Reviewerly/exHarmony huggingface dataset:
A Cloud-Based Deep Learning Framework for Early Detection of Pushing at Crowded Event Entrances

Crowding at the entrances of large events may lead to critical and life-threatening situations, particularly when people start pushing each other to reach the event faster. Automatic and timely identification of pushing behavior would help organizers and security forces to intervene early and mitigate dangerous situations. In this paper, we propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances. The proposed framework initially modifies and trains the EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it integrates the adapted model with an accurate and fast pre-trained deep optical flow model with the color wheel method to analyze video streams and identify pushing patches in real-time. Moreover, the framework uses live capturing technology and a cloud-based environment to collect video streams of crowds in real-time and provide early-stage results. A novel dataset is generated based on five real-world experiments and their associated ground truth data to train the adapted EfficientNetV2B0 model. The experimental setups simulated a crowded event entrance, while the ground truths for each video experiment was generated manually by social psychologists. Several experiments on the videos and the generated dataset are carried out to evaluate the accuracy and annotation delay time of the proposed framework. The experimental results show that the proposed framework identified pushing behaviors with an accuracy rate of 87% within a reasonable delay time.
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation

Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem. Complex degradations present in real life images make it difficult to solve this problem using naïve deep learning approaches, where models are often trained on synthetically generated image pairs. Most of the effort so far has been focused on solving the inverse problem under some constraints, such as for a limited space of blur kernels and/or assuming noise-free input images. Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations. In this paper, we propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR. In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models. The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs. IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image. IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.
Extending version-space theory to multi-label active learning with imbalanced data

Version space, defined as the subset of the hypothesis space consistent with the training samples, is an important concept in supervised learning. It has been successfully applied for evaluating the informativeness of unlabeled samples in traditional single-label active learning. Specifically, the most inconsistent samples among the version space members can reduce the size of the version space as fast as possible, these samples are given high priority for domain expert annotation, thereby the learner can construct a high-performance classifier by labeling as few samples as possible. We point out that the concept of version space has not been extended to multi-label environments yet, which hinders its application in multi-label active learning. This paper makes an attempt to extend the version space theory from single-label scenario to multi-label scenario, builds up a spatial structure for the multi-label version space, generalizes it from finite case to infinite case, puts forward a simplified representation for it and accordingly proposes a new multi-label active learning algorithm. Moreover, considering the imbalance issue in multi-label data, the algorithm is further improved by allocating different annotation numbers to the labels. Experimental comparisons verify the feasibility and effectiveness of the proposed methods.
A term function–aware keyword citation network method for science mapping analysis

Various keyword network methods are used to map scientific fields, but few studies have considered the semantic roles of keywords in such networks. This study proposes a term function–aware keyword citation network to fill this research limitation. Specifically, we first used a term function identification method to identify research questions and methods from scientific articles. Then, we constructed a question-method term citation network to represent the correlation structure of keywords. Next, we explored the topology characteristics, question-method bipartite network, and knowledge community structure of the generated network to validate its superiority in science mapping analysis. A dataset of 299,567 conference proceedings collected from the Association for Computing Machinery (ACM) digital library is used to evaluate the effectiveness of our methods. The results show that the term function identification model based on Bidirectional Encoder Representations from Transformers (BERT) achieves a score of 0.90 F1. And the question-method term citation network outperforms existing keyword citation methods in revealing association patterns between scientific knowledge and improving the interpretability of the knowledge structure of the computing field. We believe that our work expands the methodology of keyword citation network and science mapping analysis and provides guidance for considering the term function in various scenarios.
Robust two-qubit gates using pulsed dynamical decoupling

We present the experimental implementation of a two-qubit phase gate, using a radio frequency (RF) controlled trapped-ion quantum processor. The RF-driven gate is generated by a pulsed dynamical decoupling sequence applied to the ions' carrier transitions only. It allows for a tunable phase shift with high-fidelity results, in particular a fringe contrast up to $99_{-2}^{+1}\%$ is observed in Ramsey-type measurements. We also prepare a Bell state using this laser-free gate. The phase gate is robust against common sources of error. We investigate the effect of the excitation of the center-of-mass (COM) mode, errors in the axial trap frequency, pulse area errors and errors in sequence timing. The contrast of the phase gate is not significantly reduced up to a COM mode excitation $<20$ phonons, trap frequency errors of +10%, and pulse area errors of -8%. The phase shift is not significantly affected up to $<10$ phonons and pulse area errors of -2%. Both, contrast and phase shift are robust to timing errors up to -30% and +15%. The gate implementation is resource efficient, since only a single driving field is required per ion. Furthermore, it holds the potential for fast gate speeds (gate times on the order of $100~\mu$s) by using two axial motional modes of a two-ion crystal through improved setups.
Artificial intelligence to support person-centred care in breast imaging - A scoping review

To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies.The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage.3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used.The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
CeLNet: a correlation-enhanced lightweight network for medical image segmentation

Objective. Convolutional neural networks have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. As the segmentation accuracy gets constantly updated, the complexity of networks increases as well. Complex networks can achieve better performance but require more parameters and are hard to train with limited resources, while lightweight models are faster but cannot fully utilize the contextual information of medical images. In this paper, we focus on better balancing the efficiency and accuracy.Approach. We propose a correlation-enhanced lightweight network (CeLNet) for medical image segmentation, which adopts a siamese structure for weight sharing and parameter saving. Through the feature reuse and feature stacking of parallel branches, a point-depth convolution parallel block (PDP Block) is proposed to reduce the model parameters and computational cost while improving the feature extraction capability of encoder. A relation module is also designed to extract feature correlations of input slices, which utilizes global and local attention to enhance feature connections, while reducing feature differences through element subtraction, and finally obtains contextual information of associated slices to improve the segmentation performance.Main results. We conduct extensive experiments on the LiTS2017, MM-WHS and ISIC2018 datasets, and the proposed model consumes merely 5.18M parameters but achieves excellent segmentation performance, specifically, a DSC of 0.9233 in LiTS2017 dataset, an average DSC of 0.7895 on MM-WHS dataset and an average DSC of 0.8401 on ISIC2018 dataset.Significance. CeLNet achieves state-of-the-art performance in multiple datasets while ensuring lightweight.
Data-Driven Analysis of Stimulation Treatments Using Association Rule Mining

Summary Effective analysis of data collected during the well’s operational cycle is crucial to well performance, economic evaluation, and good decision-making in the upstream oil industry. Generally, the analysis of huge volumes of data stored in databases is beyond the power of traditional methods, such as curve-fitting and statistical hypothesis testing. Data mining is the practice of analyzing large databases to identify patterns, anomalies, and correlations, within the data, leading to new, hidden, and valuable knowledge that would support decisions. This article proposes a data-driven methodology for analyzing the stimulation operations data in oil/gas wells to identify the underlying rules or patterns that lead to successful operations. Association rule mining (ARM) is used in this research for rule induction purposes. The proposed approach aims to mine the frequently occurring rules, within the collected database, that guarantee the success of stimulation operations with a high degree of confidence. Finally, the proposed approach is evaluated against a set of real data from an Iranian oil field. On the basis of past stimulation operations, these extracted rules show the conditions that are most likely to lead to a successful operation. The rules identified by the proposed approach are compared against the rules that can be generated by the decision tree (DT) technique using the same data set. As the reliability of the rules is controlled by setting the minimum thresholds on support and confidence, more significant and useful rules could be derived from ARM compared to the DT technique. Using the identified rules and generated information can support the operational decisions by assisting in the design of due stimulation jobs or in selecting the appropriate candidates for future operations.
Development of Outlier Detection Algorithms for Sensors with Time-Varying Characteristics

In order to maximize production in advanced manufacturing industries, such as semiconductor manufacturing processes, equipment must perform consistently across multiple process steps. To ensure this, a process called tool-to-tool matching (TTTM) is used to align the performance of different equipment by analyzing sensor data. Although various TTTM techniques exist, there is no universally effective method. To address this, this paper aims to develop algorithms for detecting outliers from sensors that change over time by considering the unique characteristics of each sensor.
IHG4MR: Interest-oriented heterogeneous graph for multirelational recommendation

Multirelational recommendation usually considers multiple types of user–item interaction data. Recently, graph convolutional networks (GCNs) have been widely used for recommendation systems. However, most existing GCN-based recommendation models only utilize one type of interactive data, ignoring other types of interactive data. At the same time, it is worth noting that GCN-based recommendation models are prone to oversmoothing problems. This means that when GCN-based recommendation models stack more layers, they also import noisy messages from users who have no common interest with the target user, resulting in a recommendation performance bottleneck. Consequently, it is necessary to reform the traditional GCN to make it suitable for a multirelational recommendation. A novel multirelational recommendation model through an interest-oriented heterogeneous graph (IHG4MR) is proposed in this paper. To effectively capture information from high-level nodes in a heterogeneous graph, IHG4MR first utilizes user characteristics and graph structure to split users who have similar preferences and then employs these segmented users and their interacting items to build multiple new interactive graphs instead of the previous whole graph to implement graph convolution operations. IHG4MR reduces the negative information carried during embedding propagation of the graph convolutional layers, thereby mitigating the effect of oversmoothing. Extensive experimental results on two real world datasets show that IHG4MR generally outperforms existing state-of-the-art models, demonstrating the effectiveness of our model.
On Multi-conditioned Conic Fitting in Geometric Algebra for Conics

We introduce several modifications of conic fitting in Geometric algebra for conics by incorporating additional conditions into the optimisation problem. Each of these extra conditions ensure additional geometric properties of a fitted conic, in particular, centre point position at the origin of coordinate system, axial alignment with coordinate axes, or, eventually, combination of both. All derived algorithms are accompanied by a discussion of the underlying algebra and computational optimisation issues. Finally, we present examples of use on a sample dataset and offer possible applications of the algorithms.
A generalized adaptive robust distance metric driven smooth regularization learning framework for pattern recognition

In this work, we propose a novel generalized adaptive robust distance metric called Lδ(u). Compared with other distance metrics, Lδ(u) has some desirable salient properties, such as symmetry, boundedness, robustness, nonconvexity, and adaptivity, with both first-order and higher-order moments from samples. On the other hand, Lδ(u) can pick different robust distance metrics for different learning tasks during the learning process by the adaptive parameter δ. Furthermore, we apply Lδ(u) to twin extreme learning machine (TELM) and develop an smooth regularized TELM learning framework for supervised and semi-supervised classification. By introducing the structural risk minimization (SRM) principle and smoothing techniques, the learning framework perfectly overcomes the computational burden associated with the matrix inversion operation required during TELM solving, while also significantly improving performance. More importantly, the proposed learning framework not only improves the robustness of TELM, but also can effectively use the geometric information of the marginal distribution embedded in the unlabeled samples to construct a more reasonable classifier. Finally, the globally convergent and quadratic convergent fast Newton-Armijo algorithm and DC (difference of convex functions) programming algorithm (DCA) are designed to solve the proposed methods. Experimental results demonstrate the effectiveness and robustness of our methods.
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection

This paper introduces two new ensemble-based methods to reduce the data and computation costs of image classification. They can be used with any set of classifiers and do not require additional training. In the first approach, data usage is reduced by only analyzing a full-sized image if the model has low confidence in classifying a low-resolution pixelated version. When applied on the best performing classifiers considered here, data usage is reduced by 61.2% on MNIST, 69.6% on KMNIST, 56.3% on FashionMNIST, 84.6% on SVHN, 40.6% on ImageNet, and 27.6% on ImageNet-V2, all with a less than 5% reduction in accuracy. However, for CIFAR-10, the pixelated data are not particularly informative, and the ensemble approach increases data usage while reducing accuracy. In the second approach, compute costs are reduced by only using a complex model if a simpler model has low confidence in its classification. Computation cost is reduced by 82.1% on MNIST, 47.6% on KMNIST, 72.3% on FashionMNIST, 86.9% on SVHN, 89.2% on ImageNet, and 81.5% on ImageNet-V2, all with a less than 5% reduction in accuracy; for CIFAR-10 the corresponding improvements are smaller at 13.5%. When cost is not an object, choosing the projection from the most confident model for each observation increases validation accuracy to 81.0% from 79.3% for ImageNet and to 69.4% from 67.5% for ImageNet-V2. Code available at:https://github.com/carohlfs/imavis.
Clustering-based improved adaptive synthetic minority oversampling technique for imbalanced data classification

Synthetic Minority Oversampling Technique (SMOTE) and some extensions based on it are popularly used to balance imbalanced data. In this study, we concentrate on solving overfitting of the classification model caused by choosing instances to oversample that increase the occurrence of overlaps with the majority class. Our method called Clustering-based Improved Adaptive Synthetic Minority Oversampling Technique (CI-ASMOTE1) decomposes minority instances into sub-clusters according to their connectivity in the feature space and then selects minority sub-clusters which are relatively close to the decision boundary as the candidate regions to oversample. After application of CI-ASMOTE1, new minority instances are only synthesized within each connected region of the selected sub-clusters. Considering the diversity of the synthetic instances in each selected sub-cluster, CI-ASMOTE2 is put forward to extend CI-ASMOTE1 by keeping all features of those instances in the feature space as different as possible. The experimental evaluation shows that CI-ASMOTE1 and CI-ASMOTE2 improve SMOTE and its extensions, especially in the occurrence of overlaps between the minority instances and the majority instances.
User Opinion Prediction for Arabic Hotel Reviews Using Lexicons and Artificial Intelligence Techniques

Opinion mining refers to the process that helps to identify and to classify users’ emotions and opinions from any source, such as an online review. Thus, opinion mining provides organizations with an insight into their reputation based on previous customers’ opinions regarding their services or products. Automating opinion mining in different languages is still an important topic of interest for scientists, including those using the Arabic language, especially since potential customers mostly do not rate their opinion explicitly. This study proposes an ensemble-based deep learning approach using fastText embeddings and the proposed Arabic emoji and emoticon opinion lexicon to predict user opinion. For testing purposes, the study uses the publicly available Arabic HARD dataset, which includes hotel reviews associated with ratings, starting from one to five. Then, by employing multiple Arabic resources, it experiments with different generated features from the HARD dataset by combining shallow learning with the proposed approach. To the best of our knowledge, this study is the first to create a lexicon that considers emojis and emoticons for its user opinion prediction. Therefore, it is mainly a helpful contribution to the literature related to opinion mining and emojis and emoticons lexicons. Compared to other studies found in the literature related to the five-star rating prediction using the HARD dataset, the accuracy of the prediction using the proposed approach reached an increase of 3.21% using the balanced HARD dataset and an increase of 2.17% using the unbalanced HARD dataset. The proposed work can support a new direction for automating the unrated Arabic opinions in social media, based on five rating levels, to provide potential stakeholders with a precise idea about a service or product quality, instead of spending much time reading other opinions to learn that information.
Aspects of Dynamics in Dialogue Collaboration

Collaborative dialogue is an important category of human interaction and is widely studied in the literature, especially in fields that attempt to develop new technologies that enable wider varieties of collaborative dialogues. The ingredients of collaboration in dialogue are less thoroughly addressed. We describe the theoretical framework within which we are working and our approach to the construction of a theory of what may make dialogue collaborative. We study a multimodal dialogue corpus (MULTISIMO) testing for positive and negative correlations between dialogue content features and interaction features that one might reasonably imagine are related to assessments of degrees of collaboration. The duration before the second speaker’s first turn and degree of imbalance in the number of words produced by speakers negatively correlate with collaboration assessments (that is, imbalances of content and a delay in the first speaker yielding the floor lead to diminished perceptions of collaboration), while a monotonically increasing duration of focus in successive dialogue sections (rather than overall dialogue duration) correlates positively (that is, when participants are deemed to be extending the duration of the task rather than increasing speed with experience, this is perceived as collaborative).