Reviewerly/exHarmony huggingface dataset:
A new hazard event classification model via deep learning and multifractal

Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and suggestions, and such hazards can be considered as hazard events (HaE). The classification research on HaE has much irreplaceable pragmatic values. In this paper, we present a novel deep learning model termed DLF through multifractal to explore HaE classification where the motivation is that HaE can be naturally regarded as a kind of time series. Specifically, first HaE is vectorized to get HaE time series by employing BERT. Then, a new multifractal analysis method termed HmF-DFA is proposed to win HaE fractal series by analyzing HaE time series. Finally, a new hierarchical gating neural network (HGNN) is designed to process HaE fractal series to accomplish the classification of HaE from three aspects: severity, possibility and risk. We take HAZOP reports of 18 processes as cases, and launch the experiments on this basis. Results demonstrate that compared with other classifiers, DLF classifier performs better under metrics of precision, recall and F1-score, especially for the severity aspect. Also, HmF-DFA and HGNN effectively promote HaE classification. Our HaE classification system can serve application incentives to experts, engineers, employees, and other enterprises. We hope our research can contribute added support to the daily practice in industrial safety.
SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition

In this paper, a new publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition
Self-supervised Sentiment Classification based on Semantic Similarity Measures and Contextual Embedding using metaheuristic optimizer

In recent years, considerable research attention has been paid to supervised machine learning methods for Sentiment Analysis (SA). The performance of these methods heavily depends on hyperparameter tuning and varies across different contexts. In addition, getting a massive amount of labeled training data is time- and labor-consuming. As a result, unsupervised machine learning methods are getting more attention. This paper proposes a Selfsupervised sentiment analysis method that semantically generates pseudo-labels (positive or negative) for each sample using the text similarity measures. Moreover, a sentiment classifier composed of the RoBERTa transformer and a Gated Recurrent Unit (GRU) is trained by these labeled data. What is more, Whale optimization Algorithm (WOA) is employed to find the optimal values of hyperparameters. The evaluation results demonstrate that the proposed method outperforms other methods in terms of accuracy, precision, and recall.
Using Group Deep Learning and Data Augmentation in Persian Sentiment Analysis

Sentiment analysis is one of the most important areas in natural language processing, which investigates people’s opinions on products, services, and various events. The number of papers published on Persian sentiment analysis is much less than in other languages such as English, and this made our research in Persian use different approaches to boost the performance of Persian sentiment analysis. In this research, Group Deep Learning and Data Augmentation approach has been used to classify emotions in Persian, which has not been used in any previous research to analyze Persian emotions. After applying the Data Augmentation technique and using Group Deep Learning approach for classification, we achieved 96.5% accuracy in Persian sentiment analysis.
The Effects of Data Augmentation Methods on the Performance of Human Activity Recognition

In recent developments of Human Activity Recognition systems (HAR), It has been found that deep learning models are being studied by researchers, especially convolutional neural networks integrated with long shortterm memory cells such as convolutional LSTM (ConvLSTM) networks. The deep structures require large datasets which demand extensive data collection. Therefore, various data augmentation methods are under focus nowadays. Furthermore, the challenge of time-series data augmentation is to choose the method that preserves the correct labels. In this paper, we evaluated and compared six data augmentation methods (i.e., autoencoder, time warping, amplitude warping, scaling, jittering, and linear combination) utilizing ConvLSTM networks for classification. Consequently, the WISDM dataset (tri-axial accelerometer signals of six activities) was augmented to the final size of 1.5 times the original dataset. Further, the proposed ConvLSTM model was trained seven times (once with the raw dataset and six times with the augmented dataset). The results indicated classification accuracy improvements for the test data from 92% to 93%, 97% and 98%, when training the models using augmented datasets, augmented using linear combination, scaling, and jittering methods respectively. Activity-wise analysis suggested the stairing activities to be the most challenging for the model to classify when the dataset was augmented by time warping, amplitude warping as well as autoencoder.
Comparative Analysis of Transformer based Models for Question Answering

There have been numerous significant developments in Natural language processing (NLP) in past few years. Now language models can learn improved representations of the natural language due to the Transformer and the self-attention mechanism. On numerous NLP benchmarks, these attention-based models have produced excellent, cutting-edge results. The rising popularity of transfer learning is one of the contributing causes. This paper presents a comparison between some of the transformers models like BERT, ALBERT, RoBERTa, XLNET, DistilBERT, Electra, Pegasus etc. and the application of some of these models for Question-Answering (QA) system using SQUAD2 dataset. There are a lot of machine learning algorithms that which helps in solving question answering task using NLP. Previously "Bag of Words" was most common method for answering questions that were presumed by the developers. But this was a very time-consuming process for developers to frame each pair of question answer. This was very useful for chatbots but it was not able to answer question for large database. Nowadays with the help of transformers libraries, HuggingFace developed a BERT Question Answering model which scans a specified document provided by user and tries to answer the questions relating to that document. It can quickly answer from a large document.
A Supervised Deep Learning-based Approach for Bilingual Arabic and Persian Spell Correction

Spell Correction is a widely referred problem in natural language processing. Since spelling errors prevent perfect transmission of the author’s intended concepts to the audience, writers and researchers usually spend a lot of time reviewing, to detect and correct spelling errors in their writings. Therefore, an automatic tool would be a great help in this area. Persian language is very prone to these errors due to its unique features. Also, the introduction of Arabic sentences and terms into this language has increased the challenges in spelling correction. Thus, there is a need for a tool that can detect and correct spelling errors in bilingual Persian and Arabic content. In this work, a supervised deep learning-based approach is proposed which benefits from a conditional random field (CRF) recurrent neural network to correct bilingual Arabic and Persian spelling errors. In order to create a suitable data set for training and testing the model presented in the proposed approach, 220,000 sentences with Arabic and Persian content were taken. Then artificially and using the methods of producing correct and error pairs, spelling errors were generated. In the next step, using the neural network based on the conditional random field, a model was presented that takes the features extracted from the data set as input to the network and makes predictions. The design of these features is one of the important points in this type of implementation. The results of the evaluations show that the proposed approach has a good and acceptable accuracy as the first bilingual Arabic and Persian Spell Corrector.
A Pattern Recognition Framework for Signal Processing in Metaverse

Recognizing emotions in Metaverse and the real world at the same time is a significant issue that is less addressed today and is very attractive to many psychologists. In this paper, using a simple machine learning (ML) network called echo state network (ESN), speech emotion recognition (SER) has been done in both the Metaverse and the real world. Due to the recursive structure of the reservoir used in ESNs, they will have limitations for modeling high-dimensional data (because of high memory consumption). In this paper, a new structure is presented to empower ESNs for high-dimensional signal processing. To reduce the complexity caused by hyper-complex data, an octonion-based nonlinear ESN (ONESN) has been proposed. In addition, two different scenarios are designed and analyzed for how to demonstrate the functionality of the proposed networks.
Infrared Image Captioning Based on Unsupervised Learning and Reinforcement Learning

When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests.
The Development of Terminal Alkynes in Water Using DEMATEL Method

In organic chemistry, an alkyne is an unsaturated; Hydrocarbon is at least one carbon-three carbon has a bond. All three alkynes are unsaturated have a bond containing hydrocarbons, Alkynes have the general formula CnH2n-2 and three the bond is called 'acetylenic bond'. is called The functional group in the alkyne is a Carbon-carbon is three binding. Aromatics are π bonds odd number of electron in the system Planar with pairs, fully coupled and are cyclic structures. In which test to conclude from analysis and Evaluation Laboratory (DEMATEL) of complex system components a cause-and-effect chain is considered correct One of the best to identify. It values relationships Interdependence between factors and identification through visual structural modeling Important to see. Alternative: Propene (C3H6), Butene (C4H8), Pentene (C5H10), Hexene (C6H12), Heptene (C7H14). Evaluation Preference: Propene (C3H6), Butene (C4H8), Pentene (C5H10), Hexene (C6H12), Heptene (C7H14). The result it is seen that Hexene (C6H12) is got the first rank where as is the Pentene (C5H10) is having the lowest rank. The value of the dataset for Alkynes in Test and evaluate decision making the lab shows that it results in Hexene (C6H12) and top ranking.
Cross-Spectral Neural Radiance Fields

We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes camera poses across spectra during training and exploits Normalized Cross-Device Coordinates (NXDC) to render images of different modalities from arbitrary viewpoints, which are aligned and at the same resolution. Experiments on 16 forward-facing scenes, featuring color, multi-spectral and infrared images, confirm the effectiveness of X-NeRF at modeling Cross-Spectral scene representations.
On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation

Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent attention has shifted towards semi- and weakly-supervised learning. Generating an effective form of supervision with little annotations still poses major challenge in crowded scenes. In this paper we propose to impose multi-view geometrical constraints by means of a weighted differentiable tri-angulation and use it as a form of self-supervision when no labels are available. We therefore train a 2D pose estimator in such a way that its predictions correspond to the re-projection of the triangulated 3D pose and train an auxiliary network on them to produce the final 3D poses. We complement the triangulation with a weighting mechanism that alleviates the impact of noisy predictions caused by self-occlusion or occlusion from other subjects. We demonstrate the effectiveness of our semi-supervised approach on Human3.6M and MPI-INF-3DHP datasets, as well as on a new multi-view multi-person dataset that features occlusion.
SDA-SNE: Spatial Discontinuity-Aware Surface Normal Estimation via Multi-Directional Dynamic Programming

The state-of-the-art (SoTA) surface normal estimators (SNEs) generally translate depth images into surface normal maps in an end-to-end fashion. Although such SNEs have greatly minimized the trade-off between efficiency and accuracy, their performance on spatial discontinuities, e.g., edges and ridges, is still unsatisfactory. To address this issue, this paper first introduces a novel multi-directional dynamic programming strategy to adaptively determine inliers (co-planar 3D points) by minimizing a (path) smoothness energy. The depth gradients can then be refined iteratively using a novel recursive polynomial interpolation algorithm, which helps yield more reasonable surface normals. Our introduced spatial discontinuity-aware (SDA) depth gradient refinement strategy is compatible with any depth-to-normal SNEs. Our proposed SDA-SNE achieves much greater performance than all other SoTA approaches, especially near/on spatial discontinuities. We further evaluate the performance of SDA-SNE with respect to different iterations, and the results suggest that it converges fast after only a few iterations. This ensures its high efficiency in various robotics and computer vision applications requiring real-time performance. Additional experiments on the datasets with different extents of random noise further validate our SDA-SNE's robustness and environmental adaptability. Our source code, demo video, and supplementary material are publicly available at mias.group/SDA-SNE.
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors

We present a method for inferring diverse 3D models of human-object interactions from images.Reasoning about how humans interact with objects in complex scenes from a single 2D image is a challenging task given ambiguities arising from the loss of information through projection.In addition, modeling 3D interactions requires the generalization ability towards diverse object categories and interaction types.We propose an action-conditioned modeling of interactions that allows us to infer diverse 3D arrangements of humans and objects without supervision on contact regions or 3D scene geometry.Our method extracts highlevel commonsense knowledge from large language models (such as GPT-3), and applies them to perform 3D reasoning of human-object interactions.Our key insight is priors extracted from large language models can help in reasoning about human-object contacts from textural prompts only.We quantitatively evaluate the inferred 3D models on a large human-object interaction dataset and show how our method leads to better 3D reconstructions.We further qualitatively evaluate the effectiveness of our method on real images and demonstrate its generalizability towards interaction types and object categories.
A Benchmark and a Baseline for Robust Multi-view Depth Estimation

Recent deep learning approaches for multi-view depth estimation are employed either in a depth-from-video or a multi-view stereo setting. Despite different settings, these approaches are technically similar: they correlate multiple source views with a keyview to estimate a depth map for the keyview. In this work, we introduce the Robust Multi-view Depth Benchmark that is built upon a set of public datasets and allows evaluation in both settings on data from different domains. We evaluate recent approaches and find imbalanced performances across domains. Further, we consider a third setting where camera poses are available and the objective is to estimate the corresponding depth maps with their correct scale. We show that recent approaches do not generalize across datasets in this setting. This is because their cost volume output runs out of distribution. To resolve this, we present the Robust MVD Baseline model for multi-view depth estimation, which is built upon existing components but employs a novel scale augmentation procedure. It can be applied for robust multi-view depth estimation, independent of the target data. We provide code for the proposed benchmark and baseline model at https://github.com/lmb-freiburg/robustmvd.
Improving Automatic Speech Recognition for Non-native English with Transfer Learning and Language Model Decoding

ASR systems designed for native English (L1) usually underperform on non-native English (L2). To address this performance gap, (1) we extend our previous work to investigate fine-tuning of a pre-trained wav2vec 2.0 model (Baevski et al. (wav2vec 2.0: A framework for self-supervised learning of speech representations (2020). Preprint arXiv:200611477), Xu et al. (Self-training and pre-training are complementary for speech recognition. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 3030–3034 (2021)) ) under a rich set of L1 and L2 training conditions. We further (2) incorporate language model decoding in the ASR system, along with the fine-tuning method. Quantifying gains acquired from each of these two approaches separately and an error analysis allows us to identify different sources of improvement within our models. We find that while the large self-trained wav2vec 2.0 may be internalizing sufficient decoding knowledge for clean L1 speech (Xu et al. (Self-training and pre-training are complementary for speech recognition. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 3030–3034 (2021))), this does not hold for L2 speech and accounts for the utility of employing language model decoding on L2 data.
Search Behavior Prediction: A Hypergraph Perspective

At E-Commerce stores such as Amazon, eBay, and Taobao, the shopping items and the query words that customers use to search for the items form a bipartite graph that captures search behavior. Such a query-item graph can be used to forecast search trends or improve search results. For example, generating query-item associations, which is equivalent to predicting links in the bipartite graph, can yield recommendations that can customize and improve the user search experience. Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a long-tail distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as disassortative mixing.
NT-Com: A combined machine learning model for picking up first arrival

We present a combined model named NT-Com that could predict the noise of observation site in China and effectively pick up first arrivals with very high accuracy. Based on a nonlinear autoregressive exogenous model (NARX) known as a variant of recurrent neural network and classification-regression trees (CART), NT-Com picks first arrivals by detecting abrupt points in time series. Several experiments were performed to check the fitting degree and prediction accuracy of NARX on noise signals, and experiments on CART were made to measure the accuracy of first break picking. The experimental results of NT-Com are close to those of experts and better than the traditional automatic detection algorithms. Moreover, the accuracy of NT-Com can reach 96%. The time error of the P-wave first arrival pickup can be shortened to 0.5 s. Because various first arrival patterns are learned during the training process, they can be effectively identified, which leads to a much lower probability of false triggers than traditional automatic detection algorithms. Trained and tested by all seismic data in China Earthquake networks from 2009–2019, the research work finds out a solution for picking up first arrivals by combining a simple structure of the neural networks and trees, which can be applied in China with its strong generalization ability and robustness.