Reviewerly/exHarmony huggingface dataset:
Review of empirical solar radiation models for estimating global solar radiation of various climate zones of China

Utilizing solar energy requires accurate information about global solar radiation (GSR), which is critical for designers and manufacturers of solar energy systems and equipment. This study aims to examine the literature gaps by evaluating recent predictive models and categorizing them into various groups depending on the input parameters, and comprehensively collect the methods for classifying China into solar zones. The selected groups of models include those that use sunshine duration, temperature, dew-point temperature, precipitation, fog, cloud cover, day of the year, and different meteorological parameters (complex models). 220 empirical models are analyzed for estimating the GSR on a horizontal surface in China. Additionally, the most accurate models from the literature are summarized for 115 locations in China and are distributed into the above categories with the corresponding solar zone; the ideal models from each category and each solar zone are identified. Comments on two important temperature-based models that are presented in this work can help the researchers and readers to be unconfused when reading the literature of these models and cite them in a correct method in future studies. Machine learning techniques exhibit performance GSR estimation better than empirical models; however, the computational cost and complexity should be considered at choosing and applying these techniques. The models and model categories in this study, according to the key input parameters at the corresponding location and solar zone, are helpful to researchers as well as to designers and engineers of solar energy systems and equipment.
<i>DeepFIR:</i> Channel-Robust Physical-Layer Deep Learning Through Adaptive Waveform Filtering

Deep learning can be used to classify waveform characteristics ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepFIR</i> , a framework to counteract the channel action in wireless deep learning algorithms <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without retraining the underlying deep learning model</i> . The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter’s side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions. We mathematically formulate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Waveform Optimization Problem</i> <xref ref-type="disp-formula" rid="deqnWOP-deqnC1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(WOP)</xref> as the problem of finding the optimum FIR to be used on a waveform to improve the classifier’s accuracy. We also propose a data-driven methodology to train the FIRs directly with dataset inputs. We extensively evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepFIR</i> on an experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%; (ii) decreases an adversary’s accuracy by about 54% when trying to imitate other device’s fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset; (iv) increases by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times$ </tex-math></inline-formula> the accuracy of the modulation dataset.
Smokestack: Thwarting DOP Attacks with Runtime Stack Layout Randomization

Memory corruption vulnerabilities in type-unsafe languages are often exploited to perform a control-flow hijacking attack, in which an attacker uses vulnerabilities to corrupt control data in the program to eventually gain control over the execution of the program. However, widespread adoption of control-flow attack defenses such as Control-flow Integrity (CFI) has led attackers to exploit memory errors to corrupt non-control data that can not be detected by these defenses. Non-control data attacks can be used to corrupt security critical data or leak sensitive information. Moreover, recent attacks such as data-oriented programming (DOP) have generalized non-control data attacks to achieve Turing-complete computation capabilities within the programmer-specified control-flow graph, leaving previously proposed control-flow protections unable to stop these attacks.In this paper, we present a stack-layout randomization scheme that can effectively thwart DOP attacks. Our approach, called Smokestack, provides each function invocation with a randomly permuted ordering of the local stack organization. In addition, we utilize true-random value sources combined with disclosure-resistant pseudo-random number generation to ensure that an adversary cannot anticipate a function-s invocation permutation of automatic variables. Our evaluation on SPEC benchmarks and various real-world applications shows that Smokestack can stop DOP attacks with minimal overhead.
Image Forensics

From mainstream media outlets to social media and everything in between, doctored photographs are appearing with growing frequency and sophistication. The resulting lack of trust is impacting law enforcement, national security, the media, e-commerce, and more. While some types of manipulations can be detected with a careful visual examination, our visual system seems unable to reliably detect other types of manipulations. The field of image forensics has emerged to help return some trust in photography. I describe the perceptual limits of detecting manipulated images, as well as representative examples of computational techniques for authenticating images.
Visualizing quantum mechanics in an interactive simulation – Virtual Lab by Quantum Flytrap

Virtual Lab by Quantum Flytrap is a no-code online laboratory of an optical table, presenting quantum phenomena interactively and intuitively. It supports a real-time simulation of up to three entangled photons. Users can place typical optical elements (such as beam splitters, polarizers, Faraday rotators, and detectors) with a drag-and-drop graphical interface. Virtual Lab operates in two modes. The sandbox mode allows users to compose arbitrary setups. Quantum Game serves as an introduction to Virtual Lab features, approachable for users with no prior exposure to quantum mechanics. We introduce visual representation of entangled states and entanglement measures. It includes interactive visualizations of the ket notation and a heatmap-like visualization of quantum operators. These quantum visualizations can be applied to any discrete quantum system, including quantum circuits with qubits and spin chains. These tools are available as open-source TypeScript packages – Quantum Tensors and BraKetVue. Virtual Lab makes it possible to explore the nature of quantum physics (state evolution, entanglement, and measurement), to simulate quantum computing (e.g., the Deutsch-Jozsa algorithm), to use quantum cryptography (e.g., the Ekert protocol), to explore counterintuitive quantum phenomena (e.g., quantum teleportation and the Bell inequality violation), and to recreate historical experiments (e.g., the Michelson–Morley interferometer).
Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time

The motion capture method using sparse inertial sensors is an approach for solving the occlusion and economic problems in vision-based methods, which is suitable for virtual reality applications and works in complex environments. However, VR applications need to track the location of the user in real-world space, which is hard to obtain using only inertial sensors. In this paper, we present Fusion Poser, which combines the deep learning-based pose estimation and location tracking method with six inertial measurement units and a head tracking sensor that provides head-mounted displays. To estimate human poses, we propose a bidirectional recurrent neural network with a convolutional long short-term memory layer that achieves higher accuracy and stability by preserving spatio-temporal properties. To locate a user with real-world coordinates, our method integrates the results of an estimated joint pose with the pose of the tracker. To train the model, we gathered public motion capture datasets of synthesized IMU measurement data, as well as creating a real-world dataset. In the evaluation, our method showed higher accuracy and a more robust estimation performance, especially when the user adopted lower poses, such as a squat or a bow.
MMGL: Multi-Scale Multi-View Global-Local Contrastive Learning for Semi-Supervised Cardiac Image Segmentation

With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in clinical practice due to extensive expertise requirements and costly labeling efforts. Recently, contrastive learning has shown a strong capacity for visual representation learning on unlabeled data, achieving impressive performance rivaling supervised learning in many domains. In this work, we propose a novel multi-scale multi-view global-local contrastive learning (MMGL) framework to thoroughly explore global and local features from different scales and views for robust contrastive learning performance, thereby improving segmentation performance with limited annotations. Extensive experiments on the MM-WHS dataset demonstrate the effectiveness of MMGL framework on semi-supervised cardiac image segmentation, outperforming the state-of-the-art contrastive learning methods by a large margin.
Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Abstract Text-to-GraphQL (Text2GraphQL) is a task that converts the user's questions into Graph + QL (Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GraphQL task. This solution uses the Adapter pre-trained by “the linking of GraphQL schemas and the corresponding utterances” as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GraphQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GraphQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Current language generation models suffer from issues such as repetition, incoherence, and hallucinations.An often-repeated hypothesis for this brittleness of generation models is that it is caused by the training and the generation procedure mismatch, also referred to as exposure bias.In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective.We show that exposure bias leads to an accumulation of errors during generation, analyze why perplexity fails to capture this accumulation of errors, and empirically show that this accumulation results in poor generation quality.
Self-Unwrapping Phase-Shifting for Fast and Accurate 3-D Shape Measurement

Phase unwrapping is one of the most important procedures in three-dimensional phase measurement profilometry and has attracted great research interest in the past few decades. However, existing phase unwrapping methods require additional patterns or pre-assumptions to determine the fringe orders for further absolute phase retrieval, which dramatically affect the measurement efficiency. To address this problem, we propose a generic self-unwrapping phase-shifting algorithm that retrieves the absolute phase without external information or priors. To this end, we firstly embed a novel space-varying phase shift that uniquely determines the fringe order information into sinusoidal patterns, and then extract it to retrieve the absolute phase by pixel-wise calculation. Our method achieves higher efficiency over previous methods while preserving the measurement precision, and the minimum number of patterns required is only four. All theoretical, simulation and extensive experimental results demonstrate its superiority in fast and accurate 3D shape measurement.
Blockchain’s Carbon and Environmental Footprints

We analyze existing discourses surrounding blockchains’ energy consumption and look at the actors and actions involved. We also provide an evaluation of various considerations and factors that affect blockchain networks’ energy consumption and resulting environmental impacts.
Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

Purpose This paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning. Design/methodology/approach A new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers. Findings The author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction. Originality/value The author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.
Elite Chaotic Manta Ray Algorithm Integrated with Chaotic Initialization and Opposition-Based Learning

The manta ray foraging optimizer (MRFO) is a novel nature-inspired optimization algorithm that simulates the foraging strategy and behavior of manta ray groups, i.e., chain, spiral, and somersault foraging. Although the native MRFO has revealed good competitive capability with popular meta-heuristic algorithms, it still falls into local optima and slows the convergence rate in dealing with some complex problems. In order to ameliorate these deficiencies of the MRFO, a new elite chaotic MRFO, termed the CMRFO algorithm, integrated with chaotic initialization of population and an opposition-based learning strategy, is developed in this paper. Fourteen kinds of chaotic maps with different properties are used to initialize the population. Thereby, the chaotic map with the best effect is selected; meanwhile, the sensitivity analysis of an elite selection ratio in an elite chaotic searching strategy to the CMRFO is discussed. These strategies collaborate to enhance the MRFO in accelerating overall performance. In addition, the superiority of the presented CMRFO is comprehensively demonstrated by comparing it with a native MRFO, a modified MRFO, and several state-of-the-art algorithms using (1) 23 benchmark test functions, (2) the well-known IEEE CEC 2020 test suite, and (3) three optimization problems in the engineering field, respectively. Furthermore, the practicability of the CMRFO is illustrated by solving a real-world application of shape optimization of cubic generalized Ball (CG-Ball) curves. By minimizing the curvature variation in these curves, the shape optimization model of CG-Ball ones is established. Then, the CMRFO algorithm is applied to handle the established model compared with some advanced meta-heuristic algorithms. The experimental results demonstrate that the CMRFO is a powerful and attractive alternative for solving engineering optimization problems.
A High Availability Management Model Based on VM Significance Ranking and Resource Estimation for Cloud Applications

Massive upsurge in cloud resource usage stave off service availability resulting into outages, resource contention, and excessive power-consumption. The existing approaches have addressed this challenge by providing multi-cloud, VM migration, and running multiple replicas of each VM which accounts for high expenses of cloud data centre (CDC). In this context, a novel VM Significance Ranking and Resource Estimation based High Availability Management (SRE-HM) Model is proposed to enhance service availability for users with optimized cost for CDC. The model estimates resource contention based server failure and organises needed resources beforehand for maintaining desired level of service availability. A significance ranking parameter is introduced and computed for each VM, executing critical or non-critical tasks followed by the selection of an admissible High Availability (HA) strategy respective to its significance and user specified constraints. It enables cost optimization for CDC by rendering failure tolerance strategies for significant VMs only instead of all the VMs. The proposed model is evaluated and compared against state-of-the-arts by executing experiments using Google Cluster dataset. SRE-HM improved the services availability up to 19.56% and scales down the number of active servers and power-consumption up to 26.67% and 19.1%, respectively over HA without SRE-HM.
Collaborative and individual learning of geography in immersive virtual reality: An effectiveness study

Many university-taught courses moved to online form since the outbreak of the global pandemic of coronavirus disease (COVID-19). Distance learning has become broadly used as a result of the widely applied lockdowns, however, many students lack personal contact in the learning process. A classical web-based distance learning does not provide means for natural interpersonal interaction. The technology of immersive virtual reality (iVR) may mitigate this problem. Current research has been aimed mainly at specific instances of collaborative immersive virtual environment (CIVE) applications for learning. The fields utilizing iVR for knowledge construction and skills training with the use of spatial visualizations show promising results. The objective of this study was to assess the effectiveness of collaborative and individual use of iVR for learning geography, specifically training in hypsography. Furthermore, the study’s goals were to determine whether collaborative learning would be more effective and to investigate the key elements in which collaborative and individual learning were expected to differ–motivation and use of cognitive resources. The CIVE application developed at Masaryk University was utilized to train 80 participants in inferring conclusions from cartographic visualizations. The collaborative and individual experimental group underwent a research procedure consisting of a pretest, training in iVR, posttest, and questionnaires. A statistical comparison between the geography pretest and posttest for the individual learning showed a significant increase in the score ( p = 0.024, ES = 0.128) and speed ( p = 0.027, ES = 0.123), while for the collaborative learning, there was a significant increase in the score ( p &lt;0.001, ES = 0.333) but not in speed ( p = 1.000, ES = 0.000). Thus, iVR as a medium proved to be an effective tool for learning geography. However, comparing the collaborative and individual learning showed no significant difference in the learning gain ( p = 0.303, ES = 0.115), speed gain ( p = 0.098, ES = 0.185), or performance motivation ( p = 0.368, ES = 0.101). Nevertheless, the collaborative learning group had significantly higher use of cognitive resources ( p = 0.046, ES = 0.223) than the individual learning group. The results were discussed in relation to the cognitive load theories, and future research directions for iVR learning were proposed.