Reviewerly/exHarmony huggingface dataset:
FEqa: Finite element computations on quantum annealers

The solution of physical problems discretized using the finite element methods using NISQ quantum hardware remains relatively unexplored. Here, we present a unified formulation (FEqa) to solve such problems using quantum annealers. FEqa is a hybrid technique in which the finite element problem is formulated on a classical computer, and the residual is minimized using a quantum annealer. The advantages of FEqa include utilizing a single qubit per degree of freedom, enforcing Dirichlet boundary conditions a priori, reaching arbitrary solution precision, and eliminating the possibility of the annealer generating invalid results. FEqa is scalable on the classical portion of the algorithm due to its Single Program Multiple Data (SPMD) nature and does not rely on ground state solutions from the annealer. The exponentially large number of collocation points used in quantum annealing are investigated for their cosine measures, and new iterative techniques are developed to exploit their properties. The quantum annealer has clear advantages in computational time over simulated annealing, for the example problems presented in this paper solved on the D-Wave machine. The presented work provides a pathway to solving physical problems using quantum annealers.
Blockchained education: challenging the long-standing model of academic institutions

The model of higher education institutions has remained widely unchallenged over time. Although study topics keep evolving and the inclusion of increasingly sophisticated technologies have revolutionised the format of lectures and learning experiences. Their value chain remains unchanged, with institutions acting as intermediaries, between professors (knowledge) and students (recipients), and as central authorities granting and validating student knowledge. The work here presented is a review of blockchain technology and its application in education, emphasising the opportunities for disrupting the current value chain of academic institutions. Blockchain technology's prime novelty constitutes an incorruptible digital ledger of transactions, capable of recording virtually any nature of value exchange. When coupled with other emerging technologies such as the internet of things and big data, new paths for the decentralised exchange of education and recording of knowledge and skills appear. The result is an extensive analysis of the evolution of blockchain technology for education applications, along with a forecast of plausible scenarios of disruption for academic institutions. Blockchain technology is steadily advancing at accelerating rates; applications aimed at decentralising educational institutions are already available. Meanwhile, the technology keeps gaining momentum in a growing base of adepts.
Development of biologically interpretable multimodal deep learning model for cancer prognosis prediction

Robust cancer prognostication can enable more effective patient care and management, which may potentially improve health outcomes. Deep learning has proven to be a powerful tool to extract meaningful information from cancer patient data. In recent years it has displayed promise in quantifying prognostication by predicting patient risk. However, most current deep learning-based cancer prognosis prediction methods use only a single data source and miss out on learning from potentially rich relationships across modalities. Existing multimodal approaches are challenging to interpret in a biological or medical context, limiting real-world clinical integration as a trustworthy prognostic decision aid. Here, we developed a multimodal modeling approach that can integrate information from the central modalities of gene expression, DNA methylation, and histopathological imaging with clinical information for cancer prognosis prediction. Our multimodal modeling approach combines pathway and gene-based sparsely coded layers with patch-based graph convolutional networks to facilitate biological interpretation of the model results. We present a preliminary analysis that compares the potential applicability of combining all modalities to uni- or bi-modal approaches. Leveraging data from four cancer subtypes from the Cancer Genome Atlas, results demonstrate the encouraging performance of our multimodal approach (C-index=0.660 without clinical features; C-index=0.665 with clinical features) across four cancer subtypes versus unimodal approaches and existing state-of-the-art approaches. This work brings insight to the development of interpretable multimodal methods of applying AI to biomedical data and can potentially serve as a foundation for clinical implementations of such software. We plan to follow up this preliminary analysis with an in-depth exploration of factors to improve multimodal modeling approaches on an in-house dataset.
Writer-independent online signature verification based on 2D representation of time series data using triplet supervised network

Online signature verification (OSV) as a critical link of the paperless office still has tremendous challenges. In this paper, we propose a novel Writer-Independent (WI) OSV framework that includes three parts. (1) The two-dimensional (2D) representation method is designed to transform the original time-series signature data into stroke images with dynamic-static hybrid information. (2) The Channel-wise Weight Learning (CWL) mechanism is integrated into the feature extractor to mine the potential relationship between three dynamic attributes (altitude, azimuth, and pressure). (3) A new Triplet Supervised Network (TSN) that contains three weight-shared convolution neural network (CNN) streams was provided to measure the distance of [Anchor, Positive, Test]. The experimental results show that our model has at least 1.29% accuracy improvement when confronted with skilled forgery signatures than that of other classical CNNs and 11.43% than lightweight models. Moreover, the TSN model is superior to the previous OSV algorithms in the WI pattern.
HW-Forest: Deep Forest with Hashing Screening and Window Screening

As a novel deep learning model, gcForest has been widely used in various applications. However, current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies: hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy called window screening to improve the performance of our approach, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.
Augmented Reality-Based Framework Supporting Visual Inspection for Automotive Industry

Visual inspection, inside an industrial environment, has attracted considerable research attention, in terms of its relation to improving productivity and its impact on building the Industry 4.0. One of the pillars of Industry 4.0 is the Augmented Reality (AR) technology, given its beneficiary for several tasks such as maintenance, assembly, and inspection. Nevertheless, such a data presenter tool essentially relies on the data collected from other modules. A keynote technology for data collecting, storing, and exploitation is the Industrial Internet of Things (IIoT) platform. In this context, this paper proposes an innovative framework in a real case-study industry. The proposed solution supports visual inspection, relying on AR to present the data and IIoT to collect it from the production line. User acceptance tests and feedback reflect the accuracy and effectiveness of the proposed system, especially when using Hand-Held Devices (HHD).
Measuring and Mitigating Bias in AI-Chatbots

The use of artificial intelligence (AI) to train conversational chatbots in the nuances of human interactions raises the concern of whether chatbots will demonstrate prejudice similar to that of humans, and thus require bias training. Ideally, a chatbot is void of racism, sexism, or any other offensive speech, however several well-known public instances indicate otherwise (e.g., Microsoft Taybot).In this paper, we explore the mechanisms of how open source conversational chatbots can learn bias, and we investigate potential solutions to mitigate this learned bias. To this end, we developed the Chatbot Bias Assessment Framework to measure bias in conversational chatbots, and then we devised an approach based on counter-stereotypic imagining to reduce this bias. This approach is non-intrusive to the chatbot, since it does not require altering any AI code or deleting any data from the original training dataset.
Object Detection via Gradient-Based Mask R-CNN Using Machine Learning Algorithms

Object detection has received a lot of research attention in recent years because of its close association with video analysis and image interpretation. Detecting objects in images and videos is a fundamental task and considered as one of the most difficult problems in computer vision. Many machine learning and deep learning models have been proposed in the past to solve this issue. In the current scenario, the detection algorithm must calculate from beginning to end in the shortest amount of time possible. This paper proposes a method called GradCAM-MLRCNN that combines Gradient-weighted Class Activation Mapping++ (Grad-CAM++) for localization and Mask Regional Convolution Neural Network (Mask R-CNN) for object detection along with machine learning algorithms. In our proposed method, images are used to train the network, together with masks that shows where the objects are in the image. A bounding box is regressed around the region of interest in most localization networks. Furthermore, just like any classification task, the multi-class log loss is minimized during training. This model enhances the calculation time and speed, as well as the efficiency, which recognizes objects in images accurately by comparing state-of-the-art machine learning algorithms, such as decision tree, Gaussian algorithm, k-means clustering, k-nearest neighbor, and logistic regression. Among these methods, we found logistic regression performed well with an accuracy rate of 98.4%, recall rate of 99.6%, and precision rate of 97.3% with respect to ResNet 152 and VGG 19. Furthermore, we proved the goodness of fit of our proposed model using chi-square statistical method and demonstrated that our solution can achieve great precision while maintaining a fair recall level.
Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation

In recent years, sustainable sources of energies attract significant interest due to the serious environmental issues of fossil fuels. Rooftop photovoltaic (PV) panels are among the important power generation technologies exploited in many modern countries. Most PV panels are installed behind-the-meter (BTM), resulting in a lack of observability of actual load and PV power generation in a power distribution system. This paper proposes a novel supervised spatiotemporal approach to accurately disaggregate the net-load data of a set of neighboring residential units. To this end, spatiotemporal correlations of a group of neighboring residential units are modeled using a weighted undirected graph where the nodes store the temporal features. The edges reflect the spatial correlation between neighboring residential units and are determined by the information analysis technique. Afterward, a generative graph attention recurrent neural network (RNN) is devised for capturing highly nonlinear patterns of input graphs using an RNN encoder. In addition, to boost the generalization capacity and robustness of the proposed model, we reconstruct the input graphs using sparse contractive decoders. Finally, an extreme learning machine (ELM) neural network is employed to disaggregate the input net-load time-series of a set of residential units using the extracted complex spatiotemporal patterns. Experimental results on the real-world Pecan Street dataset demonstrate the superiority of the proposed RNN-ELM method over the recent BTM disaggregation techniques.
Enhancing Firefly Algorithm with Dual-Population Topology Coevolution

The firefly algorithm (FA) is a meta-heuristic swarm intelligence optimization algorithm. It simulates the social behavior of fireflies with their flash and attraction characteristics. Numerous researches showed that FA can successfully deal with some problems. However, too many attractions between the fireflies may result in high computational complexity, slow convergence, low solution accuracy and poor algorithm stability. To overcome these issues, this paper proposes an enhanced firefly algorithm with dual-population topology coevolution (DPTCFA). In DPTCFA, to maintain population diversity, a dual-population topology coevolution mechanism consisting of the scale-free and ring network topology is proposed. The scale-free network topology structure conforms to the distribution law between the optimal and potential individuals, and the ring network topology effectively reduces the attractions, and thereby has a low computational complexity. The Gauss map strategy is introduced in the scale-free network topology population to lower parameter sensitivity, and in the ring network topology population, a new distance strategy based on dimension difference is adopted to speed up the convergence. This paper improves a diversity neighborhood enhanced search strategy for firefly position update to increase the solution quality. In order to balance the exploration and exploitation, a staged balance mechanism is designed to enhance the algorithm stability. Finally, the performance of the proposed algorithm is verified via several well-known benchmark functions. Experiment results show that DPTCFA can efficiently improve the existing problems of FA to obtain better solutions.
Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods

A method for overlapping cell image segmentation is presented with a focus on multi-layer image processing in a three-phase scheme. In the first phase, a convolutional neural network is developed to provide a coarse cell segmentation with multiple output layers to identify cell cytoplasm, locations of cell nuclei, and the background, all as probabilistic image maps for the layer outputs. In the second phase, the probabilistic image maps from the convolutional neural network are used to identify locations of cell nuclei and cell cytoplasm. Then, multi-layer random walker image segmentation is used with cell nuclei as hard initial seeds and the cytoplasm estimates as soft seeds in a diffusion graph-based segmentation of the cells. With rough cell segmentation from both the trained convolutional neural network and the multi-layer random walker graph-based technique, a third phase combines and refines the cell segmentation using the Hungarian algorithm to optimise the assignment of individual pixel locations for the final cell segmentation. We evaluate the proposed method on cervical cell images generated from the International Symposium on Biomedical Imaging 2014 dataset with results that give a Dice similarity coefficient of 97.2% (compared to 93.2% for competitors) when trained on the generated dataset.
An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations

Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly surprising, novel, yet contextually relevant recommendations. Most existing methods improve serendipity in RS by learning user preferences based on item popularity or similarity. However, the effectiveness of these methods in mitigating popularity bias and generating novel and unexpected item recommendations remains poorly understood. Recent studies suggest improvement in user preference by incorporating textual opinion provided by the user on an item. Additionally, the trade-off relationship between serendipity's conflicting components, including relevance, novelty, and unexpectedness, warrants further investigation to improve the quality of top-n recommendations. Hence, this research proposes an opinion mining-based approach to learn the users' personalized preferences from the textual reviews and incorporate both rating and reviews preferences to improve the quality of the recommendation list. Next, we design a new Two-Fold Algorithmic (TFA) approach-based objective function for serendipity to mitigate the popularity bias by aggregating uncertainty based on item popularity and item similarity to user preferences. Lastly, a multi-objective evolutionary algorithm-based Serendipity Objective Optimization-based Recommendation Framework(SOORF) is designed to optimize the serendipity's conflicting components. Extensive simulations are conducted over four benchmark datasets. The Mean Absolute Precision(MAP)@n and [email protected] based evaluation findings of SOORF and TFA demonstrate an improvement of at least 8.10% and 58.48%, respectively. The [email protected] and [email protected] based evaluations on different dataset sparsity conditions are observed with an improvement of at least 13.59% and 27.73% over baseline models. The Pareto front shows the models' ability to generate surprising, novel, yet relevant recommendations.
A Simple Neural Network Models Categorical Perception of Facial Expressions

The performance of a neural network that categorizes facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks make use of interpolations of facial expressions from the Pictures of Facial Affect Database [Ekman and Friesen, 1976]. The only difference in materials between those used in the human subjects experiments [Young et al., 1997] and our materials are the manner in which the interpolated images are constructed - image-quality morphs versus pixel averages. Nevertheless, the neural network accurately captures the categorical nature of the human responses, showing sharp transitions in labeling of images along the interpolated sequence. Crucially for a demonstration of categorical perception [Hamad, 1987], the model shows the highest discrimination between transition images at the crossover point. The model also captures the shape of the reaction time curves of the human subjects along the sequences. Finally, the network matches human subjects’ judgements of which expressions are being mixed in the images. The main failing of the model is that there are intrusions of “neutral” responses in some transitions, which are not seen in the human subjects. We attribute this difference to the difference between the pixel average stimuli and the image quality morph stimuli. These results show that a simple neural network classifier, with no access to the biological constraints that are presumably imposed on the human emotion processor, and whose only access to the surrounding culture is the category labels placed by American subjects on the facial expressions, can nevertheless simulate fairly well the human responses to emotional expressions.
Secure medical data management with privacy-preservation and authentication properties in smart healthcare system

The potential of combining Internet-of-Medical-Things technology with medical cloud storage can greatly improve the current smart healthcare environment. But some critical obstacles arise when developing the two technologies. One of the most difficult aspects is how to efficiently guarantee medical data's privacy and authentication since the biomedical sensors (BMSs), implanted on patient's body, have very limited battery life and computing abilities. Another one is to check the integrity of data stored on medical cloud. In this paper, we propose a secure and lightweight, especially for the BMSs, data management model for healthcare system, in which a BMS only needs to perform symmetric encryption consisting of XOR and message-authentication-code (MAC) operations. The adopted encryption technique ensures the privacy and authentication of medical data. Moreover, an aggregated homomorphic certificateless signature scheme is also proposed for the personal-assisted device and server device, which guarantees the public verifiability of medical data stored on cloud. Meanwhile, the certificateless property naturally implies that the whole system is immune to key-escrow attack. Finally, the detailed performance analysis and comparisons with related works show that our proposed system is very competitive in terms of communication overheads and computational costs.
HR-Crime: Human-Related Anomaly Detection in Surveillance Videos

The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF-Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable for human-related anomaly detection tasks. We rely on state-of-the-art techniques to build the feature extraction pipeline for human-related anomaly detection. Furthermore, we present the baseline anomaly detection analysis on the HR-Crime. HR-Crime as well as the developed feature extraction pipeline and the extracted features will be publicly available for further research in the field.
TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas that influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generates visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post hoc interpretations and explanations of their models, develop more explainable tools, and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-Flight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.