Reviewerly/exHarmony huggingface dataset:
Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modelling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to analyse short-range motions jointly and comprehensively between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modelling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.
Two-Rail Photonic Qubit Utilizing the Quantum Holographic Imaging Idea

We present the novel approach to physical implementation of qubits with the technology of photonic chips. Proposed multi-rail qubit model, called QBell, utilizes hyper-entanglement to work in Decoherence Free Subspace on physical layer. This makes this solution robust and can result in increasing fidelity of quantum circuit used in this model. We elaborate the two-rail case. We define the QBell and discuss its internal structure. We construct also one- and two-qubit gates to make the model comprehensive and ready to implement. Proposed model utilizes the early-stage ideas for optical quantum computation, but by using the polarization and position entanglement as the resource of computation allows to avoid the general problem of them, like heralded photon technique. The technology of photonic chips allows to brake other limitations that are pointed in the text. The presented model was inspired by quantum holographic imaging and uses the holographic technique for implementing the z-rotation operation. The final product will be the photonic quantum processor using multi-rail qubits. It will find the application in many domains (e.g., medical) on earth and in the space.
Label Diffusion Graph Learning network for semi-supervised breast histological image recognition

Breast cancer has been a significant cause of female death worldwide, usually recognized by histological images. Due to the outstanding performance of computer-aided methods, many breast histological recognition algorithms are based on deep learning. Nonetheless, these advanced breast cancer recognition approaches exist two primary issues. Firstly, these methods rely on abundant pre-annotated labels, which is an expensive and heavy workload. Besides, they lack the multi-level (both pixel-level and sample-level) feature learning ability to reveal the complex semantic characteristics in histology tissue images. For these challenging problems, we proposed a Label Diffusion Graph Learning (LDGL) method, which can optimize the model in a semi-supervised manner with limited labels, and then adopt graph convolution layers to mine correlations among different breast tissues. Concretely, our LDGL model first employs a convolutional neural network and graph convolution network to extract comprehensive multi-level representations. Then it improves the identification capability through a multi-level consistency loss among unlabeled breast images. Finally, a novel pseudo-label building method is introduced to combine confidence, graph degree, and the expected calibration error to expand labeled data via credible breast samples. To prove the excellent recognition performance of our LDGL model, we compare it with several superior breast cancer histological image recognition approaches. The results indicate that our model (with only 20% labels) achieves 95.05% Accuracy, 95.27% Precision, 95.05% Recall, and 95.0% F1-score, separately. Moreover, we also verify the contribution of each module in LDGL through a series of ablation experiments. In conclusion, the evaluation proves that our LDGL model is excellent and robust for semi-supervised breast histological image recognition task.
CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer

Esophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer.The grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems.First, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model.The experimental results show that the CIABNet model outperforms other state-of-the-art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average recall of 90.31%, an average F1 value of 91.73%, and an average AUC value of 97.43%. In addition, the CIABNet model has essentially similar or identical to the ROI of pathologists in identifying histopathological images of esophageal cancer.Our experimental results prove that our proposed computer-aided diagnostic algorithm shows great potential in histopathological images of multi-differentiated types of esophageal cancer.
Gaussian RBF Centered Kernel Alignment (CKA) in the Large-Bandwidth Limit

Centered kernel alignment (CKA), also known as centered kernel-target alignment, is useful as a similarity measure between kernels and as a kernel-based similarity measure between feature representations. We prove that CKA based on a Gaussian RBF kernel converges to linear CKA in the large-bandwidth limit. The result relies on mean-centering of the feature maps and on a Hilbert-Schmidt Independence Criterion (HSIC) identity. We show that convergence onset is sensitive to the geometry of the feature representations, and that a notion of representation eccentricity, ρ, constrains the bandwidth range for which Gaussian CKA can differ noticeably from linear CKA. Our experimental results suggest that Gaussian bandwidths less than ρ should be selected in order to enable nonlinear modeling.
Quantifying Touch: New Metrics for Characterizing What Happens During a Touch

Measures of human performance for touch-based systems have focused mainly on overall metrics like touch accuracy and target acquisition speed. But touches are not atomic—they unfold over time and space, especially for users with limited fine motor function, for whom it can be difficult to perform quick, accurate touches. To gain insight into what happens during a touch, we offer 15 target-agnostic touch metrics, most of which have not been mathematically formalized in the literature. They are touch direction, variability, drift, duration, extent, absolute/signed area change, area variability, area deviation, area extent, absolute/signed angle change, angle variability, angle deviation, and angle extent. These metrics regard a touch as a time series of ovals instead of a mere (x, y) coordinate. We provide mathematical definitions and visual depictions of our metrics, and consider policies for calculating our metrics when multiple fingers perform coincident touches. To exercise our metrics, we collected touch data from 27 participants, 15 of whom reported having limited fine motor function. Our results show that our metrics effectively characterize touch behaviors including fine-motor challenges. Our metrics can be useful for both understanding users and for evaluating touch-based systems to inform their design.
Iskandar.my: Framework of Mobile Augmented Reality Travel App

In today’s world, Mobile Augmented Reality (AR) technology has been increasingly adopted by tourism industrial players in product development mainly due to the technology’s ability in improving tourists’ destination interaction through dynamic digital engagement. Mobile Augmented Reality (MAR) travel app, such as ‘Iskandar.my’, is seen capable to attract users’ excitement during travel by augmenting relevant travel-related information media, at the same time still leaving some space for tourist appreciation of the real environment. This research explores the technological framework of ‘Iskandar.my’ MAR travel apps and elaborates on the service dimension and design dimension of the app. These dimensions of the technological framework would explain the value co-creation experienced by users from the digital engagement and enlighten the possibility of being the antecedent of users’ travel satisfaction.
Reparameterized attention for convolutional neural networks

The attention mechanism has been widely explored for neural networks as it could effectively model the interdependencies among channels, spatial positions, and frames. A neural network with attention modules has uncertainties in its parameters, but training the models deterministically hardly captures the uncertainties. Modeling the parameters’ uncertainty of the attention module could facilitate flexibly capturing the representative patterns, thus promoting the generalization of the models. In this work, we propose a novel reparameterized attention strategy by modeling the uncertainty of the parameters in the attention module and performing uncertainty-aware optimization. Instead of learning deterministic parameters for the attention modules, our strategy learns variational posterior distributions. The experimental results show that our strategy could consistently improve different models’ accuracy and reduce the generalization gap without extra computation.
Machine-learning-based automatic small-angle measurement between planar surfaces in interferometer images: A 2D multilayer Laue lenses case

• A machine-learning-based method is developed to automatically measure the small angle between multiple planar surfaces in an interferometer image. • This easy-to-use and accurate method significantly simplifies the angle measurement procedure and is compatible with widely used white light interferometers. • The new method is demonstrated for a quick and precise angle measurement for the alignment of 2D multilayer Laue lenses for developing high-resolution x-ray microscopy. We report a new machine-learning-based approach to automatically measure the small angle between multiple planar surfaces characterized by white light interferometers. By applying an unsupervised clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the multiple surfaces in an interferometer image are automatically identified as distinct surfaces. The angles between every two surfaces are then calculated through the surface fitting. This method can be applied to multiple surfaces regardless of their shapes and locations and significantly simplifies the angle measurement procedure. Using the developed method, we have demonstrated a quick and precise angle measurement for the alignment of 2D Multilayer Laue Lenses (MLLs) for the development of high-resolution x-ray microscopy. This automatic, accurate, and robust small-angle measurement method is compatible with widely used white light interferometers and can be further applied to other metrology applications of interferometer results.
Pre-Inpainting Convolutional Skip Triple Attention Segmentation Network for AGV Lane Detection in Overexposure Environment

Visual navigation is an important guidance method for industrial automated guided vehicles (AGVs). In the actual guidance, the overexposure environment may be encountered by the AGV lane image, which seriously reduces the accuracy of lane detection. Although the image segmentation method based on deep learning is widely used in lane detection, it cannot solve the problem of overexposure of lane images. At the same time, the requirements of segmentation accuracy and inference speed cannot be met simultaneously by existing segmentation networks. Aiming at the problem of incomplete lane segmentation in an overexposure environment, a lane detection method combining image inpainting and image segmentation is proposed. In this method, the overexposed lane image is repaired and reconstructed by the MAE network, and then the image is input into the image segmentation network for lane segmentation. In addition, a convolutional skip triple attention (CSTA) image segmentation network is proposed. CSTA improves the inference speed of the model under the premise of ensuring high segmentation accuracy. Finally, the lane segmentation performance of the proposed method is evaluated in three image segmentation evaluation metrics (IoU, F1-score, and PA) and inference time. Experimental results show that the proposed CSTA network has higher segmentation accuracy and faster inference speed.
A method to construct exponential families by representation theory

In this paper, we give a method to construct “good” exponential families systematically by representation theory. More precisely, we consider a homogeneous space G/H as a sample space and construct an exponential family invariant under the transformation group G by using a representation of G. The method generates widely used exponential families such as normal, gamma, Bernoulli, categorical, Wishart, von Mises, Fisher–Bingham and hyperboloid distributions.
Design and Comparison of Artificial Intelligent Algorithms for Breast Cancer Classification

Breast cancer early detection is a critical factor associated to patient survival and treatment cost reduction. Nevertheless, it is difficult to obtain a diagnose at earliest stages since it does not cause any symptoms. Recently, artificial intelligence field has demonstrated to be a suitable alternative to improve classification and early detection for this affection. Therefore, this paper proposes the design and comparison of three artificial intelligent algorithms for breast cancer classification. The algorithms used for classification were a naive Bayesian network (NBN), a support vector machine (SVM), and an artificial neural network (ANN). These algorithms were trained and validated in the Breast Cancer Prediction Database, located on the Kaggle platform. This database contains ten real-valued features computed from benign and malignant tumors. The evaluation results in F1 score shown 94%, 92% and 91% for the NBN, ANN and SVM respectively. These scores were compared with state-of-the-art algorithms to demonstrate the robustness of the proposed algorithms. The comparison was made considering feature-based and image-based models. Findings shown that our feature-based algorithms obtained competitive results requiring less computational resources than image-based models. Therefore, algorithms here proposed are a good option for the development of a high-fidelity system to classify the mentioned database into the cancer and non-cancer categories.
Characterizing the research-practice gap in children’s interactive storytelling systems

Storytelling support has been and continues to be a recurring theme in child–computer interaction research, especially as it pertains to education. This work has spilled over into practice, and, in the last few decades, a large number of storytelling systems for children have been developed—both as research prototypes and commercial applications. However, through a systematic analysis of 75 research-based storytelling systems and 39 commercial systems, we find that there remains a problematic gap between research and practice with respect to products in interactive storytelling and child–computer interaction, echoing the well-documented research-practice gap in the general HCI field. We characterize the nature of this gap, highlighting design themes promoted by research-based systems that are not as prevalent in commercial systems, such as tangibility and narrative scaffolds, and vice-versa. Understanding the main areas of discrepancy between research and practice can help focus efforts to close the research-practice gap in storytelling systems for children.
Gaussian cloud and TOPSIS based power supply quality evaluation method

The accurate evaluation of power supply quality in the distribution network is regarded as an important way to improve the power quality of the user side and guide the power quality management of the power grid. Thus, a comprehensive evaluation method of the power supply quality based on the Gaussian cloud (GC) and the technique for order preference by similarity to ideal solution (TOPSIS) is proposed in this work. First, multiple evaluation indexes of the power supply quality are customized based on the measurement data of the distribution network and the power supply work order. On this basis, an index conversion method based on Gaussian cloud is adopted to convert the above indexes into Gaussian cloud distribution. Then, a comprehensive evaluation method of power supply quality based on TOPSIS is proposed for converting the similarity between Gaussian clouds into comprehensive evaluation results. Case study on power supply quality in Zhejiang province, China is employed to demonstrate the effectiveness and rationality of the proposed method. The result shows that the power supply quality can be evaluated by proposed method in an objective and reasonable manner.
CWT-DPA: Component-wise waiting time for BC-enabled data plane authentication

Software-Defined Networking (SDN) is a promising networking paradigm that brings central management and network programmability to improve existing networking systems. However, SDN has several security threats due to its untrusted control and data planes. Blockchain (BC) technology has recently provided a fault-tolerant, decentralized, and immutable ledger among untrusted parties. While the integration between SDN and BC enhances the security, it suffers from performance degradation when applied to SDN’s data plane. In traditional BC-SDN approaches, such as our previous work DPSEC (Latah and Kalkan, 2020), every switch has to wait for the final commitment of each submitted transaction, which significantly decreases the performance of the BC-SDN model. In this work, we enhance the performance of BC-SDN data plane approaches by separating switch-related and host-related transactions and propose a different waiting strategy for each type of data plane transaction. We also propose a batching technique to enhance the average latency for high-load scenarios. In addition, SDN switches and controllers utilize lattice-based signatures and Key Encapsulation Methods (KEMs) to protect against quantum adversaries. We compare the performance of CWT-DPA with existing solutions such as SSL/TLSv1.3, DPSEC (Latah and Kalkan, 2020), and AuthFlow (Mattos and Duarte, 2016).
Comparing Accuracy Between SVM, Random Forest, K-NN Text Classifier Algorithms for Detecting Syntactic Ambiguity in Software Requirements

Software requirements are ambiguous due to the ambiguity of natural language in general. The ambiguity of the requirements leads to software development errors. As a result, a multitude of approaches and techniques for detecting ambiguity in software requirements have emerged. This study used three supervised ML algorithms that used Bag-of-Words features to detect grammatical ambiguity in software requirements: support vector machine (SVM), random forest (RF), and k-nearest neighbours (KNN). RF had the highest accuracy of 86.66%, followed by SVM (80%) and KNN (70%).
Relationship Between Consumer's Social Networking Behavior and Cybercrime Victimization Among the University Students

Social media is broadly used for various reasons and one being for academic purposes in universities. Although it has its own repercussion such as cyber-crime victimization. This study intends to investigate the relationship between social networking and cybercrime victimization among university students. It identifies illegal behaviours carried through computers or other internet-connected devices. These illegal behaviours range from institutional hacking to individual victimization. In this paper, questionnaires were given to Sultan Qaboos University students of various ages to find out why and how personal attributes, social networks, user technical efficacy, and online bullying risk add to these illegal activities. The study found that consumers with high use are more prone to be targets and victims of cyber-crime. Regulating multifunction Social Networking services SNS (like Facebook) use and victimization have a mathe matically essential relationship. Also, online victimization and business social networking sites have statistically significant positive links (such as LinkedIn). Finally, cyberbullying requires social media awareness because social media sites and services save personal data.
Personal privacy VS. public safety: A hybrid model of the use of smart city solutions in fighting the COVID‐19 pandemic in Moscow

Technological advancements and big data have brought many improvements to smart city infrastructure. During the COVID-19 outbreak, smart city technologies were considered one of the most effective means of fighting the pandemic. The use of technology, however, implies collecting, processing personal data, and making the collected data publicly available which may violate privacy. While some countries were able to freely use these technologies to fight the pandemic, many others were restricted by their privacy protection legislation. The literature suggests looking for an approach that will allow the effective use of smart city technologies during the pandemic, while complying with strict privacy protection legislation. This article explores the approach applied in Moscow, Russia, and demonstrates the existence of a hybrid model that might be considered a suitable tradeoff between personal privacy and public health. This study contributes to the literature on the role of smart city technologies during pandemics and other emergencies.