Reviewerly/exHarmony huggingface dataset:
Review and analysis of augmented reality (AR) literature for digital fabrication in architecture

The use of Augmented Reality (AR) technologies has increased recently, due to the equipment update and the mature technology. For architectural design, especially in digital fabrication projects, more designers begin to integrate AR methods to achieve the visualization in the process. To help unskilled labors for holographic on-site previewing and instruction training, experimental and practice-based studies in AR for the architectural digital fabrication have emerged in recent years. Now, it is a great opportunity to discuss the topic of AR in architectural digital fabrication. By presenting a statistical review of AR technology in architecture projects, this literature review aims to review ongoing research and provide pathways for further research in architectural digital fabrication. This review article is based on information found in journal publications and conference papers in the fields of architecture, engineering, robotics, and digital fabrication, published to date (from 2010 to 2020). The review narrows the literature within these papers by filtering 84 articles through the keyword “Augmented Reality”, “Digital Fabrication” and “Assembly”. The selected articles can be categorized based on the most use of AR function in architectural digital fabrication into an order of the following three classifications with the most significant growth in the last years: (A) AR 3D holographic instruction, (B) AR data sharing, (C) AR for Human-Computer interaction. The information collected from these articles within their classifications is meant to give insight into the current state-of-the-art of AR in the architectural digital fabrication area, as well as to summarize how the topic has matured and developed over time in the research and industry literature. This article has not only analyzed the existing literature but also highlighted new emerging fields in AR research and the future trends of AR function in architectural digital fabrication.
A survey on text generation using generative adversarial networks

This work presents a thorough review concerning recent studies and text generation advancements using Generative Adversarial Networks. The usage of adversarial learning for text generation is promising as it provides alternatives to generate the so-called "natural" language. Nevertheless, adversarial text generation is not a simple task as its foremost architecture, the Generative Adversarial Networks, were designed to cope with continuous information (image) instead of discrete data (text). Thus, most works are based on three possible options, i.e., Gumbel-Softmax differentiation, Reinforcement Learning, and modified training objectives. All alternatives are reviewed in this survey as they present the most recent approaches for generating text using adversarial-based techniques. The selected works were taken from renowned databases, such as Science Direct, IEEEXplore, Springer, Association for Computing Machinery, and arXiv, whereas each selected work has been critically analyzed and assessed to present its objective, methodology, and experimental results.
An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety

Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. Therefore, this paper develops an automated deep learning based anomaly detection technique in pedestrian walkways (DLADT-PW) for vulnerable road user's safety. The goal of the DLADT-PW model is to detect and classify the various anomalies that exist in the pedestrian walkways such as cars, skating, jeep, etc. The DLADT-PW model involves preprocessing as the primary step, which is applied for removing the noise and raise the quality of the image. In addition, mask region convolutional neural network (Mask-RCNN) with densely connected networks (DenseNet) model is employed for the detection process. To ensure the better anomaly detection performance of the DLADT-PW technique, an extensive set of simulations were performed and the outcomes are investigated under distinct aspects. The obtained experimental values confirmed the superior characteristics of the DLADT-PW technique by achieving a maximum detection accuracy.
VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE

In this work we present a new construction for a batched Oblivious Pseudorandom Function (OPRF) based on Vector-OLE and the PaXoS data structure. We then use it in the standard transformation for achieving Private Set Intersection (PSI) from an OPRF. Our overall construction is highly efficient with O(n) communication and computation. We demonstrate that our protocol can achieve malicious security at only a very small overhead compared to the semi-honest variant. For input sizes $$n = 2^{20}$$ , our malicious protocol needs 6.2 s and less than 59 MB communication. This corresponds to under 450 bits per element, which is the lowest number for any published PSI protocol (semi-honest or malicious) to date. Moreover, in theory our semi-honest (resp. malicious) protocol can achieve as low as 219 (resp. 260) bits per element for $$n=2^{20}$$ at the added cost of interpolating a polynomial over n elements. As a second contribution, we present an extension where the output of the PSI is secret-shared between the two parties. This functionality is generally referred to as Circuit-PSI. It allows the parties to perform a subsequent MPC protocol on the secret-shared outputs, e.g., train a machine learning model. Our circuit PSI protocol builds on our OPRF construction along with another application of the PaXoS data structure. It achieves semi-honest security and allows for a highly efficient implementation, up to 3x faster than previous work.
Deep cascading network architecture for robust automatic modulation classification

BACKGROUND: Automatic modulation classification (AMC) plays a crucial role in cognitive radio, such as industrial automation, transmitter identification, and spectrum resource allocation. Recently, deep learning (DL) as a new machine learning (ML) methodology has achieved considerable implementation in AMC missions. However, few studies have examined the robustness of DL models under varying signal-to-noise ratio (SNR) environments. OBJECTIVE: The primary objective of this paper is to design a robust DL-based AMC model to adapt to noise changes. METHODS: The AMC task is divided into two sub-problems: SNR environment perception and modulation classification in sub-environments. A deep cascading network architecture (DCNA) is proposed to solve these two problems. DCNA is composed of an SNR estimator network (SEN) and a modulation recognition cluster network (MRCN). SEN is designed to identify the SNR levels of samples, and MRCN is composed of several subnetworks for further modulation recognition under diverse SNR settings. In addition, a label-smoothing method is proposed to promote the integration between SEN and MRCN. An auxiliary data-segmenting method is also presented to deal with the contrasting data requirements of DCNA. Note that DCNA does not utilize a specific network structure and can be generalized to various deep learning models with advanced improvements. RESULTS: Experimental results on dataset RML2016.10b show that our proposed DCNA can enhance the recognition performance of different network structures on AMC tasks. In particular, a combination of DCNA and convolutional long short-term deep neural network (CLDNN) can achieve a classification accuracy of 91.0%, outperforming the previous research. CONCLUSION: The performance of the cascading network demonstrates the significant performance advantage and application feasibility of DCNA.
Multi-objective task scheduling optimization in cloud computing based on fuzzy self-defense algorithm

A cloud computing multi-objective task scheduling optimization based on fuzzy self-defense algorithm is proposed. Select the shortest time, the degree of resource load balance and the cost of multi-objective task completion as the goal of cloud computing multi-objective task scheduling, establish a mathematical model to measure the effect of multi-objective task scheduling, and construct the objective function of cloud computing multi-objective task scheduling. The experimental results show that the cloud computing multi-objective task scheduling optimization method based on fuzzy self-defense algorithm can improve the performance of multi-objective maximum completion time, deadline violation rate and virtual machine resource utilization.
Supply chain financial service management system based on block chain IoT data sharing and edge computing

The implementation of the “Internet +” policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises.
Blockchain for IoT access control: Recent trends and future research directions

With the rapid development of wireless sensor networks, smart devices, and traditional information and communication technologies, there is tremendous growth in the use of Internet of Things (IoT) applications and services in our everyday life.IoT systems deal with high volumes of data.This data can be particularly sensitive, as it may include health, financial, location, and other personal information.Therefore, fine-grained security management in IoT demands effective access control.Several proposals discuss access control for the IoT.However, a limited focus is given to the emerging blockchain-based solutions for IoT access control.In this paper, we review the recent trends and critical needs for blockchain-based solutions for IoT access control.We identify several important aspects of blockchain, including decentralised control, secure storage and sharing information in a trustless manner for IoT access control, including their benefits and limitations.Finally, we note some future research directions on converging blockchain in IoT access control efficiently and effectively.
A bibliometric review of cryptocurrencies as a financial asset

Within a decade, cryptocurrencies have captured significant attention. After Bitcoin's emergence in 2008, new cryptocurrencies started to enter the financial market. We use bibliometric analysis to explore the cryptocurrency literature in the areas of business and management. We review and analyze 464 research articles through bibliometric measurements and social network analysis using Biblioshiny in R. Our study highlights the most influential authors, institutions, countries, and studies. Using the results of the social network analysis of the authors and countries, we illustrate the co-authorship and collaboration among authors from various countries at the institutional level and demonstrate how they have expanded the knowledge in this area. We identify four streams in the current cryptocurrency literature: (i) the determinants of cryptocurrency returns, (ii) the efficiency of cryptocurrencies, (iii) tests of portfolio diversification and sheep flock behaviour and (iv) the regulation, governance, and socio-economic impact of cryptocurrencies. Finally, we present an agenda for future research in the field.
Augmented Reality and programming education: A systematic review

In recent years, Augmented Reality (AR) usage in the learning process has been growing. AR tools and environments lead to a variety of positive outcomes and impacts for educational purposes. Similarly, AR is changing the learning process in the Computer Science (CS) Education domain. There are numerous studies that adopt the immersive AR technology in order to improve Computational Thinking (CT) or programming skills, in several contexts. However, there are not sufficient studies that analyze the meaningful characteristics or the advantages and disadvantages of AR in the field. In order to better understand the impact of AR in programming education we performed a systematic literature review. This review analyzes 31 studies in the field. It explores the evolution of this developing technology, the challenges and issues that AR offers and discusses how this work can benefit student learning and further research.
Facial Emotion Recognition using Convolution Neural Network

With the transition from laboratory-controlled to daunting in-the-wild conditions of facial Emotion recognition (FER) and the recent popularity of deep learning strategies in various fields, deep neural networks[1], [2] have rapidly been leveraged to train discriminatory representations for automated FER. The FER will allow us to recognize the Emotion of the human face that is a major blow to recent technological development. Recent FER programmers are typically concentrating on two critical issues: overfitting due to lack of appropriate training evidence and emotion-related differences such as lighting, head posture and identification bias. It encourages to improve the other innovations, such as incorporating FER into the robotic device in order to provide the robot feelings. A systematic survey of deep FER including datasets and algorithms that offer insight into these inherent issues. First, the FER scheme with the relevant context information and recommendations for the implementations to be applied at each level. Introducing the relevant datasets that are commonly used in the literature and including agreed data collection and assessment criteria for these data sets. Competitive results on commonly used metrics that can be used to develop the project to the maximum degree that, in essence, helps more developments and the environment. The main motivation to work is to improve the accuracy for the following model statement which could make an impact on the future work. Along with that a comparative analysis between the used model and transfer learning model will provide a proper novelty for the research work.
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

Multi-source unsupervised domain adaptation (MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap between source domains and target domain to some extent. Then, to fully exploit the essential semantic information across source domains, we propose a collaborative learning method for domain adaptation without seeing any data from target domain. In addition, similar to the setting of unsupervised domain adaptation, unlabeled target domain data is leveraged to further improve the performance of domain adaptation. This is achieved by additionally constraining the outputs of multiple adaptation models with pseudo labels online generated by an ensembled model. Extensive experiments and ablation studies are conducted on the widely-used domain adaptation benchmark datasets in semantic segmentation. Our proposed method achieves 59.0% mIoU on the validation set of Cityscapes by training on the labeled Synscapes and GTA5 datasets and unlabeled training set of Cityscapes. It significantly outperforms all previous state-of-the-arts single-source and multi-source unsupervised domain adaptation methods.
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network

Software defect prediction aims to identify the potential defects of new software modules in advance by constructing an effective prediction model. However, the model performance is susceptible to irrelevant and redundant features. In addition, previous studies mainly use traditional data mining or machine learning techniques for defect prediction, the prediction performance is not superior enough. For the first issue, motivated by the idea of search based software engineering, we leverage the recently proposed whale optimization algorithm (WOA) and another complementary simulated annealing (SA) to construct an enhanced metaheuristic search based feature selection algorithm named EMWS, which can effectively select fewer but closely related representative features. For the second issue, we employ a hybrid deep neural network — convolutional neural network (CNN) and kernel extreme learning machine (KELM) to construct a unified defect prediction predictor called WSHCKE, which can further integrate the selected features into the abstract deep semantic features by CNN and boost the prediction performance by taking full advantage of the strong classification capacity of KELM. We conduct extensive experiments for feature selection or extraction and defect prediction across 20 widely-studied software projects on four evaluation indicators. Experimental results demonstrate the superiority of EMWS and WSHCKE.
9.1 A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling

Low-precision computation is the key enabling factor to achieve high compute densities (T0PS/W and T0PS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in AI hardware accelerators across cloud and edge platforms. However, robust deep learning (DL) model accuracy equivalent to high-precision computation must be maintained. Improvements in bandwidth, architecture, and power management are also required to harness the benefit of reduced precision by feeding and supporting more parallel engines to achieve high sustained utilization and optimize performance within a given product power envelope. In this work, we present a 4-core AI chip in 7nm EUV technology that exploits cutting-edge algorithmic advances for iso-accurate models in low-precision training and inference [1, 2] and aggressive circuit/architecture optimization to achieve leading-edge power-performance. The chip supports fp16 (DLFIoat16 [8]) and hybrid-fp8(hfp8) [1] formats for training and inference of DL models, as well as int4 and int2 formats for highly scaled inference.
Detecting fake images by identifying potential texture difference

Fake detection has become an urgent task. Generative adversarial networks (GANs) extended to deep learning has shown its extraordinary ability in the fields of image, audio, and speech. But advanced technology benefits us, it also poses a threat to us when used in Cyber Crime. The Deepfake (common name for face manipulation methods) based on GANs can realize the replacement of different faces. Due to the development of GANs, faces generated by Deepfake can already be visually real. Deepfake can purposely replace any face to a different person, so that a fabricated event may be widely spread because of the convenience of the Internet, causing serious impacts such as personal attacks and cyber crime. Based on cutting-edge research , this paper proposes a intelligence forensic method of Deepfake detection. We first discover the subtle texture differences between real and fake image in image saliency, which shows difference in the texture of faces. To amplify this difference, we exploit guided filter with saliency map as guide map to enhance the texture artifacts caused by the post-processing and display the potential features of forgery. Resnet18 classification network efficiently learns the exposed difference and finally realizes the real and fake detection of face images. We evaluate the performance of the method and experiments verify that the proposed method can achieve the state-of-the-art detection accuracy . • The combination of traditional digital forensics and artificial intelligence will be more conducive to forgery identification. • The image saliency method can reveal the key features of the image. • The image guided filtering method can magnify the key details of the image.
COVID-19 and the use of digital technology in mathematics education

Once the COVID-19 crisis is over, will everything" return to normal" or will we instead witness an ongoing boom in online learning? A time of crisis is an opportunity for all education systems to look to the future; there is enormous potential for digital technology in mathematics education, regardless of the impact of COVID-19. In this paper, the researcher focuses on answering two research questions: (1) Is COVID-19 the gateway for digital learning in mathematics education? (2) What type of digital technology is being used in mathematics education during the COVID-19 pandemic? The study also provided a discussion on the implications that such digital technologies could have on research into the field of mathematics education and practice in addition to suggestions for future research directions on this topic. Interviews were chosen as techniques for the purpose of this research, which were undertaken with hundred and twenty mathematics teachers from different secondary schools in the Kingdom of Saudi Arabia. The researcher found that 98% of participants believed that COVID-19 is the gateway for digital learning in mathematics education. In addition, 97% claimed that the use of online education by schools had expanded greatly following the coronavirus outbreak. This has resulted in various forms of software being used to facilitate communicate between teachers and students included mobile technologies, touchscreens and pen tablets, digital library and designing learning objects in mathematics education, Massive Open Online Courses (MOOCs) in mathematics, and computer algebra systems (CAS) such as Mathematical, Maple, MuPAD, MathCAD, Derive and Maxima.