Reviewerly/exHarmony huggingface dataset:
Situated Visualization of IIoT Data on the Hololens 2

Augmented Reality for the Industrial Internet of Things (AIIT) is a use case in the larger research project Immersive Media Lab [2], which is currently being conducted at the University of Applied Sciences St.P&#x00F6;lten. This use case deals with the challenges of AR data visualization on AR HMDs in smart factories using the example of an industrial furnace. The application is intended to support people in two ways: On the one hand, new personnel should be able to quickly and easily understand the processes happening in the plant, and on the other hand, conditions for maintenance work that are invisible from the outside should be displayed in an understandable and easily consumable way directly situated at the system using AR. In addition to conventional displays (dashboards) for the representation of abstract sensor values, we use the concept of Indexical Visualization, which can reduce the cognitive load for the viewer. The system itself is expanded in AR in such a way that the information to be displayed becomes a natural part of the scene. In this paper, we describe the steps necessary to create a mixed reality industrial application for the Microsoft Hololens 2 in terms of: optimizing a digital 3D model of the facility, establishing the data interface to the plant using an OPC-UA to MQTT bridge, correctly aligning the large 3D object in the real environment, and visual metaphors for making the machine data understandable for users.
The optimization of ecological service function and planning control of territorial space planning for ecological protection and restoration

With the rapid advancement of industrialization and urbanization, the scale of cities has continued to expand, the crisis of resource shortages has appeared, and the pressure on the ecological environment has increased. Land is a natural substance that exists before humans, and it is also the first objective object that humans come into contact with after being separated from nature. When land is invested in human social production activities, it becomes a necessary and irreplaceable material condition for any social material production sector. The quality of the ecological environment in some cities continues to decline, and the ecological space is gradually compressed. This research mainly discusses the optimization of ecological service functions and planning control of land and space planning based on ecological protection and restoration. In the past, the selection of evaluation indicators for research mostly only started with some basic service functions of the ecosystem. Based on the basic service functions of the ecosystem, this paper takes into account the direct economic benefits of the ecosystem, and adds the evaluation of the importance of ecological support functions to evaluate the ecological service functions more comprehensively. From the perspective of man-land relationship and structuralism, this paper constructs an ecological space evaluation plan based on the perspective of ecosystem services. It constructs a reasonable index system and selects an appropriate scale, and sets the weight of each index for the three leading functions of supply and support service function, adjustment service function, and social service function. According to the principle of regional differences and the consistency of zoning indicators, the factors that play a leading role in various plans are integrated and selected. It transfers elements of different themes such as society, economy and ecological environment of land and space to the expression of land and space functions through indicators. This research discusses in detail the methods of using important control lines to control the main space and the design of relevant policies and regulations to solve the problems of overlapping control areas of land and space planning, mixed objects, and ambiguous powers. In order to ensure the feasibility of the downward transmission of the important control line control system, it provides reference policies, regulations and technical basis for the preparation and implementation of the entire territory and space planning. Finally, the red line area of the importance of ecological service function is delineated. The area of the red line area is 192.79km2, accounting for 20.38% of the study area. This study will provide reference for the planning of land and space.
Diffuser: A user-friendly program for diffusion chronometry with robust uncertainty estimation

Chemical diffusion in minerals has shown great potential to quantify timescales of geological processes. The presence of chemical gradients, along with favorable temperature and time conditions, lead to the formation of measurable diffusion profiles. Temporal information can be extracted from measured diffusion profiles using either analytical or numerical solutions of Fick's second law. Currently, there is a lack of widely adopted programs for diffusion studies. In addition, the uncertainties associated with timescales derived from diffusion chronometry are critical for geological studies, but are not always robustly evaluated. In many cases, only uncertainties in curve fitting parameters and temperature are considered, whereas other uncertainties, such as those associated with the experimentally determined diffusion coefficients themselves, are rarely propagated into the calculated timescales. Ignoring these uncertainties reduces the reproducibility and intercomparability of results. In response to these challenges, we present Diffuser, a user-friendly program to standardize diffusion chronometry with transparent and robust propagation of uncertainties. Using analytical and numerical methods, our program provides an automatic, visual, and efficient curve fit to extract chronological information from diffusion profiles. The method is complemented by an algorithm to propagate all uncertainties (i.e., measurement, temperature, curve fitting, and diffusion coefficient) to derived timescales. Three examples are provided to highlight how the program can recover timescales with internal consistency, efficient computing, and easy-to-use features. Our freely available and user-friendly program will hopefully increase the accessibility and consistency of diffusion modeling and thereby to facilitate more high-quality diffusion studies.
Adaptive depth-aware visual relationship detection

Visual relationship detection aims at detecting the interaction between objects from flat image, where visual appearance and spatial relationship between different objects are two key factors for detection. However, most existing methods usually extract 2D information of object from flat images, which lacks depth information compared to actual 3D space. To obtain and utilize depth information for visual relationship detection, we construct Depth VRDs dataset as an extension of the VRD dataset and propose adaptive depth-aware visual relationship detection network(ADVRD). In terms of visual appearance, we propose depth-aware visual fusion module to use additional depth visual information to guide RGB visual information where needs to be strengthened. In terms of spatial relationship, to generate a more accurate depth representation when locating object depth spatial position, we propose adaptive depth spatial location method which uses regional information variance to measure information relevance in each small region in object bounding box. Experiment results show that depth information can significantly improve the performance of our network on visual relationship detection tasks, especially for zero shots.
Integrating Rocketbox Avatars with the Ubiq Social VR platform

Having a truly ethical, unbiased technology, requires people devel-oping and using this technology to have an equal opportunity to participate in its creation. In this sense, open-access tools are a way to share best practices and enhance collaboration. In this paper, we will present the integration of the Microsoft Rocketbox avatar library into the Unity networking library Ubiq. We will see how they may contribute to the research in the field of populated virtual environments.
Employing Deep Learning Feature Extraction Models with Learning Classifiers to Diagnose Breast Cancer in Medical Images

Breast cancer became one of the most frequent cancers among women worldwide, accounting for the majority of deaths. The only method to stop breast cancer from spreading is to get a timely diagnosis and treatment. However, due to the uncertainties in the mammography technique, cancer detection is a difficult process. Pre-trained Convolutional Neural Network (CNN) models and Machine Learning (ML) classifiers can be used to provide a tool for doctors to utilize in the early identification and diagnosis of breast cancer. These methods improve the patient's chances of survival. In this research, four distinct CNN pre-trained models are compared: VGG16, VGG19, Inception v3, SqueezeNet along with various classifiers. The findings of the study provide an overview of current breast cancer diagnostic approaches.
Blind Image Deblurring via a Novel Sparse Channel Prior

Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (DCP) to the bright channel prior (BCP) as an image prior for solving the BID problem. Specifically, the above two channel priors obtained from RGB images are used to construct an innovative sparse channel prior at first, and then the learned prior is incorporated into the BID tasks. The proposed sparse channel prior enhances the sparsity of the DCP. At the same time, it also shows the inverse relationship between the DCP and BCP. We employ the auxiliary variable technique to integrate the proposed sparse prior information into the iterative restoration procedure. Extensive experiments on real and synthetic blurry sets show that the proposed algorithm is efficient and competitive compared with the state-of-the-art methods and that the proposed sparse channel prior for blind deblurring is effective.
A swarm intelligence-based robotic search algorithm integrated with game theory

This paper proposes a novel decentralize and asynchronous swarm robotic search algorithm integrated with game theory to better disperse robots in the environment while crossing obstacles and solving mazes. This prevents early convergence and improves the efficiency of the searches. In the proposed algorithm, individual robots, while searching, play a sequential game at each iteration, and based on that, choose their velocity update rule. The effectiveness of the proposed strategic game is tested in a specially designed framework. As a validation, the introduced algorithm is compared with the state-of-the-art in simple and complex search environments. The results showed that the suggested algorithm outperforms other methods both in search duration and attained path length to the target, and its success rate is equal to the one of state-of-the-art (i.e., 100% in the conducted experiments). Also, it is shown that the proposed strategic game works well in search environments with different levels of complexity and especially improves search efficiency further in complex environments.
Non-Orthogonality Measure for a Collection of Pure Quantum States

Modern optical communication technology can realize a large-scale multilevel (or M-ary) optical signal. Investigating the quantum mechanical nature of such a large-scale M-ary optical signal is essential for a unified understanding of quantum information science and optical communication technology. This article focuses on the quantum-mechanical non-orthogonality for a collection of pure quantum states and proposes a non-orthogonality index based on the least squares error criterion in quantum detection theory. First, we define the index for linearly independent signals, and the proposed index is analyzed through numerical simulations. Next, the index is applied to a highly large-scale M-ary phase-shift keying (PSK) coherent state signal. Furthermore, the index is compared with the capacity of the pure state channel with the PSK signal. As a result, it is shown that a highly large-scale M-ary PSK coherent state signal exhibits a quantum nature even when the signal transmission power is very high. Thus, the theoretical characterization of a highly large-scale M-ary coherent state signal based on the proposed index will be the first step toward a better understanding of cutting-edge optical communication technologies such as the quantum stream cipher Y00.
Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis

Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.
Mixed Reality Co-Design for Indigenous Culture Preservation &amp; Continuation

As a result of urban concentration, colonisation, increased physical distance from tribal homelands, and globalisation, many indigenous people, including M&#x0101;ori, are seeking digital solutions to connect to their culture and identity. Through co-design, close collaboration with our indigenous partners, and careful consideration of cultural context, we show that mixed reality experiences can be an effective mechanism for the growing diaspora of M&#x0101;ori to access and experience their language, genealogy, families, histories and knowledge. Inductive analysis of semi-structured interviews highlights the importance of cultural values and context in this experience, and confirms that this approach can support connections to community and culture. Our work is deeply embedded in a particular indigenous group&#x2019;s context and culture, but we believe that it holds important lessons that can generalise to other groups. In particular, collaborative co-design and recognition of cultural values throughout the project are essential for producing experiences that meet the needs of specific communities, and that reflect and respect their culture and worldview.
A Survey on Recommender System for Arabic Content

On the internet, where the number of choices of products and services is growing, users need to filter items or products to make better decisions. Recommender system is a type of information filtering system designed to provide recommendations to users based on various algorithms. These algorithms forecast the most likely products that users will buy or like based on their interests. In recent years, the number of recommender systems has increased, and famous companies have employed recommender systems to assist their users in finding the products or items that are appropriate for them. Therefore, we decided to review existing studies on recommender systems for Arabic content. Because many recommender systems focus on English content, we found a few studies in the field of recommender systems that address Arabic content. We summarize these studies based on some features, including recommender system types, domain, datasets, and if the recommender system is integrated with sentiment analysis. Finally, we discuss recommender systems with Arabic content studies, and we notice that most of these studies used sentiment analysis with recommender systems to achieve high-quality recommendations.
Progressive Training of A Two-Stage Framework for Video Restoration

As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts. Among video restorations, compressed video quality enhancement and video super-resolution are two of the main tacks with significant values in practical scenarios. Recently, recurrent neural networks and transformers attract increasing research interests in this field, due to their impressive capability in sequence-to-sequence modeling. However, the training of these models is not only costly but also relatively hard to converge, with gradient exploding and vanishing problems. To cope with these problems, we proposed a two-stage framework including a multi-frame recurrent network and a single-frame transformer. Besides, multiple training strategies, such as transfer learning and progressive training, are developed to shorten the training time and improve the model performance. Benefiting from the above technical contributions, our solution wins two champions and a runner-up in the NTIRE 2022 super-resolution and quality enhancement of compressed video challenges.
Characterizing and Understanding Distributed GNN Training on GPUs

Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accelerates training using multiple computing nodes. Maximizing the performance is essential, but the execution of distributed GNN training remains preliminarily understood. In this work, we provide an in-depth analysis of distributed GNN training on GPUs, revealing several significant observations and providing useful guidelines for both software optimization and hardware optimization.
dsODENet: Neural ODE and Depthwise Separable Convolution for Domain Adaptation on FPGAs

High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge devices with strict limitations on computational resources. In this paper, we derive a compact while highly-accurate DNN model, termed dsODENet, by combining recently-proposed parameter reduction techniques: Neural ODE (Ordinary Differential Equation) and DSC (Depthwise Separable Convolution). Neural ODE exploits a similarity between ResNet and ODE, and shares most of weight parameters among multiple layers, which greatly reduces the memory consumption. We apply dsODENet to a domain adaptation as a practical use case with image classification datasets. We also propose a resource-efficient FPGA-based design for dsODENet, where all the parameters and feature maps except for pre- and post-processing layers can be mapped onto onchip memories. It is implemented on Xilinx ZCU104 board and evaluated in terms of domain adaptation accuracy, training speed, FPGA resource utilization, and speedup rate compared to a software counterpart. The results demonstrate that dsODENet achieves comparable or slightly better domain adaptation accuracy compared to our baseline Neural ODE implementation, while the total parameter size without pre- and post-processing layers is reduced by 54.2% to 79.8%. Our FPGA implementation accelerates the inference speed by 27.9 times.
TeachInVR: A virtual reality classroom for remote education

TeachInVR is a virtual reality (VR) classroom for remote and co-located group teaching. It supports a range of teaching activities from lectures and student presentations to guided tours, group work and private study. It allows rapid creation of classes through re-use of traditional teaching materials, reducing the barriers for time-constrained teachers. It is also intended as a sandbox environment for exploring teaching concepts that are well supported by, or even unique to, VR. In particular, it provides tools for the spatial arrangement of teaching material and leveraging the spatial properties of VR for learning and recall.
Feature fusion for object detection at one map

The scale feature plays a crucial role in the detector, and existing methods adopt the feature pyramid based on multiple maps. This paper focuses on a single map and proposes an encoder called SFMF which can employ multi-scale feature fusion on a map. One of the crucial techniques underlying SFMF is a fine-grained weighting method that is used to fast discard unneeded pixel channels during the fusion process. YOLOF (you only look one-level feature) with SFMF (single feature map fusion) achieve 38.5 mAP in the ResNet50 and 40.3 mAP in the ResNet101, which improves 0.8 and 0.5 mAP than the baseline, respectively. Meta-ACON is used to auto-learn activate the neurons or not in the backbone. With the Meta-ACON and SFMF, YOLOF can achieve 39.1 and 40.4 mAP, surpassing the baseline by 1.4 and 0.6 mAP on COCO val-dev. In addition, YOLOF with SFMF achieves 54.8 mAP, improving the performance by an absolute 4.9 mAP on the aircraft detection dataset, with a slight sacrificing efficiency (1 FPS) in inference.
Automated Image Caption Generation Framework using Adaptive Attention and Bi-LSTM

Many Attention-based deep architectures have been widely adopted for image captioning. Mostly force visual attention to be active for word generation. However, spatial region specific word generation is not desired always. In this paper, to address this issue, an adaptive attention mechanism-based automated Image caption generation framework is proposed. Our model uses Inception-V3 to extract various global spatial features and the adaptive attention module helps to decide whether to attend to the image (and if so, to which regions) or to the visual sentinel maps. Further, at the decoding end, a Bi-LSTM network refines the text description. The experimental results, usingFlickr8K and Visual Assistance Datasets, demonstrate that the proposed model provides significant improvements over similar state-of-the-arts in terms of BLEU scores.
Supporting Playful Rehabilitation in the Home using Virtual Reality Headsets and Force Feedback Gloves

Virtual Reality (VR) is a promising platform for home rehabilitation with the potential to completely immerse users within a playful experience. To explore this area we design, implement, and evaluate a system that uses a VR headset in conjunction with force feed-back gloves to present users with a playful experience for home rehabilitation. The system immerses the user within a virtual cat bathing simulation that allows users to practice fine motor skills by progressively completing three cat-care tasks. The study results demonstrate the positive role that playfulness may play in the user experience of VR rehabilitation.