaching and have created prerequisites for enriching the methodology of teaching a military professional foreign language at a military school.
An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.
Co-Occurrence-Based Double Thresholding Method for Research Topic Identification

Identifying possible research gaps is a main step in problem framing, however it is increasingly tedious and expensive considering the continuously growing amount of published material. This situation suggests the critical need for methodologies and tools that can assist researchers in their selection of future research topics. Related work mostly focuses on trend analysis and impact prediction but less on research gap identification. This paper presents our first approach in automated identification of feasible research gaps by using a double-threshold procedure to eliminate the research gaps that are currently difficult to study or offer little novelty. Gaps are then found by extracting subgraphs for the less-frequent co-occurrences and correlations of key terms describing domains. A case study applying the methodology for electronic design automation (EDA) domain is also discussed in the paper.
Advances in Contextual Action Recognition: Automatic Cheating Detection Using Machine Learning Techniques

Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.
Energy-Economic-Environmental assessment of solar-wind-biomass systems for finding the best areas in Iran: A case study using GIS maps

According to the existing capacities of solar and wind in Iran and given this fact that, to reach a proper economic growth, Iran needs to increasing its capacity in the generation of power, and also noting that there is a severe water shortage in Iran and Iran is required to produce power without water consuming, present paper attempts to find the minimum price of electricity production by hybrid wind-biomass-solar configurations in the 103 station of Iran, using NASA website data and hybrid optimization model for multiple energy resources (HOMER) software. Forasmuch as building sector counting for 45% of energy using in Iran, the goal of present study is supplying the electricity for off-grid/on-grid in homemade scale. The outcomes showed that in on-grid mode, the cost of generating electricity is less than that of the off-grid. In the off-grid biomass generator-based system, Bandarabbas and Jask with 0.519 $/kWh and 0.385 $/kWh, are the most suitable stations for wind turbine applications and solar cell applications, respectively. For on-grid mode, these prices are reduced by 65 to 80%. As a general result, it can be said that Bandarabbas, Jask and Bandarabbas stations are the most suitable for wind, solar and wind-solar hybrid respectively.
An Adaptive Enhanced Technique for Locked Target Detection and Data Transmission over Internet of Healthcare Things

The incredible advancements in data transmission technology have opened up more potentials for data security than ever before. Numerous methods for data protection have been developed during the previous decades, including steganography and cryptography. The security and integrity of medical data have emerged as major barriers for healthcare service systems as the Internet of Things has evolved dramatically in the healthcare business. Communication between two devices securely is a difficult problem. Numerous cryptographic algorithms are already available, including data encryption standard (DES), Rivest–Shamir–Adleman (RSA), and advanced encryption standard (AES). In this paper, we present a hybrid security model for the protection of diagnostic text data contained in medical photographs. The proposed model is built by combining a proposed hybrid encryption system with either a 2D Discrete Wavelet Transform 1 Level (2D-DWT-1L) or a 2D Discrete Wavelet Transform 2 Level (2D-DWT-2L) steganography technique. The suggested model encrypts secret data and hides them using 2D-DWT-3L. As text covers, color and grayscale images are employed. The suggested system’s performance was tested using PSNR, SSIM, MSE, and Correlation. Associated to state-of-the-art approaches, the proposed model masked personal patient data with high capacity, imperceptibility and minimum deterioration in the received stego-image. We use MATLAB to build the proposed mechanism, and measures such as throughput and execution time are used to assess performance.
Dynamic Knowledge Distillation with Noise Elimination for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) demonstrates its superiority in detecting in complex environments due to the additional depth information introduced in the data. Inevitably, an independent stream is introduced to extract features from depth images, leading to extra computation and parameters. This methodology sacrifices the model size to improve the detection accuracy which may impede the practical application of SOD problems. To tackle this dilemma, we propose a dynamic knowledge distillation (DKD) method, along with a lightweight structure, which significantly reduces the computational burden while maintaining validity. This method considers the factors of both teacher and student performance within the training stage and dynamically assigns the distillation weight instead of applying a fixed weight on the student model. We also investigate the issue of RGB-D early fusion strategy in distillation and propose a simple noise elimination method to mitigate the impact of distorted training data caused by low quality depth maps. Extensive experiments are conducted on five public datasets to demonstrate that our method can achieve competitive performance with a fast inference speed (136FPS) compared to 12 prior methods.
Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data

With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as a discriminator, the correlation between the side information leaked by the cryptographic device, the key of the cryptographic algorithm, and other sensitive data can be explored. Then, the security of cryptographic products can be evaluated and analyzed. For the AES-128 cryptographic algorithm, combined with the CW308T-STM32F3 demo board on the ChipWhisperer experimental platform, a Correlation Power Analysis (CPA) is performed using the four most common deep learning methods: the multilayer perceptron (MLP), the convolutional neural network (CNN), the recurrent neural network (RNN), and the long short-term memory network (LSTM) model. The performance of each model is analyzed in turn when the samples are small data sets, sufficient data sets, and data sets of different scales. Finally, each model is comprehensively evaluated by indicators such as classifier accuracy, network loss, training time, and rank of side-channel attacks. The experimental results show that the convolutional neural network CNN classifier has higher accuracy, lower loss, better robustness, stronger generalization ability, and shorter training time. The rank value is 2, that is, only two traces can recover the correct key byte information. The comprehensive performance effect is better.
Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models

Elevator Pitch A quiet revolution is happening in the offices, cubicles, and boardrooms of the world. Non-IT professionals are becoming empowered by leveraging organizational data for analytics. We support this movement by offering a powerful way to make data more usable via a combination of graphical conceptual models with narratives. Longer Version We are witnessing a quiet revolution—the rise of empowered users. These non-IT professionals increasingly seek to leverage the ever-expanding amount of organizational data for analytics to support their initiatives, decisions, and actions. All too often, however, the enthusiasm of these users collides against the harsh reality—many of them lack sophisticated IT skills, and they struggle to find/access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. Conceptual models are diagrams that accurately and succinctly represent rules and patterns captured in data. Although somewhat intuitive, these models alone do not suffice, as interpreting them still requires some specialized IT knowledge. Hence, we add narratives—stories written in natural language. The narratives intuitively explain some of the challenging aspects of the conceptual models. We conducted a series of experiments and interviews with empowered users to assess our idea. These studies show the value of the conceptual models with narratives for understanding organizational data. Our work unlocks an important missing puzzle for further user empowerment—a way for non-IT professionals to get the most out of the data.
Transfer Learning for Signal Detection in Wireless Networks

The last decade has witnessed the rapid growth of deep learning (DL) applications in wireless communications, especially for channel estimation and signal detection. However, conventional DL techniques are usually trained for a specific scenario. Therefore, it is required to be retrained when applying for new wireless systems. Unfortunately, acquiring enough training data for good detection performance may be costly and time-consuming. Even if the training data is sufficient, conventional DL techniques usually require a long training time. Consequently, this can significantly impact the performance of DL techniques and hinder their robustness. To address all these limitations, this letter introduces a transfer learning framework that can transfer knowledge from a source system to improve the training process of target systems with limited training data. Our simulation results demonstrate that the proposed solution outperforms the conventional deep learning methods in terms of channel estimation and signal detection.