Reviewerly/exHarmony huggingface dataset:
OMTPlan: A Tool for Optimal Planning Modulo Theories

OMTPlan is a Python platform for optimal planning in numeric domains via reductions to Satisfiability Modulo Theories (SMT) and Optimization Modulo Theories (OMT). Currently, OMTPlan supports the expressive power of PDDL2.1 level 2 and features procedures for both satisficing and optimal planning. OMTPlan provides an open, easy to extend, yet efficient implementation framework. These goals are achieved through a modular design and the extensive use of state-of-the-art systems for SMT/OMT solving.
Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles.
Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Abstract Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems. To address these issues, we propose a new contrastive learning-based graph collaborative filtering method to learn more robust representations. The proposed method is called signal contrastive enhanced graph collaborative filtering (SC-GCF), which conducts contrastive learning on graph signals. It has been proved that graph neural networks correspond to low-pass filters on the graph signals from the graph convolution perspective. Different from the previous contrastive learning-based methods, we first pay attention to the diversity of graph signals to directly optimize the informativeness of the graph signals. We introduce a hypergraph module to strengthen the representation learning ability of graph neural networks. The hypergraph learning module utilizes a learnable hypergraph structure to model the latent global dependency relations that graph neural networks cannot depict. Experiments are conducted on four public datasets, and the results show significant improvements compared with the state-of-the-art methods, which confirms the importance of considering signal-level contrastive learning and hypergraph learning.
DMA-Net: Decoupled Multi-Scale Attention for Few-Shot Object Detection

As one of the most important fields in computer vision, object detection has undergone marked development in recent years. Generally, object detection requires many labeled samples for training, but it is not easy to collect and label samples in many specialized fields. In the case of few samples, general detectors typically exhibit overfitting and poor generalizability when recognizing unknown objects, and many FSOD methods also cannot make good use of support information or manage the potential problem of information relationships between the support branch and the query branch. To address this issue, we propose in this paper a novel framework called Decoupled Multi-scale Attention (DMA-Net), the core of which is the Decoupled Multi-scale Attention Module (DMAM), which consists of three primary parts: a multi-scale feature extractor, a multi-scale attention module, and a decoupled gradient module (DGM). DMAM performs multi-scale feature extraction and layer-to-layer information fusion, which can use support information more efficiently, and DGM can reduce the impact of potential optimization information exchange between two branches. DMA-Net can implement incremental FSOD, which is suitable for practical applications. Extensive experimental results demonstrate that DMA-Net has comparable results on generic FSOD benchmarks, particularly in the incremental FSOD setting, where it achieves a state-of-the-art performance.
A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

This study introduces HYTREM, a hybrid tree-based ensemble learning model conceived with the sustainable development of eco-friendly transportation and renewable energy in mind. Designed as a digital model, HYTREM primarily aims to enhance solar power generation systems’ efficiency via accurate solar irradiance forecasting. Its potential application extends to regions such as Jeju Island, which is committed to advancing renewable energy. The model’s development process involved collecting hourly solar irradiance and weather-related data from two distinct regions. After data preprocessing, input variables configuration, and dataset partitioning into training and testing sets, several tree-based ensemble learning models—including extreme gradient boosting, light gradient boosting machine, categorical boosting, and random forest (RF)—were employed to generate prediction values in HYTREM. To improve forecasting accuracy, separate RF models were constructed for each hour. Experimental results validated the superior performance of HYTREM over state-of-the-art models, demonstrating the lowest mean absolute error, root mean square error (RMSE), and normalized RMSE values across both regions. Due to its transparency and efficiency, this approach suits energy providers with limited computational resources. Ultimately, HYTREM is a stepping stone towards developing advanced digital twin systems, highlighting the importance of precise forecasting in managing renewable energy.
On Correspondences between Feedforward Artificial Neural Networks on Finite Memory Automata and Classes of Primitive Recursive Functions

When realized on computational devices with finite quantities of memory, feedforward artificial neural networks and the functions they compute cease being abstract mathematical objects and turn into executable programs generating concrete computations. To differentiate between feedforward artificial neural networks and their functions as abstract mathematical objects and the realizations of these networks and functions on finite memory devices, we introduce the categories of general and actual computabilities and show that there exist correspondences, i.e., bijections, between functions computable by trained feedforward artificial neural networks on finite memory automata and classes of primitive recursive functions.
Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset

Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient’s LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.
Automated software bug localization enabled by meta-heuristic-based convolutional neural network and improved deep neural network

Automatically localizing bugs is implemented to maximize the speed of the bug localizing process and minimize the developer’s burdens. However, the existing automatic bug localization approaches have not fully used the inter-relations and the intra-relations of the source files and bug reports. In this paper, we present a CDNN-based software localization model, an automatic bug localization model enabled by a Meta-heuristic-based Convolutional Neural Network, and an improved Deep Neural Network, to enhance the effectiveness of the bug localization. First, the bug features are extracted by using word embedding technologies. The hybrid Meta heuristic-based Convolutional Neural Network (HM-CNN) is developed for feature optimization, as it can guarantee reliable localization by selecting the most significant features. The hybrid Meta heuristic-based Deep Neural Network (HM-DNN) is implemented for bug localization by considering objective functions related to the error difference between the actual score and the predicted score. Here, a new combined Grey Wolf-Sun Flower Optimization (HGW-SFO) is adopted for improving the efficiency of the system. Both the CNN in feature extraction (weighted CNN optimization) and DNN in localization phases (hidden neuron optimization) are improved by Hybrid HGW-SFO. Experimental results demonstrate that the proposed CDNN-based software bug localization model is significantly superior to the other conventional methods over several benchmark datasets in locating the bug source files. From the result analysis, while considering the accuracy value of the proposed HGW-SFO-CDNN, the proposed HGW-SFO-CDNN method secures 5.8%, 5.8%, and 9.75% more advanced performance than CNN, DNN, and CDNN at the number of retrieved files as 20.
Solar spectra datasets at optimum and vertical installation angles in central Europe (Berlin) during 2020, 2021 and 2022

This article provides datasets containing three years worth of solar spectra for the optimum installation angle of 35° and the building-integrated-photovoltaics relevant vertical angle of 90°. These datasets were obtained by measuring the spectrally resolved solar spectra using a five minute interval, where two sets of spectrometers, which measure different ranges of the solar spectrum, were employed. In addition, a merged dataset of these two spectral measurements, related to every specific five minute interval measurement, is provided. An analysis and interpretation of the data using only year the 2020 is provided in "Measurement and analysis of annual solar spectra at different installation angles in central Europe" [1].
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism

Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexed data structures. In contrast, human programmers do not limit themselves to a small finite set of data types and use polymorphism to express an unbounded number of types including nested data structures, product types, and generic functions. Code-building Genetic Programming (CBGP) is a recently introduced method that compiles type-safe programs from linear genomes using stack-based compilation and a formal type system. Although prior work with CBGP has shown initial demonstrations of polymorphism inside evolved programs, we have provided a deeper exploration of these capabilities through the evolution of programs which make use of generic data types such as key-value maps, tuples, and sets, as well as higher order functions and functions with polymorphic type signatures. In our experiments, CBGP is able to solve problems with all of these properties, where every other GP system that we know of has restrictions that make it unable to even consider problems with these properties. This demonstration provides a significant step towards fully aligning the expressiveness of GP to real world programming.
Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study

Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys.We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys.First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users.A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions.A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.
EF-TTOA: Development of a UAV Path Planner and Obstacle Avoidance Control Framework for Static and Moving Obstacles

With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance.
A Blockchain-Based Incentive Mechanism for Sharing Cyber Threat Intelligence

With the development of the Internet, cyberattacks are becoming increasingly complex, sustained, and organized. Cyber threat intelligence sharing is one of the effective ways to alleviate the pressure on organizational or individual cyber security defense. However, the current cyber threat intelligence sharing lacks effective incentive mechanisms, resulting in mutual distrust and a lack of motivation to share among sharing members, making the security of sharing questionable. In this paper, we propose a blockchain-based cyber threat intelligence sharing mechanism (B-CTISM) to address the problems of free riding and lack of trust among sharing members faced in cyber threat intelligence sharing. We use evolutionary game theory to analyze the incentive strategy; the resulting evolutionarily stable strategy achieves the effect of promoting sharing and effectively curbing free-riding behavior. Then, the incentive strategy is deployed to smart contracts running in the trusted environment of blockchain, whose decentralization and tamper-evident properties can provide a trusted environment for participating members and establish trust without a third-party central institution to achieve secure and efficient cyber threat intelligence sharing. Finally, the effectiveness of the B-CTISM in facilitating and regulating threat intelligence sharing is verified through experimental simulation and comparative analysis.
Identifying geochemical anomalies using a new method of Yang Chizhong-spatial scan statistic

Identifying anomalies from geochemical data by modeling of the background and statistical evaluation of anomalies is a major concern in geochemical exploration. This study developed a novel method (namely YangScan) for extracting geochemical anomalies by using Yang Chizhong filtering and a spatial scan statistic. The Yang Chizhong filtering, a progressive filtering method on the principle of weighted moving mean of binomial coefficient, was used to construct the background whose concavity and convexity are consistent with the original data. After removing the background from the original data, a spatial scan statistic based on multidirectional optimization was developed to identify arbitrary shaped and statistically significant anomalies from the residuals. YangScan was tested on both simulated datasets and a stream sediment geochemical dataset collected in the Northwestern Jiaodong Peninsula, Eastern China. The experimental results evaluated by geology consistency, recall, and weights of evidence contrast show that YangScan outperforms the two comparision methods (i.e., the trend surface analysis method and k-nearest neighbor anomaly detector) in identifying weak and irregularly-shaped geochemical anomalies. The distribution of geochemical anomalies linked to Au mineralization identified by YangScan is highly correlated with the trending of known Au deposits and ore-controlling structures. Therefore, YangScan is a powerful tool for identifying geochemical anomalies and can provide a useful reference for mineral exploration.
Deep Convolutional Comparison Architecture for Breast Cancer Binary Classification

Early discernment of breast cancer can significantly improve the prospect of successful recovery and survival, but it takes a lot of time that frequently leads to pathologists disagreeing. Recently, much research has tried to develop the best breast cancer classification models to help pathologists make more precise diagnoses. Consequently, convolutional networks are prominent in biomedical imaging because they discover significant features and automate image processing. Knowing which CNN models are optimal for breast cancer binary classification is crucial. This work proposed architecture for finding the best CNN model. Inception-V3, ResNet-50, VGG-16, VGG- 19, DenseNet-121, DenseNet-169, DenseNet-201, and Xception are analyzed as classifiers in this paper. We have examined these deep learning techniques on the breast ultra-sound image dataset. Due to limited data, a generative adversarial network is used to improve the algorithm’s precision. Several statistical analyses are used to determine the finest convolutional technique for premature breast cancer detection using improved images in binary class scenarios. This binary classification experiment evaluates each strategy across various dimensions to determine what aspects improve success. In both normalized and denormalized conditions, the Xception maintained 95% accuracy. Xception uses the complete knowledge-digging technique and is highly advanced. Therefore, the accuracy is considered to be better than that of others.
AutoOC: Automated multi-objective design of deep autoencoders and one-class classifiers using grammatical evolution

One-Class Classification (OCC) corresponds to a subclass of unsupervised Machine Learning (ML) that is valuable when labeled data is non-existent. In this paper, we present AutoOC, a computationally efficient Grammatical Evolution (GE) approach that automatically searches for OCC models. AutoOC assumes a multi-objective optimization, aiming to increase the OCC predictive performance while reducing the ML training time. AutoOC also includes two execution speedup mechanisms, a periodic training sampling, and a multi-core fitness evaluation. In particular, we study two AutoOC variants: a pure Neuroevolution (NE) setup that optimizes two types of deep learning models, namely dense Autoencoder (AE) and Variational Autoencoder (VAE); and a general Automated Machine Learning (AutoML) ALL setup that considers five distinct OCC base learners, specifically Isolation Forest (IF), Local Outlier Factor (LOF), One-Class SVM (OC-SVM), AE and VAE. Several experiments were conducted, using eight public OpenML datasets and two validation scenarios (unsupervised and supervised). The results show that AutoOC requires a reasonable amount of execution time and tends to obtain lightweight OCC models. Moreover, AutoOC provides quality predictive results, outperforming a baseline IF for all analyzed datasets and surpassing the best supervised OpenML human modeling for two datasets.
Information extraction in handwritten historical logbooks

Document Image Understanding is a demanding Pattern Recognition problem that requires complex recognition models. This problem is even more difficult for document images with complicated layouts like tables, where the reading order is often intrinsically ambiguous, and consequently, the context is generally ambiguous as well. In this paper, we compare two machine learning approaches for extracting information in pre-printed historical tables with handwritten information. We analyze the performance of each approach at each step of the extraction process over different corpora, up to a realistic scenario where documents with different table layouts written by different hands are used. The results are good in general and show that a model based on Multilayer Perceptrons yields better results on more homogeneous documents, while another model based on Graph Neural Networks generalizes better on heterogeneous corpora.
Using artificial intelligence for global solar radiation modeling from meteorological variables

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction. In this context, this study proposes the implementation of artificial neural networks as deep learning and the XGBoost algorithm as a machine learning method for modeling the hourly global solar radiation for a humid climate such as the Rabat region. For this purpose, hourly meteorological data from the city of Rabat in Morocco are chosen in order of importance using the random forests method, for training and testing the models, namely date and time, sunshine duration, temperature, relative humidity, wind speed/direction and pressure. Subsequently, models are selected after the validation phase for testing, whose performance is evaluated using relevant statistical indicators. As a result, we retain 2 ANN and 1 XGBoost models which are eventually very close in terms of performance with a coefficient of determination value equal to 98% and 97% respectively. However, statistical indicators have proven to be effective in assessing the accuracy and fidelity of each model. Ultimately, the intent of the modeling in terms of accuracy, simplicity or fidelity is a crucial factor in the selection of the model algorithm to adopt.