Reviewerly/exHarmony huggingface dataset:
Frame-Level Rate Control for Geometry-Based LiDAR Point Cloud Compression

The state-of-the-art compression method for Light Detection And Ranging (LiDAR) point clouds is the geometry-based point cloud compression (G-PCC) standard developed by Moving Pictures Experts Group immersive media working group (MPEG-I). However, there are currently no rate control algorithms designed specifically for Geometry-based LiDAR point cloud compression (G-LPCC). In this paper, we propose the first frame-level rate control algorithm for G-LPCC. We mainly have the following contributions in our proposed rate control algorithm. First, we model the rate-distortion (R-D) relationship for both the geometry and attribute. As the geometry bitrate is mainly determined by the frame-level geometry quantizer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{G}$</tex-math></inline-formula> , we propose a relationship between the geometry bitrate and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{G}$</tex-math></inline-formula> . In addition, as the attribute bitrate can be influenced by both the attribute quantizer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{A}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{G}$</tex-math></inline-formula> , we build a relationship among the attribute bitrate, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{G}$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{A}$</tex-math></inline-formula> . Second, we propose a bit allocation algorithm between the geometry and attribute based on the R-D modeling. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{G}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q_{A}$</tex-math></inline-formula> are modeled into a proper relationship to obtain geometry and attribute bits to achieve good R-D performance. Third, we propose using the point density of LiDAR point clouds to estimate the geometry model parameters. The point density is calculated using the average distance between each point and its nearest neighbor after excluding some noisy points. The proposed rate control algorithm is implemented in the G-PCC reference software. The experimental results show that the proposed rate control algorithm can control the bitrate accurately with satisfactory R-D performance.
iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

Interactive image segmentation has been widely applied to obtain high-quality voxel-level labels for medical images. The recent success of Transformers on various vision tasks has paved the road for developing Transformer-based interactive image segmentation approaches. However, these approaches remain unexplored and, in particular, have not been developed for 3D medical image segmentation. To fill this research gap, we investigate Transformer-based interactive image segmentation and its application to 3D medical images. This is a nontrivial task due to two main challenges: 1) limited memory for computationally inefficient Transformers and 2) limited labels for 3D medical images. To tackle the first challenge, we propose iSegFormer, a memory-efficient Transformer that combines a Swin Transformer with a lightweight multilayer perceptron (MLP) decoder. To address the second challenge, we pretrain iSegFormer on large amount of unlabeled datasets and then finetune it with only a limited number of segmented 2D slices. We further propagate the 2D segmentations obtained by iSegFormer to unsegmented slices in 3D images using a pre-existing segmentation propagation model pretrained on videos. We evaluate iSegFormer on the public OAI-ZIB dataset for interactive knee cartilage segmentation. Evaluation results show that iSegFormer outperforms its convolutional neural network (CNN) counterparts on interactive 2D knee cartilage segmentation, with competitive computational efficiency. When propagating the 2D interactive segmentations of 5 slices to other unprocessed slices within the same 3D volume, we achieve 82.2% Dice score for 3D knee cartilage segmentation. Code is available at https://github.com/uncbiag/iSegFormer .
Report on the 1st simulation for information retrieval workshop (Sim4IR 2021) at SIGIR 2021

Simulation is used as a low-cost and repeatable means of experimentation. As Information Retrieval (IR) researchers, we are no strangers to the idea of using simulation within our own field---such as the traditional means of IR system evaluation as manifested through the Cranfield paradigm. While simulation has been used in other areas of IR research (such as the study of user behaviours), we argue that the potential for using simulation has been recognised by relatively few IR researchers so far. To this end, the Sim4IR workshop was held online on July 15 th , 2021 in conjunction with ACM SIGIR 2021. Building on past efforts, the goal of the workshop was to create a forum for researchers and practitioners to promote methodology and development of more widespread use of simulation for IR evaluation. Around 80 participants took part over two sessions. A total of two keynotes, three original paper presentations, and eight 'encore talks' were presented. The main conclusions from the resultant discussion were that simulation has the potential to offer solutions to the limitations of existing evaluation methodologies, but there is more research needed toward developing realistic user simulators; and the development and sharing of simulators, in the form of toolkits and online services, is critical for successful uptake. Date : 15 July, 2021. Website : https://sim4ir.org.
Learning to Transfer Prompts for Text Generation

Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning.While, it is challenging to fine-tune PLMs in a data-scarce situation.Therefore, it is non-trivial to develop a general and lightweight model that can adapt to various text generation tasks based on PLMs.To fulfill this purpose, the recent prompt-based learning offers a potential solution.In this paper, we improve this technique and propose a novel prompt-based method (PTG) for text generation in a transferable setting.First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.To consider both task-and instance-level information, we design an adaptive attention mechanism to derive the target prompts.For each data instance, PTG learns a specific target prompt by attending to highly relevant source prompts.In extensive experiments, PTG yields competitive or better results than fine-tuning methods.We release our source prompts as an open resource, where users can add or reuse them to improve new text generation tasks for future research.
Handling imbalanced data for aircraft predictive maintenance using the BACHE algorithm

Developing a prognostic model to predict an asset's health condition is a maintenance strategy that increases asset availability and reliability through better maintenance scheduling. Therefore, developing reliable vehicle health predictive models is vital in the aerospace industry, especially considering a safety–critical system such as aircraft. However, one of the significant challenges faced in building reliable data-driven prognostic models is the imbalance dataset. Training machine-learning models using an imbalanced dataset causes classifiers to be biased towards the class with majority samples, resulting in poor predictive accuracy in data-driven models. This problem can become more challenging if the imbalance ratio is extreme and classes overlap. In this paper, a novel approach called Balanced Calibrated Hybrid Ensemble Technique (BACHE) is developed to tackle the severe imbalanced classification problem. The proposed method involves the combination of hybrid data sampling and ensemble-based learning. It uses a cascading balanced approach to transfer a class imbalance problem into a sub-problem by decomposing the original problem into a set of subproblems, each characterized by a reduced imbalance ratio. Then uses a calibrated boosting with a cost-sensitive decision tree to enhance recognition of hard-to-learn patterns, which improves the prediction of the extreme minority class. BACHE is evaluated using a real-world aircraft dataset with rare component replacement instances. Also, a comparative experiment of the proposed approach with other similar existing methods is conducted. The performance metrics used are precision, recall, G-mean, and an area under the curve. The final results show that the proposed model outperforms other similar methods. Also, it can attain an excellent performance on large, extremely imbalanced datasets.
On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection

Artificial intelligence (AI) is rapidly shaping the global financial market and its services due to the great competence that it has shown for analysis and modeling in many disciplines. What is especially remarkable is the potential that these techniques could offer to the challenging reality of credit fraud detection (CFD); but it is not easy, even for financial institutions, to keep in strict compliance with non-discriminatory and data protection regulations while extracting all the potential that these powerful new tools can provide to them. This reality effectively restricts nearly all possible AI applications to simple and easy to trace neural networks, preventing more advanced and modern techniques from being applied. The aim of this work was to create a reliable, unbiased, and interpretable methodology to automatically evaluate CFD risk. Therefore, we propose a novel methodology to address the mentioned complexity when applying machine learning (ML) to the CFD problem that uses state-of-the-art algorithms capable of quantifying the information of the variables and their relationships. This approach offers a new form of interpretability to cope with this multifaceted situation. Applied first is a recent published feature selection technique, the informative variable identifier (IVI), which is capable of distinguishing among informative, redundant, and noisy variables. Second, a set of innovative recurrent filters defined in this work are applied, which aim to minimize the training-data bias, namely, the recurrent feature filter (RFF) and the maximally-informative feature filter (MIFF). Finally, the output is classified by using compelling ML techniques, such as gradient boosting, support vector machine, linear discriminant analysis, and linear regression. These defined models were applied both to a synthetic database, for better descriptive modeling and fine tuning, and then to a real database. Our results confirm that our proposal yields valuable interpretability by identifying the informative features’ weights that link original variables with final objectives. Informative features were living beyond one’s means, lack or absence of a transaction trail, and unexpected overdrafts, which are consistent with other published works. Furthermore, we obtained 76% accuracy in CFD, which represents an improvement of more than 4% in the real databases compared to other published works. We conclude that with the use of the presented methodology, we do not only reduce dimensionality, but also improve the accuracy, and trace relationships among input and output features, bringing transparency to the ML reasoning process. The results obtained here were used as a starting point for the companion paper which reports on our extending the interpretability to nonlinear ML architectures.
"Do you know existing accuracy metrics overrate time-series anomaly detections?"

An anomaly coincides with a time range in time-series data, and a detection method usually detects part of this range. Existing works assume that an expert can detect the whole anomaly range by analyzing its detected part. Based on this expert scenario, a detection method achieves a higher score if the expert can find more anomalies through predictions given by the detection method as clues. However, the existing metrics overrate imprecise and insufficient cases. The expert cannot detect any anomalies if a prediction indicates a wrong range that is unrelated with any anomalies (i.e., called an imprecise case). Moreover, they fail to detect an anomaly if a prediction indicates an insufficient anomaly range (i.e., called an insufficient case). For instance, it is difficult to understand an anomaly using a small part of the range if the anomaly has steadily changed from its original pattern over a period of time. Moreover, the existing metrics do not consider the length of incorrect predictions in their score though a prolonged incorrect prediction incoveniences the expert more than a shorter one. We deal with these problems through two concepts of cross-referencing and a weighting scheme. The cross-referencing verifies anomalies and predictions involved in imprecise and insufficient cases, preventing them from getting scores. By adopting the weighting scheme, we consider the weighted sum of scores given to predictions, wherein lengthy incorrect predictions are penalized. Based on these concepts, we propose novel metrics (i.e., eTaV and eTaff). We verify that the proposed metrics are more reasonable compared to existing metrics via evaluations using two real-world datasets, Secure Water Treatment and Hardware-in-the-Loop-based Augmented Industrial control system as well as some hypothetical datasets. Furthermore, our metrics have been verified in practice because they are employed in a anomaly detection competition (i.e., HAICon'211).
Multi-modal detection of cyberbullying on Twitter

Cyberbullying detection is one of the trending topics of research in recent years, due to the popularity of social media and the lack of limitations about using electronic communications. Detection of cyberbullying may prevent some bullying behaviors online. This paper introduces a Multi-modal system that makes use of Convolutional Neural Network (CNN), Tensor Fusion Network, VGG-19 Network, and Multi-Layer Perceptron model, for the purpose of cyberbullying detection. This system can not only analyze the messages sent but also the extra information related to the messages (meta-information) and the images contained in the messages. The proposed system is trained and tested on Twitter datasets, achieving accuracy scores of 93%, which is 4% higher than scores of the benchmark text-only model using the same dataset and 6.6% higher than previous work. With the results, we believe that the proposed system performs well and it will provide new ideas for future works.
Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.
Image fairness in deep learning: problems, models, and challenges

Abstract In recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models.
Multi-stream feature refinement network for human object interaction detection

Human–Object Interaction (HOI) detection is a crucial problem for comprehensive visual understanding, which aims to detect <human,action,object> triplets within an image. Many existing methods often exploit to integrate the human and object visual features , the spatial layout of human–object pairs, human poses, contextual information, and even object semantic information into a framework to infer the interactions, proving that all these components can contribute to improve the HOI detection. However, most methods simply concatenate these components that are not explicitly embedded in the feature learning for HOI detection. In this paper, we are trying to fuse these components explicitly using a multi-stream feature refinement network. The network extracts the visual features of humans, contexts, and objects, which receives the attentions from human poses, spatial configurations, and semantic prior knowledge of objects to refine these visual features, respectively. In addition, an additional graph neural network is employed here to learn the structural features of human–object pairs. We verify our method on V-COCO and HICO-DET datasets with extensive experiments. The experimental results demonstrate that our method is a simple yet effective for HOI detection, achieving superior performance to those state-of-the-art methods.
The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

The presented research study focuses on demonstrating the learning ability of a neural network using a genetic algorithm and finding the most suitable neural network topology for solving a demonstration problem. The network topology is significantly dependent on the level of generalization. More robust topology of a neural network is usually more suitable for particular details in the training set and it loses the ability to abstract general information. Therefore, we often design the network topology by taking into the account the required generalization, rather than the aspect of theoretical calculations. The next part of the article presents research whether a modification of the parameters of the genetic algorithm can achieve optimization and acceleration of the neural network learning process. The function of the neural network and its learning by using the genetic algorithm is demonstrated in a program for solving a computer game. The research focuses mainly on the assessment of the influence of changes in neural networks’ topology and changes in parameters in genetic algorithm on the achieved results and speed of neural network training. The achieved results are statistically presented and compared depending on the network topology and changes in the learning algorithm.
Hierarchical semantic-aware neural code representation

Code representation is a fundamental problem in many software engineering tasks. Despite the effort made by many researchers, it is still hard for existing methods to fully extract syntactic, structural and sequential features of source code, which form the hierarchical semantics of the program and are necessary to achieve a deeper code understanding. To alleviate this difficulty, we propose a new supervised approach based on the novel use of Tree-LSTM to incorporate the sequential and the global semantic features of programs explicitly into the representation model. Unlike previous techniques, our proposed model can not only learn low-level syntactic information within each statement but also the high-level semantic information between statements over the constructed semantic graph. Besides, considering that the sequential semantics is also critical for developers to understand the dependency path and data flow transmission, we propose a DFS-based method to generate the topological order of statements being processed, and then feed them as well as their in-neighboring information and syntactic embeddings into the proposed model to learn richer statement-level semantic features. Extensive experiments on multiple program comprehension tasks, e.g., code clone detection, demonstrate that our method achieves promising performance compared with other existing baselines.
Email classification analysis using machine learning techniques

Purpose In this digital era, email is the most pervasive form of communication between people. Many users become a victim of spam emails and their data have been exposed. Design/methodology/approach Researchers contribute to solving this problem by a focus on advanced machine learning algorithms and improved models for detecting spam emails but there is still a gap in features. To achieve good results, features also play an important role. To evaluate the performance of applied classifiers, 10-fold cross-validation is used. Findings The results approve that the spam emails are correctly classified with the accuracy of 98.00% for the Support Vector Machine and 98.06% for the Artificial Neural Network as compared to other applied machine learning classifiers. Originality/value In this paper, Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine (UCI) spambase email dataset to select the best features. Extensive experiments are conducted on selected features by training the different classifiers.