Reviewerly/exHarmony huggingface dataset:
Optimizing efficiency in the creation of patient-specific plates through field-driven generative design in maxillofacial surgery

Field driven design is a novel approach that allows to define through equations geometrical entities known as implicit bodies. This technology does not rely upon conventional geometry subunits, such as polygons or edges, rather it represents spatial shapes through mathematical functions within a geometrical field. The advantages in terms of computational speed and automation are conspicuous, and well acknowledged in engineering, especially for lattice structures. Moreover, field-driven design amplifies the possibilities for generative design, facilitating the creation of shapes generated by the software on the basis of user-defined constraints. Given such potential, this paper suggests the possibility to use the software nTopology, which is currently the only software for field-driven generative design, in the context of patient-specific implant creation for maxillofacial surgery. Clinical scenarios of applicability, including trauma and orthognathic surgery, are discussed, as well as the integration of this new technology with current workflows of virtual surgical planning. This paper represents the first application of field-driven design in maxillofacial surgery and, although its results are very preliminary as it is limited in considering only the distance field elaborated from specific points of reconstructed anatomy, it introduces the importance of this new technology for the future of personalized implant design in surgery.
Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks

Mobile online gaming is constantly growing in popularity and is expected to be one of the most important applications of upcoming sixth generation networks. Nevertheless, it remains challenging for game providers to support it, mainly due to its intrinsic and ever-stricter need for service continuity in the presence of user mobility. In this regard, this paper proposes a machine learning strategy to forecast user channel conditions, aiming at guaranteeing a seamless service whenever a user is involved in a handover, i.e., moving from the coverage area of one base station towards another. In particular, the proposed channel condition prediction approach involves the exploitation of an echo state network, an efficient class of recurrent neural network, that is empowered with a genetic algorithm to perform parameter optimization. The echo state network is applied to improve user decisions regarding the selection of the serving base station, avoiding game breaks as much as possible to lower game lag time. The validity of the proposed framework is confirmed by simulations in comparison to the long short-term memory approach and another alternative method, aimed at thoroughly testing the accuracy of the learning module in forecasting user trajectories and in reducing game breaks or lag time, with a focus on a sixth generation network application scenario.
Retrieval-Augmented Knowledge Graph Reasoning for Commonsense Question Answering

Existing knowledge graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieve entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) there is a lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel retrieval-augmented knowledge graph (RAKG) model, which solves the above issues using two key innovations. First, we leverage the density matrix to make the model reason along the corrected knowledge path and extract an enhanced subgraph of the knowledge graph. Second, we fuse representations of questions and graph entities through a bidirectional attention strategy, in which two representations fuse and update using a graph convolutional network (GCN). To evaluate the performance of our method, we conducted experiments on two widely used benchmark datasets: CommonsenseQA and OpenBookQA. The case study gives insight into the finding that the augmented subgraph provides reasoning along the corrected knowledge path for question answering.
Structured Matrices and Their Application in Neural Networks: A Survey

Abstract Modern neural network architectures are becoming larger and deeper, with increasing computational resources needed for training and inference. One approach toward handling this increased resource consumption is to use structured weight matrices. By exploiting structures in weight matrices, the computational complexity for propagating information through the network can be reduced. However, choosing the right structure is not trivial, especially since there are many different matrix structures and structure classes. In this paper, we give an overview over the four main matrix structure classes, namely semiseparable matrices, matrices of low displacement rank, hierarchical matrices and products of sparse matrices. We recapitulate the definitions of each structure class, present special structure subclasses, and provide references to research papers in which the structures are used in the domain of neural networks. We present two benchmarks comparing the classes. First, we benchmark the error for approximating different test matrices. Second, we compare the prediction performance of neural networks in which the weight matrix of the last layer is replaced by structured matrices. After presenting the benchmark results, we discuss open research questions related to the use of structured matrices in neural networks and highlight future research directions.
Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.
DS‐Net: Dual supervision neural network for image manipulation localization

Abstract With the rapid development of image editing technology, tampering with images has become easier. Maliciously tampered images lead to serious security problems (e.g., when used as evidence). The current mainstream methods of image tampering are divided into three types which are copy‐move, splicing and removal. Many image tampering detection methods can only detect one type of image tampering. Additionally, some methods learn features by suppressing image content, which can result in false positives when identifying tampered areas. In this paper, the authors propose a novel framework named the dual supervision neural network (DS‐Net) to localize the tampered regions of images tampered by the three tampering methods mentioned above. First, to extract richer multiscale information, the authors add skip connections to the atrous spatial pyramid pooling (ASPP) module. Second, a channel attention mechanism is introduced to dynamically weigh the results generated by ASPP. Finally, the authors build additional supervised branches for high‐level features to further enhance the extraction of these high‐level features before fusing them with low‐level features. The authors conduct experiments on various standard datasets. Through extensive experiments, the results show that the AUC scores reach 86.4% , 95.3% and 99.6% for CASIA, COVERAGE and NIST16 datasets, respectively, and the F1 scores are 56.0% , 73.4% and 82.7% , respectively. The results demonstrate that the authors’ method can accurately locate tampered regions and achieve better performance on various datasets than other methods of the same type.
Virtual Reality and Emotion

Abstract This chapter evaluates how immersive virtual reality (VR) can be used as a tool to inform an understanding of emotion and how emotion operates as a mechanism for VR effects. The authors conducted a 5-year systematic review of the VR and emotion literature (2015–2019), focusing on empirical studies that were concerned with three types of research: (1) the connection between VR and emotion from an emotion regulation perspective (e.g., mood induction, clinical applications), (2) studies that treat emotions as a mechanism to evaluate social or psychological phenomena, and (3) studies concerned with a deeper understanding of emotions or emotion theory. This perspective draws on evidence from communication research, psychology, and human–computer interaction to achieve these aims. The authors ground their investigation in an overview of immersive VR and discuss current debates in emotion research, concluding with an attempt to make connections across fields.
IOT Integration in Two-Axis Sun Tracking System with LDR Sensors in a Wheatstone Bridge Circuit

In recent years, solar energy has gained significant attention as a clean and sustainable source of power. To maximize the efficiency of solar panels, it is crucial to track the movement of the sun and ensure optimal exposure to sunlight throughout the day. In this project, propose a two-axis sun tracking system that utilizes Light Dependent Resistors (LDR) as sensors and employs the Wheatstone Bridge Circuit for accurate sun position detection. Additionally, the system integrates Internet of Things (IoT) technology to enable remote monitoring of the solar power generation process. The two-axis sun tracking system consists of two main components: the sensor module and the control module. The sensor module consists of LDR sensors strategically placed to detect the intensity of sunlight. The Wheatstone Bridge Circuit is employed to measure the resistance changes in the LDR sensors and accurately determine the sun's position relative to the solar panels. The control module processes the sensor data and generates control signals to adjust the direction of the solar panels accordingly. Furthermore, the system incorporates IoT technology to enable real-time monitoring. The collected sensor data, including the generated voltage and the panel direction, are transmitted to a cloud platform via wireless communication protocols such as Wi-Fi or cellular networks. Moreover, the integration of IoT technology facilitates remote monitoring, enabling users to optimize energy production and identify any potential issues promptly. Keywords—Wheatstone bridge, LDR sensors, DAST system, PV panel, closed-loop system, Internet of Things.
Trustworthy AI Principles to Face Adversarial Machine Learning: A Novel Study

Artificial Intelligence (AI) has witnessed significant advancements in recent years, enabling its widespread adoption across various domains. However, this progress has also given rise to new challenges, particularly in the context of adversarial machine learning. Adversarial attacks exploit vulnerabilities in AI models, resulting in their misclassification or misbehaviour. To address this critical issue, it is crucial to develop trustworthy AI systems that can withstand such adversarial threats. This paper presents a comprehensive study that covers the types of adversarial machine learning cyber-attacks, methods employed by adversaries to launch such attacks, effective defence mechanisms, and potential future directions in the field. It starts by exploring various types of adversarial ML attacks, characteristics and potential consequences of each attack type, emphasizing the risks they pose to privacy, security, and fairness in AI systems and delving into the methods employed by adversaries to launch adversarial ML attacks. By understanding the tactics used by adversaries, researchers and practitioners can develop robust defence mechanisms that can withstand these attacks. Building upon this understanding, a range of defence strategies can be invented for defending against adversarial ML attacks and emerging research areas, such as the integration of secure multi-party computation, differential privacy, and federated learning are used to enhance the resilience of AI models. By understanding the nature of adversarial attacks and implementing effective defence strategies, AI systems can be fortified against malicious manipulations. The findings of this study contribute to the development of trustworthy AI systems, ensuring their resilience, transparency, and fairness.
A Two-Stage Image Inpainting Technique for Old Photographs Based on Transfer Learning

To address the challenge of sparse old photo datasets, we apply transfer learning to image inpainting tasks. Specifically, we improve a two-stage image inpainting network that focuses on collaborative subtasks. We also design a transform module based on the cross-aggregation of windows to improve long-distance contextual information acquisition in image inpainting and enhance the integrity of images in terms of structure and texture. Our improved two-stage network has a significantly better repair performance compared to that of the current common inpainting methods. We further apply transfer learning techniques by utilizing the improved two-stage image inpainting network as the base network and decoupling the generator into a feature extractor and classifier, which consist of an encoder and a decoder, respectively. We obtain a domain-invariant feature extractor through minimax game training using source and target domain data. This feature extractor can be combined with the original encoder to restore old photo images. To verify the effectiveness of our approach, we conducted comparative experiments. Our results show that the PSNR, SSIM, and FID indexes of the model using transfer learning are 11.8%, 2.96%, and 44.4% higher than those without transfer learning, respectively. These findings suggest that applying transfer learning techniques can be an effective solution to address the challenge of sparse old photo datasets in image inpainting tasks.
Few-Shot Named Entity Recognition via Meta-Learning (Extended Abstract)

Named entity recognition (NER) is typically framed as a sequence labeling problem where the entity classes are inherently entangled together because the entity number and classes in a sentence are not known in advance, leaving the N-way K-shot NER problem so far unexplored. In our TKDE paper, we first formally define a more suitable N-way K-shot setting for NER. Then we propose FewNER, a novel meta-learning approach for few-shot NER. FewNER separates the entire network into a task-independent part and a task-specific part. During training in FewNER, the task-independent part is meta-learned across multiple tasks and the task-specific part is learned for each individual task in a low-dimensional space. At test time, FewNER keeps the task-independent part fixed and adapts to a new task via gradient descent by updating only the task-specific part, resulting in it being less prone to overfitting and more computationally efficient. Compared with pre-trained language models (e.g., BERT and ELMo) which obtain the transferability in an implicit manner (i.e., relying on large-scale corpora), FewNER explicitly optimizes the capability of "learning to adapt quickly" through meta-learning. The results demonstrate that FewNER achieves state-of-the-art performance against nine baseline methods by significant margins on three adaptation experiments (i.e., intra-domain cross-type, cross-domain intra-type and cross-domain cross-type).
Automatic Classification of TEDS Monitoring Operation Technology Research

The application of Train of EMU failures Detection System (TEDS) ensures the running safety of EMU. In order to reduce the manual workload and meet the requirements of fine classification monitoring, we researched the automatic classification technology, designed and implemented the corresponding methods, established the TEDS classification chart of all EMU trains at headquarters, and put forward the automatic calculation operation algorithm using multi-source data. Designed the generation scheme of local chart at the monitoring center, the system application function of automatic dispatching is developed, and the classification monitoring based on train running diagram was realized. The application shows that the method proposed in this paper have reduced the manual workload of work effectively, and the capability of abnormal detection has been enhanced, laid the technical foundation for the effective operation monitoring of EMU.
Toward a Unified Framework for Unsupervised Complex Tabular Reasoning

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. Specifically, we first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. The impressive performance demonstrates that UCTR can significantly reduce the dependence on manual annotation. Moreover, we also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique.
Front Matter

Computer Graphics ForumVolume 42, Issue 4 p. i-x Front Matter Front Matter First published: 26 July 2023 https://doi.org/10.1111/cgf.14896Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume42, Issue4July 2023Pages i-x RelatedInformation
Chinese Medical Named Entity Recognition Based on Label Knowledge Enhancement

To solve the problem of text complexity in Chinese medical text named entity task, a model BERT-Label-Span based on label knowledge enhancement had been proposed, which can improve the accuracy of medical information system in Chinese medical named entity recognition task. The model decomposes the problem-text joint coding into two independent coding modules and performs the joint coding based on the BERT pre-training model. Then the semantic fusion module based on the attention mechanism explicitly uses the label knowledge to enhance the representation of medical text. Finally, the span of the named entity is predicted based on the heuristic matching principle. The experiment on the CCKS2019 Chinese medical text dataset shows that the F1 value of the named entity recognition task of the model reaches 85.554, which is higher than the existing main-stream methods and proves the effectiveness of this method.
Retracted: Optimization of Data Mining and Analysis System for Chinese Language Teaching Based on Convolutional Neural Network

[This retracts the article DOI: 10.1155/2021/1148954.].
Resolving the Doubts: On the Construction and Use of ResNets for Side-Channel Analysis

The deep learning-based side-channel analysis gave some of the most prominent side-channel attacks against protected targets in the past few years. To this end, the research community’s focus has been on creating the following: (1) powerful multilayer perceptron or convolutional neural network architectures and (2) (if possible) minimal multilayer perceptron or convolutional neural network architectures. Currently, we see that, computationally intensive hyperparameter tuning methods (e.g., Bayesian optimization or reinforcement learning) provide the best results. However, as targets with more complex countermeasures become available, these minimal architectures may be insufficient, and we will require novel deep learning approaches.This work explores how residual neural networks (ResNets) perform in side-channel analysis and how to construct deeper ResNets capable of working with larger input sizes and requiring minimal tuning. The resulting architectures, obtained by following our guidelines, are significantly deeper than commonly seen in side-channel analysis, require minimal hyperparameter tuning for specific datasets, and offer competitive performance with state-of-the-art methods across several datasets. Additionally, the results indicate that ResNets work especially well when the number of profiling traces and features in a trace is large.
Re-examining tourist health, safety, and well-being in a global pandemic context.

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsBecky Liu-LastresBingjie ‘Becky’ Liu-Lastres, Ph.D., is an assistant professor in the Department of Tourism, Event, and Sport Management at Indiana University. Her main research interests include risk and crisis communication/management in tourism and hospitality, and tourist safety and security. The goal of Liu-Lastres’s research agenda is to promote safe travel and to ensure the health and well-being of tourists, organizations, and other key stakeholders within the tourism and hospitality industry.Jeff WilksJeff Wilks, Ph.D., is a qualified lawyer and psychologist specializing in travel health, safety, and well-being. He has worked on tourism safety programs for the UNWTO, ASEAN, and governments in the Middle East and Asia Pacific. Jeff is the lead author of seven tourism safety books. He serves as an Adjunct Professor of Tourism at Griffith University and is currently a consultant to the Asian Development Bank.
Retracted: Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short-Term Memory (LSTM)

[This retracts the article DOI: 10.1155/2021/9984003.].