Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Convergence of Hybrid Federated Learning with Server-Clients Collaborative Training

State-of-the-art federated learning (FL) paradigms utilize data collected and stored in massively distributed clients to train a global machine learning (ML) model, in which local datasets never leave the devices and the server performs simple model aggregation for better privacy protection. In reality, however, the parameter server often has access to certain (possibly small) amount of data, and it is computationally more powerful than the clients. This work focuses on analyzing the convergence behavior of hybrid federated learning that leverages the server dataset and its computation power for collaborative model training. Different from standard FL where stochastic gradient descent (SGD) is always computed in a parallel fashion across all clients, this architecture enjoys both parallel SGD at clients and sequential SGD at the server, by using the aggregated model from clients as a new starting point for server SGD. The main contribution of this work is the convergence rate upper bounds of this aggregate-then-advance hybrid FL design. In particular, when the local SGD keeps an $\mathcal{O}(1/t)$ stepsize, the server SGD must adjust its stepsize to scale no slower than $\mathcal{O}(1/t^{2})$ to strictly outperform local SGD with strongly convex loss functions. Numerical experiments are carried out using standard FL tasks, where the accuracy and convergence rate advantages over clients-only (FEDAVG) and server-only training are demonstrated.
Correlation Analysis of Nested Consumer Health Data: A New Look at an Old Problem

Correlation analysis is one of the most popular analytic methods for discovering the relationship between two variables in consumer health data. Nevertheless, the simple sample correlation techniques (e.g., Pearson’s or Spearman’s correlation) assume the independence of the observations, which is often violated when there are repeated measures from each subject (also called nested data). In this paper, we compare three methods for correlation calculation on nested consumer health data: (1) repeated measure correlation (rmcorr), (2) subject mean correlation (smcor), and (3) simple sample correlation (sscor). We contend that rmcorr is a more appropriate technique for the correlation analysis of nested data as it could explicitly capture the within-subject variations. In contrast, the smcor and sscor, which could only capture the cross-subject variations, may produce misleading and faulty results.
Presenting Text Layout and Color Patterns Suitable for Book Covers

We propose a design support system that automatically generates book cover designs according to several parameters set by the user. Our method for book covers is based on the technology called "Neural Style Transfer" to generate the background image for the cover. Another neural-network-based framework called "Word2Vec" is used to select a font for rendering the book title. Several cover patterns are generated according to the user specified parameters. The user can perform post-process modifications on generated cover patterns to better match one’s taste. We conducted comparison experiments where participants were presented a pair of book covers, both of which represent the same book title, one generated by the proposed system, and another taken from an existing paperback in a store. The results of user studies showed that the existing cover design obtained higher evaluations, and that 80% of the participants gave high ratings to our method when evaluated alone. As our future work, we plan to apply our method to other design tasks, such as for cover belts for books (called "obi" in Japanese).
Autonomous navigation method for substation inspection robot based on travelling deviation

A new method of edge detection is proposed in substation environment, which can realize the autonomous navigation of the substation inspection robot. First of all, the road image and information are obtained by using an image acquisition device. Secondly, the noise in the region of interest which is selected in the road image, is removed with the digital image processing algorithm, the road edge is extracted by Canny operator, and the road boundaries are extracted by Hough transform. Finally, the distance between the robot and the left and the right boundaries is calculated, and the travelling distance is obtained. The robot's walking route is controlled according to the travel deviation and the preset threshold. Experimental results show that the proposed method can detect the road area in real time, and the algorithm has high accuracy and stable performance.
CT image segmentation of COVID-19 based on UNet++ and ResNeXt

The novel coronavirus pneumonia (COVID-19) pandemic is spreading globally. Computerized tomography (CT) imaging technology plays a vital role in the fight against global COVID-19. When diagnosing new coronary pneumonia, it will be helpful if the new coronary pneumonia focus area can be automatically and accurately segmented from the CT image The doctor makes a more accurate and quick diagnosis. Aiming at the segmentation problem of neo-coronary pneumonia lesions, an automatic segmentation method based on the improved U-Net++ model is proposed. The ResNeXt network pre-trained on ImageNet is used in the encoder and decoder to extract features of effective information. The experimental results on the public data set show. The mIou, mPA, and loss of the proposed algorithm are 81.67%, 87.78%, and 0.0145 respectively. Compared with other semantic segmentation algorithms, this method can effectively segment the neo-coronary pneumonia lesion area and has good segmentation performance.
Research on Electromagnetic Performance of the Permanent Magnet Canned Motor under Different Control Strategies

Based on the analysis of two different control strategies of square wave drive and sine wave drive, the effects of the two driving modes on the performance of the permanent magnet canned motor (PMCM) are studied. The mathematical models of the PMCM under two control strategies are built, and the control mode of the motor is analyzed. The three-dimensional finite element analysis model of the motor is established, and the motor performance under the two control strategies is simulated. The motor efficiency is higher when driven by square wave. The torque ripple of the motor is smaller when the motor is driven by sine wave. It has certain engineering application value to design the control mode of permanent magnet canned motor in different applications.
Selective Targeted Transfer Learning for Malware Classification

Data driven machine learning and deep neural models demand accessibility to high-quality, clean, and enormous datasets. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL leverages gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. For effective transfer, the training (source) and test (target) data may share similar or different marginal and conditional distributions. Negative transfer may occur, due to noise accumulated during learning iterations decreasing system performance metrics [1]. In this paper, we propose a selective targeted transfer learning (STTL) method to resolve the transferability between source and target with minimal data requirements to statically detect malicious Windows portable executable files. Kernel layers from deep models map the marginal distributions of the source and target domain into a common latent space while selective source feature and hard target samples selection draws the conditional distributions closer. Here we utilize benchmark Endgame Malware Benchmark for Research (EMBER) dataset which contains more than one million malware and benign samples, to validate our work. We automatically identify source, target pairs for three different malware families (banking trojan, adware, spyware) based on statistical measures and perform feature extraction/reduction (2381 features to 29, 28, 18 features) to formulate the new training dataset for effective targeted TL. For three different families (trojan, adware, spyware malwares) with varying dimensions, we prove that acquiring a small number of intentionally well-chosen samples from the target environment (2441, 2404, 3083 which is less than 14% of target sample count), we can drastically improve model accuracy to 90.8%, 94.4%, 93.5 % in comparison to the baseline transfer learning accuracy of 80.5%, 76.1 % and 62.2%, respectively. Although, EMBER data is used in this work, our novel algorithm works on different datasets (as evident by our previous paper [2]).
Simultaneous Wireless Power and High-Rate Full-Duplex Data Transfer System Based on 4ASK

A high-rate full-duplex simultaneous wireless power and data transfer (SWPDT) system based on quaternary amplitude-shift keying (4ASK) with double-sided LCC compensation network is proposed in this paper. Based on the frequency selection characteristics of the double-tuned filter, the forward and backward data transfer circuits are connected in series and parallel with the coupling coils, respectively, to achieve the full-duplex wireless data transfer. The principles of parameter tuning and topology of the SWPDT system are introduced in detail. Moreover, the 4ASK modulation and demodulation are adopted to enhance the data rate to 120kb/s. Finally, the effectiveness of the proposed method is verified by simulation results.
Effects of station coverage on moment tensor inversion

Abstract The effects of station distribution and focal mechanism on moment tensor inversion are examined by means of numerical experiment. The far-field displacements of P and Rayleigh waves computed from fault models are inverted to the moment tensor after the error corresponding to the observation error is added to the displacement. The rms error is defined as rms of the differences between the given fault parameters and the parameters obtained from the inverted moment tensor. Experiments are made for both ideal and actual station coverages. The rms errors are found to be proportional to the random error added. For the ideal station coverages, the rms errors are smaller for strike-slip type events than for dip-slip types when only P waves are used. Both types give similar errors when only Rayleigh waves are used. For the actual station distribution of the SRO/ASRO network and earthquakes all over the world, the rms errors vary significantly with the location and mechanism of the events. The minor double couple, which must vanish if no errors are added, reaches 30 per cent of the major double couple for some events when the standard deviation of the random error is 10 per cent of the amplitude. These experiments indicate that the station coverage causes serious effects on moment tensor inversion, and we must be careful to interpret the routinely determined results. Furthermore, uniform station distribution of the global seismograph network is desirable .
Voltage control in a distribution system using active power loads

High penetration of renewable energy resources such as rooftop solar photovoltaic (PV) systems is exacerbating violations of the voltage limits in power distribution networks. Existing solutions to these over-/under-voltage issues include tap-changing transformers and shunt capacitors. However, tap changers only allow voltage changes in discrete steps and shunt capacitors cannot handle over-voltage situations. To overcome these problems, this thesis proposes voltage control in a distribution system by adjusting active power loads. The proposed solution can be used for both underand over-voltage cases at any levels of voltage adjustment. First, the active power that needs to be adjusted at selected nodes to bring the voltages within acceptable limits is calculated for under and over-voltage cases using alternating current optimal power flow (ACOPF). ACOPF is solved for three different cases by varying the cost of active power adjustment and the marginal cost from the feeder supply, which is assumed to be the market price. Secondly, demand response on electric water heaters is implemented to achieve active power adjustments at selected nodes obtained from the ACOPF results. The problem is formulated over multiple time steps to minimize the energy costs of water heaters at a specific node subject to the dynamics of the water temperature, the energy consumption, the temperature constraints, and the voltage limits at the node. The IEEE 34-bus radial distribution system is used as a case study. By solving the same problem for different price settings, it is observed that the price structure affects the energy demand only in the under-voltage system, but not in the over-voltage system. The main contribution of this thesis is to show that by adjusting active power loads, voltages in a distribution system can be maintained within the permissible limits.
An Efficient Healthcare Medication System with Clustering Algorithm Using Euclidean Distance Adjoining Data Lake

The health care services make use of the patient information along with big data analytics towards the doctor's prescription advice to patients. Still, the majority medical data is not in the standard structured standard format. So it requires enormous effort in converting the unstructured data to structured format. This paper highlights a data lake architecture which helps in decreasing the data ingestion time thereby increasing and improving the accuracy in healthcare data analytics. This facilitates the analytical process to be done completed with the removal of data silos. The new intended data lake architecture improves working analysis of data in the way of making connections with third party data providers. The Hadoop Distributed File System (HDFS) is used for storing the structures as well as unstructured data. This paper utilizes the Euclidean distance for clustering process where the patients with similar related health conditions are clustered. Then each cluster is subjected to Support Vector Machine (SVM) for the accurate doctor's prescription. The experimental results showed a decrease in ingesting data by the proposed data lake architecture. One more advantage is the precise and accurate cluster formation than the existing methods. It also improves the healthcare medication by eliminating the data silos.
Human Relationship Advice System in Metaverse World: Application Propose of CTUP Model in Future Communication

Condition, Trend, Understanding, Prediction (CTUP) are the four key technologies in situation awareness. Except for its wide application in the military, autonomous driving, and behavior analysis, CTUP is also considered as one of the social skills in human society. With the increased integration of 5G, blockchain, artificial intelligence, and other smart technologies, Metaverse is expected to change the way we communicated with each other. While concern about how to deal with the relationship in the Metaverse world has become a hot topic. As the communication skills whether in the actual or Metaverse world share some common factors, it is considered that the CTUP model could help people to have a better relationship with some advice based on the condition, trend, understanding, and prediction.
An Approach Combined CPWM with Passive Filter for Reducing EMI in a Flyback Converter

Chaotic pulse width modulation(CPWM) was used in power electronic converters to reduce electromagnetic interference (EMI) in recent decades. However, the CPWM technique has a good effect on the higher frequency but less on the lower as expectation, to give a better EMI reduction under 5MHz, this article proposes a method that combined CPWM with a passive filter for reducing EMI in a flyback converter which is the most common topology used in switching mode power supply. Compared with traditional PWM, there is about 20dB reduction can be reached by the proposed method applied in a flyback converter with 100kHz, respectively. Moreover, traditional PWM and Chaotic PWM effects on ripple were also analyzed.
Developing an Intelligent Model to Detect Micro Facial Expression

As the populace of the sector is growing continuously and those are getting older together with it, we must behavior loads of studies in-order to construct higher human carrier robot, because it's miles the destiny. These robots autonomously examine human feelings so we can provide higher offerings to people and be there while its miles required and important. Facial Expression is the maximum essential manner of detecting feelings in people and this is the subject on which the current generation focuses on. To get suitable or higher effects for facial features reputation, we've got proposed 2 strategies: they're double-channel weighted combination deep convolutionary neural community (WMDCNN) that's primarily based totally at the static pics and deep convolutionary neural community lengthy quick period reminiscence community of double channel weighted combination (WMDCNN-LSTM) that's primarily based totally on photograph series. These strategies have a quicker fee for micro facial features detection. The micro facial features are without difficulty diagnosed or detected or diagnosed with the aid of using the WMDCNN andthe bodily capabilities detected withinside the static pics with the aid of using them is dispatched to WMDCNN-LSTM. WMDCNN-LSTM research or acquires those capabilities if you want to accumulatesimilarly the temporal capabilities of the photographseries, via which we will capable of constructing a correct detection version. We have stepped forward the fee of reputation that's higher than the costs in current models.
Comparing Image Feature Extraction Methods Using Dermoscopy Noisy Images

Melanoma is a rare type of skin cancer that is regarded as the most threatening. Many studies offered a variety of effective melanoma detection strategies. The primary goal of this study is to examine existing research efforts in response to the essential and difficult problem of feature selection and extraction for skin cancer melanoma detection from dermoscopy pictures. Speeded-Up-Robust-Features (SURF), Scale-Invariant-Feature-Transformation (SIFT) and Histogram-of Oriented-Gradients (HOG) are three popular feature extraction methods that we compare. For Dermoscopic noisy pictures, we evaluate the performance of feature detection techniques. The number of valid matches found by the algorithm between the original and noisy dermoscopy images determines the efficiency of three strategies. We use dermoscopy images corrupted by three different types of noise: Gaussian noise, Salt & Pepper noise, and Speckle noise. SIFT is stable for the majority of noisy dermoscopic images, according to the findings of the trials, however it is slow. In this paper, we discovered that SURF is the best, with performance comparable to SIFT. HOG, on the other hand, proves its worth when it comes to retrieving image edge and texture information.
Family of Bounded Regulators with Variable Gains and Lyapunov Asymptotic Stability for Robot Manipulators

In this paper, the regulation problem for robot manipulators in joint space through the proposal of a new family of bounded regulators with variable gains is presented. The proposed regulators have bounded functions that replace the classical position error and the velocity; moreover, the variable gains are formed by a family of Lipchitz functions with the position error and the velocity as their arguments. This structure avoids exceeding the physical limits of the servomotors. A strict Lyapunov function is proposed to demonstrate the global and asymptotic stability. Finally, the functionality and performance of the proposal are examined by experimental results on a direct-drive-robot of 3-degrees-of-freedom against the PD regulator
Personalized Gaming Disorder Risk Analysis and Rating Based on User Preference Correlation

In this paper, we propose a rating that indicates the risk of developing a gaming disorder different from the already existing CERO rating. We divided the risk of getting a game disorder into user and game parameters. We proposed a new rating and evaluated the method using data from a sample group of users. We show that our rating can distinguish game disorder risk to users with a different set of behavior, although they are from the same CERO category.
An Integrated Human Stress Detection Sensor Using Supervised Algorithms

This paper adopts a holistic approach to stress detection issues in software and hardware phases and aims to develop and evaluate a specific low-power and low-cost sensor using physiological signals. First, a stress detection model is presented using a public data set, where four types of signals, temperature, respiration, electrocardiogram (ECG), and electrodermal activity (EDA), are processed to extract 65 features. Using Kruskal-Wallis analysis, it is shown that 43 out of 65 features demonstrate a significant difference between stress and relaxed states. K nearest neighbor (KNN) algorithm is implemented to distinguish these states, which yields a classification accuracy of 96.0 ± 2.4%. It is concluded that ECG and EDA signals are sufficient to detect stress with high accuracy while the system has fewer sensors and consumes lower power. Second, a novel sensor is developed to collect ECG and EDA signals from eighteen healthy participants aged 16 to 40 years. They were exposed to stress during an arithmetic mental task and Stroop Color-Word Test. This sensor has a battery life of 70 hours and can detect stress with 94.4 ± 2.5% accuracy. The proposed software and hardware can potentially be used in a wearable device for continuous stress detection.
DNA methylome combined with chromosome cluster-oriented analysis provides an early signature for cutaneous melanoma aggressiveness

Aberrant DNA methylation is a well-known feature of tumours and has been associated with metastatic melanoma. However, since melanoma cells are highly heterogeneous, it has been challenging to use affected genes to predict tumour aggressiveness, metastatic evolution, and patients’ outcomes. We hypothesized that common aggressive hypermethylation signatures should emerge early in tumorigenesis and should be shared in aggressive cells, independent of the physiological context under which this trait arises. We compared paired melanoma cell lines with the following properties: (i) each pair comprises one aggressive counterpart and its parental cell line, and (ii) the aggressive cell lines were each obtained from different host and their environment (human, rat, and mouse), though starting from the same parent cell line. Next, we developed a multi-step genomic pipeline that combines the DNA methylome profile with a chromosome cluster-oriented analysis. A total of 229 differentially hypermethylated genes were commonly found in the aggressive cell lines. Genome localization analysis revealed hypermethylation peaks and clusters, identifying eight hypermethylated gene promoters for validation in tissues from melanoma patients. Five CpG identified in primary melanoma tissues were transformed into a DNA methylation score that can predict survival (Log-rank test, p=0.0008). This strategy is potentially universally applicable to other diseases involving DNA methylation alterations.