Reviewerly/exHarmony huggingface dataset:
An Efficient Zero-Knowledge Dual Membership Proof Supporting Pos-and-Neg Membership Decision

In this paper, we address the problem of secure decision of membership. We present a Zero-Knowledge Dual Membership Proof (ZKDMP) protocol, which can support positive and negative (Pos-and-Neg) membership decisions simultaneously. To do it, two secure aggregation functions are used to compact an arbitrarily-sized subset into an element in a cryptographic space. By using these aggregation functions, a subset can achieve a secure representation, and the representation size of the subsets is reduced to the theoretical lower limit. Moreover, the zeros-based and poles-based secure representation of the subset are used to decide Pos-and-Neg membership, respectively. We further verify the feasibility of combining these two secure representations of the subset, so this result is used to construct our dual membership decision cryptosystem. Specifically, our ZKDMP protocol is proposed for dual membership decisions, which can realize a cryptographic proof of strict Pos-and-Neg membership simultaneously. Furthermore, the zero-knowledge property of our construction ensures that the information of the tested element will not be leaked during the implementation of the protocol. In addition, we provide detailed security proof of our ZKDMP protocol, including positive completeness, negative completeness, soundness and zero-knowledge.
Semi-Automatic Approaches for Exploiting Shifter Patterns in Domain-Specific Sentiment Analysis

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction.
Small object detection in UAV image based on slicing aided module

Unmanned aerial vehicles (UAVs) play an increasingly important role in today’s life, and play a pivotal role in many fields such as remote sensing, command, and agriculture. UAVs have the advantages of high-altitude hovering, compactness and convenience, and are one of the most ideal platforms for taking photos in the air. Since the drone can shoot at high altitude, the target object in the image is often small in size, which is easy to cause it to be blurred and difficult to distinguish. With the development of artificial intelligence technology, the application of computer vision technology to build deep learning models can automatically identify large-scale and small targets from UAV images. Although this field has been concerned by many scholars, how to deploy small target detection on UAVs and apply it to command and control remains to be studied. To this end, this study proposes a small object detection method based on a slicing aided module to automatically detect objects from UAV images. The proposed method can be widely used in scenarios such as on-site command and control, which is of great significance for the further wide application of UAVs.
Design and Application of Yoga Intelligent Teaching Platform Based on Internet of Things

With the rapid development of economy, science, and technology, people’s pursuit of quality of life continues to improve, leisure sports have gradually become a trend, and yoga, as a traditional and fashionable way of fitness, is favored by more and more people. However, the traditional training class yoga teaching time is fixed, which will have many inconveniences for professionals. However, learning according to the teaching video cannot guarantee the accuracy of the movement and may not achieve the training effect. Therefore, in this situation, intelligent e-yoga teaching is of great significance to improve the training level. This system is based on Internet of things technology, selects Kinect as the sensing carrier of human motion information, and designs an electronic yoga teaching system. The system integrates the functions of motion information acquisition and motion evaluation. First, standard yoga movements are collected through Kinect as a comparison template for yoga training. Second, the system collects the trainers’ action data, uses Hausdorff distance algorithm to evaluate the similarity of action flow, and identifies the action name based on the threshold. Third, through the motion evaluation algorithm based on joint point angle measurement, it points out the joint points whose actions are not in place. Based on the completion time of the action, the action speed is evaluated. Finally, the system outputs the evaluation results in the form of text and voice through text conversion technology. The system can carry out efficient posture recognition and can achieve the purpose of evaluating the training quality and giving guiding suggestions. It can meet the basic training needs of users and has great application value.
f-Aware Conflict Prioritization &amp; Improved Heuristics For Conflict-Based Search

Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). The main step of CBS is to expand nodes by resolving conflicts (where two agents collide). Choosing the ‘right’ conflict to resolve can greatly speed up the search. CBS first resolves conflicts where the costs (g-values) of the resulting child nodes are larger than the cost of the node to be split. However, the recent addition of high-level heuristics to CBS and expanding nodes according to f=g+h reduces the relevance of this conflict prioritization method. Therefore, we introduce an expanded categorization of conflicts, which first resolves conflicts where the f-values of the child nodes are larger than the f-value of the node to be split, and present a method for identifying such conflicts. We also enhance all known heuristics for CBS by using information about the cost of resolving certain conflicts, and with only a small computational overhead. Finally, we experimentally demonstrate that both the expanded categorization of conflicts and the improved heuristics contribute to making CBS even more efficient.
Motivational Factors as a Driver for Success for First-Year Students at a Selected Public University in South Africa

Remaining motivated is vital to enable continued focus and success for university students. This study examined motivational factors as a driver for success for first year students at a selected public university in South Africa. The study adopted a phenomenological qualitative research design and participants included 312 first year students from the University of the Witwatersrand in Johannesburg who were purposively selected from the 2020 first year Bachelor of Education cohort. Data were collected by means of open-ended survey questions in the first and sixth week of lectures. Alderfer’s Existence, Relatedness and Growth (ERG) theory (1969) and Vroom’s (1964) theories of motivations were used as the analytic framework. The qualitative findings indicated significant reliance by students on external motivating factors as compared to internal motivating factors. In practice, university structures could strengthen orientation programmes for first year students at the university as these programmes would be an external motivator to enable student success.
Learning Reliable Gradients From Undersampled Circular Light Field for 3D Reconstruction

The paper presents a 3D reconstruction algorithm from an undersampled circular light field (LF). With an ultra-dense angular sampling rate, every scene point captured by a circular LF corresponds to a smooth trajectory in the circular epipolar plane volume (CEPV). Thus per-pixel disparities can be calculated by retrieving the local gradients of the CEPV-trajectories. However, the continuous curve will be broken up into discrete segments in an undersampled circular LF, which leads to a noticeable deterioration of the 3D reconstruction accuracy. We observe that the coherent structure is still embedded in the discrete segments. With less noise and ambiguity, the scene points can be reconstructed using gradients from reliable epipolar plane image (EPI) regions. By analyzing the geometric characteristics of the coherent structure in the CEPV, both the trajectory itself and its gradients could be modeled as 3D predictable series. Thus a mask-guided CNN+LSTM network is proposed to learn the mapping from the CEPV with a lower angular sampling rate to the gradients under a higher angular sampling rate. To segment the reliable regions, the reliable-mask-based loss that assesses the difference between learned gradients and ground truth gradients is added to the loss function. We construct a synthetic circular LF dataset with ground truth for depth and foreground/background segmentation to train the network. Moreover, a real-scene circular LF dataset is collected for performance evaluation. Experimental results on both public and self-constructed datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods.
A Study on the Value of Cultural Self-confidence Cultivated by the Rise of "National Tide"

Culture, as a country's soft power, internally condenses the hearts and minds of the people, and externally displays the "image of a big country".In recent years, "National tide" has quietly emerged carrying Chinese culture.Boosting the development of "National tide" aims to strengthen cultural confidence.However, cultural inferiority and dregs restrict the development of "National tide", the growth space of "National tide" is not fully expanded, and the "pseudo-National tide" hinders the rise of "National tide", which restricts the cultivation of cultural confidence.To lead the "National tide" and strengthen cultural confidence, we should take some measures such as inheriting and developing excellent traditional culture, expanding the "National tide" development circle and strengthening the guidance of socialist core values of enterprises.
Appreciation of Frog from the Perspective of Domestication and Foreignization

Based on the translation strategies of domestication and foreignization, the author takes Howard Goldblatt's translation as the research object, and makes an appreciation of some idioms and culture-loaded words in Frog.According to different kinds of Chinese expressions, corresponding views are put forward.The author argues that domestication should be applied for some expressions that may cause cultural conflicts.For some expressions with Chinese characteristics, translators should adopt foreignization to build a bridge for the communication between different cultures.
Design and Research of Mobile Terminal-Based Heat Monitoring and Management System

With rapid development of economy and technology, people's life style has changed significantly.Specifically, physical activity has been greatly reduced while the consumption of fast food and processed food has increased, and then the obesity has become more and more common and serious problem.In order to solve this problem, a heat monitoring and management system based on mobile terminals is proposed and realized.By embedding the heat monitoring and management module into mobile terminals, the proposed system allows users to facilitate their weight management regardless of time and space.
Adaptive Image Enhancement Method Based on Gamma Correction

To address the problems of local detail loss and sharpness degradation in traditional image enhancement algorithms under low illumination conditions, an image enhancement algorithm based on the Weber-Fechner law is proposed.The adaptive gamma correction function and the adaptive contrast enhancement function are used to reduce the noise interference of the original image, the RGB mode of the original image is converted to HSV mode to improve the overall visual comfort of the image, the classical adaptive correction algorithm is optimised to separate the luminance components into blocks and obtain two images; Finally, the image fusion technique is used to extract the details from the two images and synthesise the final image.The images enhanced are clearer, brighter and more natural than the classical algorithms.
Application of Corpus to the Exploration of Senior High School English Vocabulary Teaching Strategies——Based on the Concept of English Learning Activities

The New English Curriculum Standard for Senior High School (2017 Edition) advocates the core competence-oriented "concept of English learning activities" which is the main way to achieve the curriculum goals; The Standard also advocates using modern information technology which is one of the approaches to promote students' English learning.As a product of modern information technology, "corpus" has been a new method to English vocabulary teaching.Taking the vocabulary lesson of Unit 4, Book1 of the new textbook of the People's Education Press (2019 Edition) as an example, the paper is based on the concept of English learning activities and uses corpus resources to resolve the problems existing in English vocabulary teaching in senior high school currently.This exploration will guide students to learn vocabulary and improve teaching efficiently.
Educational Management Information Construction and Management Analysis in the Big Data Era

Informatization teaching of education management (EM) has gradually led to innovation, independence, and irreplaceability of university teaching as a whole.With the continuous development and continuous promotion of education informatization, it is of great significance to the overall school running level of various universities.This article takes the informatization construction and management of EM in 50 colleges and universities(CAU) as the main research body, and conducts informatization teaching environment, informatization education mode, informatization education content, informatization EM system, informatization student management, and network security system.The establishment and implementation of the literature research method was used to collect the concepts, contents and methods of the related issues of the EM information construction; the questionnaire survey method was used for statistical analysis of 96 valid questionnaires from 50 universities; the analysis of several examples was used Use SSPS 20 statistical software to process the relevant data of the valid questionnaire; use the interview method to collect information through interviews with the leaders of the school's information management department, teachers and students.The research results show that CAU with a multimedia classroom configuration rate of 100% are beneficial to the improvement of students' learning task completion rate.Micro-class and MOOC teaching have high-quality resources and popular trends can improve the teaching effect of CAU.Informatization of EM is conducive to the improvement of teachers' teaching tasks and the completion rate of students' learning tasks in CAU, and can promote the educational development of CAU.
Performance Comparison of Irregular Face Inpainting via Deep Learning

As a specific application of image inpainting, face inpainting is a critical content in the computer vision.It plays an important role in object removal, photo editing and other fields.Deep learning has become the mainstream approach of image inpainting.In specific applications, the corrupted area of face images is usually irregular.For the classical irregular face inpainting approaches based on deep learning, this paper divides it into convolution operator optimization methods and structural information constraint methods, the former includes PConv and GConv and the latter includes EC, PRVS, MED, CTSDG.We fist describe the basic principle of each algorithm and detail about the strengths and limitations.Then we experiment on CelebA-HQ dataset, evaluate and compare the performance quantitatively and qualitatively.
Automatic Recognition of Students' Classroom Behavior Based on Computer Vision

The performance of students' classroom behavior is an important part of the evaluation of classroom teaching, and the recognition of students' classroom behavior is of great significance to the evaluation of classroom teaching.However, due to the complexity of students' classroom behavior, it is difficult to identify intelligent students' classroom behavior.Therefore, this paper proposes a classroom behavior analysis and evaluation system based on deep learning face recognition technology.The classroom behavior analysis and evaluation system judges whether students pay attention to class from three aspects: Students' side face concentration, students' head up and head down concentration, and eyes opening and closing concentration, so as to provide an objective evaluation basis for students' classroom behavior evaluation in classroom teaching.At present, to solve the problem of performance degradation of convolutional neural network with the deepening of network layers, a deep residual network based on residual structure is proposed.Comparing the accuracy of the depth residual network with that of the depth convolution neural network on this data set, the experimental results show that the former has better network recognition performance.
Overview of Naming Entities Based on Natural Language Processing

Named entity recognition is an important research direction of natural language processing.This paper first reviews the development process and main stages of named entity recognition, then expounds the research contents and methods of named entity recognition, and puts forward the key and difficult points of named entity recognition, The development process of named entity recognition is from the initial rule-based and dictionary based method to the later statistical learning method, and then to the mixed learning method and some popular learning methods.Finally, the development of named entity recognition is prospected.
GME-Dialogue-NET: Gated Multi-modal Sentiment Analysis Model Based on Fusion Mechanism

In comparison with the single mode, the utilization of multi-mode information of text, video and audio could lead to more accurate sentiment analysis. GME-Dialogue-NET, a gated multi-modal sentiment analysis model, is raised for the multi-modal emotion prediction and sentiment analysis. The model judges whether the audio or video modal is the noise through GME (Gated Multi-modal Embedding, GME) and then accepts or refuses the modal information based on the judgement. The model uses the Attention Mechanism of context vector to allocate more attention to the context with greater relevance to the current sentence. GME-Dialogue-NET divides participants of the dialogue into speaker and listener to better capture the dependence between emotion and state. It raises that the fusion mechanism CPA (Circulant-Pairwise Attention, CPA) could pay effective attention with different degrees on different modals to attain more helpful emotional and sentimental representation and thus make emotion prediction and sentiment analysis. Compared with the current model, both the weighted accuracy and the F1 score of emotion prediction were improved, especially for the three emotions of sadness, anger and excitement. In the sentiment regression task, the comparison between GME-Dialogue-NET with current advanced model Multilogue-Net shows that MAE (Mean absolute error, MAE) of GME-Dialogue-NET reduces by 0.1 percentage and the Pearson Correlation Coefficient (R) of GME-Dialogue-NET rises by 0.11 percentage.
Traceable stitching interferometry for form measurement of moderately curved freeform surfaces

We present an interferometric form measurement system that is capable of measuring the form of optical surfaces from flats to moderately curved freeforms. A Fizeau interferometer is scanned over the specimen measuring its topography and the distance to the surface in subaperture measurements. The angle between the interferometer and the specimen is adjusted for each position and additionally relevant stage angle errors are measured with two tiltmeters. An optical surface with a spherical form (50 mm diameter, 10 m radius of curvature) is measured, and an uncertainty budget yields an uncertainty of 69 nm ( k = 2 ) for its topography. The determined radius of curvature agrees well with the nominal specifications and a measurement with a coordinate measurement machine. Furthermore, a form measurement of a car’s side window is presented.
Membership Inference Attack Against Principal Component Analysis

This paper studies the performance of membership inference attacks against principal component analysis (PCA). In this attack, we assume that the adversary has access to the principal components, and her main goal is to infer whether a given data sample was used to compute these principal components. We show that our attack is successful and achieves high performance when the number of samples used to compute the principal components is small. As a defense strategy, we investigate the use of various differentially private mechanisms. Accordingly, we present experimental results on the performance of Gaussian and Laplace mechanisms under naive and advanced compositions against MIA as well as the utility of these differentially-private PCA solutions.
The Empirical Research on the Victims of Telecom Fraud in China——Based on the Record of Victims with Quantification Statistics

With the wide application of wireless communication technology and the Internet, the new financial fraud crime--telecom fraud has emerged in recent years which depend upon the modern communication technology.According to the analysis of this kind of cases, we can find that the occurrence of these criminal cases is closely related to the characteristics of victims.But the existing researches are not focused on this problem.It is very important to prevent such telecom fraud happening by analyzing the victims with quantification statistics.First, we collected several demographic categories such as age, occupation, education and gender.Using this data, we determined that the composition of the victims with different proportions.Secondly, we concluded the behavioral traits and the psychological states of these victims with text analysis by grounded theory.Following that analysis, preventive measures are proposed to solve such crimes and forestall more.