Reviewerly/exHarmony huggingface dataset:
A Summary of Virtual Reality, Augmented Reality and Mixed Reality Technologies in Film and Television Creative Industries

Virtual reality and augmented reality technologies have been applied to scientific research for many years and the achievements are remarkable. Although the application area of mixed reality is relatively narrow comparing to the first two applications, we have initially seen results. However, in fact, these technologies can be applied in film and television creation far more than that. I then illustrate some other aspects which these realistic contents can also be applied to in film and television creation in the future and the influence on the industry.
Study of High-Dimensional Data Analysis based on Clustering Algorithm

With the rapid development of big data, the scale, dimensions, diversity and sparsity of high-dimensional data restrict the effectiveness of traditional clustering algorithms. This paper mainly focuses on high-dimensional data clustering. Starting from the traditional K-means clustering algorithm and subspace clustering algorithm based on self-representation model, an improved algorithm is designed and implemented based on the existing clustering algorithm in this paper. The improved algorithm has better clustering quality by combining the "distance optimization method" and the "density method" to determine the initial clustering center. The feasibility and effectiveness of improved algorithm are verified through simulation experiments.
Predictable Performance for QoS-Sensitive, Scalable, Multi-tenant Function-as-a-Service Deployments

Abstract In this paper we present the results of our studies focused on enabling predictable performance for functions executing in scalable, multi-tenant Function-as-a-Service environments. We start by analyzing QoS and performance requirements and use cases from the point of view of End-Users, Developers and Infrastructure Owners. Then we take a closer look at functions’ resource utilization patterns and investigate functions’ sensitivity to those resources. We specifically focus on the CPU microarchitecture resources as they have significant impact on functions’ overall performance. As part of our studies we have conducted experiments to research the effect of co-locating different functions on the compute nodes. We discuss the results and provide an overview of how we have further modified the scheduling logic of our containers orchestrator (Kubernetes), and how that impacted functions’ execution times and performance variation. We have specifically leveraged the low-level telemetry data, mostly exposed by the Intel® Resource Director Technology (Intel® RDT) [1]. Finally, we provide an overview of our future studies, which will be centered around node-level resource allocations, further improving a function’s performance, and conclude with key takeaways.
Selecting image pairs for structure-from-motion by introducing the image spatial position

Image matching is a quite time-consuming task for Structure-from-Motion (SfM). In this paper, a Bag-of-Words (BoW) model that reduces the feature dimensions and introduces image spatial locations is proposed to improve the efficiency and reliability of SfM. The whole workflow includes three steps. Firstly, principal component analysis (PCA) is used to reduce the high-dimensional features to low-dimensional features, so as to improve the efficiency of retrieval vocabulary construction. Secondly, by calculating the inverse distance weighting score of query images, a comprehensive retrieval score is constructed to improve the distinguishability between similar images. Finally, by calculating the retrieval threshold and discarding the invalid matching image pairs, the image query precision is further improved. The experimental results show that compared with the VocabTree (VT) and the Hamming Embedding (HE) methods, the proposed algorithm for image matching time is reduced by 69.5% and 72.0%, respectively, while the number of sparse point clouds of the reconstruction is increased 0.6%.
Design Woodball Line Detection and Monitoring System: A Preliminary Study

Abstract This paper entails the incorporation of electronics in the sports of woodball. The decisions made by the referees in most sports nowadays are with the use of electronic technologies to assist them. For instance, the goal-line technology in football and the electronic line judge in tennis is some of the recent advances in electronic technologies that have revolutionized sports as we know it. The woodball line detection and monitoring system serves a similar purpose where it assists the referee in making decisions and modernize the sports of woodball. The current woodball sports depend entirely on referees to give out decisions. The line detection technology helps to notify the referee if the woodball is Out of Bounds (OB) whereas the monitoring system notifies the player, referee and the audience whether the gating is successful or not. However, manual assistance from the referee is still needed for starting the play after a successful gating because the player is not allowed to touch the woodball during the entire game.
Research on Target Detection Technology Based on Convolutional Neural Network

Abstract The research and application of neural network has been a research topic in recent years, and the efficient recognition of objects is undoubtedly an important part of the technology application. Convolution of the neural network is in the process of object recognition classification, has carried on the improvement of cognitive machine, thus avoiding the error back propagation in time loss problem, based on the biological neural network structure, convolution weights of neural network are more inclined to study sharing network structure, which shows good performance in the process of a number of image recognition, for translation, proportion to gather scaling and deformation and so on the many kinds of complex operation has certain robustness.
Hybrid Multidimensional Deep Convolutional Neural Network for Multimodal Fusion

The Hybrid Multidimensional Deep Convolutional Neural Network (HMDCNN) topology for the multimodal recognition of the speech, the face, the lips, and human gestures behavior is proposed. In this case a hybridization is understood to be compatible use of 2D and 3D convolutional neural networks in one multimodal architecture. Conducted researches relate to improving the understanding of complex dynamic scenes. The basic unit of the proposed hybrid system is deep neural network topology, which combines 2D and 3D convolutional neural network (CNN) for each modality with proposed intermediate-level feature fusion subsystem. Such a feature map fusion method is based on scaling procedure with a specific combination of pooling operation with non-square kernels and allows merging different type of modalities.
A Fast Adaptive Replica Recovery Algorithm Based on Access Frequency and Environment Awareness

As cloud storage adopts a distributed architecture to store massive data, how to improve the reliability of the storage center has become the focus of researchers. HDFS, the distributed file system of Hadoop, uses a sequential recovery method to recover failed replicas when the node is down, which does not take into account the priority of the replicas and the load difference between each node, resulting in request blocking and system load imbalance. Aiming at this problem, we propose a replica recovery method based on access frequency and network environment awareness, use a priority-based recovery algorithm and multi-objective decision algorithm to guarantee the response speed and realize the cluster load balance. We set up a simulation environment for verification and compared the performance of our method with various algorithms in throughput and response time. The simulation results showed that our approach effectively solved the load imbalance problem on the premise of ensuring faster response time.
Towards Assigning Diagnosis Codes Using Medication History

Prior studies have manually assessed diagnosis codes and found them to be erroneous/incomplete between 4–30% of the time. Previous methods to validate and suggest missing codes from medical notes are limited in the absence of these, or when the notes are not written in English. In this work, we propose using patients' medication data to suggest and validate diagnosis codes. Previous attempts to assign codes using medication data have focused on a single condition. We present a proof-of-concept study using MIMIC-III prescription data to train a machine-learning-based model to predict a large collection of diagnosis codes assigned on four levels of aggregation of the ICD-9 hierarchy. The model is able to correctly recall 58.2% of the ICD-9 categories and is precise in 78.3% of the cases. We evaluate the model's performance on more detailed ICD-9 levels and examine which codes and code groups can be accurately assigned using medication data. We suggest a specialized loss function designed to utilize ICD-9's natural hierarchical nature. It performs consistently better than the non-hierarchical state-of-the-art.
49‐4: <i>Late‐News Paper:</i> Prototyping of Practical e‐Tile and Estimation of its Image Impression from Distant Observers

“e‐Tile” is introduced as a novel concept for large area displays. A typical e‐Tile configuration, in which 100 pixels are mounted on a 100 mm square board, is designed and practically prototyped. Assuming a relatively large pixel size on the electronic tile, the relationship between the viewing distance and the impression of image quality was studied by calculations and subjective evaluations.
A Blockchain-based Privacy-Preserving Mechanism with Aggregator as Common Communication Point

The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration.
Modeling Multi-aspect Relationship with Joint Learning for Aspect-Level Sentiment Classification

Aspect-level sentiment classification is a crucial branch for sentiment classification. Most of the existing work focuses on how to model the semantic relationship between the aspect and the sentence, while the relationships among the multiple aspects in the sentence is ignored. To address this problem, we propose a joint learning (Joint) model for aspect-level sentiment classification, which models the relationships among the aspects of the sentence and predicts the sentiment polarities of all aspects simultaneously. In particular, we first obtain the augmented aspect representation via an aspect modeling (AM) method. Then, we design a relationship modeling (RM) approach which transforms sentiment classification into a sequence labeling problem to model the potential relationships among each aspect in a sentence and predict the sentiment polarities of all aspects simultaneously. Extensive experiments on four benchmark datasets show that our approach can effectively improve the performance of aspect-level sentiment classification compared with the state-of-the-art approaches.
A New Approach for Feature Subset Selection using Quantum Inspired Owl Search Algorithm

Feature subset selection is the approach of selecting the optimal feature subset for the classification task by removing irrelevant and redundant features. However, searching for the optimal feature subset is challenging due to its inherent exponential time complexity. To address the problem, metaheuristics are frequently used for finding the sub-optimal solution in a reasonable time constraint. In this paper, a Quantum Inspired Owl Search Algorithm (QIOSA) for feature subset selection is proposed. In this method, features are represented as quantum superposition states, and quantum rotation gate is used to accelerate the search towards an optimal set of features. Simulation experiments have done to evaluate the efficiency of the proposed approach compared to Binary Owl Search Algorithm (BOSA) proposed earlier by authors and other population-based feature selection techniques, including Binary Genetic Algorithm (BGA) and Binary Particle Swarm optimization (BPSO) with twelve publicly available benchmark datasets. The experimental results show that QIOSA improves classification accuracy and effectively reduces the number of features compared to other metaheuristic algorithms.
Research on Intelligent Volume Algorithm Based on Improved Genetic Annealing Algorithm

In order to improve the quality of the traditional exam management system test paper generation, joining the genetic algorithm of simulated annealing algorithm is put forward, and study at last on the automatic group volume algorithm update the topic the difficulty coefficient. By analyzing the simulation results, the algorithm can improve the efficiency and quality of the volume.
Design Research of Physical Virtual Experiment Based on Virtual Reality Technology

The advent of the information age has brought rapid development and progress to all walks of life in society. In this trend, information technology has gradually integrated into the education industry, and combined with various disciplines has brought a qualitative leap to education. Educational researchers and front-line teachers are currently paying attention to research topics. The physics experiment in the physics course is an important part of students' physics learning. However, in actual teaching, many middle schools have poor teaching effects due to limited experimental conditions and cannot meet the learning needs of each student. Virtual reality VR technology, as a technology with a wide imagination space function, can construct and produce physical phenomena that are difficult to observe in reality. This article attempts to design virtual reality (VR) technology for the application of physics experiments, and hopes to explore the value of virtual reality technology in physics experiment teaching.
Vehicle re-identification based on unsupervised local area detection and view discrimination

Vehicle re-identification is an important part of intelligent transportation. Although much work has been done on this subject in recent years, vehicle re-identification is still a challenging task due to its obvious illumination change, high similarity between inter-class and great changes under different views. As discriminatory local areas and vehicle view information is the key to improving the above issues, it is desirable to create a model which considers both local details and cross-view situation. In this paper, we built a vehicle re-identification framework based on unsupervised local area detection and view discrimination. First, we combine the convolution features of multiple vehicle images by channel to generate joint channel features, and constructs a discriminating region detector unsupervised by clustering the channel covariance vectors generated between the joint channel features. In the next stage, the irregular shape detection results are converted to rectangular discriminative region by using a novel local region integration algorithm. These rectangular discriminative regions are fed into a multi-branch network to extract the local–global features which contains rich detail information. Furthermore, we generate the view features by regarding the detected discriminative areas as keypoints and construct the unsupervised view discriminator. By using the view information of the vehicle, we designed a view-discrimination based reranking algorithm, which effectively reduces the error rate of identification due to view variant. In order to prove the validity of our method, we have done extensive experiments on VehicleID and VERI-Wild dataset. Experimental results show that our method is superior to other existing vehicle re-identification algorithms.
Volume I: Recovery operators in logics of formal inconsistency

There are a considerable number of logics that do not seem to share the same inferential principles. Intuitionistic logics do not include the law of the excluded middle and double negation elimination, which are fundamental inference rules in classical logic (CL). Relevance logics aim to formalize aspects of implication that are ignored by the material implication operator in classical truth-functional logic, namely the notion of relevance between the antecedent and consequent of a true implication. Paraconsistent logics are logical systems that attempt to deal with contradictions in a discriminating way. All these formal systems differ from CL. The recovery relationship is an essential element to any understanding of the connection between different systems of logic. Roughly, one logic recovers another if it is possible to specify a subsystem of the former system, which exhibits the same patterns of inference as the latter system. Sometimes, this property is also called recapture. This means expressing the inferential principles of one logic in another–reproducing all their inferential patterns. For example, one could try to recover some instances of the law of the excluded middle with a logic that rejects it globally.
Sequence Imputation using Machine Learning with Early Stopping Mechanism

In today's world, humans need data from every situation, even from conditions where humans cannot directly extract data or from places that are hazardous for human beings. Wireless sensor networks or WSNs can solve this problem. They gather the required information using their sensors and provide the human race with the necessary data. Missing data are unavoidable in WSN, due to issues such as network communication outage, sensor maintenance or failure, etc. A complete informed and accurate data can help make the right decision. Just getting the missing data is not essential; it is also crucial to get this information as quickly as possible. A sequence imputation model with an encoder-decoder model is used to identify the missing values in the sensor network. It uses SSIM, with LSTM, attention layer and an early stopping layer, which gives comparable results in the minimum time by taking into consideration the past and the future information. The training and testing data is the subset of the hourly dataset of PM2.5 data of the US Embassy in Beijing. This model was able to produce accurate values keeping in mind the time.
Learning a Cost-Effective Strategy on Incomplete Medical Data

Deep learning techniques have shown remarkable success in classification tasks. However, the prerequisite for high accuracy is that data is easily accessible, which is unrealistic since most features come at a cost. Under a medical scenario, each feature is associated with a medical test that costs a certain amount of money. And doctors would ask patients to do consecutive tests until they are confident enough to make a final diagnosis, whereas the overall cost incurred during the process is often ignored. In this paper, we propose to learn a cost-effective strategy which at the same time hastens the decision process. As is often the case where both medical records and initial feature values of a new patient are incomplete, we design a framework consisting of two components, the oracle classifier and the feature selector, to tackle the challenges. The classifier incorporates a sequence encoder that can handle any set of feature values in various sizes. And the selector efficiently learns the cost-effective strategy based on the state-of-art reinforcement learning techniques. Experimental results have shown that under the same classification accuracy, our strategy is superior to other related approaches in terms of the overall cost.
Enhancing Machine Translation by Integrating Linguistic Knowledge in the Word Alignment Module

The word alignment process, which is a critical step in statistical translation systems (SMT), has been suggested by several researchers as a promising track for enhancing neural translation system (NMT) performance in low-resource environments. Furthermore, given the negative impact on English/Arabic machine translation quality arising from the morphological richness and complexity of the Arabic language compared to the English language, we assessed in this study the relevance of the integration of morphosyntactic characteristics during the alignment phase. Indeed, we have enriched parallel corpora by morphosyntactic features such as stems, lemmas, roots, and POS tags; yet we have developed new SMT systems embedding one of these features in the word alignment phase. The test results proved the interest to use these features and highlighted the most relevant morphosyntactic information to the translation system.
Secure Decentralized Public Key Infrastructure with Multi-signature in Blockchains

Decentralized public key infrastructure (DKPI) with permissioned/permissionless blockchains has gained tremendous popularity due to the inherent transparent, decentralized, and tamper-proof nature of blockchains. Digital certificates with transport layer security/secure socket layer (TLS/SSL) which are issued by a certificate authority (CA) and which associates the public key to an individual/organization is highly centralized and is held by a small group of multinational organizations. The centralized nature of TLS/SSL certificate generation can be decentralized with blockchain technology. We propose a decentralized public key infrastructure (DPKI), with a set of nodes in the blockchain which act as certificate authorities and are used to sign and validate the public key of the user requesting the certificate, thus facilitating the issue of transparent and tamper-proof TLS/SSL certificates. In this paper, we have implemented a group signature with m nodes out of n nodes to generate a digital certificate that ensures decentralization in the blockchain.
An Automatic Clustering Algorithm Using NSGA-II with Gene Rearrangement

As an unsupervised learning method in machine learning, clustering is a way to understand and learn from data. One of the most widely used clustering algorithms is the prototype-based clustering method, which needs to know the clusters number in advance. However, the number of clusters is often unlikely to be specified with prior experience. In this paper, we proposed a data clustering method, GR-NSGAII, which does not need to pre-set the number of clusters. Instead, the number of cluster and the results are generated automatically. Unlike the benchmark partitioning algorithm-based methods, it is a multi-objective optimization clustering approach based on genetic algorithm NSGA-II, in which only the objective functions were used to guide the clustering partitioning. Two optimization criteria, i.e., the sum of generalized sample variance and the Calinski-Harabasz index, were simultaneously optimized to control the number of clusters in a reasonable range. In addition, a technique named gene rearrangement combined with cluster merging was used to get better clustering results. In contrast with the state-of-the-art multi-objective evolutionary clustering methods, GR-NSGAII has better performance.