Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Domain Adaptive Structure Learning

Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of training data bias towards labeled samples. In this paper, we introduce an adaptive structure learning method to regularize the cooperation of SSL and DA. Inspired by the multi-views learning, our proposed framework is composed of a shared feature encoder network and two classifier networks, trained for contradictory purposes. Among them, one of the classifiers is applied to group target features to improve intra-class density, enlarging the gap of categorical clusters for robust representation learning. Meanwhile, the other classifier, serviced as a regularizer, attempts to scatter the source features to enhance the smoothness of the decision boundary. The iterations of target clustering and source expansion make the target features being well-enclosed inside the dilated boundary of the corresponding source points. For the joint address of cross-domain features alignment and partially labeled data learning, we apply the maximum mean discrepancy (MMD) distance minimization and self-training (ST) to project the contradictory structures into a shared view to make the reliable final decision. The experimental results over the standard SSDA benchmarks, including DomainNet and Office-home, demonstrate both the accuracy and robustness of our method over the state-of-the-art approaches.
Users’ Perceptions of Key Blockchain Features in Games

The blockchain is an emerging technology that has the potential to revolutionize the gaming industry among a wide range of different business fields. So far, only a few studies have been conducted about blockchain gaming. This study introduces a mobile game utilizing blockchain asset tokens and smart contracts. It was developed for research purposes and used to demonstrate blockchain-based games using semi-structured interviews. This study follows the exploratory research paradigm, which aims to map research of little-known areas. This study focuses on how participants perceived blockchain attributes such as trust, transparency, and user-generated content and how this affected engagement and their willingness to play the game again. Based on our evaluation, generating blockchain assets positively impacted player retention. According to the results, providing genuine asset ownership through the blockchain contributes to environmental engagement and self-engagement, as well as player retention. Another positive blockchain feature discovered from the interview data is user-generated content implementation into games.
Monitoring of Single-Phase Induction Motor through IoT Using ESP32 Module

The condition monitoring of rotating machines for critical applications plays an important role in reducing downtime. With Industry 4.0, the role of IoT in online condition monitoring of electrical machines has gained considerable significance. The main aim of the paper is the use of IoT for online monitoring of motor parameters like current, temperature, vibration, and humidity and observing its online trending using a web server. Data can be accessed in form of graphs and widgets by visiting the web page. The advantage of this project is the real-time monitoring of the motor from any remote area and in case of any abnormality operating personnel can take necessary steps for preventing complete breakdown. The proposed work can help industry people in online monitoring of motors and in the future work can be extended for fault prediction and classification.
CRC-Oriented Error Detection Architectures of Post-quantum Cryptography Niederreiter Key Generator on FPGA

Providing error detection constructions for Internet of nano-Things in constrained applications is of prominent importance. The Niederreiter cryptosystem falls into the category of code-based public-key cryptography. It is a relatively well-established scheme, but its key size and performance overheads have traditionally hindered its efficiency to be utilized for traditional computers. However, with the arrival of quantum computers, the Niederreiter cryptosystem is believed to be secure against attacks enabled by such computers, even though it has been previously shown that it is still vulnerable to fault injection and natural hardware defects. In this paper, we present fault detection schemes for the different blocks in the key generation of the Niederreiter cryptosystem using binary Goppa codes. These blocks perform finite field operations such as addition, multiplication, squaring, and inversion. The schemes are derived for different parameter sizes in order to have more flexibility and be able to choose according to the overheads to be tolerated and the required level of security. Moreover, we implement our fault detection schemes on Xilinx field-programmable gate array (FPGA) family Kintex UltraScale+ (device xcku5p-ffvd900-1-i) to benchmark the overhead induced of the proposed approaches.
ZuKon 2030: An Innovative Learning Environment Focused on Sustainable Development Goals

Global challenges such as the COVID-19 pandemic highlight the need for visionary innovations in chemical education. We recently deigned a new learning environment called "ZuKon 2030", which addresses sustainability from a chemical perspective while simultaneously introducing students to the sustainable development goals (SDGs) of the United Nations Agenda 2030. It integrates modern and innovative learning strategies by creating a format that can be used in either face-to-face or distance learning settings. In addition to the modules designed as exit games embedded in the overarching narrative of a fictional science conference, the Future Conference, paper materials enriched with Augmented Reality are used in order to allow students to complete the modules independently. In this paper, we present the basic concept of the learning environment and concretely show the implementation of the concept. We use the module "The Mystery of Coral Death" as an example, and we report our results and experiences with the entire learning environment. In the preliminary study, the students rated the hybrid learning environment mostly positively in the areas of thematic content, realization of the learning environment, digital content, and game elements.
DMRNet++: Learning Discriminative Features With Decoupled Networks and Enriched Pairs for One-Step Person Search

Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i.e., pedestrian detection and person re-identification. The dominant fashion is termed as the one-step person search that jointly optimizes detection and identification in a unified network, exhibiting higher efficiency. However, there remain major challenges: (i) conflicting objectives of multiple sub-tasks under the shared feature space, (ii) inconsistent memory bank caused by the limited batch size, (iii) underutilized unlabeled identities during the identification learning. To address these issues, we develop an enhanced decoupled and memory-reinforced network (DMRNet++). First, we simplify the standard tightly coupled pipelines and establish a task-decoupled framework (TDF). Second, we build a memory-reinforced mechanism (MRM), with a slow-moving average of the network to better encode the consistency of the memorized features. Third, considering the potential of unlabeled samples, we model the recognition process as semi-supervised learning. An unlabeled-aided contrastive loss (UCL) is developed to boost the identification feature learning by exploiting the aggregation of unlabeled identities. Experimentally, the proposed DMRNet++ obtains the mAP of 94.5% and 52.1% on CUHK-SYSU and PRW datasets, which exceeds most existing methods.
Going beyond books to using e-books in education: a systematic literature review of empirical studies

With the development of educational technologies, electronic books (e-books) have been improved and adapted to cater to new teaching models, as well as to overcome several challenges reported by printed book portability. The literature about the benefits of e-books in education is still fragmented as several studies have reported distinct disadvantages in addition to advantageous of using them. Currently, no prior study has systematically reviewed the research on the ways e-books have been employed in education and the associated benefits and challenges. To address this gap, this study conducts a systematic review of 123 empirical studies on e-books in education. The findings show that the development and adoption of e-books by countries vary, calling for more international collaborations to facilitate the adoption of e-books worldwide. Additionally, several challenges of e-books were identified, such as eye fatigue and lack of knowledge on using e-books in education by both students and teachers. Therefore, it is suggested that more design strategies and training about using e-books can be conducted to enhance both teaching and learning experiences. Finally, it is argued that future research may focus on designing intelligent and open e-books to expand their use in different contexts and provide more personalized learning.
Keywords-enhanced Deep Reinforcement Learning Model for Travel Recommendation

Tourism is an important industry and a popular entertainment activity involving billions of visitors per annum. One challenging problem tourists face is identifying satisfactory products from vast tourism information. Most of travel recommendation methods regard the recommendation procedure as a static process and only focus on immediate rewards. Meanwhile, they often infer user intensions from click behaviors and ignore the informative keywords of the clicked products. To this end, in this article, we present a Keywords-enhanced Deep Reinforcement Learning model (KDRL) framework. Specifically, we formalize travel recommendation as a Markov Decision Process and implement it upon the Actor–Critic framework. It integrates keyword information into the reinforcement learning–(RL) based recommendation framework by devising novel state representation and reward function and learns the travel recommendation and keywords generation simultaneously. To the best of our knowledge, this is the first time that keywords are explicitly discussed and used in RL-based travel recommendations. Extensive experiments are performed on the real-world datasets and the results clearly show the superior performance of KDRL compared with the baseline methods.
MarsQuakeNet: A More Complete Marsquake Catalog Obtained by Deep Learning Techniques

Abstract NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) seismometer has been recording Martian seismicity since early 2019, and to date, over 1,300 marsquakes have been cataloged by the Marsquake Service (MQS). Due to typically low signal‐to‐noise ratios (SNR) of marsquakes, their detection and analysis remain challenging: while event amplitudes are relatively low, the background noise has large diurnal and seasonal variations and contains various signals originating from the interactions of the local atmosphere with the lander and seismometer system. Since noise can resemble marsquakes in a number of ways, the use of conventional detection methods for catalog curation is limited. Instead, MQS finds events through manual data inspection. Here, we present MarsQuakeNet (MQNet), a deep convolutional neural network for the detection of marsquakes and the removal of noise contamination. Based on three‐component seismic data, MQNet predicts segmentation masks that identify and separate event and noise energy in time‐frequency domain. As the number of cataloged MQS events is small, we combine synthetic event waveforms with recorded noise to generate a training data set. We apply MQNet to the entire continuous 20 samples‐per‐second waveform data set available to date (&gt;1,000 Martian days), for automatic event detection and for retrieving denoised amplitudes. The algorithm reproduces all high quality, as well as majority of low quality events in the manual, carefully curated MQS catalog. Furthermore, MQNet detects ∼60% additional events that were previously unknown with mostly low SNR, that are verified in manual review. Our analysis on the event rate confirms seasonal trends and shows a substantial increase in the second Martian year.
Exploring the (lack of) facilitative effect of emoji for word processing

We explore whether emoji were associatively linked to emotion concepts represented in emotion-laden words, in line with the Associative-Propositional Evaluation model. Specifically, we tested these principles by exploring whether emotionally-congruent emoji could enhance word processing (Study 1 & 2) and recall (Study 3). In Study 1, participants completed a lexical decision task where word valence was manipulated. Emoji were appended to words which were either congruent, incongruent, or controls. No effects were found for emoji valence on response accuracy or latency. Study 2 presented words which varied in valence alongside congruent or control emoji where self-report valence evaluations were obtained. No effects were observed for emoji valence on word valence evaluations. Study 3 included emoji as primes to test the effect on word recall. No effects were found on word recall accuracy. Overall, the current findings suggest that emoji do not support word processing and may not be associatively linked to emotion concepts.
Using the Think Aloud Protocol in an Immersive Virtual Reality Evaluation of a Virtual Twin

Employing virtual prototypes and immersive Virtual Reality (VR) in usability evaluation can save time and speed up the iteration process during the design process. However, it is still unclear whether we can use conventional usability evaluation methods in VR and obtain results comparable to performing the evaluation on a physical prototype. Hence, we conducted a user study with 24 participants, where we compared the results obtained by using the Think Aloud Protocol to inspect an everyday product and its virtual twin. Results show that more than 60% of the reported usability problems were shared by both the physical and virtual prototype, and the in-depth qualitative analysis further highlights the potential of immersive VR evaluations. We report on the lessons we learned for designing and implementing virtual prototypes in immersive VR evaluations.
Evaluating Difficulty Adjustments in a VR Exergame for Younger and Older Adults: Transferabilities and Differences

Virtual reality (VR) exergames have the potential to train cognitive and physical abilities. However, most of these spatial games are developed for younger users and do not consider older adults with different design requirements. Yet, to be entertaining and efficient, the difficulty of games has to match the needs of players with different abilities. In this paper, we explore the effects of individually calibrating a starting difficulty and adjusting it: i) exactly as calibrated before, ii) 50% more difficult, and iii) 50% less difficult. In a user study, we compare the effects of using these adjustments on reaction times and subjective measures on younger (n=30) and older adults (n=9). The results show that most of the users prefer a faster-paced VR game in terms of enjoyment, but this also resulted in a higher perceived workload. Compared to the younger adults, the older adults rated the game more positive in terms of higher enjoyment and eagerness to play the game again, as well as lower perceived workload. This emphasizes the need for games to be designed for the user group they are intended for; both in terms of cognitive-physical difficulty and game content. Furthermore, we reflect on the transferability of the results obtained from testing with the younger adults and highlight their potential, especially for identifying suggestions and issues with the gameplay.
Automated cloud resources provisioning with the use of the proximal policy optimization

Abstract Many modern applications, both scientific and commercial, are deployed to cloud environments and often employ multiple types of resources. That allows them to efficiently allocate only the resources which are actually needed to achieve their goals. However, in many workloads the actual usage of the infrastructure varies over time, which results in over-provisioning and unnecessarily high costs. In such cases, automatic resource scaling can provide significant cost savings by provisioning only the amount of resources which are necessary to support the current workload. Unfortunately, due to the complex nature of distributed systems, automatic scaling remains a challenge. Reinforcement learning domain has been recently a very active field of research. Thanks to combining it with Deep Learning, many newly designed algorithms improve the state of the art in many complex domains. In this paper we present the results of our attempt to use the recent advancements in Reinforcement Learning to optimize the cost of running a compute-intensive evolutionary process by automating the scaling of heterogeneous resources in a compute cloud environment. We describe the architecture of our system and present evaluation results. The experiments include autonomous management of a sample workload and a comparison of its performance to the traditional automatic threshold-based management approach. We also provide the details of training of the management policy using the proximal policy optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to further scenarios.
A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&amp;DS). In Fed-IW&amp;DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&amp;DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&amp;DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox &amp; the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&amp;DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&amp;DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.
Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.
Dissipative evolution of quantum Gaussian states

Recent works on quantum resource theories of non-Gaussianity, which are based upon the type of tools available in contemporary experimental settings, put Gaussian states and their convex combinations on equal footing. Motivated by this, in this article, we derive a new model of dissipative time evolution based on unitary Lindblad operators which, while does not preserve the set of Gaussian states, preserves the set of their convex combinations, i.e. so-called quantum Gaussian states. As we demonstrate, the considered evolution proves useful both as a description for random scattering and as a tool in dissipator engineering.
Intelligent Networked Music Performance Experiences

Abstract A Networked Music Performance (NMP) is defined as what happens when geographically displaced musicians interact together while connected via network. The first NMP experiments begun in the 1970s, however, only recently the development of network communication technologies has created the necessary infrastructure needed to successfully create an NMP. Moreover, the widespread adoption of network-based interactions during the COVID-19 pandemic has generated a renewed interest towards distant music-based interaction. In this chapter we present the Intelligent networked Music PERforMANce experiENCEs (IMPERMANENCE) as a comprehensive NMP framework that aims at creating a compelling performance experience for the musicians. In order to do this, we first develop the neTworkEd Music PErfoRmANCe rEsearch (TEMPERANCE) framework in order to understand which are the main needs of the participants in a NMP. Informed by these results we then develop IMPERMANENCE accordingly.