Reviewerly/exHarmony huggingface dataset:
Consideration of success probability and performance optimization in non-Gaussian continuous variable quantum teleportation

Non-Gaussian operations have been shown to enhance the fidelity of continuous variable quantum teleportation. However, a disadvantage of these non-Gaussian operations is that they are probabilistic in nature. In this article, we study the trade-off between teleportation fidelity and success probability for optimal performance of the teleportation protocol, which to the best of our knowledge, has never been studied before. To this end, we first derive a unified expression for the Wigner characteristic function describing three non-Gaussian states, photon subtracted, photon added, and photon catalyzed two-mode squeezed vacuum states. We then utilize it to obtain the fidelity of teleportation for input coherent and squeezed vacuum states using the aforementioned non-Gaussian resource states. We optimize the product of the relative enhancement in fidelity and the probability of state preparation by tuning the transmissivity of the beam splitters involved in implementing non-Gaussian operations. This leads to a scenario that can be effectively implemented in a lab to enhance fidelity. It turns out that among all the considered non-Gaussian resource states, the symmetric one-photon subtracted TMSV state is the most advantageous. We provide the associated optimal squeezing and beam splitter transmissivity values for the considered non-Gaussian resource states, which will be of significant interest to the experimental community. We also consider the effect of imperfect photon detectors on teleportation fidelity. Further, we expect the derived Wigner characteristic function to be useful in state characterization and other quantum information processing protocols.
Stereoscopic Projection Policy Optimization Method Based on Deep Reinforcement Learning

Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy optimization research, a deep learning framework is selected according to the research problems, and a DRL stereoscopic project policy model based on the asynchronous advantage actor–critic (A3C) algorithm, which uses two groups of neural networks, is constructed. The optimized stereoscopic projection policy is obtained by the interactive learning between the DRL model and the simulation. The effectiveness of the cooperative optimization policy between the DRL and the simulation experiment is verified.
Supervised Machine Learning Techniques based Human Age, Gender and Emotion Detection

The proposed work is “Supervised machine learning based Human Age, Gender and Emotion Detector” is a project to help people avoid being victims of frauds. Automated age and gender detection has been generally used in our daily lives that we come across, majorly in a person to computer interaction, visual surveillance, biometric analysis, electronics and other applications of commercial use. By recognizing the emotions of a person, we can improve the recommendation system. The existing methods have quite satisfying performance on real-world images if facial expressions of input image is neutral or calm, it lacks significantly in age prediction when facial expressions are altered. For image classification, a convolutional neural networks (CNNs) pre=trained are used on ImageNet from Caffe, a modifiable platform for state-of-the-art deep learning algorithms and a set of reference models. The YOLOv3 (You Only Look Once V3) algorithm was employed for such purposed having a desirable ability to serve the required purpose.
Enhancing Health Monitoring using Efficient Hyperparameter Optimization

Nowadays, healthcare problems among elders have been increasing at an unprecedented rate, and every year, more than a quarter of the elderly people face weakening injuries such as unexpected falls, etc. resulting in broken bones and serious injuries in some cases. Sometimes, these injuries may go unnoticed, and the resulting health consequences can have a considerable negative impact on their quality of life. Constant surveillance by trained professionals is impossible owing to the expense and effort. The detection of physical activities by different sensors and recognition processes is a key topic of research in wireless systems, smartphones and mobile computing. Sensors document and keep track of the patient's movements, to report immediately when any irregularity is found, thus saving a variety of resources. Multiple types of sensors and devices are needed for activity identification of a person's various behaviours that record or sense human actions. This work intends to gather relevant insights from data gathered from sensors and use it to categorize various human actions with machine learning using appropriate feature selection and hyperparameter tuning, and then compare the implemented models based on their performance. Understanding human behaviour is very useful in the healthcare industry, particularly in the areas of rehabilitation, elder care assistance, and cognitive impairment.
From business to clinical trials: a systematic review of the literature on fraud detection methods to be used in central statistical monitoring

Data-driven decisions can be suboptimal when the data are distorted by fraudulent behaviour. Fraud is a common occurrence in finance or other related industries, where large datasets are handled and motivation for financial gain may be high. In order to detect and prevent fraud, quantitative methods are used. Fraud, however, is also committed in other circumstances, e.g. during clinical trials. The article aims to verify which analytical fraud-detection methods used in finance may be adopted in the field of clinical trials. We systematically reviewed papers published over the last five years in two databases (Scopus and Web of Science) from the field of economics, finance, management and business in general. We considered the broad scope of data mining techniques including artificial intelligence algorithms. As a result, 37 quantitative methods were identified with the potential of being fit for application in clinical trials. The methods were grouped into three categories: pre-processing techniques, supervised learning and unsupervised learning. Our findings may enhance the future use of fraud-detection methods in clinical trials.
Research on the Teaching Reform of Network Application Development under the Background of First-class Specialty

With the goal of cultivating application-oriented and innovative talents, based on the teaching philosophy of "student-centered, output-oriented, continuous improvement", combined with modern information technology, and based on online and offline mixed teaching has become an inevitable trend of curriculum construction universities. This paper discusses the teaching reform research situation of “Network Application Development” course under the background of first-class Specialty, mixed teaching design, course teaching content and organization, course assessment and course evaluation, comprehensively exploring the new ideas of curriculum reform, and cultivating new engineering innovative talents.
Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.
Spike-based statistical learning explains human performance in non-adjacent dependency learning tasks

Grammar acquisition is of significant importance for mastering human language. As the language signal is sequential in its nature, it poses the challenging task to extract its structure during online processing. This modeling study shows how spike-timing dependent plasticity (STDP) successfully enables sequence learning of artificial grammars that include non-adjacent dependencies (NADs) and nested NADs. Spike-based statistical learning leads to synaptic representations that comply with human acquisition performances under various distributional stimulus conditions. STDP, therefore, represents a practicable neural mechanism underlying human statistical grammar learning. These findings highlight that initial stages of the language acquisition process are possibly based on associative learning strategies. Moreover, the applicability of STDP demonstrates that the non-human brain possesses potential precursor abilities that support the acquisition of linguistic structure.
Modeling Education Studies Indexed in Web of Science Using Natural Language Processing

Easier access to information and resources allowed researchers to conduct more studies and publish most of them electronically. They are indexed in scholarly citation databases such as Web of Science and Scopus. These databases index huge volumes of research reports. Even though they offer search engine filtering options, it is still hard to locate the publications in which their contents are closely related. Artificial intelligence technologies, such as Natural Language Processing, allow documents to be categorized based on their content. Top2Vec is an unsupervised topic modeling algorithm that enables users to categorize documents semantically. The purpose of the current study is twofold: (1) to provide users with the ability to group documents applying Natural Language Processing techniques, and (2) to reveal the topics with the highest number of articles indexed in the ‘education scientific disciplines’ category within the Web of Science Core Collection scholarly database in 2021. Colab notebook used to type Python codes for executing Top2Vec algorithm. This study yielded 68 distinct topics among the 8125 articles published in 2021 and indexed in the Web of Science database under the Education Scientific Disciplines category. After modeled topics were ranked from the topic having the largest number of documents (i.e., N=549) to the topic having the least number of documents (i.e., N=29), the first eight topics' findings were presented and discussed. These eight most studies topics are listed as follows: Physics (N=549), online education and covid (N=438), Chemistry (N=381), Math and Reasoning (N=377), Psychology and Emotions (N=257), Educational Diversity (N=228), Health and Life (N=223), Mentoring and Leadership (N=204).
Research on the Assistance of MySQL Database Platform to the Renewal Design of the Interior Space of Urban Existing Housing

Abstract In response to the topic of this conference, this paper mainly adopts the strategy of introducing database technology into the renewal and renovation of existing housing. The construction industry, particularly those involved in the renovation of existing homes, must adapt to the new information era by applying information transformation strategies. In the architecture sector, it is still utilized for the storage and visualization of project and product information, but that is about all for the time being. According to the findings of this study, the database platform should be utilized more extensively in such a case. Through the establishment of a new online platform, residents will be able to engage in the design process to a significant degree. This study, which used WAMP as its foundation and database technology as its vehicle, created a core database on the supporting and accessory systems of existing housing in order to provide people with an interactive platform for free renewal design and, as a result, make it easier for people to become involved in the redevelopment and renovation of existing housing.
TIC y enseñanza de las matemáticas en secundaria: Melilla

The incorporation of technology by teachers as a teaching tool is a challenge. Our aim is to analyse whether ICT resources influence the use of pedagogical models and active methodologies by secondary school teachers in Melilla to teach Mathematics. In this cross-sectional quantitative study, a sample n= 61 (83.56% of the population) was evaluated using a validated 20-item questionnaire with three indicators: B.1 Methodologies and Pedagogical Models, B.2 Teaching Practice and C.3 ICT Resources in the teaching environment. Statistical power allows extrapolation of the findings.
Designing motion graphics as a zero waste lifestyle campaign media for the Indonesian people

Abstract Since Covid-19 there have been many changes in the order of people’s lives, there are various new regulations such as the obligation to wear masks when leaving the house, maintaining distance, working from home, or restrictions on leaving the house. These social restrictions have an impact on increasing the volume of plastic waste due to online transactions. This problem causes environmental damage, such as what happened in Gorontalo waters between Hulonthalangi and Dumbo Raya sub-districts, Gorontalo City, polluted with microplastics, the food chain in Gorontalo waters is automatically contaminated with microplastics from the research results of the Nusantara River Expeditionary Team in collaboration with the Institute for Humanities and Development Studies in October 2022. Based on this, campaign actions need to be taken. This Final Project with the title “Designing Motion Graphics as a Zero Waste Lifestyle Campaign Media for the Indonesian People” aims to assist the government’s role in preventing environmental damage due to waste and to provide information, education, and an invitation to the target audience to participate. In the success of this campaign. Motion graphic design as a media campaign applies a mixed media art style as a visual representation of graphic elements, typeface, and layouts.
CKG: Dynamic Representation Based on Context and Knowledge Graph

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on \textbf{C}ontext and \textbf{K}nowledge \textbf{G}raph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. The experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERT$_{Base}$ (88.5).
Extractive Summarization on Food Reviews

Text summarization is a technique to summarize the content of a sizeable text but meanwhile it keep the key information. Extractive summarization and abstractive summarization are the main techniques for text summarization. TextRank algorithm, an extractive summarization technique is applied to perform automatic text summarization in this study. Furthermore, GloVe pre-trained word embedding model is used to map each word from the reviews to vector representation. In the end, PageRank algorithm is applied to rank the sentences based on their sentence ranking scores. The more important and relevant sentences which can be the representatives of summary will be placed in a higher rank. The objective of our study is to extract top five reviews with the highest sentence ranking scores which can form a summary to provide a conspectus of a cookies brand in Amazon food reviews. An analysis of the customer perception based on the summary generated is conducted to understand their needs and level of satisfactions. The final summary demonstrates that Amazon customer reviews for certain cookies brand are generally positive.
AR-based Accessibility Verification Method for Smart Manufacturing System with Human Motion Capture

With the development of industrial production towards personalization and intelligence, process validation of manufacturability has become a complex and time-consuming problem in manufacturing system. Smart manufacturing requires manufacturing system to have the ability of rapid response to various product designs. However, there is an obvious time span between product design and accessibility verification process. Augmented reality (AR) constructs an environment of virtual reality integration, which brings people intuitive visual feeling and natural interaction feedback. It can be easily applied to the early stage of product accessibility verification. This paper presents a digital human assisted accessibility verification system based on AR, which can perform accessibility verification in the product design stage. In this system, augmented reality glasses are used to realize the natural interaction between human and product 3D model, and a motion capture device is used to realize the superposition of digital human and operators. This paper expounds the above methods and related devices in detail, verifies the feasibility of this system through an industrial example, and shows its application potential in smart manufacturing system.
Initial Cluster Centers Based on Moving Two Lines Approximation in K-means Algorithm

The main shortcoming of K-means clustering algorithm is its great dependence on the initial cluster center point. Based on the moving two lines approximation model, this paper gives a method to pick the initial cluster center of k-means clustering. Numerical experiments and comparison criteria show that this method can get better clustering effect.
Glass classification and identification based on logistic regression analysis and K-means++ clustering algorithm

During the weathering process, a large number of internal elements are exchanged with environmental elements, resulting in a change in the proportion of glass composition, which affects the correct judgement of glass categories. By comparing the chemical composition of high potassium glass and lead-barium glass, this paper finds that the proportion of silicon dioxide and potassium oxide is significantly higher in the high potassium glass category, while the proportion of lead oxide and barium oxide is high in the lead-barium glass category, and the above-mentioned substances with large differences in content are defined as classified substances. A logistic regression analysis was carried out to derive a regression equation between the classified substances and the glass type, which was used as a basis for determining the glass type. Based on this, a K-means++ clustering algorithm was used to perform subclass classification, resulting in three subclasses for each type. Finally, the glass of unknown composition was identified according to the established model.
Credit Card Fraud Detection Based on Support Vector Machine

Due to the increasing popularity cashless transactions, credit card fraud has become one of the most common frauds and caused huge harm to the financial institutions and individuals in real life. In this academic paper, the algorithm Support Vector Machine (SVM) is used to build models to deal with the credit card fraud detection problem with the performance metrics AUC and F1-score. The experiment dataset is named Credit Card Transactions Fraud Detection Dataset from the Kaggle website. After the step of preprocessing, the dataset is split into the training, testing and validation dataset with 11 numerical features and a label feature called “is_fraud”. The inner parameter “class_weight” of the SVM algorithm in Python is set as “balanced” to deal with the imbalanced datasets. The main method to find the optimized models is using the GridSearchCV function in Python library sklearn. After tuning the hyperparameters and handling the overfitting phenomenon, the optimized models for the two metrics are found. The parameter values of the best model for AUC are C=10, class_weight= “balanced”, g =0.01, kernel = “rbf”. The training AUC is 0.87 and testing AUC is 0.90. The parameter values of the final optimized model for F1-score are C=0.8, class_weight= “balanced”, g =0.06, kernel = “rbf”. The final training F-score is 0.305 and testing F-score is 0.260.
Open Chinese Internet Sarcasm Corpus Construction: An Approach

Sarcasm is a commonly-used language phenomenon particularly on the Internet, which is often to convey criticism or negative emotions. A proper sarcasm corpus to help sarcasm study and detection can contribute to linguistic research and assist sentiment analysis, but an open Chinese corpus is found extremely lacking. In this paper, we referenced existing methods and data and constructed a balanced open Chinese Internet sarcasm corpus in a new approach to improve efficiency and data quality. The balanced open corpus contains multi-source and labeled 2,000 texts selected from bigger corresponding origin datasets. In our corpus, sarcasm and non-sarcasm, longer and shorter texts are both in 1:1 ratio.
Hybrid Attention Fusion in Dense Crowd Counting

One of appealing approaches to guiding deep parameter optimization, is attentional supervision, which inspires intelligence in complex networks at a fraction of the cost, but there is still room for improvement. First, the real dense scene with varying scales and uneven density distribution of human heads, the density map cannot be clearly expressed. Second, the heavily occluded areas are extremely similar to the complex background, which further aggravates the counting error. Therefore, we propose a dual-track attention network that distinguishes between global and local information, which is responsible for the target overlap and background confusion problems, respectively, and finally converges and normalizes with the feature map to transform the multi-channel attention map into a single-channel density map. Meanwhile the heterogeneous pyramid design alleviates the distress of scale variation and density dissimilarity. Experiments on several official datasets prove the effectiveness of the scheme to enhance key information and overcome confounding factors.