Reviewerly/exHarmony huggingface dataset:
Photovoltaic power forecasting with a long short-term memory autoencoder networks

In many developed countries, photovoltaic solar power, which is considered the most cost-effective renewable energy source, accounts for a major portion of electricity production. The photovoltaic (PV) power generation is unpredictable and imprecise due to its high variation that can be caused of meteorological elements, to reduce the negative influence of the use of PV power, accurate PV power prediction is of crucial significance for the secure and efficient operation of photovoltaic power system operation. In light of this, we propose a long short-term memory (LSTM) autoencoder (AE) for photovoltaic power forecasting. Initially, to generate encoded sequences the LSTM-encoder extracts the characteristics from the input data. Then the LSTM-decoder decoded the encoded sequences to advance them to the last dense layer for photovoltaic power prediction. Furthermore, we conducted experiments using a 23.40 kW PV power plants dataset from DKASC in Australia. The results have confirmed that the LSTM-AE model can achieve better prediction accuracy than the benchmark deep learning methods, in terms of mean absolute error (MAE), mean square error (MSE), mean bias error (MBE), root mean square error (RMSE) and coefficient of determination (R $$^2$$ ) performance measures. When the results are analyzed, the LSTM-AE model gives the best results with average RMSE, MAE, and R $$^2$$ to 0.0762 kW, 0.0389 kW, and 99.93 $$\%$$ , respectively. The experimental results confirm that proposed method with the highest R $$^2$$ values and minimum forecasting errors compared to the benchmark models can effectively improve stable performance and achieve better accurate photovoltaic power forecasting.
FCG-Net: An innovative full-scale connected network for thyroid nodule segmentation in ultrasound images

Ultrasonic imaging is more susceptible to poor contrast and speckle noise than other imaging modalities, which poses certain challenges to computer-aided automatic segmentation of thyroid nodules. Nine popular segmentation networks for thyroid nodule in ultrasound images were well trained with the same training strategy and the performances on the public dataset and the clinical dataset were well compared using different indicators. Among them, UNet3+ achieved the best performance. However, it brings more parameters compared to other lightweight networks, which limits its application in portable diagnosis. This paper proposed an innovative thyroid nodule segmentation network FCG-Net that using full-scale skip connection to extracted multi resolution features, and Ghost bottleneck was introduced in the encoder and decoder process to reduce computational complexity of feature maps. Compared to UNet3+, the parameters of the model were reduced from 27 million to 11 million, and the inference speed was improved by 1.07 FPS. Meanwhile, the segmentation performance of FCG-Net exceeds that of UNet3+. 729 ultrasound images from the public dataset and clinical ultrasound images were used to validate the performance of the network by 5-fold cross-validation. The accuracy, precision, sensitivity, specificity and Dice of the FCG-Net were 0.9488, 0.8579, 0.8984, 0.9627 and 0.8670 respectively. The network not only improves the accuracy of ultrasonic diagnosis but also reduces the requirements of hardware and calculation, which is significant to promote the development of portable ultrasonic diagnosis of thyroid nodule.
Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review

Solar irradiation data is essential for the feasibility of solar energy projects. Notably, the intermittent nature of solar irradiation influences solar energy use in all forms, whether energy or agriculture. Accurate solar irradiation prediction is the only solution to effectively use solar energy in different forms. The estimation of solar irradiation is the most critical factor for site selection and sizing of solar energy projects and for selecting a suitable crop selection for the area. But the physical measurement of solar irradiation, due to the cost and technology involved, is not possible for all locations across the globe. Numerous techniques have been implemented to predict solar irradiation for this purpose. The two types of approaches that are most frequently employed are empirical techniques and artificial intelligence (AI). Both approaches have demonstrated good accuracy in various places of the world. To find out the best method, a thorough review of research articles discussing solar irradiation prediction has been done to compare different methods for solar irradiation prediction. In this paper, articles predicting solar irradiation using AI and empirical published from 2017 to 2022 have been reviewed, and both methods have been compared. The review showed that AI methods are more accurate than empirical methods. In empirical models, modified sunshine-based models (MSSM) have the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM). The NSM has a little lower accuracy than MSSM and SSM, but the NSM can give good results in sunshine data unavailability. Also, the literature review confirmed that simple empirical models could predict accurately, and increasing the empirical model's polynomial order cannot improve results. Artificial neural networks (ANN) and Hybrid models have the highest accuracy among AI methods, followed by support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS). The increase in efficiency by hybrid models is minimal, but the complexity of models requires very sophisticated programming knowledge. ANN's most important input factors are maximum and minimum temperatures, temperature differential, relative humidity, clearness index and precipitation.
Measuring the Impact of Augmented Reality in Flipped Learning Mode on Critical Thinking, Learning Motivation, and Knowledge of Engineering Students

Digital electronics is a fundamental subject for engineering students, and it enables the students to learn design-based approaches and solve complex engineering problems. Students learn about minimization techniques for reducing the hardware components and size of the circuit by solving complex Boolean equations. The Karnaugh map (K-map) is one such technique utilized in digital electronics to solve complex Boolean equations and design AND-OR-INVERT (AOI) logical diagrams. The K-map technique involves several steps to solve the Boolean expression, and students often find it difficult to follow the K-map process. In this study, an AR-based learning system was developed using Unity 3D and Vuforia SDK that aimed to teach the students about the step-wise operation of the K-map technique. An experimental study was conducted with 128 undergraduate engineering students to determine the impact of the AR learning system on the critical thinking skills, learning motivation, and knowledge gain of students. The students were divided into two groups: experimental group (N = 64) and control group (N = 64). The AR learning system was implemented in flipped learning mode and utilized to provide in-class activities during the learning. The experimental group students utilized the AR learning system for in-class activities whereas control group students performed in-class activities using the traditional approach. The experimental outcomes indicate that the use of AR technology has a significant positive impact on the critical thinking skills, learning motivation, and knowledge gain of students. The study also found that critical thinking skills and learning motivation have a significant positive correlation with the knowledge gain of students in the experimental group.
Prolog Education in Selected Secondary Schools in Bulgaria

This article presents our activities for introducing the training of Prolog programming to the secondary school. The beginning was the development of an appropriate curriculum. The results of an experiment conducted with a group of selected students are summarized in this paper. A project is briefly presented, the purpose of which is to provide an opportunity to share knowledge and experience about Prolog programming and at the same time help build a community of interested students. In the conclusion, we have tried to summarize our experience which would possibly help to introduce Prolog programming in secondary school in other conditions.
Writing Correct Prolog Programs

This article describes a somewhat new way of thinking about Prolog programming. It was motivated by a video and presentation by Leslie Lamport [6] in which he argued for a simple model of computation in which, to develop a program, one uses conventional mathematical language with the necessary invariants being front and center. He used as a motivating example the problem of finding the greatest common divisor (GCD) of two positive integers. I felt his model of computation was too simple to be useful for complex programs, but I liked his essential idea. I thought I’d like to apply it to the computational model(s) of logic programming, in particular to Prolog. It led to a somewhat different way of thinking about how to develop Prolog programs that takes advantage of both bottom-up and top-down thinking. This article explores this program development strategy using the GCD problem as a motivating example.
Characterizing Low-Frequency Qubit Noise

Fluctuations of the qubit frequencies are one of the major problems to overcome on the way to scalable quantum computers. Of particular importance are fluctuations with a correlation time that exceeds the decoherence time due to decay and dephasing by fast processes. The statistics of the fluctuations can be characterized by measuring the correlators of the outcomes of periodically repeated Ramsey measurements. We describe qubit dynamics during repeated measurements and evaluate the two-time correlator for noise from two-level systems and two- and three-time correlators for Gaussian noise. The explicit expressions for the correlators are compared with simulations. We find that, even though the three-time correlators for noise from two-level systems and for Gaussian noise are generally significantly different, already ten two-level systems can mimic Gaussian noise provided they are symmetric. This is not the case for asymmetric systems.
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the com- ing graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence.
Profile-based diffuse reflectance corrections for improved optical property measurement of spherical fruit with spatial frequency domain imaging

Diffuse reflectance (Rd), demodulated from spatial frequency domain imaging (SFDI), is strongly influenced by the surface profile of target sample, and the optical properties (absorption coefficient μa and reduced scattering coefficient μ's) extracted from the Rd are prone to large estimation errors caused by curved surface of the fruit. This study proposes a profile-based Rd correction method, aiming to improve the accuracy of optical property measurement for spherical fruit, such as apples, as well as enhance fruit bruise detection through improved optical properties. Firstly, the surface height of the test object was obtained by performing the phase-measuring profilometry (PMP) in SFDI. Secondly, the object surface reflection angle was calculated using the proposed angle calculation method for spherical samples. The Modified Lambertian Correction (MLC) method was then refined to minimize the effect of the incident angle on the Rd correction. The performance of the Rd correction method was validated using standard reflector and Teflon spheres. The results showed that the average error of the PMP measurement with the height range of 0–50 mm was within 1 mm, the relative error of the corrected Rd was within 2 %, and the standard deviation of the Rd for the Teflon sphere was reduced by about 60 % after correction. Finally, the proposed Rd correction method was applied to measure the optical properties of four spherical fruit (‘Golden Delicious’, ‘Red Fuji’ apples, lemons, and oranges). The results revealed that the standard deviations of the measured μa and μ's of the spherical fruit were greatly reduced by about 70 % and 40 % after correction. The improved optical property mappings enhanced the early bruise detection of apples due to the fact that the optical property contrast between the non-bruised and bruised tissues were significantly increased.
GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. A new data structure with a sort-mapping strategy is proposed to enhance the search ability and reduce memory usage. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.
Exploring the Pedagogical Use of AI-Powered Chatbots Educational Perceptions and Practices

By providing personalized and adaptable learning experiences, artificial intelligence (AI) has the potential to revolutionize education. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. Chatbot technology has the potential to revolutionize the way educational content is accessed, created, understood, and implemented. In this research paper, we examine the current state of AI-powered chatbots in education and discuss the advantages and disadvantages of using chatbots in this context. A case study of an AI unit planner (Copilot) built on top of the GPT-3 application in K-12 settings will also be presented, examining how chatbots are used to assist teachers in lesson design and the extent to which teachers are familiar with them. Finally, the implications of chatbot technology for the future of education are discussed, along with recommendations for future research directions.
Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

In this article, forecast models based on a hybrid architecture that combines recurrent neural networks and shallow neural networks are presented. Two types of models were developed to make predictions. The first type consisted of six models that used records of exported active energy and meteorological variables as inputs. The second type consisted of eight models that used meteorological variables. Different metrics were applied to assess the performance of these models. The best model of each type was selected. Finally, a comparison of the performance between the selected models of both types was presented. The models were validated using real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type had a root mean square error (RMSE) of 0.19, a mean square error (MSE) of 0.03, a mean absolute error (MAE) of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower accuracy in the metrics: RMSE = 0.24, MSE = 0.06, MAE = 0.10, correlation coefficient = 0.95, and determination coefficient = 0.90. Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy generation of the solar plant.
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection

In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation strategy to get dense pseudo-labels, which can retain more potential supervision information for the student network. On the other hand, instead of traditional fixed thresholds, we propose a dynamic threshold manner to generate pseudo-labels, which can guarantee the quality and quantity of pseudo-labels during the whole training process. Benefiting from these two components, our DDS3D outperforms the state-of-the-art semi-supervised 3d object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist under the same configuration of 1% samples. Extensive ablation studies on the KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models will be made publicly available at https://github.com/hust-jy/DDS3D
3D Spectral Domain Registration-Based Visual Servoing

This paper presents a spectral domain registration-based visual servoing scheme that works on 3D point clouds. Specifically, we propose a 3D model/point cloud alignment method, which works by finding a global transformation between reference and target point clouds using spectral analysis. A 3D Fast Fourier Transform (FFT) in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{R}^{3}$</tex> is used for the translation estimation, and the real spherical harmonics in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{SO}(3)$</tex> are used for the rotations estimation. Such an approach allows us to derive a decoupled 6 degrees of freedom (DoF) controller, where we use gradient ascent optimisation to minimise translation and rotational costs. We then show how this methodology can be used to regulate a robot arm to perform a positioning task. In contrast to the existing state-of-the-art depth-based visual servoing methods that either require dense depth maps or dense point clouds, our method works well with partial point clouds and can effectively handle larger transformations between the reference and the target positions. Furthermore, the use of spectral data (instead of spatial data) for transformation estimation makes our method robust to sensor-induced noise and partial occlusions. We validate our approach by performing experiments using point clouds acquired by a robot-mounted depth camera. Obtained results demonstrate the effectiveness of our visual servoing approach.
A survey on neural-symbolic learning systems

In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they have been found to lack effective reasoning and cognitive ability. On the other hand, symbolic systems exhibit exceptional cognitive intelligence but suffer from poor learning capabilities when compared to neural systems. Recognizing the advantages and disadvantages of both methodologies, an ideal solution emerges: combining neural systems and symbolic systems to create neural-symbolic learning systems that possess powerful perception and cognition. The purpose of this paper is to survey the advancements in neural-symbolic learning systems from four distinct perspectives: challenges, methods, applications, and future directions. By doing so, this research aims to propel this emerging field forward, offering researchers a comprehensive and holistic overview. This overview will not only highlight the current state-of-the-art but also identify promising avenues for future research.
Measuring Attribution in Natural Language Generation Models

Abstract Large neural models have brought a new challenge to natural language generation (NLG): it has become imperative to ensure the safety and reliability of the output of models that generate freely. To this end, we present an evaluation framework, Attributable to Identified Sources (AIS), stipulating that NLG output pertaining to the external world is to be verified against an independent, provided source. We define AIS and a two-stage annotation pipeline for allowing annotators to evaluate model output according to annotation guidelines. We successfully validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset). We provide full annotation guidelines in the appendices and publicly release the annotated data at https://github.com/google-research-datasets/AIS.
Cloth texture preserving image-based 3D virtual try-on

3D virtual try-on based on a single image can provide an excellent shopping experience for Internet users and has enormous business potential. The existing methods of processing the clothed 3D human body generated from the virtual try-on images are reconstructed in 3D models by extracting the depth information from the input images. However, the generated results are unstable and often fail to capture the high-frequency information loss detail features in the larger spatial background during the process of downsampling for depth prediction, and the loss of the generator gradient when predicting the occluded areas in the high-resolution images. To address this problem, we propose a multi-resolution parallel approach to obtain low-frequency information and retain as much of the high-frequency depth features in the images during depth prediction; at the same time, we use a multi-scale generator and discriminator to more accurately infer the feature images of the occluded regions to generate a fine-grained dressed 3D human body. Our method not only provides better details and effects to the final 3D mannequin generation for 3D virtual fitting, but also significantly improves the user’s try-on experience than previous studies, as evidenced by our higher quantitative and qualitative evaluations.