Reviewerly/exHarmony huggingface dataset:
Clinical Application to Improve the “Depth Perception Problem” by Combining Augmented Reality and a 3D Printing Model

In our experience with intraoperative evaluation and educational application of augmented reality technology, an illusion of depth has been a major problem. To improve this depth perception problem, we conducted two experiments combining various three-dimensional models and holograms and the observation angles using an augmented reality device.In experiment 1, when observing holograms projected on the surface layer of the model (bone model) or holograms projected on a layer deeper than the model (body surface model), the observer's first impression regarding which model made it easier to understand positional relationships was investigated. In experiment 2, to achieve a more quantitative evaluation, the observer was asked to measure the distance between two specific points on the surface and deep layers from two angles in each of the above combinations. Statistical analysis was performed on the measurement error for this distance.In experiment 1, the three-dimensional positional relationships were easier to understand in the bone than in the body surface model. In experiment 2, there was not much difference in the measurement error under either condition, which was not large enough to cause a misunderstanding of the depth relationship between the surface and deep layers.Any combination can be used for preoperative examinations and anatomical study purposes. In particular, projecting holograms on a deep model or observing positional relationships from not only the operator's viewpoint, but also multiple other angles is more desirable because it reduces confusion caused by the depth perception problem and improves understanding of anatomy.
Digital forensics challenges and readiness for <scp>6G Internet of Things (IoT)</scp> networks

Abstract The development of sixth‐generation (6G) wireless communication technology is expected to provide super high‐speed data transmission, and advanced network performance than the current fifth‐generation (5G) and be fully functional by the 2030s. This development will have a significant impact and add improvements to digital extended reality (XR), autonomous systems, vehicular ad hoc networks (VANETs), artificial intelligence (AI), underwater communications, blockchain technology, pervasive biomedical informatics and smart cities built on the digital infrastructure backbone of the Internet of Things (IoT). The ubiquitous nature of this large‐scale 6G‐enabled IoT that offers faster connectivity capabilities and integrates both terrestrial and non‐terrestrial networks will not only create new data security and privacy issues but also provide a treasure trove of digital evidence useful for digital forensic examiners investigating security incidents and cybercrime. However, for digital forensic examiners, evidence collection, preservation and analysis will become a priority in the successful deployment of 6G IoT networks. In this study, we define key applications of 6G network technology to the Internet of Things and its existing architectures. The survey introduces potential digital forensic challenges and related issues affecting digital forensic investigations specific to 6G IoT networks. Finally, we highlight and discuss forensic readiness and future research directions for identified challenges within the 6G IoT network environments. This article is categorized under: Digital and Multimedia Science &gt; IoT Forensics
A Comparison of Commercial Sentiment Analysis Services

Abstract Empirical insights into promising commercial sentiment analysis solutions that go beyond the claims of their vendors are rare. Moreover, due to the constant evolution in the field, previous studies are far from reflecting the current situation. The goal of this article is to evaluate and compare current solutions using two experimental studies. In the first part of the study, based on tweets about airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro)F1, time performance, and service level agreements (SLA). Furthermore, we compare two of the services in depth with multiple data sets and over time. The services tested here are Google Cloud Natural Language API and MeaningCloud Sentiment Analysis API. For evaluating the results over time, we use the same data set as in November 2020. In addition, further topic-specific and general Twitter data sets are used. The experiments show that the IBM Watson NLU and Google Cloud Natural Language API solutions may be preferred when negative text detection is the primary concern. When tested in July 2022, the Google Cloud Natural Language API was still the clear winner compared to the MeaningCloud Sentiment Analysis API, but only on the airline service quality data set; on the other data sets, both services provided specific benefits and drawbacks. Furthermore, we detected changes in the sentiment classification over time with both services. Our results motivate that an independent, critical, and longitudinal experimental analysis of sentiment analysis services can provide interesting insights into their overall reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real data and analyze potential weaknesses and margins of error before making an investment.
Quantum Pulse Gate Attack on IM/DD Optical Key Distribution Exploiting Symbol Shape Distortion

Intensity modulation/direct detection (IM/DD) optical key distribution (OKD) is a method to generate a secret key whose security against passive eavesdropping is guaranteed by the shot noise inherent to the photodetection process. Here the effects of intensity-dependent symbol shape distortion on the IM/DD OKD security are investigated assuming that the eavesdropper can implement temporal mode demultiplexing using e.g. the quantum pulse gating technique. The quantitative analysis includes key generation based on either hard- or soft-decoding of the detected signal as well as the impact of excess detection noise. A simple, rule-of-thumb relation between the severity of the symbol shape distortion and the signal strength required to ensure key security is presented.
Applying the DEMATEL−ANP Fuzzy Comprehensive Model to Evaluate Public Opinion Events

Network public opinion is a mirror reflecting people’s will, and evaluating its urgency can help to find hidden social crises. Research on public opinion in the field of machine learning usually focuses on micro-sentiment judgment, which is unable to offer support for the evaluation of public opinion events without additional data, and research from the perspective of artificial weighting has the disadvantage of the confusion of explanation. Judging the urgency of public opinion events is usually based on human perception, which is fuzzy and conforms to the attribute of fuzzy mathematics. Therefore, the index system in this paper was constructed in line with five principles, from which the weights were scientifically evaluated by integrating the DEMATEL and ANP model, and fuzzy mathematics was applied to determine the urgency level of public opinion. The result has three-fold significance. First, the index system constructed was more closely linked. Second, the integration of the DEMATEL and ANP weight calculating model took the interdependence of indicators fully into account. Third, fuzzy mathematics provided support for determining the public opinion crisis level, especially in the absence of immediate dissemination data.
A full-automatic test system for the intelligent substation based on GOOSE/SV signal simulation

Abstract With the wide application of one-key sequence control in intelligent substations, the testing of one-key sequential control and error prevention logic has become an important work before production. Due to the limitation of testing methods, most of the tests are conducted during on-site commissioning, which has a large workload, lower efficiency, and a high probability of missed testing. Therefore, this paper proposes a full-automatic test system for the intelligent substation based on GOOSE/SV signal simulation. It can automatically verify the error prevention logic and the one-key sequence control of the monitoring system through the combination of GOOSE/SV signals at different statuses, which provides technical support for the site to improve efficiency and safety.
Finding Structure in One Child's Linguistic Experience

Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this question using a recent longitudinal dataset collected from a single child, consisting of egocentric visual data paired with text transcripts. We train both language-only and vision-and-language neural networks and analyze the linguistic knowledge they acquire. In parallel with findings from Jeffrey Elman's seminal work, the neural networks form emergent clusters of words corresponding to syntactic (nouns, transitive and intransitive verbs) and semantic categories (e.g., animals and clothing), based solely on one child's linguistic input. The networks also acquire sensitivity to acceptability contrasts from linguistic phenomena, such as determiner-noun agreement and argument structure. We find that incorporating visual information produces an incremental gain in predicting words in context, especially for syntactic categories that are comparatively more easily grounded, such as nouns and verbs, but the underlying linguistic representations are not fundamentally altered. Our findings demonstrate which kinds of linguistic knowledge are learnable from a snapshot of a single child's real developmental experience.
Sustainable decision-making game for the next generation of smart citizens

Abstract Cities are increasingly facing the consequences of climate change, it is therefore urgent to take actions to reach the targets set by the UN Sustainable Development Goals. By 2050, 70% of the world population will live in cities, engaging citizens in the sustainable transformation of the built environment is crucial. Moreover, children and youth require specific attention to secure their active participation in the process. ByMaker is a serious game developed to facilitate the participation of children and youth in sustainable urban transformation. The objective of ByMaker is to improve the knowledge about sustainability and raise awareness on the importance of making balanced decisions. While the environmental dimension is already well covered and assimilated by children and youth, the social and economic ones remain more challenging to understand. The method uses a 3D map of the concrete city, interactive elements of popular landmarks and friendly characters of local historical figures. The players are invited to experiment multiple combination of choices corresponding to different scenarios of urban development such as road and transportation, urban nature, residential buildings and public space. The impact of their decisions on the overall sustainability of their city is presented as visual feedback. Scenarios in the game focus on the realization of SDGs’ ambitions and objectives. The vision is both mid-term oriented, addressing the climate urgency and long-term focused to anticipate future needs. Furthermore, ByMaker presents innovative technological solutions to play with. The game was tested in three classes in primary schools in Trondheim, Norway. The feedback guided the further development and improvements such as better visualization, interaction features and simplified scenario descriptions. By playing with ByMaker, children succeeded to learn, experiment, and create their sustainable city.
A deep image segmentation‐based method for stitching ancient‐book images without an overlapping region

Abstract With continuous advancements in ancient‐book digitization and preservation research, the problems with the stitching of ancient‐book images have become increasingly prominent, as traditional feature‐mapping‐based methods cannot satisfactorily stitch non‐overlapping images. To realize the accurate stitching of the left and right pages of ancient‐book images, this paper proposes a method for ancient‐book image stitching to meet the requirements of their digitization in back‐wrapped binding and other binding forms. First, a dataset of the black text frames from ancient‐book images was established and then used to train a VGG16‐UNet network for the extraction of black text frames. Then, the Douglas–Peucker algorithm was used to fit the black text frames and filter outliers. Finally, a sliding matching algorithm based on the position information of black text frames was proposed for the rectification of misalignments. The results showed that the method achieved a satisfying stitching effect and had good robustness.
SOCIAL MACHINE AND ALIENATION IN THE CONTEXT OF ONTOLOGY OF NATURE AND ONTOLOGY OF SOCIAL REALITY

The incompleteness of the instinctive foundations of human existence forms the basic feature of a specifically human form of existence, on which special emphasis was placed even in the works of the classics of philosophical anthropology (M. Scheler, H. Plessner, A. Gelen). The obvious conclusion from such a statement is that a person does not have a constituted, objective generic nature and is forced to overcome this incompleteness, which determines the main dynamics of his existence. Such a reading of the problem of man as an insufficient being leads to the phenomenon of fundamental artificiality, self-referentiality of man and his collectivity (N. Luhmann).
Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods.
Multimodal Deep Learning Methods on Image and Textual Data to Predict Radiotherapy Structure Names

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This paper presents novel deep learning methods on structure sets by integrating multimodal data compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU). These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Evaluation with macro-averaged F1 score shows that our model with single-modal textual data usually performs better than previous studies. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain.
Application of the Artificial Intelligence Technique to Recognize and Analyze from the Image Data

Recently, there have been numerous applications of deep learning-based approaches such as speech recognition, face detection or risk prediction. This investigation introduces a novel approach to deploying the deep learning technique to evaluate physical gestures. First, the system's overall scheme and the local setup are discussed. Then, the scoring mechanism to reward is conceptually established to estimate human performance. This score is immediately demonstrated how hard to imitate the reference model. Later, some statistics indicators, for instance, timing, type of actions or emotion, could be visually analyzed and performed. The contributions of this paper are (1) to initially innovate the overall framework of the system, (2) to create the scoring mechanism to evaluate and (3) to denote the indicators to assess human performance. From these results, it has been seen that the proposed solution could be applied in teaching activities, sports analytics or gesture-based control.
The mediating role of absorptive capacity in the relationship between transformational leadership and corporate entrepreneurship

Absorptive capacity is widely acknowledged as a critical driver of innovation, business development, and self-renewal through the acquisition, transformation, assimilation, and application of knowledge. Despite the importance of the relationship between transformational leadership and corporate entrepreneurship, there exists a research gap due to the insufficient exploration of the mediating impact of absorptive capacity. This study endeavors to fill the existing research gap by delving into the interplay between transformational leadership and corporate entrepreneurship, taking absorptive capacity into consideration as a mediating factor. Surveys were collected from 154 middle managers representing a broad range of healthcare institutions in the United Arab Emirates. The study utilized a two-step structural equation modelling approach and provided empirical evidence to support the hypothesis that absorptive capacity mediates the relationship between transformational leadership and corporate entrepreneurship. These findings contribute to the resource-based view theory by demonstrating the effective role of transformational leadership in promoting corporate entrepreneurship through the development of absorptive capacity.
Decomposing Synthesized Strategies for Reactive Multi-agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has been used to solve sequential decision problems by a collection of intelligent agents interacting in a shared environment. However, the design complexity of MARL strategies increases with the complexity of the task specifications. In addition, current MARL approaches suffer from slow convergence and reward sparsity when dealing with multi-task specifications. Linear temporal logic works as one of the software engineering practices to describe non-Markovian task specifications, whose synthesized strategies can be used as a priori knowledge to train the multi-agents to interact with the environment more efficiently. In this paper, we consider multi-agents that react to each other with a high-level reactive temporal logic specification called Generalized Reactivity of rank 1 (GR(1)). We first decompose the synthesized strategy of GR(1) into a set of potential-based reward machines for individual agents. We prove that the parallel composition of the reward machines forward simulates the original reward machine, which satisfies the GR(1) specification. We then extend the Markov Decision Process (MDP) with the synchronized reward machines. A value-iteration-based approach is developed to compute the potential values of the reward machine based on the strategy structure. We also propose a decentralized Q-learning algorithm to train the multi-agents with the extended MDP. Experiments on multi-agent learning under different reactive temporal logic specifications demonstrate the effectiveness of the proposed method, showing a superior learning curve and optimal rewards.
Poster: StreamToxWatch – Data Poisoning Detector in Distributed, Event-based Environments

StreamToxWatch, or ToxWatch for short, is an early-stage ensemble architecture for data poisoning detection and monitoring in online learning systems over streams. Detecting data poisoning is difficult, especially in distributed streaming systems where statistical baselines change on the fly and across the system. For that reason, ToxWatch employs a combination of input, (adversarial) conceptual drift, and model performance monitors intended to observe anomalous behaviors and phenomena across the system and to offer targeted detection signals to downstream applications.
Color Image Encryption Using a Sine Variation of the Logistic Map for S-Box and Key Generation

The proliferation of image sharing on the Internet has mandated the presence of secure methods of data encryption that operate on and are specifically suited for visual data. This work proposes a 4 stage image encryption scheme that utilizes a variation of the Logistic map to generate a key and a substitution box (S-box). Rule 30 Cellular Automata is used to generate a second key, while a second S-box, from the literature, is also utilized. The proposed scheme, on assessment, has shown to be highly efficient, and has comparable encryption strength to other, similar encryption schemes from the literature.