Reviewerly/exHarmony huggingface dataset:
On the Information Coupling and Propagation of Visual 3D Perception in Vehicular Networks With Position Uncertainty

Visual 3D perception is a key interface between vehicles and the environment which provides rich perception information for autonomous vehicles. In vehicular visual 3D perception, camera position uncertainty can often degrade the perception and localization performance, and the information coupling and propagation among 3D points and cameras exist ubiquitously. However, these factors do not receive the deserving concern in previous algorithms which mainly focus on the consideration of efficiency. In this paper, we develop a statistical framework for visual 3D perception in vehicular networks from the perspective of information gains. Specifically, we first derive the influence of camera position uncertainty on perception and localization quality with geometric interpretations given by the information ellipsoid, and also present the perception loss caused by uncertainty in the camera deployment application. Then we determine the information coupling and propagation among 3D points and cameras in the vehicular network, propose the visual information graph to characterize the coupling, and derive the hierarchical structure in propagation. Moreover, we also propose the equivalent circuit for the quasi-tree network to interpret the hierarchical structure and derive the blocking and the end-link effects for network reduction. Our results provide guidelines for the development of efficient uncertainty- and coupling-aware visual 3D perception techniques in vehicular networks.
STEEX: Steering Counterfactual Explanations with Semantics

As deep learning modelsCord, Matthieu are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a trained classification model. In this work, we address the problem of producing cotual explanations for high-quality images and complex scenes. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausible and sparse modifications which preserve the overall scene structure. Furthermore, we introduce the concept of "region-targeted counterfactual explanations", and a corresponding framework, where users can guide the generation of counterfactuals by specifying a set of semantic regions of the query image the explanation must be about. Extensive experiments are conducted on challenging datasets including high-quality portraits (CelebAMask-HQ) and driving scenes (BDD100k). Code is available at: https://github.com/valeoai/STEEX .
A Modified Heat Diffusion Based Method for Enhancing Physical Images

In this paper, we present a new denoising technique based on a modification on the heat equation. This method is mainly presented to enhance some physical images that were carried out in a physical laboratory during some experiments. A one-dimensional example using a vector and a two-dimensional example using matrices are showed in this paper to clarify how applying the modified way of solution on a heat diffusion equation can remove some existing defects in these images. In addition, we are going to make a model for a heat diffusion in two directions x and y and assemble letters from components in matrices to apply our experiment on some other different colorful and greyscale images. Finally, we calculate PSNR values for our method and compare them with some research methods to conclude that enhancing images by removing undesired defects can be done by the proposed method.
Product and Process Analysis of Machine Translation into the Inflectional Language

This study focuses on the influence of quality of Machine Translation (MT) output on a translator’s performance. We analyze the translator’s effort by product analysis and process analysis. The product analysis consists of MT quality evaluation according to the Dynamic Quality Framework; using error typology and the criteria such as fluency and adequacy. We examine translator’s effort from the point of view of typing time, in the context of MT quality—focusing on error rate in language, accuracy, terminology, and style, and also in fluency and adequacy to the source text. We have found that the translator’s performance is influenced by MT quality. The typing time is very closely related to errors in language, accuracy, terminology, and style as well as to fluency and adequacy. We used the Mann-Whitney test to compare the productivity of post-editing of MT with human translation. The results of the study have shown that post-editing—compared to human translation of journalistic text from English into the inflectional Slovak language is more effective.
Learning to Search in Task and Motion Planning With Streams

Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream [Garrett et al. 2020)] have focused on optimistic planning with an incrementally growing set of objects until a feasible trajectory is found. However, this set is exhaustively expanded in a breadth-first manner, regardless of the logical and geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in block-stacking manipulation tasks.
A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm

In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance.Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures.The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds.In this research, an entropy assisted threshold is implemented for the benchmark RGB images.The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur's and Tsallis for a chosen image threshold.This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5).After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work.The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study.
RETRACTED: Research on Security of Accounting Information System in the Era of Big Data

Abstract Since the 21st century, information technology has made great progress. Especially in recent years, the concept of big data has attracted increasing attention. The construction and maintenance of accounting information security and accounting information system are also facing great opportunities and challenges. At present, in the era of big data, the importance of accounting information security and accounting work is increasing. Failure to make a good analysis of accounting information system will lead to disclosure of financial information, and even harm the economic interests of enterprises in serious cases. This paper takes strengthening information security as the theme and analyzes the development of accounting information system security.
FaceTuneGAN: Face autoencoder for convolutional expression transfer using neural generative adversarial networks

In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression. We propose a first adaptation of image-toimage translation networks, that have successfully been used in the 2D domain, to 3D face geometry. Leveraging recently released large face scan databases, a neural network has been trained to decouple factors of variations with a better knowledge of the face, enabling facial expressions transfer and neutralization of expressive faces. Specifically, we design an adversarial architecture adapting the base architecture of FUNIT and using SpiralNet++ for our convolutional and sampling operations. Using two publicly available datasets (FaceScape and CoMA), FaceTuneGAN has a better identity decomposition and face neutralization than state-of-the-art techniques. It also outperforms classical deformation transfer approach by predicting blendshapes closer to ground-truth data and with less of undesired artifacts due to too different facial morphologies between source and target.
Idea, Architecture, and Applications of 5G Enabled IoMT Systems for Smart Health Care System

The health sector is one of the important sectors of the Indian economy. The average expenditure on smart healthcare can be saved using the smart healthcare systems. Using Smart Healthcare System, death rate can be reduced and life span can be increased. Smart healthcare includes eHealth, mHealth services, smart home services, electronic database, and intelligent medical devices. Use of IoT based smart healthcare systems is increasing rapidly for disease diagnosis, patient’s treatment, and better quality of life. However, it produces a massive amount of data from different applications. It is the need of an hour to process and monitor real time data remotely with accuracy. As there are some limitations of IoT in healthcare like the massive amount of data generated, data security/privacy concerns, and more. This issue can be resolved by 5G technology. It has the capability to tackle the large amount of data with minimum delay and fulfills the requirement, such as high bandwidth, ultra-low latency, and ultra-high energy efficiency. Both 5G and IoT have great utility in the health sector. This research paper presents components, architecture, and applications of 5G enabled Internet of Medical Things (IoMT). This paper also covers how the 5G enabled IoMT supports the smart healthcare system and comparison of available wireless communication technologies for 5G enabled IoT. Requirements and technologies involved in 5G enabled IoMT Systems are presented along with their impact.
Hateful and Other Negative Communication in Online Commenting Environments: Content, Structure and Targets

Information and communication technologies are increasingly interacting with modern societies.One specific manifestation of this interaction concerns hateful and other negative comments in online environments.Various terms appear to denote this communication, from flaming, indecency and intolerance to hate speech.However, there is still a lack of an umbrella term that broadly captures this communication.Therefore, this paper introduces the concept of socially unacceptable discourse, which serves as the basis for an empirical study that evaluated online comments scraped from the Facebook pages of the three most-visited Slovenian news outlets.Machine-learning algorithms were used to narrow the focus to topics related to refugees and LGBT rights.Ten thousand comments were manually coded to identify and structure socially undesirable discourse.The results show that about half of all comments belonged to this type of discourse, with a surprisingly stable level and structure across media (i.e., right-wing versus mainstream) and topics.Most of these comments could also be considered a potential violation of hate speech legislation.In the context of these findings, the political and ideological consequences and implications of mediatised emotions are discussed.
A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint

Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively.
Opportunities of and threats to consumer well-being in the age of Fourth Industrial Revolution (IR 4.0) technologies

Purpose Fourth Industrial Revolution (IR 4.0) technologies have strong potential to affect consumer well-being, positively or negatively, so the current paper aims to review potential opportunities and threats that these technologies represent for consumers in several core economic sectors: health care, education, financial services, manufacturing and retailing. Design/methodology/approach This paper proposes a conceptual framework for how IR 4.0 technologies affect consumer well-being in five representative sectors: health care, education, financial services, manufacturing and retailing. The authors argue that the potential transformations of these specific sectors, facilitated by these technologies, may have profound effects on consumer well-being, with urgent public policy implications. Findings Emerging technologies, such as artificial intelligence, robotics, the Internet of Things, three-dimensional printing, machine learning and blockchain, provide customers with novel approaches toward decisions regarding health, education, finances and other fundamental parts of their lives. The organizations that provide these services, such as hospitals, universities and banks, actively adopt the innovations offered by IR 4.0. These evolving and disruptive technologies thus are changing reality for consumers and providers. Originality/value This paper proposes some novel public policy implications of IR 4.0 technologies for consumer well-being, and it outlines further research directions that can enhance understanding of relevant technologies and the consequences of their use for society.
Viral sticks, virtual stones: addressing anonymous hate speech online

Woods and Ruscher examine hate speech as a multifaceted phenomenon that has adapted to novel communication contexts and opportunities. In particular, online communication represents a growing opportunity for individuals to engage in anonymous hate speech, a situation that merits additional research for several reasons. The authors posit, first, that this specific type of hate speech warrants focused research because it may not altogether fit into pre-established categories of hate speech events, a matter that may have implications for the likelihood of legal censure. Second, this incarnation of hate speech may be best understood in terms of constitutive harms, relative to other harms, that are themselves understudied. Finally, anonymous hate speech online may inflict unique and additional harms by virtue of the unique context of the Internet and the ease of anonymity for perpetrators. Woods and Ruscher provide an overview of research on the types and harms of hate speech, and a consideration of the impact both of anonymity and of the Internet on these harms, as well as describe an illustrative example of a type of hate speech online that is often anonymous—derogatory Internet memes—in an effort to encourage further research into this novel incarnation of words that wound.
Detecting and Characterizing SMS Spearphishing Attacks

Although spearphishing is a well-known security issue and has been widely researched, it is still an evolving threat with emerging forms. In recent years, Short Message Service (SMS) has been revealed as a new distribution channel for spearphishing messages, which already has caused a serious impact in the real world, but has not yet attracted enough attention from the academic community. In this paper, we report the first systemic study to spotlight this emerging threat, SMS spearphishing attack. Through cooperating with a leading security vendor, we obtain 31.96M real-world spam messages that span three months. We design and implement a novel NLP-based detection algorithm, and uncover 90,801 spearphishing messages on the entire dataset. And then, a large-scale measurement was performed on the detected messages to reveal and understand the characteristics of SMS spearphishing attack. Our findings are multi-fold. We discover that SMS spearphishing has a significant negative impact on the real-world, and a large number of victims have been affected. And the distribution of active illicit types between spearphishing message and common spam is quite inconsistent. At the micro-level, to evade detection and increase the probability of success, adversary campaigns have evolved a set of sophisticated strategies. Our research highlights the impact of SMS spearphishing attack is prominent. We call on different communities to work together to mitigate this emerging security threat.
Analysis of Sports Event Management Teaching from the Perspective of Multimedia considering Multiobjective Teaching Optimization Algorithm

At present, the combination of multimedia technology and physical education has been extensively applied, and the reform of sports event management has also attracted the attention of researchers. Reform and innovation have been carried out on the effective utilization of physical education resources and the image display of sports actions with great difficulties in the process of teaching and cross school and class synchronous learning, which has not only enriched the approaches and methods of sports management and teaching but also achieved certain effects.
Smart City Data Platform for Real-Time Processing and Data Sharing

The concept of a smart city comes with the need to support a data platform that can gather, process and export the data of millions of sensors, coming from different sources, with information in different formats, in a scalable approach for realtime and historical data visualization, processing, and actuation in the city. This paper proposes a Data Platform for the Aveiro Tech City Living Lab, to gather, process, visualize and actuate on mobility, environmental and network data. The architecture of the platform provides a real open platform that is accessible for third-parties to collect data and to experiment their own solutions, through a secure and open data platform at their disposal. The results with respect to the amount of data gathered and examples of data show how this platform can be used to develop new applications and use both real-time and historical data for future predictions and actuations in a smart city.
Recurrence quantification analysis of Q&amp;A behavior from the perspective of explicit and tacit knowledge – an empirical study based on Zhihu's hashtags

Purpose The purpose of this research is to investigate the time structure characteristics of collaborative knowledge production behaviors in Q&amp;A (question-and-answer) communities for explicit and tacit knowledge, and systematically investigate the supply side and the demand side of knowledge production. Design/methodology/approach Taking Zhihu as the research object, using the methods of recurrence plot and recurrence quantification analysis, this paper analyzes the recursive characteristics of the motion trajectories of the three behavioral sequences of questioning, answering, and discussion, qualitatively and quantitatively analyzing the generation and evolution mechanism of explicit and tacit knowledge. Findings The results show that compared with the demand-side behavior sequence, the supply-side behavior sequence exhibits higher stability, complexity and periodicity. Compared with the tacit knowledge topics, the demand-side behavior sequence of the explicit knowledge topics shows stronger nonlinearity, and the supply-side behavior sequence shows lower complexity. Originality/value The research conclusions provide preliminary evidence for the effectiveness of the recurrence plot method in distinguishing different types of knowledge production behaviors and have important application value for the “crowdsourcing” knowledge generation and identification under the knowledge economy and the sustainable development of the socialized question-and-answer community.
Using digital humanity approaches to visualize and evaluate the cultural heritage ontology

Purpose This study applies digital humanity tools (Gephi and Protégé) for establishing and visualizing ontologies in the cultural heritage domain. According to that, this study aims to develop a novel evaluation approach using five ontology indicators (data overview, visual presentation, highlight links, scalability and querying) to evaluate the knowledge structure presentation of cultural heritage ontology. Design/methodology/approach The researchers collected and organized 824 pieces of government’s open data (GOD), converted GOD into the resource description framework format, applied Protégé and Gephi to establish and visualize cultural heritage ontology. After ontology is built, this study recruited 60 ontology participants (30 from information and communications technology background; 30 from cultural heritage background) to operate this ontology and gather their different perspectives of visual ontology. Findings Based on the ontology participant’s feedback, this study discovered that Gephi is more supporting than Protégé when visualizing ontology. Especially in data overview, visual presentation and highlight links dimensions, which is supported visualization and demonstrated ontology class hierarchy and property relation, facilitated the wider application of ontology. Originality/value This study offers two contributions. First, the researchers analyzed data on East Asian architecture with novel digital humanities tools to visualize ontology for cultural heritage. Second, the study collected participant’s feedback regarding the visualized ontology to enhance its design, which can serve as a reference for future ontological development.
Lightweight Multi-Scale Asymmetric Attention Network for Image Super-Resolution

Recently, with the development of convolutional neural networks, single-image super-resolution (SISR) has achieved better performance. However, the practical application of image super-resolution is limited by a large number of parameters and calculations. In this work, we present a lightweight multi-scale asymmetric attention network (MAAN), which consists of a coarse-grained feature block (CFB), fine-grained feature blocks (FFBs), and a reconstruction block (RB). MAAN adopts multiple paths to facilitate information flow and accomplish a better balance of performance and parameters. Specifically, the FFB applies a multi-scale attention residual block (MARB) to capture richer features by exploiting the pixel-to-pixel correlation feature. The asymmetric multi-weights attention blocks (AMABs) in MARB are designed to obtain the attention maps for improving SISR efficiency and readiness. Extensive experimental results show that our method has comparable performance with fewer parameters than the current advanced lightweight SISR.