Reviewerly/exHarmony huggingface dataset:
Information Cartography in Association Rule Mining

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitable for the user. Recently, an information cartography has been proposed for creating structured summaries of information and visualizing with methodology called “metro maps”. This was applied to several problem domains, where pattern mining was necessary. The aim of this study is to develop a method for automatic creation of metro maps of information obtained by Association Rule Mining and, thus, spread its applicability to the other machine learning methods. Although the proposed method consists of multiple steps, its core presents metro map construction that is defined in the study as an optimization problem, which is solved using an evolutionary algorithm. Finally, this was applied to four well-known UCI Machine Learning datasets and one sport dataset. Visualizing the resulted metro maps not only justifies that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users.
Characterizing the boundary of the set of absolutely separable states and their generation via noisy environments

We characterize the boundary of the convex compact set of absolutely separable states, referred as {\bf AS}, that cannot be transformed to entangled states by global unitary operators, in $2\otimes d$ Hilbert space. However, we show that the absolutely separable states of rank-$(2d-1)$ are extreme points of such sets. We then discuss conditions to examine if a given full-rank absolutely separable state is an interior point or a boundary point of {\bf AS}. Moreover, we construct two-qubit absolutely separable states which are boundary points but not extreme points of {\bf AS} and prove the existence of full-rank extreme points of {\bf AS}. Properties of certain interior points are also explored. We further show that by examining the boundary of the above set, it is possible to develop an algorithm to generate the absolutely separable states which stay outside the maximal ball. By considering paradigmatic noise models, we find the amount of local noise which the input entangled states can sustain, so that the output states do not become absolutely separable. Interestingly, we report that with the decrease of entanglement of the pure input state, critical depolarizing noise value, transferring an entangled state to an absolutely separable one, increases, thereby showing advantages of sharing nonmaximally entangled states. Furthermore, when the input two-qubit states are Haar uniformly generated, we report a hierarchy among quantum channels according to the generation of absolutely separable states.
AWAP: Adaptive weighted attribute propagation enhanced community detection model for bitcoin de-anonymization

Bitcoin is a kind of decentralized cryptocurrency and widely used in online payment partially for its anonymity mechanism. The anonymity, however, also attracts the usage of cryptocurrency by criminals in ransomware and money laundering, and limits its further application and development. In this paper, we aim to improve Bitcoin’s auditability with de-anonymization. Many previous studies have used heuristic clustering or supervised machine learning to analyze the historical transactions for identifying user behaviors. However, heuristic clustering only considers the topological structure of the transaction graph and ignores the transaction attributes. While supervised learning is usually limited by the size of labeled datasets, resulting in an unsatisfactory accuracy . To resolve the above problems, we propose an A daptive W eighted A ttribute P ropagation enhanced community detection model, named AWAP, which considers both the transaction’s topological structure and the transaction attributes. We first parse the transaction data from the public ledger and construct a bipartite graph to describe correlations between addresses and transactions. Then, we use a five-step feature engineering pipeline to extract Bitcoin address attributes and build an attribute graph. Finally, we design an adaptive weighted attribute propagation algorithm running on the attribute graph to classify the Bitcoin addresses and identify user behaviors. Extensive experiments highlight that AWAP model achieves 12% higher accuracy and 25% higher F-score on average, compared to the state-of-the-art Bitcoin address classifiers and other community detection algorithms. To evaluate the effectiveness of AWAP, we also present two case studies on Bitcoin address classification and Bitcoin trace-ability in ransomware.
EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification

Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from the Web are labeled using Amazon Mechanical Turk crowd-sourcing tool by human labelers. ImageNet is useful for transfer learning because of the sheer volume of its dataset and the number of object classes available. Transfer learning using pretrained models is useful because it helps to build computer vision models in an accurate and inexpensive manner. Models that have been pretrained on substantial datasets are used and repurposed for our requirements. Scene recognition is a widely used application of computer vision in many communities and industries, such as tourism. This study aims to show multilabel scene classification using five architectures, namely, VGG16, VGG19, ResNet50, InceptionV3, and Xception using ImageNet weights available in the Keras library. The performance of different architectures is comprehensively compared in the study. Finally, EnsemV3X is presented in this study. The proposed model with reduced number of parameters is superior to state-of-of-the-art models Inception and Xception because it demonstrates an accuracy of 91%.
Mask Wearing Classification using CNN

Indonesian official team for handling COVID-19 stated that from total 358,659 tested people, 42,762 of them are confirmed to be positive COVID-19, approximately 11.7% from total tested people and continues to increase every day. Therefore, wearing a mask becomes a new habit to adapt new normal life, in order to prevent COVID-19 virus spread in Indonesia. It is important for everyone to wear a mask especially in public places with high intense of human interaction. Public area managers need to monitor and ensure everyone is wearing mask properly. To do a better public places monitoring, this work proposes a deep-learning based image classification model to detect whether a person is wearing a mask or not wearing a mask in real-time condition. Proposed architecture is using Convolutional Neural Network (CNN) to learn from annotated private dataset to match the Indonesian characteristics. Result gives 97% accuracy for classifying face.
Linear optimal transport embedding: provable Wasserstein classification for certain rigid transformations and perturbations

Abstract Discriminating between distributions is an important problem in a number of scientific fields. This motivated the introduction of Linear Optimal Transportation (LOT), which embeds the space of distributions into an $L^2$-space. The transform is defined by computing the optimal transport of each distribution to a fixed reference distribution and has a number of benefits when it comes to speed of computation and to determining classification boundaries. In this paper, we characterize a number of settings in which LOT embeds families of distributions into a space in which they are linearly separable. This is true in arbitrary dimension, and for families of distributions generated through perturbations of shifts and scalings of a fixed distribution. We also prove conditions under which the $L^2$ distance of the LOT embedding between two distributions in arbitrary dimension is nearly isometric to Wasserstein-2 distance between those distributions. This is of significant computational benefit, as one must only compute $N$ optimal transport maps to define the $N^2$ pairwise distances between $N$ distributions. We demonstrate the benefits of LOT on a number of distribution classification problems.
Cascaded Feature Warping Network For Unsupervised Medical Image Registration

Deformable image registration is widely utilized in medical image analysis, but most proposed methods fail in the situation of complex deformations. In this paper, we present a cascaded feature warping network to perform the coarse-to-fine registration. To achieve this, a shared-weights encoder network is adopted to generate the feature pyramids for the unaligned images. The feature warping registration module is then used to estimate the deformation field at each level. The coarse-to-fine manner is implemented by cascading the module from the bottom level to the top level. Furthermore, the multi-scale loss is also introduced to boost the registration performance. We employ two public benchmark datasets and conduct various experiments to evaluate our method. The results show that our method outperforms the state-of-the-art methods, which also demonstrates that the cascaded feature warping network can perform the coarse-to-fine registration effectively and efficiently.
Witnessing the survival of time-energy entanglement through biological tissue and scattering media

We demonstrate the preservation of time-energy entanglement of near-IR photons through thick biological media ($\leq$1.55 mm) and tissue ($\leq$ 235 $\mu$m) at room temperature. Using a Franson-type interferometer, we demonstrate interferometric contrast of over 0.9 in skim milk, 2% milk, and chicken tissue. This work supports the many proposed opportunities for nonclassical light in biological imaging and analyses from sub-shot noise measurements to entanglement-enhanced fluorescence imaging, clearly indicating that the entanglement characteristics of photons can be maintained even after propagation through thick, turbid biological samples.
Driving and tilt‐hovering – An agile and manoeuvrable aerial vehicle with tiltable rotors

Driving and tilt-hovering help unmanned aerial vehicles extend their time of operation and the visible range of the equipped sensors. Standard quadrotors are underactuated, whose translational and rotational motions are strongly coupled. As a result, it is impossible to maintain the attitude invariably while changing position. Meanwhile, larger flying speed results in greater air resistance, which is also not desirable. A novel aerial platform with tiltable rotors is proposed. Compared with standard quadrotors, the aerial vehicle has three special capabilities: vector-flying, tilt-hovering, and driving. The platform consists of two tilting-axes to change the direction of thrusts with respect to the aircraft body. To guarantee flexible manoeuvres, a geometric tracking control scheme that adapts on the fly to the thrust vectoring is exploited. Manoeuvrability of the proposed vehicle in different manoeuvres is demonstrated, such as flight with vectoring thrust that can reduce the flight resistance, tilt-hovering expanding the reachable set of the equipped sensors. The prototype equipped with an electromechanical controller is constructed, and several associated preliminary experiments are performed. The feasibility of the mechanical design and the geometric control strategy are demonstrated.
Visual analytics for technology and innovation management

Abstract The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.
Sample Specific Generalized Cross Entropy for Robust Histology Image Classification

The accuracy of deep learning classifiers trained using the cross entropy loss function suffers even when a fraction of training labels are wrong or input images are uninformative. Training images and labels for computational pathology are often noisy due to the difficulty in signal localization and certain disease classifications being subjective discretization of the underlying continuums of disease conditions. For training classifiers robust to input and label noise, we propose a modified and sample-specific version of generalized cross entropy loss. We take advantage of the bootstrapping properties in deep learning models in order to design loss functions that are aware of the difficulty of classifying individual samples, due to either the label noise or the lack of a strong visual signal. We carry out extensive experiments to validate our approach by comparing against the models trained using other loss functions. The superior performance of our methodology in all the experiments support the requirement of using robust loss functions for histology image classification.
A fixed structure learning automata‐based optimization algorithm for structure learning of Bayesian networks

Abstract One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem.
Taking ‘A walk through dementia’: exploring care home practitioners’ experiences of using a virtual reality tool to support dementia awareness

Abstract Emerging research has outlined the possibility for virtual reality (VR) experiences, which situate users into the perspective of someone living with dementia, to enhance dementia awareness. Currently, there is limited VR research that engages care home practitioners. It is imperative this population has high levels of dementia education given their requirements to provide care and support to residents, many of whom will be living with the condition. This paper reports on an exploratory qualitative study designed to elicit the experiences of care home practitioners who engaged with the VR application: ‘A walk through dementia’. Twenty practitioners, across four care homes in the United Kingdom, watched the VR scenarios and provided their views on the experience and the potential for the VR tool to be developed into a wider training programme to support dementia awareness. Data were collected via focus group discussions. Following an inductive thematic analysis, we constructed three themes. These suggested participants perceived the VR application offered them a convincing and immersive experience that was insightful and evocative , and provided ‘next-level’ dementia-awareness training that enabled them to reflect on care practices. Although the findings highlight important challenges for practitioners and developers wishing to use VR within dementia care, they suggest this application may be an engaging experiential learning tool that can provide care home staff with deeper cognitive and emotional awareness of living with dementia. Further work, drawing on these preliminary insights, is required to ensure the VR tool can be incorporated into a training programme that can positively contribute to the ‘dementia-friendly communities’ agenda.
A chaotic grey wolf optimizer for constrained optimization problems

Abstract Bio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well‐known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching‐Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way.
Celltrack R-CNN: A Novel End-To-End Deep Neural Network For Cell Segmentation And Tracking In Microscopy Images

Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine. In this study, we propose a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework, where cell detection and segmentation are performed with a current instance segmentation pipeline and cell tracking is implemented by integrating Siamese Network with the pipeline. Besides, tracking performance is improved by incorporating spatial information into the network and fusing spatial and visual prediction. Our approach was evaluated on the DeepCell benchmark dataset. Despite being simple and efficient, our method outperforms state-of-the-art algorithms in terms of both cell segmentation and cell tracking accuracies.
An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.
AAA: Fair Evaluation for Abuse Detection Systems Wanted

User-generated web content is rife with abusive language that can harm others and discourage participation. Thus, a primary research aim is to develop abuse detection systems that can be used to alert and support human moderators of online communities. Such systems are notoriously hard to develop and evaluate. Even when they appear to achieve satisfactory performance on current evaluation metrics, they may fail in practice on new data. This is partly because datasets commonly used in this field suffer from selection bias, and consequently, existing supervised models overrely on cue words such as group identifiers (e.g., gay and black) which are not inherently abusive. Although there are attempts to mitigate this bias, current evaluation metrics do not adequately quantify their progress. In this work, we introduce Adversarial Attacks against Abuse (AAA), a new evaluation strategy and associated metric that better captures a model’s performance on certain classes of hard-to-classify microposts, and for example penalises systems which are biased on low-level lexical features. It does so by adversarially modifying the model developer’s training and test data to generate plausible test samples dynamically. We make AAA available as an easy-to-use tool, and show its effectiveness in error analysis by comparing the AAA performance of several state-of-the-art models on multiple datasets. This work will inform the development of detection systems and contribute to the fight against abusive language online.