Reviewerly/exHarmony huggingface dataset:
Self-Supervised Occlusion Detection and Avoidance using Differentiable Rendering

Deep Learning-based computer vision methods have achieved state-of-the-art performance in the last decade, prompting a renaissance in the field of Artificial Intelligence (AI). Still, despite recent successes, many challenges remain including the robustness of these solutions to disturbances of the input, such as occlusion. While some solutions exist for neural network-based object detection under partial occlusions, these methods still implement the object detector as a passive observer in the scene. We argue, however, that an active object detector agent may be able to achieve better results by changing its own position to resolve occlusion, when possible. In this paper, an OpenAI Gym-compatible virtual environment is presented that enables the creation of realistic occlusion datasets via differentiable rendering. A trained neural network is also presented that predicts the occlusion mask and the optimal camera movement via self-supervised learning in the environment. Our experiments presented in this paper show that the neural network model is able to outperform the gradient-based optimization, and efficiently avoid occlusion in simulated scenes containing multiple objects.
Perception and evaluation of selected cloud computing factors in the light of conducted research among small and medium-sized enterprises

Cloud computing is now an increasingly common solution used by enterprises, regardless of their size or business profile. Cloud computing is a solution that measurably affects the company's operations, offering many benefits and solutions. As a result, more enterprises have decided to implement cloud computing, expecting many benefits from the acceleration of management processes or the convenience of activities carried out in the daily operations. Therefore, cloud computing is the subject of research in terms of its impact on the activities of enterprises. However, no analyzes have been carried out so far to illustrate the perception of various aspects of cloud computing depending on the size of enterprises. Thus, the article's primary goal is to identify significant differences in the perception of selected aspects of cloud computing by small and medium enterprises. For this purpose, the analyses carried out showed substantial differences in the perception of selected aspects of cloud computing by the surveyed groups of companies.
RuleSQLova: Improving Text-to-SQL with Logic Rules

Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based SQLova model combined with execution-guided (EG) decoding strategy has achieved a super-human performance on the WikiSQL dataset. However, through our fine-grained error analysis, we find that SQLova cannot handle well the aggregation operator selection for nu-meric columns, due to the lack of column type information to distinguish between textual and numeric columns. Besides, most predicted value spans in the WHERE clause have the same meaning with the ground truth, but they do not match exactly, leading to unnecessary errors. Therefore we propose RuleSQLova model, which enhances the SQLova base model with logic rules to deal with these two major weaknesses of SQLova. It first incorporates four logic rules into the model to constrain the aggregation operator prediction for numeric columns, using the general framework of iterative rule knowledge distillation. Then it leverages another logic rule for post-processing before EG decoding to ensure the consistency between predicted value spans and values in the table column. Experimental results indicate that RuleSQLova model offers significant and consistent improvements over SQLova, outperforming competitive sketch-based models on the WikiSQL dataset, and our method also brings improvements to the sketch-based models on the Spider dataset.
Local Attention Pyramid for Scene Image Generation

In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction

Prediction of annual crop yields at a county granularity is important for national food production and price stability. In this paper, towards the goal of better crop yield prediction, leveraging recent graph signal processing (GSP) tools to exploit spatial correlation among neighboring counties, we denoise relevant features via graph spectral filtering that are inputs to a deep learning prediction model. Specifically, we first construct a combinatorial graph with edge weights that encode county-to-county similarities in soil and location features via metric learning. We then denoise features via a maximum a posteriori (MAP) formulation with a graph Laplacian regularizer (GLR). We focus on the challenge to estimate the crucial weight parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> , trading off the fidelity term and GLR, that is a function of noise variance in an unsupervised manner. We first estimate noise variance directly from noise-corrupted graph signals using a graph clique detection (GCD) procedure that discovers locally constant regions. We then compute an optimal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> minimizing an approximate mean square error function via bias-variance analysis. Experimental results from collected USDA data show that using denoised features as input, performance of a crop yield prediction model can be improved noticeably.
An Ensemble Learning Algorithm Based on Resampling and Hybrid Feature Selection, with an Application to Software Defect Prediction

In recent years, software defect prediction(SDP) based on ensemble learning has attracted extensive attention. Hevertheless, the forthcoming integrated learning means still have some shortcomings in improving the divergence of base classifiers. How to improve the divergence of base classifiers is the major challenge in ensemble learning. In this paper, we advance the new ensemble learning algorithm named E_RHFS, and apply it to SDP. In E_RHFS, we first need the weighted complexity-based SMOTE to manage imbalanced data; Second, the resampling technology is used to generate multiple sampling sets from the balanced data. Moreover, for each sampling set, a hybrid feature selection method is used to select features, which combines the granular decision entropy and the Random Subspace Method (RSM); Third, a base classifier is constructed on each sampling set; Finally, all base classifiers are combined by voting, so as to obtain the final ensemble classifier for software defect prediction. Test on many relevant data sets express that E_RHFS be able to get better performance than existing ensemble methods.
Web-based Election Monitoring Integrating Barangay Geospatial Mapping

Web-based election monitoring in the Philippines has proven to be quite beneficial to the community. The Commission on Elections has granted external sources like the media and verified non-government organizations access to election returns. The local media relies on its parent firm for data on the local mayoral contest to keep track of the votes. The general public relies on radio broadcasts and social media live streams for election results, but voting information, particularly the map, lacks precise graphical representations of votes. The researchers opted to design and implement a web-based system for automating the visual display of the mayoral campaign election. In this project, the quick application development technique was used. With less time, the development was rapid enough to construct a prototype on how to graphically portray the voting results by leveraging Google cloud infrastructure, open source, and PHP web technologies. The local media partner was amazed by the technology during the proposal and completely supports the data to be obtained from the COMELEC, such as the number of registered voters by barangay and municipality. During testing, the ease of encoding and verification was evident. As a consequence, broadcasts of recent vote information, including the barangays, are now faster and more exact, provided in graphical form (charts, graphs, and geographical maps) until the end of the election period. Thus, geospatial mapping is an excellent method for showing election results. Furthermore, this research significantly solves the main problem of producing detailed graphical presentations of election results by developing web applications with geovisualization that can be used for smart cities and municipalities addressing the Fourth Industrial Revolution and fostering innovation. The utilization of internet technologies and comprehensive voting information has considerably boosted the community's faith and trust in the voting process.
NAM Net: Meta-Network with Normalization-based Attention for Few-Shot Learning

Few-shot learning aims to learn a classifier to recognize unseen classes with limited labeled examples. The scarcity of training data remains a challenging problem in few-shot classification. Many few-shot learning methods focus on the structure of meta-network models and there is often no dedicated study of meta-knowledge representation. In this paper, a meta-learning strategy is introduced and a meta-network NAM Net is proposed. Taking the advantage of ‘learning to learn’, the model acquires meta-knowledge via few-shot classification tasks and applies it to new few-shot scenarios. Specifically, the meta-learner learns the best parameters of the feature extraction and similarity metric modules. The distance between the support feature and query feature is obtained by a learnable metric function, which leads to the classification result. The base learner migrates the meta-knowledge to the target class to perform classification in a new few-shot episode. Moreover, the Normalization-based Attention Module is adapted to feature extractor to enhance meta-knowledge representation. Compared with few-shot learning benchmarks, NAM Net is effective and achieves higher accuracy in both 5-way 1-shot and 5-shot classification tasks.
Cyberbullying Through the Lens of Data Science

The objective of this systematic literature review is to find the scientific contributions that involve the topics of cyberbullying, data science techniques and microlearning. The proposed research question is how data science engages with data treatment for microlearning about fighting bullying and cyberbullying. To answer it, four important digital libraries were selected, and the search was performed using an established search string. Two hundred eighty-three studies were preliminary found; after an initial review, 69 were chosen to be part of the process. This literature review follows the guidelines established by Kitchenham and Charters. After applying the review protocol, which guarantees the rigour and replicability of the study, it was found that not many studies involve the initial terms; therefore, this scientific article is an initial step toward an in-depth analysis of this area of study.
Optimizing Geometric Constellations for Phase Noise Channels Using Deep Learning

We optimize 16 and 64QAM constellations and detectors via end-to-end learning for residual laser phase noise. At the bit-error-rate (BER) of 1e-3, the required signal-to-noise ratio (SNR) is shown to reduce about 0.9 and 1 dB for 16 and 64-QAM, respectively.
Variance of the Gradient Also Matters: Privacy Leakage from Gradients

Distributed machine learning (DML) enables model training on a large corpus of decentralized data from users and only collects local models or gradients for global synchronization on the cloud. Recent studies show that a third party can recover the training data in the DML system through publicly shared gradients. Our investigation has revealed that existing techniques (e.g., DLG) can only recover the training data on uniform weight distribution and fail to recover the training data on other weights initialization (e.g., normal distribution) or during the training stage. In this work, we provide an analysis of how weight distribution can affect the training data recovery from gradients. Based on this analysis, we propose a self-adaptive privacy attack from gradients, SAPAG—a general gradient attack algorithm that can recover the training data in DML with any weight initialization and in any training phase. Our algorithm exploits not only the gradients but also the variance of gradients. Specifically, we exploit the variance of gradients distribution and the Deep Neural Network (DNN) architecture and design an adaptive Gaussian kernel of gradient difference as a distance measure. Our experimental results on various benchmark datasets and tasks demonstrate the generalizability of SAPAG. SAPAG outperforms the state-of-the-art algorithms in terms of both the data recovery performance and the recovery speed.
A Named Entity Recognition Method For Chinese Winter Sports News Based On RoBERTa-WWM

In the task of Named Entity Recognition (NER) of winter sports text news, for the problem that common Pre-trained Language Model (PLM)trained with a common corpus have limited semantic representation capabilities for sentence and words in the winter sports domain, and the problem that Chinese BERT PLM, trained based on a single character masking approach, cannot understand Chinese semantic features well, this paper proposes a Named Entity Recognition method for Chinese winter sports news based on RoBERTa-WWM model. Firstly, we add many proper nouns and terminologies in the domain of winter sports to lexicon of the LTP word segmentation tool used for pre-training of RoBERTa-WWM model, so that the LTP can segment the text more accurately, thus the RoBERTa-WWM model can achieve more accurate Whole Word Masking (WWM) pre-training. After that, we pre-train the RoBERTa-WWM model on abundant unlabeled winter sports text news data with the premise of more accurate WWM pre-training. In the end, the pre-trained RoBERTa-WWM model is used as the word embedding layer to connect with the downstream task model BILSTM-CRF for fine-tuning the NER task by using a hierarchical learning approach, to obtain better accuracy of entity recognition results. The experimental results show that the NER model trained by the method proposed in this paper achieves an F1 value of 93.56% in the NER test of actual winter sports textual news, which is 0.77% higher than the F1 value of the common RoBERTa-WWM-BiLSTM-CRF model, 1.81% higher than the F1 value of the common BERT-BiLSTM-CRF model, and 4.88% higher than the F1 value of the BiLSTM-CRF benchmark model.
Real-Time 3-D Human Action Recognition Based on Hyperpoint Sequence

Real-time 3-D human action recognition has broad industrial applications, such as surveillance, human–computer interaction, and healthcare monitoring. By relying on complex spatio-temporal local encoding, most existing point cloud sequence networks capture spatio-temporal local structures to recognize 3-D human actions. To simplify the point cloud sequence modeling task, we propose a lightweight and effective point cloud sequence network referred to as SequentialPointNet for real-time 3-D action recognition. Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions. First, we define a novel type of point data, hyperpoint, to better describe the temporally changing human appearances. A theoretical foundation is provided to clarify the information equivalence property for converting point cloud sequences into hyperpoint sequences. Second, the point cloud sequence modeling task is decomposed into a hyperpoint embedding task and a hyperpoint sequence modeling task. Specifically, for hyperpoint embedding, the static point cloud technology is employed to convert point cloud sequences into hyperpoint sequences, which introduces inherent frame-level parallelism; for hyperpoint sequence modeling, a hyperpoint-mixer module is designed as the basic building block to learning the spatio-temporal features of human actions. Extensive experiments on three widely-used 3-D action recognition datasets demonstrate that the proposed SequentialPointNet achieves a competitive classification performance with up to 10× faster than existing approaches.
Super-Resolution for Cross-Sensor Optical Remote Sensing Images

Generative adversarial network (GAN) models are becoming popular in the field of remote sensing for generating high spatial resolution images from their low resolution versions. In this study, four models including two basic Super-resolution GAN models and two non-GAN Deep Learning models were trained and tested to achieve 2.5m, and 5m spatial resolution from their 10m spatial resolution satellite data. The comparison of results showed that the SRGAN model performed better than the other deep learning models. The performance metrics were also found to be consistent with available literature.
Multi-Level Clustering Algorithm for Pedestrian Trajectory Flow Considering Multi-Camera Information

With the development of surveillance video systems from a single camera to a multi-camera network, the traditional video target trajectory clustering algorithm does not consider the camera position and field of view, resulting in poor clustering results. To solve this problem, this paper proposes a multi-level clustering algorithm for semi-supervised video target geographic flow. The proposed method is based on flow space and uses geographic flow hierarchical representation for video target trajectories to obtain geographic flow sub-segment sets at different levels. Meanwhile, this method takes the camera number as a label and performs semi-supervised clustering on the flow sub-segment sets at different levels. In this way, the clustering center set at the corresponding level is obtained, and the general trend of a large number of geographic flows is effectively described with cluster centers. The proposed method makes up for the shortcomings that traditional algorithms can only cluster at a single level and do not consider camera information, and its effectiveness is verified by a series of experiments.
MarsNet: Automated Rock Segmentation With Transformers for Tianwen-1 Mission

The Mars exploration mission of China named Tianwen-1 is being carried out as scheduled. The Navigation and Terrain Cameras (NaTeCam) equipped on the Zhurong Rover play an essential role in obstacle recognition. The main obstacles on the Martian surface are rocks of different sizes, which influence the path planning of Zhurong Rover in scientific exploration. Most existing semantic segmentation methods are based on the U-Net architecture with ResNet or other backbones, and features extracted by these methods lack long-range dependencies. To fully exploit the context information, we propose the MarsNet framework for the Mars image, which combines transformers with the convolutional neural network (CNN) as the backbone, and hybrid dilated convolution (HDC) is also employed to the decoder path to help detect the huge rocks. Besides, since there are few open-source datasets for rock segmentation for Mars, we establish a segmentation dataset from the Martian surface image, named TWMARS, captured by NaTeCam. Extensive experiments are conducted on the TWMARS dataset, and the experimental results demonstrate that MarsNet achieves accurate rock segmentation and outperforms state-of-the-art methods. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BUPT-ANT-1007/MarsNet</uri> .
TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks

Predicting the performance of production code prior to actual execution is known to be highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which demonstrates that high-accuracy performance prediction is possible for the special case of predicting unit test execution times. TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model. We evaluate TEP-GNN using four real-life Java open source programs, based on 922 test files mined from the projects' public repositories. We find that our approach achieves a high Pearson correlation of 0.789, considerable outperforming a baseline deep learning model. Our work demonstrates that FA-ASTs and GNNs are a feasible approach for predicting absolute performance values, and serves as an important intermediary step towards being able to predict the performance of arbitrary code prior to execution.
Long- and Short-term Attention Network for Knowledge Tracing

Knowledge Tracing (KT) is an important part of intelligent online education and is the key to personalized guidance for students' learning process. It aims at dynamically estimate students' knowledge status based on history answer records and predict whether they will answer the next question correctly. Predicting students' knowledge is a difficult task because student learning is a dynamic process and students' knowledge status is constantly changing. However, existing approaches often ignore the fact of students' stage development of learning ability and do not take into account the impact of short-term knowledge states on the next moment. Existing models do not emphasize the importance of students' long-term knowledge states and short-term knowledge states. In this paper, we propose a new Long- and Short-term Attention network Knowledge Tracing model (LSAKT). Specifically, we divided the sequences into subsequences based on time stamps, with the first attention layer learning the student's long-term knowledge state based on the interactional performance with the problem, while the other attention layer learns the student's short-term knowledge state based on the last sequence. Finally, we integrate the long-term knowledge state and the short-term knowledge state to form the student's final knowledge state. We evaluated the proposed model on four publicly available datasets, and the experimental results showed that the LSAKT approach achieved a fantastic result.