Reviewerly/exHarmony huggingface dataset:
Peer-to-peer: The Social Transmission of Symptoms Online

Social learning can be highly adaptive-for example, avoiding a hotplate your friend just burnt themselves on-but it has also been implicated in symptom transmission. Social learning is particularly pertinent given the rapid increase in the use of online mediums for social interaction. Yet, little is known about the social transmission of symptoms online or social chains extending further than a single model-observer interaction.To explore whether socially induced symptoms could be propagated through a three-generation social transmission chain in an online setting.We explored the social transmission of cybersickness following a virtual reality (VR) experience through online webcam interactions. One hundred and seventy-seven adults viewed a VR video in one of four links along a social transmission chain, after: viewing an actor model cybersickness to the VR video (First-Generation); viewing the First-Generation participant undergo VR (Second-Generation); viewing the Second-Generation participant undergo VR (Third-Generation); or naïve (Control).Cybersickness was strongest in First-Generation participants, indicating social transmission from the model. This was mediated by expectancy and anxiety. Whether or not subsequent generations experienced cybersickness depended on what the observed participant verbally reported, which is consistent with social transmission.Results demonstrate that symptoms can be readily transmitted online, and that expectancy and anxiety are involved. Although it is inconclusive as to whether symptoms can propagate along a social transmission chain, there is some evidence of protection from symptoms when a model who does not report any symptoms is observed. As such, this research highlights the role of social transmission in the modulation of symptoms through virtual mediums.Social learning is a ubiquitous cognitive process whereby our own behaviors and experiences are influenced by observing others. Occasionally, this can involve the observation of an individual experiencing negative outcomes (e.g., pain or symptoms) following exposure to a treatment or intervention (e.g., consumption of medicine). Previous research has found that individuals may experience an increase in symptoms due to this social learning, even when their treatment has no active components. While research has primarily explored situations in which there is one model and one observer, it was of interest as to whether these socially induced symptoms can be transmitted beyond the first observer. Moreover, with social interaction through online mediums such as social media and video conferencing becoming more common, it was also of interest as to whether these symptoms can be transmitted online. The present findings highlight the significant role of social learning in symptom transmission, even when interactions occur online. With expectancy and anxiety being key features of this social transmission, this study highlights important implications for understanding how individuals can learn about their own future experiences through the observation of others.
Federated Learning With Server Learning for Non-IID Data

Federated Learning (FL) has gained popularity as a means of distributed learning using local data samples at clients. However, recent studies showed that FL may experience slow learning and poor performance when client samples have different distributions. In this paper, we consider a server with access to a small dataset, on which it can perform its own learning. This approach is complementary to and can be combined with other approaches, e.g., sample sharing among clients. We study and demonstrate the benefits of proposed approach via experimental results obtained using two datasets - EMNIST and CIFAR10.
Design and Implementation of Travel Website Based on Java Web

With the continuous improvement of people's living standards, China's tourism industry has developed vigorously, and various tourism websites have sprung up. However, due to the late start of informatization in China, compared with developed countries, the degree of informatization in tourism in China is relatively low. Therefore, this paper realizes the system of travel website based on JavaWeb. The system generally adopts a relatively convenient B/S structure, including the front end and the back end. The front-end adopts Ajax, JQuery, HTML, CSS and other technologies, considering the response speed of users. The back-end mainly uses the technologies of Web containers such as Sevlet and Filter, and refers to the design pattern of three-tier architecture to process the back-end, which effectively standardizes the development process and provides convenience for testing and maintenance. In the aspect of database, MySQL database and Redis cache are used to store data hierarchically, and Durid connection pool is used to manage database connection resources and further improve the response performance of database.
Cloud-Assisted Private Set Intersection via Multi-Key Fully Homomorphic Encryption

With the development of cloud computing and big data, secure multi-party computation, which can collaborate with multiple parties to deal with a large number of transactions, plays an important role in protecting privacy. Private set intersection (PSI), a form of multi-party secure computation, is a formidable cryptographic technique that allows the sender and the receiver to calculate their intersection and not reveal any more information. As the data volume increases and more application scenarios emerge, PSI with multiple participants is increasingly needed. Homomorphic encryption is an encryption algorithm designed to perform a mathematical-style operation on encrypted data, where the decryption result of the operation is the same as the result calculated using unencrypted data. In this paper, we present a cloud-assisted multi-key PSI (CMPSI) system that uses fully homomorphic encryption over the torus (TFHE) encryption scheme to encrypt the data of the participants and that uses a cloud server to assist the computation. Specifically, we design some TFHE-based secure computation protocols and build a single cloud server-based private set intersection system that can support multiple users. Moreover, security analysis and performance evaluation show that our system is feasible. The scheme has a smaller communication overhead compared to existing schemes.
Model Segmentation for Storage Efficient Private Federated Learning with Top $r$ Sparsification

In federated learning (FL) with top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> sparsification, millions of users collectively train a machine learning (ML) model locally, using their personal data by only communicating the most significant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> fraction of updates to reduce the communication cost. It has been shown that the values as well as the indices of these selected (sparse) updates leak information about the users' personal data. In this work, we investigate different methods to carry out user-database communications in FL with top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> sparsification efficiently, while guaranteeing information theoretic privacy of users' personal data. These methods incur considerable storage cost. As a solution, we present two schemes with different properties that use MDS coded storage along with a model segmentation mechanism to reduce the storage cost at the expense of a controllable amount of information leakage, to perform private FL with top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> sparsification.
Nature-inspired Path Planning for Robot Swarms

As the development of robotics technique, a raising number of scenarios call for coordination of large robotic swarms. One key problem for robotic swarms is distribution motion planning. This paper introduces some popular creature-inspired approaches for path optimization, which include Ant Colony System, Firefly algorithm, and Artificial Bee Colony. Their principle and methodology will be introduced specifically. Also, the author lists the final performance of these algorithms and has a systematic evaluation, and finally draws the conclusion that the ACO could find a nearly optimized but not rigorously optimized solution, ABC do the best work in minimizing the path length, and FA is the most practical algorithm in reality.
General particle swarm optimization algorithm

In order to make full use of the individual extreme information of each particle, and promote optimization performance of particle swarm optimization algorithm, a new particle swarm optimization algorithm is proposed and named as generalized particle swarm optimization (GPSO). GPSO algorithm modifies the standard PSO algorithm speed update formula, in the current particle, cognitive part of speed update formula introduces the rest particles individual extreme information, it makes every particle get n (particle swarm size) update speeds and n corresponding location update values, and GPSO chooses the particle which has optimal adaptive value as the final iteration particle. similar to the PSO algorithm, Global extremum of GPSO and individual extremum of particle are updated. the nature of the GPSO algorithm is analyzed, GPSO algorithm enhances the capacity of information exchange and sharing between the particles and has a downward compatibility for the PSO algorithm, which theoretically ensures that the GPSO algorithm has better search performance than the PSO algorithm. The simulation results show that the proposed algorithm is effective.
The integration of digital twin and serious game framework for new normal virtual urban exploration and social interaction

COVID-19 has disconnected humanity, reduced social interaction in physical urban areas, and led to the new normal, which describes the anticipated changes in human life and professionals due to the impact of the pandemic. In addition, as part of digital transformation, the post-pandemic New Normal includes accelerating digital solutions and new standards for virtual urban exploration and planning. This study applies the concept of digital twin and serious game design and uses Minecraft, a game-based platform that inspires creative, inclusive learning through play, for virtual urban exploration and the development of social interaction among participants. Dadaocheng, a historical area of Taipei city, is then studied as a case study with the Geoboxers application to develop the prototype for a co-creation experiment in Minecraft. The prototype development and the results of the experiment are then used in the Analytic Hierarchical Process (AHP) method to evaluate a set of influential criteria proposed in this study through pairwise comparisons by expert panelists. The results of the AHP analysis reveal users' simultaneous preferences for urban planning and social interaction with urban characteristics (22.14%), urban exploration (12.29%), and 3D models (11.97%). Subsequently, the research results showed a need to promote the integration of digital twins and serious game applications as digital tools for urban exploration and social interaction, increasing post-pandemic virtual urban planning and applying new urban design techniques. This study also contributes to the acceleration of digital transformation in urban planning and management.
Modulation identification method based on time-frequency analysis and support vector machine

For the problem of modulation recognition method of high complexity and low recognition rate under low signal-noise ratio (SNR), this paper presents a modulation recognition method based on time-frequency analysis and support vector machine (SVM). This method uses characteristic parameters of time domain, frequency domain and wavelet energy entropy as the input of the support vector machine classifier, and can effectively identify three signal modulation types of Frequency-shift keying (FSK), Frequency Modulation (FM) and Amplitude Modulation (AM). The results show that the AM modulated signal can be accurately identified by this method even under low SNR. Relatively speaking, FM and AM do not perform well at low SNR, but the correct recognition rate can still reach 85%. At the same time, the proposed method has a better recognition performance than the traditional single feature extraction method, the no feature processing method, and the decision tree classification method.
Transformational design for food systems: Cultural, social and technological challenges1

Due to climate changes, resources availability and evolving markets, the food system is developing towards an articulated and complex ecology, with fast transformations occurring in food production, preparation, delivery and disposal. In this context, innovation is needed not just to ideate solutions to deal with a fast-changing system but also to accompany the change adopting a systemic long-term approach. We reflect on the transformational potential of design in the food sector enabled by digital technologies, one of the current major drivers of change. We define two levels of changes implying digital technologies, those that radically change the food system and those enabling changes within a given system. These levels are exemplified with case studies documented in literature and with students’ projects showing how transformational design can help grasp the complexity of current problems, and question the current status quo by facilitating a dialogue among stakeholders to stimulate behaviour change without prescribing it. In this article we encourage a paradigm shift of design from craft activity to a holistic approach of systemic thinking where the designer assumes the role of promoter and facilitator of change. Reflections on challenges at cultural, social and technological levels are provided in the conclusion section.
Towards understanding the mechanism through which reward and punishment motivate or demotivate behaviours

Persuasive gamified systems are effective tools for motivating behaviour change using various persuasive strategies. In line with the reinforcement theory, some persuasive gamified systems employ reward and punishment in their design to achieve the intended behavioural outcome. Research has argued both in favour and against using these strategies in behaviour change applications due to mixed results with respect to their effectiveness. However, there is a lack knowledge about how interventions using these strategies could motivate or demotivate behaviours. Therefore, this paper explores the mechanism through which Reward and Punishment motivate or demotivate behaviours with respect to their strengths and weaknesses. The results of large-scale exploratory studies (N = 1768) uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of Reward and Punishment at motivating behaviour change. These include their ability to engage users and make behaviour fun, reinforce commitments to goals, and reveal some consequences of bad behaviour. We also compared the perceived effectiveness of reward and punishment quantitatively.
Year of sustainability, openness, and new roles: A Ukrainian university library in wartime

One year of russia’s full-scale military invasion of Ukraine to seize its territory and destroy the Ukrainian people as a nation, its language, culture, scientific achievements, and educational traditions have demonstrated the incredible adaptability of universities and their libraries. This paper aims to analyze the administrative aspect of the urgent adaptation of library teams of Ukrainian higher education institutions to martial law conditions in the example of the Scientific Library of the Ukrainian State University of Science and Technologies (USUST). The paper considers the experience of survival, resilience, and adaptation of the USUST library to the war, which can be extrapolated to other libraries of Ukrainian higher education institutions. In addition, the study comparatively analyzes quantitative indicators of five university libraries as of January 1, 2022, and January 1, 2023. The main vectors of library activity are distance, openness, unification, and improvement of the single information space. The primary motivating factors for librarians’ resilience and adaptation to wartime working conditions are belief in the Armed Forces and the victory of Ukraine. The study emphasizes the importance of university libraries’ support of open educational resources as one of the methods of overcoming the Ukrainian linguocide. Library directors in wartime, with predominantly female staff, note the impact of psychological factors on their professional activities. Despite the threatening trends, libraries continue to support educational and scientific processes in their universities, launching new directions of activity.
Sturgeon-MKIII: Simultaneous Level and Example Playthrough Generation via Constraint Satisfaction with Tile Rewrite Rules

Completability is a key aspect of procedural level generation. In this work, we present a constraint-based approach to level generation for 2D tile-based games that simultaneously generates a level and an example playthrough of the level demonstrating its completability. The approach represents game mechanics as tile rewrite rules, which allows a variety of games and mechanics (beyond simple pathfinding) to be incorporated. The mechanics are represented as constraints in the same problem along with the constraints used to generate the level itself. Thus, the solution to the constraint problem contains both a level and a playthrough of the level. We demonstrate the flexibilty of the system and of tile rewrite rules in several applications, including lock-and-key dungeons, platformers, puzzles, and match-three style games.
FedUA: An Uncertainty-Aware Distillation-Based Federated Learning Scheme for Image Classification

Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks. In addition to data security issues, fundamental challenges in this type of learning include the imbalance and non-IID among clients’ data and the unreliable connections between devices due to limited communication bandwidths. The above issues are intractable to FL. This study starts from the uncertainty analysis of deep neural networks (DNNs) to evaluate the effectiveness of FL, and proposes a new architecture for model aggregation. Our scheme improves FL’s performance by applying knowledge distillation and the DNN’s uncertainty quantification methods. A series of experiments on the image classification task confirms that our proposed model aggregation scheme can effectively solve the problem of non-IID data, especially when affordable transmission costs are limited.
DRL: Dynamic rebalance learning for adversarial robustness of UAV with long-tailed distribution

Adversarial robustness has attracted extensive studies in various fields by increasing the interpretability of deep learning and enhancing the understanding of neural network models. In realistic scenarios such as UAV control system, imbalanced datasets are a consensus. Therefore, how to solve the adversarial robustness on imbalanced datasets is a more and more inescapable problem in UAV control system. There have been some works on adversarial robustness on imbalanced datasets, which bring us a deeper understanding of vulnerability of deep neural networks and the generation of adversarial examples. To adjust the classification plane after training, the long-tailed robustness framework is usually designed to be multi-stage, and different classifiers are used in different stages, which can improve the robustness through multi learning. The existing methods are hardly considered to effectively handle the long-tailed robustness problem in UAV control system. To explore the intrinsic features of long-tailed robustness, we propose a one-stage robustness framework. First, we study different classifiers and propose a general cosine classifier. By changing the general cosine classifier adaptively, the model obtains a more robust classification. Then, we analyze the scalability of the focal loss and design a focal-margin loss. Finally, we design a category focus mobile learning strategy, obtained more robust features by changing the learning emphasis with this strategy. From this, we design a simple and efficient one-stage dynamic adversarial robustness method DRL under long-tailed distribution, which consists of an adaptive cosine classifier and a focal-margin loss under long-tailed mobile learning. The extended experiments demonstrate the superiority of our approach over other state-of-the-art methods, and the effectiveness of the designed module. This method can effectively solve the long-tailed robustness problem on UAV control system and other terminals.
Secure Information Sharing Approach for Internet of Vehicles Based on DAG-Enabled Blockchain

Information sharing in vehicular networks has great potential for the future Internet of Vehicles (IoV). Vehicles and roadside units (RSUs) can exchange perceptual information and driving experience to enable intelligent transportation applications such as autonomous driving and road condition analysis. However, ensuring secure and efficient information sharing among vehicles is challenging. While traditional blockchain can guarantee the tamper-proof nature of shared information, it cannot be directly applied in large-scale vehicle networks due to its slow consensus process. Therefore, we propose an information sharing approach based on a directed acyclic graph (DAG), in which shared information is encapsulated into sites instead of blocks. We also propose a driving decision-based tip selection algorithm (DDB-TSA) and design a reputation-based rate control strategy (RBRCS) to ensure secure and efficient information sharing. Simulation results show that our approach reduces consensus latency, improves information sharing efficiency, and provides a more secure information sharing environment compared to existing DAG-enabled blockchain systems.
SAM-GAN: Supervised Learning-Based Aerial Image-to-Map Translation via Generative Adversarial Networks

Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image translation based on generative adversarial networks has led to rapid progress in aerial image-to-map translation. Still, the generated results could be better regarding quality, accuracy, and visual impact. This paper proposes a supervised model (SAM-GAN) based on generative adversarial networks (GAN) to improve the performance of aerial image-to-map translation. In the model, we introduce a new generator and multi-scale discriminator. The generator is a conditional GAN model that extracts the content and style space from aerial images and maps and learns to generalize the patterns of aerial image-to-map style transformation. We introduce image style loss and topological consistency loss to improve the model’s pixel-level accuracy and topological performance. Furthermore, using the Maps dataset, a comprehensive qualitative and quantitative comparison is made between the SAM-GAN model and previous methods used for aerial image-to-map translation in combination with excellent evaluation metrics. Experiments showed that SAM-GAN outperformed existing methods in both quantitative and qualitative results.