Reviewerly/exHarmony huggingface dataset:
Assessing Virtual Reality's potential to influence emotional states from negative to provide an instant positive effect

Human beings' emotional states are strong yet delicate where any trigger of any size can cause it to reverse/worsen instantly into an uncontrollable state, such as a Panic attack. Panic Attacks are an episode of heightened negative emotions that causes difficulty in breathing, increased heartbeat and shaking to name a few. This study explores Virtual Reality (VR), an immersive technology, as a solution to manipulate emotional states from negative to provide an instant positive effect, instant being at 10 seconds for this study. By experimenting on nine participants who went through two negative and positive scenarios, their heart rate, as they changed from negative to positive scenario were analyzed. The findings proved to be fruitful as all participants experienced a decrease in heart rate, at least in one of the markers set at either 10 seconds or 1 minute. This study is an initial step with positive outcomes that VR does have the potential to manipulate and influence emotional states from negative to positive instantly.
Named Entity Recognition Model of Power Equipment Based on Multi-feature Fusion

Extracting useful information from a large number of text files in the power field is of great significance to power informatization, and the identification of power equipment entities is a key part. Aiming at the difficulties of entity recognition of power equipment in the field of Chinese electric power, such as complex entity names and difficult identification of rare entities, this paper proposes a Chinese named entity recognition model based on multi-feature fusion. From the knowledge of the electric power field (concise dictionary of electric technical terms, English dictionary of electric power terms, etc.), a large number of electric power professional terms are sorted out to construct the electric power field dictionary, and then text segmentation and part-of-speech tagging are carried out under the guidance of it. Integrate various features of characters, words and word categories into input vectors and input them into the BiLSTM-CRF model for sequence labeling. The experimental results show that the entity recognition model proposed in this paper improves the recognition effect of Chinese named entities in the field of power equipment.
PiMS: A Pre-ML Labelling Tool

Machine Learning (ML) techniques in clinical decision support systems are scarce due to the limited availability of clinically validated and labelled training data sets. We present a framework to (1) enable quality controls at data submission toward ML appropriate data, (2) provide in-situ algorithm assessments, and (3) prepare dataframes for ML training and robust stochastic analysis. We developed and evaluated PiMS (Pandemic Intervention and Monitoring Systems): a remote monitoring solution for patients that are Covid-positive. The system was trialled at two hospitals in Melbourne, Australia (Alfred Health and Monash Health) involving 109 patients and 15 clinicians.
Bistable Josephson Junction-Based True Random Number Generator Without Inductors

True random number generators are essential for many applications including security and statistical simulations. A novel superconductive true random number generator is proposed based on inductor-less bistable Josephson junction circuits. The recently proposed design methodology, based on all-Josephson junctions, promises higher area efficiency and simpler circuit design. In this brief, this methodology is extended to a superconductive circuit design that extracts randomness from thermal noise present in electronic circuits. A high throughput of 16 GHz is achieved using the proposed design, with no bias or correlation in the output sequence. The quality of the generated randomness is assessed using NIST SP 800-22 statistical tests. A 100 Mb binary sequence is demonstrated to pass all 15 statistical tests using recommended parameters as per NIST guidelines. The simulation methodology, described in detail, is based on the MIT-LL SFQ5EE 10 kA/cm2 Nb standard process and open source Verilog-A model.
Object Detection: A Comparative Study to Find Suitable Sensor in Smart Farming

Crop-loss is one of the major factors that led the farmers to ruin their lives and everything they possess. Internet of Things (IoT) is an advanced technology that is effectively benefitting various fields nowadays. We have tried to use this technology to save the lives of farmers by saving their crops from being ruined. For this, we have proposed a model that is going to be used in the agricultural fields to prevent wildlife attacks that is one of the major factors of crop loss. As we know that Animal hunting is one of the punishable offences in many countries. We want the animals to run away from the field without any physical harm. In this paper, we have tried to do a comparative study of sensors that would be most preferable for our proposed model. We want to find out which sensor is most suitable for the detection of wild animals in the agriculture field. For finding that we have done some experiments and observed the results to justify our sensor selection. As we want to choose the most suitable sensors for our crop protection model.
Conditions for the successful mastering of virtual reality technology by schoolchildren

This article describes the results of the work of the “School of Virtual Reality and Neurotechnology”, which functioned within the framework of the Talant22 school. During this training course, schoolchildren used VR headsets (Oculus Rift S and Class VR) to learn the basics of interacting with virtual (VR) and augmented realities (AR), and also used the free Google Street View mobile application to create their own VR content (photosphere). We have obtained evidence that video games as a hobby leads to more successful mastering of virtual reality technology by children. AAA games and a deeper knowledge of physics (within the school curriculum or wider) can make this mastering even more successful. We have also established the fact that developed skills in various game mechanics, controller operation skill and the perception of 3D space and experience in the following genres of video games are also key: simulation, shooter, action and adventure. It has been found that the best way to quickly adapt to a virtual environment and learn how to interact with virtual objects is obtained with the experience of playing on video game consoles. In addition, it turns out that playing video games as a hobby contributes to a deeper knowledge of the English and is a kind of additional practice. The use of VR in the educational process is accompanied by multiple positive effects, while the manifestation of different negative side effects turned out to be low, and the proposed tasks were rated by schoolchildren as simple and exciting. In this regard, we see that the further development of gamification should be closely associated with the process of introducing video games into the education. The main principles we promote — motivation through entertainment and game as the most important process, in our opinion, can stimulate students to master new areas of knowledge and create their own VR content. Thus, a deep revision of gamification approaches and further research in this area is required.
Face Mask Detection: A Deep Learning Approach

The global health is being ravaged by the COVID-19 coronavirus epidemic. The healthcare industry is a complete disaster. As human wants to recover from the outbreak of COVID-19 and seeks to revert to normalcy, there is an increase in unease among those who expect to resume their everyday routines in reality. Numerous preventative measures have been implemented to avoid the transmission of this virus, notably the use of a masks, which the World Health Organization (WHO) significantly and positively instructs. According to multiple research, using a face mask minimizes the likelihood of viral propagation while also providing an impression of security. It is, nevertheless, impossible to independently track the proposal's application. Therefore, technology is incredibly important. The goal of this study is to explain a CNN-based system for recognizing occurrences of a person with or without a mask. It would enable it easy to keep track of safety violations, encourage the use of facial masks, and establish a safe working environment.
Multi-View Stereo and Depth Priors Guided NeRF for View Synthesis

In this paper, we present a new framework for view synthesis of novel view based on Neural Radiance Fields(NeRF). We aim to address two main limitations of NeRF. Firstly, we propose to combine multi-view stereo into NeRF to help construct general neural radiance fields across different scenes. Specifically, We build a MVS-Encoding Feature Volume with average groupwise correlation to aggregate the multi-view appearance and geometry feature for every source view. And then we use an MLP to encode neural radiance fields by using the scene-dependent features interpolated from the MVS-Encoding Feature Volumes. This makes our model can be applied to other unseen scenes without any per-scene fine-tuning, and render realistic images with few images. If more training images are provided, our method can be fine-tuned quickly to render more realistic images. In fine-tuning phase, we propose a depth priors guided sampling method, which can make the model represent more accurate geometry for corresponding scenes and so render high-quality images of novel view. We evaluate our method on three common datasets. The experiment results show that our method performs better than other baselines, neither without or with fine-tuning. And the depth priors guided sampling method can be easily applied on other methods based on Neural Radiance Fields to further improve the quality of rendered images.
Adaptive and Efficient Qubit Allocation Using Reinforcement Learning in Quantum Networks

Quantum entanglement brings high-speed and inherently privacy-preserving transmission for information communication in quantum networks. The qubit scarcity is an important issue that cannot be ignored in quantum networks due to the limited storage capacity of quantum devices, the short lifespan of qubits, and so on. In this article, we first formulate the qubit competition problem as the Cooperative-Qubit-Allocation-Problem (CQAP) by taking into account both the waiting time and the fidelity of end-to-end entanglement with the given transmission link set. We then model the CQAP as a Markov Decision Process (MDP) and adopt a Reinforcement Learning (RL) algorithm to self-adaptively and cooperatively allocate qubits among quantum repeaters. Further, we introduce an Active Learning (AL) algorithm to improve the efficiency of the RL algorithm by reducing its trial-error times. Simulation results demonstrate that our proposed algorithm outperforms the benchmark algorithms, with 23.5 ms reduction on the average waiting time and 19.2 improvement on the average path maturity degree, respectively.
The Voltage Prediction of a Buck Converter Using Machine Learning Approaches

Machine learning is a part of artificial intelligence and a method of analyzing data that convert to automatic operations the building of analytical models. It is aimed on the proposal that systems can learn from materials on their own, identify patterns, and build decisions with little or no human assistance. The paper reviews the machine learning as a process for teaching computers to learn from experience or directly from data, based on a predefined equation as a model. We used four types of decision tree as machine learning methods for data set classification, such as PERTree, M5P, RandomTree and RandomForest. First, we give the equations for buck converter as a model, then we teach the computer to make predictions by his own. The Buck DC-DC converter decrease voltage by using a transformer, so the output voltage is always less than or equal to the input voltage. Second, the way we gain the database and WEKA software are described. WEKA operate with.arff file format, so we first convert our database in the required format. Then we present and discuss the results obtained using different types of classification.
Online Knowledge Distillation with Multi-Architecture Peers

Knowledge distillation is a technique that effectively trains a lightweight model with strong generalization ability under the supervision of a powerful teacher model. However, a well-trained teacher model is really time-consuming. Therefore, online knowledge distillation emerged, which uses the aggregated results of multiple student models to help each student model to converge. But existing online distillation methods have several drawbacks, such as unavailable teacher models and the lack of diversity among student models. These hinder the further improvement of network performance. In this work, we propose a new online knowledge distillation technique that trains diverse architecture auxiliary peers to enhance the final effect of knowledge distillation. We propose a Multi-Architecture Peers (MAP) mechanism, which enhances student models to learn diverse features and delivers a powerful ensemble result. Furthermore, we introduce a Feature Learning Module (FLM) that efficiently learns rich semantic information by combing feature maps generated from the last block of student models and generates precise weights for each student model. The experimental results on three image classification datasets: CIFAR10, CIFAR100, and SVHN show that our method trains diverse student models and achieves much better results than alternative methods.
Person search by similarity of clothing using clothing database

A method searchinf for a person by focusing on the clothing of the target is proposed. YoloV3 is used to extract the area of the person the 360 degree image by RICO Theta Z1. The extracted image is compared with the photo to be searched by the cherasteristic of the person’s wear. The ResNet model is trained using the Category and Attribute Prediction Benchmark of the Deepfashion dataset, which contains images of various outfits. This model is used for discrimination by calculating the similarity between the clothing of the person in the camera and the clothing of the person in one photo.
Semi-Supervised Learning Algorithm Based on AFS Theory and LogitBoost Algorithm

There are a wide variety of classification problems in the world, so the classification task has always been a fundamental and important machine learning problem. However, many scholars have focused on the accuracy and training speed of classification models, while neglecting an important factor of classifier models: interpretability. In many cases, it is extremely difficult to collect labeled data, so the use of both labeled and unlabeled data to train classifiers that generate better semi-supervised learning is also the focus of many scholars. For the above reasons, this paper proposes a new classifier, which is based on the axiomatic fuzzy set(AFS) theory. It not only realizes the interpretability of the fuzzy theory, but also uses the LogitBoost algorithm to verify the inconsistency of the axiomatic fuzzy set theory.
Exploration of design methods and tools for virtual, augmented and mixed reality

This paper shows an overview of design practices of the XR-lab at the Amsterdam University of Applied Sciences, The Netherlands. Over the course of six years, interdisciplinary teams of students have delivered 55+ prototypes in virtual, augmented, and mixed reality for a variety of 40+ clients. As human-computer interaction is entering a new evolutionary phase towards human-computer integration, new opportunities in extended reality (XR) have the potential to funda-mentally alter human characteristics and abilities. Therefore, this paper begins with taking a philosophical stance on ‘being human’ and the anthropological concept of ‘liminality’ in XR-experiences. A further exploration of the concept of 'emotional rehearsal spaces' uses know-how from performance art, dance, architecture, and dra-maturgy. Insights from tangible practices at the XR-lab show the cultural journey in XR-collaborations. This is made visible through a quick and dirty experiment on artistic thinking, design thinking, and system thinking, which shows how interdisciplinary collaborations are able to ignite new combinations of thought in design teams and individual professionals. Finally, we show an overview of specific design methods and tools that have been explored at the XR-lab over the years.
Fast and robust state estimator for a kind of nonlinear systems: one method from two perspectives

This paper aims to solve the fast and robust state estimation problem for a kind of nonlinear systems based on the modulating functions method. The estimated system state is exactly given by algebraic integral formulas proved by two different means: the traditional modulating functions method which transfers the system into the input-output differential equation and the new proposed one which is based on the state-space representation form. The proposed estimator is non-asymptotic and robust against corrupting noises in the output signal. The modulating functions applied in the proposed estimator with specific properties are defined and constructed. The effectiveness and advantage of the proposed method are illustrated by a provided example.
Discover the ICS Landmarks Based on Multi-stage Clue Mining

In recent years, the rapidly increasing landscape of industrial control systems (ICS) devices has made the ICS geolocation more important. However, IP-based geolocation cannot provide high accuracy geographical locations for ICS devices. Commercial databases only provide coarse mappings between IP hosts and physical locations. Measured-based geolocation relies on the number of high-quality landmarks. In this paper, we present a novel framework called OSI-Geo for serving high-quality landmark mining of ICS devices. The main idea is that there are many location-indicating clues in the open-source information exposed by ICS devices, which can be utilized to find their physical locations. The OSI-Geo automatically collects location-indicating clues to generate ICS landmarks at large-scale. We conduct real-world experiments for validating the effectiveness and performance of our method. The results show that OSI-Geo can accurately collect clues with over 99% recall and precision. Based on those clues, 36,872 stable landmarks, covering 162 countries and 5,596 cities, are obtained. Among them, there are 30,290 (82%) fine-grained landmarks accurate to street-level at least. The accuracy of IP geolocation has been improved significantly based on the ICS landmarks. Thus, OSI-Geo achieves effectively landmark mining for ICS devices.
Project-Based Learning: Bridging the Gap Between Algorithm and Architecture in Neural Network Course

Neural network has shown its powerful ability in many research fields in the recent years. By using different network structures, many new algorithms are developed to enhance the accuracy. Along with the algorithm development, corresponding architectures are also proposed for the acceleration. However, pure algorithm may not be hardware friendly. As a result, we need to find an optimal trade-off between algorithmic accuracy and architectural efficiency. To help students build the gap between algorithm and architecture, this paper introduces a project-based learning. The project is called learned image compression, which is composed of three phases: algorithm design, architecture mapping and algorithm-architecture co-optimization. Through the project, the students are expected to develop a neural network with high image compression ratio and hardware performance. Furthermore, these kind of knowledge can be extended to any neural network applications.
Growth of agile software development methodologies in a web apps scenario

The software industry is changing rapidly, and software firms look for processes, approaches, and methodologies that focus on effective software development gaining stakeholder satisfaction. Software development models mainly help firms to deploy projects in a smooth way by using specific methods and techniques. The challenge of choosing between traditional software development methodologies and agile development methodologies plays a crucial role when firms are looking for software processes, approaches and tools that help to create stable products. The choice of a software development model depends on project timeframe, business requirements, availability of resources, team, client involvement and perception. The traditional models face shortcomings of being rigid, difficulty in accommodating changing requirements and heavy documentation. Agile models represent a major innovation from traditional, plan-based approaches to software engineering. The objective of this paper is to review the concept of agile and traditional methodologies in terms of definitions, processes, and impact on the delivery of innovative products and compare between agile and traditional software engineering models. The implementation of scrum agile model in a software firm with web application (webapps) domain is also presented in this paper.
Master-Slave Policy Collaboration for Actor-Critic Methods

Actor-critic methods of deep reinforcement learning are widely used to address continuous control tasks. However, the difficulty in balancing exploration and exploitation, as well as the limitation in the learning efficiency of actors, slow down the overall learning progress, and lead to suboptimal policies. To alleviate these problems, we introduce a policy collaboration mechanism using the master-slave architecture (MSPC). At different stages of training, actors are divided into master actors and slave actors, where the master actors with better performance dominate the training, and the slave actors extract the knowledge of the master actors through policy distillation, thus improving the learning and collaborating efficiency for all actors. Moreover, we propose a new experience replay mechanism, called CER, to further improve the exploration ability and performance of master actors. Finally, we demonstrate empirically the advantages of MSPC by applying it to existing state-of-the-art actor-critic methods in Mujoco robot simulation tasks. We also provide a demonstration showing that MSPC+CER improves over MSPC for sample efficiency and learning speed.
Parallel Hypergraph Convolutional Neural Networks for Image Annotation

In recent years, the application of graph neural networks in automatic image annotation has become more mature. However, there still exist several problems. First of all, the regular graph structure network modeling is not accurate enough, and it is not flexible enough to deal with multi-label and heterogeneous data. Second, some algorithms only use graph convolutional networks to construct sample or label graphs, limiting the fusion and extension of multi-modes. In this paper, we propose a semi-supervised automatic image annotation method with parallel hypergraph convolutional neural networks(PHCN). The algorithm combines hypergraph convolutional network (HCN) with graph convolutional network (GCN) to improve the annotation performance under semi-supervised learning. In addition, We connect the label graph with the sample hypergraph, further consider the distribution of labels and features, and aggregate the features. Experiments on three benchmark image annotation datasets show that our method is superior to other existing state-of-the-art methods.