Reviewerly/exHarmony huggingface dataset:
Learning to refine object boundaries

Existing Deep-Learning-based contour detectors suffer from the issues of the sharpness and the correctness of their predictions, which often need offline post-processing to sharpen the results as well as improve model performance. In this work, we present a novel method that can learn to refine object contour in training and directly output crisp object boundaries in inference. To this end, we first introduce a keypoint-focal loss that draws point-based attention to the isolated contour annotations. It allows an edge detector to jointly optimize the appearance thickness and the localization accuracy of predictions in the training procedure. Moreover, we present a regularization loss to further improve the performance of an edge detector. Lastly, we present the Contour Transformer model for precisely localizing object boundaries in images. We repurpose and integrate a Transformer-style hyper module into an encoder–decoder network, effectively aggregating global contextual information on high-level features and significantly enhancing the discriminative power for classifying foreground/background pixels. We train and test our Attention and Contour Transformer detector (ACTD) on four widely adopted datasets, i.e., BSDS500, NYUD, Multi-Cue, and RoadNet. The proposed method achieves an ODS F-score of 0.826 on BSDS500 and an ODS F-score of 0.783 on NYUD, outperforming previous top detectors.
Conceptual orthospaces—Convexity meets negation

Neural networks and in general subsymbolic learning approaches perform well on usual learning tasks, but they are black boxes lacking desired properties such as explainability or trustworthiness. Fully integrated symbolic/subsymbolic systems account for these properties, e.g., through the injection of symbolic information into a subsymbolic learning framework. Systems relying on embeddings can be considered as specific examples of such systems where facts, expressed in some logic, are embedded into a continuous space. Instances mentioned in the facts are mapped to points and the relations/concepts mentioned in the facts are mapped to a region in that space. This allows to exploit geometric regularities in order to tackle typical learning tasks such as link prediction. Embeddings provide the first step towards filling the gap between qualitative, Tarskian style semantics, which is used for deductive reasoning over the facts, and quantitative structures, which are used for representing objects, relations, and concepts for learning purposes. However, to enable meaningful reasoning, embeddings of relations and concepts are not allowed to be shaped arbitrarily. Especially, convex sets turned out to be appropriate due to their computational advantages and due to their foundation in cognition. Convexity can be defined with a ternary betweenness relation and concepts can be defined as betweenness-closed (= convex) sets. Though many interesting phenomena of cognitive reasoning can be explained in such a framework, it is at least not obvious how to use betweenness for other, more logico-formal aspects of reasoning that, e.g., require defining logical operators. In particular, for the logical operator of negation other mathematical structures such as the orthoframes of Goldblatt (1974) [13] have proven more useful. In this paper, we provide results on the connection between convexity and orthoframes. In particular, we investigate the construction of convex concepts equipped with an orthogonality relation. We give a universal construction of a betweenness relation over an orthoframe and show that based on Euclidean betweenness (thus using the classical notion of convexity) and under some natural restrictions, convex cones are the only structures capable of modeling such a space.
Visual question generation for explicit questioning purposes based on target objects

Visual question generation aims to focus on some target objects in an image to generate questions with certain questioning purposes. Existing studies mainly utilize an answer to extract the target object corresponding to the questioning purpose for questioning. However, answers fail to accurately and completely map to every target object, such as the objects corresponding to the answer are ambiguous or the answers are the relationship between multiple objects. To address this problem, we propose a content-controlled question generation model, which generates questions based on a given target object set specified from an image. Considering that the target objects have different contributions during the generation process, we design a recurrent generative architecture to explicitly control attention to different objects and their corresponding image information at each generative stage. Extensive experiments on the VQA v2.0 dataset and the Visual7w dataset show that the proposed model outperforms the state-of-the-art models and can controllably generate questions with specified content.
Consumer-Perceived Risks and Sustainable Development of China’s Online Gaming Market: Analysis Based on Social Media Comments

Online gaming constitutes an indispensable facet of China’s digital economy, catalyzing consumer discussions on social media platforms. This study employs a comprehensive natural language processing framework, encompassing topic mining, multi-label classification, and sentiment analysis, to evaluate consumers’ psychological perceptions of the risks associated with online games through social media comments. This study identifies 11 distinct perceived risk topics, including “Excessive Temptation”, “Entry Regulation”, and “Culture Implantation”. Numerous comments encompass multiple topics, each infused with diverse emotional inclinations, thus unveiling disparate consumer perspectives. These findings underscore the critical significance of addressing potential perceived risks and mitigating negative consumer emotions for enterprises operating within online gaming. Such measures are pivotal to maintaining a brand image, business reputation, and enduring growth. Furthermore, this study extends valuable insights to regulatory bodies, contributing to enhancing administrative efficiency, safeguarding consumer rights, and fostering a robust and sustainable trajectory within China’s online gaming market.
On the use of sentiment analysis for linguistics research. Observations on sentiment polarity and the use of the progressive in Italian

This article offers a conceptual and methodological contribution to linguistics by exploring the potential value of using sentiment analysis (SA) for research in this field. Firstly, it discusses the limitations and advantages of using SA for linguistics research including the wider epistemological implications of its application outside of its original conception as a product reviews analysis tool. Methodologically, it tests its applicability against an established linguistic case: the correlation between subjective attitudes such as surprise, irritation and discontent and the use of the progressive. The language example is Italian for which this function of the progressive form has not been analyzed yet. The analysis applies FEEL-IT, a state-of-the-art transformer-based machine learning model for emotion and sentiment classification in Italian on language samples from various sources as collected in Evalita-2014 (238,556 words). The results show statistically significant correlations between negative subjective attitudes and the use of the progressive in line with previous accounts in other languages. The article concludes with a few additional propositions for practitioners and researchers using SA.
From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining

In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm’s superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs.
AI APPLICATIONS IN BLOCKCHAIN TECHNOLOGY

Blockchain technology is one of the most important and hot topics because of the last bull run in 2021 and the dramatic increase of knowledge in this sphere. Many famous hedge funds and big companies are investing in the cryptocurrency sphere to diversify their portfolio, and mostly all the CEOs of technological giants (e.g. Google, Tesla, Twitter, etc.) consider blockchain as a “future of financial systems”. This sphere covers not only the finance sector but also any direction where centralized authorities participate. With appropriate consensus mechanisms created and in case they are proven to work from both efficiency and security aspects, any type of central-ized service may become decentralized empowering people to have more control on tools that they are using. Despite its power of usage; we can also consider blockchain goals and tasks from Artificial intelligence (AI) perspective. Blockchains are a great source of high-quality data, which we can use to solve various complex and exciting problems. Due to the huge amount of data that AI models provide, there is a need for data scientists and techniques to analyze data fast, and effectively and build pipelines to extract newly generated data from the blockchain. This article will provide a possi-ble set of problems AI tries to solve, and our study case, where its usage would be helpful.
Property graph representation learning for node classification

Abstract Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node classification, link prediction and recommendation. Many real-world graphs can be characterized as the property graphs, because besides the structure information, there exists rich property information related to each node in the graphs. Many existing graph representation learning methods—e.g. random walk-based methods like and , focus only on the structure of graph for learning the node embedding. Although graph representation learning based on neural networks (e.g. typical methods such as ) uses the property of nodes as the initial features of nodes and then aggregates feature information of the neighbours, their limitation is that the neighbourhood of a node is considered to be uniform—i.e. there is no way to differentiate among neighbours of a node when learning a node embedding. Additionally, their definition of neighbourhood is local, i.e. only nodes connected to the current node are considered as neighbours. Hence, those methods fail to capture implicit/latent relationships among nodes, which are implicit in the given structure. In this study, our aim is to improve the performance of graph representation learning methods on property graphs. We present a new framework called ()—a graph representation learning framework to address above-mentioned limitations. Our proposed framework relies on the notion of latent neighbourhood, as well as systematic sampling of neighbouring nodes to obtain better representation of the nodes. The experimental results on five publicly available graph datasets demonstrate that outperforms state-of-the-art baselines for the task of node classification. We further evaluate the superiority of our proposed formulation by defining a novel quantitative metric to measure the usefulness of the sampled neighbourhood in the graph.
Exploring English newspaper reading of Chinese college English majors in mobile-assisted language learning

Abstract With advances in mobile technology, mobile devices have been widely applied in language learning. English newspaper reading apps are emerging language learning tools for Chinese English majors. However, little has been published on reading in mobile-assisted language learning (MALL) environments. Studies of English newspaper reading are limited to the classroom environment. To explore English newspaper reading in MALL, this study analysed the features of English newspaper reading apps, usage patterns, and users’ language learning strategies by adopting both quantitative and qualitative methods. This research found that (1) apps incorporating multimedia technology mainly provide vocabulary instruction. Most English majors prefer English newspaper reading apps with explanations; (2) Learners tend to use WeChat as the main reading platform instead of utilising apps in combination; and (3) The most frequently used strategies by English majors are affective strategies, while the least frequently used strategies are social strategies, although most English newspaper reading apps support collaborative learning. The study is expected to inspire the design of English newspaper reading apps and the reform of the English newspaper reading course.
Tensor tomography of the residual stress field in graded-index YAG’s single crystals

Abstract This work presents an application of tensor field tomography for non-destructive reconstructions of axially symmetric residual stresses in a graded-index YAG single crystal for the case of beam deflection. The axis of the cylinder coincides with the crystallographic axis [001] of the single crystal and it has an axially symmetric refractive index distribution. The transformation of the polarization of light is measured in a plane orthogonal to the axis of the cylinder. Stresses are determined within the framework of the Maxwell piezo-optic law (linear dependence of the permittivity tensor on stresses) and small rotation of quasi principal stress axes. This paper generalizes the method of integrated photoelasticity for the case of ray deflection.
A Feature Map is Worth a Video Frame: Rethinking Convolutional Features for Visible-Infrared Person Re-identification

Visible-Infrared Person Re-identification (VI-ReID) aims to search for the identity of the same person across different spectra. The feature maps obtained from the convolutional layers are generally used for loss calculation in the later stages of the model in VI-ReID, but their role in the early and middle stages of the model remains unexplored. In this article, we propose a novel Rethinking Convolutional Features (ReCF) approach for VI-ReID. ReCF consists of two modules: Middle Feature Generation (MFG), which utilizes the feature maps in the early stage to reduce significant modality gap, and Temporal Feature Aggregation (TFA), which uses the feature maps in the middle stage to aggregate multi-level features for enlarging the receptive field. MFG generates middle modality features in the form of a learnable convolution layer as a bridge between RGB and IR modalities, which is more flexible than using fixed-parameter grayscale images and yields a better middle modality to further reduce the modality gap. TFA first treats the convolution process as a video sequence, and the feature map of each convolution layer can be considered a worthwhile video frame. Based on this, we can obtain a multi-level receptive field and a temporal refinement. In addition, we introduce a color-unrelated loss and a modality-unrelated loss to constrain the modality features for providing a common feature representation space. Experimental results on the challenging VI-ReID datasets demonstrate that our proposed method achieves state-of-the-art performance.
Development of Software for the Organization of Training of TPP Workers on the Basis of Digital Twins of Equipment

The chapter considers the task of developing software that uses digital duplicates of equipment to organize training of thermal power plant (TTP) personnel. This solution can also be used in the educational process of higher education institutions for engineering specialties. To solve the problem of implementing a prototype software developed mathematical and algorithmic support for the construction of mathematical models of various pieces of equipment for use in the digital twins of thermal power plant equipment and thermal schemes. In the course of software development the following mathematical models have been created: steam turbine cylinder model, surface-type low-pressure heater model, condenser model. Algorithmic support for building models was synthesized for use in plant personnel training systems and organization of educational processes in institutions of higher education for engineering specialties. The simulation of negative phenomena in accordance with a pre-designed scenario allows the educational process to be implemented with high efficiency. The efficiency is conditioned not only by the possibility of generation of different equipment operation modes, but also by taking into account the influence of different negative factors on the operability. The tools to create scenarios of abnormal situations: fixing the values of certain parameters, single-step, step change of parameters, creating a linear function of change of physical parameter of the object in time. The programming language for realization of the simulator software is chosen.
Advancing remote consultation through the integration of blockchain and ant colony algorithm

To guide the more reasonable and fair allocation of medical resources, to solve the problem of fee prices negotiated by various subjects in the medical and health system and patient payment, and to solve the problem of how to ensure the privacy, accuracy, consistency and traceability of data in the process of collecting patient information in each hospital, according to the operation process of a remote consultation service, a decentralized remote intelligent consultation blockchain model is proposed. The model uses the improved ant colony algorithm under a smart contract and studies the practicality of the improved ant colony algorithm on the multi-node remote consultation service simulation platform. According to the experimental analysis results, the improved ant colony algorithm can automatically execute and effectively match the target population under the smart contract.
Illumination-Aware Cross-Modality Differential Fusion Multispectral Pedestrian Detection

Multispectral information fusion technology is a practical approach to enhance pedestrian detection performance in low light conditions. However, current methods often overlook the impact of illumination on modal weights and the significance of inter-modal differential information. Therefore, this paper proposes a novel illumination-aware cross-modality differential fusion (IACMDF) model. The weights of the different modalities in the fusion stage are adaptively adjusted according to the illumination intensity of the current scene. On the other hand, the advantages of the respective modalities are fully enhanced by amplifying the differential information and suppressing the commonality of the twin modalities. In addition, to reduce the loss problem caused by the importance occupied by different channels of the feature map in the convolutional pooling process, this work adds the squeeze-and-excitation attention mechanism after the fusion process. Experiments on the public multispectral dataset KAIST have shown that the average miss rate of our method is substantially reduced compared to the baseline model.
CAM-FRN: Class Attention Map-Based Flare Removal Network in Frontal-Viewing Camera Images of Vehicles

In recent years, active research has been conducted on computer vision and artificial intelligence (AI) for autonomous driving to increase the understanding of the importance of object detection technology using a frontal-viewing camera. However, using an RGB camera as a frontal-viewing camera can generate lens flare artifacts due to strong light sources, components of the camera lens, and foreign substances, which damage the images, making the shape of objects in the images unrecognizable. Furthermore, the object detection performance is significantly reduced owing to a lens flare during semantic segmentation performed for autonomous driving. Flare artifacts pose challenges in their removal, as they are caused by various scattering and reflection effects. The state-of-the-art methods using general scene image retain artifactual noises and fail to eliminate flare entirely when there exist severe levels of flare in the input image. In addition, no study has been conducted to solve these problems in the field of semantic segmentation for autonomous driving. Therefore, this study proposed a novel lens flare removal technique based on a class attention map-based flare removal network (CAM-FRN) and a semantic segmentation method using the images in which the lens flare is removed. CAM-FRN is a generative-based flare removal network that estimates flare regions, generates highlighted images as input, and incorporates the estimated regions into the loss function for successful artifact reconstruction and comprehensive flare removal. We synthesized a lens flare using the Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) datasets, which are road scene open datasets. The experimental results showed that semantic segmentation accuracy in images with lens flare was removed based on CAM-FRN, exhibiting 71.26% and 60.27% mean intersection over union (mIoU) in the CamVid and KITTI databases, respectively. This indicates that the proposed method is significantly better than state-of-the-art methods.
Crowd Wisdom vs in-House Expertise: A Comprehensive Analysis of Quality Assurance Approaches

In the dynamic landscape of software quality assurance, this research paper conducts an insightful comparative analysis of the contrasting methodologies: crowd-sourced testing and in-house testing. By closely examining their individual merits, limitations, and adaptability across diverse project contexts, the study explores crucial metrics encompassing cost-effectiveness, comprehensive test coverage, and adept defect detection. Integrating quantitative data alongside real-world instances, the paper not only elucidates the intricate factors pivotal in making informed decisions between these methodologies but also provides practical, well-grounded recommendations for their efficacious implementation. The study serves as a compass, empowering software practitioners and decision-makers with a discerning outlook to deftly navigate and choose the testing strategy that impeccably aligns with their project requisites and overarching organizational goals.