Reviewerly/exHarmony huggingface dataset:
A software-based approach in designing a rooftop bifacial PV system for the North Hall of Residence, IUT

Abstract Bifacial rooftop photovoltaic panels appear to be an excellent means of power generation in this era of urbanization, especially for land-limited countries like Bangladesh. This paper presents a software-based approach to design and simulate a bifacial solar-panel-based energy model on the rooftop of the North Hall of Residence of the Islamic University of Technology, Gazipur. This vertically mounted model investigates the feasibility and applicability of such an energy model in a university residence, situated in a load-shedding-prone area. Hence, three prominent software platforms, namely PVSOL, PVsyst and System Advisor Model (SAM), are brought into action and rigorous simulations are performed for three different orientations; promising outcomes are observed in terms of annual energy yield, bifacial gain (BG) and consumption coverage of the grid and PV model. The annual energy demand of the North Hall is ~444 733.5 kWh. The three orientations can generate annually 92 508.62, 94 643.48 and 86 758.94 kWh, respectively. Hence, it is evident that the proposed orientations can supply almost 19–21% of the site’s annual demand. Monthly BG analysis shows an overall increase in energy gain of 13%, 15.6% and 6% for Orientation-1, Orientation-2 and Orientation-3, respectively. A rigorous comparative analysis and deviation analysis among the software results has been accomplished to gain more insight into the feasibility of the proposed system. Thus, we have focused on a detailed software-based estimation of energy production for different orientations of the PV panels, considering several factors, which will provide prior knowledge and assessment before going for hardware implementation in the future.
Sentiment Classification Using fastText Embedding and Deep Learning Model

Along with the growing popularity of deep learning to handle classification problems, various deep learning models have emerged having complex architectures. The disadvantage of a model with a complex architecture is the computational problem in which takes longer training time than a simple model. The desire to take advantage of sentiment classification in real-time applications is the reason for using a simpler model architecture but still paying attention to the model performance. Furthermore, this study also investigates how fastText embedding affects the performance of a sentiment classification model. In this paper, we propose two sentiment classification models with simple architecture. The first model is the single-layered Bidirectional Gated Recurrent Unit (BiGRU) model with fastText embedding, and the second one is the single-layered Convolutional Neural Network (CNN) model with fastText embedding. Both models can provide competitive results compared to the baseline model that has previously been compared with other models with complex architectures. The best accuracy is produced by the fastText + CNN model, with 80% of accuracy for the MR dataset and 84% of accuracy for the SST2 dataset. This study shows that the use of CNN for sentiment classification can provide competitive results compared to the BiLSTM and BiGRU models. This study also indicates that the use of fastText embedding can improve the performance of the single-layered BiLSTM model.
Overcoming model bias for robust offline deep reinforcement learning

State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly interact with their environment to collect millions of observations. This makes it hard to transfer their success to industrial control problems, where simulations are often very costly or do not exist, and exploring in the real environment can potentially lead to catastrophic events. Recently developed, model-free, offline RL algorithms, can learn from a single dataset (containing limited exploration) by mitigating extrapolation error in value functions. However, the robustness of the training process is still comparatively low, a problem known from methods using value functions. To improve robustness and stability of the learning process, we use dynamics models to assess policy performance instead of value functions, resulting in MOOSE (MOdel-based Offline policy Search with Ensembles), an algorithm which ensures low model bias by keeping the policy within the support of the data. We compare MOOSE with state-of-the-art model-free, offline RL algorithms BRAC, BEAR and BCQ on the Industrial Benchmark and MuJoCo continuous control tasks in terms of robust performance, and find that MOOSE outperforms its model-free counterparts in almost all considered cases, often even by far.
On Measuring and Controlling the Spectral Bias of the Deep Image Prior

Abstract The deep image prior showed that a randomly initialized network with a suitable architecture can be trained to solve inverse imaging problems by simply optimizing it’s parameters to reconstruct a single degraded image. However, it suffers from two practical limitations. First, it remains unclear how to control the prior beyond the choice of the network architecture. Second, training requires an oracle stopping criterion as during the optimization the performance degrades after reaching an optimum value. To address these challenges we introduce a frequency-band correspondence measure to characterize the spectral bias of the deep image prior, where low-frequency image signals are learned faster and better than high-frequency counterparts. Based on our observations, we propose techniques to prevent the eventual performance degradation and accelerate convergence. We introduce a Lipschitz-controlled convolution layer and a Gaussian-controlled upsampling layer as plug-in replacements for layers used in the deep architectures. The experiments show that with these changes the performance does not degrade during optimization, relieving us from the need for an oracle stopping criterion. We further outline a stopping criterion to avoid superfluous computation. Finally, we show that our approach obtains favorable results compared to current approaches across various denoising, deblocking, inpainting, super-resolution and detail enhancement tasks. Code is available at https://github.com/shizenglin/Measure-and-Control-Spectral-Bias .
Progressive Unsupervised Learning for Visual Object Tracking

In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from back-ground in a contrastive learning way. We then employ the BD model to progressively mine temporal corresponding patches (i.e., patches connected by a track) in sequential frames. As the BD model is imperfect and thus the mined patch pairs are noisy, we propose a noise-robust loss function to more effectively learn temporal correspondences from this noisy data. We use the proposed noise robust loss to train backbone networks of Siamese trackers. Without online fine-tuning or adaptation, our unsupervised real-time Siamese trackers can outperform state-of-the-art unsupervised deep trackers and achieve competitive results to the supervised baselines.
Dual-Objective Collision-Free Path Optimization of Arc Welding Robot

In order to solve the dual-objective path optimization problem of arc welding robot, a complete and novel methodology is proposed, including modeling, collision-free path search and global path optimization. First of all, the grid method is utilized to accurately model the welding workpieces and surrounding environment. Afterwards, an adaptive extension bidirectional RRT* algorithm (AB-RRT*) is used to search for collision-free paths between any two weld seams. Finally, aiming to minimize path length and energy consumption, the global path is optimized through the improved discrete NSGA-III algorithm (IDNSGA-III). Simulation results show that the AB-RRT* algorithm can search for optimal or sub-optimal solutions with higher efficiency. The IDNSGA-III algorithm can properly maintain the trade-off between convergence and distribution. Furthermore, it can obtain satisfactory solutions in a shorter time and provide effective guidance and help for engineers to plan the actual path.
Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features

In pedestrian detection, it is often affected by factors such as changes in the position of moving targets, changes in illumination, and complex background interference. Therefore, the traditional pedestrian feature detection method is affected by the complex environment, which will cause a certain deviation in the detection accuracy. This study combines hog and LTP (Local Trinary Patterns) feature detection technology to improve the efficiency of pedestrian detection. Constructing a pedestrian detection system based on SVM (Support Vector Machine) classifier trained by hog and LTP features, and constructs a pedestrian detection system according to the actual needs. Moreover, this paper designs a pedestrian detection experiment based on HOG (Histogram of Oriented Gradient) and LTP feature training SVM classifier. In addition, this paper uses the weighted fusion method to fuse the color map features with the depth map features, and finally uses the classifier to detect pedestrians. Experiments show that the new pedestrian detection system has a certain effect.
Efficient and Robust Distributed Matrix Computations via Convolutional Coding

Distributed matrix computations - matrix-matrix or matrix-vector multiplications - are well-recognized to suffer from the problem of stragglers (slow or failed worker nodes). Much of prior work in this area is (i) either sub-optimal in terms of its straggler resilience, or (ii) suffers from numerical problems, i.e., there is a blow-up of round-off errors in the decoded result owing to the high condition numbers of the corresponding decoding matrices. Our work presents a convolutional coding approach to this problem that removes these limitations. It is optimal in terms of its straggler resilience, and has excellent numerical robustness as long as the workers' storage capacity is slightly higher than the fundamental lower bound. Moreover, it can be decoded using a fast peeling decoder that only involves add/subtract operations. Our second approach has marginally higher decoding complexity than the first one, but allows us to operate arbitrarily close to the storage capacity lower bound. Its numerical robustness can be theoretically quantified by deriving a computable upper bound on the worst case condition number over all possible decoding matrices by drawing connections with the properties of large block Toeplitz matrices. All above claims are backed up by extensive experiments done on the AWS cloud platform.
A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series

To understand the behavior of complex phenomena, data collection and data analysis are the two basic key issues in this process. The most significant hard problem experimenters may face is the optimality selection of the dataset which provides valuable information about the behavior of the phenomena under the experimentation. An experiment with an optimal dataset allows more significant parameters to be estimated with minimum variance and without bias. Unfortunately, the collected datasets of many real-life experiments are not optimal due to the existence of outliers. An outlier is an observation that deviates significantly from other experimental data points arouse suspicions that it was generated by a different mechanism. The presence of even a few outliers leads to misspecification of model, biased estimation of parameters, and poor forecasts. Therefore, removing the outliers from the collected datasets is the critical and significant step before analyzing the data. This paper gives a hybrid feedforward neural network algorithm for detecting outliers as single points as well as small and large clusters in non-stationary multivariate time series using robust measures of location and dispersion matrix. From various perspectives, the performance of the proposed algorithm is compared with the existing algorithms under different scenarios using simulated datasets.
Participatory complexity in tourism policy: Understanding sustainability programmes with participatory systems mapping

Linear logic models are insufficient to understand how interventions work in complex areas such as sustainable tourism. We present Participatory Systems Mapping (PSM), a novel method to develop shared understandings and collective management of complex policy issues among stakeholders. We use PSM with stakeholders in Barcelona to support the design of an upcoming evaluation of an existing sustainability programme. Discussion during workshops, and analysis of the PSM map produced, suggest sharing best practices and improving peer-to-peer learning are pivotal to improving sustainability. We show how a complex systems approach, implemented via PSM, can provide a more holistic understanding of the contexts and interactions of tourism policy. We offer learning and guidance on how the method can be used by others.
Multi-Decoding Deraining Network and Quasi-Sparsity Based Training

Existing deep deraining models are mainly learned via directly minimizing the statistical differences between rainy images and rain-free ground truths. They emphasize learning a mapping from rainy images to rain-free images with supervision. Despite the demonstrated success, these methods do not perform well on restoring the fine-grained local details or removing blurry rainy traces. In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rain-free background layer and a rainy layer containing intact rain streaks. To this end, we introduce the quasi-sparsity prior to train network so as to generate two sparse layers with intact textures of different objects. Then we explore the low-value prior to compensate sparsity, forcing all rain streaks to enter into one layer while non-rain con-tents into another layer to restore image details. We introduce a multi-decoding structure to specially supervise the generation of multi-type deraining features. This helps to learn the most contributory features to deraining in respective spaces. Moreover, our model stabilizes the feature values from multi-spaces via information sharing to alleviate potential artifacts, which also accelerates the running speed. Extensive experiments show that the proposed de-raining method outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.
PRISMA sensor evaluation: a case study of mineral mapping performance over Makhtesh Ramon, Israel

The Italian Space Agency (ASI)’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral remote sensing (HSR) satellite was launched on 22 March 2019. This spectral imaging sensor, with 238 bands across the 400–2500 nm range and 30-m spatial resolution, can advance remote-sensing studies for vast applications worldwide. The present study evaluates the capability of the sensor’s L1 (Top-of the- atmosphere TOA radiance) and L2D (reflectance) products for mapping minerals and geology in Makhtesh Ramon (MR), a national park in southern Israel that covers approximately 200 km2. The exceptional geological features in this area, with hardly any vegetation and mostly clear skies year-round, make it an ideal site for remote-sensing studies in general and HSR studies in particular. The quality of the PRISMA sensor’s technical and thematic performance was assessed by examining its radiometry and spectral products against a highly calibrated airborne HSR sensor (AisaFENIX 1K; 420 spectral bands, range 375–2500 nm, 1.5-m spatial resolution). Airborne and field data acquisition captured the entire MR area, followed by comprehensive fieldwork with a portable ASD FieldSpec spectrometer (400–2500 nm spectral range). The PRISMA sensor’s radiance performance was evaluated by examining the top of the atmosphere radiance product against simulated radiance using the MODTRAN® radiative transfer code. The simulated radiance used in-situ ground reflectance measurements in several locations across MR. The L1 and L2D products presented fair results with some outliers for PRISMA’s L2D SWIR 2 long-wavelength region. The L2D product was further studied to map mineral occurrences and demonstrated promising results compared to MR’s well-known geology and mineral distribution, and it was highly correlated to the high-spatial-resolution spectral mapping generated by the AisaFENIX 1K airborne sensor. Based on these results, we successfully mapped different types of minerals, such as iron oxides in the VNIR, gypsum in SWIR 1, and clay in SWIR 2. The PRISMA data’s reflectance interference across the longer wavelengths of SWIR 2 did not permit fine mapping for carbonates, probably because of the L2D’s poor performance in this spectral range. The quantitative performance of PRISMA’s mineral mapping was judged relative to the quantitative products of the AisaFENIX 1K sensor for these same minerals, revealing about 80% accuracy for PRISMA’s products.
Blind Deblurring for Saturated Images

Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that pixels around saturated regions are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding these pixels during the deblurring process, which sometimes simultaneously removes the informative edges around saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during the deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods.
Virtual Reality-Based Treatment Approaches in the Field of Substance Use Disorders

Abstract Purpose Substance use disorders (SUD) are burdening chronic conditions characterized by high relapse rates despite severe negative consequences. Substance-related cues that elicit craving by means of automatic physiological and behavioural responses have long been suggested to predict relapse. One major mechanism contributing to relapse behaviour are cue-induced behavioural approach tendencies towards the addictive agent. Recently, there has been an emerging interest in virtual reality (VR)-based approaches to assess and modify craving and its related responses. This review aims at elucidating (1) VR techniques applied in the field of SUD, (2) VR as an induction/assessment tool for biopsychological correlates of craving and (3) VR-based therapeutic approaches. Findings There is an emerging number of studies focusing on different substances of abuse incorporating VR in craving induction/assessment as well as therapy. Despite some limitations as missing of randomized controlled clinical trials with large samples and missing data on the long-term effects of VR treatment, the VR approach showed consistent results in eliciting and reducing craving across different substances. Summary This review suggests virtual reality as a promising tool for the assessment and treatment of craving among individuals with substance use disorders. Because of its ecological validity, VR unifies the benefits of a laboratory setting with the advantages of a realistic environment. Further studies with large samples and randomized controlled clinical trials using more homogenous VR techniques as well as assessment of objective biophysiological craving markers are required.
Robust Earthquake Early Warning at a Fraction of the Cost: ASTUTI Costa Rica

Abstract We show that a fixed smartphone network can provide robust Earthquake Early Warning for at least two orders of magnitude less cost than scientific‐grade networks. Our software and cloud‐based data architecture that we have constructed for the Alerta Sismica Temprana Utilizando Teléfonos Inteligentes (ASTUTI; Earthquake Early Warning Utilizing Smartphones) network in Costa Rica is easily scaled and exported. Implementation comprises provisioning and installing modern smartphones in judicious locations. Stand‐up time for regionally operational networks can be on the order of days. We evaluated a non‐parametric ground‐motion detection and alerting strategy that would alert the entire Costa Rican population of any event with a ground motion detection threshold of 0.55–0.65 %g at four neighboring stations. During a 6‐month evaluation period ASTUTI detected and alerted on five of 13 earthquakes with M w 4.8–5.3 that caused felt Modified Mercalli Intensity shaking levels of 4.3–6. The system did not produce any false alerts and the undetected events did not produce wide‐spread or significant felt shaking. System latencies were less than or similar to scientific‐grade latencies. Alerts for all five detected events would have reached the capital city, San Jose, before strong S ‐wave shaking. This would have afforded time for Drop Cover Hold On actions by most residents. Two of the five alerts were triggered by P ‐waves suggesting that smartphone‐based networks could approach the fastest theoretical EEW performance, especially with future expected improvements in smartphone sensors and processing algorithms.
Explainable Deep Classification Models for Domain Generalization

Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in the form of a saliency map conveying how much each pixel contributed to the network’s decision. Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the image regions that directly correspond to the ground-truth object. We quantify explainability using an automated metric, and using human judgement. We propose explainability as a means for bridging the visual-semantic gap between different domains where model explanations are used as a means of disentagling domain specific information from otherwise relevant features. We demonstrate that this leads to improved generalization to new domains without hindering performance on the original domain.
Futuristic blockchain based scalable and cost-effective 5G vehicular network architecture

In the present era, smart and efficient vehicular network architectures are necessary due to fast technological advancements in vehicles. Many problems arise in these complex networks, which can be handled using blockchain and the Internet of Things (IoT). We proposed a comprehensive blockchain based 5G vehicular network architecture, which is cost-effective, scalable, secure, and handles various vehicular network issues in a smart city. The proposed architecture consists of all essential components like reputation system, incentive mechanism, and priority based techniques to handle different limitations in the literature. Simulations results for different scenarios depict the high execution cost of a single controller node, minor node, and ordinary node as 106305, 85864, and 65491 gas values and transaction cost as 130521, 109824, and 89195 gas values. The results depict the effectiveness of the proposed architecture in terms of scalability, time and cost-effectiveness.