Reviewerly/exHarmony huggingface dataset:
Recurrent attention unit: A new gated recurrent unit for long-term memory of important parts in sequential data

Gated recurrent unit (GRU) is a variant of the recurrent neural network (RNN). It has been widely used in many applications, such as handwriting recognition and natural language processing. However, GRU can only memorize the sequential information, but lacks the capability of adaptively paying attention to important parts in the sequences. In this paper, we propose a novel RNN model, called recurrent attention unit (RAU), which can seamlessly integrate the attention mechanism into the interior of the GRU cell by adding an attention gate. The attention gate enhances the ability of RAU to remember long-term information and pay attention to important parts in the sequential data. Extensive experiments on adding problem, image classification, sentiment classification and language modeling show that RAU consistently outperforms GRU and other related models.
A hybrid machine learning method with explicit time encoding for improved Malaysian photovoltaic power prediction

Nowadays, with the growing interest in green energy, further improvements in photovoltaic (PV) power systems are needed. In this regard, the main aim is to find an optimal method to predict the output power of PV systems to maintain a sustainable operation. Hence, this work proposes a hybrid Machine Learning (ML) method LASSO-RFR for an hourly PV power output prediction. The model consists of Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest Regressor (RFR), where the former model makes a prediction and the latter fine tune it by the addition or subtraction of a relatively small value. The proposed model outperformed other models when tested on real data recorded from 2016 to 2019 for three Malaysian PV systems, namely Thin-Film (TF), Monocrystalline (MC), and Polycrystalline (PC). LASSO-RFR attained the lowest root mean square error (RMSE) of 23.7, 18.2, and 20.8 Wh/m2 for the TF, MC, and PC, respectively. This work also highlights the importance of explicit time encoding in improving PV power prediction. Although it is used to be ignored in the literature when developing ML models, the time feature is the second most influencing factor of PV power prediction after solar irradiance, as shown by the SHAP analysis (shapely additive explanation). For the study implications, the developed prediction model can assist the industry in predicting 1 h ahead of PV power output, demand-side management, and building operations and maintenance.
Dynamic graph transformer for 3D object detection

LiDAR-based 3D detection is critical in autonomous driving perception systems. However, point-based 3D object detection that directly learns from point clouds is challenging owing to the sparsity and irregularity of LiDAR point clouds. Existing point-based methods are limited by fixed local relationships and the sparsity of distant and occluded objects. To address these issues, we propose a dynamic graph transformer 3D object detection network (DGT-Det3D) based on a dynamic graph transformer (DGT) module and a proposal-aware fusion (PAF) module. The DGT module is built on a dynamic graph and graph-aware self-attention module, which adaptively concentrates on the foreground points and encodes the graph to capture long-range dependencies. With the DGT module, DGT-Det3D has better capability to detect distant and occluded objects. To further refine the proposals, our PAF module fully integrates the proposal-aware spatial information and combines it with the point-wise semantic features from the first stage. Extensive experiments on the KITTI dataset demonstrate that our approach achieves state-of-the-art accuracy for point-based methods. In addition, DGT brings significant improvements when combined with state-of-the-art methods on the Waymo open dataset.
Improving Line-Point Zero Knowledge

Recent advances in fast protocols for vector oblivious linear evaluation (VOLE) have inspired a family of new VOLE-based lightweight designated-verifier NIZK protocols (Weng et al., S&P 2021, Baum et al., Crypto 2021, Dittmer et al., ITC 2021, Yang et al., CCS 2021). In particular, the Line-Point Zero Knowledge (LPZK) protocol of Dittmer et al. has the advantage of being entirely non-cryptographic given a single instance of a random VOLE correlation.
Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Abstract Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in visual representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.
Bayesian game model based unsupervised sentiment analysis of product reviews

Sentiment Analysis is a task of computationally recognizing and contextualizing opinions stated in a text. We mainly assess whether the writer's attitude towards a specific topic, or a product, is positive, negative, or neutral. Numerous machine learning and fuzzy logic methods have been reconnoitered for sentiment analysis. Yet, the application of mathematical optimization techniques for sentiment tagging is still unexplored. This study presents a novel mathematical framework for sentiment analysis of reviews based on Game Theory. We identify whether the sentiment of a review is positive or negative. In the first step, we comprehend a review and derive context scores from review comments using the SentiWordNet lexicon. We comprehensively combine the computed context and rating scores using the Bayesian Game Model to deduce the sentiment of reviews. Experimental results on three benchmark review datasets, viz. Food, Mobile, and Electronics demonstrate that the proposed model yields state-of-the-art results. We also statistically validated the stability and correctness of the results. The proposed model ensures rational and consistent results. The utility of the game theory model for sentiment analysis creates a new paradigm for diverse NLP tasks.
Affinity based fuzzy kernel ridge regression classifier for binary class imbalance learning

The class imbalance learning (CIL) problem indicates when one class have very low proportions of samples (minority class) compared to the other class (majority class). Even though kernel ridge regression (KRR) shows high generalization ability at a considerably quicker learning speed than conventional machine learning algorithms, it fails to achieve an excellent result for CIL problems. To address this inherent limitation of KRR, a novel affinity-based fuzzy KRR (AFKRR) is proposed for dealing with the binary CIL problem. In AFKRR, an affinity-based fuzzy membership value is linked to each training sample. The affinity of the majority class datapoint is measured using the support vector data description (SVDD) trained by the majority class datapoints. The classification ability of the proposed AFKRR is calculated using the area under the receiver optimal characteristics curve (AUC), F-measure and Geometric mean. AFKRR's performance is compared with the support vector machine (SVM), affinity and class probability-based fuzzy SVM (ACFSVM), entropy-based fuzzy SVM (EFSVM), improved density weighted least squares SVM (IDLSSVM-CIL), KRR and intuitionistic fuzzy KRR (IFKRR) models on a few real-world as well as a few artificial imbalanced datasets. Experimental outcomes reveal the usability and efficacy of the proposed AFKRR model.
An uncertainty hybrid model for risk assessment and prediction of blast-induced rock mass fragmentation

Blasting is an important mining operation that usually produce several damaging consequences. Adverse rock fragmentation due to bench blasting is one of them. Hence, analysis of risk level and accurate estimation of particle size distribution of fragment size are of interest. This research developed a new model to simultaneously predict and risk assessment of rock fragmentation using 64 collected data from blasting operated in the Zarshouran gold mine in Iran. In this regard, a newly rock engineering system (RES) is developed based on the reliability information of Z-number theory and causal-effect relationship of the fuzzy cognitive map (FCM). This approach is named the reliability rock engineering causality system (RRECS). To do this, 15 principal effective parameters on rock fragment size were considered in the RRECS modeling process. The uncertainty of the interaction matrix was reduced using Z-number concept. Besides, the weight of effective parameters updated based on the combination of the nonlinear Hebbian algorithm (NLH) and differential evolution algorithm (DE) in FCM. The RRECS performance was validated by statistical linear and non-linear models. The results show R2, RSME, BIAS, and Accuracy using the proposed RRECS model calculated to be 0.957, 1.956, 0.001, and 96.741 for training and 0.931, 0.996, 0.016, and 99.053 for testing parts, respectively. Therefore, RRECS has performed better than the exponential, power, logarithmic, polynomial, and linear models. Furthermore, the sensitivity analysis results revealed that the hole diameter and powder factor parameters have the highest and lowest sensitivity on fragmentation, respectively.
Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.
Fuzzing deep-learning libraries via automated relational API inference

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL systems can lead to serious consequences, and may even threaten human lives. As a result, a growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, e.g., PyTorch and TensorFlow, which serve as the foundations for building, training, and running DL models. Prior work on fuzzing DL libraries can only generate tests for APIs which have been invoked by documentation examples, developer tests, or DL models, leaving a large number of APIs untested. In this paper, we propose DeepREL, the first approach to automatically inferring relational APIs for more effective DL library fuzzing. Our basic hypothesis is that for a DL library under test, there may exist a number of APIs sharing similar input parameters and outputs; in this way, we can easily “borrow” test inputs from invoked APIs to test other relational APIs. Furthermore, we formalize the notion of value equivalence and status equivalence for relational APIs to serve as the oracle for effective bug finding. We have implemented DeepREL as a fully automated end-to-end relational API inference and fuzzing technique for DL libraries, which 1) automatically infers potential API relations based on API syntactic/semantic information, 2) synthesizes concrete test programs for invoking relational APIs, 3) validates the inferred relational APIs via representative test inputs, and finally 4) performs fuzzing on the verified relational APIs to find potential inconsistencies. Our evaluation on two of the most popular DL libraries, PyTorch and TensorFlow, demonstrates that DeepREL can cover 157% more APIs than state-of-the-art FreeFuzz. To date, DeepREL has detected 162 bugs in total, with 106 already confirmed by the developers as previously unknown bugs. Surprisingly, DeepREL has detected 13.5% of the high-priority bugs for the entire PyTorch issue-tracking system in a three-month period. Also, besides the 162 code bugs, we have also detected 14 documentation bugs (all confirmed).
Truth Serum

We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to other parties. Our active inference attacks connect two independent lines of work targeting the integrity and privacy of machine learning training data.
Accurate three-dimensional measurement based on polarization-defocused encoded structured light

Polarized structured light is an important method to measure shiny surface. In this paper, a type of structured light encoded by polarized light is proposed. A coding strategy based on polarization and light intensity is adopted to improve the quality of the projection fringe. A type of robust phase-shift fringe is generated using the characteristics of a liquid crystal display projector by the defocus method. Compared with other polarized structured light coding methods, the proposed method requires simpler equipment and no additional custom instruments. Experiments show that the method could effectively improve the robustness of fringes. Experiments suggest that the range accuracy is improved by 2.4%.
High resolution photovoltaic power generation potential assessments of rooftop in China

Rooftop photovoltaic system plays an important role in solar energy power generation especially in urban. In this paper, we present an assessment method for the PV power generation potential of rooftop in China. Using machine learning model processes the big data that consists of the gross domestic product, building footprint, road length and population, at a high geographic resolution of 10 km by 10 km. The result shows that the rooftop generation potential in China is 3.27×109 MWh annually, which is close to half of the total electricity generation of China mainland in 2020, and will contribute to 2.41×109 tons of CO2 emission reduction per year. The regional results are counted by provinces and cities, showing Shandong province is the one with highest potential of 0.275×109 MWh. On the whole, the western region covers a large area with sufficient solar radiation, while the eastern region has greater photovoltaic power generation potential because of its available roof resources. The development potential of first tier, new first tier and second tier cities is generally greater than that of third and fourth tier cities.
2D Human pose estimation: a survey

Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e.g., images, videos, or signals). It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields. Deep learning techniques allow learning feature representations directly from the data, significantly pushing the performance boundary of human pose estimation. In this paper, we reap the recent achievements of 2D human pose estimation methods and present a comprehensive survey. Briefly, existing approaches put their efforts in three directions, namely network architecture design, network training refinement, and post processing. Network architecture design looks at the architecture of human pose estimation models, extracting more robust features for keypoint recognition and localization. Network training refinement tap into the training of neural networks and aims to improve the representational ability of models. Post processing further incorporates model-agnostic polishing strategies to improve the performance of keypoint detection. More than 200 research contributions are involved in this survey, covering methodological frameworks, common benchmark datasets, evaluation metrics, and performance comparisons. We seek to provide researchers with a more comprehensive and systematic review on human pose estimation, allowing them to acquire a grand panorama and better identify future directions.
An Effective Ensemble Machine Learning Approach to Classify Breast Cancer Based on Feature Selection and Lesion Segmentation Using Preprocessed Mammograms

Background: Breast cancer, behind skin cancer, is the second most frequent malignancy among women, initiated by an unregulated cell division in breast tissues. Although early mammogram screening and treatment result in decreased mortality, differentiating cancer cells from surrounding tissues are often fallible, resulting in fallacious diagnosis. Method: The mammography dataset is used to categorize breast cancer into four classes with low computational complexity, introducing a feature extraction-based approach with machine learning (ML) algorithms. After artefact removal and the preprocessing of the mammograms, the dataset is augmented with seven augmentation techniques. The region of interest (ROI) is extracted by employing several algorithms including a dynamic thresholding method. Sixteen geometrical features are extracted from the ROI while eleven ML algorithms are investigated with these features. Three ensemble models are generated from these ML models employing the stacking method where the first ensemble model is built by stacking ML models with an accuracy of over 90% and the accuracy thresholds for generating the rest of the ensemble models are >95% and >96. Five feature selection methods with fourteen configurations are applied to notch up the performance. Results: The Random Forest Importance algorithm, with a threshold of 0.045, produces 10 features that acquired the highest performance with 98.05% test accuracy by stacking Random Forest and XGB classifier, having a higher than >96% accuracy. Furthermore, with K-fold cross-validation, consistent performance is observed across all K values ranging from 3−30. Moreover, the proposed strategy combining image processing, feature extraction and ML has a proven high accuracy in classifying breast cancer.
Financial development, institutional improvement, poverty reduction: The multiple challenges in West Africa

Poverty continues to be one of the biggest difficulties confronting humanity today, and eradicating poverty has become one of the most difficult tasks facing developing countries in their drive for long-term development. Accordingly, this study assesses the moderating role of institutional quality (measured by rule of law, regulation quality, control of corruption, government effectiveness, political stability, and voice and accountability) in the finance-poverty nexus for a sample of 16 West African countries spanning 2002–2019. The findings show that finance (represented by domestic credit, and money supply) reduces poverty; however, the weak institutional quality reduces the positive effect of finance on poverty reduction. The study establishes that the institutional quality thresholds at which domestic credit could reduce poverty are: rule of law (0.6), regulation quality (0.1), control of corruption (1.6), government effectiveness (0.1), and political stability (0.3) on the scale of −2.5–2.5. Finally, the institutional quality thresholds at which money supply could reduce poverty are: rule of law (2.5), regulation quality (0.3), control of corruption (2.0), government effectiveness (1.5), and political stability (1.4) on the scale of −2.5–2.5, whilst the interaction effect of domestic credit, money supply, and voice and accountability were insignificant. The study concludes that policymakers must enhance the institutional environment in West Africa to boost financial development to reduce poverty.
Developing robust benchmarks for driving forward AI innovation in healthcare

Machine learning technologies have seen increased application to the healthcare domain. The main drivers are openly available healthcare datasets, and a general interest from the community to use its powers for knowledge discovery and technological advancements in this more conservative field. However, with this additional volume comes a range of questions and concerns — are the obtained results meaningful and conclusions accurate; how do we know we have improved state of the art; is the clinical problem well defined and does the model address it? We reflect on key aspects in the end-to-end pipeline that we believe suffer the most in this space, and suggest some good practices to avoid reproducing these issues. Finding good benchmarks is an important and pervasive problem in machine learning for healthcare. This Perspective highlights key aspects that require scrutiny in the whole process of benchmark generation and use, including problem formulation, creation of datasets, development of a suite of machine learning models and evaluation of these models.