Reviewerly/exHarmony huggingface dataset:
Time-division technique for quantum optical receivers utilizing single-photon detector array technology and spatial-multiplexing

Free-space quantum key distribution (QKD) has been gaining popularity in recent years due to its advantages in creating networking options for the quantum internet. One of the main challenges to be addressed in QKD is the achievable secret key rate, which must meet current and future demand. Some of the existing solutions include the use of higher bandwidth electronics, untrusted relay architectures such as Twin-Field QKD, or high dimensional QKD. In this work, we proposed the use of a combination of spatial-multiplexing and time-division techniques, together with the use of 2D single-photon avalanche diode arrays to increase the final throughput. The main challenge in a free-space scenario is the effects introduced by turbulence. This paper demonstrates how appropriate time-division of the spatial-modes can reduce the quantum bit error rate due to optical crosstalk from 36% to 0%. With this technique, we believe the future need for superconducting nanowires single photon detectors, in some free-space QKD applications, can be relaxed, obtaining more cost-effective receiver systems.
Fault Detection in Wireless Sensor Networks Data Using Random Under Sampling and Extra-Tree Algorithm

As a highly diverse cyber-physical system, Wireless Sensor Network (WSN) is vulnerable to various failures, which can have catastrophic consequences for safety, economy, and system dependability. Due to the various deployments and limitations of sensor resources, proper detection and diagnosis of failures or faults in WSNs is a complex problem. In this study, a supervised machine learning-based approach is used. To address this issue, the authors employ Random Under Sampling (RUS) sampling method, which is used to overcome class imbalance, and Extra-Tree (ET) classification algorithm to examine sensor behavior through data to find and diagnose problems. The performance of the proposed scheme is compared with advanced machine learning algorithms such as Support Vector Machine (SVM) and Random Forest (RF). The efficiency of the suggested scheme is compared based on the measuring parameters of Accuracy, Recall, Precision, F1-Score, and AUC-ROC Score. This study's results showed that the Random Under Sampling (RUS) sampling method could negatively and positively impact the performance of machine learning models generated to predict WSN data faults. Such as the performance results of one of the classification algorithms used, Support Vector Machine (SVM), the performance of the resulting model on the Accuracy measurement parameter has a value range between 0.29 to 0.83, depending on the model parameters used. In comparison, the Extra- Tree algorithm generates the best model performance on the Accuracy measurement parameter of 96% on all models with the model parameters used.
MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address this matching problem caused by one-stage association, we propose a novel multi-stage association method, which consists of a hierarchical matching module and a customized track management module. Specifically, the hierarchical matching module defines the reliability of the objects by associating multimodal detections, and matches detections with trajectories based on the reliability in turn, which increases the utilization of true detections, and, thus, guides accurate association. Then, based on the reliability of the trajectories provided by the matching module, the customized track management module sets maximum missing frames with differences for tracks, which decreases the number of identity switches of the same object and, thus, further improves the association accuracy. By using the proposed multi-stage association method, we develop a tracker called MSA-MOT for the 3D multi-object tracking task, alleviating the inherent matching problem in one-stage association. Extensive experiments are conducted on the challenging KITTI benchmark, and the results show that our tracker outperforms the previous state-of-the-art methods in terms of both accuracy and speed. Moreover, the ablation and exploration analysis results demonstrate the effectiveness of the proposed multi-stage association method.
Berry Phase of Two Impurity Qubits as a Signature of Dicke Quantum Phase Transition

In this paper, we investigate the effect of the Dicke quantum phase transition on the Berry phase of the two impurity qubits. The two impurity qubits only have dispersive interactions with the optical field of the Dicke quantum system. Therefore, the two impurity qubits do not affect the ground state energy of the Dicke Hamiltonian. We find that the Berry phase of the two impurity qubits has a sudden change at the Dicke quantum phase transition point. Therefore, the Berry phase of the two impurity qubits can be used as a phase transition signal for the Dicke quantum phase transition. In addition, the two impurity qubits change differently near the phase transition point at different times. We explain the reason for the different variations by studying the variation of the Berry phase of the two impurity qubits with the phase transition parameters and time. Finally, we investigated the variation of the Berry phases of the two impurity qubits with their initial conditions, and we found that their Berry phases also have abrupt changes with the initial conditions. Since the Dicke quantum phase transition is already experimentally executable, the research in this paper helps to provide a means for manipulating the Berry phase of the two impurity qubits.
Detecting Fake Reviews with Generative Adversarial Networks for Mobile Social Networks

With the growth of mobile social networks (MSNs), crowdsourced information could be used for recommendation to mobile users. However, it is quite vulnerable to Sybil attacks, where attackers post fake information or reviews to mislead users for business benefits. To address this problem, existing detection models mainly use graph-based techniques or extract features of users. However, these approaches either rely on strong assumptions or lack generalization. Therefore, we propose a novel Sybil detection model based on generative adversarial networks (GANs), which contains a feature extractor, a domain classifier, and a Sybil detector. First, the feature extractor is proposed to identify the rich information in the review text with the neural network model of TextCNN. Second, the domain classifier is implemented by a neural network discriminator and is able to extract common features. Third, the Sybil detector is utilized to discriminate the fake review. Finally, the minimax game between the domain classifier and Sybil detector forms a GAN and enhances the overall generalization ability of the model. Extensive experiments show that our model has a high detection accuracy against Sybil attacks.
Personalized Relationships-Based Knowledge Graph for Recommender Systems with Dual-View Items

The knowledge graph has received a lot of interest in the field of recommender systems as side information because it can address the sparsity and cold start issues associated with collaborative filtering-based recommender systems. However, when incorporating entities from a knowledge graph to represent semantic information, most current KG-based recommendation methods are unaware of the relationships between these users and items. As such, the learned semantic information representation of users and items cannot fully reflect the connectivity between users and items. In this paper, we present the PRKG-DI symmetry model, a Personalized Relationships-based Knowledge Graph for recommender systems with Dual-view Items that explores user-item relatedness by mining associated entities in the KG from user-oriented entity view and item-oriented entity view to augment item semantic information. Specifically, PRKG-DI utilizes a heterogeneous propagation strategy to gather information on higher-order user-item interactions and an attention mechanism to generate the weighted representation of entities. Moreover, PRKG-DI provides a score feature as a filter for individualized relationships to evaluate users’ potential interests. The empirical results demonstrate that our approach significantly outperforms several state-of-the-art baselines by 1.6%, 2.1%, and 0.8% on AUC, and 1.8%, 2.3%, and 0.8% on F1 when applied to three real-world scenarios for music, movie, and book recommendations, respectively.
A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations

Despite the growing availability of artificial intelligence models for predicting type 2 diabetes, there is still a lack of personalized approaches to quantify minimum viable changes in biomarkers that may help reduce the individual risk of developing disease. The aim of this article is to develop a new method, based on counterfactual explanations, to generate personalized recommendations to reduce the one-year risk of type 2 diabetes. Ten routinely collected biomarkers extracted from Electronic Medical Records of 2791 patients at low risk and 2791 patients at high risk of type 2 diabetes were analyzed. Two regions characterizing the two classes of patients were estimated using a Support Vector Data Description classifier. Counterfactual explanations (i.e., minimal changes in input features able to change the risk class) were generated for patients at high risk and evaluated using performance metrics (availability, validity, actionability, similarity, and discriminative power) and a qualitative survey administered to seven expert clinicians. Results showed that, on average, the requested minimum viable changes implied a significant reduction of fasting blood sugar, systolic blood pressure, and triglycerides and a significant increase of high-density lipoprotein in patients at risk of diabetes. A significant reduction in body mass index was also recommended in most of the patients at risk, except in females without hypertension. In general, greater changes were recommended in hypertensive patients compared to non-hypertensive ones. The experts were overall satisfied with the proposed approach although in some cases the proposed recommendations were deemed insufficient to reduce the risk in a clinically meaningful way. Future research will focus on a larger set of biomarkers and different comorbidities, also incorporating clinical guidelines whenever possible. Development of additional mathematical and clinical validation approaches will also be of paramount importance.
A New Adaptive Technique for Multicomponent Signals Reassignment Based on Synchrosqueezing Transform

In this paper, we tackle the issue of making adaptive the reassignment of multicomponent signals using synchrosqueezing transforms. Indeed, depending on the frequency modulation of the modes making up these signals, the reassignment results with synchrosqueezing transforms are significantly different depending on the so-called order of the transform. In this paper, we introduce a new technique to choose locally the best order of the synchrosqueezing transform to represent a multicomponent signal, and study its behavior in terms of mode reconstruction on both simulated and real signals.
Secure banking and international trade digitization using blockchain

Traditional trade processes suffer from a great number of issues about intermediaries, information latency and trust, which, in turn, hinder overall process efficiency. The emerging blockchain technology may have potentials to mitigate those issues by revolutionizing business processes across enterprise borders in various industries. The security of smart contract technology and anti-money-laundering is utilized in tandem to combat the flaws of traditional banking. By decentralizing we can circumvent some of its defects, and achieve autonomous, fast and safe foreign goods trade. The purpose of this paper is to examine the current obstacles in letter of credit and bank transfers related to the imported foreign goods, and suggest a solution using the blockchain technology. Comparative analysis and feasibility study were conducted to identify and validate the prospects, in terms of facilitating process flow and enhancing overall trade performance, of the proposed blockchain-based international trade process model. This study contributes to the conceptual design of a blockchain- and smart-contract based process along with a provision of practical case in business process re-engineering. Further endeavors devoted to blockchain research and application across different sectors are suggested to reach better performance of business process operations.
Using provocative design to foster electronic informed consent innovation

Abstract Background The development of technological applications in clinical research, such as electronic informed consent (eIC), is on the rise. The involvement of end users throughout the design process of eIC is of utmost importance to improve the current informed consent process. Methods Using a provocative design, we conducted interviews with 30 clinical research participants. Provotypes were used as a starting base to discuss various aspects relevant to eIC. By providing a medium to encourage divergent thinking, participants’ views and concerns were solicited. Thematic analysis was undertaken using NVivo. Results The majority of participants placed trust in the principal investigator or the hospital to perform the role of eIC hosting party. Differing opinions were reported on the amount of information required related to stakeholders’ access to an eIC system, and thus, to participants’ personal data, to enable trust in an eIC system. Nevertheless, this study indicates a general willingness of participants to share personal data with physicians and pharmaceutical companies on an international level, and to receive requests for new research studies via an eIC system. Participants suggested to tailor an eIC system based upon their preferences, for example, regarding whom they want to share their personal data with. Moreover, they expressed a desire to choose how they can contact the research team, and to indicate which study-related information they would like to receive electronically. In addition, positive opinions were voiced on the integration of a test to assess participants’ understanding before providing their eIC. Conclusions Following a research through design approach, insights have been generated which inform the design of eIC. Provotypes were designed to help participants think beyond what is familiar to them. Study findings revealed that not all situations were perceived as provocative, because of participants’ motivation to advance scientific research and the trust they place in the research team. Nevertheless, the use of provocative design resulted in additional insights, generated by clinical research participants, which could be considered in the further design of eIC.
Stereo Image Matching Using Adaptive Morphological Correlation

A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.
The development of Brazilian women’s and gender studies: a bibliometric diagnosis

This study analyzes the characteristics and development of Brazilian research on women’s and gender studies using a new comprehensive data source that is still hardly explored in bibliometric studies called 1Findr. The search expression forms a part of a methodological proposition for similar studies, based on the literature review of other analogous studies and on the historical and current characteristics of Brazil. We analyzed 31,609 Brazilian articles on women’s and gender studies based on bibliometric indicators including activity, collaboration, and thematic association, with excerpts from the 1970s to 2019. Our results show that, initially, research was linked to the health sciences and was carried out in institutions in the Southeast and South of the country; however, this trend started changing since the 1990s, when two journals specialized in the area were first developed in Brazil and when researchers in the human and social sciences started working with this subject. Since then, the volume of articles, collaboration, and research has grown throughout the country, although production is still concentrated in the Southeast and South. Federal public universities are the main research actors, and researchers prioritize national journals and the Portuguese language. However, the main characteristic, which increases over time, is plurality: in relation to science in general and to other areas of Brazilian research, women’s and gender studies present a greater plurality in terms of its publication characteristics, for example, in relation to the amount and percentage of languages. Plurality is also observed in the fact that, over the years, researchers from new disciplines have started to publish on gender studies, making the area more and more inter- and multidisciplinary, and also consolidating it scientifically, in the sense that it has become a subject of interest for all areas of research. Its presence in journals with the best ratings in the Brazilian research system supports the understanding that this is a scientifically consolidated area. In contrast, despite its continuous growth, collaboration is a rare phenomenon, and specific characteristics of studies carried out with foreign partners can be observed. Collaboration clusters among national institutions are characterized, among other factors, by their geographic proximity and the central role of major universities, such as Universidade de São Paulo, and others associated with the history of the area in the country, such as Universidade de Campinas, Universidade Federal de Santa Catarina and Universidade Federal do Rio Grande do Sul. The dataset obtained, prepared, and used in the present research is available for new studies.
Recognition of students’ abnormal behaviors in English learning and analysis of psychological stress based on deep learning

The recognition of students’ learning behavior is an important method to grasp the changes of students’ psychological characteristics, correct students’ good learning behavior, and improve students’ learning efficiency. Therefore, an automatic recognition method of students’ behavior in English classroom based on deep learning model is proposed. The deep learning model is mainly applied to the processing of English classroom video data. The research results show that the video data processing model proposed in this paper has no significant difference between the data obtained from the recognition of students’ positive and negative behaviors and the real statistical data, but the recognition efficiency has been significantly improved. In addition, in order to verify the recognition effect of the deep learning model in the real English classroom environment, the statistical results of 100 recognition result maps are compared with the results of manual marking, and the average recognition accuracy of 100 recognition effect maps is finally obtained, which is 87.33%. It can be concluded that the learning behavior recognition model proposed in this paper has a high accuracy and meets the needs of daily teaching. It further verifies that the developed behavior recognition model can be used to detect students’ behavior in English class, which is very helpful to analyze students’ psychological state and improve learning efficiency.
Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms

Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.
Sentiment Analysis on Malaysian Airlines with BERT

Sentiment analysis has been a popular research area in Natural Language Processing (NLP), where sentiments expressed through text data including positive, negative and neutral sentiments are analyzed and predicted. It is often performed to evaluate customer satisfaction and understand customer needs for businesses. In the airline industry, millions of people today use social networking sites such Twitter, Skytrax, TripAdvisor and more to express their emotions, opinions, reviews and share information about the aircraft service. This creates a treasure trove of information for the airline company, showcasing different points of views about the airline’s brand online and providing insightful information. Hence, this paper experiments with six different sentiment analysis models in order to determine and develop the best model to be used. The model with the best performance was then used to determine the social status, company reputation, and brand image of Malaysian airline companies. In conclusion, the BERT model was found to have the best performance out of the six models tested, scoring an accuracy of 86 percent.