Reviewerly/exHarmony huggingface dataset:
Robust Extreme Learning Machine for estimation of triangular, rectangular, and parabolic weirs

In this paper, for the first time, the DC of triangular, rectangular, and parabolic weirs is simulated by a new learning machine called “Robust Extreme Learning Machine” (RELM). The used laboratory data are divided into two categories: training (70% data) and testing (30% data). In the next step, the number of neurons inside the hidden layer is examined. For the structure of the proposed RELM algorithm, 10 hidden layer neurons are embedded. For the learning machine used in the present study, six different activation functions are evaluated that the Sigmoid activation function has better performance and is used for the RELM structure. Next, the calibration parameter of the RELM algorithm is discussed. The optimal regularization parameter is selected for the present study equal to 0.0001. Then, using parameters affecting the DC, four RELM models are developed. By performing various analyzes, the superior RELM model and the most effective input parameters are identified. Also, comparing the performance of the RELM model with ELM shows the superiority of the RELM algorithm. For the superior model, a relative derivative sensitivity analysis is performed to investigate the behaviour of the input parameters on the DC. Finally, an equation for estimating the DCs of triangular, rectangular, and parabolic weirs is proposed. • Discharge coefficient was simulated using Robust Extreme Learning Machine (RELM). • Sensitivity analysis of parameter affecting the discharge coefficient. • Hyperparameters of the RELM algorithm were tuned.
Analysis on noise impact in algorithm-based quantum computing benchmark

Different kinds of quantum computing prototypes and simulators have been developed rapidly, and entry-level cloud-based quantum computing services begun to emerge. Various noise in quantum computer had significant impact on its execution results, which were also focus of benchmark and performance evaluation. Based on IBM Quantum Lab platform, several application algorithms have been executed in both quantum backend and simulator to verify the impact of circuit depth, different quantum noise model and parameters on the fidelity of quantum computing results. The prospect of quantum computing benchmark evaluation was also discussed.
An Improved EIoU-Yolov5 Algorithm for Blood Cell Detection and Counting

At present, detecting blood cells commonly depends on artificial count by observing microscope. Artificial observation takes a lot of manpower. In the object detection algorithm for real-time detection, the representative algorithm of the real-time object detection algorithm is Yolov5 algorithm. However, the accuracy of Yolov5 algorithm is low. In order to improve the accuracy of the algorithm, realize the automation of blood detection and reduce the workload of manual count, this paper proposes an improved algorithm EIoU-YOLOV5 based on Yolov5, which improves the Loss function of prediction box by replacing CIoU Loss with EIoU Loss. Experimental results on the common data set BCCD show that, compared with the original Yolov5 algorithm, the Recall of EIou-YOLOV5 algorithm increased from 0.855 to 0.917, which increased by 6.2 percentage points. Therefore, it reduced the missed rate effectively. The result of mAP@0.5 is increased from 0.899 to 0.922, which is raised by 2.3 percentage points. Among them, the detection of platelet increased the most. It increased from 0.858 to 0.92, which increased by 6.2 percentage points. Therefore, the improved EIoU-yolov5 algorithm can better assist clinical diagnosis.
Improve on Entity Recognition Method Based on BiLSTM-CRF Model for the Nuclear Technology Knowledge Graph

The accuracy of entity recognition is particularly important for knowledge graph construction. The traditional Named Entity recognition (NER) model mainly includes HMM, CRF, BiLSTM, BiLSTM-CRF, etc. It is difficult to solve the problem of word meaning confusion resulting from the wrong separation at the end of the entity when using the four models for the labeled nuclear technology knowledge data sets. In order to improve the entity recognition effect and address the problem of polysemy in nuclear technology knowledge data set, an improved nuclear technology entity recognition method based on the BERT-BiLSTM-CRF combination model was proposed by comparative experiments. According to the results, it can conclude that the application of the BERT model instead of the Word2Vec algorithm for word vector training is helpful to the model recognition, and the exclusive dictionary word segmentation and part of speech classification of nuclear technology texts contribute to improving the quality of labeled data. The experiment verified that the usage of BERT pre-training model can solve the problem of polysemy in NER to some extent. Meanwhile, it validated the precision in nuclear technology knowledge entity recognition for collected data, which could be used in pre-procedure in nuclear technology knowledge graph construction.
A Hybrid Asymmetric Integer-Only Quantization Method of Neural Networks for Efficient Inference

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing. Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices. In order to reduce the complexity of deploying neural networks on Integer-only hardware, most of the current quantization methods use a symmetric quantization mapping strategy. However, the robustness and generalization of this quantization mapping strategy are poor. It is difficult for the quantized model to meet the accuracy requirements, especially for complicated tasks with higher accuracy requirements. In this paper, an efficient hybrid asymmetric Integer-only quantization method for different types of neural network layers is proposed. The proposed method can resolve the contradiction between the quantization accuracy and the ease of implementation, and balance the trade-off between clipping range and quantization resolution, and thus improve the accuracy of the quantized neural network. The results show that compared with the traditional symmetric quantization method, the accuracy of the proposed method can be improved up to 2.02% for classification models and up to 5.52% for the Yolo-v3 tiny target detection model, enabling the neural network to be easily deployed and implemented on embedded devices.
Binocular Camera Calibration Based on BP Neural Network Optimized by Improved Particle Swarm Optimization

Binocular vision technology is widely used in daily life, and binocular camera calibration is of great significance. Aiming at the problem that the training accuracy of the current camera calibration method based on neural network is not high enough, this paper proposes a binocular camera calibration method based on the improved particle swarm optimization BP neural network (APSO-BPNN), and introduces the adaptive particle swarm optimization algorithm to optimize the BP neural network. A binocular camera calibration model is constructed, and real data are used to verify the effectiveness and feasibility of the method. The experimental results show that the dual-target calibration method based on APSO-BPNN has higher calibration accuracy than the traditional neural network calibration method.
An IoT-Oriented Gesture Recognition System Based on ResNet-Mediapipe Hybrid Model

In this paper, we propose the ResNet-Mediapipe hybrid model and a new Internet of things (IoT) control system in order to implement gesture control of IoT devices. In the process of designing the ResNet-Mediapipe workflow, we improve the ResNet-50 and integrate it with Mediapipe to improve recognition speed and accuracy. Besides, we also create a brand-new gesture dataset with more than 5700 pictures, which is richer than other open-source gesture datasets. Experiments show that ResNet-Mediapipe can capture hand position in a variety of situations and recognize gestures in real-time with an accuracy rate of over 98.5% and recognition speed up to 45 fps, which is more accurate and faster than other gesture recognition methods. The new gesture-controlled IoT system we propose is equipped with hardware that includes Raspberry Pi, STM32F103, ESP8266, EG200U, and various IoT devices. This system can realize gesture recognition based on ResNet-Mediapipe and control numerous IoT devices. Besides, we design a human-computer interface for the system on the Ali IoT cloud platform and a set of gestures used for human-computer interaction, which enable the users to monitor IoT devices' information on the Web page and choose different IoT devices to control. The experiment proves that this project provides a precise and convenient way for people to interact with gestures.
Multi-attribute Context-Aware Item Recommendation Method Based on Deep Learning

With the wide application of deep learning in the field of recommendation, item recommendation has gradually become a new hotspot. The existing item recommendation methods ignore the context relationship between multiple attributes and fail to make full use of the potential structural interaction relationship. This paper proposes a new multi-attribute context-aware item recommendation method (MCIR) based on deep learning to recommend a series of suitable items for characters in the match. The attribute attention encoder (AAE) module proposed in this method mainly decouples the local and global features of multiple attributes, and the global multiple aggregation (GMA) module integrates the obtained context features in a graph structure. Extensive experiments on the kaggle public game dataset show that our method significantly outperforms previous methods in terms of Precision, F1 and MAP compared to other existing methods.
YOLO Method Analysis and Comparison for Real-Time Human Face Detection

Face detection plays a huge role in the fields of computer vision and pattern recognition. YOLOv3 is a fast single-stage detector method. However, if consider real applications like mobile devices which have limited memory and computation. YOLOv3 with darknet-53 still has a complex architecture, that can have an impact on computing costs. This study specifically focuses on the comparative analysis of the YOLOv3 method on the problem of human face detection by proposing YOLOv3-tiny that adopts YOLOv3. However, with a reduced architecture to increase detection speed and make the method lightweight, so as compatible with the needs of low-power devices with limited memory and computing. Both methods were trained with human facial data. The results of the training with 50 epochs YOLOv3 outperformed YOLOv3-tiny by a difference of 7% precision, 3% recall, and 10% mAP. However, in terms of time consumption during the training process and the size of the resulting weight. YOLOv3-Tiny shows much better results, which only takes 39 minutes, and weighs size only 16.6 MB, while YOLOv3 takes 5 hours 9 minutes with weighs 117 MB. Both methods were then tested on RGB and Grayscale images. Tests are carried out based on the distance of the face and the number of faces. The test results of the YOLOv3 are very good at predicting close faces with the highest value of 95% and getting the same results on many faces with an average value of 95% accuracy. YOLOv3-tiny achieves lower accuracy than YOLOv3 on both image types. The highest result of YOLOv3-tiny is close face with 87% accuracy. However, YOLOv3-tiny excels in detection speed. The final test is based on the speed of CCTV video inference. YOLOv3-tiny significantly outperformed YOLOv3 with detection speeds of 104.1 FPS on the GPU and 8.7 FPS on the CPU. Meanwhile, YOLOv3 is only able to detect faces at a speed of 30.2 FPS on the GPU and 1.6 FPS on the CPU.
Generalized Westerink-Roufs Model for Predicting Quality of Scaled Video

Resolution is a fundamental property of encoded video. Understanding the impact of resolution on quality as an independent parameter can help design better, more efficient systems, such as selecting optimum rendition in adaptive video streaming applications. One known quality model that considers resolution for predicting the perceived picture quality is the Westerink and Roufs (WR) model, which establishes the relationship between subjective quality and two parameters of viewing setup: angular resolution and viewing angle. This paper first validates the WR model on recent datasets and shows that it is reasonably accurate. We then propose a generalization of this model, allowing operation in a broader range of parameters and with more graceful saturation in extended regions. We then validate the performance of the proposed Generalized WR model on the new datasets and show that the proposed model achieves even a better fit to the recent datasets. We also demonstrate that the proposed Generalized model can account for the differences in scaling algorithms, including more advanced ML-based methods such as super-resolution. We conclude with a discussion of several possible applications of this model, including its use to guide the rendition selection decisions in streaming players and adapt that decision logic based on the upsampling algorithms used at the player.
Deep Learning-Based Light Field Image Quality Assessment Using Frequency Domain Inputs

Light Field (LF) cameras capture angular and spa-tial information and, consequently, require a large amount of resources in memory and bandwidth. To reduce these requirements, LF contents generally need to undergo compression and transmission protocols. Since these techniques may introduce distortions, the design of Light-Field Image Quality Assessment (LFI - IQA) methods are important to monitor the quality of the LF image (LFI) content at the user side. The majority of the existing LFI-IQA methods work in the spatial domain, where it is more difficult to analyze changes in the spatial and angular domains. In this work, we present a novel NR LFI-IQA, which is based on a Deep Neural Network that uses Frequency domain inputs (DNNF-LFIQA). The proposed method predicts the quality of an LF image by taking as input the Fourier magnitude spectrum of LF contents, represented as horizontal and ver-tical Epipolar Plane Images (EPI)s. Specifically, DNNF-LFIQA is composed of two processing streams (streaml and stream2) that take as inputs the horizontal and the vertical epipolar plane images in the frequency domain. Both streams are composed of identical blocks of convolutional neural networks (CNNs), with their outputs being combined using two fusion blocks. Finally, the fused feature vector is fed to a regression block to generate the quality prediction. Results show that the proposed method is fast, robust, and accurate.
When Every Millisecond Counts: The Impact of Delay in VR Gaming

This paper presents the finding of a subjective study investigating the impact of delay on a VR gaming experience, applying an experimental design to a virtual game of squash. 32 participants were asked to rate the stability of the VR experience while playing the game, with results showing an impact of delay even at the lowest level of 20 ms added delay. With 120 ms added delay, most participants judged the experience to be fully unstable. Exploring individual differences, we also found that males and experienced gamers were more critical, or more sensitive, to higher levels of delay, compared to females and less experienced gamers.
Human Influential Factors Assessment During At-Home Gaming with an Instrumented VR Headset

Human influential factors (HIFs) play a crucial role in a gamer's perceived immersive media experience, especially in virtual reality (VR) applications where cybersickness can be present. Typically, questionnaires have been used to gauge a gamer's experience focusing on factors such as sense of presence and engagement, to name a few. Questionnaires, however, can be time consuming and do not allow real-time assessment. To this end, affective/physiological computing tools have emerged as potential instrumental measures of HIFs. Affective tools, however, typically require the user to be in controlled laboratory settings. Therefore, the results may have limited transferability to everyday “in the wild” settings. This paper aims to fill this gap. First, we developed a plug-and-play instrumented VR headset capable of measuring multiple physiological signals, including electroencephalography (EEG), electrocardiography (ECG), and electro-oculography (EOG). Instrumented headsets were dropped off at the homes of eight novice VR gamers, together with a gaming laptop and a data streaming laptop, and players were asked to play Half-Life Alyx at the comfort of their homes. At the end of the game, users were asked to answer a questionnaire about their experience, including perceived sense of immersion, presence, emotions, realism, and engagement using 10- point scales. Several HIF biometrics were then extracted from the neurophysiological signals and shown to significantly correlate with the questions related to experience features. These findings suggest that real-time VR gamer experience measurement can be possible in highly ecological settings.
Designing Real-time, Continuous QoE Score Acquisition Techniques for HMD-based 360°VR Video Watching

Watching HMD-based 360° video has become in-creasing popular as a medium for immersive viewing of photo-realistic content. To evaluate subjective video quality, researchers typically prompt users to provide an overall Quality of Experience (QoE) score after viewing a stimulus. However, since users can adjust their viewport throughout a 360° video, a higher level of spatiotemporal granularity is needed for adaptive 360° video streaming. To address this, we design several real-time, continuous QoE annotation input and peripheral visualization techniques, with the goal of minimizing mental workload and distraction during score acquisition. Drawing on two parallel co-design sessions with seven experts, we find that touchpad and joystick are most suitable for continuous input, with DotMorph (circle with tick label that varies in filling) for peripheral state feedback. We contribute design findings for testing QoE score acquisition techniques during HMD-based 360° video watching, which enable more precise optimization of adaptive video streaming quality.
QoE of Frame Stalls in Remote 6-DOF VR

Remotely rendered Virtual Reality (VR) typically has to rely upon less capable and less reliable communication channels as compared to locally rendered VR. Such communication channels pose the risk of impairing the VR experience with temporary disturbances in form of frame stalls that would not be present in local VR. However, it is not obvious to which extent the durations of temporary stalls affect the Quality of Experience (QoE) in interactive 6-degrees-of-freedom (DOF) VR, and neither to which extent present reprojection/warping techniques mitigate any adverse effects of such stalls on user perception. In this paper, subjective experiments were conducted with N =29 users to quantify the relationship between QoE and single, isolated stall durations in interactive 6-DOF remote VR when stalls either freeze the frame in place, or when frames are reprojected by the VR driver. Results indicate that given a 90 fps headset and a simple task that requires moderate head-movement; (1) short, isolated stalls of up to 4 frames go unnoticed on average, no matter whether reprojection is used or not, (2) the number of perceived stalls when using reprojection is the same or lower than the reference (no stall) in the range 1–4 frame stalls, (3) reprojection entails significantly higher user ratings for stalls ranging from 8 frames and beyond, (4) the improvement of using reprojection over freezes in terms of making more stalling events imperceptible is highest in the range 8–16 frames.
Decentralized platforms: Goals, challenges, and solutions

Today there is a trend to deploy both commercial and social applications and services on decentralized platforms. This corresponds to the demands of modern society for openness and transparency of information, freedom of access, and equal rights of participants. However, the more sophisticated architecture of decentralized platforms poses new challenges to the developers of such systems. This paper discusses the goals, tasks, and problems that arise during the transition from traditional (centralized) technologies to decentralized ones, and some potential ways to solve them.
Multisensory Immersive Experiences: A Pilot Study on Subjective and Instrumental Human Influential Factors Assessment

Recent technological advances have allowed for virtual reality applications to burgeon. With virtual reality (VR), so-called human influential factors play a crucial role in the final perceived immersive media experience (IMEx). While two individuals can use the same VR headset, play the same game in the same location, and have the same goals, the two individuals can have very different experiences, with varying perceptions of immersion, presence, realism, engagement, and cybersickness. This can be particularly true in multisensory immersive experiences where, in addition to audio-visual stimuli, olfactory and haptic feedback can be used. In this paper, we describe a pilot study in which a VR game was developed to combine audio-visual, olfactory, and haptic feedback to the user in real-time. After game play, participants were asked about their IMEx using five scales: realism, immersion, presence, engagement, and overall quality of experience (QoE). Moreover, using an instrumented VR headset we measure electroencephalography (EEG), electrocardiography (ECG), and electrooculography (EOG) signals and compute several instrumental measures of human influential factors, including an engagement index, arousal and valence indices, frontal alpha asymmetry, heart rate, several EEG sub-band powers, and eye blink rate. Using the subjective ratings, we measure the contribution that each IMEx subscale has on overall QoE, as a function of the type of sensory stimuli used. Results on 11 participants suggest very different contributions once smells and haptics are incorporated, relative to traditional audio-visual experiences. We also report on several instrumental measures that showed significant correlations with the IMEx subscales, suggesting that, in the future, real-time instrumental QoE measurement of multisensory experiences could be possible.
Role of Intricate Pottery Visualization in Ceramic Manufacturing

Layered manufacturing, the underlying technology of 3-D printing, has made rapid strides over the last 30 years. We discuss layered manufacturing from the artist's perspective, especially for intricate ceramic pottery. We contend that opportunities exist for applying visualization to the foremost problems plaguing layered manufacturing. Virtual pottery involves meeting two conflicting constraints: rapid visualization during modeling and accurate rapid prototyping during manufacturing. Artists simultaneously need both low polygon shape representation for interactive visualization and adequate representation for generating accurate printable models. Artists also face the additional complexities of adding surface details that cannot be achieved by hand and handling materials like clay used in the manufacturing of real pottery. Illustrated by a system we have developed that uses sound resonance patterns to create volumetric textures for virtual pottery, we show how visualization helps address both these problem areas.