Reviewerly/exHarmony huggingface dataset:
Two-mode photon added Schrödinger cat states: nonclassicality and entanglement

The concept of a photon added two-mode Schrödinger cat state in which both modes are independent is introduced, and their nonclassical properties and entanglement are studied. The introduced states emerge as eigenstates of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>a</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>a</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> , where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mo>,</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>f</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> are nonlinear functions of the number operator, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>a</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> <mml:mo>,</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mi>a</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> are annihilation operators. We study the evolution of these states under canonical transformation using the parity operator for the case of standard coherent states of the harmonic oscillator. The nonclassical properties of these states are evaluated especially by considering sub-Poissonian photon statistics and photon number distribution. Interestingly, the addition of photons leads to shifting the region in which photon number distribution shows oscillatory behavior. In addition, the entanglement of introduced states is quantitatively analyzed using concurrence. We observe that the state approaches maximum entanglement more rapidly after the addition of photons.
Design and Implementation of Balance Ability Assessment Training System for Special Children Based on Education Cloud Integration

Many children with special needs have body balance disorders, which will not only bring inconvenience to their daily lives but also have a certain impact on their spirits, making them insecure. It will lead to a decline in their social skills, which will also seriously affect their study and work as well as reduce their quality of life. The purpose of this paper is to design and discuss a system for evaluating and training the balance ability of children with special needs based on the integration of education cloud. The balance ability evaluation module of the system performs a series of calculations on the projection coordinates of the center of gravity of the human body in various postures, and the obtained parameter values are the basis of the evaluation. This paper analyzes the role of education cloud integration in the evaluation of balance ability of special children, evaluates and trains the balance ability of special children, and verifies the feasibility of the balance ability evaluation and training system for special children. This study found that in a feasibility trial evaluating the balance ability of children with special needs, the balance stability of the tested children was improved, and the overall balance ability improved by 60%. The balance ability evaluation training system developed in this paper can effectively improve the independent standing ability of young children. Children over the age of 4 do not have any difficulties in understanding and mastering movements, and training difficulties for children are also applicable. At the same time, the results of these exercises also showed that this kind of exercise can help children maintain their balance.
Step Into My Mind Palace: Exploration of a Collaborative Paragogy Tool in VR

Virtual Reality (VR) can mediate remote collaborative learning and can support pedagogical processes like paragogy. Within education, methods such as spaced repetition and memory palaces exist to support the cognitive process of remembering. We identify an opportunity to enhance learner-led collaborative paragogy involving these methods through immersive VR experiences. We present CleVR, a VR-mediated collaboration-based system that supports the memory palace and spaced repetition techniques. As an exploratory study, we aim to identify the applicability, viability and user perception for such a system combining these two techniques in VR. CleVR is a novel implementation which provides a location-driven metaphor to populate and present multiple resources related to a topic for peer-led exploration. We discuss the design and provide a prototype implementation of CleVR. We conducted two studies, a targeted expert user review and a broader proof of concept survey. The results of the studies show interesting outcomes, with the system described as ‘engaging’, ‘useful’ and ‘fun’. Our findings provide insights to the potential of using Virtual Reality Learning Environments (VRLE) geared towards collaborative learner-led activities.
Bimodal Information Fusion Network for Salient Object Detection based on Transformer

Inspired by the excellent performance achieved by the Transformer in processing computer vision tasks in recent years, this paper investigates and designs a bimodal information fusion network (BIFNet) for salient object detection (SOD) based on Transformer. Compared with most existing CNN-based or hybrid CNN+Transformer-based models, BIFNet builds on a pure Transformer framework for both the feature extraction and fusion phases. It treats bimodal features as sequence-to-sequence context information to learn cross-modality context-aware feature representation. Specifically, we first utilize the weight-sharing Swin Transformer as a Siamese backbone to extract bimodal hierarchical sequence features. Next, we design two modules, namely dual self-and-mutual attention (DSMA) module and multi-scale fusion (MSF) module, to further explore saliency cues and fully and effectively fuse information of the two modalities. We apply BIFNet to RGB-D SOD and RGB-T SOD bimodal detection tasks, and comprehensive experimental results on public benchmark datasets demonstrate the superiority of our proposed method.
A Survey of Offline Handwritten Signature Verification Based on Deep Learning

Handwritten signatures are biometrics and a point of contention in the scientific community, the process used to verify whether a person’s signature is genuine. In the past ten years, the application of handwritten signature technology in the fields of administration, finance, handling legal disputes, and security has been greatly developed, and many researchers have focused on applying systems based on handwritten signature analysis and processing to new fields on the possibility. After several years of disorderly development in this field of research, it is time to assess the applicability of its current developments to formulate a structured path forward. In this paper, we provide a systematic review of the literature on offline handwritten signatures over the past 10 years, focusing on the most prominent and promising deep learning-based signature verification methods, and attempt to elicit possible future research directions on this topic.
Material Dialogues for First-Order Logic in Constructive Type Theory

Material dialogues are turn taking games which model debates about the satisfaction of logical formulas. A novel variant played over first-order structures gives rise to a notion of first-order satisfaction. We study the induced notion of validity for classical and intuitionistic first-order logic in the constructive setting of the calculus of inductive constructions. We prove that such material dialogue semantics for classical first-order logic admits constructive soundness and completeness proofs, setting it apart from standard model theoretic semantics of first-order logic. Furthermore, we prove that completeness with regards to intuitionistic material dialogues fails in constructive and classical settings. The results concerning classical material dialogues have been mechanized using the Coq interactive theorem prover.
A Novel CNN-based Autoencoder with Channel Feedback for Intelligent Maritime Communications

Driven by the rapid growth of maritime business, the research of reliable maritime communications has attracted great attention from both academic and industry. This paper proposes a novel convolutional neural networks (CNN) -based autoencoder with channel feedback (CNN-AE-CF) for intelligent maritime communications with complex and changeable environment. A CNN-AE-CF is comprised of CNN layers, which inherits the breakthrough characteristics of CNN, such as generalization, feature learning, classification, and fast training convergence. In the CNN-AE-CF, we introduce a feedback channel and a feedback decoder at the transmitter to decode the feedback signals. Then, the transmitter combines feedback signals with input signals for secondary encoding. We leverage Rician fading channel to simulate the marine environment, in which CNN-AE-CF is trained. Finally, simulation results illustrate the superiority in terms of reliability and robustness. The proposed autoencoder is promising for intelligent maritime communications.
Design and Implementation of Community Pension Service Quality Evaluation System Based on Clustering Algorithm

The healthy development of community elderly care service is very important. Studying the quality evaluation of community elderly care services and establishing a complete, scientific, and objective community elderly care service method can establish the online reputation of enterprises and help elderly users obtain cost-effective elderly care services. This is very important for healthy development. This paper puts forward a new evaluation model for the quality of community elderly care service. The evaluation method based on the model is designed, the quality evaluation model is constructed, and the community evaluation system is developed. From the aspects of requirement analysis, design, implementation, and testing, the specific implementation process of the system is described. This paper puts forward a community evaluation model which accords with the clustering model: community elderly care service quality evaluation method. AIS-OR mathematical modeling is carried out. Under the guidance of the model, the secondary indicators are analyzed and determined, and the mathematical model of indicator evaluation is constructed. We use text mining technology and emotion analysis to determine the relationship between comment emotion and community elderly care service quality online and build a quality emotion feature library. The naive Bayesian algorithm is used to classify the online evaluation of the service quality of community elderly care services, which verified by experiments and completed the design and development of the community elderly care service quality evaluation system based on the project needs.
A Systematic Literature Review of Virtual Reality Education and COVID-19 Safety

Within the year 2020, there has been a rapid rise in COVID-19 risks and documentation. Virtual reality education processes have been studied for over 20 years, while in-person interaction has been the primary source of education across all ages. A systematic analysis was conducted to understand current literature focused on virtual reality education and how recent COVID-19 safety needs have been met through these processes implemented. Databases used for data collection included HarzingHarzing’s Publish or Perish, Google Scholar, Vicinitas, Scopus, Web of Science, ResearchGateResearchGate, and SpringerLink. Core software used for analysis were VOS Viewer, MAXQDAMaxqda, BibExcel, CiteSpaceCitespace, and Mendeley. The results show that due to how recent COVID-19 is, few publications show any overlap between the COVID-19 safety and virtual reality education implemented. However, due to the success of virtual reality education processes in past studies, there is potential for future positive impact on learning processes within the COVID-19 safety regulations. Future work was evaluated through documenting current studies awarded at the National Science Foundation.
Static and Dynamic Demonstration of the Ecological Level of Ethnic Cultural Industries Based on the Internet of Things and Environmental Responsibility

The cultural industry of ethnic minorities carries the wisdom of many ancients. In today’s modernization, ethnic minority cultural industry products can still impress people’s hearts with exquisite handicrafts, natural materials, and strong culture. The rise in demand for ethnic cultural products will bring about an increase in the demand for raw materials for handmade products, which is bound to have an impact on the environment. This paper is aimed at modeling and analyzing the ecological level of minority cultural industries based on the background of the Internet of Things (IoT) and environmental responsibility. In the experiment, this paper analyzed the ecological level of the traditional fish skin industry by taking the Hezhe people in Heilongjiang as an example. Aiming at the cultural industry of the Hezhe people, this paper selected the fish skin industry as the object of empirical analysis from the fish culture it represents. According to the ecological level of the industry, this paper selected the three first-level indicators of production energy saving level, ecological environmental protection level, and production emission reduction level and its 12 subordinate second-level indicators to model and analyze the fish skin industry of the Hezhe people. The analysis results showed that the industrial ecological level of the Hezhe fish skin industry in the past 16 years reached the standard rate of more than 75%. However, the ecological level index for the three years of 2018, 2019, and 2020 was between 0.3 and 0.5, which indicated that the ecological level in these three years was very low. This was caused by the mechanization of the handicraft industry in recent years.
Thermal Infrared Tracking Method Based on Efficient Global Information Perception

To solve the insufficient ability of the current Thermal InfraRed (TIR) tracking methods to resist occlusion and interference from similar targets, we propose a TIR tracking method based on efficient global information perception. In order to efficiently obtain the global semantic information of images, we use the Transformer structure for feature extraction and fusion. In the feature extraction process, the Focal Transformer structure is used to improve the efficiency of remote information modeling, which is highly similar to the human attention mechanism. The feature fusion process supplements the relative position encoding to the standard Transformer structure, which allows the model to continuously consider the influence of positional relationships during the learning process. It can also generalize to capture the different positional information for different input sequences. Thus, it makes the Transformer structure model the semantic information contained in images more efficiently. To further improve the tracking accuracy and robustness, the heterogeneous bi-prediction head is utilized in the object prediction process. The fully connected sub-network is responsible for the classification prediction of the foreground or background. The convolutional sub-network is responsible for the regression prediction of the object bounding box. In order to alleviate the contradiction between the vast demand for training data of the Transformer model and the insufficient scale of the TIR tracking dataset, the LaSOT-TIR dataset is generated with the generative adversarial network for network training. Our method achieves the best performance compared with other state-of-the-art trackers on the VOT2015-TIR, VOT2017-TIR, PTB-TIR and LSOTB-TIR datasets, and performs outstandingly especially when dealing with severe occlusion or interference from similar objects.
Data Analytics on eXtended Reality (XR) for Pedagogy Education: Educators’ Awareness for the Future of Education in Smart Cities

The future of education in smart cities is connected with the metaverse and eXtended Reality (XR) applications. Educators know so little or nothing about helpful digital learning opportunities because they never heard about them during their pedagogic studies. To rapidly change this, European educational institutions join forces in the Erasmus + XR4Ped project to provide answers to questions and help foster digitisation in higher education in general and promote XR-based Virtual Reality (VR), Augmented Reality (AR) and the combination and further extension of both to Extended Reality (XR) immersive learning in particular. This poster will present the results of the anonymous data provided through a qualitative online questionnaire, highlighting the differences of XR technologies’ awareness in Europe.
DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.
Security of Social Networks: Lessons Learned on Twitter Bot Analysis in the Literature

Twitter is one of the popular social network platforms used by both humans and bots to share information, and to distribute misinformation or disinformation. The goal of this research is to explore state-of-the-art Twitter bot detection systems: Botometer, and Tweetbotornot. In doing so, we aim to understand their characteristics, similarities, and differences as well as to identify additional methods to improve their performances. Evaluations performed on 15 datasets show that the proposed methods used as add-ons to Botometer were able to improve the detection of bot/human accounts on Twitter using the simple characteristics of Twitter accounts.
A Belief Rule Based Expert System to Diagnose Alzheimer’s Disease Using Whole Blood Gene Expression Data

Alzheimer’s disease (AD) is a degenerative neurological disease that is the most common cause of dementia. It is also the fifth-greatest reason for death in adults aged 65 and over. However, there is no accurate way of diagnosing neurological Alzheimer’s disorders in medical research. Blood gene expression analysis offers a realistic option for identifying those at risk of AD. Blood gene expression patterns have previously proved beneficial in diagnosing several brain disorders, despite the blood-brain barrier’s restricted permeability. The most extensively used statistical machine learning and deep learning algorithms are data-driven and do not address data uncertainty. Belief Rule-Based Expert System (BRBES) is an approach that can identify various forms of uncertainty in data and reason using evidential reasoning. No previous research studies have examined BRBES’ performance in diagnosing AD. As a result, this study aims to identify how effective BRBES is at diagnosing Alzheimer’s disease from blood gene expression data. We used a gradient-free technique to optimize the BRBES because prior research had shown the limits of gradient-based optimization. We have also attempted to address the class imbalance problem using BRBES’ consequent utility parameters. Finally, after 5-fold cross-validation, we compared our model to three classic ML models, finding that our model had a greater specificity than the other three models across all folds. The average specificity of our models for all folds was 32%
The effect of asynchronous online English classes on Korean college students: Focusing on self-directed learning ability and English achievement

The purpose of this study is to investigate the effects of asynchronous online English classes on learners’ self-directed learning ability and English achievement. In addition, this study is to find out the relationship between their self-directed learning ability and English achievement. The study was conducted for 15 weeks with 72 Korean college students. The results were as follows. First, asynchronous online English classes were found to be effective in improving the students’ English achievement. Second, asynchronous online English classes were effective in improving the students’ self-directed learning ability. Third, among the seven factors of self-directed learning ability, the students showed more improvement in three factors, ‘original approach’, ‘voluntary planning’, ‘inquisitive nature’, which showed a statistical difference between pre- and post-test. Fourth, there was a correlation between students’ English achievement and self-directed learning ability. However, among the seven factors, only ‘original approach’ was correlated with English achievement. This study helps teachers to understand how learners’ self-directed learning works in asynchronous online English classes. Through the study, it is concluded that asynchronous online English classes can be effective in college students’ self-directed learning abilities and English learning. Based on the study, some guidelines for effective online English classes were suggested.