Reviewerly/exHarmony huggingface dataset:
Augmented Reality Interface for Robot-Sensor Coordinate Registration

Abstract Accurate registration of Cartesian coordinate systems is necessary to facilitate metrology-based solutions for industrial robots in production environments. Conducting coordinate registration between industrial robots and their metrological systems requires measuring multiple points in the robot’s and sensor system’s coordinate frames. However, operators lack intuitive tools to interface, visualize, and characterize the quality of the selected points in the robot workspace for robot–sensor coordinate registration. This article proposes an augmented reality system for human-in-the-loop, robot–sensor coordinate registration to efficiently record and visualize the pose-dependent quality of computing the robot–sensor transformation. Furthermore, this work establishes metrics to define the relative quality of measurement points used in robot–sensor coordinate registration, which is shown by the augmented reality application. Experiments were conducted demonstrating the augmented reality environment in addition to investigating the pose dependency of the measurement point quality. The results indicate that the proposed metrics highlight the dependency of the poses on both robot and sensor placement and that the augmented reality system can provide a human-in-the-loop interface for robot–sensor coordinate registration.
An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification

Abstract Clustering is a technique used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, clustering for probability density functions (CDF) has been studied to capture data uncertainty. In CDF, automatic clustering is a clever technique that can determine the number of clusters automatically. However, current automatic clustering algorithms update the new probability density function (pdf) based on the weighted mean of all previous pdfs , resulting in slow convergence. This paper proposes an efficient automatic clustering algorithm for pdfs. In the proposed approach, the update of is based on the weighted mean of , where is the number of pdfs and . This technique allows for the incorporation of recently updated pdfs, leading to faster convergence. This paper also pioneers the applications of certain CDF algorithms in the field of surface image recognition. The numerical examples demonstrate that the proposed method can result in a rapid convergence at some early iterations. It also outperforms other state‐of‐the‐art automatic clustering methods in terms of the Adjusted Rand Index and the Normalized Mutual Information. Additionally, the proposed algorithm proves to be competitive when clustering material images contaminated by noise. These results highlight the applicability of the proposed method in the problem of surface image recognition.
A user experience analysis for a mobile Mixed Reality application for cultural heritage

Abstract Mixed Reality has emerged as a valuable tool for the promotion of cultural heritage. In this context, in particular, the metaphor of virtual portals allows the virtual visit of monuments that are inaccessible or no longer exist in their original form, integrating them into the real environment. This paper presents the development of a Mixed Reality mobile application that proposes a virtual reconstruction of the church of Sant’Elia in Ruggiano, in the southern province of Lecce (Italy). By placing the virtual portal in the same place where the entrance of the church was located, the user can cross this threshold to enter inside and make a virtual journey into the past. The user experience was evaluated by administering a questionnaire to 60 users who tried the application. From the data collected, four user experience factors were identified (interest, focus of attention, presence and usability), which were compared between young and old, male and female users, and between users who had already visited the church in person and all other users. In general, the scores reveal a total independence of the other three factors from usability and a very high level of interest.
Down-scale simplified non-local attention networks with application to image denoising

Non-local (NL) attention modules or transformer-based methods have been widely applied in various image processing tasks. However, the computation of the long-range similarity is very expensive, which greatly limits the further application of the NL attention modules. Motivated by the recurrence law of image patches across different scales, we propose an efficient down-scale simplified NL (DSNL) attention module. In our method, the deep feature maps are divided into several feature maps in the coarse scales, which contain the cleaner version of feature patches in the original feature maps. Then the NL attention can be implemented on smaller and clearer feature maps. Numerical experiments ons image denoising demonstrate that the proposed attention module consistently outperforms the original patch-based NL attention modules on both visual quality and GPU time. The classical ResNet which integrates the proposed attention module can product favorable results compared to many state-of-the-art methods.
NeuralReshaper: single-image human-body retouching with deep neural networks

In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks. To achieve globally coherent reshaping effects, our approach follows a fit-then-reshape pipeline, which first fits a parametric 3D human model to a source human image and then reshapes the fitted 3D model with respect to user-specified semantic attributes. Previous methods rely on image warping to transfer 3D reshaping effects to the entire image domain and thus often cause distortions in both foreground and background. In contrast, we resort to generative adversarial nets conditioned on the source image and a 2D warping field induced by the reshaped 3D model, to achieve more realistic reshaping results. Specifically, we separately encode the foreground and background information in the source image using a two-headed UNet-like generator, and guide the information flow from the foreground branch to the background branch via feature space warping. Furthermore, to deal with the lack-of-data problem that no paired data exist (i.e., the same human bodies in varying shapes), we introduce a novel self-supervised strategy to train our network. Unlike previous methods that often require manual efforts to correct undesirable artifacts caused by incorrect body-to-image fitting, our method is fully automatic. Extensive experiments on both indoor and outdoor datasets demonstrate the superiority of our method over previous approaches.
Quantization to speedup approximate nearest neighbor search

Abstract The quantization-based approaches not only are the effective methods for solving the problems of approximate nearest neighbor search, but also effectively reduce storage space. However, many quantization-based approaches usually employ fixed nprobes to the search process for each query. This will lead to extra query consumption. Additionally, we observed that as the number of points in each cluster center of product quantization increases, the query cost also increases. To address this issue, we propose an acceleration strategy based on the IVF-HNSW framework to further speed up the query process. This strategy involves introducing an adaptive termination condition for queries and reducing the number of data points accessed by building HNSW results. Through extensive experiments, we have shown that our proposed method significantly accelerates the nearest neighbor search process.
The Irresistible Simplicity of Preventing Harm

Abstract In Loomis v. Amazon.com, the California Court of Appeal confronts the most pressing products liability issue of our time: the extent to which an online marketplace is liable for injuries caused by defective products sold on its platform. In a trailblazing concurrence destined to enter the torts canon, Justice John Wiley highlights Amazon’s ability to influence the safety of the products it makes available for sale, and uses the case to remind us that the “deep structure of modern tort law” is built to “minimize the social costs of accidents.” Similarly, Justice Wiley cuts a clear path through the thicket of the economic loss rule, institutional (or vicarious) liability and punitive damages cases by following the torts lodestar: the irresistible simplicity of preventing harm.
Fraud Detection under Siege: Practical Poisoning Attacks and Defense Strategies

Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this article, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle , which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: During the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.
Bayesian multitask learning for medicine recommendation based on online patient reviews

We propose a drug recommendation model that integrates information from both structured data (patient demographic information) and unstructured texts (patient reviews). It is based on multitask learning to predict review ratings of several satisfaction-related measures for a given medicine, where related tasks can learn from each other for prediction. The learned models can then be applied to new patients for drug recommendation. This is fundamentally different from most recommender systems in e-commerce, which do not work well for new customers (referred to as the cold-start problem). To extract information from review texts, we employ both topic modeling and sentiment analysis. We further incorporate variable selection into the model via Bayesian LASSO, which aims to filter out irrelevant features. To our best knowledge, this is the first Bayesian multitask learning method for ordinal responses. We are also the first to apply multitask learning to medicine recommendation. The sample code and data are made available at GitHub: https://github.com/thrushcyc-github/BMull.We evaluate the proposed method on two sets of drug reviews involving 17 depression/high blood pressure-related drugs. Overall, our method performs better than existing benchmark methods in terms of accuracy and AUC (area under the receiver operating characteristic curve). It is effective even with a small sample size and only a few available features, and more robust to possible noninformative covariates. Due to our model explainability, insights generated from our model may work as a useful reference for doctors. In practice, however, a final decision should be carefully made by combining the information from the proposed recommender with doctors' domain knowledge and past experience.The sample code and data are publicly available at GitHub: https://github.com/thrushcyc-github/BMull.
SapiPin: Observations on PIN-code typing dynamics

Abstract In this paper, we report on PIN-code typing behaviour on touchscreen devices of 112 subjects. Detailed statistical analysis revealed that the major di erence between subjects is in inter-key latency. Key-press duration variations are insignificant compared to inter-key latency variations. Subjects were grouped into meaningful clusters using clustering. The resulting clusters were of slow, medium, and fast typists. The dataset was split randomly into two equal size subsets. The first subset was used to train different synthetic data generators, while the second subset was used to evaluate an authentication attack using the generated synthetic data. The evaluation showed that slow typists were the hardest to attack. Both the dataset and the software are publicly available at https://github.com/margitantal68/sapipin_paper .
Optimal sufficient requirements on the embedded Ising problem in polynomial time

Abstract One of the central applications for quantum annealers is to find the solutions of Ising problems. Suitable Ising problems, however, need to be formulated such that they, on the one hand, respect the specific restrictions of the hardware and, on the other hand, represent the original problems which shall actually be solved. We evaluate sufficient requirements on such an embedded Ising problem analytically and transform them into a linear optimization problem. With an objective function aiming to minimize the maximal absolute problem parameter, the precision issues of the annealers are addressed. Due to the redundancy of several constraints, we can show that the formally exponentially large optimization problem can be reduced and finally solved in polynomial time for the standard embedding setting where the embedded vertices induce trees. This allows to formulate provably equivalent embedded Ising problems in a practical setup.
DegreEmbed: Incorporating entity embedding into logic rule learning for knowledge graph reasoning

Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human problem solving. However, KGs are usually huge and there are inevitably missing facts in KGs, thus undermining applications such as question answering and recommender systems that are based on knowledge graph reasoning. Link prediction for knowledge graphs is the task aiming to complete missing facts by reasoning based on the existing knowledge. Two main streams of research are widely studied: one learns low-dimensional embeddings for entities and relations that can explore latent patterns, and the other gains good interpretability by mining logical rules. Unfortunately, the heterogeneity of modern KGs that involve entities and relations of various types is not well considered in the previous studies. In this paper, we propose DegreEmbed, a model that combines embedding-based learning and logic rule mining for inferring on KGs. Specifically, we study the problem of predicting missing links in heterogeneous KGs from the perspective of the degree of nodes. Experimentally, we demonstrate that our DegreEmbed model outperforms the state-of-the-art methods on real world datasets and the rules mined by our model are of high quality and interpretability.
A study on requirement optimization of intelligent health care information system for rural elderly based on improved FKM-TOPSIS method

Against the backdrop of China’s young and middle-aged population moving to the cities to work, older adults in rural areas of China are facing even more severe old-age and healthcare dilemmas. Under such circumstances, how to improve the functions of age-appropriate smart wearable products to meet the needs of older adults in rural areas for old age and health care has become a focal issue in Chinese society. In this paper, we change the traditional Fuzzy Kano Model (FKM) research method, quantify the research results, and use the superiority and inferiority coefficient formula to scientifically classify user needs after conducting research on different functions of smart wearable devices and obtaining different user needs: M denotes must-be demand; O denotes performance demand; A denotes attractive demand; and I denote indifferent demand. A theoretical multidimensional analysis of the generated data was performed to determine the requirements priority of the rural elderly. Then, using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach, compute the relative closeness of user satisfaction, i.e., the priority of functional needs. Finally, testing the product prototypes of “PC-based and mobile-based” information systems of smart wearable devices and identifying the most critical demand concentrated on emergency assistance for rural older adults. Such as 120 Call, Call Emergency Contacts, Positioning System, Blood Pressure Testing, Heartbeat Pulse Monitoring, etc. These functions can effectively reduce the health risks of older adults in critical emergencies, confirm the scientific character of user demand prioritization, establish the viability of the study, and provide new ideas for intelligent product-related research to continue.
Textual outlier detection with an unsupervised method using text similarity and density peak

Abstract Text mining is an intriguing area of research, considering there is an abundance of text across the Internet and in social medias. Nevertheless outliers pose a challenge for textual data processing. The ability to identify this sort of irrelevant input is consequently crucial in developing high-performance models. In this paper, a novel unsupervised method for identifying outliers in text data is proposed. In order to spot outliers, we concentrate on the degree of similarity between any two documents and the density of related documents that might support integrated clustering throughout processing. To compare the e ectiveness of our proposed approach with alternative classification techniques, we performed a number of experiments on a real dataset. Experimental findings demonstrate that the suggested model can obtain accuracy greater than 98% and performs better than the other existing algorithms.
A Transverse Hamiltonian Approach to Infinitesimal Perturbation Analysis of Quantum Stochastic Systems

This paper is concerned with variational methods for open quantum systems with Markovian dynamics governed by Hudson-Parthasarathy quantum stochastic differential equations. These QSDEs are driven by quantum Wiener processes of the external bosonic fields and are specified by the system Hamiltonian and system-field coupling operators. We consider the system response to perturbations in these operators and introduce a transverse Hamiltonian which encodes the propagation of the perturbations through the unitary system-field evolution. This approach provides an infinitesimal perturbation analysis tool which can be used for the development of optimality conditions in quantum control and filtering problems. As performance criteria, such settings employ quadratic (or more complicated) cost functionals of the system and field variables to be minimized over the energy and coupling parameters of system interconnections. We demonstrate an application of the transverse Hamiltonian variational approach to a mean square optimal coherent quantum filtering problem for a measurement-free field-mediated cascade connection of a quantum system with a quantum observer.
Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation

Abstract Automatic detection of tissue types on whole-slide images (WSI) is an important task in computational histopathology that can be solved with convolutional neural networks (CNN) with high accuracy. However, the black-box nature of CNNs rightfully raises concerns about using them for this task. In this paper, we reformulate the task of tissue type detection to multiple binary classification problems to simplify the justification of model decisions. We propose an adapted Bag-of-local-Features interpretable CNN for solving this problem, which we train on eight newly introduced binary tissue classification datasets. The performance of the model is evaluated simultaneously with its decision-making process using logit heatmaps. Our model achieves better performance than its non-interpretable counterparts, while also being able to provide human-readable justification for decisions. Furthermore, the problem of data scarcity in computational histopathology is accounted for by using data augmentation techniques to improve both the performance and even the validity of model decisions. The source code and binary datasets can be accessed at: https://github.com/galigergergo/BolFTissueDetect .
Improved multi-level fuzzy integrated assessment algorithm for tourism environment carrying ability based on cloud model

In recent years, tourism has developed rapidly and made great contributions to the economic enhancement of various regions; While tourism environment carrying ability assessment is the key to tourism sustainable development. The randomness and fuzziness of the traditional multi-level fuzzy comprehensive tourism environmental carrying ability assessment model cannot be combined effectively. In view of this, to construct a reasonable and objective assessment model, this study improves the multi-level fuzzy comprehensive tourism environmental carrying ability assessment model based on cloud model. The results indicate that the unimproved model judges that this tourism environment carrying ability level corresponds to level 2 for selecting tourism destination. And it is in a suitable load state. The evaluation results on the foundation of cloud model improved multi-level fuzzy comprehensive tourism environmental carrying ability assessment show that its Ex is 5.748, En is 1,296 and He is 0.1, which is between moderate to slightly overloaded, and the overall state is moderate, but there is a tendency to develop towards slightly overloaded. The evaluation results of the improved model are more intuitive in showing the carrying capacity of the tourism environment, and these evaluation results are more objective and reliable, which verifies the applicability of the research model. This research model provides a theoretical basis and data support for the study of tourism environment carrying capacity.