Reviewerly/exHarmony huggingface dataset:
A novel adaptive kernel-guided multi-condition abnormal data detection method

Data acquisition devices and environmental factors may lead to abnormal data and misjudgment of mechanical health status in mechanical work. A new method for abnormal data detection in mechanical equipment health monitoring is proposed in this paper. Firstly, an adaptive kernel bandwidth local outlier factor (AKLOF) detection method is proposed. Furthermore, multiple time-domain statistical features are obtained from sliding window segmentation data, and the anomaly degree of each data segment is calculated. Then, the relationship between the sliding window length and the maximum AKLOF function is studied, and the optimal sliding window length is obtained. Subsequently, the AKLOF value and threshold are calculated according to the optimal sliding window length to detect the final abnormal data. Four groups of experiments result show that the method can accurately identify all the missing data, and can also effectively detect other types of abnormal data, which provide guarantee for equipment condition monitoring.
Joint alignment of the distribution in input and feature space for cross-domain aerial image semantic segmentation

Benefiting from the development of deep learning, researchers have made significant progress and achieved superior performance in the semantic segmentation of remote sensing (RS) data. However, when encountering an unseen scenario, the performance of a trained model deteriorates dramatically because of the domain shift. Unsupervised domain adaptation (UDA) provides an alternative to address the issue. Aligning the high-level representations via adversarial learning is a popular way, but it is difficult when there is a large gap in input space. With this consideration, we design a framework to jointly align the distribution in input and feature space. For input space alignment, we unify the resolution for the consistency of content and propose a lightweight module named Digital Number Transformer (DNT) to reduce the visual differences. For feature space alignment, we design a Multi-Scale Feature aggregation (MSF) module and introduce the Fine-Grained Discriminator (FGD) to conduct a category-level alignment at multiple layers so that features can be fully aligned and negative transfer can be reduced. We carried out experiments in diverse cross-domain scenarios, including the discrepancy in geographic position and the discrepancy in both geographic position and imaging mode. Comprehensive experiments demonstrate that our method outperforms other state-of-the-art methods in all scenarios.
Exploring viewport features for semi-supervised saliency prediction in omnidirectional images

Compared with the annotated data for the 2D image saliency prediction task, the annotated data for training omnidirectional image (or 360° image) saliency prediction models are not sufficient. Most existing fully-supervised saliency prediction methods for omnidirectional images (ODIs) adopt a scheme, first training the methods on a labeled large 2D image saliency prediction dataset and then fine-tuning the methods on the labeled tiny ODI saliency prediction dataset. However, this strategy is time-consuming and may not inadequately mine the visual features built in ODIs. To explore the visual attributes targeted at ODIs and address the shortage of labels on these ODIs, in this paper, we propose an end-to-end semi-supervised network, namely VFNet, which relies on viewport features and only utilizes ODIs as training data, for ODI saliency prediction. Concretely, we adopt consistency regularization as our semi-supervised learning framework. The predictions between main and auxiliary saliency inference networks in the VFNet enforce consistency. Aiming at ODIs, we introduce a new form of perturbation, i.e., DropView, to improve the effectiveness of consistency regularization. By randomly dropping out different 360° cubemap viewport features before the auxiliary saliency inference network, the proposed DropView enhances the robustness of the final ODI saliency prediction. To adaptively interact with the equirectangular and different cubemap viewport features according to their contributions, we introduce a Viewport Feature Adaptive Integration (VFAI) module and deploy the VFAI module at different levels in the VFNet to raise the capacity of feature encoding of our VFNet. Compared with state-of-the-art fully-supervised methods, our VFNet with fewer labeled training data achieves competitive performance demonstrated by extensive experiments on two publicly available datasets.
Evaluating classifier predictive performance in multi‐class problems with balanced and imbalanced data sets

Abstract A major issue in classification problems arises when dealing with class imbalance, which requires the adoption of a suitable performance measure able to handle imbalanced data sets. This paper introduces the Balanced and its weighted version Balanced as classifier performance measures suitable for both balanced and imbalanced data sets. The performances of the proposed measures are compared against those of other well‐known performance measures through an empirical comparison using several algorithms and data sets. Moreover, the applicability of Balanced is showcased through an illustrative example dealing with steel plate faults classifications, where class imbalance typically occurs due to non‐common defects which, though rare, may seriously impact steel quality.
Zero‐knowledge‐based distributed auditing protocol

Abstract Blockchain‐based data outsourcing has attracted a huge deal of attention in recent years. However, key management and reliance on pre‐defined or randomly‐selected third‐party auditors (TPAs) are still challenging owing to refusing to collaborate or break auditing policies. Additionally, it is impossible to store secrets in smart contracts (SCs) for signing or auditing. Thus, in the present study, a zero‐knowledge (ZK)‐based distributed auditing protocol (DAP) is provided. In this protocol, a user‐based key generation mechanism offers users security against curious authorities based on no TPA causing them to reveal no knowledge about under‐auditing data. Based on Pointcheval's and Sanders's signature scheme, as a ZK‐based randomizable signature applied in an SC to handle the auditing process, the proposed DAP provides confidential transactions and user accountability. Evaluation indicates that the proposed ZK‐based DAP is efficient for the user side and it is the fastest DAP in the blockchain.
The Contribution of the User Experiences Goals for Designing Better Cobots: A Systematic Literature Review

Collaborative robots are an indispensable element of both industry 4.0 and industry 5.0, the latter of which gives special emphasis to the human facet of the human-robot collaboration. To facilitate such an interaction, attention should be given to the design of the cobot, including its interface, which enables communication with the user. Programming through the interface and performing a task with the robotic device are responsible for the user experience (UX), which comprises both pragmatic and hedonic aspects. In order to design the most positive experience for users, their perspectives must be considered, which is achieved through the identification of UX goals. In this respect, a systematic review was conducted to revise the UX goals present in the literature. The following seven UX goals were identified: safety, relationship, usability, inspiration, flexibility, efficiency, and accomplishment. These findings represent the first systematic categorization of UX goals for the specific design of cobots, that should empirically be tested.
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms

The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way.In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to an SQL database.The execution of an experiment can be fully determined by a configuration file, which means the experiment results can be reproduced easily, given only the configuration file.fseval has high test coverage, continuous integration, and rich documentation.The package is open source and can be installed through PyPI.
Design and Usability Testing of an Augmented Reality (AR) Environment in Pharmacy Education—Presenting a Pilot Study on Comparison between AR Smart Glasses and a Mobile Device in a Laboratory Course

An essential feature of pharmacy education is the teaching of theoretical knowledge with the support of practical work in the laboratory. When properly utilized, laboratory activities have the potential to enhance students’ achievement, conceptual understanding, and positive attitudes towards learning. In this pilot study, an augmented reality (AR) environment was designed and introduced for teaching laboratory skills in pharmacy education at the university level. The AR environment was used by pharmacy students (n = 36), featuring gate questions, information screens, Quick Response codes, think-aloud questions, and instant feedback. The environment was utilized with smart glasses and mobile devices with the aim of comparing the support to students’ performance. User experience was evaluated through self-efficacy beliefs and anxiety towards the technology. As a result, students found the environment a useful supplement to traditional laboratory teaching. Smart glasses and mobile devices were both accepted with great positivity but neither being clearly preferred over the other. Smart glasses were noted to provide sufficient feedback in the right stages of work. In contrast, mobile devices promoted the learning process more than the smart glasses. The self-efficacy results for mobile device use were higher, especially related to device handling and operating the AR environment. The pilot study gives educators valuable insights on the usability of AR technology in guiding laboratory tasks, although future work should involve larger and more diverse samples, as well as different learning tasks.
A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition

With the continuous development of communication technology, the wireless communication environment becomes more and more complex with various intentional and unintentional signals. Radio signals are modulated in different ways. The traditional radio modulation recognition technology cannot recognize the modulation modes accurately. Consequently, the communication system has embraced Deep Learning (DL) models as they can automatically recognize the modulation modes and have better accuracy. This paper systematically summarizes the related contents of radio Automatic Modulation Recognition (AMR) based on DL over the last seven years. First, we summarize the current research status of modulation recognition and the necessity of AMR research based on DL. Then, we review current radio AMR methods based on DL. In addition, we also propose a network model of AMR based on Convolutional Neural Network (CNN) and prove its effectiveness. Finally, we highlight existing challenges and research directions of radio AMR based on DL.
Image classification using gaussian curvature based local tetra pattern descriptor with enhanced feature selection technique

A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques.
AI BASED GESTURE AND SPEECH RECOGNITION TOOL FLOW FOR EDUCATORS

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 6395-6400ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1596Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Improved SSD Model for Pedestrian Detection in Natural Scene

The indagation improves the SSD network to improve the target detection performance in unmanned driving at night. In target detection, the goal is to identify and locate the different types of objects present in an image. The first-level target detection method pulls categorization information and target position information for use by the second-level target detection algorithm using the featured MAP created by the deep network. The depth characteristics, on the other hand, are processed using long-distance convolution and downsampling. Given the lack of geographical information, research alludes to the concept of semantic segmentation and proposes a method for improving the first-level target identification algorithm SSD by mixing shallow characteristics from the backbone network with deep features obtained through sampling. In addition, this research enhances the generation method and loss function of the preselection box by taking into account the peculiarities of pedestrian detection data. Undertakings to experiments on the two data sets provided by INRIA and Caltech show that the improved model USSD reported in this paper improves both the efficiency of detection and speed of retrieval.
Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography

Background Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. Purpose To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. Material and Methods From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance ( P &lt; 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P &lt; 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P &lt; 0.001) and accuracy (71.3% to 79.7%, P &lt; 0.001) significantly improved with AI assistance. Conclusion AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.
Data science for empowerment: understanding the data science training landscape for women and girls in Africa

The increasing datafication of African societies has led to a proliferation of data science-related training opportunities.These trainings provide young people with the opportunity to learn the skills to work on Data science, with some focused specifically on women and girls.While this is encouraging and brings new opportunities for women and girls to participate in the knowledge economy, it is important to understand the wider context of data science training in Africa, in particular, how women and girls experience their (data science) education, and how this knowledge can impact their lives, sustain livelihoods and bring empowerment.Through a review of the literature, as well as an examination of different pedagogical approaches and practices used by various formal and informal training programs in Africa, we examined the experience of women and girls.We conducted a mapping of the training and networks that have been set up to provide knowledge and skills and to empower women in data science.We highlight some of the facilitators that have positively contributed to a greater participation of women and girls in data science education, while also revealing some of the barriers and structural impediments to fair access to training for women in data science.
The Dahu graph-cut for interactive segmentation on 2D/3D images

Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each's complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user's efforts.
Multiset neurons

The present work addresses the adoption of multiset similarity, more specifically the real-valued Jaccard and coincidence indices, as an element underlying artificial neurons. The overlap/interiority index, cosine similarity, and Euclidean distance are also considered. Special attention is given to the features adopted to characterize the patterns provided as input to neurons. After presenting the basic concepts related to real-valued multisets and the coincidence similarity index, including the generalization of the real-valued Jaccard and coincidence indices to higher orders, as well as a description of their special properties, we introduce multiset neurons with and without a non-linearity stage. Important issues related to the selectivity and sensitivity of the implemented comparisons, as well as the effect of localized perturbations of individual features are then addressed. We proceed to study the response of single linear multiset neurons respectively to the detection of gaussian two-dimensional stimulus in presence of displacement, magnification, intensity variation, noise and interference from additional patterns. It is shown that the real-valued Jaccard and coincidence approaches tend to be more robust and effective than the interiority index and the cross-correlation. The coincidence-based neurons are shown to have enhanced overall performance respectively to the considered type of data and perturbations. The potential of multiset neurons is further illustrated with respect to the challenging problem of color and gray-level image segmentation. The reported concepts, methods, and results have several implications not only for pattern recognition and machine learning, but also regarding several other related areas including signal processing, image analysis, and neuroscience.
Gymnastic skills on a balance beam with simulated height

Virtual reality (VR) is a valuable tool for simulating dangerous situations and training under these conditions with a reduced possibility of injury. This could be beneficial within different sports scenarios. In gymnastics, the height of the balance beam can be dangerous and frightening, especially for beginners. Here, a simulated height can reduce the participant's risk of injury and facilitate entry to balance beam gymnastics. However, the investigation of sports performance in a virtual environment is rare. Therefore, the current study aims to compare beginners' performance in balance beam tasks between the real world (RW) and VR. 34 sports students executed gymnastic balancing forward, backward and the extension of one leg to the front, the side and the back on a regular balance beam in the RW and on a balance beam with simulated height in VR (using a head-mounted display). We analyzed beginners' performance in both conditions (RW and VR) regarding different criteria for each balance beam task. Statistical analyses show significant differences in performance with better results in RW (p < .05). Especially the body tension and the upper body posture were superior in RW. One explanation can be the more difficult regulation of the balance in VR. For the tasks where the motion took part in a fixed position on the beam, the further performance aspects were similar for RW and VR. When the participants moved along the beam, further performance aspects, like the leg extension, were better in RW. In total, the participants executed the VR balance beam tasks with decreased danger of injury due to the simulated height of the balance beam, but their performance was slightly limited. We conclude that for the first contact with the height of a balance beam, VR is a suitable tool to reduce the danger of injury occurring from falling off the beam and facilitate entry to balance beam gymnastics.