Reviewerly/exHarmony huggingface dataset:
Obfuscation Algorithm for Privacy-Preserving Deep Learning-Based Medical Image Analysis

Deep learning (DL)-based algorithms have demonstrated remarkable results in potentially improving the performance and the efficiency of healthcare applications. Since the data typically needs to leave the healthcare facility for performing model training and inference, e.g., in a cloud based solution, privacy concerns have been raised. As a result, the demand for privacy-preserving techniques that enable DL model training and inference on secured data has significantly grown. We propose an image obfuscation algorithm that combines a variational autoencoder (VAE) with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions. A binary classifier is trained on secured coronary angiographic frames to evaluate the utility of obfuscated images in the context of model training. Two possible attack configurations are considered to assess the security level against artificial intelligence (AI)-based reconstruction attempts. Similarity metrics are employed to quantify the security against human perception (structural similarity index measure and peak signal-to-noise-ratio). Furthermore, expert readers performed a visual assessment to determine to what extent the reconstructed images are protected against human perception. The proposed algorithm successfully enables DL model training on obfuscated images with no significant computational overhead while ensuring protection against human eye perception and AI-based reconstruction attacks. Regardless of the threat actor’s prior knowledge of the target content, the coronary vessels cannot be entirely recovered through an AI-based attack. Although a drop in accuracy can be observed when the classifier is trained on obfuscated images, the performance is deemed satisfactory in the context of a privacy–accuracy trade-off.
Secure and Efficient Data Storage Operations by Using Intelligent Classification Technique and RSA Algorithm in IoT-Based Cloud Computing

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm.
Investigating the Impact of Triangle and Quadrangle Mesh Representations on AGV Path Planning for Various Indoor Environments: With or Without Inflation

In a factory with different kinds of spatial atmosphere (warehouses, corridors, small or large workshops with varying sizes of obstacles and distribution patterns), the robot’s generated paths for navigation tasks mainly depend on the representation of that environment. Hence, finding the best representation for each particular environment is necessary to forge a compromise between length, safety, and complexity of path planning. This paper aims to scrutinize the impact of environment model representation on the performance of an automated guided vehicle (AGV). To do so, a multi-objective cost function, considering the length of the path, its complexity, and minimum distance to obstacles, is defined for a perfect circular robot. Unlike other similar studies, three types of representation, namely quadrangle, irregular triangle, and varying-size irregular triangle, are then utilized to model the environment while applying an inflation layer to the discretized view. Finally, a navigation scenario is tested for different cell decomposition methods and an inflation layer size. The obtained results indicate that a nearly constant coarse size triangular mesh is a good candidate for a fixed-size robot in a non-changing environment. Moreover, the varying size of the triangular mesh and grid cell representations are better choices for factories with changing plans and multi-robot sizes due to the effect of the inflation layer. Based on the definition of a metric, which is a criterion for quantifying the performance of path planning on a representation type, constant or variable size triangle shapes are the only and best candidate for discretization in about 59% of industrial environments. In other cases, both cell types, the square and the triangle, can together be the best representation.
Infusing Knowledge from Wikipedia to Enhance Stance Detection

Stance detection infers a text author's attitude towards a target.This is challenging when the model lacks background knowledge about the target.Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection.We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding.Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection. 1
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems

Content Warning: some examples in this paper may be offensive or upsetting.Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoherent viewpoints that erode a user's trust in the moral integrity of the system.Moral deviations are difficult to mitigate because moral judgments are not universal, and there may be multiple competing judgments that apply to a situation simultaneously.In this work, we introduce a new resource, not to authoritatively resolve moral ambiguities, but instead to facilitate systematic understanding of the intuitions, values and moral judgments reflected in the utterances of dialogue systems.The MORAL INTEGRITY CORPUS, MIC , is such a resource, which captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs).Each RoT reflects a particular moral conviction that can explain why a chatbot's reply may appear acceptable or problematic.We further organize RoTs with a set of 9 moral and social attributes and benchmark performance for attribute classification.Most importantly, we show that current neural language models can automatically generate new RoTs that reasonably describe previously unseen interactions, but they still struggle with certain scenarios.Our findings suggest that MIC will be a useful resource for understanding and language models' implicit moral assumptions and flexibly benchmarking the integrity of conversational agents.To download the data, see
Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes

Driven by emerging technologies such as edge computing and Internet of Things (IoT), recent years have witnessed the increasing growth of data processing in a distributed way. Federated Learning (FL), a novel decentralized learning paradigm that can unify massive devices to train a global model without compromising privacy, is drawing much attention from both academics and industries. However, the performance dropping of FL running in a heterogeneous and asynchronous environment hinders its wide applications, such as in autonomous driving and assistive healthcare. Motivated by this, we propose a novel mechanism, called Fed2A: Federated learning mechanism in Asynchronous and Adaptive Modes. Fed2A supports FL by (1) allowing clients and the collaborator to work separately and asynchronously, (2) uploading shallow and deep layers of deep neural networks (DNNs) adaptively, and (3) aggregating local parameters by weighing on the freshness of information and representational consistency of model layers jointly. Moreover, the effectiveness and efficiency of Fed2A are also analyzed based on three standard datasets, i.e., FMNIST, CIFAR-10, and GermanTS. Compared with the best performance among three baselines, i.e., FedAvg, FedProx, and FedAsync, Fed2A can reduce the communication cost by over 77%, as well as improve model accuracy and learning speed by over 19% and 76%, respectively.
Canceling microwave crosstalk with fixed-frequency qubits

Scalable quantum information processing requires that modular gate operations can be executed in parallel. The presence of crosstalk decreases the individual addressability, causing erroneous results during simultaneous operations. For superconducting qubits which operate in the microwave regime, electromagnetic isolation is often limited due to design constraints, leading to signal crosstalk that can deteriorate the quality of simultaneous gate operations. Here, we propose and demonstrate a method based on the alternative-current Stark effect for calibrating the microwave signal crosstalk. The method is suitable for processors based on fixed-frequency qubits, which are known for high coherence and simple control. The optimal compensation parameters can be reliably identified from a well-defined interference pattern. We implement the method on an array of seven superconducting qubits and show its effectiveness in removing the majority of crosstalk errors.
The EPOS Research Infrastructure: a federated approach to integrate solid Earth science data and services

The European Plate Observing System (EPOS) is a Research Infrastructure (RI) committed to enabling excellent science through the integration, accessibility, use and re-use of solid Earth science data, research products and services, as well as by promoting physical access to research facilities. This article presents and describes the EPOS RI and introduces the contents of its Delivery Framework. In November 2018, EPOS ERIC (European Research Infrastructure Consortium) has been granted by the European Commission and was established to design and implement a long-term plan for the integration of research infrastructures for solid Earth science in Europe. Specifically, the EPOS mission is to create and operate a highly distributed and sustainable research infrastructure to provide coordinated access to harmonized, interoperable and quality-controlled data from diverse solid Earth science disciplines, together with tools for their use in analysis and modelling. EPOS relies on leading-edge e-science solutions and is committed to open access, thus enabling a step towards the change in multidisciplinary and cross-disciplinary scientific research in Earth science. The EPOS architecture and its Delivery Framework are discussed in this article to present the contributions to open science and FAIR (Findable, Accessible, Interoperable, and Reusable) data management, as well as to emphasize the community building process that supported the design, implementation and construction of the EPOS RI.
Self-Explaining Social Robots: An Explainable Behavior Generation Architecture for Human-Robot Interaction

In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.
The devil in the tail: Cluster consolidation plus cluster adaptive balancing loss for unsupervised person re-identification

Unsupervised person re-identification (Re-ID) is to retrieve pedestrians from different camera views without supervision information. State-of-the-art methods are usually built upon training a convolution neural network with pseudo labels generated by clustering. Unfortunately, the pseudo labels are highly unbalanced and heavily noisy, carrying ineffective or even erroneous supervision information. To address these deficiencies, we present an effective clustering and reorganization approach, called Cluster Consolidation, which aims to separate a small proportion of unreliable data points from each cluster. This approach benefits to improve the quality of the pseudo labels, but also yields more tiny clusters. Thus, we further propose a Cluster Adaptive Balancing (CAB) loss to effectively train the network with the imbalance pseudo labels, where our CAB loss is able to automatically balance the importance of each cluster. We conduct extensive experiments on widely used person Re-ID benchmark datasets and demonstrate the effectiveness of our proposals.
2×N twin-field quantum key distribution network configuration based on polarization, wavelength, and time division multiplexing

Abstract Developing quantum key distribution (QKD) has been recently directed toward distance extension and network expansion for real-world secure communications. Considering a recent report on a quantum communication network over 4,600 km, it seems that QKD networks using conventional protocols have been sufficiently studied. However, although the twin-field QKD (TF-QKD) proposed for long-distance QKD has been studied deeply enough to succeed the demonstrations over 428- and 511-km deployed fibers, TF-QKD networks have been verified only for a ring network. In this work, we propose a star topological 2 × N TF-QKD network scheme, where the coherence maintenance issue, being the primary obstacle to implementing TF-QKD, can be minimized by the automatic mode-matching feature of the Sagnac-based plug-and-play architecture. A lower number of active controllers is required for our scheme in comparison with one-way TF-QKD networks. Moreover, our scheme adopts a cost-effective configuration that requires only a single pair of single-photon detectors for the entire network system. We conducted a proof-of-concept experiment over a 50-km fiber successfully, achieving an average secret key rate of 1.31 × 10 −4 bit per pulse (1.52 bit per second) with the finite-size effect.
Class DojoLMS in the Interactive Learning of PAUD Educators in the Disruption Era 4.0

With the change in the learning system from conventional to online in the current pandemic, it is a challenge for teachers, especially early childhood educators, to find interactive learning methods. Therefore, in this paper, we will focus on the Learning Management System (LMS) strategy used by educators to continue to provide quality interactive learning to early childhood education level students. Because until now, the problem faced by Early Child Education (ECE) teachers is that there is no research on dual-function learning applications, both for communication and giving learning assignments. The research will be conducted using the slovin method and surveys of 70 teacher respondents who have experience using learning applications and 30 of them are experts in assessing the visualization of a system. interviews were conducted with a time span of one month from February - March 2021. Aim of the research is to provide learning application solutions that have dual functions and are interactive for early childhood targets. An Interactive learning application for the main target of early childhood, where from the learning media studied ranging from Class Dojo, Whatsapp, Google meet, Youtube, Worksheets, and email, Class dojo which received high votes from 70 respondents by getting 72% results became findings study. This dojo class application can be a recommendation for educators' learning media because it has complete features and can be improved communication between educators and guardians of students through the additional contact feature of the student guardian group of the dojo application.
Content-Based Video Retrieval With Prototypes of Deep Features

The rapid development in the area of information and communication technologies has enabled the transfer of high-resolution, large-sized videos, and video applications have also evolved according to data quality levels. Content-based video retrieval (CBVR) is an essential video application because it can be applied to various domains, such as surveillance, education, sports, and medicine. In this paper, we propose a CBVR method based on prototypical category approximation (PCA-CBVR), which calculates prototypes of deep features for each category to predict the user’s query video category without a classifier. We also undertake fine searching to retrieve the video most similar to the user’s query video from the predicted category database of videos. The proposed PCA-CBVR approach is efficient in terms of its computational cost and maintains meaningful information of the videos. It does not need to train a classifier even when the database is updated and uses all deep features without any dimension reduction step, such as those in CBVR studies. Moreover, we conduct fine-tuning of the 3D CNN feature extractor based on a few-shot learning approach for better domain adaptation ability and apply salient frame sampling instead of uniform frame sampling to improve the performance of the PCA-CBVR method. We demonstrate the performance capability of the proposed PCA-CBVR approach through experiments on various benchmark video datasets, in this case the UCF101, HMDB51, and ActivityNet datasets.
Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design

With the development of information technology, digital media art came into being. Digital media art interface design, as a window for direct communication with users, has a direct impact on the user’s experience, so it has attracted more and more people’s attention. In the context of the continuous development of artificial intelligence, artificial intelligence algorithms have penetrated into all fields of social development. This article is based on the integration of graphic elements with artificial intelligence algorithms to build a digital media art interface design system. According to the analysis of system requirements, combined with the algorithm of artificial intelligence graphic elements, the system hierarchy is constructed from the user model, window model, and display model; software development and driver development are realized through the media library, and the window and common controls are realized. On this basis, the creation and display of windows and common controls are realized, and the design of the core functions of the system is completed to ensure the effect of system functions. The final conclusion shows that in actual design applications, the algorithm in this paper has a very high probability of finding the optimal solution, about 90%, when the number of iterations is small. Adjust parameters α and β, when <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>α</mi> <mo>=</mo> <mn>0.6</mn> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>β</mi> <mo>=</mo> <mn>0.4</mn> </math> , are the best parameters; the image reaches the highest PSNR value, and the image restoration quality is the best; after system testing, the running time of this system is shortened by an average of 1-2 seconds, and the target detection task accuracy rate is 100%; the system is evaluated by questionnaires. The overall satisfaction of the design interface remains at about 80%, which is a great improvement compared to the previous one. Finally, it satisfies the requirements of the digital media art interface design better, and it is worthy of further exploration.
The Impact of Mobile Learning on the Effectiveness of English Teaching and Learning—A Meta-Analysis

Mobile devices have been widely used in both teaching and learning English as a second or foreign language. The impact of using various mobile devices on the learning outcome has thus been subject to an increasing number of educational experiments. However, the results of these experiments have not always been consistent or conclusive. In order to evaluate the effectiveness of mobile learning, it is necessary to employ a meta-analysis approach to synthesize results taken from previous experiments. This study examined 29 experiments, including both true and quasi, on the learning outcome of those learners who used mobile devices in their English learning process. Following <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PRISMA$ </tex-math></inline-formula> guidelines for conducting meta-analysis, this study has found that mobile learning has a highly positive effect on English learning outcomes with a weighted effect size of 0.893. The effect of mobile learning is subject to potential moderating variables, such as learners’ levels, intervention time, language learning areas being targeted, hardware and software, and implementation setting. Taken as a whole, the results indicate that mobile learning is affording a change in English learning that does not undermine pre-existing models of English language pedagogy. Therefore, progressive exploration and implementation of mobile learning effectiveness appears to be of some importance to English teaching and learning stakeholders.