Reviewerly/exHarmony huggingface dataset:
Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method

The main objective of this paper is to present a novel approach for determining PV potential through an optimization method. The novel approach considers the importance of technical and economic potential simultaneously for determining the optimal configuration of PV systems using a digital surface model. The integration of photovoltaic systems is conditioned mainly by the location and type of installation - configuration. Thus, more and more photovoltaic systems are being integrated into urban areas. For the further successful integration of photovoltaic systems into networks and the successful establishment of appropriate policies and directives, it is essential to properly assess the photovoltaic potential. Based on the described methodology, the potential determination is made for a completed area of 75,537 m2 using a digital surface model of the observed area. The annual values of physical, geographical, technical, and economic potentials were 19.57 GWh, 7.54 GWh, 875.50 MWh, and 19.64 kWh, respectively. The methodologies presented in the paper are based on detailed meteorological data for 20 years, a digital surface model of the observed area, a novel optimization approach, and multi-year data of electricity prices on markets. The presented results can be an excellent basis for further analyzes of determining the photovoltaic potential.
Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods

This study compares score- and feature-based methods for estimating forensic likelihood ratios for text evidence. Three feature-based methods built on different Poisson-based models with logistic regression fusion are introduced and evaluated: a one-level Poisson model, a one-level zero-inflated Poisson model and a two-level Poisson-gamma model. These are compared with a score-based method that employs the cosine distance as a score-generating function. The two types of methods are compared using the same data (i.e., documents attributable to 2,157 authors) and the same features set, which is a bag-of-words model using the 400 most frequently occurring words. Their performances are evaluated via the log-likelihood ratio cost (Cllr) and its composites: discrimination (Cllrmin) and calibration (Cllrcal) cost. The results show that (1) the feature-based methods outperform the score-based method by a Cllr value of 0.14-0.2 when their best results are compared and (2) a feature selection procedure can further improve performance for the feature-based methods. Some distinctive performance characteristics associated with likelihood ratios produced using the feature-based methods are described, and their implications will be discussed with real forensic casework in mind.
A Novel Robust Kalman Filter Based on Switching Gaussian-Heavy-Tailed Distribution

In this brief, the state estimation problems of systems with unknown non-stationary heavy-tailed noises are investigated. First, we present a new switching Gaussian-heavy-tailed (SGHT) distribution, which can model the noises by adaptive learning of the switching probability between the Gaussian distribution and the newly designed heavy-tailed distribution. Then, the SGHT distribution is expressed as a hierarchical Gaussian presentation by utilizing two auxiliary variables satisfying the categorical distribution and the Bernoulli distribution respectively. After-wards, a new SGHT distribution based robust Kalman filter (SGHT-RKF) is derived by applying the variational Bayesian (VB) inference. Finally, the simulations are performed to illustrate the superior performance of the developed filter as compared with existing filters.
Writing More Compelling Creative Appeals: A Deep Learning-Based Approach

In this paper, we present a deep learning-based decision support tool to assist individuals in writing effective creative appeals.
Dynamic Modeling and Hybrid Fireworks Algorithm-Based Path Planning of an Amphibious Robot

The task of path planning in amphibious environments requires additional consideration due to the complexity of the amphibious environments. This paper presents a path planning method for an amphibious robot named “AmphiRobot” with its dynamic constraints considered. First, an explicit dynamic model using Kane’s method is presented. The hydrodynamic parameters are obtained through computational fluid dynamics simulations. Furthermore, a path planning method based on a hybrid fireworks algorithm is proposed, combining the fireworks algorithm and bare bones fireworks algorithm, aiming at the amphibious robot’s characteristics of multiple motion modes and working environments. The initially planned path is then smoothed using Dubins path under constraints determined by the dynamic model. Simulation reveals that the performance of the hybrid fireworks algorithm approach is better than the fireworks algorithm and bare bones fireworks algorithm is applied separately in the amphibious environment scenarios.
High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing app, 'Taiwan Social Distancing App'; however, the effectiveness of this tracing app depends on its acceptance and uptake among the general population. We measured the acceptance of three hypothetical tracing technologies (telecommunication network tracing, a government app, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we found a high acceptance of all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modeling revealed that acceptance increased with the perceived technology benefits, trust in the providers' intent, data security and privacy measures, the level of ongoing control, and one's level of education. Acceptance decreased with data sensitivity perceptions and a perceived low policy compliance by others among the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and in the future.
Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier

In this article, we present a smart gravimetric system for the automatic security monitoring of the accesses to public places with some entrance ticket or pass required (e.g., railway stations, subway stations, museums, exhibitions). The main objective is to spot illegal behaviors, for example, two people trying to enter using only one ticket; simultaneously, other events of interest can also be detected, for example, the passage of a person with disabilities or a person with a stroller. These pieces of information can be used to arrange the appropriate assistance or to conduct statistical analyses on the monitored place frequentation. The proposed system is based on a robust gravimetric footboard, composed of steel plates laid on load cells. The data processing, classification included, is totally executed on embedded STM32 microcontrollers, following the edge-computing paradigm. The classification exploits a MobileNetV2, a class of neural networks for computer vision, especially created for embedded and mobile applications. We realized a prototype of the gravimetric system for laboratory testing and we acquired a set of samples for the considered event classes to assess the MobileNet network capabilities. We compared the performance of this implementation with a more classic VGG16 convolutional neural network (CNN) running on a PC machine. We show that in the face of akin classification results and similar execution times between the two, the embedded MobileNet version brings many advantages, namely portability, cost, size, energy consumption reduction, and finally simplification in the installation procedure.
Integrating big data and blockchain to manage energy smart grids—TOTEM framework

The demand for electricity is increasing exponentially day by day, especially with the arrival of electric vehicles. In the smart community neighborhood project, electricity should be produced at the household or community level and sold or bought according to the demands. Since the actors can produce, sell, and buy according to the demands, thus the name prosumers. ICT solutions can contribute to this in several ways, such as machine learning for analyzing the household data for customer demand and peak hours for the usage of electricity, blockchain as a trustworthy platform for selling or buying, data hub, and ensuring data security and privacy of prosumers. TOTEM: Token for controlled computation is a framework that allows users to analyze the data without moving the data from the data owner's environment. It also ensures the data security and privacy of the data. Here, in this article, we will show the importance of the TOTEM architecture in the EnergiX project and how the extended version of TOTEM can be efficiently merged with the demands of the current and similar projects.
Comparison between a quantum annealer and a classical approximation algorithm for computing the ground state of an Ising spin glass

Finding the ground state of an Ising spin glass on general graphs belongs to the class of NP-hard problems, widely believed to have no efficient polynomial-time algorithms to solve them. An approach developed in computer science for dealing with such problems is to devise approximation algorithms; these are algorithms, whose run time scales polynomially with the input size, that provide solutions with provable guarantees on their quality in terms of the optimal unknown solution. Recently, several algorithms for the Ising spin-glass problem on a bounded degree graph that provide different approximation guarantees were introduced. D-Wave, a Canadian-based company, has constructed a physical realization of a quantum annealer and has enabled researchers and practitioners to access it via their cloud service. D-Wave is particularly suited for computing an approximation for the ground state of an Ising spin glass on its Chimera and Pegasus graphs-both with a bounded degree. To assess the quality of D-Wave's solution, it is natural to compare it to classical approximation algorithms specifically designed to solve the same problem. In this work, we compare the performance of a recently developed approximation algorithm to solve the Ising spin-glass problem on graphs of bounded degree against the performance of the D-Wave computer. We also compared the performance of D-Wave's computer in the Chimera architecture against the performance of a heuristic tailored specifically to handle the Chimera graph. We found that the D-Wave computer was able to find better approximations for all the random instances of the problem we studied-Gaussian weights, uniform weights, and discrete binary weights. Furthermore, the convergence times of D-Wave's computer were also significantly better. These results indicate the merit of D-Wave's computer under certain specific instances. More broadly, our method is relevant to a wider class of performance comparison studies, and we suggest that it is important to compare the performance of quantum computers not only against exact classical algorithms with exponential run-time scaling, but also against approximation algorithms with polynomial run-time scaling and a provable guarantee of performance.
High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

Camera calibration is the most important aspect of computer vision research. To address the issue of insufficient precision, therefore, a high precision calibration algorithm for binocular stereo vision camera using deep reinforcement learning is proposed. Firstly, a binocular stereo camera model is established. Camera calibration is mainly divided into internal and external parameter calibration. Secondly, the internal parameter calibration is completed by solving the antihidden point of the camera light center and the camera distortion value of the camera plane. The deep learning fitting value function is used based on the internal parameters. The target network is established to adjust the parameters of the value function, and the convergence of the value function is calculated to optimize reinforcement learning. The deep reinforcement learning fitting structure is built, the camera data is entered, and the external parameter calibration is finished by continuous updating and convergence. Finally, the high precision calibration of the binocular stereo vision camera is completed. The results show that the calibration error of the proposed algorithm under different sizes of checkerboard calibration board test is only 0.36% and 0.35%, respectively, the calibration accuracy is high, the value function converges quickly, and the parameter calculation accuracy is high, the overall time consumption of the proposed algorithm is short, and the calibration results have strong stability.
AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening

Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.
Short text classification for Arabic social media tweets

With the rapid growth in the number of tweets published daily on Twitter, automated classification of tweets becomes necessary for broad diverse applications (e.g., information retrieval, topic labeling, sentiment analysis, rumors detection) to better understand what these tweets are, and what the users are expressing in this social platform. Text classification is the process of assigning one or more pre-defined categories to text according to its content. Tweets are short, and the short text does not have enough contextual information, which is part of the challenge in their classification. Adding to the challenge is the increase in ambiguity since the diacritical marking is not explicitly specified in most Modern Standard Arabic (MSA) texts. Not to mention the Arabic tweets are known to contain fused text of MSA and dialectal Arabic. In this paper, we propose a scheme to classify the textual tweets in the Arabic language based on its linguistic characteristics and content into five different categories. We explore two different textual representations: word embedding using Word2vec and stemmed text with term frequency-inverse document frequency (tf-idf). We tested three different classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Random Forest (RF). All the classifiers had their hyperparameters tuned. We collected and manually annotated a dataset of approximately 35,600 Arabic tweets for the experiments. Statistically, the RF and the SVM with radial basis function (RBF) kernel performed equally well when used with stemming and tf-idf, achieving macro-F1 scores ranging between 98.09% and 98.14%. The GNB with word embedding was disappointingly low performer. Our result tops the current state-of-the-art score of 92.95% using a deep learning approach, RNN-GRU (recurrent neural network-gated recurrent unit).
Joint Semantic Understanding with a Multilevel Branch for Driving Perception

Visual perception is a critical task for autonomous driving. Understanding the driving environment in real time can assist a vehicle in driving safely. In this study, we proposed a multi-task learning framework for simultaneous traffic object detection, drivable area segmentation, and lane line segmentation in an efficient way. Our network encoder extracts features from an input image and three decoders at multilevel branches handle specific tasks. The decoders share the feature maps with more similar tasks for joint semantic understanding. Multiple loss functions are automatically weighted summed to learn multiple objectives simultaneously. We demonstrate the effectiveness of this framework on a BerkeleyDeepDrive100K (BDD100K) dataset. In the experiment, the proposed method outperforms the competing multi-task and single-task methods in terms of accuracy and maintains a real-time inference at more than 37 frames per second.
PaaRPN: Probabilistic anchor assignment with region proposal network for visual tracking

Recently, visual trackers based on region proposal networks (RPN) have attracted widespread attention due to their relatively high efficiency and excellent performance. RPN-based trackers mainly combine a classification branch and a regression branch to predict a target’s state. These branches are all under the guidance of pre-defined anchor boxes. RPN-based trackers, however, first compute the Intersection-over-Union (IoU) between the anchor boxes and ground truth boxes, and then use a fixed IoU threshold to separate negative and positive training samples. The limit of this design lies in the fact that these trackers lack an analysis of the actual content of the intersecting regions, which may include distractor objects or few meaningful regions of the tracked target. In this research, we propose a probabilistic anchor assignment with region proposal network (PaaRPN) that can adaptively separate anchors into negative samples and positive samples according to the model’s current learning status. To this end, we first calculate the classification scores of the anchor boxes conditioned on the current model and fit a probability distribution to the classification scores. The whole tracking model is then trained with anchor boxes separated into negative and positive samples in a probabilistic manner. Moreover, we introduce an online learning method in the PaaRPN framework that enables the model to have powerful discriminative abilities by exploiting both background and target appearance information. We tested the PaaRPN tracker on six tracking benchmarks to exhibit the effectiveness of the proposed method. In particular, our model outperforms a strong RPN tracker, SiamRPN++, with AUC scores improvements of 0.613 → 0.657 and 0.496 → 0.565 on UAV123 and LaSOT, respectively.
Security threats and approaches in E-Health cloud architecture system with big data strategy using cryptographic algorithms

Cloud computing refers to the use of the Internet to deliver software to cloud consumers. For storing and analyzing huge volumes of data from social media, enterprises, organizations, government sectors, and other sources, cloud infrastructure is essential. As a result, a large storage capacity is required to store all of the data on the cloud. The existing system still has a lot of work to do in terms of improving DDoS attacks, data breaches, and data loss. To address the aforementioned difficulties, many academics have focused on the creation of powerful and sensitive data, the lack of data recovery, and data storage, utilizing cloud protection strategies to capitalize on them. We propose a Big Data E-Healthcare Information System that utilizes cloud platforms to connect hospitals, patients, and health centers in this paper. We eliminate anything with third-party monitoring, enabling hospital and clinic patients to view the same data at the same time. and helps protect static or dynamic protection from hacker's observation, modification, or interference using RSA Cryptographic Algorithms.
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack

Deep neural networks are discovered to be non-robust when attacked by imperceptible adversarial examples, which is dangerous for it applied into medical diagnostic system that requires high reliability. However, the defense methods that have good effect in natural images may not be suitable for medical diagnostic tasks. The pre-processing methods (e.g., random resizing, compression) may lead to the loss of the small lesions feature in the medical image. Retraining the network on the augmented data set is also not practical for medical models that have already been deployed online. Accordingly, it is necessary to design an easy-to-deploy and effective defense framework for medical diagnostic tasks. In this paper, we propose a Robust and Retrain-Less Diagnostic Framework for Medical pretrained models against adversarial attack (i.e., MedRDF). It acts on the inference time of the pretrained medical model. Specifically, for each test image, MedRDF firstly creates a large number of noisy copies of it, and obtains the output labels of these copies from the pretrained medical diagnostic model. Then, based on the labels of these copies, MedRDF outputs the final robust diagnostic result by majority voting. In addition to the diagnostic result, MedRDF produces the Robust Metric (RM) as the confidence of the result. Therefore, it is convenient and reliable to utilize MedRDF to convert pretrained non-robust diagnostic models into robust ones. The experimental results on COVID-19 and DermaMNIST datasets verify the effectiveness of our MedRDF in improving the robustness of medical diagnostic models.
MBN: Towards Multi-Behavior Sequence Modeling for Next Basket Recommendation

Next basket recommendation aims at predicting the next set of items that a user would likely purchase together, which plays an important role in e-commerce platforms. Unlike conventional item recommendation, the next basket recommendation focuses on capturing item correlations among baskets and learning the user’s temporal interest from the past purchasing basket sequence. In practice, most users interact with items in various kinds of behaviors. The multi-behavior data sheds light on user’s potential purchasing intention and resolves noisy signals from accidentally purchased items. In this article, we conduct an empirical study on real datasets to exploit the characteristics of multi-behavior data and confirm its positive effects on next basket recommendation. We develop a novel Multi-Behavior Network (MBN) model that captures item correlations and acquires meta-knowledge from multi-behavior basket sequences effectively. MBN employs the meta multi-behavior sequence encoder to model temporal dependencies of each individual behavior and extract meta-knowledge across different behaviors. Furthermore, we design the recurring-item-aware predictor in MBN to realize the high degree of the repeated occurrences of items, leading to better recommendation performance. We conduct extensive experiments to evaluate the performance of our proposed MBN model using real-world multi-behavior data. The results demonstrate the superior recommendation performance of MBN compared with various state-of-the-art methods.