Reviewerly/exHarmony huggingface dataset:
Crowd Localization From Gaussian Mixture Scoped Knowledge and Scoped Teacher

Crowd localization is to predict each instance head position in crowd scenarios. Since the distance of pedestrians being to the camera are variant, there exists tremendous gaps among scales of instances within an image, which is called the intrinsic scale shift. The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic. To this end, the paper concentrates on access to tackle the chaos of the scale distribution incurred by intrinsic scale shift.We propose Gaussian Mixture Scope (GMS) to regularize the chaotic scale distribution. Concretely, the GMS utilizes a Gaussian mixture distribution to adapt to scale distribution and decouples the mixture model into sub-normal distributions to regularize the chaos within the sub-distributions. Then, an alignment is introduced to regularize the chaos among sub-distributions. However, despite that GMS is effective in regularizing the data distribution, it amounts to dislodging the hard samples in training set, which incurs overfitting. We assert that it is blamed on the block of transferring the latent knowledge exploited by GMS from data to model. Therefore, a Scoped Teacher playing a role of bridge in knowledge transform is proposed. What' s more, the consistency regularization is also introduced to implement knowledge transform. To that effect, the further constraints are deployed on Scoped Teacher to derive feature consistence between teacher and student end. With proposed GMS and Scoped Teacher implemented on four mainstream datasets of crowd localization, the extensive experiments demonstrate the superiority of our work. Moreover, comparing with existing crowd locators, our work achieves state-of-the-art via F1-measure comprehensively on four datasets.
An Evolutionary Multitasking Algorithm With Multiple Filtering for High-Dimensional Feature Selection

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula> method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this article devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer (CSO) to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a CSO is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on 18 high-dimensional datasets.
Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> orkflow <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> cheduling in <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> og <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> omputing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ree <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> enetic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> rogramming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50%) on all the tested scenarios than existing state-of-the-art methods.
A Multimodal Dynamic Hand Gesture Recognition Based on Radar–Vision Fusion

Regarding increasingly complex scenarios in hand gesture recognition (HGR), it is challenging to implement a reliable HGR due to the non-adaptability of individual sensors to the environment and the discrepancy of personal habits. Multisensor fusion has been deemed an effective way to overcome the limitations of a single sensor. However, there is a lack of research on HGR to effectively establish bridges linking multimodal heterogeneous information. To address this issue, we propose a novel multimodal dynamic HGR method based on a two-branch fusion deformable network with Gram matching. First, a time-synchronized method is designed to preprocess the multimodal data. Second, a two-branch network is proposed to implement gesture classification based on radar-vision fusion. The input convolution is replaced by the deformable convolution to improve the generalization of gesture motion modeling. The long short-term memory (LSTM) unit is utilized to extract the temporal features of dynamic hand gestures. Third, Gram matching is presented as a loss function to mine high-dimensional heterogeneous information and maintain the integrity of radar-vision fusion. The experimental results indicate that the proposed method effectively improves the adaptability of the classifier to complex environments and exhibits satisfactory robustness to multiple subjects. Furthermore, ablation analysis shows that deformable convolution and Gram loss not only provide reliable gesture recognition but also enhance the generalization ability of the proposed methods in different field-of-view scenarios.
Object Detection Based on Swin Deformable Transformer-BiPAFPN-YOLOX

Object detection technology plays a crucial role in people's everyday lives, as well as enterprise production and modern national defense. Most current object detection networks, such as YOLOX, employ convolutional neural networks instead of a Transformer as a backbone. However, these techniques lack a global understanding of the images and may lose meaningful information, such as the precise location of the most active feature detector. Recently, a Transformer with larger receptive fields showed superior performance to corresponding convolutional neural networks in computer vision tasks. The Transformer splits the image into patches and subsequently feeds them to the Transformer in a sequence structure similar to word embeddings. This makes it capable of global modeling of entire images and implies global understanding of images. However, simply using a Transformer with a larger receptive field raises several concerns. For example, self-attention in the Swin Transformer backbone will limit its ability to model long range relations, resulting in poor feature extraction results and low convergence speed during training. To address the above problems, first, we propose an important region-based Reconstructed Deformable Self-Attention that shifts attention to important regions for efficient global modeling. Second, based on the Reconstructed Deformable Self-Attention, we propose the Swin Deformable Transformer backbone, which improves the feature extraction ability and convergence speed. Finally, based on the Swin Deformable Transformer backbone, we propose a novel object detection network, namely, Swin Deformable Transformer-BiPAFPN-YOLOX. experimental results on the COCO dataset show that the training period is reduced by 55.4%, average precision is increased by 2.4%, average precision of small objects is increased by 3.7%, and inference speed is increased by 35%.
Detecting Weak Physical Signal from Noise: A Machine-Learning Approach with Applications to Magnetic-Anomaly-Guided Navigation

Detecting a weak physical signal immersed in overwhelming noises entails separating the two, a task for which machine learning is naturally suited. In principle, such a signal is generated by a nonlinear dynamical system of intrinsically high dimension for which a mathematical model is not available, rendering unsuitable traditional linear or nonlinear state-estimation methods that require an accurate system model (e.g., extended Kalman filters). We exploit the architectures of reservoir computing and feed-forward neural networks (FNNs) with time-delayed inputs to solve the weak-signal-detection problem. As a prototypical example, we apply the machine-learning schemes to Earth's magnetic anomaly field-based navigation. In particular, the time series are collected from the interior of the cockpit of a flying aircraft during different maneuvering phases, where the overwhelmingly strong noise background is the result of other components of Earth's magnetic field and the fields generated by the electronic devices in the cockpit. We demonstrate that, when combined with the traditional Tolles-Lawson model for Earth's magnetic field, the articulated machine-learning schemes are effective for accurately detecting the weak anomaly field from the noisy time series. The schemes can be applied to detecting weak signals in other domains of science and engineering.
Breast cancer: toward an accurate breast tumor detection model in mammography using transfer learning techniques

Female breast cancer has now surpassed lung cancer as the most common form of cancer globally. Although several methods exist for breast cancer detection and diagnosis, mammography is the most effective and widely used technique. In this study, our purpose is to propose an accurate breast tumor detection model as the first step into cancer detection. To guarantee diversity and a larger amount of data, we collected samples from three different databases: the Mammographic Image Analysis Society MiniMammographic (MiniMIAS), the Digital Database for Screening Mammography (DDSM), and the Chinese Mammography Database (CMMD). Several filters were used in the pre-processing phase to extract the Region Of Interest (ROI), remove noise, and enhance images. Next, transfer learning, data augmentation, and Global Pooling (GAP/GMP) techniques were used to avoid imagery overfitting and to increase accuracy. To do so, seven pre-trained Convolutional Neural Networks (CNNs) were modified in several trials with different hyper-parameters to determine which ones are the most suitable for our situation and the criteria that influenced our results. The selected pre-trained CNNs were Xception, InceptionV3, ResNet101V2, ResNet50V2, ALexNet, VGG16, and VGG19. The obtained results were satisfying, especially for ResNet50V2 followed by InceptionV3 reaching the highest accuracy of 99.9%, and 99.54% respectively. Meanwhile, the remaining models achieved great results as well, proving that our approach starting from the chosen filters, databases, and pre-trained models with the fine-tuning phase and the used global pooling technique is effective for breast tumor detection. Furthermore, we also managed to determine the most suitable hyper-parameters for each model using our collected dataset.
Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis

While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar data", for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment's effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39-0.63] vs. avatar HR = 0.40 [95% CI, 0.31-0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.
Research on the construction of event logic knowledge graph of supply chain management

Knowledge graph technology plays an important role in knowledge supporting for efficient supply chain management (SCM) of manufacturing enterprises, a SCM knowledge graph can be constructed based on the relevant case corpus. In order to solve the problems of coarse concept granularity of event ontology knowledge in SCM cases, potential words of characters in the existing entity recognition models may not be matched or matched incorrectly, key features of entities with different lengths may interfere with each other in the expression of attention mechanism, and lack of a large number of annotation training samples, a top-down construction method of SCM event logic knowledge graph (ELKG) is proposed. Firstly, SCM event argument classes and class relations are defined, an event logic ontology model is built, and the event argument entities according to event logic ontology are labeled. Then, an active learning event argument entity recognition (EAER) model based on two-stage generative adversarial network (GAN) is proposed. In GAN generator, an EAER model based on binocular attention-based stacked BiLSTM with CNN (BACSBN) is proposed, which combines word-level character feature attention mechanism and n-gram pooling feature attention mechanism to improve the attention to character features of constituent words and highlight entity key information with different lengths, respectively. Two-stage GAN adversarial training and label space attention mechanism are introduced to select the correct predicted label samples for active learning training. The experimental results show that BACSBN can improve the entity recognition accuracy, and the two-stage GAN's active learning can further improve the recognition effect on the basis of full annotation sample training, and can still maintain a high accuracy in the absence of a large number of manual annotation data. Further, according to the sentence pattern and keyword matching, the matching relations of argument entities are completed, and the SCM ELKG is constructed to provide knowledge support for autonomous SCM.
An intuitionistic fuzzy representation based software bug severity prediction approach for imbalanced severity classes

In order to improve software reliability and quality, the triager must assess the severity of the software bug and allocate suitable resources on time. However, the triager faces many difficulties in understanding various software bugs that involve lots of uncertainty and irregularities. Additionally, it can be challenging for the triager to determine the severity of bugs that are semantically close to multiple severity labels. To address these problems, a topic modeling and intuitionistic fuzzy similarity measure-based software bug severity prediction technique (IFSBSP) is proposed in this paper. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the severity classes in software bug repositories. Then topic modeling is used to generate topics based on the probability of underlying uncertainty in software bugs. Using these topics, the intuitionistic fuzzy membership, non-membership, and hesitancy membership degrees of a software bug are calculated for multiple severity labels. Then, 15 IFS techniques are investigated for a new bug in order to compute its similarity to multiple severity labels. The Eclipse, Mozilla, Apache, and NetBeans software bug repositories are used to evaluate the performance of IFSBSP and the state-of-the-art models. On these software bug repositories, the IFSBSP model outperforms state-of-the-art models by achieving accuracy of 91.6%, 90.9%, 88.1%, and 92.9% and an F-measure of 90.7%, 91.1%, 89.3%, and 91.7%, respectively.
Hey?

As food delivery robots are spreading onto streets and college campuses worldwide, users' views of these robots will depend on their direct and indirect interactions with the robots and their conversations with other people, such as those with whom they are ordering food via a robot. We examined if being in a group of 2 to 3 people affects the acceptance and trust of the robot compared to being an individual user. First-time users of the food delivery robot service (N = 60) ordered food either as an Individual or in a Group. We measured the acceptance and trust of the robots after three Exposures (pre-exposure, after ordering food on the app, and after the robots delivered the food). Results indicated that Individual users had more acceptance and trust compared to Group users. Further, as hypothesized, groups had more variation in acceptance and trust compared to individual users, consistent with patterns of group polarization i.e., group members influencing each other's perceptions to become more positive or negative. Further analysis demonstrated that group members were highly influenced by their groupmates. Designers and restaurant operators should consider how to enhance group members' experience of delivery robots.
Achieving Decentralized and Dynamic SSO-Identity Access Management System for Multi-Application Outsourced in Cloud

Existing single sign-on (SSO) access control systems typically rely on the traditional protocols requiring additional authentication mechanism and/or identity providers. As the growing demand in outsourcing system resources such as data and applications to the cloud platform, implementing traditional SSO models to support efficient and fine-grained access control for multi-user and multi-application environment is not practical. In this paper, we propose a blockchain-based identification and access management (IAM) scheme called D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM to provide strong security measures for controlling SSO access to resources in the cloud. At a core of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM, all access control processes are done by the smart contracts and blockchain where the access transactions are well retained for the accountability. In our system, the SSO authentication is based on highest authentication level and hashed-based token that significantly reduces the communication overhead for multi-system authentication. For the authorization system, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM enables fine-grained access through the access policy modeled in document database written and enforced to each customer. Finally, we conducted the experiments on Google cloud to show that our D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -IAM system is efficient for the implementation. The performance test showed that our proposed system is approximately 4 times efficient than the average processing time of three existing works.
Transformer-based structuring of free-text radiology report databases

To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies.A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist. First, a system based on human-defined rules was applied for annotation of all reports (termed "silver labels"). Second, 18,000 reports were manually annotated in 197 h (termed "gold labels") of which 10% were used for testing. An on-site pre-trained model (Tmlm) using masked-language modeling (MLM) was compared to a public, medically pre-trained model (Tmed). Both models were fine-tuned on silver labels only, gold labels only, and first with silver and then gold labels (hybrid training) for text classification, using varying numbers (N: 500, 1000, 2000, 3500, 7000, 14,580) of gold labels. Macro-averaged F1-scores (MAF1) in percent were calculated with 95% confidence intervals (CI).Tmlm,gold (95.5 [94.5-96.3]) showed significantly higher MAF1 than Tmed,silver (75.0 [73.4-76.5]) and Tmlm,silver (75.2 [73.6-76.7]), but not significantly higher MAF1 than Tmed,gold (94.7 [93.6-95.6]), Tmed,hybrid (94.9 [93.9-95.8]), and Tmlm,hybrid (95.2 [94.3-96.0]). When using 7000 or less gold-labeled reports, Tmlm,gold (N: 7000, 94.7 [93.5-95.7]) showed significantly higher MAF1 than Tmed,gold (N: 7000, 91.5 [90.0-92.8]). With at least 2000 gold-labeled reports, utilizing silver labels did not lead to significant improvement of Tmlm,hybrid (N: 2000, 91.8 [90.4-93.2]) over Tmlm,gold (N: 2000, 91.4 [89.9-92.8]).Custom pre-training of transformers and fine-tuning on manual annotations promises to be an efficient strategy to unlock report databases for data-driven medicine.• On-site development of natural language processing methods that retrospectively unlock free-text databases of radiology clinics for data-driven medicine is of great interest. • For clinics seeking to develop methods on-site for retrospective structuring of a report database of a certain department, it remains unclear which of previously proposed strategies for labeling reports and pre-training models is the most appropriate in context of, e.g., available annotator time. • Using a custom pre-trained transformer model, along with a little annotation effort, promises to be an efficient way to retrospectively structure radiological databases, even if not millions of reports are available for pre-training.