Reviewerly/exHarmony huggingface dataset:
A novel image retrieval strategy based on transfer learning and hand-crafted features for wool fabric

The application of image retrieval techniques in industrial settings aims at rapid and accurate retrieval of the same or similar products from an archive to facilitate the production process. Content-based image retrieval and its applications on fabrics focused on the appearance differences and ignored the delicate differences, thus the retrieval fineness cannot meet the demands of industrial applications. In this study, a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features. The pre-trained model based on ImageNet was fine-tuned to extract features for automatic classification. The images in the wool fabric image database were classified into different categories. Meanwhile, the distribution probabilities were used to build the retrieval strategy. To form the 'feature database' in each category, oriented FAST and rotated BRIEF (ORB) was utilized for feature extractions and the vector of locally aggregated descriptors was adopted for feature aggregation. Ball tree was implemented to search the nearest neighbors for the final results. Based on the above methods, the query image was first classified automatically, and then the images were retrieved based on the retrieval strategy. A large-scale image database of wool fabrics with 82,073 images was built as the benchmark to evaluate the efficacy of the proposed method. Experiments indicated that the proposed strategy is effective for delicate image retrieval. The combination of transfer learning and hand-crafted features can discriminate the appearance and delicate differences, being superior to the existing methods on representing the delicate fabric differences. The proposed method can provide referential assistance for the production crew and reducing manual labor in the factory.
Genetic Programming With Knowledge Transfer and Guided Search for Uncertain Capacitated Arc Routing Problem

The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming (GP) is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this article, we propose a novel GP method that removes phenotypic duplicates from the source individuals to initialize the target individuals. Furthermore, assuming that the transferred knowledge used in initialization already includes all the important knowledge explored for the source problem, it is more effective to explore new regions that have not been explored for the source problem. Therefore, we propose novel genetic operators that prohibit the search from revisiting the source individuals when solving the target problem. To speed up the revisit check, we propose to adapt a powerful hashing method for routing policies that greatly improves the efficiency of the genetic operators. Our experimental results show that the proposed method significantly outperforms the existing GP approaches with knowledge transfer in terms of both initial and final solution quality.
A Two-Stage Convolutional Neural Network for Joint Demosaicking and Super-Resolution

As two practical and important image processing tasks, color demosaicking (CDM) and super-resolution (SR) have been studied for decades. However, most literature studies these two tasks independently, ignoring the potential benefits of a joint solution. In this paper, aiming at efficient and effective joint demosaicking and super-resolution (JDSR), a well-designed two-stage convolutional neural network (CNN) architecture is proposed. For the first stage, by making use of the sampling-pattern information, a pattern-aware feature extraction (PFE) module extracts features directly from the Bayer-sampled low-resolution (LR) image, while keeping the resolution of the extracted features the same as the input. For the second stage, a dual-branch feature refinement (DFR) module effectively decomposes the features into two components with different spatial frequencies, on which different learning strategies are applied. On each branch of the DFR module, the feature refinement unit, namely, densely-connected dual-path enhancement blocks (DDEB), establishes a sophisticated nonlinear mapping from the LR space to the high-resolution (HR) space. To achieve strong representational power, two paths of transformations and the channel attention mechanism are adopted in DDEB. Extensive experiments demonstrate that the proposed method is superior to the sequential combination of state-of-the-art (SOTA) CDM and SR methods. Moreover, with much smaller model size, our approach also surpasses other SOTA JDSR methods.
BRIBOT: Towards a Service-Based Methodology for Bridging Business Processes and IoT Big Data

We envisage that BPM and IoT Big Data will be the two pillars of next-generation Process-Aware Information Systems (PAIS). While IoT enables BPM to perceive and react to realtime events in the physical world, BPM can equip IoT with a well-developed modelling and implementation platform. However, the integration of BPM and IoT is facing paradigm misalignment challenges including mismatch of programming mechanisms, mismatch of resource management mechanisms, and mismatch of adaptation mechanisms. In this paper, we present the vision and architectural solution of the recently funded NSFC-DFG cooperation research project BRIBOT, which aims to develop novel service-based approaches and techniques for these challenges. The paper presents the BRIBOT methodology that comprises four parts: abstraction and servitization of IoT data, resource space that handles service and data assets, modelling and transformation of IoT and business events, and IoT-event-driven process awareness and adaptation.
Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation

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Abduction with probabilistic logic programming under the distribution semantics

In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach.
Classifying User Requirements from Online Feedback in Small Dataset Environments using Deep Learning

An overwhelming number of users access app repositories like App Store/Google Play and social media platforms like Twitter, where they provide feedback on digital experiences. This vast textual corpus comprising user feedback has the potential to unearth detailed insights regarding the users’ opinions on products and services. Various tools have been proposed that employ natural language processing (NLP) and traditional machine learning (ML) based models as an inexpensive mechanism to identify requirements in user feedback. However, they fall short on their classification accuracy over unseen data due to factors like the cost of generating voluminous de-biased labeled datasets and general inefficiency. Recently, Van Vliet et al. [1] achieved state-of-the-art results extracting and classifying requirements from user reviews through traditional crowdsourcing. Based on their reference classification tasks and outcomes, we successfully developed and validated a deep-learning-backed artificial intelligence pipeline to achieve a state-of-the-art averaged classification accuracy of ∼87% on standard tasks for user feedback analysis. This approach, which comprises a BERT-based sequence classifier, proved effective even in extremely low-volume dataset environments. Additionally, our approach drastically reduces the time and costs of evaluation, and improves on the accuracy measures achieved using traditional ML-/NLP-based techniques.
Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture

Recent monocular human performance capture approaches have shown compelling dense tracking results of the full body from a single RGB camera. However, existing methods either do not estimate clothing at all or model cloth deformation with simple geometric priors instead of taking into account the underlying physical principles. This leads to noticeable artifacts in their reconstructions, e.g. baked-in wrinkles, implausible deformations that seemingly defy gravity, and intersections between cloth and body. To address these problems, we propose a person-specific, learning-based method that integrates a simulation layer into the training process to provide for the first time physics supervision in the context of weakly supervised deep monocular human performance capture. We show how integrating physics into the training process improves the learned cloth deformations, allows modeling clothing as a separate piece of geometry, and largely reduces cloth-body intersections. Relying only on weak 2D multi-view supervision during training, our approach leads to a significant improvement over current state-of-the-art methods and is thus a clear step towards realistic monocular capture of the entire deforming surface of a clothed human.
Trainable Discrete Feature Embeddings for Quantum Machine Learning

Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Cryptanalysis of compressive interference-based optical encryption using a U-net deep learning network

We present a deep learning (DL) framework based on a U-type convolutional neural network (U-net) to analysis the security of optical interference-based encryption system with sparsity constraints. In the optical cryptosystem, two phase-only masks (POMs) can be obtained by using phase retrieval algorithm (PRA) with the constraints of complementary binary amplitude masks (BAMs). By integrating the interference technique and phase encoding, the encryption scheme achieves a high security level and is immune to conventional attack methods. Here, we investigate and construct a neural network architecture for evaluating the security of the cryptosystem based on the idea of U-net. In this method, we achieve the approximate equivalent model of the compressive interference-based optical cryptosystem by using a U-net model, which has been pre-trained by a series of ciphertext–plaintext pairs. It can be found that the cryptosystem can be broken by using the proposed DL network. The plaintexts can be retrieved from their corresponding ciphertexts without the use of security keys, such as the diffraction distances, the BAMs, etc. The results of numerical experiments are presented to show the good performance of our proposed DL-based method.
Anchor-free YOLOv3 for mass detection in mammogram

Automatic detection of mass in mammograms is a big challenge and plays a crucial role in assisting radiologists for accurate diagnosis. Applying excellent deep learning based detection networks to medical image is a direction. In this paper, an anchor-free YOLOv3 network is proposed for mass detection in mammogram. The main improvements are as follows. Anchor-based (AB) detection scheme may involve too many negative boxes, which may aggravate the unbalance between positive boxes and negative boxes. Aiming at this problem, a novel anchor-free (AF) detection scheme is developed for box regression. To alleviate the problem caused by using mean square error (MSE) as box regression loss, generalized intersection over union (GIoU) loss is adopted to train bounding box regression. Focal loss is adopted as the objectness prediction loss, which can prevent the network degradation caused by the vast number of easy negatives. Moreover, a new feature aggregation manner by summing is designed and employed in the up to down pathway. Comparative experiments are performed on two databases (publicly available dataset INbreast and private dataset TXMD). For INbreast dataset, the proposed method achieved 0.95 true positive rate (TPR) with 1.7 false positives per image (FPs/I). For TXMD dataset, the proposed method achieved 0.94 TPR with 5.75 FPs/I. Experimental results demonstrate that the proposed method outperforms YOLOv3 network and gets a promising result compared with existing mass detection methods.
Attitude Control of a Moving Mass–Actuated UAV Based on Deep Reinforcement Learning

A moving mass–actuated unmanned aerial vehicle (MAUAV) is controlled by mass sliders installed inside the airframe and has the advantages of high aerodynamic efficiency and good stealth performance. However, designing a controller for it faces severe challenges due to the strong nonlinearity and coupling of its dynamics. To this end, we proposed an attitude controller based on deep reinforcement learning for the MAUAV. It directly maps the states to the needed deflection of the actuators and is an end-to-end controller. For the sparse reward problem, the reward function required for training is reasonably designed through reward shaping to hasten the algorithm’s training speed. In training, random initialization and parameter perturbation are used to strengthen the final policy’s robustness further. The simulation results tentatively demonstrate that the proposed controller is not only robust but suboptimal. Compared with an active disturbance rejection controller (ADRC) optimized by the particle swarm algorithm, our controller still guarantees a 100% success rate in multiple unlearned scenarios, meaning it has good generalization ability.
A Two-Way alignment approach for unsupervised multi-Source domain adaptation

Domain adaptation aims at transferring knowledge from labeled source domain to unlabeled target domain. Current advances primarily concern single source domain and neglect the setting of multiple source domains. Previous unsupervised multi-source domain adaptation (MDA) algorithms only consider domain-level alignment, while neglecting the category-level information among multiple domains and the instance variations inside each domain. This paper introduces a Two-Way alignment framework for MDA (TWMDA), which considers both domain-level and category-level alignments, and addresses the instance variations. We first align the target and multiple sources on the domain-level by an adversarial learning process. To circumvent the drawbacks of adversarial learning, we further reduce the domain gap on the category-level by minimizing the distance between the category prototypes and unlabeled target instances. To address the instance variations, we design an instance weighting strategy for diverse source instances. The effectiveness of TWMDA is demonstrated on three benchmark datasets for image classification.
Effect of minerals and heavy metals in sand samples of Ponnai river, Tamil Nadu, India

River sand samples have been collected from Ponnai river, Tamil Nadu, India for characterization of minerals and heavy metals by different spectroscopic techniques. Initially, the samples were subjected by Fourier Transform-Infra Red (FT-IR) spectroscopic technique and infra-red absorption bands values are observed in the range of 515-520, 695-700, 775-780 cm-1 which shows the presence of quartz in all the samples. Similarly, infra-red peaks were absorbed for feldspar, kaolinite, calcite, gibbsite and organic carbon and confirmed by X-Ray diffraction (XRD) technique. Additionally, zircon, aragonite, magnetite and kyanite minerals were identified in the samples using only the XRD method. The concentration of heavy metals such as Pb, Cr, Zn, Ni, Hg, As, Mn, Cu has been determined by flame atomic absorption spectrometry (FAAS). An average metal concentration measured in mg kg-1 were: Pb 0.12, As 0.15, Hg 0.13, Cu 2.80, Zn 10.15 Cr 12.70, Ni 2.86 and Mn 104.94 and hence found in the order of Mn > Cr > Zn > Ni > Cu > As > Hg > Pb. These average values do not exceed the world average value and hence potentially do not affect the quality of sand in the river. In addition to that, presences of heavy metals are confirmed by scanning electron microscope equipped with energy dispersive X-ray spectrometry (SEM/EDS) analysis. In order to understand the possible natural and anthropogenic sources of heavy metals, multivariate statistical techniques such as Pearson correlation, principal component and cluster analysis were performed. Results obtained from the statistical techniques were good agreement with each other.
Cybercrime Victimization and Problematic Social Media Use: Findings from a Nationally Representative Panel Study

According to criminological research, online environments create new possibilities for criminal activity and deviant behavior. Problematic social media use (PSMU) is a habitual pattern of excessive use of social media platforms. Past research has suggested that PSMU predicts risky online behavior and negative life outcomes, but the relationship between PSMU and cybercrime victimization is not properly understood. In this study, we use the framework of routine activity theory (RAT) and lifestyle-exposure theory (LET) to examine the relationship between PSMU and cybercrime victimization. We analyze how PSMU is linked to cybercrime victimization experiences. We explore how PSMU predicts cybercrime victimization, especially under those risky circumstances that generally increase the probability of victimization. Our data come from nationally representative surveys, collected in Finland in 2017 and 2019. The results of the between-subjects tests show that problematic PSMU correlates relatively strongly with cybercrime victimization. Within-subjects analysis shows that increased PSMU increases the risk of victimization. Overall, the findings indicate that, along with various confounding factors, PSMU has a notable cumulative effect on victimization. The article concludes with a short summary and discussion of the possible avenues for future research on PSMU and cybercrime victimization.
An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification

As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method.
Explainable AI, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare

Advances in AI technologies have resulted in superior levels of AI-based model performance. However, this has also led to a greater degree of model complexity, resulting in “black box” models. In response to the AI black box problem, the field of explainable AI (xAI) has emerged with the aim of providing explanations catered to human understanding, trust, and transparency. Yet, we still have a limited understanding of how xAI addresses the need for explainable AI in the context of healthcare. Our research explores the differing explanation needs amongst stakeholders during the development of an AI-system for classifying COVID-19 patients for the ICU. We demonstrate that there is a constellation of stakeholders who have different explanation needs, not just the “user”. Further, the findings demonstrate how the need for xAI emerges through concerns associated with specific stakeholder groups i.e., the development team, subject matter experts, decision makers, and the audience. Our findings contribute to the expansion of xAI by highlighting that different stakeholders have different explanation needs. From a practical perspective, the study provides insights on how AI systems can be adjusted to support different stakeholders’ needs, ensuring better implementation and operation in a healthcare context.
The Influence of in-VR Questionnaire Design on the User Experience

Researchers study the user experience in Virtual Reality (VR) typically by collecting either sensory data or using questionnaires. While traditional questionnaire formats present it through web-based survey tools (out-VR), recent studies investigate the effects of presenting questionnaires directly in the virtual environment (in-VR). The in-VR questionnaire can be defined as an implemented user-interface object that allows interaction with questionnaires in VR that do not break the immersion. Integrating questionnaires directly into the virtual environment, however, also challenges design decisions.
Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments

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