Reviewerly/exHarmony huggingface dataset:
SSpoon

One key strategy of combating obesity is to slow down eating; however, this is difficult to achieve due to people's habitual nature. In this paper, we explored the feasibility of incorporating shape-changing interface into an eating spoon to directly intervene in undesirable eating behaviour. First, we investigated the optimal dimension (i.e., Z-depth) and ideal range of spoon transformation for different food forms that could affect bite size while maintaining usability. Those findings allowed the development of SSpoon prototype through a series of design explorations that are optimised for user's adoption. Then, we applied two shape-changing strategies: instant transformations based on food forms and subtle transformations based on food intake) and examined in two comparative studies involving a full course meal using Wizard-of-Oz approach. The results indicated that SSpoon could achieve comparable effects to a small spoon (5ml) in reducing eating rate by 13.7-16.1% and food consumption by 4.4-4.6%, while retaining similar user satisfaction as a normal eating spoon (10ml). These results demonstrate the feasibility of a shape-changing eating utensil as a promising alternative to combat the growing concern of obesity.
Quantum and Blockchain for Computing Paradigms Vision and Advancements

Blockchain and quantum technology breakthroughs are currently being debated publicly across a variety of forums. There are numerous applications and capabilities that can provide transparent, redundant, secure, accountable environments thanks to these technologies. To ensure resource-dependent high security requirements, certain cryptographic primitives and protocols can be used effectively. Quantum proofs, safe quantum solutions, and anti-quantum systems will assess any system for quantum attacks and create a secure quantum computing system. Therefore, this work intends to encourage experts from different fields to provide technology-integrated solutions that combine cost-effective and quality service, quickly, securely, and meet requirements. Researchers will be encouraged to provide helpful overviews and guidance in dealing with real-time applications from post-quantum technologies.
Zero-shot sim-to-real transfer of reinforcement learning framework for robotics manipulation with demonstration and force feedback

Abstract In the field of robot reinforcement learning (RL), the reality gap has always been a problem that restricts the robustness and generalization of algorithms. We propose Simulation Twin (SimTwin) : a deep RL framework that can help directly transfer the model from simulation to reality without any real-world training. SimTwin consists of a RL module and an adaptive correct module. We train the policy using the soft actor-critic algorithm only in a simulator with demonstration and domain randomization. In the adaptive correct module, we design and train a neural network to simulate the human error correction process using force feedback. Subsequently, we combine the above two modules through digital twin to control real-world robots, correct simulator parameters by comparing the difference between simulator and reality automatically, and then generalize the correct action through the trained policy network without additional training. We demonstrate the proposed method in an open cabinet task; the experiments show that our framework can reduce the reality gap without any real-world training.
Academic Map and Hotspot Analysis of China’s Legal Construction Research since the Eighteenth National Congress of the Communist Party of China: Visual Analysis Based on CiteSpace

Since the 18th National Congress of the Communist Party of China, the research on the construction of the rule of law in China has received keen attention from scholars. In this study, CSSCI literature published from 2012 to 2022 included in CNKI, Beijing Wanfang, and Chongqing VIP Chinese databases were used as data templates. NoteExpress software was used to deduplicate and organize, and 4,603 literature related to the construction of the rule of law in China were obtained. The citespace5.8 software has carried out a visual analysis, and the results show that: (1) The research on the "rule of law China" as the overall concept in the academic circle first appeared in 2013, and the overall stability was stable, and the local volatility increased. (2) The relationship between researchers and research institutions in this field is relatively loose, and the high-yield author group has not yet been formed. (3) Through the visualization of keyword co-occurrence map and keyword clustering map and the analysis of high-frequency words and centrality, four academic maps and research hotspots are mainly proposed: core cognition of the construction of the rule of law in China, internal theory, external Practice orientation and construction path. (4) Future research on the construction of the rule of law in China strengthens the legal response to scientific and technological information, pays attention to the research on foreign-related fields of the construction of the rule of law in China, and insists on promoting the combination of "domestic rule of law" and "foreign rule of law."
Optimization of University Archives Management under the Application of Blockchain Technology in the Digital Age

The centralized database can store a variety of electronic archives. Electronic archives face a variety of network attack vulnerabilities as information technology and network technology continue to advance. Furthermore, these archives will be readily forged and tampered with by internal management or external attackers. Data security and authenticity problems prevail in China’s management system for archives. First, this exploration elaborates the blockchain technology, distributed database technology, and distributed database system structure. Secondly, blockchain technology is applied to the authenticity protection of electronic archives. Then, an optimization model of university archives based on blockchain technology is constructed. Finally, this exploration investigates the current use of blockchain technology for college archive management systems. The questionnaire is used to understand the current university personnel’s views on the college archive management system under the application of blockchain. The survey results suggest that most people support the digital college archive management system. At present, the operation efficiency of the college archive management system still needs to be improved, and the quality of archives search should be promoted. Therefore, the college archive management system still needs to optimize the archive’s efficiency as well as quality. According to the above survey results, this investigation gives suggestions for optimizing the college archive management system using blockchain technology, as well as some suggestions and references for further management.
Cryptanalysis of Rocca and Feasibility of Its Security Claim

Rocca is an authenticated encryption with associated data scheme for beyond 5G/6G systems. It was proposed at FSE 2022/ToSC 2021(2), and the designers make a security claim of achieving 256-bit security against key-recovery and distinguishing attacks, and 128-bit security against forgery attacks (the security claim regarding distinguishing attacks was subsequently weakened in the full version in ePrint 2022/116). A notable aspect of the claim is the gap between the privacy and authenticity security. In particular, the security claim regarding key-recovery attacks allows an attacker to obtain multiple forgeries through the decryption oracle. In this paper, we first present a full key-recovery attack on Rocca. The data complexity of our attack is 2128 and the time complexity is about 2128, where the attack makes use of the encryption and decryption oracles, and the success probability is almost 1. The attack recovers the entire 256-bit key in a single-key and nonce-respecting setting, breaking the 256-bit security claim against key-recovery attacks. We then extend the attack to various security models and discuss several countermeasures to see the feasibility of the security claim. Finally, we consider a theoretical question of whether achieving the security claim of Rocca is possible in the provable security paradigm. We present both negative and positive results to the question.
Big Data Enabled the Development of Public Sports Health Emergency Corpus: Taking MACPHE as an Example

This study aims to make public sports health emergency corpus as a way to deal with public health emergency such as COVID-19, reducing the losses affected by an illness or health condition that has occurred frequently in recent years. On this basis, this paper analyzes the research status of emergency language services at home and abroad, discusses the significance and principles of Multimodal Aligned Corpus Public Health Emergency (shorted for MACPHE) construction, and develops technical processing paths and building procedures for MACPHE. Finally, it was emphasized that the construction of MACPHE and emergency language resources are important parts of the national language service capacity. Furthermore, on the basis of big data, a modal architecture of MACPHE was given and analyzed in the field of public health service.
Classroom Simulation System of Oral English Teaching Based on a Network Computer

The aim is to study the classroom simulation system of oral English teaching based on a network computer. A classroom simulation system for oral English teaching is proposed. Firstly, the third-party software “Baidu voice” recognition system is introduced into the oral training module of the system, which introduces the field of Baidu artificial intelligence into the system. Baidu AI open platform provides permanent and free speech technology services, including speech recognition, semantic analysis, speech synthesis, and other functions. Then, from the perspective of modern educational technology students, this paper attempts to design an artificial intelligence system module for middle school English teaching and explore the specific implementation conditions and ideas of the system. Finally, the students in a middle school are divided, including 37 in the top class and 34 in the normal class. The simulation results show that the system not only studies the listening and speaking materials but also deeply analyzes the data tables. The functions of English listening, speaking, homework release, and query are realized.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -photon bundle statistics on different solid-state platforms

The term $N$-photon bundles has been coined for a specific type of photon emission, where light quanta are released from a cavity only in groups of $N$ particles. This emission leaves a characteristic number distribution of the cavity photons that may be taken as one of their fingerprints. We study this characteristic $N$-photon bundle statistics considering two solid-state cavity quantum electrodynamics (cQED) systems. As one example, we consider a semiconductor quantum-dot--microcavity system coupled to longitudinal acoustic phonons. There, we find the environmental influence to be detrimental to the bundle statistics. The other example is a superconducting qubit inside a microwave resonator. In these systems, pure dephasing is not important and an experimentally feasible parameter regime is found, where the bundle statistics prevails.
Takeaways from an experimental evaluation of eXpress Data Path (XDP) and Data Plane Development Kit (DPDK) under a Cloud Computing environment

The popularization of the Linux kernel at data center servers became a significant factor to consider when developing or deploying network applications. New “packet processing frameworks” emerged to ensure fast packet processing inside these servers that rely on the Linux kernel, XDP and DPDK being the two main ones. Alongside this, Cloud Computing environments dominated the data center scenario, bringing virtualization to the center of these servers. This research compares DPDK and XDP packet processors when subject to a cloud computing environment with I/O, CPU, and network load. We first describe an architecture that implements frameworks and emulates a cloud environment. We measure throughput and packet loss while varying the number of competing VMs, CPU usage by the frameworks, and packet size and present our results in the form of takeaways. Furthermore, we conclude that the virtual environment can impact the performance of both frameworks depending on the received packet size and the type of workload the cloud environment performs. A significant case to highlight is when the cloud environment performs network load, and the servers receive small-sized packets, creating both throughput degradation and increasing packet loss. We conclude our experiments by conducting statistical non-parametric hypothesis tests to enhance our conclusions and have a closer look at the results.
A conceção de cuidados de enfermagem dirigida aos prestadores de cuidados espelhada na documentação

Background: Caregivers must acquire knowledge and develop skills that ensure the safety of the care delivered and guarantee their own self-care.Nurses take on the role of facilitators in this transition process, with nursing information systems being used as tools to safeguard the continuity and quality of care.Objective: To understand nursing care design for caregivers.Methodology: This is a mixed method study with a descriptive exploratory research design, analyzing 163 files of caregiver documentation and using a questionnaire (N = 72).Results: It was possible to identify the underreporting of caregivers.Caregivers were predominantly elderly women with moderate burden who reported difficulties in instrumental activities and needed support from an additional caregiver or a formal support network.Conclusion: Identifying the needs and difficulties of caregivers combined with the observed underreporting strengthens the need for bringing the models in use closer to the models presented, thus translating this process into an improvement of quality standards in nursing care for caregivers.
Analysis of Psychological Shaping Function of Music Education under the Background of Artificial Intelligence

In order to solve the problem of integrating intelligent technology into music teaching, this paper puts forward the methods of using intelligent technology to optimize the music teaching system; to enhance the effectiveness of music psychological guidance, music intelligent creation, the development of Yi Guzheng platform, the integration of online sparring technology, and the rational use of Mu class platform, and to ensure the construction of loop curriculum system with "intelligent piano." RBF algorithm has strong data processing ability, which can ensure the operation quality of music score, music score and performance learning modules, and effectively strengthen the training effect of music psychological function. The artificial intelligence platform is more progressive in evaluation. It can use emotion to evaluate courses, grasp the course direction in advance, ensure the construction quality of psychological function, and improve the effect of music teaching; take 100 people as the basis of students, and measure the time proportion of students' music learning in the intelligent system. cjσj refers to the learning situation of music courses within the learning time X of students with excellent psychological quality of music. This result is parallel. Y represents the best score of music output by each algorithm. 2024 music intelligent platforms have collected various course resources with difficulty coefficients of 1 to 5, and the course resources of pictures and videos are sufficient. It can provide students with comprehensive music psychological education and give full play to the teaching advantages of intelligent technology. It can be seen from the data: the operation of music score, music score and performance module viewed in March 2021, with high transportation times and resource download times, indicating that the operation is in good condition; The error correction accuracy of the system is greater than 99%, indicating that the system has strong error correction ability.
FedHarmony: Unlearning Scanner Bias with Distributed Data

The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, combining datasets across sites leads to two challenges: first, an increase in undesirable non-biological variance due to scanner and acquisition differences - the harmonisation problem - and second, data privacy concerns due to the inherently personal nature of medical imaging data, meaning that sharing them across sites may risk violation of privacy laws. To overcome these restrictions, we propose FedHarmony: a harmonisation framework operating in the federated learning paradigm. We show that to remove the scanner-specific effects, for our scenario we only need to share the mean and standard deviation of the learned features, helping to protect individual subjects’ privacy. We demonstrate our approach across a range of realistic data scenarios, using real multi-site data from the ABIDE dataset, thus showing the potential utility of our method for MRI harmonisation across studies. Our code is available at https://github.com/nkdinsdale/FedHarmony .
Adversarially Robust Prototypical Few-Shot Segmentation with Neural-ODEs

Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to the lack of a large number of training examples. In this paper, we provide a framework to make few-shot segmentation models adversarially robust in the medical domain where such attacks can severely impact the decisions made by clinicians who use them. We propose a novel robust few-shot segmentation framework, Prototypical Neural Ordinary Differential Equation (PNODE), that provides defense against gradient-based adversarial attacks. We show that our framework is more robust compared to traditional adversarial defense mechanisms such as adversarial training. Adversarial training involves increased training time and shows robustness to limited types of attacks depending on the type of adversarial examples seen during training. Our proposed framework generalises well to common adversarial attacks like FGSM, PGD and SMIA while having the model parameters comparable to the existing few-shot segmentation models. We show the effectiveness of our proposed approach on three publicly available multi-organ segmentation datasets in both in-domain and cross-domain settings by attacking the support and query sets without the need for ad-hoc adversarial training.
Explainability does not improve biochemistry staff trust in artificial intelligence-based decision support

Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user trust, yet this has not been established in healthcare contexts. For advanced algorithms such as artificial neural networks, the generation of explanations is not trivial, but requires the use of a second algorithm. The assumption of improved user trust should therefore be investigated to determine if it justifies the additional complexity.Biochemistry staff completed a wrong blood in tube (WBIT) error identification task with the help of an ADS system. One-half of the volunteers were provided with both ADS predictions and explanations for those predictions, while the other half received predictions alone. The two groups were compared in terms of their rate of agreement with ADS predictions, as an index of user trust, and WBIT error detection performance. Since the AI model used to generate predictions was known to out-perform laboratory staff, increased trust was expected to improve user performance.Volunteers reviewed 1590 sets of results. The volunteers provided with explanations demonstrated no difference in their rate of agreement with the ADS system compared to volunteers receiving predictions alone (83.3% versus 81.8%, p = 0.46). The two volunteer groups were also equivalent in accuracy, sensitivity and specificity for WBIT error identification (p-values >0.78).For a WBIT error identification task, there was no evidence to justify the additional complexity of explainability on the grounds of increased user trust.
Study on the Application of Traditional Chinese Auspicious Images in Environmental Art Design Based on VR Senses

Virtual reality is high-tech involving multiple disciplines developed at the end of the 20th century. Virtual reality technology can be used in many fields, such as remote control, business, education, entertainment, visual simulation, military, art, architecture, and network. Essentially, virtual reality technology has three basic characteristics: immersion, interaction, and imagination. This study analyzes the advantages of VR technology in the specific application of environmental art design based on case studies and considers the impact of VR technology on human beings from multiple dimensions such as sociology and philosophy. First, it analyzes the application and advantages of VR technology in various stages of the environmental art design. Secondly, combined with cases, it explains the advantages of VR technology in four directions: exhibition, interior, landscape, and planning. Then, it finds out the problems in the application and their solutions. In addition, to solve these problems, you need to explore the answers from more fields such as philosophy and sociology. Then, it discusses the development trend of environmental art design under VR technology and how designers should keep up with the time. Finally, as a designer of the new era, we apply VR technology to our own design and creation practice. We also analyzed the many forms of expression of auspicious images. Through the actual research and induction of modern designers, we summed up its main forms and expressions and drew some conclusions that everyone recognized, which can provide a reference for future designers.
College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

The “New Era Artificial Brain Power Improvement Plan” issued by the State Council and the “Computer Reasoning Development Activity Plan for Colleges and Universities” issued by the Ministry of Education clearly pointed out that it is necessary to attach importance to the combination of modern education and artificial intelligence. As a global language, English is becoming more and more important in global trade. Under the premise of educational plan changes in the new environment, English teaching is gradually reflected in the cultivation of college students’ English application ability. This research studies the current college English audio-visual-oral teaching mode from the perspective of artificial intelligence and analyzes the current situation of traditional English teaching by explaining the meaning of artificial intelligence, which mainly includes four stages: literature research, model construction, empirical research, and result analysis. In light of BP neural network, the showing assessment model of school English audio-visual-oral is laid out, and the change technique of showing method of school English audio-visual-oral under AI is advanced. Experiments show that this model achieves better teaching quality than the models constructed by traditional GA algorithm and BSA algorithm and is more suitable for promotion in college English teaching.