Reviewerly/exHarmony huggingface dataset:
DHGECON: A multi-round conversational recommendation method based on dynamic heterogeneous encoding

Multi-round conversational recommendation (MCR), fulfilling a real–time recommendation task for users through interactively asking attributes and recommending items, can be regarded as a multi-step Markov decision-making process. Thus, in MCR, the key point is how to appropriately guide and characterize the dynamic interactive process in the conversation and to mine the dynamic relationship among the user, items and attributes to determine such policies as when to ask (attributes) and recommend (items), what (attributes) to ask and what (items) to recommend. Recent works mainly use statistical information involved in the conversation process to characterize the conversation without comprehensively considering the dynamic relationship among the user, items and attributes and may consequently have a negative impact on accurate capture of the user’s real-time preferences. To address this issue, we propose a multi-round conversational recommendation method based on dynamic heterogeneous encoding called DHGECON. Firstly, a dynamic heterogeneous graph with three types of node (users, items, and attributes) is constructed to characterize the proceeding conversation. Secondly, a heterogeneous graph-based encoder which adaptively updates the attention weight of nodes is designed to mine and represent the dynamic high-order semantic relationship among the user, items and attributes. Finally, the encoded information is fed into the decision-making module to get the action (i.e., recommending items or asking attributes) for guiding the next round conversation. Experimental results show that, compared with the state-of-the-art existing methods, the proposed method has a significant improvement in terms of major evaluation metrics over four real-world datasets.
Variable-length sequence model for attribute detection in the image

Holistic scene understanding is a challenging problem in computer vision. Most recent researches in this field were focusing on the object detection, the semantic segmentation and the relationship detection tasks. The attribute can provide meaningful information for the object instance, thus the object instance can be expressed more detail in the scene understanding. However, most researches in this field have been limited to several special conditions. Such as, several researches were just focusing on the attribute of special object class, because their solutions were aimed at a limited-scenarios, their methods are hardly to generalize in other scenarios. We also find that most of the research for multi-attribute detection task were only regarding each attribute as binary class and simply use the multi-binary-classifier method for the attribute detection. But these strategies above not consider the relation between each pair of the attributes, they will fall into trouble in the “imperfect” attribute dataset (which is labeled with the missing and incomplete annotations), and they will have low performance in the long-tail attribute class (which has lower rank of annotation and more missing labels). In this paper, we focus on the multi-attribute detection for a variant of object classes and take the relation between attributes into consideration. We propose a GRU-based model to detect a variable-length attribute sequence with a customized loss compute method to solve the “imperfect” attribute dataset problem. Furthermore, we perform ablative studies to prove the effectiveness of each part of our method. Finally, we compare our model with several existed multi-attribute detection methods on VG (Visual Genome) and CUB200 bird datasets to prove the superior performance of the proposed model.
A Comparative Study of Prepositions in English-Chinese Translation

Prepositions can be regarded as virtual words, which are used frequently in English and Chinese, flexible in application and changeable in meaning, which are used to express the relationship between words and sentences, affect whether the target language readers can effectively understand the original text, and play an important role in English-Chinese translation. By comparing the differences between English and Chinese prepositions, this paper discusses the classification of English and Chinese prepositions from different angles, analyzes the diversity of prepositions based on specific translation examples, and studies the five translation methods of English preposition equivalence, omission, addition, conversion and reverse translation to accurately convey information, hoping to learn and progress together with the majority of readers and translators.
Interpretation of the “Research Results Report on the 2021 King Sejong Institute Non-Face-to-Face Education Operation Consulting” and its Implications for Online Confucius Institutes

In 2019, the spread of the COVID-19 pandemic affected the operations of many educational institutions around the world, with some institutions being forced to close or suspend classes. In this situation, the King Sejong Institute Foundation in South Korea actively provided support for the operation of King Sejong Institute, and the Online King Sejong Institute was launched and expanded ahead of schedule. In November 2021, the King Sejong Institute Foundation issued a tender announcement, investing 100,000,000 won (including VAT) in a tender titled “2021 King Sejong Institute Non-Face-to-Face Education Status Survey and Education Operation Consulting”, with the aim of providing professional consulting support for non-face-to-face education operations to solve local difficulties and lay the foundation for future development of non-face-to-face education. In August 2022, the online informatization team of the King Sejong Foundation released the “Research Results Report on the 2021 King Sejong Institute Non-Face-to-Face Education Operation Consulting Project”, comprehensively showing the background, development process and research results of the non-face-to-face education operation consulting tool of King Sejong Institute. This article will introduce the background of the “Report” and sort out and interpret its content in order to provide inspiration and reference for Chinese online Confucius Institutes in their online operations in the post-pandemic era.
Countermeasures on Classroom Teaching Quality of Young Teachers in Higher Vocational Colleges from The Perspective of Teaching Supervision and Evaluation

In recent years, with the rapid development of higher education in China, more and more outstanding young teachers with master's degree, doctor's degree and professional talents come to the platform of higher vocational colleges every year. Young teachers have become the backbone of the teachers of higher vocational colleges. Wenzhou Polytechnic (hereinafter referred to as the "School"), as a national demonstration higher vocational college, has deeply implemented the strategy of "strengthening the school with talents" and recruited talents widely. However, among these young teachers, there are some problems, such as non-normal major, short teaching time, not rich teaching experience and weak scientific research ability. Therefore, how to improve the classroom teaching quality of young teachers has become a problem that higher vocational colleges must think about. Based on the problems existing in the classroom teaching quality of young teachers from the perspective of school teaching supervision and course evaluation, this paper analyzes and proposes countermeasures from the aspects of teaching plan preparation, teaching link, ideological and political integration, classroom management, etc., in order to improve the level and quality of classroom teaching of young teachers, boost the high quality development of vocational education in schools and cultivate high-level technical skills talents.
Study on The Form of Reservation of Food Orders in Campus Canteens in The Post-epidemic Era

In order to reduce the pressure of dining in the school canteens and to facilitate the purchase of meals for students, the use of information technology to achieve the reservation of meals and delivery services in the school canteens has become a key issue. Based on this situation, we designed a canteen ordering system using HTML, CSS and JavaScript, combined with a MySQL database and a WeChat applet as the basic platform. The system combines the offline campus cafeteria with the mobile Internet terminal, thus realising the basic function of ordering food in the canteen. The system mainly contains a user ordering module and a merchant management module. Users can view dishes, order and evaluate them; merchants can.
Synchronous spatio-temporal signature verification via Fusion Triplet Supervised Network

Handwritten signature has been regarded as one of the most accepted and practical means of person verification since ancient times. Unlike existing methods to trace the signer only considering a single spatial or temporal information, in this paper, we propose to combine two forms of signature data (stroke images and sensor signals) to realize person verification using the novel Fusion Triplet Supervised Network (fuseTSN). First, the Electronic Handwritten Signature Acquisition System 1.0 (EHS-AS 1.0) connected to the Wacom tablet is developed to construct a synchronous static–dynamic Chinese signature dataset (SynCS) and to transform existing dynamic signature datasets into static–dynamic datasets. Then, the hybrid feature extractor (ResCNN-BiLSTM with attention) as the backbone is embedded into the fuseTSN model, mapping the images and time-series signal into a common hyperspace. Furthermore, the similarity of the input [Anchor, Positive, Test] is measured to produce the final output. Finally, the proposed fuseTSN is tested on three public datasets and also evaluated on our SynCS dataset comprised of 24000 unique triplet label samples. The results show that the accuracy of the fuseTSN model is at least 5.01% higher than that of a unimodal system with only stroke image or sensor data on the SynCS dataset and outperforms the current state-of-the-art multimodal methods for writer-independence (WI) verification on public datasets. In addition, we present the fuseTSN’s reliable judgment basis for spatial strokes in the heat-map and further find that the combination of time attributes (OA and OE) can significantly improve the model performance.
On the existence of balanced generalized de Bruijn sequences

A balanced generalized de Bruijn sequence with parameters (n,l,k) is a cyclic sequence of n bits such that (a) the number of 0's equals the number of 1's, and (b) each substring of length l occurs at most k times. We determine necessary and sufficient conditions on n,l, and k for the existence of such a sequence.
A kernel Stein test for comparing latent variable models

We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the marginal distribution of the observed variables is intractable. The proposed test generalizes the recently proposed kernel Stein discrepancy (KSD) tests (Liu et al., 2016, Chwialkowski et al., 2016, Yang et al., 2018) to the case of latent variable models, a much more general class than the fully observed models treated previously. The new test, with a properly calibrated threshold, has a well-controlled type-I error. In the case of certain models with low-dimensional latent structure and high-dimensional observations, our test significantly outperforms the relative Maximum Mean Discrepancy test, which is based on samples from the models and does not exploit the latent structure.
A complementary and contrastive network for stimulus segmentation and generalization

Existing convolutional neural networks (CNNs) have achieved remarkable performance in medical image segmentation tasks. However, they still fail to generalize well to unseen datasets due to the limited size and diversity of training data as well as distribution shifts. Meanwhile, CNN-based methods have inherent limitations in capturing global contexts and suffer semantic dilution issues in the decoder stage, which leads to suboptimal predictions especially under low inter-class discrepancy and complex backgrounds. In this paper, we propose a novel framework named CCNet that learns complementary and contrastive features for accurate segmentation. Firstly, a novel complementary feature extraction module is formulated to learn global–local features by coordinating Transformer and CNN-style parallel branches. Secondly, a global context refinement module is constructed to adaptively generate a set of layer-specific global maps, so as to remedy semantic dilution. Thirdly, a mutual attentive module is designed to alleviate background confusion, in which contrastive cues are mutually captured from the foreground and background view by cascaded dual attention blocks. Moreover, we implement synthetic data augmentation to deal with training data scarcity and distribution shifts, thereby improving the out-of-distribution generalization of our model. Extensive experiments demonstrate that our CCNet achieves outstanding performance in polyp, skin lesion, and nuclei segmentation tasks, outperforming the state-of-the-arts.
Two-dimensional Fibonacci words: Tandem repeats and factor complexity

If x is a non-empty string then the repetition xx is called a tandem repeat. Similarly, a tandem in a two-dimensional array X is a configuration consisting of copies of a same primitive block W that touch each other with one side or a corner. In [6], Apostolico and Brimkov have proved various bounds for the number of tandems in a two-dimensional word of size m×n. Of the two types of tandems considered therein, they also proved that, for one type, the number of occurrences in an m×n Fibonacci array attained the general upper bound, O(m2nlogn). In this paper, we derive an expression for the exact number of tandems in a given finite Fibonacci array fm,n. As a required result, we derive the factor complexities of fm,n, m,n≥0 and that of the infinite Fibonacci word f∞,∞. Generations of f∞,∞ and fm,n, for any given m,n≥1 using a two-dimensional homomorphism are also achieved.
Spatial multifractal spectrum distribution method for breast ultrasonic image classification

The point-wise multifractal spectrum (MFS) of the ultrasonic image in the time-frequency domain is studied in this paper. With the theoretical derivation, the spatial multifractal spectrum (SMFS) distribution, as an extension of the conventional MFS, is proposed to analyze and characterize the spatial multiscale multifractal feature. Furthermore, the homogeneous region statistical MFS (HRS-MFS) with respect to different physical regions and the singularity-exponent-domain statistical multi-resolution (SSMR) feature image can be extracted from the renormalized SMFS data cube. The SMFS is rigorously derived from the two-dimensional multifractal spectrum and the Pseudo Wigner-Ville distribution (PWVD). In addition, both the SSMR feature images based on the SMFS and the singularity exponent of the 2D-PWVD is proved to be the SNR independence in the Gaussian white noise (GWN) background, which ensures the robust estimation of SMFS features of images under low SNR conditions. Furthermore, the HRS-MFS and the SSMR feature images of the breast ultrasound images (BUSI) are extracted to discriminate the specificity of the tumor regions. Then, a novel BUSI classification method based on the SMFS and deep learning network is proposed and tested on the public BUSI datasets. The experiment results indicate that the SMFS method can significantly reduce the risk of the ‘intermediate effects’. In fact, the classification accuracy of the original deep network is considerately improved by 9.3 % with the SMFS method, and 94.8 % classification accuracy is achieved, which is superior by 3.3 % to the state-of-art method.
Human Motion Recognition in Dance Video Images Based on Attitude Estimation

With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.
A Study on Influence of local project based learning on high school students' awareness of local and career path determinant factors

The purpose of this study is to elucidate the influence of local project based learning on high school students' awareness of the local. In the future, the purpose is to examine the direction of local project based learning that will be a factor in determining the career path of high school students in local cities. In this study, a questionnaire survey was conducted on the survey subjects who learned local project based learning. A multivariate analysis was performed to clarify the structure of awareness from learning evaluation to the change in awareness to the local and the factors that determine career paths. The results are as follows：1) As the results of analysis about evaluation of local project based learning, two factors were extracted: community connection and self-actualization, and self-directed learning and discussion. 2) As the results of analysis about awareness in students about locality, three factors were extracted: self-understanding and willingness to act, personal future vision, and interest in the local. 3) As the results of analysis about career path determinants, five factors were extracted: local orientation, external factors, learning orientation, self-orientation, and convenience. 4) It is important to have a dialogue with local people as a direction of local project based learning.
The experience of introducing telepathology in Mongolia

Anatomical pathology care services play an essential role in cancer diagnosis through histological analysis, effective treatment of patients, and determination of prognosis. Therefore, quality control is necessary for the diagnosis of pathology. Based on this need, telepathology technology is rapidly developing in the world. This study aimed to share the experience of implementing telepathology case consultation between Mongolian and Japanese expert pathologists.The study included 173 cases that required telepathology consultation, which was complicated and doubtful in diagnosis, submitted by Mongolian pathologists between May 2019 and April 2022. The scanned digital slides were transmitted with the help of the LOOKREC cloud-based system, and the expert pathologists of Hiroshima University Hospital, Japan, browsed the images through the data on the internet and their advice and made a mutual diagnosis.During the study period, 173 cases were consulted. Out of 58.4% of all cases, consultation reports were released in 2022. The majority of the cases in 2020 had a mean standard deviation turn-around time of 4.2±6.2 days. The most cases were from the lung and mediastinum were 29.4%, followed by head and neck at 12.6%, the bone at 11.9%, lymph nodes at 8.4%, GIT at 7.7%, soft tissues at 6.3%, etc. Comparing the sample submission of biopsy and cytology was significantly higher in the under 10 years of an experienced group than over 10 years of an experienced group (p<.005). The diagnostic agreement between submitter Mongolian pathologists and expert Japanese pathologists was 82.7%, and disagreement was 17.3% of all cases, with a sensitivity of 67.3% and specificity of 85.5%.Telepathology could save many lost opportunities and play an essential role in developing quality control and surgical pathology in Mongolia. This digital technology and the appropriate strategy and policy of the government could accelerate the overall pathology field development.
A reliable region information driven kriging-assisted multiobjective rough fuzzy clustering algorithm for color image segmentation

Multiobjective clustering algorithms (MOCAs) are becoming increasingly popular with the merit of segmenting images from multiple perspectives. The performances of MOCAs highly depend on their fitness functions. However, most existing MOCAs adopt one pair of complementary fitness functions, which always measure intra-class compactness and inter-class separation, respectively. This may result in insufficient ability to recognize and mine feature structures from complex images. Moreover, information within color images, such as region information and uncertain information, can barely receive enough attention in MOCAs. To resolve these problems, we propose a reliable region information driven Kriging-assisted multiobjective rough fuzzy clustering algorithm (RRI-KMRFC). Firstly, a reliability-based region information extraction strategy (RRIES) is designed to obtain reliable image information with satisfactory regional homogeneity and abundant image details. Secondly, the derived region information is used to construct three complementary fitness functions, focusing on rough intra-class compactness, rough inter-class separation, and regional consistency, respectively. Such fitness functions can effectively identify the clustering structure, maintain contour details, and characterize uncertain information from color images. To efficiently optimize the proposed functions, an incremental Kriging-assisted evolutionary framework is presented to decrease the expensive function evaluations in which an improved infill sampling strategy is devised to assist in finding unexplored areas in the decision space. Finally, a rough fuzzy clustering validity index with reliable region information is proposed to select the optimal trade-off solution. Experiments performed on Berkeley and Weizmann images confirm the effectiveness and robustness of RRI-KMRFC.
A new lattice-based online/offline signatures framework for low-power devices

Lattice-based online/offline signature scheme enjoys the advantage of resisting quantum feature besides the short online response time. The existing Fiat-Shamir with aborts paradigm candidates do not have perfect online/offline property, and the hash-sign-switch ones usually increase the signature sizes. In this paper we mainly address its online time and the length of signature issues, and propose a new and efficient paradigm of its construction for lattice assumptions over rings. In this paradigm the signer tries to sign for a random string and the message respectively. Specifically, we first discuss the generic construction of Fiat-Shamir lattice-based signatures. Then for a large class of lattice-based relations over rings, we propose an improved framework designed for low-power devices. This new framework allows the signer to extract a part of the signature only for a random string chosen in offline time, and thus can obtain the whole signature by less online time. In addition, the resulting scheme slightly reduces the signature size compared to the Fiat-Shamir counterparts. The performance analysis shows that our construction is efficient.