Reviewerly/exHarmony huggingface dataset:
Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks

Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10−4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.
Deep Metric Learning for Open World Semantic Segmentation

Classical close-set semantic segmentation networks have limited ability to detect out-of-distribution (OOD) objects, which is important for safety-critical applications such as autonomous driving. Incrementally learning these OOD objects with few annotations is an ideal way to enlarge the knowledge base of the deep learning models. In this paper, we propose an open world semantic segmentation system that includes two modules: (1) an open-set semantic segmentation module to detect both in-distribution and OOD objects. (2) an incremental few-shot learning module to gradually incorporate those OOD objects into its existing knowledge base. This open world semantic segmentation system behaves like a human being, which is able to identify OOD objects and gradually learn them with corresponding supervision. We adopt the Deep Metric Learning Network (DMLNet) with contrastive clustering to implement open-set semantic segmentation. Compared to other open-set semantic segmentation methods, our DMLNet achieves state-of-the-art performance on three challenging open-set semantic segmentation datasets without using additional data or generative models. On this basis, two incremental few-shot learning methods are further proposed to progressively improve the DMLNet with the annotations of OOD objects.
Exploiting Explanations for Model Inversion Attacks

The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy. Explainable artificial intelligence (XAI) provides more information to help users to understand model decisions, yet this additional knowledge exposes additional risks for privacy attacks. Hence, providing explanation harms privacy. We study this risk for image-based model inversion attacks and identified several attack architectures with increasing performance to reconstruct private image data from model explanations. We have developed several multi-modal transposed CNN architectures that achieve significantly higher inversion performance than using the target model prediction only. These XAI-aware inversion models were designed to exploit the spatial knowledge in image explanations. To understand which explanations have higher privacy risk, we analyzed how various explanation types and factors influence inversion performance. In spite of some models not providing explanations, we further demonstrate increased inversion performance even for non-explainable target models by exploiting explanations of surrogate models through attention transfer. This method first inverts an explanation from the target prediction, then reconstructs the target image. These threats highlight the urgent and significant privacy risks of explanations and calls attention for new privacy preservation techniques that balance the dual-requirement for AI explainability and privacy.
A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence

The task of detecting fraudulent reviewers is of great importance to E-commerce platforms. Existing research has invested much effort into developing comprehensive features and advanced techniques to detect fraudulent reviewers. However, most of these studies have ignored the data imbalance problem inherent in fraudulent reviewer detection: non-fraudulent reviewers are the majority, while fraudulent reviewers are the minority in real practice. To fill this gap, we propose a novel approach called ImDetector to detect fraudulent reviewers while handling data imbalance based on weighted latent Dirichlet allocation (LDA) and Kullback–Leibler (KL) divergence. Specifically, we develop a weighted LDA model to extract the latent topics of reviewers distributed on the review features. Asymmetric KL divergence is adopted to make the similarity measure between reviewers biased toward the fraudulent minority when using the K-nearest-neighbor for classification. By mapping the reviewers to the latent topics of features derived from the weighted LDA model and measuring the similarities between reviewers using asymmetric KL divergence, the data imbalance problem in fraudulent reviewer detection is alleviated. Extensive experiments on the Yelp.com dataset demonstrate that the proposed ImDetector approach is superior to the state-of-the-art techniques used for fraudulent reviewer detection. We also explain the experimental results and present the managerial implications of this paper.
EigenGAN: Layer-Wise Eigen-Learning for GANs

Recent studies on Generative Adversarial Network (GAN) reveal that different layers of a generative CNN hold different semantics of the synthesized images. However, few GAN models have explicit dimensions to control the semantic attributes represented in a specific layer. This paper proposes EigenGAN which is able to unsupervisedly mine interpretable and controllable dimensions from different generator layers. Specifically, EigenGAN embeds one linear subspace with orthogonal basis into each generator layer. Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations. By traversing the coefficient of a specific eigen-dimension, the generator can produce samples with continuous changes corresponding to a specific semantic attribute. Taking the human face for example, EigenGAN can discover controllable dimensions for high-level concepts such as pose and gender in the subspace of deep layers, as well as low-level concepts such as hue and color in the subspace of shallow layers. Moreover, in the linear case, we theoretically prove that our algorithm derives the principal components as PCA does. Codes can be found in https://github.com/LynnHo/EigenGAN-Tensorflow.
Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem

This article presents the Ant Colony Optimization algorithm to solve the Travelling Salesman Problem. The proposed algorithm implements three novel techniques to enhance the overall performance, lower the execution time and reduce the negative effects particularly connected with ACO-based methods such as falling into a local optimum and issues with settings of control parameters for different instances. These techniques include (a) the node clustering concept where transition nodes are organised in a set of clusters, (b) adaptive pheromone evaporation controlled dynamically based on the information entropy and (c) the formulation of the new termination condition based on the diversity of solutions in population. To verify the effectiveness of the proposed principles, a number of experiments were conducted using 30 benchmark instances (ranging from 51 to 2,392 nodes with various nodes topologies) taken from the well-known TSPLIB benchmarks and the results are compared with several state-of-the-art ACO-based methods; the proposed algorithm outperforms these rival methods in most cases. The impact of the novel techniques on the behaviour of the algorithm is thoroughly analysed and discussed in respect to the overall performance, execution time and convergence.
Augmented reality technology based on school physical education training

Physical education is a programme that combines skills and athletic experience. Applied coaching in augmented reality (AR) has rarely been utilized in school physical education. Visual coaching has also been used in athletic activities, but it neither includes immersive practice nor equally embodies academic learning and athletic skills. Recently, techno pedagogical methods have been introduced to utilize constructive teaching and learning progressions through information communication techniques (ICT). The main goal of this work is to analyze the effect of teaching by applying increased physical education realities for spatial orientation creation and acquisition in contrast to conventional exhibition education. To educate school students in physical education advancements, the AR method of training is efficient, particularly to obtain better performance in students' engagement in sports. However, the massively augmented reality renderings limit the operating environment to a costlier, high-complexity integrated unit, and it is impossible to run AR simulations on normal computers. Considering the above issues, this article designs and suggests an augmented reality (AR) solution for school physical education training based on augmented reality technologies: a cloud network, Internet of things (IoT), and remote users. AR simulation outcomes explored that sportsperson performance data and input from sports trainers, the positive impact of the augmented reality environment, is demonstrated to enhance the school physical education systems' training and learning ability.
Blockchain applications in the supply chain management in German automotive industry

The automotive industry appears to be a particularly attractive application case for blockchain technology adoption. Blockchain is one of the innovations that has much potential to change current business processes in German original equipment manufacturers (OEMs) and enable new services for their customers. This paper combines collective case study and in-depth interviews to explore the potentials and existing challenges of blockchain technology-based applications at German OEMs. The results suggest that blockchain applications have advantages in aggregating product information, securing transaction information, and establishing a reliable supply chain. Based on the TOE (technology, organisation and environment) model, our case study shows that the biggest obstacles for blockchain technology adoption in the automotive supply chain include: technology immaturity, lack of guidance and industry standards, non-cooperation of chain members, and legislative ambiguity. Based on auto manufacturers investigated, blockchain technology is perceived to have great potentials in reducing process costs, ensure product quality, and enhance the automotive supply chain’s visibility and digitisation.
Prioritizing Energy Blockchain Use Cases Using Type-2 Neutrosophic Number-Based EDAS

The modern power systems are evolving in parallel to the development of other technological trends such as decarbonization and digitalization.While the penetration of renewable energy resources is increasing within the national and regional energy mix, emerging digitalization technologies, such as artificial intelligence and blockchain technology are shaping modern power systems.Especially blockchain technology has a very high potential to disrupt the current and future energy sector landscape by enabling various use cases in this domain.This paper aims to prioritize different energy use cases where blockchain technology can actively be utilized to create additional value.This study proposes a Type-2 Neutrosophic Number (T2NN) based Evaluation based on Distance from Average Solution (EDAS) to evaluate and rank a set of existing use cases of an energy blockchain system.Testing and validation of the model is done through a comparison against one alternative T2NN based Multi-Criteria Decision Making (MCDM) model and an existing approach from literature.In addition, a sensitivity analysis is performed, revealing that changing criteria weightings do not affect the ranking order of the use cases of the energy blockchain system.Prioritizing the use cases can assist the companies, standardization bodies, and related government authorities to make better decisions for their operations, such as ranking the investment decisions.
Cryptographic system based on double parameters fractal sorting vector and new spatiotemporal chaotic system

• We propose a class of sorting vectors with fractal characteristics of irregular, self-similar and infinitely iterative. • We reveal a new spatiotemporal chaotic system with the model of double parameters fractal sorting vector coupled map lattice. • We present the permutation-diffusion synchronous encryption method in mathematical linguistics. • We construct a new cryptographic system to improve the security and resistance to attacks of image encryption . Information security and image encryption have rapidly developed in recent years. This study presents an iterative method for vectors with fractal characteristics. Based on the idea of combining fractal and algebraic theories, the double parameters fractal sorting vector (DPFSV) was originally proposed, which incorporates features such as complexity, self-similarity, and iteration. Based on the practical application requirements, the DPFSV has a good regulatory vector length and increases the diversity of information location change laws. Hence, this study uses the DPFSV to control iterative node relationships in spatiotemporal chaotic systems. The new spatiotemporal chaotic system proposed based on DPFSV exhibits better dynamics than that of the coupled map lattice (CML) system, verified by comparing their Kolmogorov–Sinai entropy, bifurcation diagram, information entropy, and mutual information. Therefore, DPFSV–CML is more suitable in cryptography than CML. Combining the DPFSV and spatiotemporal chaotic system, this study constructs a new cryptographic system to implement permutation–diffusion synchronous encryption. This new encryption method has a good encryption effect and is resistant to various attacks. The security of this proposed cryptographic system is verified through various security analyses and simulated attack validations.
Yoga Pose Estimation and Feedback Generation Using Deep Learning

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one's own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one's health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.
Visibility classification and influencing-factors analysis of airport: A deep learning approach

This paper discusses the visibility classification and influencing-factor analysis of an airport, which were carried out through a data-driven deep learning approach and multiple nonlinear regression analysis. First, a deep integrated model for airport visibility classification was developed, and the relationship between runway visual range (RVR) and surface meteorological elements for different reasons was analyzed. To extract more low-level features from nighttime images, the original images were transformed into pseudo-color images, and the image pairs were input into the classification model. The proposed deep integrated model combined two popular convolutional neural network (CNN) models: VGG-16 and Xception. The results showed that the accuracy of the proposed model was 87.64%, with the F1-score being 88.58%; it achieved the best performance among other well-known experimental CNNs. Afterward, the relationships between RVR and meteorological optical range (MOR), source light intensity (I), and background light (BL) were analyzed, and the results showed that RVR increased with increasing I under the same MOR and BL conditions; however, it decreased with increasing log (BL) values under the same I and MOR conditions. RVR increased as MOR increased when other conditions remained unchanged. Subsequently, the multivariate regression analysis was conducted to fit the relationship between MOR and surface meteorological elements, and the results showed that the cubic curve was the best-fit curve with R-square values of 0.937 and 0.860 in December 2019 and March 2020, respectively. Finally, combined with the analysis of the factors influencing the RVR, a significant negative correlation was observed with relative humidity, along with a significant positive correlation with temperature and a positive correlation with average wind speed in the winter of 2019 and spring of 2020. However, the pressure factor showed the opposite correlation, possibly because the arrival of cold air in winter increases the ground pressure, which is conducive for the dissipation of pollutants. Meanwhile, the increase in temperature and decrease in pressure in spring increases the chance of disturbances in the atmosphere, thereby increasing the likelihood of adverse visibility factors. ◆ Pseudo-color images were found beneficial for improving the accuracy of visibility identification. ◆ An integrated model combined VGG-16 and Xception achieved the best performance compared to that of other popular models. ◆ The relationship between RVR and MOR, I, and BL was found. ◆ The multivariate regression analysis between MOR and surface meteorological elements in different seasons was conducted. ◆ There is a correlational relationship between RVR and surface meteorological elements.
Unsupervised domain adaptation via distilled discriminative clustering

Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source domain data that share a common label space but follow a different distribution. Most of the recent methods take the approach of explicitly aligning feature distributions between the two domains. Differently, motivated by the fundamental assumption for domain adaptability, we re-cast the domain adaptation problem as discriminative clustering of target data, given strong privileged information provided by the closely related, labeled source data. Technically, we use clustering objectives based on a robust variant of entropy minimization that adaptively filters target data, a soft Fisher-like criterion, and additionally the cluster ordering via centroid classification. To distill discriminative source information for target clustering, we propose to jointly train the network using parallel, supervised learning objectives over labeled source data. We term our method of distilled discriminative clustering for domain adaptation as DisClusterDA. We also give geometric intuition that illustrates how constituent objectives of DisClusterDA help learn class-wisely pure, compact feature distributions. We conduct careful ablation studies and extensive experiments on five popular benchmark datasets, including a multi-source domain adaptation one. Based on commonly used backbone networks, DisClusterDA outperforms existing methods on these benchmarks. It is also interesting to observe that in our DisClusterDA framework, adding an additional loss term that explicitly learns to align class-level feature distributions across domains does harm to the adaptation performance, though more careful studies in different algorithmic frameworks are to be conducted.
Conceptual challenges for interpretable machine learning

Abstract As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes.