Reviewerly/exHarmony huggingface dataset:
Generation of Time-Bin Entanglement in a Silicon Waveguide With Variable Channel Bandwidths

Entangled photon pairs based on silicon straight waveguides have been applied in the area of quantum information owing to the compactness of the devices and high nonlinearity of the silicon material. In order to fully take advantage of the broad emission spectrum of photon pairs for quantum-key-distribution, dense wavelength division multiplexer (DWDM) is often required. However, the relationship between the channel bandwidth of DWDM and the two-photon interference visibility remains unambiguous, in which the high visibility of quantum interference fundamentally reduces quantum bit error rate in quantum-key-distribution system. In this paper, time-bin entangled photon pairs with a raw visibility of 94.3% ± 3.3% are generated in a silicon chip with a channel bandwidth of 0.1 nm, showing a violation of Bell’s inequality by more than three standard deviations, and a fidelity of 0.942 ± 0.02 is obtained by quantum state tomography. Applying variable channel bandwidths, the corresponding two-photon interference visibilities are measured, fitting well with the theoretical model incorporating multiple parameters such as optical loss, dark count probability, and averaged photon-pair number in DWDM channel.
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection

In a number of applications where anonymity is critical, users act under pseudonyms to preserve their privacy. For instance, in scientific peer review using forums like OpenReview.net, reviewers make comments on papers that are publicly viewable. Reviewers who have been assigned multiple papers operate under different pseudonyms across their papers to remain anonymous. Other examples of publicly visible tasks where users operate under pseudonyms include Wikipedia editing and cryptocurrency transactions.
Validation volume reduction with tracking in sensor coordinates

Tracking in sensor coordinates has numerous benefits, because the filter bypasses the burden of nonlinear coordinates transformation, if consistency in the Cartesian space is guaranteed. Taking advantage of recent breakthroughs in coordinate conversion methods related to bistatic r-u-v (range and direction sines u,v ), we develop the r-u-v filter to optimize the association gate volume while maintaining Cartesian consistency. The paper focuses on materializing theoretical gains due to r-u-v tracking in a scenario with a glide vehicle subject to aerodynamic drag and gravity in a guided trajectory. Previous r-u-v filters are mixed-coordinate filters because realistic dynamics is difficult to express in r-u-v, but the present work manages to account for drag, gravity and guidance in r-u-v state equations without excessive complexity. Linear measurement model helps reduce gate volume, especially by accentuating the range accuracy of the sensor. In the presence of the “contact lens” phenomenon, gate volume reduction can be furthered by replacing the Gaussian ellipsoid gate which has a lot of empty space with a fuller and better fitted contact-lens-shaped gate region. Comparative simulations show that benefits are dependent on the sampling frequency and can be attributed to the choice of coordinates used by the filter.
Exploring how well Large-scale Masked Language Models can Recognize Grammatical Errors

In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to what a non-language model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method. This suggests that (i) the BERT-based method should have a good knowledge of the grammar required to recognize certain types of error and that (ii) it can use the knowledge to estimate whether the given word is correct or erroneous after fine-tuning with few training samples, which explains its high generalization ability in grammatical error detection. We further show with pseudo error data that it actually exhibits such nice properties for recognizing various types of error. Finally, based on these findings, we discuss a cost-effective method for detecting grammatical errors with feedback comments to learners.
A Requirements-Driven Framework for Automatic Data Visualization

Data visualization is an essential method for analyzing big data. Regarding the increasing demands on data visualization generation and understanding, more professional knowledge and skills are required, which are difficult to meet in practice. In most cases, people visualize data using existing templates which might not fit their requirements. We believe that it is essential to establish the connections between users’ visualization requirements and visualization solutions. In this paper, we propose a four-layer visualization framework to systematically and automatically map user requirements to data visualization solutions. Specifically, the framework is designed based on typical visual features and attributes and establishes mappings based on their semantics. Based on this framework, we have implemented a web-based prototype, which can automate the generation of visualization solutions from user visualization requirements. To evaluate the framework, we conducted a case study with one participant using the developed prototype and received positive feedback and suggestions.
A Survey on Hiding Data Using Video Steganography

Steganography’s capability to transmit data secretly has made it more popular in recent decades. Steganography secretly transmits information via a cover media such as a text file, picture file, audio file, or video file. As a result of its potential for concealing a more significant quantity of sensitive data, video steganography is gaining popularity. Compression methods and video deduplication are provided in this study as they pertain to the use of video in clandestine communication. When conducting surveys, both processed and raw footage are taken into account. Moreover, we discuss whether such an encryption method is even possible. This study covers state of the art in covert video embedding and encryption methods. Numerous compression techniques are discussed here, and data is also more securely stored in motion vectors in dynamic scenes.
Research on classroom behavior analysis and optimization strategies based on convolutional neural network in smart learning environment

Realizing the analysis and prediction of attention and learning emotion recognition in teacher-student adaptive interaction in a smart teaching environment is a major development need in the fields of intelligent guidance, teacher classroom evaluation, and classroom stress analysis.However, classroom education and online education big data are characterized by cross-modality, multi-source heterogeneity, content redundancy and structural confusion, and the secrecy of teacher-student relationship between different types of courses is high, which brings great challenges to the analysis and prediction of attention and learning emotion recognition in teacher-student adaptive interaction.This paper is based on the research idea of "sentiment analysis-data modeling-group discovery-behavior prediction", and innovatively uses the core algorithm of convolutional neural network to overcome the key problems of attention and learning emotion recognition and prediction in teacher-student adaptive interaction in the intelligent teaching environment.This paper presents an innovative approach to the analysis and prediction of attention and learning emotions in teacher-student adaptive interaction in a smart teaching environment.The method proposed in this paper achieves 96.88% accuracy in practical application at the expense of computation time.This study is expected to form a key method for the analysis of teacher-learner adaptive interaction states driven by multimodal data in a smart teaching environment, which provides optimized strategies and theoretical support for personalized smart teaching, and has high theoretical significance and application value.
Construction of Refined Assessment System for Practical Education of Engineering Majors under the Perspective of Craftsmanship

With the advent of a new era in Chinese socialism, the role and significance of human resources as the primary resource for economic and social development has become increasingly evident. From the perspective of practical education, the cultivation of applied talents with the spirit of craftsmanship has become a new demand in the new era, in conjunction with a refined assessment system for practical education that incorporates this spirit. Taking the Fire Engineering program at Southwest Forestry University as the research object, a questionnaire survey was used to evaluate the satisfaction of students with the training and assessment system, with a total score of 72.25 points. The satisfaction with the assessment system was at an above-average level, with scores of 82 for teaching methods evaluation, 75 for teacher evaluation, 67.5 for student evaluation, and 62.75 for learning methods evaluation. Using the expert scoring method, four refined assessment indicators of practical education were evaluated as "very important," including teacher and student dimensions, individual development of students, basic knowledge application ability, and comprehensive quality. The results of this study supplement the theoretical basis of the refined assessment system for practical education and provide reference opinions.
A Study of the Correlation between Life Events and Adaptability of Private University Students

College students; Life events; Adaptability
RGB Image Encryption Approach Built on a New Chaotic System With DNA Coding

In this study, we presented a hybrid method of a new 4D hyper-chaotic system with DNA encoding for the encryption of RGB images. The suggested encryption algorithm consists of three stages: permutation, confusion, and DNA coding. First, color components are permuted utilizing the index of the chaotic sequences are constructed from the new chaotic system to remove the high correlation among adjacent pixels. Second, we performed the XOR between the chaotic sequences and the permuted image from the previous phase. Finally, a set of DNA processes (DNA addition, DNA XOR, and DNA subtraction) are performed between the DNA sequences of the confusing image with the DNA chaotic sequences to obtain the final encoded image. Through tests like key space, key sensitivity, information entropy, and the histogram, the simulation analysis shows that the suggested method is secure and effective, and it has the ability to resist various attacks.
The Seismic Vulnerability of Buildings in the Central Zone of Peru through the Application of the Rapid Visual Detection Method

In order to predict the occurrence of major earthquakes, the world scientific community carried out the World Seismicity Map, establishing the most seismic zone known as the Pacific Ring of Fire, where Peru is included, as a result of the earthquakes that have occurred and that have led to structural damage to buildings and loss of human lives throughout the history of disasters in the country.The city of Chupuro is located in the central zone of Peru, categorized as a level III seismic zone, which makes it susceptible to seismic events.That is why the purpose was to determine the seismic vulnerability in 226 buildings in the city of Chupuro that were analyzed using the rapid visual detection method (RVS), with the use of the data collection sheet stipulated by the FEMA P-154 methodology, which is quite conservative and is based mainly on the category, structural system, soil and topography, providing a quick evaluation in any type of building and place.The field investigation identified the buildings according to the number of floors, type of construction, year of construction, irregularities in floor plan and height; they showed that 61% of the buildings are immersed to suffer seismic damage and hazards.In addition, these buildings showed that they are prone to suffer grade 2 (moderate damage) and grade 3 (severe damage) damage, mostly comprised of masonry buildings of medium height.It is concluded that there is a clear probability that the damage state of the buildings is 53.1% Grade 2 and 27.0%Grade 3 leading to moderate and severe damage respectively.Finally, with this we want to initiate actions to follow in order to achieve a country less vulnerable to seismic events with adequate management policies that include the population and authorities.
Key Competences of Students of Professional Subjects Using Activating Methods

Nowadays, it is necessary to use not only classic teaching methods be used in the teaching process but also non-traditional methods that can activate students to work independently and to find appropriate solutions to problems. At secondary vocational schools, the basis of students' education is teaching vocational subjects. Graduates of secondary vocational schools are expected not only to have professional knowledge but also the ability to communicate, solve problems independently, work in a team, and orient themselves in information. Therefore, teachers should include as many teaching methods as possible in teaching professional subjects, which will enable students to develop and improve key competencies. Our goal is to describe examples of several specific activating teaching methods that can be applied to teaching various vocational subjects.
Study of traffic improvement planning and design assistant decision making system based on city brain

With the continuous improvement of the city brain, lacking application and exploration in depth of implement scenarios for collection, aggregation and dynamic operation of total factor traffic data in the field of traffic improvement planning has been realized. Taking the key urban scenes as the research object, the paper deeply explores the traffic operation mechanism, establishes an integrated systematic treatment process of "operation monitoring - problem diagnosis - supply and demand analysis - scheme design - effect evaluation", provides all-round perception, analysis and judgment support for urban managers. Taking the Peking University People’s Hospital as an example. A system is established in the paper to perceive total factors of traffic behavior participants, to optimize scheme design by combining traditional improvement measures and information means and to evaluate the improvement scheme through integrated microscopic traffic simulation model The application demonstration rudimental system of "panoramic perception - monographic study - effect evaluation" is established to realize the closed loop of decision-making process systematization, with a view to providing reference of the transformation of traffic improvement planning and design technology system under the information background.
Blockchain-based public audit scheme for smart home data integrity

With the gradual improvement of people's demand for quality of life, the smart home industry is developing rapidly. A large number of smart home devices concentrate a large amount of data, so smart home systems mostly use data cloud outsourcing to manage data. Meanwhile, the centralized cloud servers used by traditional smart home solutions bring single-point failure problems and data privacy protection risks. To solve the above issues, we propose a blockchain-based public audit solution for smart home data integrity. The scheme uses blockchain and IPFS instead of centralized cloud servers, and supports dynamic attributes of data in cloud servers. Further, we extend the bulk auditing functionality. Finally, the security analysis and performance analysis show that our scheme outperforms existing scheme, and the audit computation overhead is reduced by 38%. When the number of users of the batch audit function is greater than 10, the average audit computation overhead per user is significantly reduced, which can effectively improve audit efficiency.
Recent advances in locality-sensitive hashing and its performance in different applications

This paper investigates the recent progress of Locality-sensitive hashing (LSH) under different metrics and its adaptive performance in different application scenarios. LSH is an approximate nearest neighbor query algorithm, which aims to perform fast similarity finding in high-dimensional space. In recent years, the improvement directions of LSH can be divided into three categories: 1) constructing suitable function families under different metric indexes, 2) constructing detection sequences by perturbation, and 3) expanding the radius to improve the retrieval range. Since metric indexing is more widely used, this paper focuses on a comprehensive and schematic review of this approach. computational simplicity and efficiency of LSH, which is prominent in the fields of image, recommendation, and de-duplication, we review the latest research on LSH in different application scenarios to provide an advanced general framework and a better understanding of LSH in combination with other fields. In conclusion, various algorithms for LSH are evolving rapidly, and it is expected to see more diverse application scenarios for these methods in the future.
Rainfall Prediction using Extreme Gradient Boosting

Abstract: Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made. Previous research on rainfall prediction uses models that have their limitations and thus produce poor performance. This study aims to build a multivariate rainfall prediction model using the best performing technique to date namely the Extreme Gradient Boosting. This model is built based on 7 years of historical weather data collected by the weather station.
Robust AAC Audio Watermarking Algorithm Based on Particle Swarm Optimization Algorithm

A robust ACC audio watermarking algorithm based on the particle swarm optimization algorithm is proposed in this paper. The Pearson correlation coefficients of the QMDCT coefficients of the left and right channels of the audio are calculated, and the carrier elements of the left and right channels are decomposed by QR. For the decomposed matrix, different operations are performed on the non-zero elements in the upper matrix according to the different watermark information to achieve embedding. In order to improve the overall performance of the watermarking algorithm, the particle swarm optimization algorithm is used to divide the carrier elements, and the audibility and robustness of the watermarked audio are fully considered. The experimental results show that this method has a significant improvement in imperceptibility and provides good robustness against common audio signal processing such as added noise, filtering, and recompression.
Digital Image Correlation with a Prism Camera and Its Application in Complex Deformation Measurement

Given the low accuracy of the traditional digital image correlation (DIC) method in complex deformation measurement, a color DIC method is proposed using a prism camera. Compared to the Bayer camera, the Prism camera can capture color images with three channels of real information. In this paper, a prism camera is used to collect color images. Relying on the rich information of three channels, the classic gray image matching algorithm is improved based on the color speckle image. Considering the change of light intensity of three channels before and after deformation, the matching algorithm merging subsets on three channels of a color image is deduced, including integer-pixel matching, sub-pixel matching, and initial value estimation of light intensity. The advantage of this method in measuring nonlinear deformation is verified by numerical simulation. Finally, it is applied to the cylinder compression experiment. This method can also be combined with stereo vision to measure complex shapes by projecting color speckle patterns.
Ontology for Blind SQL Injection

In cyberspace, there exists a prevalent problem that heavily occurs to web application databases and that is the exploitation of websites by using SQL injection attacks. This kind of attack becomes more difficult when it comes to blind SQL vulnerabilities. In this paper, we will first make use of this vulnerability, and subsequently, we will build an ontology (OBSQL) to address the detection of the blind SQL weakness. Therefore, to achieve the exploitation, we reproduce the attacks against a website in production mode. We primarily detect the presence of the vulnerability, after we use our tools to abuse it. Last but not least, we prove the importance of applying ontology in cybersecurity for this matter. The mitigation techniques in our ontology will be addressed in our future work.
Transforming Healthcare: Exploring Blockchain-Based Patient Record Management Systems for Secure and Efficient Data Sharing

Blockchain technology has a great potential for improving efficiency, security and privacy of Electronic Health Records (EHR) sharing systems.However, existing solutions relying on a centralized database are susceptible to traditional security problems such as Denial of Service (DoS) attacks and a single point of failure similar to traditional database systems.Medical data contains multiple records of patient data that are important for subsequent treatment and future research.However, it needs to be stored and shared securely to protect the privacy of the data.Blockchain is widely used in the management of healthcare data because of its decentralized and tamper-proof features.Blockchain-based patient record management systems are gaining increasing attention in the healthcare industry due to their potential to improve data security, transparency, and interoperability.In this paper, we provide an overview of different ways of implementing blockchain-based patient record management systems, including private blockchain, smart contract, tokenization, DApp, and consortium blockchain.We review the advantages and limitations of each approach and analyze the performance measures of blockchain-based electronic medical record systems.We conclude by highlighting the challenges and opportunities for further research and development in this field.Our findings suggest that blockchain-based patient record management systems have the potential to transform the healthcare industry by enabling secure, efficient, and patient-centered data sharing.
Laser-amplified nonvolatile charge trapping effect in semiconductor quantum dot structures

Optoelectronic memory is attracting tremendous attention as an emerging strategy to emulate the human visual system. However, most devices to date focus on converting visual information in real time, rarely meeting the expectation of memorizing that information. Here, we report the discovery of a light-induced nonvolatile trapping effect that shows remarkable long-term storage of optical signals in a molybdenum disulfide quantum dot memory structure. Moreover, the charge trapping capability can be enhanced almost threefold after laser irradiation, in contrast to pure electrical means. We ascribe this to barrier modulation driven by the electric and light fields. By designing an optoelectronic memory array, we demonstrate that this effect can be applied simultaneously for image sensing and preprocessing, as well as in neuromorphic reinforcement learning. This work could enable the development of photosensitive artificial intelligence devices.