Reviewerly/exHarmony huggingface dataset:
Modular Clinical Decision Support Networks (MoDN)—Updatable, interpretable, and portable predictions for evolving clinical environments

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
Cognitive synonymy: a dead parrot?

Abstract Sentences $$\varphi$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>φ</mml:mi> </mml:math> and $$\psi$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ψ</mml:mi> </mml:math> are cognitive synonyms for one when they play the same role in one’s cognitive life. The notion is pervasive (Sect. 1), but elusive: it is bound to be hyperintensional (Sect. 2), but excessive fine-graining would trivialize it and there are reasons for some coarse-graining (Sect. 2.1). Conceptual limitations stand in the way of a natural algebra (Sect. 2.2), and it should be sensitive to subject matters (Sect. 2.3). A cognitively adequate individuation of content may be intransitive (Sect. 3) due to ‘dead parrot’ series: sequences of sentences $$\varphi _1, \ldots , \varphi _n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>φ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>φ</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:mrow> </mml:math> where adjacent $$\varphi _i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> and $$\varphi _{i+1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> are cognitive synonyms while $$\varphi _1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$\varphi _n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>φ</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:math> are not (Sect. 3.1). Finding an intransitive account is hard: Fregean equipollence won’t do (Sect. 3.2) and a result by Leitgeb shows that it wouldn’t satisfy a minimal compositionality principle (Sect. 3.3). Sed contra , there are reasons for transitivity, too (Sect. 3.4). In Sect. 4, we come up with a formal semantics capturing this jumble of desiderata, thereby showing that the notion is coherent. In Sect. 5, we re-assess the desiderata in its light.
Supporting AI-powered real-time cyber-physical systems on heterogeneous platforms via hypervisor technology

Abstract The heavy use of machine learning algorithms in safety-critical systems poses serious questions related to safety, security, and predictability issues, requiring novel architectural approaches to guarantee such properties. This paper presents an architecture solution that leverages heterogeneous platforms and virtualization technologies to support AI-powered applications consisting of modules with mixed criticalities and safety requirements. The hypervisor exploits the security features of the Xilinx ZCU104 MPSoCs to create two isolated execution environments: a high performance domain running deep learning algorithms under the Linux operating system and a safety-critical domain running control and monitoring functions under the freeRTOS real-time operating system. The proposed approach is validated by a use case consisting of an unmanned aerial vehicle capable of tracking moving targets using a deep neural network accelerated on the FGPA available on the platform.
AssistML: an approach to manage, recommend and reuse ML solutions

Abstract The adoption of machine learning (ML) in organizations is characterized by the use of multiple ML software components. When building ML systems out of these software components, citizen data scientists face practical requirements which go beyond the known challenges of ML, e. g., data engineering or parameter optimization. They are expected to quickly identify ML system options that strike a suitable trade-off across multiple performance criteria. These options also need to be understandable for non-technical users. Addressing these practical requirements represents a problem for citizen data scientists with limited ML experience. This calls for a concept to help them identify suitable ML software combinations. Related work, e. g., AutoML systems, are not responsive enough or cannot balance different performance criteria. This paper explains how AssistML, a novel concept to recommend ML solutions, i. e., software systems with ML models, can be used as an alternative for predictive use cases. Our concept collects and preprocesses metadata of existing ML solutions to quickly identify the ML solutions that can be reused in a new use case. We implement AssistML and evaluate it with two exemplary use cases. Results show that AssistML can recommend ML solutions in line with users’ performance preferences in seconds. Compared to AutoML, AssistML offers citizen data scientists simpler, intuitively explained ML solutions in considerably less time. Moreover, these solutions perform similarly or even better than AutoML models.
Experimental optical computing of complex vector convolution with twisted light

Orbital angular momentum (OAM), emerging as an inherently high-dimensional property of photons, has boosted information capacity in optical communications. However, the potential of OAM in optical computing remains almost unexplored. Here, we present a highly efficient optical computing protocol for complex vector convolution with the superposition of high-dimensional OAM eigenmodes. We used two cascaded spatial light modulators to prepare suitable OAM superpositions to encode two complex vectors. Then, a deep-learning strategy is devised to decode the complex OAM spectrum, thus accomplishing the optical convolution task. In our experiment, we succeed in demonstrating 7-, 9-, and 11-dimensional complex vector convolutions, in which an average proximity better than 95% and a mean relative error <6 % are achieved. Our present scheme can be extended to incorporate other degrees of freedom for a more versatile optical computing in the high-dimensional Hilbert space.
Snapture—a Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition

Abstract As robots are expected to get more involved in people’s everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communication and, thus, are an integral part of seamless human-robot interaction (HRI). Recent years have witnessed an immense evolution of computational models powered by deep learning. However, state-of-the-art models fall short of expanding across different gesture domains, such as emblems and co-speech. In this paper, we propose a novel hybrid hand gesture recognition system. Our Snapture architecture enables learning both static and dynamic gestures: by capturing a so-called snapshot of the gesture performance at its peak, we integrate the hand pose and the dynamic movement. Moreover, we present a method for analyzing the motion profile of a gesture to uncover its dynamic characteristics, which allows regulating a static channel based on the amount of motion. Our evaluation demonstrates the superiority of our approach on two gesture benchmarks compared to a state-of-the-art CNNLSTM baseline. Our analysis on a gesture class basis unveils the potential of our Snapture architecture for performance improvements using RGB data. Thanks to its modular implementation, our framework allows the integration of other multimodal data, like facial expressions and head tracking, which are essential cues in HRI scenarios, into one architecture. Thus, our work contributes both to integrative gesture recognition research and machine learning applications for non-verbal communication with robots.
Adaptive sparse modeling in spectral &amp; spatial domain for compressed image restoration

Block discrete cosine transform (BDCT) is an indispensable component of modern image and video coding standards, specifically for its decorrelation and superior energy compaction aspects. BDCT typically employs block-specific quantization, which results in unpleasant compression-blocking artifacts which predominates at low bit rates. The proposed method aims to minimize these blocking artifacts to generate high-quality images under the framework of the alternating direction method of multipliers (ADMM) optimization. The proposed method exploits the local structures identified and extracted via wavelet-patch-based sparse representation and non-local self-similarity identified and extracted via group-based sparse representation, which are subsequently combined optimally employing ADMM. Moreover, the method uses a Gaussian quantization noise model, which allows a more precise and reliable assessment. An adaptive regularization parameter is used, which integrates spectral and spatial domain sparse representations with multi-resolution dictionaries and PCA-based dictionary. The proposed algorithm improves the overall practicality of the process and outperforms existing methods in terms of objective measures like structural similarity index measure, peak signal-to-noise ratio and visual perception.
Increasing the Value of XAI for Users: A Psychological Perspective

Abstract This paper summarizes the psychological insights and related design challenges that have emerged in the field of Explainable AI (XAI). This summary is organized as a set of principles, some of which have recently been instantiated in XAI research. The primary aspects of implementation to which the principles refer are the design and evaluation stages of XAI system development, that is, principles concerning the design of explanations and the design of experiments for evaluating the performance of XAI systems. The principles can serve as guidance, to ensure that AI systems are human-centered and effectively assist people in solving difficult problems.
A tamper-resistant timed secure data transmission protocol based on smart contract

Many time-sensitive scenarios need to decrypt data at a specified time. The timed-release encryption (TRE) primitive can meet this requirement. However, in the single-time server TRE model, there is a single point of failure problem. Therefore, we propose a tamper-resistant timed secure data transmission protocol based on smart contracts. Firstly, by decomposing the ciphertext into ciphertext fragments, the amount of deposit that a single middleman needs to submit is reduced. Secondly, it provides the system with security redundancy that changes with the decomposition mode. Thirdly, the sender is required to submit the hash value of each ciphertext fragment to the blockchain network at the same time as sending data, so that the receiver can quickly verify the authenticity of the ciphertext to resist substitution attack. Security analysis shows that the proposed protocol model can resist interruption attacks, release-ahead attacks and replacement attacks. Finally, we conduct a monetary cost test on the Ethereum's Rinkeby test network. The results show that our running cost is almost double compared with the existing similar scheme, but it is still very low and almost negligible compared with the value of the content and the expected profits it brings.
Placement of Multiple Virtual Objects in Physical Space in Augmented Reality Applications

Introduction. The challenges of placing virtual objects in a real-world environment limit the potential of augmented reality (AR) technology. This situation identifies a gap in scientific knowledge that requires additional research. Therefore, the main task of this study was to develop a method for optimal placement of virtual objects, in which the objective function of comfort was minimized. This approach is aimed at improving AR systems and developing the corresponding theory. Materials and Methods. The conducted research was based on the analysis of the placement of virtual objects in AR/VR applications with particular emphasis on optimization. The concept of comfort of placement was proposed, taking into account the size of the object and the distance to the boundaries of free space in X, Y, Z coordinates. Results. As part of the study, formulas were obtained for the optimal placement of objects with an arbitrary comfort function. The basic criterion was to minimize the difference between comfort levels from different sides of the object. It was found that a successful placement of objects required taking into account their size and comfort zones, as well as solving a system of n linear equations. Discussion and Conclusion. The results obtained make an important contribution to the study of the problem of placing virtual objects in AR/VR/MR. They open up new opportunities for improving user interaction and conducting further research in the field of spatial computing. Possible directions for further development are dynamic adjustments and integration of the results into various XR scenarios.
Health Monitoring Apps: An Evaluation of the Persuasive System Design Model for Human Wellbeing

In the current era of ubiquitous computing and mobile technology, almost all human beings use various self-monitoring applications. Mobile applications could be the best health assistant for safety and adopting a healthy lifestyle. Therefore, persuasive designing is a compulsory element for designing such apps. A popular model for persuasive design named the Persuasive System Design (PSD) model is a generalized model for whole persuasive technologies. Any type of persuasive application could be designed using this model. Designing any special type of application using the PSD model could be difficult because of its generalized behavior which fails to provide moral support for users of health applications. There is a strong need to propose a customized and improved persuasive system design model for each category to overcome the issue. This study evaluates the PSD model and finds persuasive gaps in users of the Mobile Health Monitoring application, developed by following the PSD model. Furthermore, this study finds that users misunderstand health-related problems when using such apps. A misunderstanding of this nature can have serious consequences for the user’s life in some cases.
Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition

Communication is integral to every human’s life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. Image classification models can be used to create assistive systems to address this communication barrier. This paper conducts a comprehensive literature review and experiments to find the state of the art in sign language recognition. It identifies a lack of research in Norwegian Sign Language (NSL). To address this gap, we created a dataset from scratch containing 24,300 images of 27 NSL alphabet signs and performed a comparative analysis of various machine learning models, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) on the dataset. The evaluation of these models was based on accuracy and computational efficiency. Based on these metrics, our findings indicate that SVM and CNN were the most effective models, achieving accuracies of 99.9% with high computational efficiency. Consequently, the research conducted in this report aims to contribute to the field of NSL recognition and serve as a foundation for future studies in this area.
Youth Practices of Reading as a Form of Life and the Digital World

The proliferation of digital technologies is precipitating a transformation in the socio-cultural fabric of human existence. The present study is dedicated to investigating the coexistence of various reading practices among contemporary youth in the modern era. The advent of new forms of reading has resulted in a shift from conventional paper-based reading to electronic formats, which, in turn, has transformed the practice of reading and the way of life associated with it. The methodological foundation of this research is the socio-philosophical theory that the practice of reading, rooted in the habitus of reading, is enacted by practitioners, and organized through public initiatives. The context of the reading practice system is a distinct historical system of circumstances in which practices are reproduced. This study encompasses an empirical component, focusing on the examination of reading practices among young individuals in a large modern city, specifically within the confines of Yekaterinburg (N = 200). The research was conducted between December 2021 and January 2022. This study permits an analysis of the constituent elements of the reader’s habitus model as a form of life.
Impact of endorsement policy on the performance of blockchain‐based <scp>VANET</scp>

Abstract With the revolution in technology, VANET has emerged as a new generation of technology and used globally for intelligent traffic management systems. VANET uses a PKI‐based protocol to maintain privacy and security. Still, some crucial issues remain unresolved, like blind belief in communicated messages, lack of vehicle's interest in participation, revealing the sender vehicle's identity, and maintaining integrity. The solution to these issues will prevent vehicles from propagating fake messages while concurrently protecting the confidentiality of each involved vehicle against tracking attacks. Blockchain, a peer‐to‐peer, immutable, distributed ledger‐based technology, can be adapted as a robust solution to overcome critical challenges and make VANET more secure and reliable. This paper proposes a novel blockchain‐based VANET architecture, blockchain‐based vehicular ad‐hoc network (B‐VANET), implemented on the Hyperledger Fabric platform. B‐VANET aims to secure the network by validating road events before actually updating it on the network. For validation, the trust factor of each vehicle and their past involvement in the network is maintained. Trust factor value decides the trustworthiness of a vehicle. Based on this trust factor, a dynamic endorsement policy is designed. Endorsement policy, part of the smart contract, plays a vibrant role in Hyperledger Fabric. It decides how many and which particular peers can endorse the transaction to make it valid and add it to the ledger. Multiple possible approaches to endorsement policies are identified for B‐VANET and are analyzed using the Hyperledger Caliper tool. These approaches are evaluated for data integrity, confidentiality, and real‐time system issues. An appropriate approach for the B‐VANET system is recognized, giving the best throughput and latency in all possible scenarios. It is analyzed that compared to the worst approach, the best approach achieves an almost 15% increase in throughput and reduces latency by nearly half. We also compared the performance of B‐VANET and other approaches to show that our proposed system better meets VANET's security needs.
Neural dynamics for improving optimiser in deep learning with noise considered

Abstract As deep learning evolves, neural network structures become increasingly sophisticated, bringing a series of new optimisation challenges. For example, deep neural networks (DNNs) are vulnerable to a variety of attacks. Training neural networks under privacy constraints is a method to alleviate privacy leakage, and one way to do this is to add noise to the gradient. However, the existing optimisers suffer from weak convergence in the presence of increased noise during training, which leads to a low robustness of the optimiser. To stabilise and improve the convergence of DNNs, the authors propose a neural dynamics (ND) optimiser, which is inspired by the zeroing neural dynamics originated from zeroing neural networks. The authors first analyse the relationship between DNNs and control systems. Then, the authors construct the ND optimiser to update network parameters. Moreover, the proposed ND optimiser alleviates the non‐convergence problem that may be suffered by adding noise to the gradient from different scenarios. Furthermore, experiments are conducted on different neural network structures, including ResNet18, ResNet34, Inception‐v3, MobileNet, and long and short‐term memory network. Comparative results using CIFAR, YouTube Faces, and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise‐free and noise‐polluted conditions. The source code is publicly available at https://github.com/LongJin‐lab/ND .
High-Capacity Reversible Data Hiding in Encrypted Images Based on 2D-HS Chaotic System and Full Bit-Plane Searching

Using the local correlation of carrier images to embed secret data in MSBs is a popular scheme for reversible data hiding in encrypted images (RDHEI). However, most existing methods based on this scheme face challenges in achieving complete compression while ensuring security. In this study, an RDHEI method that uses a two-dimensional chaotic system and full bit-plane search (FBPS) is proposed. Specifically, the content owner provides cover images and employs a chaotic system to generate chaotic sequences for inter-block non-symmetrical permutation and intra-block diffusion. The special encryption method, combined with chaos, not only preserves the correlation of pixels within a block but also ensures an extremely high level of security. The FBPS technique is applied to detect all smooth bit planes, not limited to continuous MSBs. For data embedding, ‘0’ and ‘1’ are used to record all smooth and rough bit planes to achieve thorough compression. The results of the experiment show that our proposed method provides a high level of security and achieves 2.142 bit/pixel and 2.339 bit/pixel on the typical datasets BOSSbase (Break Our Steganographic System) and BOWS-2 (Break Our Watermarking System 2nd). Compared with the state-of-the-art methods, the embedding capacity has also been significantly improved.