Reviewerly/exHarmony huggingface dataset:
Friend-as-Learner: Socially-Driven Trustworthy and Efficient Wireless Federated Edge Learning

Recently, wireless edge networks have realized intelligent operation and management with edge artificial intelligence (AI) techniques (i.e., federated edge learning). However, the trustworthiness and effective incentive mechanisms of federated edge learning (FEL) have not been fully studied. Thus, the current FEL framework will still suffer untrustworthy or low-quality learning parameters from malicious or inactive learners, which undermines the viability and stability of FEL. To address these challenges, the potential social attributes among edge devices and their users can be exploited, while not included in previous works. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> ocial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> ederated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> dge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> earning framework (SFEL) over wireless networks, which recruits trustworthy social friends as learning partners. First, we build a social graph model to find like-minded friends, comprehensively considering the mutual trust and learning task similarity. Besides, we propose a social effect based incentive mechanism for better personal federated learning behaviors with both complete and incomplete information. Finally, we conduct extensive simulations with the Erdos-Renyi random network, the Facebook network, and the classic MNIST/CIFAR-10 datasets. Simulation results demonstrate our framework could realize trustworthy and efficient federated learning over wireless edge networks, and it is superior to the existing FEL incentive mechanisms that ignore social effects.
Education in Emergencies, Inequities, and the Digital Divide

The COVID-19 pandemic upended the daily life of every individual throughout the world and has had an impact on every sphere of life. However, its impact on education, especially higher education, is profound. This chapter is based on a recent study, which explored teaching and learning in higher education in Bangladesh during the COVID-19 lockdown. The chapter focuses on how switching from face-to-face to online distance teaching and learning within a short period of time has created educational inequality and the digital divide among teachers and students in higher education in Bangladesh. Based on the findings from the recent surveys (teachers' and students' perspectives), this chapter provides recommendations for the stakeholders to support teachers and students during the emergency remote teaching and learning. It also shows how different strategies could minimize educational inequality and the digital divide during the time of crisis and how the COVID-19 pandemic lockdown impacted on the transition and transformation of teaching and learning in Bangladesh.
Three novel quantum-inspired swarm optimization algorithms using different bounded potential fields

Abstract Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb-like square root potential fields, respectively. To show the computational efficacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fixed-dimension multimodal. As a finding, the algorithm inspired in the Lorentz potential field presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential field inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fixed-multimodal functions.
Optimal teleportation via noisy quantum channels without additional qubit resources

Abstract Quantum teleportation exemplifies how the transmission of quantum information starkly differs from that of classical information and serves as a key protocol for quantum communication and quantum computing. While an ideal teleportation protocol requires noiseless quantum channels to share a pure maximally entangled state, the reality is that shared entanglement is often severely degraded due to various decoherence mechanisms. Although the quantum noise induced by the decoherence is indeed a major obstacle to realizing a near-term quantum network or processor with a limited number of qubits, the methodologies considered thus far to address this issue are resource-intensive. Here, we demonstrate a protocol that allows optimal quantum teleportation via noisy quantum channels without additional qubit resources. By analyzing teleportation in the framework of generalized quantum measurement, we optimize the teleportation protocol for noisy quantum channels. In particular, we experimentally demonstrate that our protocol enables to teleport an unknown qubit even via a single copy of an entangled state under strong decoherence that would otherwise preclude any quantum operation. Our work provides a useful methodology for practically coping with decoherence with a limited number of qubits and paves the way for realizing noisy intermediate-scale quantum computing and quantum communication.
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation

Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models.However, there exists a discrepancy on lowfrequency words between the distilled and the original data, leading to more errors on predicting low-frequency words.To alleviate the problem, we directly expose the raw data into NAT by leveraging pretraining.By analyzing directed alignments, we found that KD makes low-frequency source words aligned with targets more deterministically but fails to align sufficient low-frequency words from target to source.Accordingly, we propose reverse KD to rejuvenate more alignments for lowfrequency target words.To make the most of authentic and synthetic data, we combine these complementary approaches as a new training strategy for further boosting NAT performance.We conduct experiments on five translation benchmarks over two advanced architectures.Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words.Encouragingly, our approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets, respectively.Our code, data, and trained models are available at https://github.com/longyuewangdcu/RLFW-NAT.
Sparse representation based compressive video encryption using hyper-chaos and DNA coding

This work proposes a new and efficient sparse representation-based spatial compression of video signals integrated with a hyper-chaotic DNA coding-based encryption module that provides higher security. The betterment of compression efficiency is achieved by introducing sparse coding on the video frames, and higher security is obtained by applying 5D hyper-chaotic DNA coding on the sparse coded frames. This novel technique is applied to a large set of video signals for testing and compares its performance with some recently proposed video coding and encryption systems. To test and measure the efficiency and reliability of different encoding and encryption schemes, we consider a set of well-defined quality metrics, e.g., compression ratio, bit rate in compression, PSNR, NPCR, UACI, correlation coefficients, and histogram analysis, and measure the values of those metrics for different encoding and encryption modules. In addition, an ablation analysis is done. It shows improved quality matrices when we utilize DNA coding along with the 5D hyper-chaotic system-based encryption mechanism. We also perform the differential attack analysis for testing the security of the encryption modules. The quantitative measurements of these quality metrics for different algorithms demonstrate the superiority of the proposed algorithm to the other algorithms in efficiency, quality, and security.
TAR: Generalized Forensic Framework to Detect Deepfakes Using Weakly Supervised Learning

Deepfakes have become a critical social problem, and detecting them is of utmost importance. Also, deepfake generation methods are advancing, and it is becoming harder to detect. While many deepfake detection models can detect different types of deepfakes separately, they perform poorly on generalizing the detection performance over multiple types of deepfake. This motivates us to develop a generalized model to detect different types of deepfakes. Therefore, in this work, we introduce a practical digital forensic tool to detect different types of deepfakes simultaneously and propose Transfer learning-based Autoencoder with Residuals (TAR). The ultimate goal of our work is to develop a unified model to detect various types of deepfake videos with high accuracy, with only a small number of training samples that can work well in real-world settings. We develop an autoencoder-based detection model with Residual blocks and sequentially perform transfer learning to detect different types of deepfakes simultaneously. Our approach achieves a much higher generalized detection performance than the state-of-the-art methods on the FaceForensics++ dataset. In addition, we evaluate our model on 200 real-world Deepfake-in-the-Wild (DW) videos of 50 celebrities available on the Internet and achieve 89.49% zero-shot accuracy, which is significantly higher than the best baseline model (gaining 10.77%), demonstrating and validating the practicability of our approach.
A Blockchain and Self-Sovereign Identity Empowered Digital Identity Platform

Most of the existing identity systems are built on top of centralized storage systems. Storing identity data on these types of centralized storage platforms(e.g cloud storage, central servers) becomes a major privacy concern since various types of attacks and data breaches can happen. With this research, we are proposing blockchain and self-sovereign identity based digital identity (KYC - Know Your Customer) platform “Casper” to address the issues on centralized identity systems. “Casper ” is an Android/iOS based mobile identity wallet application that combines the integration of blockchain and a self-sovereign identity-based approach. Unlike centralized identity systems, the actual identities of the customer/users are stored in the customers’ mobile wallet application. The proof of these identities is stored in the blockchain-based decentralized storage as a self-sovereign identity proof. Casper platforms’ Self-Sovereign Identity(SSI)-based system provides a Zero Knowledge Proof(ZKP) mechanism to verify the identity information. Casper platform can be adopted in various domains such as healthcare, banking, government organization etc. As a use case, we have discussed building a digital identity wallet for banking customers with the Casper platform. Casper provides a secure, decentralized and ZKP verifiable identity by using blockchain and SSI based approach. It addresses the common issues in centralized/cloud-based identity systems platforms such as the lack of data immutability, lack of traceability, centralized control etc.
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text.The complexity makes automatic evaluation of NLG particularly challenging.Previous work has typically focused on a single task and developed individual evaluation metrics based on specific intuitions.In this paper, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog).Information alignment between input, context, and output text plays a common central role in characterizing the generation.With automatic alignment prediction models, we develop a family of interpretable metrics that are suitable for evaluating key aspects of different NLG tasks, often without need of gold reference data.Experiments show the uniformly designed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in each of diverse tasks, including text summarization, style transfer, and knowledgegrounded dialog. 1
Multi-Objective Optimization-Based Recommendation for Massive Online Learning Resources

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the “information overload” problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary Algorithm-based online learning Resource Recommendation Model to balance the system’s accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm.
Plan-then-Generate: Controlled Data-to-Text Generation via Planning

Recent developments in neural networks have led to the advance in data-to-text generation.However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications.In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-totext models.Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG.The results show that our model is able to control both the intrasentence and inter-sentence structure of the generated output.Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
The Simulation Game—Virtual Reality Therapy for the Treatment of Social Anxiety Disorder: A Systematic Review

(1) Background: With the term Virtual reality (VR) we refer to a three-dimensional environment generated by the computer, in which subjects interact with the environment as if they were really inside it. The most used VR tools are the so-called HMD (head-mounted display) which make it possible to achieve what theorists define "direct mediated action". The aim of our systematic review is specifically to investigate the applications of virtual reality therapy for the treatment of social anxiety disorder, also known as social phobia. The most common treatment for social anxiety disorder is represented by "in vivo exposure therapy" (iVET). This method consists of exposing the participant, in a gradual and controlled way, to anxious stimuli, with the goal to change the subject's response to the object or situation that is causing the fear. However, the main flaw of "in Vivo therapies" is represented by both the huge costs involved and the possible disturbance variables that can hinder the execution of the therapeutic treatment. Virtual reality exposure therapy could therefore, if confirmed in its effectiveness, constitute a solution to eliminate these two defects demonstrated by "in vivo exposure therapy". The goal is to use VR as a means for the clinician to build a tailor-made path for the participant in order to make him acquire "in virtual" those skills necessary for a good adaptation in the "real" world. (2) Methods: From February 2021 until the date of submission of the article (September 2021), we conducted a systematic review aiming to verify the effectiveness of virtual reality exposure therapy (VRET) for the treatment of SAD. (3) Results: We identified a total of 205 unique articles. Among these, 20 full-text articles were assessed for eligibility and 5 of these met the eligibility criteria and were, therefore, included in the final systematic review. (4) Conclusions: Virtual reality therapies proved to be a valid alternative to the acquisition of social skills suitable for improving the symptoms of SAD. Although there has not been a significant difference between VRET and iVET, the low costs and flexibility of VRET open up new scenarios for achieving greater psychophysical well-being.
DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments.
The effects of financial inclusion and the business environment in spurring the creation of early-stage firms and supporting established firms

This study investigates the role of financial inclusion and the institutional environment in promoting entrepreneurship. Using the panel vector autoregressive model for a sample of 43 countries over the period from 2001 to 2018, we find that both financial inclusion and the institutional environment positively affect early-stage and established firms. In particular, our results reveal that basic financial products are important to stimulate the creation of early-stage firms, whereas access to credit supports established firms’ viability. Our findings also indicate that lower business regulatory, tax and bureaucracy burdens; better access to commercial infrastructure; ease of entry to markets and government programs are essential for developing entrepreneurship. In particular, one of the most important implications of our findings is the vital role that government programs play in supporting entrepreneurship. Accordingly, several policies targeting promotion of entrepreneurship have been proposed and discussed.
Dual U-Net residual networks for cardiac magnetic resonance images super-resolution

Heart disease is a vital disease that has threatened human health, and is the number one killer of human life. Moreover, with the added influence of recent health factors, its incidence rate keeps showing an upward trend. Today, cardiac magnetic resonance (CMR) imaging can provide a full range of structural and functional information for the heart, and has become an important tool for the diagnosis and treatment of heart disease. Therefore, improving the image resolution of CMR has an important medical value for the diagnosis and condition assessment of heart disease. At present, most single-image super-resolution (SISR) reconstruction methods have some serious problems, such as insufficient feature information mining, difficulty to determine the dependence of each channel of feature map, and reconstruction error when reconstructing high-resolution image.To solve these problems, we have proposed and implemented a dual U-Net residual network (DURN) for super-resolution of CMR images. Specifically, we first propose a U-Net residual network (URN) model, which is divided into the up-branch and the down-branch. The up-branch is composed of residual blocks and up-blocks to extract and upsample deep features; the down-branch is composed of residual blocks and down-blocks to extract and downsample deep features. Based on the URN model, we employ this a dual U-Net residual network (DURN) model, which combines the extracted deep features of the same position between the first URN and the second URN through residual connection. It can make full use of the features extracted by the first URN to extract deeper features of low-resolution images.When the scale factors are 2, 3, and 4, our DURN can obtain 37.86 dB, 33.96 dB, and 31.65 dB on the Set5 dataset, which shows (i) a maximum improvement of 4.17 dB, 3.55 dB, and 3.22dB over the Bicubic algorithm, and (ii) a minimum improvement of 0.34 dB, 0.14 dB, and 0.11 dB over the LapSRN algorithm.Comprehensive experimental study results on benchmark datasets demonstrate that our proposed DURN can not only achieve better performance for peak signal to noise ratio (PSNR) and structural similarity index (SSIM) values than other state-of-the-art SR image algorithms, but also reconstruct clearer super-resolution CMR images which have richer details, edges, and texture.
When software engineering meets quantum computing

research-article Open Access Share on When software engineering meets quantum computing Authors: Shaukat Ali Simula Research Laboratory, Oslo, Norway Simula Research Laboratory, Oslo, NorwayView Profile , Tao Yue Simula Research Laboratory, Oslo, Norway Simula Research Laboratory, Oslo, NorwayView Profile , Rui Abreu University of Porto, Portugal University of Porto, PortugalView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 4April 2022 pp 84–88https://doi.org/10.1145/3512340Online:19 March 2022Publication History 0citation7,630DownloadsMetricsTotal Citations0Total Downloads7,630Last 12 Months7,630Last 6 weeks324 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF