Reviewerly/exHarmony huggingface dataset:
Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care

Background . In critical care, intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions, which has always been a challenging task. Recently, deep learning models such as recurrent neural networks (RNNs) have demonstrated their strong potential on predicting such events. However, in real deployment, the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate. Methods . In this study, we propose a novel self-correcting mechanism for RNN to fill in this gap. Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp, so that the model can “learn” from previous predictions. We also proposed a regularization method that takes into account not only the model’s prediction errors on the labels but also its estimation errors on the input data. Results . We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury (AKI) prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset. Conclusions . The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.
Discernible Effect of Video Quality for Distorted Vehicle Detection using Deep Neural Networks

In the transport sector, the rapid development of applications in communications and information technologies has led to the evolution of the intelligent transportation system (ITS). For sustainable development of road transport and traffic management, ITS played a vital role, especially in traffic efficiency and safety. However, with better road infrastructure, the growing number of vehicles on the road, and fast-track highways, vehicle detection in real-time is an important and challenging task. The real-time vehicle detection systems must distinguish and find vehicles in foggy, cloudy, and rainy environments, as these environmental conditions may reflect the noise effect in the captured videos. Therefore, in this work, the effect of video quality on object detection in self-driving scenarios is analyzed and tested. Deep learning algorithms, Res Net and YOLO, trained with high-quality video streams, are used for object detection in lower quality video streams. In this regard, the video quality is reduced with the increase in the quantization parameter using H.264, by the addition of Gaussian noise, and by introducing motion blur. Following this, the encoded videos are processed for vehicle detection, and it is found that with an increase in quantization parameters the effect on vehicle detection is negligible. In the degraded video through the Gaussian noise, the vehicles' detection fails above 1.5 dB added noise, while in the case of motion blur the detection failed when the motion blur exceeded 53 %.
A System for Visual Analysis of Objects Behavior in Surveillance Videos

Closed-circuit television (CCTV) surveillance systems are employed in different scenarios to prevent a variety of threats, producing a large volume of video footage. Several surveillance tasks consist of detecting/tracking moving objects in the scene to analyze their behavior and comprehend their role in events that occur in the video. Such analysis is unfeasible if manually performed, due to the large volume of long duration videos, as well as due to intrinsic human limitations, which may compromise the perception of multiple strategic events. Most of smart surveillance approaches designed for moving objects analysis focus only on the detection/tracking process, providing a limited comprehension of objects behavior, and rely on automatic procedures with no/few user interaction, which may hamper the comprehension of the produced results. Visual analytics techniques may be useful to highlight behavior patterns, improving the comprehension of how the objects contribute to the occurrence of observed events in the video. In this work, we propose a video surveillance visual analysis system for identification/ exploration of objects behavior and their relationship with events occurrence. We introduce the Appearance Bars layout to perform a temporal analysis of each object presence in the scene, highlighting the involved dynamics and spatial distribution, as well as its interaction with other objects. Coordinated with other support layouts, these bars represent multiple aspects of the objects behavior during video extent. We demonstrate the utility of our system in surveillance scenarios that shows different aspects of objects behavior, which we relate to events that occur in the videos.
An Efficient Deep Learning Framework for People Detection in Overhead Images

People detection is an important issue in video surveillance applications. The factors such as severe occlusions and scene perspective distortions in real application scenarios complicate this task. This article proposes an efficient deep learning framework for detecting people from overhead images. The proposed method uses a multi-scale Yolov4-Tiny algorithm for person detection. The multi-scale feature of the algorithm enables it to successfully detect people who appear small in an image. A comparative analysis was made with non-maximum suppression (NMS) algorithms, which significantly affect the detection performance. In this context, it is aimed to determine the most suitable NMS algorithm for the multi-scale Yolov4-Tiny algorithm. The performance of this method has been evaluated by various experiments. The experiments were carried out on two datasets consisting of many difficulties such as light, distance, occlusion, and wear a hat. As a result of the experiments, 94.85% mean average precision (mAP) value was obtained with the proposed method. The proposed method not only shows promising results, but can also work in real-time applications due to low computational cost.
Signal Classification Using a Mechanically Coupled MEMS Neural Network

This paper reports on the implementation and operation of the first micro-electromechanical (MEMS) network to perform basic neural computing. The device is comprised of a mechanically coupled network of three electrostatically controlled micro-structures with two of the coupled structures acting as the input layer and the third as the output (computing) layer. It has been shown that such device can be programed by application of appropriate bias voltages to the electrostatic control electrodes so that it can distinguish between a ramp (gradually increasing) input signal and a step (abruptly rising) input signal. The results serve as the proof of concept and a pre-cursor to performing more complex computational tasks using coupled micro-structures acting as a network of interacting neurons.
RestHAR: Residual Feature Learning Transformer for Human Activity Recognition from Multi-sensor Data

In recent times, surge in the use of smartphones in our daily lives has created a huge opportunity for paving the road towards human-centric computing by utilizing the rich data which gets recorded by it’s multiple sensors. Sensor-based human activity recognition has a tremendous amount of real-world applications such as health monitoring, surveillance, smart homes, and ambient assisted living. This paper presents a joint residual feature extractor and a transformer-based deep neural network for end-to-end human activity recognition using raw multi-sensor data captured from smartphones or wearable devices. Unlike conventional handcrafted feature extraction, this approach outperforms all present approaches showing state-of-the-art generalizable performance over multiple benchmark datasets. It achieves a test accuracy of 95.2% on the UCI HAR dataset and 96.4% test accuracy on the WISDM dataset.
Research on Time Series Anomaly Detection: Based on Deep Learning Methods

Abstract Time series anomaly detection has always been an important research direction. The early time series anomaly detection methods are mainly statistical methods and machine learning methods. With the powerful functions of deep neural network being continuously mined by researchers, the effect of deep neural network in anomaly detection task has been significantly better than the traditional methods. In view of the continuous development and application of deep neural networks such as transformer and graph neural network (GNN) in time series anomaly detection in recent years, the body of research lacks a comparative evaluation of deep learning methods in recent years. This paper studies various deep neural networks suitable for time series, which are divided into three categories according to anomaly detection methods. The evaluation is conducted on public datasets. By analyzing the evaluation criteria, this paper discusses the performance of each model, as well as the problems and development direction in the field of time series anomaly detection in the future. This study found that in the time series anomaly detection task, transformer is suitable for dealing with long-time series prediction, and studying the graph structure of time series may be the best way to deal with time series anomaly detection in the future
Evaluation system of college physical education teaching reform based on wireless sensor network

The traditional evaluation system of physical education reform in colleges and universities is usually designed with I/O module. This evaluation system is not real-time and it is difficult to meet the requirements of many evaluation indexes. In order to solve the problem of weak anti-interference and real-time performance of the evaluation system, this paper designs the evaluation system of College Physical Education Reform Based on wireless sensor network from two aspects of hardware design and software design. The hardware part mainly designs the evaluation interface, evaluation circuit, processor and display; The software part analyzes the principle of software evaluation, develops a new evaluation system and a new software evaluation process, and completes the work through three steps: communication information collection, communication information evaluation processing and information display. Compared with the traditional system, the experimental results show that the system can complete the accurate evaluation in a short time, and can quickly transmit the evaluation results to the LCD, which has strong real-time performance, effectively solves the problem of long delay in the traditional system, ensures the evaluation effect, and improves the quality of teaching reform.
A nonstationary and non‐Gaussian moving average model for solar irradiance

Abstract Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two‐way‐street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite‐based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second‐order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non‐Gaussian behavior; we develop a nonstationary and non‐Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood‐based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.
Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.
A Game Recommendation Method Based on Machine Learning

The emergence and popularity of the Internet have brought users a large amount of information to meet their information needs. Still, the rapid development of the Internet has brought about a significant increase in the amount of online information, making it impossible for users to obtain the part of the information that is useful to them. The efficiency of using information has been reduced instead. This is when a recommender system is needed to recommend the content of interest to users based on their information. Any website or platform has its own independent recommender system, and collaborative filtering is one of the traditional recommender algorithms. This paper uses steam's users' data to cluster and label separately to create four different collaborative filtering game-recommender models based on historical purchases, game time, and the relationship between items. They then compare their advantages and disadvantages to determine which model is more suitable for game recommender systems.
YOLO Algorithm-Based Surrounding Object Identification on Autonomous Electric Vehicle

An autonomous vehicle must be equipped with a camera, which works by providing visual input that is used to detect objects around the autonomous electric vehicle. Currently, no method has been implemented in real-time. Thus, this study utilized the You Only Look Once (YOLO) algorithm to detect objects in real-time around the autonomous electric vehicle. The objects were limited to humans, motorcycles, and cars. The results showed that the most compatible YOLO model for the system was the Tiny YOLOv4 model which was built with the darknet framework. The simulation experiment showed that detection accuracy was 80% and was able to transmit information in a form of data location of the object to the microcontroller. A success rate of 100% was obtained from 10 tests. Hence, it showed that the YOLO was able to detect objects and provided input to the steering control system. Meanwhile, the depth information method was used to measure the distance of the object to the vehicle in real-time with an accuracy of 60%. Real-time testing was conducted to test whether the autonomous electric vehicle can avoid objects in front of it by providing input from the detection results of the Tiny- YOLOv4 model object. The success rate of the system in real-time experiments was 100%.
Face Tracking for Flying Robot Quadcopter based on Haar Cascade Classifier and PID Controller

Abstract The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.
Badminton Backcourt Stroke Route Planning Method Based on Deep Learning

In order to improve the planning ability of the badminton backcourt stroke line, this study designs a badminton backcourt stroke line planning method based on deep learning. Firstly, the trajectory adaptive learning method of motion primitives is used to design the hitting line nodes and path space, so as to construct the shortest distributed grid structure model of the hitting line. Then, the constraint parameters of hitting route planning are analyzed, and then the hitting position and player posture are controlled according to node positioning and shortest path optimization deployment. Finally, the adaptive optimization of the route planning process is realized by combining the deep learning method. The simulation results show that this method has good learning control ability and good convergence performance and improves the reliability of badminton backcourt hitting line planning.
Search Queries to Internet as a Tool for Mid-term Forecast of Cryptocurrencies (XRP, Waves, ETH) on GMDH-based models

In spite of optimistic predictions of experts in recent years, cryptocurrencies still hold an inferior position on financial market due to the inability to provide a satisfactory forecast of their dynamics. The latter essentially depends on various subjective factors: attitude of authorities, interest of business community and trust of population. Our hypothesis is that subjective factors are well reflected in Internet queries, and in this study we confirm it experimentally. In the paper, we describe formation of descriptors&#x0027; vocabulary, creation of barometers, and building of predictive models. The source information covers the period 2019&#x2013;2021 (2 years) with the step of 1 week. In the paper we consider 3 cryptocurrencies (XRP, Waves, ETH), 2 forecast horizons (week, month), 2 forecast periods (calm, crisis), 3 models (autoregression, regression, hybrid), 2 basic algorithms from the GMDH Shell software (combinatorial, neural network). We show that preliminary smoothing of cryptocurrencies dynamics essentially reduces forecast error. Baseline is presented by the popular Holt-Winters method. The results allow us to recommend the proposed approach for experts of cryptocurrency market.
Scrumlity: An Agile Framework Based on Quality Assurance

Agile is a methodology where continuous iterations and testing take place during the entire Software Development Life Cycle of a product. Scrum is one of many agile frame-works and is considered the most popular or widely adopted framework. Although Scrum presents several advantages such as incremental deliveries at the end of each sprint, stakeholders and product owners can change the requirements during the process and Scrum can adapt very quickly to these changes. Process and final product quality continue to be Scrum’s main challenges. Several authors have attempted to improve the quality of Scrum projects, changing some aspects of the framework such as including new quality practices, a quality role, as well as quality processes. However, the quantification of quality is still a challenge. This study suggests a change to Scrum including a quality role and some artifacts to evaluate quality before, during, and after each sprint. The proposed framework, called Scrumlity, was used for several weeks in real projects; Tests were conducted after each sprint to evaluate the acceptance rating for this new framework. Results obtained after this exercise indicate that there are several improvements to development phases and that developers generally accept the framework.
Detection of Personality Traits Through Handwriting Analysis Using Machine Learning Approach

The human emotions and behaviors differ from person to person. Human handwriting is a unique one like finger print, which carries the richest information to gain the insights into the physical, mental and emotional state of the writer. The science of analyzing handwriting is called graphology. Graphological rules are defined by considering the features of the handwriting. The proposed approach is carried out with the intention of finding how successful is the computer-aided handwriting analysis and the most appropriate machine learning classification approach for graphological feature classification. An application is developed to intake handwriting samples and to output personality trait of the writer. The application analyzes 3 major features: pen pressure, page margin and word size of a handwriting sample. Three hundred handwriting samples were tested, and the outputs were stored as a dataset. The dataset is used to train and test support vector machine and K-nearest neighbor models. The classification reports depict high accuracies of 96% and 85% for SVM and KNN models, respectively. Application was tested with a small team of candidates and received good responses. More than 65% of them were satisfied with the functionality and the output of the application. The proposed methodology is open for further enhancements one like handwriting sample can be segmented further into words and characters and can analyze the features of them.