Reviewerly/exHarmony huggingface dataset:
A Fast Light Baking System for Mobile VR Game Based on Edge Computing Framework

Metaverse development has often focused on bettering virtual reality technologies due to benefits in establishing immersion in virtual environment1. Its dependency on VR technology brings limitations including the lack of high-quality graphics and a lack of mobility. The mobile platform is the most prevalent interface for users to gain VR experience. There is a big challenge that mobile VR must face: it has limited computing power, but VR requires high rendering speed and image quality. The traditional solution is to complete lighting and shading computation offline and bake the result to light map. Thus, only texture lookups are needed at runtime which allows very high rendering speed. The problem of the traditional baking is that they usually take seconds to minutes to finish. In this paper, we introduce a novel edge computing framework to reduce the baking time to less than a second. Our method uses reservoir-based spatiotemporal importance resampling (ReSTIR) to bake low resolution light maps at edge server. At mobile side, the light maps are enlarged using super resolution technique. We also use denoiser to make sure the baked image is noise-free. The experimental results show that our method can have near real-time performance for scene with average complexity. The bottleneck is the bandwidth of mobile device when performing super resolution. Once it gets improved, we can expect our entire edge computing framework becomes real-time.
Analysis and Implementation of Key Technologies in Spoken English Automatic Recognition and Evaluation System

The rapid development of information and network in the 21st century has changed the way of human life, and the communication between people is more convenient. The research of speech recognition technology has high commercial value. How to use this technology more naturally, reliably and at low cost in business and daily life is its development direction. In the future, human beings can achieve some things more conveniently and quickly through voice interaction, and enjoy more modern services. This paper designs a complete intelligent system driven by voice, including voice recognition, voice evaluation, intelligent dialogue and other core AI technologies. The system allows learners to interact with AI virtual characters naturally, and simulate different application scenarios for training.
Relaxed Symmetric Non-negative Latent Factor Analysis for Large-scale Undirected Weighted Networks

A large-scale undirected weighted network (LUWN) is described by a symmetric high-dimensional and sparse (SHiDS) matrix. To correctly represent its symmetry, existing models adopt a strong symmetry assumption, i.e., reducing the solution space to adopt a unique latent factor matrix to describe an SHiDS matrix’s symmetry. Yet it may impair a resultant model’s representativeness to its numerical features. Aiming at addressing this issue, this work proposes a Relaxed Symmetric Non-negative latent factor analysis (RSN) model that adopts three-fold ideas: a) Introducing a triple equation constraint into its learning objective for relaxing the strong symmetry assumption, thereby greatly improving its representativeness to the numerical features of an SHiDS matrix; b) Adopting the framework of Alternating Direction Method of Multipliers to fast realize its learning objective subject to multiple constraints; and c) utilizing a data density-oriented principle during its modeling and optimization, thereby precisely representing an SHiDS matrix’s imbalanced data. Empirical studies on four industrial SHiDS matrices describing real LUWNs demonstrate that RSN outperforms state-of-the-art models in representing an SHiDS matrix precisely, as well as achieves highly competitive computational efficiency. Hence, this work greatly advances the area of LUWN analysis.
Robust Quantum Sensing

Quantum sensors can be potentially used to outperform the classical sensors. The uncertainty of the estimation of the target field scales as \(T^{-0.5}\) with classical sensors where T denotes the total measurement time. On the other hand, about the uncertainty by using quantum sensors, it is in principle possible to obtain a scaling of 1/T, which is called a quantum scaling. However, since the quantum states are fragile against decoherence, it is not clear whether the quantum sensing is practically useful or not. Here, we propose a scheme to achieve the scaling of 1/T about the uncertainty with quantum sensors under the effect of dephasing. The crucial idea is to use quantum teleportation for the suppression of the dephasing. The quantum teleportation presents a system-environment correlation from growing while the quantum state acquires an information about the target fields. Our results suggest a practical approach to achieve the quantum scaling with the quantum sensing.
Quality Evaluation of Image Dataset based on Label File

The quality of the dataset affects the accuracy of the artificial intelligence model, but it is a lot of work to manually detect errors related to the quality evaluation of the dataset, and it may not be possible to perform quality evaluation through simple viewing. Therefore, we propose an image dataset quality measurement model, including nine evaluation metrics, and analyze the evaluation metrics from three aspects: definition, calculation formula and description. Based on the label file, the quality of the dataset file and the content of the dataset is evaluated, and the evaluation standard is given to judge whether the quality of the dataset is qualified. The measurement model and evaluation criteria proposed in this article were verified against the Cifar-10 dataset and the COVID-CT dataset, and the problems of label accuracy and label category imbalance were found, which proved the effectiveness of the method in this paper.
Active Learning with Numerical Feature Annotation

Active learning is an algorithm that aims to minimize the labeling effort in model training by wisely choosing the key data for labeling in an iterative and alternately querying labeled data and training procedure. While most of studies focus on the query strategy such as selecting the informative instances for labeling in each run, some others consider the query on features, or the query on both of the features and instances simultaneously to build effective model more efficiently than before. A typical strategy of querying features can take care only the categorical features. In this work, we extend the possibility of querying numerical features as well. We consider a Vector Quantization (VQ) technique to transform the numerical features to categorical ones and then all the features, as well as instances can all be evaluated using a unified criterion in the feature/instance selection step. Moreover, we study multivariate VQ as well as univariate VQ to find the best approach for the qunatization before sending the numerical features to categorical ones. After all, we can have active learning that deals with the selection on informative instances and features at the same time. We need to point out that not all numerical features could be appropriate to become categorical ones in the aforementioned transformation. As in our evaluation, some data from medical analysis and others are perfect to be one of such active learning applications. Along this line, we study the difference between applying the proposed feature/instance combined active learning to various data sets. A set of different vector quantization methods shall also be examined to find the best setting for effective active learning experience.
Data-driven Verification using Efficient Active Learning

Verification of performance for complex nonlinear dynamical systems requires an accurate estimation of their domains of attraction. In this paper, we estimate these regions borrowing tools from statistical learning to obtain an active learning algorithm that relies entirely on data. The proposed technique combines efficient uncertainty sampling with a modified representative sampling technique to arrive at a formulation that fuses informativeness with representativeness in the learning paradigm. We demonstrate our approach on an illustrative Van der Pol oscillator system to characterize the utility of the novel active learning framework in a broad setting.
Simulation-based verification of the shape measurement mechanism of micro structures beyond the diffraction limit using speckle interferometry

A method that can measure three-dimensional shapes beyond the diffraction limit of a lens has been proposed that does not require focusing on an image of the measured object, as in traditional processing. In this method, the shape of the microstructure can be measured by high-resolution detection of the amount of phase change of the speckle pattern with a lateral shift of the object. In this study, the measurement mechanism of the method was investigated using computer simulations. It was found that the measurement is realized by a combination of the perfect optical system and speckle interferometry using the scattered light as the illumination light. In addition, the validity of the simulation results was verified using a real optical system.
Learning Motion Features from Dynamic Images of Depth Video for Human Action Recognition

Depth video based action recognition is an important topic in machine vision tasks. Most existing methods design handcrafted features or directly use convolutional neural networks to obtain spatial temporal information in the depth video. However, these methods can not make good use of the 3D information contained in the depth video. In this work, the depth video is projected onto four planes to capture the information of the action in different dimensions. The pixel value of the projection image is determined by the distance between the projected point and the projection plane. Dynamic images of the four projection videos are calculated, which contain different dynamic information of the action on the four planes. Finally, a four-stream network is constructed to learn discriminative motion features. The visualization of the dynamic images shows that the four dynamic images contain different information of the action, and combining them makes it easier for action recognition. The proposed method is evaluated on the benchmark dataset, and the results validate the effectiveness of the method.
Wearable Augmented Reality System Using Head-Mounted Projector

In this paper, we propose a wearable augmented reality system that can be used without restrictions on location and can present images that are fixed in the real environment. In the proposed system, a projector is mounted on the head so that an image is always projected in the user’s line of sight. By estimating the position and orientation of the head in real time, the system presents images of objects in the virtual space as if the objects were fixed in the real space. In addition, by placing the projector close to the viewpoint, the user can see an image without distortion regardless of the shape of the projection surface. Using this system, a pseudo-large screen is realized by fixing a virtual object on the projected surface, which realizes the expansion of work space. The prototype system was developed using a small, lightweight projector and a tracking camera. We confirmed that the virtual object were fixed in the real space even if the user is moving the head. We also confirmed that the user was able to see an undistorted image regardless of the shape of the projection surface.
Data-Augmented Emoji Approach to Sentiment Classification of Tweets

The Natural Language Processing field has made great strides recently. As a result, many challenging tasks are being given better solutions. One of these tasks is Sentiment Analysis, which is the subject of this work. We propose a novel methodology to classify the sentiment of tweets, based on BERT and focusing on emoji. Our method also employs data augmentation to improve its generalization ability. Experiments on two Brazilian Portuguese datasets – TweetSentBR and 2000-tweets-BR – show that our methodology produces better results than BERT and outperforms the previously published results for TweetSentBR, with accuracy of 0.7726 (6.3 percentage points (p.p.) of improvement) and F\(_{1}\) score of 0.7514 (9.5 p.p. of improvement), as well as for 2000-tweets-BR, with accuracy of 0.8247 (14.5 p.p. of improvement) and F\(_{1}\) score of 0.8035 (23.4 p.p. of improvement).
Dress Code Surveillance at Power Grid Construction Site via Object Detection

Constructions at the power grid working site have potential high risks if the construction rules are not well obeyed. The dress codes at the power grid working site are critical rules to protect the workers' safety. The most fundamental dress code for the workers to follow is to wear the uniform and helmet when entering the working site and wear the safety rope for the aerial operation. However, the negligence of workers or the lack of surveillance can lead to irregular dress situations and may further result in serious accidents. In this paper, with the help of deep learning technology, we proposed to establish a surveillance system for the dress code of power grid workers via object detection. Specifically, we borrow YOLOv5 as the backbone detection model. To detect the dress of the power grid workers, we construct a dataset that contains the common uniform, helmet, and safety belt to fine-tune the pre-trained YOLOv5 model. Experimental results verify the feasibility of our method to provide real-time surveillance of the workers' dress code on the power grid working site.
The Regulatory Regime for Cryptocurrency in the Present Global Order

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A Parallelized DynTARM Algorithm for the Discovery of Predictive Co-Occurrences Within Streaming Time Stamped Data

Predictive analytics utilizes information extracted from big data (both real-time and historical) in order to forecast potential future occurrences with an acceptable level of reliability. As time progresses, especially in this information age, trends change at an explosive rate. As a result, it can be especially hard for companies and individuals to react in time to take advantage and/or properly react to these changes. Some examples of these changing trends are emerging disease, increased hospital patients at certain times of the year, and the increase/decrease in traffic accidents under certain conditions. The main objective of this project is to develop a parallelized in-memory pattern-mining framework for dynamic data mining. Utilizing an in-memory database allows for faster information processing, dynamic structure growth as time progresses, and advanced co-occurrence discovery. By parallelizing and making medications to the structure, this project plans to increase processing speed allow for the discovery of new, previously undiscovered co-occurrence types, and to decrease the wait time between discovery of a new occurrence and implementation.
Partitioning some multivariate distributions

This is a study of the behavior under partition of the sample space of three multivariate distributions: multinomial, multinomial-Dirichlet, and Dirichlet. A general theorem is given, of which all three are special cases.
The Calibration for Camera and Correction of Image

Most of the information needed by the machine vision system is contained in the picture, when the traditional binocular camera is used to collect, the photos taken by the camera are easily distorted, which leads to the error of the collected information. A series of MATLAB algorithms are used to correct the image distortion, and the least square method and curvature contrast are used to verify the distortion correction.
Learning Diachronic Embedding and Time-Encoding Sequences for Temporal Knowledge Graph Completion

In the era of big data, "Information Explosion" has come into being, which make data types complex and data structure loose. Knowledge graphs (KGs) are mainly used to organize, manage, and retrieve structured information. The research of KG has made outstanding contributions for machines to understand the human world. However, the current work mainly focus on the static knowledge graph which ignores the important temporal information and makes the fact data obsolete or even invalid soon. Therefore, more and more attention has been paid to the research on temporal knowledge graph, which enables "knowledge" to keep pace with time and update automatically. In this paper, we propose a novel temporal knowledge graph embedding model, and extends it to its downstream task - temporal knowledge graph completion. In order to construct this model, we introduce the core ideas of Diachronic embedding and Time-encoding sequence predicates. The former encapsulates entity and time into the same function, and the latter tags the time label to relation. With the two functional components, we can get a new triple carrying rich temporal information. The proposed embedding model can be potentially combined with any static model. The results of experiment show that our model has successfully improved the performance of link prediction problem on temporal KG datasets, compared with the corresponding static and diachronic embedding model.
An Efficient Distributed Coverage Pattern Mining Algorithm

Mining of coverage patterns from transactional databases is one of the data mining tasks. It has applications in banner advertising, search engine advertising and visibility computation. In general, most real-world transactional databases are typically large. Mining of coverage patterns from large transactional databases such as query log transactions on a single computer is challenging and time-consuming. In this paper, we propose Distributed Coverage Pattern Mining (DCPM) approach. In this approach, we employ a notion of the summarized form of Inverse Transactional Database (ITD) and replicate it at every node. We also employ an efficient clustering-based method to distribute the computational load of extracting coverage patterns among the Worker nodes. We performed extensive experiments using two real-world datasets and one synthetic dataset. The results show that the proposed approach significantly improves the performance over the state-of-the-art approaches in terms of execution time and data shuffled.
Recurrent Neural Network with Adaptive Gating Timescales Mechanisms for Language and Action Learning

Inspired by the neurons’ differences in membrane time-scales, the multiple timescale recurrent neural network model (MTRNN) adopts the hierarchical architecture with increasing time-scales from bottom to top layers. Based on this idea, the recent adaptive and continuous time recurrent neural networks (ACTRNN) and the gated adaptive continuous time recurrent neural network (GACTRNN) develop the novel learning mechanism on the time-scales. In this paper, we test the performance of GACTRNN using the dataset obtained from a real-world humanoid robot’s object manipulation experiment. By using trainable timescale parameters with the gating mechanism, it can be observed that the GACTRNN can better learn the temporal characteristics of the sequences. Besides, to eliminate the effects of parameters’ overgrowing with a large data-set, we improve the GACTRNN model and propose the MATRNN model. In this model, the sigmoid function is used instead of exponential function. We compare the performances of the CTRNN, GACTRNN and MATRNN models, and find that the GACTRNN and MATRNN models perform better than the CTRNN model with the large-scale dataset. By visualizing the timescales adapting in the training process, we also qualitatively show that the MATRNN model performs better than the GACTRNN model in terms of stability with the dataset.
On the Design of a Model of Vocational Guidance Work with High School Students

This article analyzes the conditions for creating a model of career guidance at a civil aviation university. It describes the creation and description of the interactive model of professional orientation of civil aviation university students in interaction with educational institutions of secondary general education. The advantage of online career guidance services over traditional methods is shown.
Human Head Pose Estimation Through Temporal Enhanced and Accurate Self-Supervised Depth Prediction

Integrating depth information is beneficial for human head pose estimation. However, in current methods, the depth is acquired by hardware devices, which is cumbersome for daily-life applications. In this paper, we propose to leverage the depth from a depth prediction network to estimate the head pose. Benefit from the projection loss, only RGB videos are necessary to train the network. Furthermore, considering the temporal information is ignored in current depth prediction models, we design a Double Temporal Correlation Network (DTCNet), where the features of adjacent frames are fused to boost current feature. After obtaining the depth, head point clouds can be constructed. Finally, an improved ICP algorithm is proposed to register the point clouds of two views and realize pose estimation. In the experiments, the effectiveness of DTCNet is demonstrated on the KITTI dataset and that of our head pose estimation framework is demonstrated on the Biwi and 300W-LP datasets.
A Study on the journey of Natural Language Processing models: from Symbolic Natural Language Processing to Bidirectional Encoder Representations from Transformers

In this digital era, Natural language Processing is not just a computational process rather it is a way to communicate with machines as humanlike. It has been used in several fields from smart artificial assistants to health or emotion analyzers. Imagine a digital era without Natural language processing is something which we cannot even think of. In Natural language Processing, firstly it reads the information given and after that begins making sense of the information. After the data has been properly processed, the real steps are taken by the machine throwing some responses or completing the work. In this paper, I review the journey of natural language processing from the late 1940s to the present. This paper also contains several salient and most important works in this timeline which leads us to where we currently stand in this field. The review separates four eras in the history of Natural language Processing, each marked by a focus on machine translation, artificial intelligence impact, the adoption of a logico-grammatical style, and an attack on huge linguistic data. This paper helps to understand the historical aspects of Natural language processing and also inspires others to work and research in this domain.
Boltzmann Machine Using Superconducting Circuits

We study the design and optimization of the Boltzmann machine hardware using superconducting circuits as a new stochastic information processing method. The Boltzmann machine is an artificial neural network of stochastic binary models wherein the energy function is determined by the given set of parameters, and the output is obtained by stochastic state transitions of the system to the energy stable states according to the Boltzmann distribution. By adjusting the set of parameters, arbitrary functions can be embedded in energy-stable states, which have applications in data dimensionality reduction and generative models. The hardware of a Boltzmann machine using superconducting circuits consists of quantum flux parametrons (QFPs), one of the superconducting circuits, magnetically coupled to each other. In this study, we designed the Boltzmann machine hardware in which logic gates such as NOR are embedded in energy-stable states. Furthermore, we applied maximum likelihood estimation (MLE), machine learning method, as an operating-point optimization method, and confirmed the effectiveness of this method in experiments.