Reviewerly/exHarmony huggingface dataset:
RoFL: Robustness of Secure Federated Learning

Even though recent years have seen many attacks exposing severe vulnerabilities in Federated Learning (FL), a holistic understanding of what enables these attacks and how they can be mitigated effectively is still lacking. In this work, we demystify the inner workings of existing (targeted) attacks. We provide new insights into why these attacks are possible and why a definitive solution to FL robustness is challenging. We show that the need for ML algorithms to memorize tail data has significant implications for FL integrity. This phenomenon has largely been studied in the context of privacy; our analysis sheds light on its implications for ML integrity. We show that certain classes of severe attacks can be mitigated effectively by enforcing constraints such as norm bounds on clients’ updates. We investigate how to efficiently incorporate these constraints into secure FL protocols in the single-server setting. Based on this, we propose RoFL, a new secure FL system that extends secure aggregation with privacy-preserving input validation. Specifically, RoFL can enforce constraints such as L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> bounds on high-dimensional encrypted model updates.
Continual Observation under User-level Differential Privacy

In the foundational work of Dwork et al. [15] on continual observation under differential privacy (DP), two privacy models have been proposed: event-level DP and user-level DP. The latter provides a much stronger notion of privacy, as it allows a user to contribute an arbitrary number of items. Under event-level DP, their mechanisms match the optimal utility bounds in the static setting up to polylogarithmic factors for all union-preserving functions. Unfortunately, in contrast to this strong result for event-level DP, their user-level DP mechanisms have weak utility guarantees and many restrictions on the data. In this paper, we take an instance-specific approach, designing continual observation mechanisms for a number of fundamental functions under user-level DP. Our mechanisms do not need any a priori restrictions on the data, while providing utility guarantees that degrade gracefully as the hardness of the data increases. For the count and sum function, our mechanisms are down-neighborhood optimal, matching the static setting up to polylogarithmic factors. For other functions, they do not match the static case, but we prove that this is inevitable, which is the first separation result for continual observation under differential privacy.
SoK: Distributed Randomness Beacons

Motivated and inspired by the emergence of blockchains, many new protocols have recently been proposed for generating publicly verifiable randomness in a distributed yet secure fashion. These protocols work under different setups and assumptions, use various cryptographic tools, and entail unique trade-offs and characteristics. In this paper, we systematize the design of distributed randomness beacons (DRBs) as well as the cryptographic building blocks they rely on. We evaluate protocols on two key security properties, unbiasability and unpredictability, and discuss common attack vectors for predicting or biasing the beacon output and the countermeasures employed by protocols. We also compare protocols by communication and computational efficiency. Finally, we provide insights on the applicability of different protocols in various deployment scenarios and highlight possible directions for further research.
Conditional generative adversarial network for generation of three-dimensional porous structure of solid oxide fuel cell anodes with controlled volume fractions

A structure generation model based on a generative adversarial network (GAN) is developed to synthesize artificial porous microstructures of solid oxide fuel cell (SOFC) anodes. Different from the conventional framework of GANs, additional training is performed for the generator to control statistical parameters, namely, volume fractions, of the generated structures. The developed model is validated by comparing the synthesized structures with the real electrode microstructures obtained by three-dimensional microscopy analysis. Microstructural parameters, such as volume fraction, specific surface area, and triple-phase boundary density, are used for the comparison in addition to the visual observation. The effect of the input vector size for the generator and the definition of the loss on the ability to generate realistic structures and control the volume fractions of the structures is investigated. The developed model successfully generates realistic anode microstructures with accurately controlled volume fractions, even for compositions not included in the training datasets. It is also found that the balance between the losses influences the accuracy of the volume fraction control and diversity of the generated structures. The GAN model developed is expected to be helpful in constructing a digital twin of electrode fabrication and evaluation processes.
Check-in to the future: Exploring the impact of contemporary information technologies and artificial intelligence on the hotel industry

The hotel industry is undergoing significant change as a result of technological advancements and shifting guest preferences. Artificial intelligence (AI) is playing an increasingly important role in this transformation, allowing hotels to provide personalized, efficient, and sustainable experiences that meet the needs of modern travelers. This paper provides an overview of the role and application of AI in the hotel industry, highlighting its benefits and challenges as well as presenting case studies that show how hotels are currently utilizing AI technology. According to the review of literature, AI technology can be used to optimize hotel operations, improve the guest experience, and boost sustainability efforts. However, the use of AI technology in the hotel industry raises concerns about data privacy and security, job displacement, and the need for human supervision to ensure ethical and responsible deployment. Finally, the paper discusses the future directions of AI in the hotel industry, highlighting the emergence of voice technology and augmented reality as potential areas for future innovation. This paper adds to the growing body of literature on the use of AI in the hotel industry and provides useful insights for hotel managers, technology providers, and researchers.
Dispute-Free Scalable Open Vote Network Using zk-SNARKs

The Open Vote Network is a self-tallying decentralized e-voting protocol suitable for boardroom elections. Currently, it has two Ethereum-based implementations: the first, by McCorry et al., has a scalability issue since all the computations are performed on-chain. The second implementation, by Seifelnasr et al., solves this issue partially by assigning a part of the heavy computations to an off-chain untrusted administrator in a verifiable manner. As a side effect, this second implementation became not dispute-free; there is a need for a tally dispute phase where an observer interrupts the protocol when the administrator cheats, i.e., announces a wrong tally result. In this work, we propose a new smart contract design to tackle the problems in the previous implementations by (i) preforming all the heavy computations off-chain hence achieving higher scalability, and (ii) utilizing zero-knowledge Succinct Non-interactive Argument of Knowledge (zk-SNARK) to verify the correctness of the off-chain computations, hence maintaining the dispute-free property. To demonstrate the effectiveness of our design, we develop prototype implementations on Ethereum and conduct multiple experiments for different implementation options that show a trade-off between the zk-SNARK proof generation time and the smart contract gas cost, including an implementation in which the smart contract consumes a constant amount of gas independent of the number of voters.
ASVMK: A novel SVMs Kernel based on Apollonius function and density peak clustering

Support Vector Machine (SVM) is one of the successful methods of machine learning. SVM uses kernel tricks to efficiently learn non-linear classification tasks. The kernel function is applied to each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable. The Gaussian kernel in Radial-Basis Function (RBF) is a popular kernel function used in various kernelized learning methods. In the RBF kernel, the sigma parameter (bandwidth of kernel function) should be fine-tuned, which is a challenging job. If the sigma value is very small, the decision boundary is highly non-linear. On the other hand, if the sigma value is large, the decision boundary tends to be linear. In this paper, an efficient kernel method called the Apollonius SVM Kernel (ASVMK) is introduced. Our algorithm adjusts the width of the kernel based on the data density and the representativeness of each class. The main idea is to introduce a kernel without adjusting the bandwidth of the kernel based on landmark points and the Apollonius similarity function. ASVMK finds the correlation of dense points and their neighbors in the data and tries to map the original non-linear observations in the highest dimensional space from these features. Reducing memory problems and low computations due to the shrinkage of the gram matrix are some of the benefits of the proposed kernel. The proposed method outperformed the state-of-the-art methods by up to 11%.
From Data to Action: Exploring AI and IoT-Driven Solutions for Smarter Cities

The emergence of smart cities demands harnessing advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) and promises to unlock cities' potential to become more sustainable, efficient, and ultimately livable for their inhabitants. This work introduces an intelligent city management system that provides a data-driven approach to three use cases: (i) analyze traffic information to reduce the risk of traffic collisions and improve driver and pedestrian safety, (ii) identify when and where energy consumption can be reduced to improve cost savings, and (iii) detect maintenance issues like potholes in the city's roads and sidewalks, as well as the beginning of hazards like floods and fires. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights that enhance security, energy efficiency, and sustainability, while highlighting the potential of AI and IoT-driven solutions for smart city development.
Cognitive Reinforcement for Enhanced Post Construction Aiming Fact-Check Spread

Despite the success of fact-checking agencies in presenting timely fact-checking reports on the main topics, the same success is not achieved for the dissemination of these reports. This work presents the definition of a set of heuristics applicable to messages (posts) in the microblogging environment, with the aim of increasing their engagement and, consequently, their reach. The proposed heuristics focus on two main tasks: summarisation, emotion-personality reinforcement. The results were evaluated through an experiment conducted with twenty participants, comparing the engagement of actual and generated posts. From the results of the experiment, it can be concluded that the strategy used by the generator is at least better than the one used by the fact-checking journal Snopes in its Twitter posts.
Free viewing biases for complex scenes in preschoolers and adults

Adult gaze behaviour towards naturalistic scenes is highly biased towards semantic object classes. Little is known about the ontological development of these biases, nor about group-level differences in gaze behaviour between adults and preschoolers. Here, we let preschoolers (n = 34, age 5 years) and adults (n = 42, age 18-59 years) freely view 40 complex scenes containing objects with different semantic attributes to compare their fixation behaviour. Results show that preschool children allocate a significantly smaller proportion of dwell time and first fixations on Text and instead fixate Faces, Touched objects, Hands and Bodies more. A predictive model of object fixations controlling for a range of potential confounds suggests that most of these differences can be explained by drastically reduced text salience in pre-schoolers and that this effect is independent of low-level salience. These findings are in line with a developmental attentional antagonism between text and body parts (touched objects and hands in particular), which resonates with recent findings regarding 'cortical recycling'. We discuss this and other potential mechanisms driving salience differences between children and adults.
Incorporating an Intelligent Tutoring System into the DiscoverOChem Learning Platform

The current work develops intelligent tutoring aspects for the DiscoverOChem learning platform. Intelligent tutoring systems are technology-based learning systems that can adapt the learning experience to better serve individual users. DiscoverOChem (www.discoverochem.com) is a free Internet-based platform for learning undergraduate-level organic chemistry. Data from previous years of students were used to analyze how well individual students performed on various pages of the platform. Correlations between pairs of pages were analyzed. Predictive models, which use a user's results on previous pages to predict that user's likely performance on upcoming pages, were developed and evaluated. The most successful set of models, which utilizes random forests of one-branch decision trees, was incorporated into the DiscoverOChem platform as a recommender system. This system helps individual users to identify pages that are likely to challenge them and provides targeted recommendations about which previous pages to review in order to help them become better prepared to succeed on the upcoming page. We anticipate that learners will benefit from this new individualization of their learning experiences. We also anticipate that the general 6-step framework that was used to develop this system will be broadly useful for creating intelligent learning platforms for other subjects as well.
Effective collaborative learning for early childhood teachers: structural, motivational and sustainable features

Research on teachers' professional learning and development (PLD) has investigated the nature and design of such learning initiatives, itemising characteristics that promote high-quality PLD and outcomes for individual teachers. However, much learning for teachers happens as they work together to consider and discuss their ability to implement their new learning. Features that create teacher collaboration have not been as explicitly pinpointed within the existing literature. We report a content analysis of literature aimed at identifying and grouping features of what we term 'teachers' collaborative learning' (TCL), specifically for early childhood teachers. Three interdependent themes that promote collaboration were identified. First, structural features reflect how effective TCL is organised. Second, motivational features promote the depth and quality of teachers' learning experience. The third set of features are those that promote sustainability of both changes and improvements in teaching and learning arising from TCL. We argue that an understanding of effective TCL for early childhood teachers is enhanced when many of these features function together cohesively. We conclude with evidence-based considerations for designing PLD. We highlight the need to address power differences in PLD, respond to cultural issues affecting knowledge building, and understand challenges faced in teachers sustaining collaborative learning.
Technology-Enhanced Learning in Health Sciences: Improving the Motivation and Performance of Medical Students with Immersive Reality

Numerous studies suggest that immersive reality (IR) is an educational technology with great potential in the field of health sciences. Its integration allows for an increase in the motivation and academic performance of students. In this sense, this research aims to study the self-perception of motivation and performance levels obtained by students who are completing their degree in medicine at the University of Seville after experiencing a session with IR. To achieve this, 136 student participants answered two questionnaires, the IMMS and the academic performance test. The results show high levels of motivation during the IR session, where the interaction with “hot spots” predominates. In the same way, the measured performance results are quite great. For this reason, it is concluded that the potential of using IR as an educational technology is evident, and new lines of related research are opened.
Mining Eye-Tracking Data for Text Summarization

In this study, we introduce and evaluate a novel extractive text summarization methodology, “SummarEyes,” based on the visual interaction of the user with the text, using eye-tracking data, as opposed to the traditional approaches based on analysis of textual content only. We conducted a large-scale user study aiming to collect eye-tracking data while reading the text to be summarized. We utilized various user’s implicit attention metrics to generate novel eye-tracking-based text summarization models and compared them both to eye-tracking models typically using only a single feature of the gaze duration and to traditional, as well as state-of-the-art summarization methods, based solely on textual features. The models’ quality was evaluated in terms of ROUGE scores using intrinsic evaluation on the datasets we had generated, relating gaze behavior to personalized and DUC gold-standard summaries. The experimental results showed that “SummarEyes” significantly outperformed the other summarizers in predicting both the user’s personalized summarization and the generic gold standard summaries. With the increasing availability of eye-tracking technology, this research can lead to a new generation of effective user-centric text summarization tools.
Impact of a robot and tangible object (R&amp;T) integrated learning system on elementary EFL learners’ English pronunciation and willingness to communicate

Research on how the use of social robots helps improve English as Foreign Language (EFL) young learners’ pronunciation and willingness to communicate (WTC) is understudied. This study developed a robot and tangible objects (R&T) learning system and examined its impact on elementary EFL learner’s English pronunciation and WTC. The R&T system consists of a robot, a tablet, a cellphone, and sets of tangible objects that enable students to physically interact with the system. A total of 29 elementary students were engaged in location-specific tasks with the R&T system 40 min a day for five days. Data were collected through pronunciation tests, willingness to communicate questionnaires, and video recordings of students’ interaction with the R&T system. Paired-samples t test showed students improved in both pronunciation and willingness to communicate after interacting with the R&T system. Video recordings found active engagement in the form of multiple tries and active help-seeking. Finally, a missed learning opportunity captured in the video data provided directions for future refinement in robot and task design.
Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Skeleton-based human action recognition based on Neural Architecture Search (NAS.) adopts a one-shot NAS strategy. It improves the speed of evaluating candidate models in the search space through weight sharing, which has attracted significant attention. However, directly applying the one-shot NAS method for skeleton recognition requires training a super-net with a large search space that traverses various combinations of model parameters, which often leads to overly large network models and high computational costs. In addition, when training this super-net, the one-shot NAS needs to traverse the entire search space of the complete skeleton recognition task. Furthermore, the traditional method does not consider the optimization of the search strategy. As a result, a significant amount of search time is required to obtain a better skeleton recognition network model. A more efficient weighting model, a NAS skeleton recognition model based on the Single Path One-shot (SNAS-GCN) strategy, is proposed to address the above challenges. First, to reduce the model search space, a simplified four-category search space is introduced to replace the mainstream multi-category search space. Second, to improve the model search efficiency, a single-path one-shot approach is introduced, through which the model randomly samples one architecture at each step of the search training optimization. Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. With these three steps, the entire network architecture of the recognition model (and its weights) is fully and equally trained significantly. The search and training costs will be greatly reduced. The search-out model is trained by the NTU-RGB + D and Kinetics datasets to evaluate the performance of the proposed model’s search strategy. The experimental results show that the search time of the proposed method in this paper is 0.3 times longer than that of the state-of-the-art method. Meanwhile, the recognition accuracy is roughly comparable compared to that of the SOTA NAS-GCN method.
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph Attention Network for UAV Swarms

Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated significant advantages in some real-world scenarios, but the limited communication range of UAVs poses great challenges to multi-UAV collaborative decision-making. By constructing the multi-UAV cooperation problem as a multi-agent system (MAS), the cooperative decision-making among UAVs can be realized by using multi-agent reinforcement learning (MARL). Following this paradigm, this work focuses on developing partially observable MARL models that capture important information from local observations in order to select effective actions. Previous related studies employ either probability distributions or weighted mean field to update the average actions of neighborhood agents. However, they do not fully consider the feature information of surrounding neighbors, resulting in a local optimum often. In this paper, we propose a novel partially multi-agent reinforcement learning algorithm to remedy this flaw, which is based on graph attention network and partially observable mean field and is named as the GPMF algorithm for short. GPMF uses a graph attention module and a mean field module to describe how an agent is influenced by the actions of other agents at each time step. The graph attention module consists of a graph attention encoder and a differentiable attention mechanism, outputting a dynamic graph to represent the effectiveness of neighborhood agents against central agents. The mean field module approximates the effect of a neighborhood agent on a central agent as the average effect of effective neighborhood agents. Aiming at the typical task scenario of large-scale multi-UAV cooperative roundup, the proposed algorithm is evaluated based on the MAgent framework. Experimental results show that GPMF outperforms baselines including state-of-the-art partially observable mean field reinforcement learning algorithms, providing technical support for large-scale multi-UAV coordination and confrontation tasks in communication-constrained environments.