Reviewerly/exHarmony huggingface dataset:
Efficiency increasing of the bidirectional teleportation protocol via weak and reversal measurements

Abstract In this suggested version of the bidirectional teleportation protocol, it is assumed that the used quantum channel passes through an amplitude damping channel. Therefore, some of its quantum correlations (entanglement) are lost and, consequently, its efficiency to implement this protocol decreases. The weak and the reversal measurements are used to recover the losses of these correlations, where the negativity, as a measure of entanglement is improved. In this context, we discussed the effect of the noisy strength on the fidelities of the bidirectional teleported states between the users. It is shown that, by applying the weak and the reversal measurements (WRM) on the initial quantum channel between the users, the fidelities of the teleported states are improved. Moreover, we showed that, the upper bounds of the teleported states depend on the initial states of the triggers and the strengths of WRM. It is worth noting that the WRM improves the quantum correlations of the shared channel and, hence, the fidelity of the teleported state if the initial fidelity of the teleported state is larger than 0.5.
Evaluating the Performance of Machine Learning Algorithms in Gaze Gesture Recognition Systems

Despite the utility of gaze gestures as an input method, there is a lack of guidelines available regarding how to design gaze gestures, what algorithms to use for gaze gesture recognition, and how these algorithms compare in terms of performance. To facilitate the development of applications that leverage gaze gestures, we have evaluated the performance of a combination of template-based and data-driven algorithms on two custom gesture sets that can map to user actions. Template-based algorithms had consistently high accuracies but the slowest runtimes, making them best for small gesture sets or accuracy-critical applications. Data-driven algorithms run much faster and scale better to larger gesture sets, but require more training data to achieve the accuracy of the template-based methods. The main takeaways for gesture set design are 1) gestures should have distinct forms even when performed imprecisely and 2) gestures should have clear key-points for the eyes to fixate onto.
Heterogeneous information network embedding based on multiperspective metapath for question routing

Question routing is an important task on community question answering websites. Network embedding methods have achieved great success in question routing. However, most network embedding methods focus on the content of the questions and the answering history of the answerers. Answers that are similar to the accepted answer are treated in the same way as bad answers only because they are not "accepted". As a result, the profiles of users are not fully assessed in these methods. To solve these problems, we propose a multiperspective metapath-based representation learning network for question routing, namely, MPQR. (1) MPQR learns multiperspective representations of question answerers, question raisers and questions based on a heterogeneous information network (HIN) that is constructed from answering records and voting information. Interest and expertise representations of users are learned at the same time. (2) A scoring function outputs the probability of each answerer providing the best answer. Pointwise loss and pairwise loss are combined to rank the answerers. The pointwise loss helps MPQR give more attention to potential answers with higher numbers of votes than bad answers. Experiments on real-world datasets show that MPQR outperforms state-of-the-art network embedding methods that ignore voting information.
The fuzzy filter based on the method of areas’ ratio

The paper describes a new fuzzy filter based on the method of areas' ratio which allows to reduce noise during filtering signals. To expand the functionality of the method of areas' ratio two computational procedures were developed to eliminate errors inherent in classical defuzzification models, namely a narrow range of defuzzification and insensitivity of a defuzzification model. Presented various computational procedures for the fuzzy filter can change the properties of the output variable resulting. As an example, the proposed mathematical model of the Fuzzy Filter based on the Method of Areas' Ratio illustrated its distinctive characteristics are shown. Firstly, the Fuzzy Filter based on the Method of Areas' Ratio model has the property of continuity. Secondly, computational procedures provide an increase in the performance of the fuzzy filter. Using detailed numerically calculated Root Mean Square Error and Mean Absolute Percentage Error evaluated the proposed model of the fuzzy filter with other filters such as Kalman Filter, Fuzzy Kalman Filter, Ensemble Kalman Filter and Fuzzy Extended Kalman Filter, Basic defuzzification distributions, Fuzzy mean, Quality method, Root mean square and New weighted trapezoid median average. One of the main goals of the article was to confirm the hypothesis about the possibility of using a fuzzy filter based on the method of area's ratio for filtering signals. As well as studies of the sensitivity of the proposed fuzzy filter is based on the Root Mean Square Error and Mean Absolute Percentage Error coefficients. These coefficients were established during the experimental studies and showed that the sensitivity of the fuzzy filter based on the method of area's ratio is better than other filters.
Efficient Unbounded Fully Attribute Hiding Inner Product Encryption in Cloud-Aided WBANs

By integrating cloud computing and traditional wireless body area networks (WBANs), the emerging cloud-aided WBANs for smart health care have been significantly developed in recent years. However, this new technique that is now in its infancy days also suffers from some challenging issues resulted from untrustworthy cloud servers, such as data security. While attribute-based encryption seems to be suitable for ensuring data security in such complex scenarios, the flaw of existing ABE works in policy hiding inevitably leads to extra privacy issues. For efficiently guaranteeing data security without revealing anything privacy information about the data owner, we adopt the conception named inner-product encryption (IPE) in this article and propose a full attribute hiding IPE scheme that concurrently achieves unbounded attribute vectors and acceptable communication as well as computation efficiency. Compared with the only existing IPE scheme presented by Okamoto and Takashima that simultaneously achieves full attribute hiding and unbounded attributes, our scheme not only covers all the merits in their scheme but also has the high practicality due to tinier size of public parameters and more desirable computation efficiency. Eventually, performance comparisons and implementation results reveal that our article is highly feasible and effective than other works.
CIM-Spin: A Scalable CMOS Annealing Processor With Digital In-Memory Spin Operators and Register Spins for Combinatorial Optimization Problems

Recently, annealing processors based on the Ising model have received rising attention as efficient alternative hardware for solving combinatorial optimization problems. After mapping a problem to the hardware Ising model, we can observe the natural convergence behavior of the Ising model and find potential solutions to the problem. The quantum annealing processor has shown effectiveness in finding better solutions to the problems by using quantum bits (qubits) with annealing based on their quantum tunneling behaviors. However, the annealing processor based on the emerging quantum technology faces challenges such as limited scalability, high energy consumption, and operating costs due to the extremely low operating temperature. As a low-cost alternative, a CMOS annealing processor has been recently developed and drawn increasing attention thanks to the advantages over its quantum counterpart, including better scalability and lower energy consumption. In this work, we present a digital CMOS annealing processor with in-memory spin operators and register spins. The proposed CMOS annealing processor achieves > <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> energy efficiency and faster operation than state-of-the-art works.
Machine translation in the multilingual classroom

Abstract Machine Translation (MT), the process by which a computer engine such as Google Translate or Bing automatically translates a text from one language into another without any human involvement, is increasingly used in professional, institutional and everyday contexts for a wide range of purposes. While a growing number of studies has looked at professional translators and translation students, there is currently a lack of research on non-translator users and uses in multilingual contexts. This paper presents a survey examining how, when and why students at Leiden University’s Faculty of Humanities use MT. A questionnaire was used to determine which MT engines students use and for what purposes, and gauge their awareness of issues concerning privacy, academic integrity and plagiarism. The findings reveal a widespread adoption of Google Translate and indicate that students use MT predominantly to look up single words, as an alternative to a dictionary. Many seemed sceptical about the value of MT for educational purposes, and many assumed that the use of MT is not permitted by lecturers for graded assignments, especially in courses focusing on language skills. The results demonstrate a clear need for more MT literacy. Students may not need practical training in how to use MT, but there is much room for improvement in terms of when and why they use it.
A wide interpretable Gaussian Takagi–Sugeno–Kang fuzzy classifier and its incremental learning

While wide fuzzy classifiers which combine several TSK fuzzy sub-classifiers with some aggregation strategy like weighting have been widely developed to achieve good classification performance and/or interpretability, the aggregation strategies inevitably deteriorate the interpretability of the whole ensemble and hinder them from developing the corresponding incremental algorithm easily yet efficiently for online learning. To avoid this weakness and ensure enhanced generalization capability and interpretability, this work attempts to develop a novel wide interpretable Gaussian Takagi–Sugeno–Kang (TSK) fuzzy classifier (WIG-TSK) by simultaneously training all the Gaussian TSK fuzzy sub-classifiers without any aggregation strategy, just like a single structure. Each fuzzy rule of WIG-TSK generates its Gaussian fuzzy antecedent on randomly chosen features by exerting the Gaussian random selection on the original features of data. WIG-TSK has four merits: (1) Gaussian random selection of features guarantees both its easy interpretability and the avoidance of rule-explosion curse, and hence WIG-TSK is suitable to deal with multi- and even high-dimensional data. (2) all the sub-classifiers of WIG-TSK can be simultaneously trained in parallel without any aggregation strategy, and hence the corresponding incremental learning can be developed to dynamically update the whole structure of WIG-TSK. (3) WIG-TSK has a solid theoretical guarantee to be structurally equivalent to a more complicated zero-order Gaussian TSK fuzzy classifier. (4) WIG-TSK theoretically has better generalization capability than the corresponding wide combination structure. The effectiveness of WIG-TSK is manifested by the experimental results on eighteen benchmarking datasets and a commonly-used UCI dataset in terms of classification accuracy, running speed and interpretability.
Dynamic imputation for improved training of neural network with missing values

To train a neural network with an incomplete dataset containing missing values, the dataset is required to be completed in advance. The conventional approach applies missing value imputation before training, and consistently uses the same imputations to update the neural network. However, some inaccurate imputations would negatively affect the generalization performance of the neural network. In this study, we propose a dynamic imputation method to improve the training of a neural network in the presence of missing values. We use a dynamic imputer that can provide different imputations for the same missing value. At each training epoch, imputations for all missing values are newly obtained using the dynamic imputer, and are then used to update the neural network. By diversifying the imputations with the dynamic imputer during the training, the neural network is expected to exhibit more robustness to imputation inaccuracies, thereby improving the generalization performance. The effectiveness of the proposed method was demonstrated on benchmark datasets with various missing rates.
Internet of health things driven deep learning-based system for non-invasive patient discomfort detection using time frame rules and pairwise keypoints distance feature

Nowadays, digitization has opened up various possibilities in the healthcare sector. Smart healthcare is becoming a field of remarkable transformations and growth in the new era of smart cities and digitally interconnected societies. In smart healthcare systems, multiple sensors, devices, and sources are interconnected through networks and collect valuable data that gives insights about the patient's health status and ongoing health trends. Internet of Health Things (IoHT) is a concept that represents the identifiable sensors, devices, and sources connected to the internet and that can be utilized in various automated patient monitoring applications to provide remote healthcare services. This article aims to provide an IoHT driven deep learning-based system for non-invasive patient discomfort detection. The developed system comprises of an RGB camera device to identify discomfort in a patient's body instead of utilizing any wearable sensors or devices. In order to identify movement and discomfort, the system utilizes the YOLOv3 model to detect the patient's body and the Alphapose method to extract different keypoints information of the patient's body. Applying association rules, the detected keypoints are then converted into six central body organs. To detect a patient's discomfort, pairwise distance is measured between detected keypoints, and finally, time frame rules are applied to set a threshold in order to analyze movements as either associated with normal or discomfort conditions. The patient's body is monitored continuously for multiple frames, based on which comfort and discomfort levels are distinguished. Different video sequences are used for experimental evaluations; results reveal the effectiveness of the developed system by achieving a True Positive Rate of 95% with a 6% False Positive Rate.
Investigating the Effect of Preprocessing Arabic Text on Offensive Language and Hate Speech Detection

Preprocessing of input text can play a key role in text classification by reducing dimensionality and removing unnecessary content. This study aims to investigate the impact of preprocessing on Arabic offensive language classification. We explore six preprocessing techniques: conversion of emojis to Arabic textual labels, normalization of different forms of Arabic letters, normalization of selected nouns from dialectal Arabic to Modern Standard Arabic, conversion of selected hyponyms to hypernyms, hashtag segmentation, and basic cleaning such as removing numbers, kashidas, diacritics, and HTML tags. We also experiment with raw text and a combination of all six preprocessing techniques. We apply different types of classifiers in our experiments including traditional machine learning, ensemble machine learning, Artificial Neural Networks, and Bidirectional Encoder Representations from Transformers (BERT)-based models to analyze the impact of preprocessing. Our results demonstrate significant variations in the effects of preprocessing on each classifier type and on each dataset. Classifiers that are based on BERT do not benefit from preprocessing, while traditional machine learning classifiers do. However, these results can benefit from validation on larger datasets that cover broader domains and dialects.
Video Based Fall Detection Using Human Poses

Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exist some challenges, such as lighting variation, complex background, which degrade the accuracy and generalization ability of these approaches. Meanwhile, large computation cost limits the application of existing fall detection approaches. To alleviate these problems, a video based fall detection approach using human poses is proposed in this paper. First, a lightweight pose estimator extracts 2D poses from video sequences, and then 2D poses are lifted to 3D poses. Second, we introduce a robust fall detection network to recognize fall events using estimated 3D poses, which increases respective field and maintains low computation cost by dilated convolutions. The experimental results show that the proposed fall detection approach achieves a high accuracy of 99.83% on large benchmark action recognition dataset NTU RGB+D and real-time performance of 18 FPS on a non-GPU platform, 63 FPS on a GPU platform.
Stochastic Analysis of Double Blockchain Architecture in IoT Communication Networks

In this article, we present practical stochastic modeling and detailed performance analysis of our double blockchain (DBC) from Hao <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2021) for secure information and reputation data management in large-scale wireless Internet of Things (IoT) networks. Specifically, the DBC is a private blockchain deployed on a cloud-fog communication network which is composed of an information blockchain (IBC) storing large amounts of IoT data in the cloud layer and a reputation blockchain (RBC) storing reputation data of the IoT devices in the near-terminal fog layer. The locations of the fog layer nodes are modeled according to a random Poisson point process (PPP) over a given 2-D area to approximate the stochastic property of real-world wireless node deployments. Furthermore, we assume that the number of IoT devices transmitting to the fog nodes also follow a random Poisson distribution. Based on these models, we derive novel closed-form expressions for the storage size, transmission latency, and tampering time of the IoT fog nodes in our DBC architecture. Numerical simulations highlight high storage scalability, low latency, and superior security of the DBC design, and provide insights into the performance gains for different fog node and IoT device densities.
Understanding Public Opinion Toward the #StopAsianHate Movement and the Relation With Racially Motivated Hate Crimes in the US

#StopAsianHate and #StopAAPIHate are two of the most commonly used hashtags that represent the current movement to end hate crimes against the Asian American and Pacific Islander communities. We conduct a social media study of public opinion toward the #StopAsianHate and #StopAAPIHate movement based on 46 058 Twitter users across 30 states in USA ranging from March 18, 2021 to April 11, 2021. To facilitate fine-grained analyses, the demographic information of the Twitter users, including age, gender, race/ethnicity, social capital, political affiliation, geolocation, family, income, and religious status, is either retrieved from the user profiles or inferred using classifiers. We find that the movement attracts more participation from women, younger adults, Asian, and Black communities. It is noteworthy that most of them are also active in other online movements related to racial or social issues, such as #BlackLivesMatter or #SayHerName; 51.56% of the Twitter users show direct support, 18.38% are news about anti-Asian hate crimes, and 5.43% show a negative attitude toward the movement. By conducting logistic regression, we find that the public opinion varies across user characteristics. Furthermore, among the states with most racial bias-motivated hate crimes, the negative attitude toward the #StopAsianHate and #StopAAPIHate movement is the weakest. The majority of the top influencers are Asian–American reporters, journalists, or politicians. To the best of our knowledge, this is the first large-scale social media-based study to understand public opinion toward the #StopAsianHate and #StopAAPIHate movement. We hope that our study can provide insights and promote research on anti-Asian hate crimes and ultimately help address such a serious societal issue for the common benefits of all communities.
A Hybrid System for Learning Classical Data in Quantum States

Deep neural network powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore’s Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high intensity training data. Meanwhile, quantum computing has demonstrated its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrated quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world’s largest supercomputers. To this end, quantum-based learning has become an area of interest, with the potential of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, compared with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 33.33% faster.
Noise-robust voice conversion with domain adversarial training

Voice conversion has made great progress in the past few years under the studio-quality test scenario in terms of speech quality and speaker similarity. However, in real applications, test speech from source speaker or target speaker can be corrupted by various environment noises, which seriously degrade the speech quality and speaker similarity. In this paper, we propose a novel encoder-decoder based noise-robust voice conversion framework, which consists of a speaker encoder, a content encoder, a decoder, and two domain adversarial neural networks. Specifically, we integrate disentangling speaker and content representation technique with domain adversarial training technique. Domain adversarial training makes speaker representations and content representations extracted by speaker encoder and content encoder from clean speech and noisy speech in the same space, respectively. In this way, the learned speaker and content representations are noise-invariant. Therefore, the two noise-invariant representations can be taken as input by the decoder to predict the clean converted spectrum. The experimental results demonstrate that our proposed method can synthesize clean converted speech under noisy test scenarios, where the source speech and target speech can be corrupted by seen or unseen noise types during the training process. Additionally, both speech quality and speaker similarity are improved.