Reviewerly/exHarmony huggingface dataset:
The Perceptual Science of Augmented Reality

Augmented reality (AR) systems aim to alter our view of the world and enable us to see things that are not actually there. The resulting discrepancy between perception and reality can create compelling entertainment and can support innovative approaches to education, guidance, and assistive tools. However, building an AR system that effectively integrates with our natural visual experience is hard. AR systems often suffer from visual limitations and artifacts, and addressing these flaws requires basic knowledge of perception. At the same time, AR system development can serve as a catalyst that drives innovative new research in perceptual science. This review describes recent perceptual research pertinent to and driven by modern AR systems, with the goal of highlighting thought-provoking areas of inquiry and open questions.
Contrastive structure and texture fusion for image inpainting

Most recent U-Net based models have shown promising results for the challenging tasks in image inpainting field. However, they often generate content with blurred textures and distorted structures due to the lack of semantic consistency and texture continuity in the missing regions. In this paper, we propose to restore the missing areas at both structural and textural levels. Our method is built upon a U-Net structure, which repairs images by extracting semantic information from high to low resolution and then decoding it back to the original image. Specifically, we utilize the high-level semantic features learned in encoder to guide the inpainting of structure-aware features of its adjacent low-level feature map. Meanwhile, low-level feature maps have clearer texture compared with high-level ones, which can be used as a prior for textural repair of high-level feature maps. subsequently, a module is used to fuse the two repaired feature maps (i.e., structure-aware and texture-aware features) reasonably and obtain a feature map with reasonable semantics. Moreover, in order to learn more representative high-level semantics feature, we design the model as a siamese network for contrastive learning. Experiments on practical data show that our method outperforms other state-of-the-art methods.
An efficient hybrid computer-aided breast cancer diagnosis system with wavelet packet transform and synthetically-generated contrast-enhanced spectral mammography images

Contrast-enhanced spectral mammography (CESM) is an emerging modality for breast cancer diagnosis. This work investigates the feasibility of a computationally-efficient computer-aided diagnosis (CAD) system for breast lesion classification. Moreover, to avoid the need for intravenous contrast agents, we investigated the synthesis of contrast-enhanced (SynCESM) images. A total of 504 pairs of low-energy (LE) and CESM images were collected from 160 female subjects. A semi-automatic active-contour method was used for lesion segmentation. Then, 20 morphological and textural features were extracted. To improve the computational efficiency of the proposed system, the wavelet packet transform (WPT) was applied. Then, the same features were extracted from the WPT-approximated segmented lesions. Using LE images, a sigmoid-kernel SVM classifier exhibited a 90.20% accuracy, an 88.39% sensitivity, an 88.26% specificity, and a 0.92 AUC. The per-frame classification time was significantly reduced from 1.1046 s to 0.0734 s with WPT. Using CESM images, we achieved a 93.26% accuracy, a 95.94% sensitivity, a 93.37% specificity, a 0.94 AUC, and a 0.0657 s classification time. Interestingly, with SynCESM images, reasonable performance was still obtained with a 92.14% accuracy, a 93.87% sensitivity, a 91.58% specificity, a 0.93 AUC and a 0.0657 s classification time. Finally, with hybrid pairs of LE and CESM images, we got the best performance with a 96.87% accuracy, a 97.23% sensitivity, a 95.47% specificity, a 0.98 AUC, and a 0.0696 s classification time. The results demonstrate several advantages of the proposed system including its clinical feasibility, lower complexity, and reduced need for contrast agents via synthetic data generation.
On dynamics of 4-D blinking chaotic system and voice encryption application

A new four-dimensional chaotic system with four cross-terms is investigated in this study. The dynamical behaviors of the system and codimension-one bifurcations are analyzed both theoretically and numerically. The influences of the system’s parameters on its nonlinear dynamics are explored. Then, a proposed four-dimensional blinking system is introduced based on the new chaotic system. More interestingly, the possible occurrence of ghost attractors in the phase space of the blinking system is verified and has been illustrated through several examples. The encryption keystream was generated dynamically using pre-treated chaotic mixed sequences and was used to mask the voice bitstream to protect voice data integrity. Finally, a voice encryption example employing the 4-D blinking chaotic system of the proposed system is presented.
Functionality of a Technical University Teacher: Targets

The university teachers’ tasks are being updated, their labor functions are being revised and supplemente, with changes at the higher education system. The good organization of a modern university teacher is subject to numerous external targets. The perception of these targets, as well as the personal value of the activity components, are important. The purpose of the article are the identification of tasks for a technical university teacher from the standpoint of the Higher School strategic targets, and determination of their solution limitations. The paper reveals the strategic tasks hierarchy of higher technical education – for technical universities, departments and their teachers; the academic community attitude review to strategic objectives is carried out (including: how the strategic objectives are understood by teachers, how priorities and emphasis are placed in these tasks); the priorities of technical universities teachers are evaluated in terms of their tasks solving; restrictions are indicated in solving these problems, and these restrictions connection with the working conditions of the technical universities teaching staff. The paper points out the balancing problem of the teachers work, and the emerging dissonance among the goals, tasks set for them, and private indicators of their activities effectiveness. The engineering education strategic goals and objectives should proceed from the fact, that this is a mutually responsible process of the qualified technical specialists training, from the authors’ point of view. This is especially topical for the returning process to the engineers training.
Optoelectronic coherent Ising machine for combinatorial optimization problems

Hopfield networks are iterative procedures able to solve combinatorial optimization problems. New studies regarding algorithm-architecture adequacy are fostered by the re-emergence of hardware implementations of such methods in the form of Ising machines. In this work, we propose an optoelectronic architecture suitable for fast processing and low energy consumption. We show that our approach allows effective optimization relevant to statistical image denoising.
Hate Speech Classifiers Learn Normative Social Stereotypes

Abstract Social stereotypes negatively impact individuals’ judgments about different groups and may have a critical role in understanding language directed toward marginalized groups. Here, we assess the role of social stereotypes in the automated detection of hate speech in the English language by examining the impact of social stereotypes on annotation behaviors, annotated datasets, and hate speech classifiers. Specifically, we first investigate the impact of novice annotators’ stereotypes on their hate-speech-annotation behavior. Then, we examine the effect of normative stereotypes in language on the aggregated annotators’ judgments in a large annotated corpus. Finally, we demonstrate how normative stereotypes embedded in language resources are associated with systematic prediction errors in a hate-speech classifier. The results demonstrate that hate-speech classifiers reflect social stereotypes against marginalized groups, which can perpetuate social inequalities when propagated at scale. This framework, combining social-psychological and computational-linguistic methods, provides insights into sources of bias in hate-speech moderation, informing ongoing debates regarding machine learning fairness.
Efficient quality control of whole slide pathology images with human-in-the-loop training

Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. For instance, WSIs contain multiple types of tissue regions, at least some of which might not be relevant to the diagnosis. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into 6 broad tissue regions-epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. HistoROI is trained using a novel human in-the-loop and active learning paradigm that ensures variations in training data for labeling efficient generalization. HistoROI consistently performs well across multiple organs, despite being trained on only a single dataset, demonstrating strong generalization. Further, we have examined the utility of HistoROI in improving the performance of downstream deep learning-based tasks using the CAMELYON breast cancer lymph node and TCGA lung cancer datasets. For the former dataset, the area under the receiver operating characteristic curve (AUC) for metastasis versus normal tissue of a neural network trained using weakly supervised learning increased from 0.88 to 0.92 by filtering the data using HistoROI. Similarly, the AUC increased from 0.88 to 0.93 for the classification between adenocarcinoma and squamous cell carcinoma on the lung cancer dataset. We also found that the performance of the HistoROI improves upon HistoQC for artifact detection on a test dataset of 93 annotated WSIs. The limitations of the proposed model are analyzed, and potential extensions are also discussed.
Bi-Attention enhanced representation learning for image-text matching

Image-text matching has become a research hotspot in recent years. The key point of image-text matching is to accurately measure the similarity between an image and a sentence. However, most existing methods either focus on the inter-modality similarities between regions in image and words in text or the intra-modality similarities within image regions or words, such that they cannot well exploit detailed correlations between images and texts. Furthermore, existing methods typically train their models using a triplet ranking loss, which relies on the similarity of randomly sampled triples. Since the weights of positive and negative samples are not adjusted, it cannot provide enough gradient information for training, resulting in slow convergence and limited performance. To address the above problems, we propose an image-text matching method named Bi-Attention Enhanced Representation Learning (BAERL). It builds a self-attention learning sub-network to exploit intra-modality correlations within image regions or words and a co-attention learning sub-network to exploit inter-modality correlations between image regions and words. Then, representations obtained by two sub-networks capture holistic correlations between images and texts. In addition, BAERL uses the self-similarity polynomial loss instead of triplet ranking loss to train the model. The self-similarity polynomial loss can adaptively assign appropriate weights to different pairs based on their similarity scores so as to further improve the retrieval performance. Experiments on two benchmark datasets demonstrate the superior performance of the proposed BAERL method over several state-of-the-art methods.
Computer-based performance feedback: Effects of error message complexity on cognitive, metacognitive, and motivational outcomes

In computer-based assessments, immediate performance feedback on incorrect responses may have major cognitive and metacognitive benefits but it may also be demotivating for students by mirroring task-related failure. This experiment investigated the impact of three different error clarification types. We systematically varied the presence and complexity of immediate error messages (1 × 4 between-subjects design) and analyzed the effects of feedback with different levels of error clarification complexity on students' cognition, metacognition, and motivational state. Our sample included 439 university students who worked on 12 computer-based constructed-response geometry tasks. The students received either no feedback or feedback with different error message complexities after incorrect responses (i.e., Knowledge of Results [KR], Knowledge of Correct Response [KCR], or Elaborated Feedback [EF]) paired with confirmatory KCR feedback after correct responses. All feedback types, namely KR, KCR, and EF, significantly enhanced error correction and equally fostered students' metacognitive calibration accuracy. Moreover, error correction increased with the time students' spent on the error-related feedback (even for simple KR messages), which suggests that students actively used the additional time to reflect on their mistakes. Nevertheless, KCR and EF increased error correction more effectively than KR. Students' motivational state after correct responses did not significantly differ after receiving feedback compared to no feedback. After incorrect responses, we found a negative effect on students' motivational state for KR feedback. This detrimental effect was in contrast to EF, which mitigated the negative effect compared to KR feedback and showed a similar motivational state level as the KCR and no feedback group.
Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles

With the 6G-enabled Internet of Vehicles (IoV), the Intelligent Transportation System (ITS) uses new communication technologies and smart data analysis to make transportation systems more innovative and effective. After the emergence of 6G, focusing on emergency message dissemination scenarios in IoVs, software-defined vehicular fog computing (SDVF) networks can be instrumental in enhancing vehicle-to-everything communication for sharing the road, traffic, and accident information with low latency. Nevertheless, the emergency messages are susceptible to intrusion attacks, especially Sybil attacks that could produce numerous bogus clients or collaborate with compromised devices. Thus, we propose the Federated Learning Entrusted Misbehaviour Detection System (FLEMDS) with vehicle selection to support the 6G-enabled vehicles in combating Sybil attackers. As a result, Sybil attack detection is carried out locally in the vehicles employing the federated learning on-vehicle AI technique. The FLEMDS employs a three-level model weight aggregation process at three locations to improve detection accuracy. To minimize the learning and detection latency, federated learning and software-defined vehicular fog computing are combined in the FLEMDS. We employ a fuzzy logic-based FL-vehicle selection (FLBFLVS) technique in the Road-Side Units (RSUs) and Base Stations (BSs) to choose suitable FL-vehicles as clients for the participation of local training in the FL process. The experimental results substantiate that the FLEMDS with FLBFLVS capitulates with a higher detection accuracy of 87% for a minimal number of global aggregations. Furthermore, FLEMDS with FLBFLVS is compared with the state-of-the-art FL-based frameworks. The outcomes show that the proposed schemes yield a faster convergence rate as well as a decrease in the time consumption of computation and communication.
Individual fairness for local private graph neural network

Graph neural networks (GNNs) have shown superior performance in learning node representation for various graph inference tasks and play a pivotal role in high-stakes decision scenarios. However, GNNs may magnify the bias in the original data and make discriminatory decisions toward individuals, reducing user trust. Some research advances have been made to improve the individual fairness of GNNs and increase trust. However, they raise privacy issues when the acquired data involves sensitive information. To address this challenge, we propose a GNN privacy protection method called local private feature-individual​ fairness graph neural network, LPF-IFGNN for node features based on local differential privacy (LDP) that can strike a balance between fairness and privacy. Specially, we propose an LDP mechanism that considers privacy issues in normalization and can compress and perturb features. In addition, we employ a convolution layer that aggregates multi-hop node features using the mean function to denoise and avoid promoting individual fairness about the perturbed features. We consider the situation where node labels are also required to be protected, and we further propose LPL-LPF-IFGNN based on LDP. Experimental results on five real-world datasets show that LPL-LPF-IFGNN outperforms the state-of-the-art fairness baseline by 41.75% on ACC and 5.02% on NDCG@10 on private data with a feature privacy budget of 1 and a label privacy budget of 0.5. Experiments further indicate that our methods can achieve a good balance between model utility and individual fairness for private node data with LDP guarantees.
ACU<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e1379"><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>E-Net: A novel predict–refine attention network for segmentation of soft-tissue structures in ultrasound images

Segmentation of anatomical structures in ultrasound images is a challenging task due to existence of artifacts inherit to the modality such as speckle noise, attenuation, shadowing, uneven textures and blurred boundaries. This paper presents a novel attention-based predict-refine network, called ACU2E-Net, for segmentation of soft-tissue structures in ultrasound images. The network consists of two modules: a predict module, which is built upon our newly proposed attentive coordinate convolution; and a novel multi-head residual refinement module, which consists of three parallel residual refinement modules. The attentive coordinate convolution is designed to improve the segmentation accuracy by perceiving the shape and positional information of the target anatomy. The proposed multi-head residual refinement module reduces both segmentation biases and variances by integrating residual refinement and ensemble strategies. Moreover, it avoids multi-pass training and inference commonly seen in ensemble methods. To show the effectiveness of our method, we collect a comprehensive dataset of thyroid ultrasound scans from 12 different imaging centers, and evaluate our proposed network against state-of-the-art segmentation methods. Comparisons against state-of-the-art models demonstrate the competitive performance of our newly designed network on both the transverse and sagittal thyroid images. Ablation studies show that proposed modules improve the segmentation Dice score of the baseline model from 79.62% to 80.97% and 82.92% while reducing the variance from 6.12% to 4.67% and 3.21% in transverse and sagittal views, respectively.
Refinement Co‐supervision network for real‐time semantic segmentation

Abstract Semantic segmentation is a fundamental technology for autonomous driving. It has a high demand for inference speed and accuracy. However, a good trade‐off between accuracy and latency is yet not present in existing semantic segmentation approaches. Due to the limitation of speed, the authors cannot increase the number of network layers without limit and cannot design modules like in the networks without real‐time. It is a challenging problem how to design a model with good performance under limited resources. To alleviate these issues, in this study, the authors propose a refinement co‐supervision network (RCNet), which is real‐time on a high‐resolution image (1024×2048). The authors first construct the context refinement module, which can provide low computation cost way for obtaining the large receptive field and context information. Furthermore, a boundary co‐supervision mechanism is proposed. It strengthens the optimisation of easily neglected boundaries and small targets. Experimental results reveal that the proposed RCNet outperforms seven representative semantic segmentation methods.
Face expression recognition based on NGO-BILSTM model

Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization.In this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender.The results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively.In conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition.
A Text Analytics Framework for Supplier Capability Scoring Supported by Normalized Google Distance and Semantic Similarity Measurement Methods

Abstract The unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, they do not lend themselves well to computational analysis. The objective of this work is to propose framework to enable automated classification and ranking of manufacturing suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics framework is supported by a formal thesaurus that uses Simple Knowledge Organization System (SKOS) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.