Reviewerly/exHarmony huggingface dataset:
An adaptive robust defending algorithm against backdoor attacks in federated learning

To address the backdoor attacks in federated learning due to the inherently distributed and privacy-preserving peculiarities, we propose RDFL including four components: selecting the eligible parameters to compute the cosine distance; executing adaptive clustering; detecting and removing the suspicious malicious local models; performing adaptive clipping and noising operations. We evaluate the performance of RDFL compared with the existing baselines on MNIST, FEMNIST, and CIFAR-10 datasets under non-independent and identically distributed scenario, and we consider various attack scenarios, including the different numbers of malicious attackers, distributed backdoor attack, different poison ratios of local data and model poisoning attack. Experimental results show that RDFL can effectively mitigate the backdoor attacks, and outperforms the compared baselines.
Scientific and methodological provision of digitalization of education in Ukraine: status, problems, prospects

Digitalization of education becomes an imperative for reforming the education sector, the main and primary task of effective development of the information society in Ukraine. Digitalization of education is impossible without taking into account global trends, in particular the intensive development of such technologies as augmented and virtual reality, artificial intelligence, the Internet of Things, robotics, blockchain, media education, cloud-based environments, gamification, interdisciplinary, etc. The transition to a digital society, the large-scale COVID-19 pandemic, and the state of martial law in Ukraine affected the functioning of the national education system, both in terms of management, organization of the educational process, communication, assessment of educational achievements, and in relation to the creation and use of digital educational content. The National Academy of Educational Sciences of Ukraine provides constant scientific and methodological support to pedagogical workers in overcoming the identified difficulties and problems of online education. Scientists of the National Academy of Educational Sciences of Ukraine research, create and implement a wide range of electronic resources, educational programs and educational and methodological materials to ensure the digital transformation of the national education. The results of research and the experience of educators regarding the digitalization of the educational process in educational institutions of various levels during the COVID-19 pandemic and under the state of martial law are presented. They are aimed at preschool education, full general secondary education, education of children with special educational needs, education of gifted students, professional, higher education, advanced training of scientific, scientific-pedagogical and managerial personnel of education, etc. The report, prepared for the General Meeting of the National Academy of Educational Sciences of Ukraine, which took place on November 18-19, 2022, revealed the most significant initiatives regarding the digitalization of Ukrainian education, in the initiation and implementation of which the scientists of the Academy take an active part. Based on the analysis of the current stage of the digitalization of the national education and with the aim of concentrating the efforts of scientists, the pedagogical community, and education managers on its defining directions, the participants of the General Meetings of the National Academy of Educational Sciences of Ukraine approved the Recommendations on the digital transformation of education of Ukraine prepared by the National Academy of Educational Sciences of Ukraine.
An Interoperable Blockchain Security Frameworks Based on Microservices and Smart Contract in IoT Environment

In the Internet of Things (IoT), technological developments have increased the significance of federated cloud systems with integrated cloud providers for exchange transactions. Monolithic IoT systems implement service-oriented architecture (SOA), which is complex for supporting scalability and communicating transactions in a federated cloud system. One weakness of conventional security methods is that they depend on a centralized party, which means there is a single point of failure for the system. In contrast, blockchain (BC) and microservice (MS) technologies allow services to split for independent tasks. In this research paper, we introduce BC security managers based on MS technology for federated cloud systems in an IoT environment. In addition, we present the design of the Federation Security System Manager (FSSM) MS with interoperability features. This enables the exchange of transactions between permissioned BC managers at different cloud providers, with some constraints. Furthermore, a security framework based on MSs and BCs is implemented to ensure security and protect access control. The security functions are deployed based on a smart contract between the permissioned BC managers to achieve interoperability. Finally, we introduce the development process of the proposed framework, which allows for interoperability and ensures the security and privacy of the participating data for a distributed IoT based on the federated cloud system.
Congestion Control Using In-Network Telemetry for Lossless Datacenters

In the Ethernet lossless Data Center Networks (DCNs) deployed with Priority-based Flow Control (PFC), the head-of-line blocking problem is still difficult to prevent due to PFC triggering under burst traffic scenarios even with the existing congestion control solutions.To address the head-of-line blocking problem of PFC, we propose a new congestion control mechanism.The key point of Congestion Control Using In-Network Telemetry for Lossless Datacenters (ICC) is to use In-Network Telemetry (INT) technology to obtain comprehensive congestion information, which is then fed back to the sender to adjust the sending rate timely and accurately.It is possible to control congestion in time, converge to the target rate quickly, and maintain a near-zero queue length at the switch when using ICC.We conducted Network Simulator-3 (NS-3) simulation experiments to test the ICC's performance.When compared to Congestion Control for Large-Scale RDMA Deployments (DCQCN), TIMELY: RTT-based Congestion Control for the Datacenter (TIMELY), and Re-architecting Congestion Management in Lossless Ethernet (PCN), ICC effectively reduces PFC pause messages and Flow Completion Time (FCT) by 47%, 56%, 34%, and 15.3×, 14.8×, and 11.2×, respectively.
Molecular Energy Landscapes of Hardware-Efficient Ansätze in Quantum Computing

Rapid advances in quantum computing have opened up new opportunities for solving the central electronic structure problem in computational chemistry. In the noisy intermediate-scale quantum (NISQ) era, where qubit coherence times are limited, it is essential to exploit quantum algorithms with sufficiently short quantum circuits to maximize qubit efficiency. The procedural construction of hardware-efficient ansätze provides one approach to design such circuits. However, refining the accuracy of the global minimum by increasing circuit depth may lead to a proliferation of local minima that hinders global optimization. To investigate this phenomenon, we explore the energy landscapes of hardware-efficient circuits to identify ground-state energies of the hydrogen, lithium hydride, and beryllium hydride molecules. We also propose a simple dimensionality reduction procedure that reduces quantum gate depth while retaining high accuracy for the global minimum, simplifying the energy landscape, and hence speeding up optimization from both software and hardware perspectives.
Nonlinear transformations in quantum computation

While quantum computers are naturally well suited to implementing linear operations, it is less clear how to implement nonlinear operations on quantum computers. However, nonlinear subroutines may prove key to a range of applications of quantum computing from solving nonlinear equations to data processing and quantum machine learning. Here we develop a series of basic subroutines for implementing nonlinear transformations of input quantum states. Our algorithms are framed around the concept of a weighted state, a mathematical entity describing the output of an operational procedure involving both quantum circuits and classical postprocessing.
Quantum Instruction Set Design for Performance

A quantum instruction set is where quantum hardware and software meet. We develop characterization and compilation techniques for non-Clifford gates to accurately evaluate its designs. Applying these techniques to our fluxonium processor, we show that replacing the iSWAP gate by its square root SQiSW leads to a significant performance boost at almost no cost. More precisely, on SQiSW we measure a gate fidelity of up to 99.72% and averaging at 99.31%, and realize Haar random two-qubit gates with an average fidelity of 96.38%. This is an average error reduction of 41% for the former and a 50% reduction for the latter compared to using iSWAP on the same processor.
An Incentive-Compatible Mechanism for Decentralized Storage Network

The dominance of a few big companies in the storage market arising various concerns including single point of failure, privacy violation, and oligopoly. To eliminate the dependency on such a centralized storage architecture, several Decentralized Storage Network (DSN) schemes such as Filecoin, Sia, and Storj have been introduced. DSNs leverage blockchain technology to create a storage platform such that the micro storage providers can also participate in the storage market. To verify the accurate data storage by the storage providers during a storage contract, DSNs apply a Proof of Storage (PoS) scheme to continuously inspect the storage service. However, continuous verification of the storage provider imposes an extra cost to the network and therefore end-users. Moreover, DSN's PoS verification is vulnerable to a service denying attack in which the storage provider submits valid PoS to the network while denying the service to the client. Considering the benefits and existing challenges of DSNs, this paper introduces a novel incentive-compatible DSN scheme. In this scheme, the PoS is conducted only if the client submits a challenge request. We model the storage service as a non-cooperative repeated dynamic game and set the players' payoffs such that the storage provider's dominant strategy is to honestly follow the storage contract. Our proposed mechanism leverages the smart-contract and oracle network to govern the storage agreement between the client and storage provider efficiently. Furthermore, our scheme is independent of a specific blockchain platform but can be plugged into any blockchain platform with smart-contract execution capability. As a proof of concept, we have implemented our scheme using solidity language and chainlink oracle network. The performance analysis demonstrates the applicability of our scheme. The outcome of this paper is a new incentive-compatible mechanism designed carefully for the blockchain-based DSN. The proposed mechanism utilizes different tools including game-theory, smart-contract, oracle network, and Merkle tree to improve the security and performance of storage verification in DSN.
Quantum encryption of superposition states with quantum permutation pad in IBM quantum computers

Abstract We present an implementation of Kuang and Bettenburg’s Quantum Permutation Pad (QPP) used to encrypt superposition states. The project was conducted on currently available IBM quantum systems using the Qiskit development kit. This work extends previously reported implementation of QPP used to encrypt basis states and demonstrates that application of the QPP scheme is not limited to the encryption of basis states. For this implementation, a pad of 56 2-qubit Permutation matrices was used, providing 256 bits of entropy for the QPP algorithm. An image of a cat was used as the plaintext for this experiment. The plaintext was randomized using a classical XOR function prior to the state preparation procedure. To create corresponding superposition states, we applied a novel operator defined in this paper. These superposition states were then encrypted using QPP, with 2-qubit Permutation Operators, producing superposition ciphertext states. Due to the lack of a quantum channel, we omitted the transmission and executed the decryption procedure on the same IBM quantum system. If a quantum channel existed, the superposition ciphertext states could be transmitted as qubits, and be directly decrypted on a different quantum system. We provide a brief discussion of the security, although the focus of the paper remains on the implementation. Previously we have demonstrated QPP operating in both classical and quantum computers, offering an interesting opportunity to bridge the security gap between classical and quantum systems. This work broadens the applicability of QPP for the encryption of basis states as well as superposition states. We believe that quantum encryption schemes that are not limited to basis states will be integral to a secure quantum internet, to reduce vulnerabilities introduced by using two separate algorithms for secure communication between a quantum and a classical computer.
IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation

Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) short-term: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) long-term: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an Interest Dynamics modeling framework using generative Neural Processes, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms the state-of-the-art on various evaluation metrics.
Website quality evaluation: a model for developing comprehensive assessment instruments based on key quality factors

Purpose The field of website quality evaluation attracts the interest of a range of disciplines, each bringing its own particular perspective to bear. This study aims to identify the main characteristics – methods, techniques and tools – of the instruments of evaluation described in this literature, with a specific concern for the factors analysed, and based on these, a multipurpose model is proposed for the development of new comprehensive instruments. Design/methodology/approach Following a systematic bibliographic review, 305 publications on website quality are examined, the field's leading authors, their disciplines of origin and the sectors to which the websites being assessed belong are identified, and the methods they employ characterised. Findings Evaluations of website quality tend to be conducted with one of three primary focuses: strategic, functional or experiential. The technique of expert analysis predominates over user studies and most of the instruments examined classify the characteristics to be evaluated – for example, usability and content – into factors that operate at different levels, albeit that there is little agreement on the names used in referring to them. Originality/value Based on the factors detected in the 50 most cited works, a model is developed that classifies these factors into 13 dimensions and more than 120 general parameters. The resulting model provides a comprehensive evaluation framework and constitutes an initial step towards a shared conceptualization of the discipline of website quality.
Adaptive Slicing-Aided Hyper Inference for Small Object Detection in High-Resolution Remote Sensing Images

In the field of object detection, deep learning models have achieved great success in recent years. Despite these advances, detecting small objects remains difficult. Most objects in aerial images have features that are a challenge for traditional object detection techniques, including small size, high density, high variability, and varying orientation. Previous approaches have used slicing methods on high-resolution images or feature maps to improve performance. However, existing slicing methods inevitably lead to redundant computation. Therefore, in this article we present a novel adaptive slicing method named ASAHI (Adaptive Slicing Aided Hyper Inference), which can dramatically reduce redundant computation using an adaptive slicing size. Specifically, ASAHI focuses on the number of slices rather than the slicing size, that is, it adaptively adjusts the slicing size to control the number of slices according to the image resolution. Additionally, we replace the standard non-maximum suppression technique with Cluster-DIoU-NMS due to its improved accuracy and inference speed in the post-processing stage. In extensive experiments, ASAHI achieves competitive performance on the VisDrone and xView datasets. The results show that the mAP50 is increased by 0.9% and the computation time is reduced by 20–25% compared with state-of-the-art slicing methods on the TPH-YOLOV5 pretrained model. On the VisDrone2019-DET-val dataset, our mAP50 result is 56.4% higher, demonstrating the superiority of our approach.
Wearable Drone Controller: Machine Learning-Based Hand Gesture Recognition and Vibrotactile Feedback

We proposed a wearable drone controller with hand gesture recognition and vibrotactile feedback. The intended hand motions of the user are sensed by an inertial measurement unit (IMU) placed on the back of the hand, and the signals are analyzed and classified using machine learning models. The recognized hand gestures control the drone, and the obstacle information in the heading direction of the drone is fed back to the user by activating the vibration motor attached to the wrist. Simulation experiments for drone operation were performed, and the participants’ subjective evaluations regarding the controller’s convenience and effectiveness were investigated. Finally, experiments with a real drone were conducted and discussed to validate the proposed controller.
Consecutive multiscale feature learning-based image classification model

Abstract Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that results in stable performance improvements in numerous real-life applications. However, currently available state-of-the-art methods primarily rely on a parallel multiscale feature extraction approach, and despite exhibiting competitive accuracy, the models lead to poor results in efficient computation and low generalization on small-scale images. Moreover, efficient and lightweight networks cannot appropriately learn useful features, and this causes underfitting when training with small-scale images or datasets with a limited number of samples. To address these problems, we propose a novel image classification system based on elaborate data preprocessing steps and a carefully designed CNN model architecture. Specifically, we present a consecutive multiscale feature-learning network (CMSFL-Net) that employs a consecutive feature-learning approach based on the usage of various feature maps with different receptive fields to achieve faster training/inference and higher accuracy. In the conducted experiments using six real-life image classification datasets, including small-scale, large-scale, and limited data, the CMSFL-Net exhibits an accuracy comparable with those of existing state-of-the-art efficient networks. Moreover, the proposed system outperforms them in terms of efficiency and speed and achieves the best results in accuracy-efficiency trade-off.
Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach

Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data.