Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification

Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular clouds and aerosols. The complexity of weather conditions in terms of many variable parameters and their inherent unpredictability limit the performance and accuracy of solar power forecasting models. As renewable power penetration in electricity grids increases due to the rapid increase in the installation of photovoltaics (PV) systems, the resulting challenges are amplified. A regional PV power prediction system is presented and evaluated by providing forecasts up to 72 h ahead with an hourly time resolution. The proposed approach is based on a local radiation forecast model developed by Blue Sky. In this paper, we propose a novel method of deriving forecast equations by using an irradiance classification approach to cluster the dataset. A separate equation is derived using the GEKKO optimization tool, and an algorithm is assigned for each cluster. Several other linear regressions, time series and machine learning (ML) models are applied and compared. A feature selection process is used to select the most important weather parameters for solar power generation. Finally, considering the prediction errors in each cluster, a weighted average and an average ensemble model are also developed. The focus of this paper is the comparison of the capability and performance of statistical and ML methods for producing a reliable hourly day-ahead forecast of PV power by applying different skill scores. The proposed models are evaluated, results are compared for different models and the probabilistic time series forecast is presented. Results show that the irradiance classification approach reduces the forecasting error by a considerable margin, and the proposed GEKKO optimized model outperforms other machine learning and ensemble models. These findings also emphasize the potential of ML-based methods, which perform better in low-power and high-cloud conditions, as well as the need to build an ensemble or hybrid model based on different ML algorithms to achieve improved projections.
Resolution Tradeoff in Gameplay Experience, Performance, and Simulator Sickness in Virtual Reality Games

Higher resolution is one of the main directions and drivers in the de-velopment of Virtual Reality (VR) Head-Mounted Displays (HMDs). However, given their associated higher cost, it is unclear the benefits of having higher resolution on user experience, especially in VR games. This research aims to investigate the effects of resolution in gameplay experience and simulator sickness for VR games. To this end, we designed an experiment to collect gameplay experience, simulator sickness (SS), and player performance data with a VR First-Person Shooter game. Our results indicate that 2K resolution is an important threshold for an enhanced gameplay experience without affecting performance and increasing SS levels.
Spurious Correlations in Reference-Free Evaluation of Text Generation

Model-based, reference-free evaluation metrics have been proposed as a fast and cost-effective approach to evaluate Natural Language Generation (NLG) systems.Despite promising recent results, we find evidence that reference-free evaluation metrics of summarization and dialog generation may be relying on spurious correlations with measures such as word overlap, perplexity, and length.We further observe that for text summarization, these metrics have high error rates when ranking current state-ofthe-art abstractive summarization systems.We demonstrate that these errors can be mitigated by explicitly designing evaluation metrics to avoid spurious features in reference-free evaluation.
A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.
Exploring the Relationship between Interactions and Learning Performance in Robot-Assisted Language Learning

Robot-assisted language learning is a pattern that uses a social robot in a class to enhance learning performance through human-robot interaction. While this pattern receives increasing attention, few review articles generalize whether each interactive behavior improves learning performance. This study delves into the answers to three research questions. The results show that the synthesized and prerecorded human voices are appropriate for different teaching activities. Verbal communication except for egocentric small talk and L1 translation between the robot and learners are indispensable to improve students’ confidence and learning gains. Moderate employment of nonverbal interactions helps students increase concentration, motivation, and retention of vocabulary, while undue interactions give rise to counterproductive effects. Based on the findings, future research on robot-assisted language learning is suggested to pay attention to the effectiveness of an independent interaction. Another valuable focus is the proper combination of interactive behaviors that suit different teaching tasks. A suitable level of robotic sociability is also worthy of exploration. Educators teaching with a robot should make full use of verbal and nonverbal interactive behaviors to boost students’ confidence, motivation, engagement, and learning gains. Meanwhile, they need to be cautious about the excessive employment of interactions.
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation

Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are available. Although the quality of VC is crucial for this approach, it is challenging to learn a stable VC model because the amount of data is limited in low-resource scenarios, and highly expressive speech has large acoustic variety. To address this issue, we propose a novel data augmentation method that combines pitch-shifting and VC techniques. Because pitch-shift data augmentation enables the coverage of a variety of pitch dynamics, it greatly stabilizes training for both VC and TTS models, even when only 1,000 utterances of the target speaker's neutral data are available. Subjective test results showed that a FastSpeech 2-based emotional TTS system with the proposed method improved naturalness and emotional similarity compared with conventional methods.
ChapterBreak: A Challenge Dataset for Long-Range Language Models

While numerous architectures for long-range language models (LRLMs) have recently been proposed, a meaningful evaluation of their discourse-level language understanding capabilities has not yet followed.To this end, we introduce CHAPTERBREAK, a challenge dataset that provides an LRLM with a long segment from a narrative that ends at a chapter boundary and asks it to distinguish the beginning of the ground-truth next chapter from a set of negative segments sampled from the same narrative.A fine-grained human annotation reveals that our dataset contains many complex types of chapter transitions (e.g., parallel narratives, cliffhanger endings) that require processing global context to comprehend.Experiments on CHAPTERBREAK show that existing LRLMs fail to effectively leverage long-range context, substantially underperforming a segment-level model trained directly for this task.We publicly release our CHAPTERBREAK dataset to spur more principled future research into LRLMs. 1
Estimation of Photovoltaic Energy in China Based on Global Land High-Resolution Cloud Climatology

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds.
immersivePOV: Filming How-To Videos with a Head-Mounted 360° Action Camera

How-to videos are often shot using camera angles that may not be optimal for learning motor tasks, with a prevalent use of third-person perspective. We present immersivePOV, an approach to film how-to videos from an immersive first-person perspective using a head-mounted 360° action camera. immersivePOV how-to videos can be viewed in a Virtual Reality headset, giving the viewer an eye-level viewpoint with three Degrees of Freedom. We evaluated our approach with two everyday motor tasks against a baseline first-person perspective and a third-person perspective. In a between-subjects study, participants were assigned to watch the task videos and then replicate the tasks. Results suggest that immersivePOV reduced perceived cognitive load and facilitated task learning. We discuss how immersivePOV can also streamline the video production process for content creators. Altogether, we conclude that immersivePOV is an effective approach to film how-to videos for learners and content creators alike.
A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data

There is growing interest in using evidence generated from clinical practice data to support regulatory, coverage and other healthcare decision-making. A graphical framework for depicting longitudinal study designs to mitigate this barrier was introduced and has found wide acceptance. We sought to enhance the framework to contain information that helps readers assess the appropriateness of the source data in which the study design was applied.For the enhanced graphical framework, we added a simple visualization of data type and observability to capture differences between electronic health record (EHR) and other registry data that may have limited data continuity and insurance claims data that have enrollment files.We illustrate the revised graphical framework with 2 example studies conducted using different data sources, including administrative claims only, EHR only, linked claims and EHR, as well as specialty community based EHRs with and without external linkages.The enhanced visualization framework is important because evaluation of study validity needs to consider the triad of study question, design, and data together. Any given data source or study design may be appropriate for some questions but not others.
3D human pose estimation with cross-modality training and multi-scale local refinement

This paper aims to study two major research problems for 3D human pose estimation using depth data. First we seek an effective way for applying the RGB pre-trained 2D CNN model to 3D pose field, so as to transfer large-scale RGB annotation information to depth domain. In particular, we proposed a cross-modality CNN training strategy, where the key idea is to set a partial Batch Normalization (BN) layer within the RGB pre-trained 2D CNN model to weaken the distribution divergence between the RGB and depth data during training. To involve richer 3D descriptive cues, the raw depth data is appended with the normal vector map. Albeit coarse-to-fine human pose estimation with local refinement is helpful to enhance performance. While the way for setting the optimal local observation scale is not well addressed. Towards this crucial problem, we propose to fuse the multi-scale local information jointly. A multi-scale local refinement network is proposed accordingly, where the small local region focuses on capturing the fine information. On the other hand, the large local region contains richer semantic contextual information. The experiments on two 3D human pose estimation datasets with depth data verify the effectiveness and real-time running capacity of our proposition.
Deep parameter-free attention hashing for image retrieval

Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add channel-based or spatial-based attention modules. However, embedding these modules into the network can increase the complexity of model and lead to over fitting in the training process. In this study, a novel deep parameter-free attention hashing (DPFAH) is proposed to solve these problems, that designs a parameter-free attention (PFA) module in ResNet18 network. PFA is a lightweight module that defines an energy function to measure the importance of each neuron and infers 3-D attention weights for feature map in a layer. A fast closed-form solution for this energy function proves that the PFA module does not add any parameters to the network. Otherwise, this paper designs a novel hashing framework that includes the hash codes learning branch and the classification branch to explore more label information. The like-binary codes are constrained by a regulation term to reduce the quantization error in the continuous relaxation. Experiments on CIFAR-10, NUS-WIDE and Imagenet-100 show that DPFAH method achieves better performance.
Public Perspectives on Exposure Notification Apps: A Patient and Citizen Co-Designed Study

Canada deployed a digital exposure notification app (COVID Alert) as a strategy to support manual contact tracing. Our aims are to (1) assess the use, knowledge, and concerns of the COVID Alert app, (2) identify predictors of app downloads, and (3) develop strategies to promote social acceptability. A 36-item questionnaire was co-designed by 12 citizens and patients partnered with 16 academic researchers and was distributed in the province of Québec, Canada, from May 27 to 28 June 2021. Of 959 respondents, 43% had downloaded the app. Messaging from government sources constituted the largest influence on app download. Infrequent social contacts and perceived app inefficacy were the main reasons not to download the app. Cybersecurity, data confidentiality, loss of privacy, and geolocation were the most frequent concerns. Nearly half of the respondents inaccurately believed that the app used geolocation. Most respondents supported citizen involvement in app development. The identified predictors for app uptake included nine characteristics. In conclusion, this project highlights four key themes on how to promote the social acceptability of such tools: (1) improved communication and explanation of key app characteristics, (2) design features that incentivize adoption, (3) inclusive socio-technical features, and (4) upstream public partnership in development and deployment.
Direct conversion of Greenberger–Horne–Zeilinger state to Knill–Laflamme–Milburn state in decoherence-free subspace

Several schemes are proposed to realize the conversion of photonic polarized-entangled Greenberger-Horne-Zeilinger state to Knill-Laflamme-Milburn state in decoherence-free subspace (DFS) via weak cross-Kerr nonlinearity and X-quadrature homodyne measurement with high fidelity. DFS is introduced to decrease the decoherence effect caused by the coupling between the system and the environment. Optimizations to improve the success rate and utilization of residual states are further investigated. This study indicates important applications for quantum information processing in the future.
Providing dementia education with augmented reality: a health sciences and medicine feasibility pilot study

Understanding the key physiology and anatomy of the brain, and the mechanisms underlying dementia, represents essential components within a medical curriculum. This study assessed the implementation feasibility of an augmented reality (AR) three-dimensional (3D) brain visualisation learning mode and the knowledge improvements in medical students when compared to a text-based pamphlet. The pamphlet group learnt from a double-sided information pamphlet, while the AR group used an AR app. In AR, participants held a cube in front of the camera on the tablet, rendered on-screen as a 3D brain model, and received a narrated lesson containing the same information as the pamphlet verbatim. Both resources were also evaluated for perceived usefulness via pre-post tests and written survey. A total of 24 students participated in the study. A significant overall difference in knowledge scores (p < 0.001) was found for all participants but without significant differences between groups. Prior education was a significant covariate for pre-post change (p = 0.016) across all participants but had no impact on group outcomes. Positive feedback was received on both resources where the majority perceived them as easy to use, enjoyable, and helped develop their knowledge of dementia. Both the text-based pamphlet and AR delivery modes improved knowledge, although neither was significantly superior to the other. However, the AR lesson was perceived highly for learning, and has the potential for implementation within a medical programme.
An Alignment-based Multi-Perspective Online Conformance Checking Technique

Conformance checking is a class of process mining techniques, which contrasts the modeled behaviors with the observed behaviors of a process, to detect, locate and explain the deviations between them.Even though deviations can occur anytime and in any perspectives, currently there is no such conformance checking technique available, which is able to take into account other perspectives than the control-flow perspective of the investigated process when computing the conformance statistics on running, incomplete cases.In this paper, a multi-perspective online conformance checking technique is introduced, which aims to confront the modeled behaviors in form of a Data Petri net process model with a stream of events as the observed behaviors.For the conformance checking, two existing techniques were merged: a prefix-alignment-based technique, which is able to compute conformance statistics for incomplete process executions by applying incremental heuristic search, and an alignment-based multi-perspective conformance checking technique, which is able to compute conformance statistics for complete process instances while focusing on multiple perspectives.
Traceable, trustworthy and privacy preserving agri-food supply chains

The globally complex trade practices have given rise to supply chain digitization for data storage and traceability of agricultural produce. However, these traceability systems suffer from issues such as scattering of information across multiple silos and susceptibility to erroneous or modified data which could lead to compromised food safety through fraud or counterfeiting. Ensuring the credibility of information about a product is another challenge as the available data may not be the true representation of the physical events in a product's lifecycle. Traceability and transparency come at the expense of sharing business-sensitive data. Exposure of product related data makes it difficult to pro-tect trade secrets such as shipments, pricing and trade frequency while simultaneously ensuring the provenance/traceability to the consumers. In this paper, we highlight the need for agri-food supply chain traceability and present a generic framework for the traceability model with blockchain as a key component. The framework includes building blocks from data, storage, application and blockchain layers. Finally, we share our work in applying blockchain technology for supply chain management - with a focus on fresh produce as an example use-case. We also highlight the challenges and future opportunities in adopting blockchain for supply chain resilience.