Reviewerly/exHarmony huggingface dataset:
Heterosexual, gay, and lesbian people’s reactivity to virtual caresses on their embodied avatars’ taboo zones

Abstract Embodying an artificial agent through immersive virtual reality (IVR) may lead to feeling vicariously somatosensory stimuli on one’s body which are in fact never delivered. To explore whether vicarious touch in IVR reflects the basic individual and social features of real-life interpersonal interactions we tested heterosexual men/women and gay men/lesbian women reacting subjectively and physiologically to the observation of a gender-matched virtual body being touched on intimate taboo zones (like genitalia) by male and female avatars. All participants rated as most erogenous caresses on their embodied avatar taboo zones. Crucially, heterosexual men/women and gay men/lesbian women rated as most erogenous taboo touches delivered by their opposite and same gender avatar, respectively. Skin conductance was maximal when taboo touches were delivered by female avatars. Our study shows that IVR may trigger realistic experiences and ultimately allow the direct exploration of sensitive societal and individual issues that can otherwise be explored only through imagination.
Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g., illumination changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image translation. Then, a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under challenging environments with illumination variance, vegetation, and night-time images. Moreover, real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.
Multisensor Data Fusion Based on Modified Belief Entropy in Dempster–Shafer Theory for Smart Environment

Multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers. Since uncertainty in the data is a central constraint for data fusion and decision-making systems. Dempster-Shafer's evidence theory is an appropriate method for modeling and fusing uncertain information. In this paper, a novel data fusion scheme is proposed based on the modified belief entropy of the basic probability assignments (BPAs) to quantify the uncertainty in the information and fused them by Dempster-Shafer evidence theory. The proposed DFUDS (data fusion based on measuring uncertainty in Dempster-Shafer) scheme considers the available redundant information in the body of evidence (BoEs). The BoEs obtained from the sensor data are processed by proposed belief entropy, and fuse all pieces of evidence by Dempster's rule of combination to transfer the conflicting data into decision-making results. Extensive computer simulation results show that the proposed scheme outperforms in terms of the degree of uncertainty, evidence, reasoning, and decision accuracy under active contexts of the smart environment.
WiFi CSI gesture recognition based on parallel LSTM-FCN deep space-time neural network

In this study, we developed a system based on deep space-time neural networks for gesture recognition. When users change or the number of gesture categories increases, the accuracy of gesture recognition decreases considerably because most gesture recognition systems cannot accommodate both user differentiation and gesture diversity. To overcome the limitations of existing methods, we designed a one- dimensional parallel long short-term memory-fully convolutional network (LSTM-FCN) model to extract gesture features of different dimensions. LSTM can learn complex time dynamic information, whereas FCN can predict gestures efficiently by extracting the deep, abstract features of gestures in the spatial dimension. In the experiment, 50 types of gestures of five users were collected and evaluated. The experimental results demonstrate the effectiveness of this system and robustness to various gestures and individual changes. Statistical analysis of the recognition results indicated that an average accuracy of approximately 98.9% was achieved.
A Systematic Literature Review towards the Research of Game-Based Learning with Augmented Reality

Changes in the lifestyles of human beings keep up with science and technology in tandem with the growth of science and technology. As in all fields, expectations are influenced by the advancement of science and technology for the use of emerging technologies in educational environments. By empowering the physical world with virtual information, Augmented Reality (AR) creates new opportunities for education. The impact of uncertainty and scientific advances in the field of application has modified the definition of “AR Technology” to “AR Environments”. Within the scope of this study, it is aimed to analyze scientific studies on game-based learning and the use of AR in educational activities systematically. In this context, the “education area”, “the level of education of the participant”, “dependent variables”, “data collection tools”, “research methods used”, “type of AR used”, “software used in the development phase of the application”, factors such as “advantages and limitations towards the use of AR in a learning environment” are taken into consideration. In line with the purpose of the research and the specified criteria, a total of 53 scientific studies, which were scanned from scientific journals published in Turkey and abroad between 2016 and 2020, were analyzed using the systematic literature review method. As a result, findings regarding the effects of the game and AR-based learning environments in the learning processes for students are discussed. In addition, several recommendations for the creation and use of new educational games and AR applications have been created, which will help the gaps in AR studies and students' learning processes in terms of potential study.
Literacy can enhance syntactic prediction in spoken language processing.

Language comprehenders can use syntactic cues to generate predictions online about upcoming language. Previous research with reading-impaired adults and healthy, low-proficiency adult and child learners suggests that reading skills are related to prediction in spoken language comprehension. Here, we investigated whether differences in literacy are also related to predictive spoken language processing in non-reading-impaired proficient adult readers with varying levels of literacy experience. Using the visual world paradigm enabled us to measure prediction based on syntactic cues in the spoken sentence, prior to the (predicted) target word. Literacy experience was found to be the strongest predictor of target anticipation, independent of general cognitive abilities. These findings suggest that (a) experience with written language can enhance syntactic prediction of spoken language in normal adult language users and (b) processing skills can be transferred to related tasks (from reading to listening) if the domains involve similar processes (e.g., predictive dependencies) and representations (e.g., syntactic). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Dementia Care, Fall Detection, and Ambient-Assisted Living Technologies Help Older Adults Age in Place: A Scoping Review

We aimed to describe recent technologic advances in the three domains of dementia care, falls, and home supports; summarize existing literature on usability; and identify knowledge gaps.A comprehensive search of five databases for recent peer-reviewed publications was conducted in May 2020. Independent reviewers performed title/abstract review, full-text screening, data extraction, and study characteristic summarization.Out of 2,696 citations, 151 articles were retrieved for full-text evaluation, after which 54 studies were included in this scoping review. For each domain, different technologies are available to enhance the health and well-being of older adults; many users deemed them usable and useful. Technologies targeted improving function, psychosocial and cognitive status, home safety, and caregiver burden. Barriers to widespread uptake include privacy concerns, suboptimal user experience, and willingness to accept assistance.Technologic innovations directed toward dementia care, fall detection, and ambient-assisted living can aid older adults "aging in place."
Learning from crowds in digital pathology using scalable variational Gaussian processes

The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using gold-standard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the class-conditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well.
Path planning in nuclear facility decommissioning: Research status, challenges, and opportunities

During nuclear facility decommissioning, workers are continuously exposed to high-level radiation. Hence, adequate path planning is critical to protect workers from unnecessary radiation exposure. This work discusses recent development in radioactive path planning and the algorithms recommended for the task. Specifically, we review the conventional methods for nuclear decommissioning path planning, analyze the techniques utilized in developing algorithms, and enumerate the decision factors that should be considered to optimize path planning algorithms. As a major contribution, we present the quantitative performance comparison of different algorithms utilized in solving path planning problems in nuclear decommissioning and highlight their merits and drawbacks. Also, we discuss techniques and critical consideration necessary for efficient application of robots and robotic path planning algorithms in nuclear facility decommissioning. Moreover, we analyze the influence of obstacles and the environmental/radioactive source dynamics on algorithms' efficiency. Finally, we recommend future research focus and highlight critical improvements required for the existing approaches towards a safer and cost-effective nuclear-decommissioning project.
Blockchain Technology and its Applications Across Multiple Domains: A Survey

Blockchain technology has become an active area of research and a technological option for many businesses and industrial communities. With its distributed, decentralized, and trustless nature, blockchain can provide businesses with new opportunities and benefits through increased efficiency, reduced costs, enhanced integrity and transparency, better security, and improved traceability. Although blockchain’s largest applications have been in the finance and banking sector, we now see experiments and proposed applications in different fields. This paper provides an overview of blockchain technology; it brings together all the key design features, characteristics, and benefits of blockchain that make it a superior and unique technology, and it presents the popular consensus protocols and taxonomy of blockchain systems. Additionally, the paper surveys blockchain-based applications across multiple domains such as in finance, insurance, supply chain management, energy, advertising and media, real estate and healthcare. It aims at examining the industries’ key issues, blockchain solutions and use cases. The paper highlights three broad limitations that blockchain technology presents: scalability, security, and regulation, and shows how these challenges could impact blockchain application and adoption.
Reference-Frame-Independent Measurement-Device-Independent Quantum Key Distribution Over 200 km of Optical Fiber

Reference-frame-independent (RFI) measurement-device-independent (MDI) quantum key distribution (QKD) is immune to all detector side-channel attacks and is insensitive to slow drifts of reference frames; thus, it is a promising candidate for the realization of long-distance quantum secure communications. However, its practical performance is predominantly limited by the finite-data-size effect compared with standard MDI QKD. By combining the collective constraint and joint-study strategy with our parameter estimation processes for the RFI-MDI-QKD protocol, we reduce the influence of the finite-data-size effect and substantially improve the protocol's performance under realistic experimental conditions. Furthermore, a proof-of-principle experimental demonstration is conducted, and a transmission distance of up to 200 km is achieved. Therefore, this study represents a further step toward long-distance quantum secure communications.
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead significantly restricts their practical deployments on resource-limited devices. In this paper, we proposed a novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models. In particular, a channel-splitting super-resolution network can first be constructed from a target teacher network as a compact student network. Then, we propose a novel contrastive loss to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer. Extensive experiments demonstrate that the proposed CSD scheme effectively compresses and accelerates several standard SR models such as EDSR, RCAN and CARN. Code is available at https://github.com/Booooooooooo/CSD.
Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology

In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze the data in order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy system.
wav2vec-C: A Self-Supervised Model for Speech Representation Learning

Wav2vec-C introduces a novel representation learning technique combining elements from wav2vec 2.0 and VQ-VAE. Our model learns to reproduce quantized representations from partially masked speech encoding using a contrastive loss in a way similar to Wav2vec 2.0. However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2.0 network from the quantized representations in a way similar to a VQ-VAE model. The proposed self-supervised model is trained on 10k hours of unlabeled data and subsequently used as the speech encoder in a RNN-T ASR model and fine-tuned with 1k hours of labeled data. This work is one of only a few studies of self-supervised learning on speech tasks with a large volume of real far-field labeled data. The Wav2vec-C encoded representations achieves, on average, twice the error reduction over baseline and a higher codebook utilization in comparison to wav2vec 2.0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability

Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. However, the generated voice is often not perceptually identifiable by its intended emotion category. To address this problem, we propose a new interactive training paradigm for ETTS, denoted as i-ETTS, which seeks to directly improve the emotion discriminability by interacting with a speech emotion recognition (SER) model. Moreover, we formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization. Experimental results demonstrate that the proposed i-ETTS outperforms the state-of-the-art baselines by rendering speech with more accurate emotion style. To our best knowledge, this is the first study of reinforcement learning in emotional text-to-speech synthesis.
The SwissCovid Digital Proximity Tracing App after one year: Were expectations fulfilled? 

Digital proximity tracing has been promoted as a major technological innovation for its potential added benefits of greater speed, wider reach and better scalability compared with traditional manual contact tracing. First launched in Switzerland on 25 June 2020, the SwissCovid digital proximity tracing app has now been in use for more than one year. In light of this milestone, we raise the questions: What is currently known about the role of SwissCovid in mitigating the pandemic? Were the expectations fulfilled? In this review, we will summarise the current state of the literature from empirical studies on the adoption, performance and effectiveness of SwissCovid. The review consists of three sections. The first section summarizes findings from effectiveness studies, which suggest that SwissCovid exposure notifications contributed to preventive actions in 76% of exposure notification recipients and were associated with a faster quarantine time in some SwissCovid user groups. The second describes the public perception and current state of adoption of SwissCovid in Switzerland in light of prevalent misconceptions and overemphasised expectations. the third places the evidence on SwissCovid in an international context. Specifically, we compare key performance indicators of SwissCovid, which are of similar magnitude as for digital proximity tracing apps from other European countries. Using findings from Switzerland, we subsequently derive a preliminary measure of the population-level effectiveness of digital proximity tracing apps. We estimate that exposure notifications may have contributed to the notification and identification of 500 to 1000 SARS-CoV-2-positive app users per month. We explore why this effectiveness estimation is somewhat lower when compared with Germany or the United Kingdom. In light of the presented evidence, we conclude that digital proximity tracing works well in specific contexts, such as in mitigating non-household spread. However, future applications of digital proximity tracing should invest into stakeholder onboarding and increased process automatization – without deviating from the principles of voluntariness and user privacy.
Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Question classification is a crucial task for answer selection. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. Machine learning models based on traditional and deep learning classifiers were used. The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The classifying question categories achieved with average micro F1 -score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The highest average micro F1 -score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset.