Reviewerly/exHarmony huggingface dataset:
Analyzing causes of urban blight using cognitive mapping and DEMATEL

Blight is a concept not commonly discussed. However, blight is a problem that exists in the lives of many people, especially if they reside in urban areas. Blight originates whenever properties are neglected, contributing to both a functional and social depreciation process and ultimately leading to uninhabitable dwellings. Despite being blighted, these properties and surrounding neighborhoods often are occupied by families who fail to have sufficient income to afford residences that meet minimum standards or to live in neighborhoods free from drug trafficking and prostitution or other forms of crime. Blight may spread rapidly, thus, experts must, in a timely manner, analyze its causes, which are essential to preventing and mitigating blight problems. The purpose of this study is to seek an understanding of blight and identify its causal factors. The generic methods commonly applied in previous blight research present limitations that this study aims to overcome by using cognitive mapping and the decision making trial and evaluation laboratory (DEMATEL) technique. This dual methodology provides a more transparent and less restrictive approach for analyzing and complying with the dynamics of cause-and-effect relationships among variables. Group debate involving a panel of specialists in this field identified six causation clusters based on the experts’ experience and knowledge. The resulting framework and its application were validated both by these specialists and the head of the Territorial and Environmental Assessment and Monitoring Division of Cascais City Council Strategic Planning Department, Portugal.
Deep learning-based methods for natural hazard named entity recognition

Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities.
Improving the performance of practical decoy-state quantum key distribution with advantage distillation technology

Abstract Quantum key distribution (QKD) provides a promising solution for sharing information-theoretic secret keys between two remote legitimate parties. To improve the maximal transmission distance and the maximal error rate tolerance, we apply the advantage distillation technology to analyze the security of practical decoy-state QKD systems. Based on the practical experimental parameters, the device-dependent QKD protocols and the measurement-device-independent QKD protocols have been respectively analyzed, and our analysis results demonstrate that the advantage distillation technology can significantly improve the performance of various QKD protocols. In the four-state and six-state device-dependent QKD protocols, we prove that the maximal transmission distance can be improved from 142 km to 180 km and from 146 km to 187 km respectively. In the four-state and six-state measurement-device-independent QKD protocols, we prove that the maximal transmission distance can be improved from 195 km to 273 km and from 200 km to 282 km respectively.
Scalable multi-view clustering with graph filtering

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches.
Introduction to Virtual Reality

Chapter 1 sets the stage for the entire book by laying the background of virtual reality, and defining the term as it is used in the book Understanding Virtual Reality. Additionally, this chapter defines a number of related technologies, concepts, and media including augmented reality, cyberspace, telepresence, virtual worlds, and collaborative environments. The idea that virtual reality is actually a medium is introduced, as well as five key elements of a virtual reality experience: the virtual world, immersion (physical and mental), interactivity, and the people on the creating and receiving sides of the medium. The chapter explores a variety of ways a virtual world can be manifested in virtual reality and discusses the three primary paradigms for manifesting virtual reality experiences: head-based, stationary, and hand-based systems. In order to enable the reader to understand the following chapters, Chapter 1 introduces a number of other concepts, such as presence, computational simulation, and others that are essential to understanding virtual reality. Finally, this chapter presents a timeline that highlights key innovations and ideas related to developing the idea of virtual reality.
Efficient text document clustering with new similarity measures

In this paper, two new similarity measures, namely distance of term frequency-based similarity measure (DTFSM) and presence of common terms-based similarity measure (PCTSM), are proposed to compute the similarity between two documents for improving the effectiveness of text document clustering. The effectiveness of the proposed similarity measures is evaluated on reuters-21578 and WebKB datasets for clustering the documents using K-means and K-means++ clustering algorithms. The results obtained by using the proposed DTFSM and PCTSM are significantly better than other measures for document clustering in terms of accuracy, entropy, recall and F-measure. It is evident that the proposed similarity measures not only improve the effectiveness of the text document clustering, but also reduce the complexity of similarity measures based on the number of required operations during text document clustering.
Anomaly Detection for Agricultural Vehicles Using Autoencoders

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases.
Assessing cloud QoS predictions using OWA in neural network methods

Abstract Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.
Robust deep auto-encoding network for real-time anomaly detection at nuclear power plants

Detecting anomaly conditions in nuclear reactor is a critical issue in safety management of Nuclear Power Plants (NPPs). Conventionally, the operating status are monitored in transient data with pre-designed labels by human operators or basic diagnosis systems. Nowadays, continuous time series data from multi-sensors are increasingly collected and emerging unlabeled abnormal status are monitored during the operation, making it challenging to capture both spatial and temporal dependency at each time steps without supervised labels. In this paper, a robust unsupervised Multi-Variate Convolutional GRU Encoder-Dncoder (MVCGED) method is proposed to perform anomaly detection and fault diagnosis in multi-sensor operation time series data. Specifically, MVCGED first construct each time steps into signature matrices to maintain both spatial and temporal features via sliding windows with inner-correlation and forget mechanism. Subsequently, A CNN feature extraction network, CNN-based GRU encoding network and CNN decoding network are implemented successively to capture and reconstruct the hidden patterns of the signature matrices. Finally, the reconstruction loss are further utilized to detect anomalies and diagnose faults. Extensive empirical studies based on PCTRAN nuclear power plant operation data demonstrate that MVCGED outperforms commonly-used baseline methods.
Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks

Automatic sign language recognition is a challenging task in machine learning and computer vision. Most works have focused on recognizing sign language using hand gestures only. However, body motion and facial gestures play an essential role in sign language interaction. Taking this into account, we introduce an automatic sign language recognition system based on multiple gestures, including hands, body, and face. We used a depth camera (OAK-D) to obtain the 3D coordinates of the motions and recurrent neural networks for classification. We compare multiple model architectures based on recurrent networks such as Long Short-Term Memories (LSTM) and Gated Recurrent Units (GRU) and develop a noise-robust approach. For this work, we collected a dataset of 3000 samples from 30 different signs of the Mexican Sign Language (MSL) containing features coordinates from the face, body, and hands in 3D spatial coordinates. After extensive evaluation and ablation studies, our best model obtained an accuracy of 97% on clean test data and 90% on highly noisy data.
A decentralized secured grid integration system using APEBC technique with multi access AI framework

Now days huge amounts of outsourced data are done especially in the cloud based amenities. Providing data confidentiality and data security on cloud data are the important key factors. Generally, data stored on cloud in the encrypted format. Based on the user demand for accessing data from the cloud server, a retrieving key is issued through the third party is mandatory. Here, the data security is depending on the third party. If the third party is not genuine, then the data security is questionable. Based on this issue, the novel based cloud storage mechanism with accessing governor using Authenticated Privacy Ethereum based Blockchain (APEBC) technique is proposed. This technique is based on Encoded Policy Attribute Based Encrypted embedded with Ethereum concept. The third party concept is not necessary in this proposed work as it is based on decentralized. The ethereum based blockchain technique allows the end user to set legal access periods. So, the end users can access and decrypt their data during on that access periods alone. The details of settings the legal access periods and tracking the access duration are stored and processed with the blockchain technique. Finally, the proposed work is implemented and examined based on the security standards. The demonstration shows that the proposed work is feasible in all aspects.
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification

Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing.Previous studies mainly focus on learning text representation and modeling label correlation.However, they neglect the rich knowledge from the existing similar instances when predicting labels of a specific text.To address this oversight, we propose a k nearest neighbor (kNN) mechanism which retrieves several neighbor instances and interpolates the model output with their labels.Moreover, we design a multi-label contrastive learning objective that makes the model aware of the kNN classification process and improves the quality of the retrieved neighbors during inference.Extensive experiments show that our method can bring consistent and considerable performance improvement to multiple MLTC models including the state-of-the-art pretrained and non-pretrained ones.
Unsupervised Learning-Based Framework for Deepfake Video Detection

With the continuous development of computer hardware equipment and deep learning technology, it is easier for people to swap faces in videos by currently-emerging multimedia tampering tools, such as the most popular deepfake. It would bring a series of new threats of security. Although many forensic researches have focused on this new type of manipulation and achieved high detection accuracy, most of which are based on supervised learning mechanism with requiring a large number of labeled samples for training. In this paper, we first develop a novel unsupervised detection manner for identifying deepfake videos. The main fundamental behind our proposed method is that the face region in the real video is taken by the camera while its counterpart in the deepfake video is usually generated by the computer; the provenance of two videos is totally different. Specifically, our method includes two clustering stages based on Photo-Response Non-Uniformity (PRNU) and noiseprint feature. Firstly, the PRNU fingerprint of each video frame is extracted, which is used to cluster the full-size identical source video (regardless of its real or fake). Secondly, we extract the noiseprint from the face region of the video, which is used to identify (re-cluster for the task of binary classification) the deepfake sample in each cluster. Numerical experiments verify our proposed unsupervised method performs very well on our own dataset and the benchmark FF++ dataset. More importantly, its performance rivals that of the supervised-based state-of-the-art detectors.
Syntax in the Treetops

A proposal that syntax extends to the domain of discourse in making core syntax link to the conversational context. In Syntax in the Treetops, Shigeru Miyagawa proposes that syntax extends into the domain of discourse by making linkages between core syntax and the conversational participants. Miyagawa draws on evidence for this extended syntactic structure from a wide variety of languages, including Basque, Japanese, Italian, Magahi, Newari, Romanian, and Spanish, as well as the language of children with autism. His proposal for what happens at the highest level of the tree structure used by linguists to represent the hierarchical relationships within sentences—“in the treetops”—offers a unique contribution to the new area of study sometimes known as “syntacticization of discourse.” Miyagawa's main point is that syntax provides the basic framework that makes possible the performance of a speech act and the conveyance of meaning; although the role that syntax plays for speech acts is modest, it is critical. He proposes that the speaker-addressee layer and the Commitment Phrase (the speaker's commitment to the addressee of the truthfulness of the proposition) occur together in the syntactic treetops. In each succeeding chapter, Miyagawa examines the working of each layer of the tree and how they interact.
Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image characteristics of infrared vehicle detection, and then discusses the improved YOLOv5 algorithm in detail. The algorithm uses the DenseBlock module to increase the ability of shallow feature extraction. The Ghost convolution layer is used to replace the ordinary convolution layer, which increases the redundant feature graph based on linear calculation, improves the network feature extraction ability, and increases the amount of information from the original image. The detection accuracy of the whole network is enhanced by adding a channel attention mechanism and modifying loss function. Finally, the improved performance and comprehensive improved performance of each module are compared with common algorithms. Experimental results show that the detection accuracy of the DenseBlock and EIOU module added alone are improved by 2.5% and 3% compared with the original YOLOv5 algorithm, respectively, and the addition of the Ghost convolution module and SE module alone does not increase significantly. By using the EIOU module as the loss function, the three modules of DenseBlock, Ghost convolution and SE Layer are added to the YOLOv5 algorithm for comparative analysis, of which the combination of DenseBlock and Ghost convolution has the best effect. When adding three modules at the same time, the mAP fluctuation is smaller, which can reach 73.1%, which is 4.6% higher than the original YOLOv5 algorithm.
Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection.A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset.Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively.The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.
Development of Superconducting Single-Photon and Photon-Number Resolving Detectors for Quantum Applications

Single-photon detectors based on superconducting thin films have become a viable class of technologies for widespread usage in quantum optics. In this tutorial paper we introduce the key performance metrics required of them for quantum information processing and related fields. We review the latest records achieved by such devices, study technical details regarding recent improvements in superconducting nanowire detectors and transition-edge sensors, and present representative applications in cutting-edge research areas that benefit from these advances.
Building Korean DMZ Metaverse Using a Web-Based Metaverse Platform

Metaverse is a compound word of “Meta” and “Universe”, meaning a world that transcends reality, a new virtual world. Due to the COVID-19 pandemic, non-face-to-face further accelerated the activation of the Metaverse. The Metaverse has the attractiveness of arousing user interest, high service scalability, and the potential to combine multiple revenue models. Therefore, many industries are adopting the Metaverse. The Republic of Korea has been on a ceasefire, since 1953, after the Korean War. After the war, the DMZ was decided in an area of 2 km from north to south (total of 4 km), centered on the Military Demarcation Line (MDL). The DMZ area is the largest nature conservation area in Asia and a military area where public access is strictly restricted. The Ministry of Unification is trying to reduce tensions and establish peace in the DMZ area by raising the interest of Korean people, as well as people around the world, through the DMZ Metaverse application. The Korean government has its own e-Government framework and cloud operating environments. In order to adopt the Metaverse to Korean government agencies and operate them sustainably, it is essential to support the e-Government framework and the cloud environments. In this paper, the design features, architecture, models, and functions of OnTwins, a Metaverse platform serviceable in the Korean government IT operating environment, are elaborated in detail. The DMZ Metaverse public service built on the platform will also be introduced. Finally, after comparing it with other Metaverse platforms, future research directions are discussed.