Reviewerly/exHarmony huggingface dataset:
Efficient and Privacy-Preserving Feature Importance-based Vertical Federated Learning

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about a largely overlapping set of data samples, to collaboratively train a global model. The quality of data owners' local features affects the performance of the VFL model, which makes feature selection vitally important. However, existing feature selection methods for VFL either assume the availability of prior knowledge on the number of noisy features or prior knowledge on the post-training threshold of useful features to be selected, making them unsuitable for practical applications. To bridge this gap, we propose the Federated Stochastic Dual-Gate based Feature Selection (FedSDG-FS) approach. It consists of a Gaussian stochastic dual-gate to efficiently approximate the probability of a feature being selected. FedSDG-FS further designs a local embedding perturbation approach to achieve differential privacy for local training data. To reduce overhead, we propose a feature importance initialization method based on Gini impurity, which can accomplish its goals with only two parameter transmissions between the server and the clients. The enhanced version, FedSDG-FS++, protects the privacy for both the clients' training data and the server's labels through Partially Homomorphic Encryption (PHE) without relying on a trusted third-party. Theoretically, we analyze the convergence rate, privacy guarantees and security analysis of our methods. Extensive experiments on both synthetic and real-world datasets show that FedSDG-FS and FedSDG-FS++ significantly outperform existing approaches in terms of achieving more accurate selection of high-quality features as well as improving VFL performance in a privacy-preserving manner.
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning

Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate scale quantum (NISQ) computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions, while having access only to limited training data. Existing generalization analyses, while identifying important general trends and scaling laws, cannot be used to assign reliable and informative “error bars” to the decisions made by quantum models. In this article, we propose a general methodology that can reliably quantify the uncertainty of quantum models, irrespective of the amount of training data, of the number of shots, of the ansatz, of the training algorithm, and of the presence of quantum hardware noise. The approach, which builds on probabilistic conformal prediction, turns an arbitrary, possibly small, number of shots from a pre-trained quantum model into a set prediction, e.g., an interval, that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">provably</i> contains the true target with any desired coverage level. Experimental results confirm the theoretical calibration guarantees of the proposed framework, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quantum conformal prediction</i> .
The Instructional Media to Teach English for Young Learner

The purpose of this article is to discover the applicable Instructional Media to help teacher in teaching English for young learners for different situations. This study used library research, which this research is a series of activities related to the method of collecting library data. Instructional Media is a facilitation used to implement instruction given by the teacher. It is also facilitate students to have a better understanding to the material. In a similar vein Adegun (1997) says instructional media are things which are intended to help the teacher to teach more effectively and enable the students to learn more readily. Considering the characteristics of young learner, the media used have to be interest for students. From the discussion, there are five medias can be used to teach English for young learners, such as realia, pictures, boards, flipcharts, and Internet.
Unlocking my heart: Fostering hotel brand love with service robots

While the presence of robots in the hotel industry is undeniable, there remains a wealth of untapped potential for service robots in shaping and promoting hotel brands. The present study seeks to address this issue by proposing and validating a research model that includes the effects of perceived anthropomorphism, perceived intelligence, and perceived animacy on authenticity, which serves as a prerequisite for brand love. An online questionnaire on self-administration was administered to 324 respondents. Through the use of a multi-analytical hybrid structural equation modeling-artificial neural network (SEM-ANN) technique, the obtained data was empirically validated. This research provides (i) an indication that service authenticity and brand authenticity are prerequisites for users to experience hotel brand love from hotel robots, (ii) two key aspects that influence the service authenticity of robots through human-like and technological characteristics, and (iii) an exploration of the further impact of human-like attributes on brand authenticity in the use of robotic services in hotels. In conclusion, these results have important theoretical and practical insights that can help advance the hotel and tourism industry.
IDLGen: Automated Code Generation for Inter-parameter Dependencies in Web APIs

The generation of code templates from web API specifications is a common practice in industry. However, existing tools neglect the dependencies among input parameters (so-called inter-parameter dependencies), extremely common in practice and usually described in natural language. As a result, developers are responsible for implementing the corresponding validation logic manually, a tedious and error-prone process. In this paper, we present IDLGen, an approach for the automated generation of validation code for inter-parameter dependencies in web APIs. Specifically, we exploit the IDL4OAS extension for specifying inter-parameter dependencies as a part of OpenAPI Specification (OAS) files. To make our approach applicable in practice, we present an extension of the popular OpenAPI Generator tool ecosystem, automating the generation of Java and Python code for the management of inter-parameter dependencies in web APIs. Evaluation results show the effectiveness of the approach in accelerating the development of APIs, generating up to 9.4 times more lines of Java code than the current generator. This leads to average time savings ranging from 16 to 24 min when implementing API operations including between one and three dependencies, when compared to manual coding. More importantly, the generated code mitigates human errors, making web APIs significantly more reliable.
Student Perspective of Learning in Research Courses in Law Under the Flipped Classroom Modality

Objective: The purpose was to determine the factors that best describe the use of learning strategies of Peruvian law students who took research courses using the Flipped Classroom approach. Method: A Questionnaire (LSQ-U α = 0.88) was administered to 160 subjects, consisting of 57 items scored on an ordinal scale; grouped into three macro dimensions with an acceptable reliability: Motivational Strategies (α = 0.82), Cognitive Strategies (α = 0.73) and Metacognitive Strategies (α = 0.81). An Exploratory Factor Analysis [Bartlett (X2 p: &lt;0.01) and KMO (0.836)] was performed. Results: Two factors emerged. Factor 1-Self-Regulated Learning (%σ2: 56.889), reflects the use applicability and self-reinforcement, together with organization, generative elaboration, and anchoring elaboration. Metacognitively, they make use of planning and revision. Factor 2-Motivational (%σ2: 17.270), privileges positive association, gradual approach, and applicability. Conclusion: The Flipped Classroom defines a profile that moves away from memorization, it enhances learning and stimulates the use of metacognition, thus revealing its benefits. The results show that FC is also an appropriate didactic option for virtual classes in the context of pandemic and post-pandemic, whose greatest need is related to a teaching profile and the management of technical computer skills. Practical Implications: The information obtained from this work can be considered in the design and planning of systematic teaching and learning strategies that focus on students' attention, to support them in their search for better performance and academic success as indicators of the quality of the university system.
Enhancing Query Performance Using Simultaneous Execution and Vertical Query Splitting

in recent years, the significance of data mining algorithms has increased with the fast growth of data. Elements are often repeated when dealing with transitions that fetch data. Therefore, one of the most essential issues in data mining is mining common item collections. Here we tackle an algorithm that finds common elements appearing in transitions and tries to split the data accordingly into different tables, called vertical fragmentation. This gives you high time efficiency. Here we use FP growth for the mining phase. Another smart splitting method has been proposed that converts the database into separate tables.
Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.
Real-time Lightweight Target Detection Network under Autonomous Driving

Abstract Target detection in traffic scenes has been a focal point concerning computer vision, especially in the context of autonomous driving, including the recognition of vehicles, pedestrians, and traffic signs. However, the computational power of devices operating in traffic scenarios is limited, placing stringent demands on computational resources and latency. To address these challenges, this study proposed a lightweight detection algorithm, GV2-YOLO, which prioritizes both speed and accuracy. First, we apply the GhostNetv2 architecture and incorporate the Ghost module to decrease the parameters of the backbone feature extraction network. Second, the algorithm also integrates SPPF and Slim-neck by GSConv techniques, which effectively reduces the computational complexity while ensuring the resulting significant reduction in detection accuracy. Our proposed algorithm achieves a mAP of 84.7% on the CCTSDB dataset. The algorithm has a total parameter count of 9.1 M, making it ideal for deployment in embedded autonomous driving platforms.
Monintainer: An orchestration-independent extensible container-based monitoring solution for large clusters

Container virtualization has recently gained popularity due to its low performance and resource allocation overhead. The rise of this technology can be attributed to the advancement of cloud computing and the adoption of micro-services architecture. These new approaches offer a more efficient and fine-grained system design through the benefits of containerization, such as isolation, portability, and improved performance. However, container-based systems have created new challenges in monitoring due to their automated flexibility, ephemerality, and the increasing number of containers in a system. So there is a practical need for effective monitoring and performance management tools. This paper analyses the key performance metrics for machine, container and application services, including CPU usage, memory usage, disk usage, and network usage. Furthermore, we review several widespread tools for collecting and monitoring these metrics and present the Monintainer tool. It is a solution designed to monitor entire container-based systems, from applications to their underlying infrastructure, allowing users to better understand their systems' behaviour in run-time. The tool's results can aid container-based systems' design, implementation and optimization.
Demonstrating EMMA: Embodied MultiModal Agent for Language-guided Action Execution in 3D Simulated Environments

Alessandro Suglia, Bhathiya Hemanthage, Malvina Nikandrou, George Pantazopoulos, Amit Parekh, Arash Eshghi, Claudio Greco, Ioannis Konstas, Oliver Lemon, Verena Rieser. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.
Generating Discourse Connectives with Pre-trained Language Models: Conditioning on Discourse Relations Helps Reconstruct the PDTB

We report results of experiments using BART (Lewis et al., 2019) and the Penn Discourse Tree Bank (Webber et al., 2019) (PDTB) to generate texts with correctly realized discourse relations. We address a question left open by previous research (Yung et al., 2021; Ko and Li, 2020) concerning whether conditioning the model on the intended discourse relation—which corresponds to adding explicit discourse relation information into the input to the model—improves its performance. Our results suggest that including discourse relation information in the input of the model significantly improves the consistency with which it produces a correctly realized discourse relation in the output. We compare our models’ performance to known results concerning the discourse structures found in written text and their possible explanations in terms of discourse interpretation strategies hypothesized in the psycholinguistics literature. Our findings suggest that natural language generation models based on current pre-trained Transformers will benefit from infusion with discourse level information if they aim to construct discourses with the intended relations.
Investigating Effective Geometric Transformation for Image Augmentation to Improve Static Hand Gestures with a Pre-Trained Convolutional Neural Network

Hand gesture recognition (HGR) is a challenging and fascinating research topic in computer vision with numerous daily life applications. In HGR, computers aim to identify and classify hand gestures. The limited diversity of the dataset used in HGR is due to the limited number of hand gesture demonstrators, acquisition environments, and hand pose variations despite previous efforts. Geometric image augmentations are commonly used to address these limitations. These augmentations include scaling, translation, rotation, flipping, and image shearing. However, research has yet to focus on identifying the best geometric transformations for augmenting the HGR dataset. This study employed three commonly utilized pre-trained models for image classification tasks, namely ResNet50, MobileNetV2, and InceptionV3. The system’s performance was evaluated on five static HGR datasets: DLSI, HG14, ArabicASL, MU HandImages ASL, and Sebastian Marcell. The experimental results demonstrate that many geometric transformations are unnecessary for HGR image augmentation. Image shearing and horizontal flipping are the most influential transformations for augmenting the HGR dataset and achieving better classification performance. Moreover, ResNet50 outperforms MobileNetV2 and InceptionV3 for static HGR.
I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem

The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.
Decentering Through Design: Bridging Posthuman Theory with More-than-Human Design Practices

While decentering the human has been a key approach in posthumanist HCI, there are still questions and tensions around it.To address them, we outline emergent notions of decentering, tracing it back from HCI to critical posthumanism and connecting epistemological developments in the humanities to design scholarship.Then, reviewing how decentering is understood and practiced in HCI, we distill five emerging dimensions for articulating more-thanhuman practices.We conclude by unpacking "decentering through design" as an ongoing material practice through which more-than-human designers not only materialize (apply) posthuman theory but also "make" posthuman knowledge in their own unique ways.
DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models

DeepFake data contains realistically manipulated faces - its abuses pose a huge threat to the security and privacy-critical applications. Intensive research from academia and industry has produced many deepfake/detection models, leading to a constant race of attack and defense. However, due to the lack of a unified evaluation platform, many critical questions on this subject remain largely unexplored. (i) How is the anti-detection ability of the existing deepfake models? (ii) How generalizable are existing detection models against different deepfake samples? (iii) How effective are the detection APIs provided by the cloud-based vendors? (iv) How evasive and transferable are adversarial deepfakes in the lab and real-world environment? (v) How do various factors impact the performance of deepfake and detection models? To bridge the gap, we design and implement DEEPFAKER , a unified and comprehensive deepfake-detection evaluation platform. Specifically, DEEPFAKER has integrated 10 state-of-the-art deepfake methods and 9 representative detection methods, while providing a user-friendly interface and modular design that allows for easy integration of new methods. Leveraging DEEPFAKER , we conduct a large-scale empirical study of facial deepfake/detection models and draw a set of key findings: (i) the detection methods have poor generalization on samples generated by different deepfake methods; (ii) there is no significant correlation between anti-detection ability and visual quality of deepfake samples; (iii) the current detection APIs have poor detection performance and adversarial deepfakes can achieve about 70% ASR (attack success rate) on all cloud-based vendors, calling for an urgent need to deploy effective and robust detection APIs; (iv) the detection methods in the lab are more robust against transfer attacks than the detection APIs in the real-world environment; (v) deepfake videos may not always be more difficult to detect after video compression. We envision that DEEPFAKER will benefit future research on facial deepfake and detection.
Cross-Parallel Transformer: Parallel ViT for Medical Image Segmentation

Medical image segmentation primarily utilizes a hybrid model consisting of a Convolutional Neural Network and sequential Transformers. The latter leverage multi-head self-attention mechanisms to achieve comprehensive global context modelling. However, despite their success in semantic segmentation, the feature extraction process is inefficient and demands more computational resources, which hinders the network’s robustness. To address this issue, this study presents two innovative methods: PTransUNet (PT model) and C-PTransUNet (C-PT model). The C-PT module refines the Vision Transformer by substituting a sequential design with a parallel one. This boosts the feature extraction capabilities of Multi-Head Self-Attention via self-correlated feature attention and channel feature interaction, while also streamlining the Feed-Forward Network to lower computational demands. On the Synapse public dataset, the PT and C-PT models demonstrate improvements in DSC accuracy by 0.87% and 3.25%, respectively, in comparison with the baseline model. As for the parameter count and FLOPs, the PT model aligns with the baseline model. In contrast, the C-PT model shows a decrease in parameter count by 29% and FLOPs by 21.4% relative to the baseline model. The proposed segmentation models in this study exhibit benefits in both accuracy and efficiency.
Optimal Entanglement Distillation Policies for Quantum Switches

The quantum switch is a vital component for the development of the quantum internet. In an entanglement distribution network, the function of a quantum switch is to generate elementary entanglement with its clients followed by entanglement swapping to distribute end-to-end entanglement of sufficiently high fidelity between clients. The threshold on entanglement fidelity is any quality of service requirement specified by the clients as dictated by the application they run on the network. We consider a discrete time model for a quantum switch that attempts generation of fresh elementary entanglement with clients in each time step in the form of maximally entangled qubit pairs, or Bell pairs, which succeed probabilistically; the successfully generated Bell pairs are stored in noisy quantum memories until they can be swapped. We focus on establishing the value of entanglement distillation of the stored Bell pairs prior to entanglement swapping in presence of their inevitable aging, i.e., decoherence: For a simple instance of a switch with two clients, an exponential decay of entanglement fidelity, and a wellknown probabilistic but heralded two-to-one distillation protocol, given a threshold end-to-end entanglement fidelity, we use the Markov Decision Processes framework to identify the optimal action policy - to wait, to distill, or to swap that maximizes throughput. We compare the switch's performance under the optimal distillation-enabled policy with that excluding distillation. Simulations of the two policies demonstrate the improvements that are possible in principle via optimal use of distillation with respect to average throughput, average fidelity and jitter of end-to-end entanglement, as functions of fidelity threshold. Our model thus helps capture the role of entanglement distillation in mitigating the effects of decoherence in a quantum switch in an entanglement distribution network, adding to the growing literature on quantum switches.