Reviewerly/exHarmony huggingface dataset:
The Nuclear Word Family List: A List of the Most Frequent Family Members, Including Base and Affixed Words

Abstract This article introduces the NFL7 (Nuclear Family List 7), a list of the 2,887 most frequent “nuclear” word families, that is, families that include just the most frequent family members and exclude those that constitute less than 7% of family occurrences. The NFL7 was developed by using a dedicated computer program, the Nuclear List Builder (freely available to users). To construct the list, we used that tool to reduce the complete BNC/COCA lists of the 3,000 most frequent word families from 19,062 to 7,293 word types and from 9,132 to 5,610 lemmas. Despite this reduction, the NFL7 compares favorably with other lists in terms of text coverage, and it includes a small number of the most frequent derivational affixes. We argue that the nuclearization of the list makes it suitable for nonadvanced learners, for teaching and testing both receptive and productive knowledge, and for instruction in basic morphology.
Uncertainty Reduction for Model Adaptation in Semantic Segmentation

Traditional methods for Unsupervised Domain Adaptation (UDA) targeting semantic segmentation exploit information common to the source and target domains, using both labeled source data and unlabeled target data. In this paper, we investigate a setting where the source data is un-available, but the classifier trained on the source data is; hence named "model adaptation". Such a scenario arises when data sharing is prohibited, for instance, because of privacy, or Intellectual Property (IP) issues.To tackle this problem, we propose a method that reduces the uncertainty of predictions on the target domain data. We accomplish this in two ways: minimizing the entropy of the predicted posterior, and maximizing the noise robustness of the feature representation. We show the efficacy of our method on the transfer of segmentation from computer generated images to real-world driving images, and transfer between data collected in different cities, and surprisingly reach performance comparable with that of the methods that have access to source data.
User Experience Evaluation in Intelligent Environments: A Comprehensive Framework

‘User Experience’ (UX) is a term that has been established in HCI research and practice, subsuming the term ‘usability’. UX denotes that interaction with a contemporary technological system goes far beyond usability, extending to one’s emotions before, during, and after using the system and cannot be defined only by studying the fundamental usability attributes of effectiveness, efficiency and user satisfaction. Measuring UX becomes a substantially more complicated endeavor when the interaction target is not just a technological system or application, but an entire intelligent environment and the systems contained therein. Motivated by the imminent need to assess, measure and quantify user experience in intelligent environments, this paper presents a methodological and conceptual framework that provides concrete guidance for UX research, design and evaluation, explaining which UX parameter should be measured, how, and when. An evaluation of the framework indicated that it can be valuable for researchers and practitioners, assisting them in planning, carrying out, and analyzing UX studies in a comprehensive and thorough manner, thus enhancing their understanding and improving the experiences they design for intelligent environments.
Social Virtual Reality Platform Comparison and Evaluation Using a Guided Group Walkthrough Method

As virtual reality (VR) headsets become more commercially accessible, a range of social platforms have been developed that exploit the immersive nature of these systems. There is a growing interest in using these platforms in social and work contexts, but relatively little work into examining the usability choices that have been made. We developed a usability inspection method based on cognitive walkthrough that we call guided group walkthrough . Guided group walkthrough is applied to existing social VR platforms by having a guide walk the participants through a series of abstract social tasks that are common across the platforms. Using this method we compared six social VR platforms for the Oculus Quest. After constructing an appropriate task hierarchy and walkthrough question structure for social VR, we ran several groups of participants through the walkthrough process. We undercover usability challenges that are common across the platforms, identify specific design considerations and comment on the utility of the walkthrough method in this situation.
Partial Synchronization to Accelerate Federated Learning Over Relay-Assisted Edge Networks

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a global model with highly distributed data located on mobile devices. Aiming to optimize the communication efficiency for gradient aggregation and model synchronization among large-scale devices, we propose a relay-assisted FL framework. By breaking the traditional transmission-order constraint and exploiting the broadcast characteristic of relay nodes, we design a novel synchronization scheme named Partial Synchronization Parallel (PSP), in which models and gradients are transmitted simultaneously and aggregated at relay nodes, resulting in traffic reduction. We prove that PSP has the same convergence rate as the sequential synchronization approaches via rigorous analysis. To further accelerate the training process, we integrate PSP with any unbiased and error-bounded compression technologies and prove that the convergence properties of the resulting scheme still hold. Extensive experiments are conducted in a distributed cluster environment with real-world datasets and the results demonstrate that our proposed approach reduces the training time up to 37 percent compared to state-of-the-art methods.
Informative Feature Disentanglement for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. The strategy of aligning the two domains in latent feature space via metric discrepancy or adversarial learning has achieved considerable progress. However, these existing approaches mainly focus on adapting the entire image and ignore the bottleneck that occurs when forced adaptation of uninformative domain-specific variations undermines the effectiveness of learned features. To address this problem, we propose a novel component called Informative Feature Disentanglement (IFD), which is equipped with the adversarial network or the metric discrepancy model, respectively. Accordingly, the new network architectures, named IFDAN and IFDMN, enable informative feature refinement before the adaptation. The proposed IFD is designed to disentangle informative features from the uninformative domain-specific variations, which are produced by a Variational Autoencoder (VAE) with lateral connections from the encoder to the decoder. We cooperatively apply the IFD to conduct supervised disentanglement for the source domain and unsupervised disentanglement for the target domain. In this way, informative features are disentangled from the domain-specific details before the adaptation. Extensive experimental results on three gold-standard domain adaptation datasets, e.g., Office31, Office-Home and VisDA-C, demonstrate the effectiveness of the proposed IFDAN and IFDMN models for UDA.
Smarter real estate marketing using virtual reality to influence potential homebuyers' emotions and purchase intention

Purpose This paper aims to investigate the potentials of virtual reality (VR) for residential real estate marketing to influence house purchase intention. Design/methodology/approach Based on the relevant literature in consumer behaviour, this study hypothesised the relationships between atmosphere with pleasure and arousal emotions and the subsequent influence of emotions towards house purchase intention in a virtual environment. A within-subjects experimental design was conducted with 60 real potential homebuyers to test the hypotheses. Data were analysed using paired samples t -test and partial least squares-structural equation modelling (PLS-SEM). Findings Results revealed that there is a significant difference in the atmosphere and house purchase intention between real and virtual environments. On the other hand, pleasure and arousal emotions evoked in real and virtual environments showed no significant difference. The results show that the atmosphere significantly affects pleasure and arousal, where pleasure, in turn, has a significant effect on purchase intention, and arousal showed an insignificant effect on purchase intention in the virtual environment. Research limitations/implications Due to budget limitation, this study was constrained to the use of HTC Vive as the VR equipment and evaluation of only one type of housing design. Practical implications This study contributes to facilitating the revitalisation of real estate marketing with the integration of VR by providing notable empirical results and recommendations based on the research findings. Originality/value This study extends the current knowledge from the stimulus-organism-response framework for a smart real estate marketing strategy using VR.
Novel pseudo random key &amp; cosine transformed chaotic maps based satellite image encryption

Image encryption converts the images into unrecognizable forms that seems like a white noise. Digital Chaos has also emerged as one of the important technique to design secure and efficient image encryption schemes. The chaos theory possesses various desirable properties required for the encrypting images like initial state sensitivity, unpredictability and behavioural complexity. However, image encryption schemes suffer from vulnerabilities like differential attack, statistical, known/chosen plaintext attack, and brute force attack. This paper proposes a novel approach to generate a pseudorandom key. This pseudo random key is combined with Logistic map (LM), cosine transformed Logistic map (CTLM) and cosine transformed Logistic-Sine Map (CTLSM), one by one, to implement three secure and efficient methods for satellite image encryption. The scheme uses the 384-bit of share key to perform encryption during the process. The proposed approaches are tested for parameters such as Entropy, Correlation Coefficient (CC), Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Avalanche effect, Bit Correct Ratio (BCR) and Peak Signal to Noise Ratio (PSNR). The work also analyses the various cryptanalytic attacks on the proposed chaos and novel Pseudo random key combinations. The results show that the proposed pseudo random key and CTLSM combination outperforms the other two combinations, and is more efficient in resisting all type of attacks as well.
A Novel Scheme of Image Encryption Based on Elliptic Curves Isomorphism and Substitution Boxes

In this manuscript, we propose an image encryption technique by using isomorphic elliptic curves which are proved to be effective against side-channel attacks and have efficient key size as compared to other public-key structures.The proposed technique counts on the Kobltiz method to convert plain images into elliptic curve points.These points are then mapped to the isomorphic curve and are the reason for diffusion in the ciphertext.Besides this diffusion mechanism, elliptic curve points are also involved in the construction of small substitution boxes for confusion.This proposed confusion and diffusion technique also provides quality security in response to well-known cryptographic attacks as established by the number of statistical results and security analysis.
Systematic Review and Meta-Analysis of Virtual Reality in Mental Healthcare: Effects of Full Body Illusions on Body Image Disturbance

Background: Body image (BI) disturbances have been identified in both clinical and non-clinical populations. Virtual reality (VR) has recently been used as a tool for modulating BI disturbances through the use of eliciting a full body illusion (FBI). This meta-analysis is the first to collate evidence on the effectiveness of an FBI to reduce BI disturbances in both clinical and non-clinical populations. Methods: We performed a literature search in MEDLINE (PubMed), EMBASE, PsychINFO, and Web of Science with the keywords and synonyms for “virtual reality” and “body image” to identify published studies until September 2020. We included studies that (1) created an FBI with a modified body shape or size and (2) reported BI disturbance outcomes both before and directly after the FBI. FBI was defined as a head-mounted display (HMD)-based simulation of embodying a virtual body from an egocentric perspective in an immersive 3D computer-generated environment. Results: Of the 398 identified unique studies, 13 were included after reading full-texts. Four of these studies were eligible for a meta-analysis on BI distortion inducing a small virtual body FBI in healthy females. Significant post-intervention results were found for estimations of shoulder width, hip width, and abdomen width, with the largest reductions in size being the estimation of shoulder circumference (SMD = −1.3; 95% CI: −2.2 to −0.4; p = 0.004) and hip circumference (SMD = −1.0; 95% CI: −1.6 to −0.4; p = 0.004). Mixed results were found in non-aggregated studies from large virtual body FBIs in terms of both estimated body size and BI dissatisfaction and in small virtual body FBI in terms of BI dissatisfaction. Conclusions: The findings presented in this paper suggest that the participants' BIs were able to conform to both an increased as well as a reduced virtual body size. However, because of the paucity of research in this field, the extent of the clinical utility of FBIs still remains unclear. In light of these limitations, we provide implications for future research about the clinical utility of FBIs for modulating BI-related outcomes.
Privacy in the Time of COVID-19: Divergent Paths for Contact Tracing and Route-Disclosure Mechanisms in South Korea

In response to COVID-19, Korea has implemented digital contact tracing and patient route disclosure schemes.While the former has been embraced more willingly, the latter has been shunned due to privacy concerns, demonstrating that privacy is highly dynamic and is of contextual value.
A Collision Avoidance Method Based on Deep Reinforcement Learning

This paper set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor. This research aims to determine whether a deep reinforcement learning-based collision avoidance method is superior to the traditional methods, such as potential field-based methods and dynamic window approach. Besides, the proposed obstacle avoidance method was developed as one of the capabilities to enable each robot in a novel robotic system, namely the Self-reconfigurable and Transformable Omni-Directional Robotic Modules (STORM), to navigate intelligently and safely in an unknown environment. A well-conceived hardware and software architecture with features that enable further expansion and parallel development designed for the ongoing STORM projects is also presented in this work. A virtual STORM module with skid-steer kinematics was simulated in Gazebo to reduce the gap between the simulations and the real-world implementations. Moreover, comparisons among multiple training runs of the neural networks with different parameters related to balance the exploitation and exploration during the training process, as well as tests and experiments conducted in both simulation and real-world, are presented in detail. Directions for future research are also provided in the paper.
A novel image encryption algorithm based on least squares generative adversarial network random number generator

Abstract In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.
Mssa-Net: Multi-Scale Self-Attention Network For Breast Ultrasound Image Segmentation

Ultrasound imaging is one of the most commonly used diagnostic tools to detect and classify abnormalities of the women breast. Automatic ultrasound image segmentation provides radiologists a second opinion to increase diagnosis accuracy. Deep neural networks have recently been employed to achieve better image segmentation results than conventional approaches. In this paper, we propose a novel deep learning architecture, a Multi-Scale Self-Attention Network (MSSA-Net), which can be trained on small datasets to explore relationships between pixels to achieve better segmentation accuracy. Our MSSA-Net integrates rich local features and global contextual information at different scales and applies self-attention to multi-scale feature maps. We evaluate the proposed MSSA-Net on three public breast ultrasound datasets and compare its performance with six state-of-the-art deep neural network-based approaches in terms of five metrics. MSSA-Net achieves best overall segmentation results and improves the second best approach by 1.21% for Jaccard Index (JI) and 0.94% for Dice's Coefficient (DSC).
AutomaticAI – A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning

Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way.
Errors of Machine Translation of terminology in the patent text from English into Chinese

This paper summarizes eight types of error of terminology in the patent text in the output of Machine Translation from English into Chinese, including term being mistranslated as a verb, term being mistranslated as a common noun, term being redundantly translated, term being mistranslated as a homophone, term being mistranslated as a wrong term, term being mistranslated due to Chinese expression, term being mistranslated without initial, and term being mistranslated due to wrong acronym. These errors can be solved by the translator before Machine Translation and the translator can identify and correct these errors by pre-editing of the source text.
An improved spatially controlled reaction–diffusion equation with a non-linear second order operator for image super-resolution

In this work, we introduce an efficient second-order reaction–diffusion (R.D.) equation for noise removal and image super-resolution. The main idea is to decompose the image into two components: cartoon and texture parts using a new spatially controlled diffusion obtained by incorporating into R.D. equation an anti-diffusion effect modelled by a space dependent parameter γ. Hence, the proposed equation leads to a noticeable improvement in texture structure preservation. Moreover, we demonstrate an existence result of global solutions and analyse the asymptotic behaviour of the equation. Furthermore, an approximation of the sensitive parameter γ is established using a learning optimization technique. As a result, the anti-diffusion parameter γ can be efficiently computed to increase the quality of the restored image and preserve its texture. The proposed approach is tested and validated using multiple numerical experiments alongside a comparison with some competitive methods for a variety of applications including image de-noising, decomposition and super-resolution.
PipeZK: Accelerating Zero-Knowledge Proof with a Pipelined Architecture

Zero-knowledge proof (ZKP) is a promising cryptographic protocol for both computation integrity and privacy. It can be used in many privacy-preserving applications including verifiable cloud outsourcing and blockchains. The major obstacle of using ZKP in practice is its time-consuming step for proof generation, which consists of large-size polynomial computations and multi-scalar multiplications on elliptic curves. To efficiently and practically support ZKP in real-world applications, we propose PipeZK, a pipelined accelerator with two subsystems to handle the aforementioned two intensive compute tasks, respectively. The first subsystem uses a novel dataflow to decompose large kernels into smaller ones that execute on bandwidth-efficient hardware modules, with optimized off-chip memory accesses and on-chip compute resources. The second subsystem adopts a lightweight dynamic work dispatch mechanism to share the heavy processing units, with minimized resource underutilization and load imbalance. When evaluated in 28 nm, PipeZK can achieve 10x speedup on standard cryptographic benchmarks, and 5x on a widely-used cryptocurrency application, Zcash.