Reviewerly/exHarmony huggingface dataset:
"REFLEXIVITY AND EMOTIONS IN QUALITATIVE FIELDWORK WITH HEROIN USERS"

Motivation for the paper: Doing qualitative research fieldwork implies some ethical and personal issues to take in account such as reflexivity and emotions.Objectives: Describe and analyze challenges in managing emotions while researching hidden populations with substance abuse problems.What was done: Results come from an ethnographic study with heroin male and female patients attending a methadone clinic.How it was done and validated: 100 open-ended qualitative interviews were done with the aim of reaching a subjective approach related to the onset of heroin consumption and the eventual assistance to the clinical space.Thematic analysis was use and saturation validity was obtained.
Research on the Application of Software Engineering in the Construction of Enterprise Information Platforms

With the rapid development of our country's economy and science and technology, all kinds of enterprises are constantly looking for more effective technology strategies to improve their product quality.It can be said that the birth of this information system for the company to provide a good solution.In the process of building enterprise informationization, if it can be well applied, it will have a great impact on the construction results.Based on this point of view, this paper analyzes the application of software engineering technology in our country.
The Orientation and Enlightenment of Tiktok on Ideological and Political Education of College Students

With the upgrading and iteration of Internet technology and the rapid development of social media, Tiktok, as the representative of personal media platform, is deeply loved by contemporary college students.At the same time, only when "Tiktok" endows ideological and political education with the function of making it the flow entrance to spread mainstream values, can it give correct education and guidance to college students and better help them grow and become talents.In the information age, the high utilization rate of "Tiktok" among college students makes college students become a huge user group of "Tiktok".Spreading the mainstream value in "Tiktok" can make college students accept it imperceptibly.The use of "Tiktok" to carry out ideological and political education is a development beyond the main channel and main position of ideological and political education classroom teaching, which fills the blank field of ideological and political education for college students."Tiktok" can realize the organic combination of text, music and video, and the information in the video is highly concentrated, targeted and oriented, which has a more visual, intuitive and three-dimensional performance effect for ideological and political education.
An Application of Mind Mapping to English Vocabulary Teaching in Senior High Schools

In the traditional vocabulary teaching, only a simple introduction of the meaning and usage of words is boring in class, and only a simple memory after class.Such a cycle will lead to the weakening of students' interest in learning.Students think that learning English is just memorizing words, and there is no fun at all.This is the problem existing in the current English vocabulary teaching.The application of mind mapping in vocabulary teaching can solve this problem.In vocabulary teaching, the presentation of mind map can make students learn to think， so that students will be deeply impressed with the knowledge points they have learned.As a new teaching method, it is of great significance to apply mind mapping in English vocabulary teaching.It can cultivate students' interest in learning English, improve their attention and thinking ability.However, when applying mind mapping to teach students knowledge, teachers should also realize that mind mapping is not omnipotent.Teachers should learn to apply mind mapping scientifically and reasonably in teaching.
Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images Based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge

Registration of brain scans with pathologies is difficult, yet important research area. The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences. The organizers introduced the task of aligning pre-operative to follow-up magnetic resonance images of glioma. The main difficulties are connected with the missing data leading to large, nonrigid, and noninvertible deformations. In this work, we describe our contributions to both the editions of the BraTS-Reg challenge. The proposed method is based on combined deep learning and instance optimization approaches. First, the instance optimization enriches the state-of-the-art LapIRN method to improve the generalizability and fine-details preservation. Second, an additional objective function weighting is introduced, based on the inverse consistency. The proposed method is fully unsupervised and exhibits high registration quality and robustness. The quantitative results on the external validation set are: (i) IEEE ISBI 2022 edition: 1.85, and 0.86, (ii) MICCAI 2022 edition: 1.71, and 0.86, in terms of the mean of median absolute error and robustness respectively. The method scored the 1st place during the IEEE ISBI 2022 version of the challenge and the 3rd place during the MICCAI 2022. Future work could transfer the inverse consistency-based weighting directly into the deep network training.
Research on the Cultivation of Aesthetic Ability in Music Aesthetic Education Practice Teaching in Colleges Based on Big Data

At the present stage, schools gradually pay attention to quality education, cultivating students' aesthetic ability as an important link of quality education in schools, naturally also attracting the attention of schools.Music teaching is an important course to enhance students' aesthetic ability.To carry out practical teaching of music, aesthetic education is of great significance to strengthen the construction of moral education and enhance students' aesthetic ability.Based on this, this paper studies the cultivation of students' aesthetic ability in the practice and teaching of music aesthetic education.Through the investigation of the current situation of aesthetic ability cultivation, the following conclusions are drawn: most students' musical aesthetic ability is low; there are 74.84%students' evaluation of music aesthetic teaching is average.After the investigation of the current situation of the cultivation of students' aesthetic ability, this paper puts forward the cultivation strategy of students' aesthetic ability by combining big data (BD), such as cultivating students' aesthetic interest, enhancing aesthetic understanding, carrying out diversified practical activities and evaluating the practical teaching of music aesthetic education.
Cultivation of Students' Craftsmanship Spirit in Vocational Physics Education: A Practical Study

Vocational physics education plays a crucial role in cultivating students' practical skills and craftsmanship spirit.This paper presents a practical study on fostering students' craftsmanship spirit in vocational physics education, exploring effective teaching methods and strategies.The research findings demonstrate that teaching measures such as emphasizing practical instruction, cultivating students' innovative thinking, and stimulating their interest in learning have a positive impact on nurturing students' craftsmanship spirit.The results of this study have certain reference value for the practice of vocational physics education.
Multi-step actor-critic framework for reinforcement learning in continuous control

Continuous control is an important issue in control theory.It controls an agent to take action in continuous spaces for transiting from one state to another until achieving the desired goal.A useful tool for this issue is the reinforcement learning where an optimal policy is learned for the agent by maximizing the cumulative reward of the state transitions.However, most existing reinforcement learning methods consider only the one-step transition and one-step reward in each state.In this case, it is hard to recognize the information hidden in the sequence of the previous states and accurately estimate the cumulative reward.Therefore, these methods cannot learn the optimal policy both fast and effectively for continuous control.To solve this problem, in this paper, we propose a new framework, called Multi-step Actor-critic Framework (MAF) for reinforcement learning.In MAF, the convolutional deterministic policy is used to learn the information hidden in the sequence of the previous states by convolutional neural networks, and then n-step temporal difference learning is used to accurately estimate the cumulative reward by considering the rewards from n-step states.Based on an effective reinforcement learning method, TD3, the implementation of our MAF is in nTD3.The theoretical analysis and experiment illustrate that our nTD3 can learn the policy not only better but also faster than the existing RL methods for continuous control.
Towards a Scalable Digital Skills Training Architecture for Resource-Constrained Environments: The Case of Ayitic Goes Global in Haiti

In developing countries such as Haiti, which are marked by high unemployment and gender inequality, online education has the potential to change lives. Returns on education are particularly high in Information Communications Technology (ICT)-intensive jobs and IT outsourcing offers opportunities for remote employment, providing alternatives for economic diversification and job creation that are particularly relevant for youth and women. However, the problem faced by many developing countries, is that traditional models, frameworks, architectures, and platforms for online learning do not lend themselves well to their context and, therefore, it is important to develop context-specific platforms. This need for suitable platforms has motivated the research question that this paper seeks to address, that is: What is the appropriate architecture that supports learning strategies for delivering scalable digital skills training in a resource-constrained environment? We propose an architecture that was developed specifically for blended learning in resource-constrained environments and describe how a prototype for this was designed, built, and deployed in Haiti. The initial responses to the application of the architecture, including the testimonials of the participants and the interest expressed by other countries of the region in adopting the proposed architecture, have been extremely positive.
Improving News Recommendation via Bottlenecked Multi-task Pre-training

Recent years have witnessed the boom of deep neural networks in online news recommendation service. As news articles mainly consist of textual content, pre-trained language models~(PLMs) (e.g. BERT) have been widely adopted as the backbone to encode them into news embeddings, which would be utilized to generate the user representations or perform the semantic matching. However, existing PLMs are mostly pre-trained on large-scale general corpus, and have not been specially adapted for capturing the rich information within news articles. Therefore, their produced news embeddings may be not informative enough to represent the news content or characterize the relations among news. To solve it, we propose a bottlenecked multi-task pre-training approach, which relies on an information-bottleneck encoder-decoder architecture to compress the useful semantic information into the news embedding. Concretely, we design three pre-training tasks, to enforce the news embedding to recover the news contents of itself, its frequently oc-occurring neighbours, and the news with similar topics. We conduct experiments on the MIND dataset and show that our approach can outperform competitive pre-training methods.
Bootstrapping Query Suggestions in Spotify's Instant Search System

Instant search systems present results to the user at every keystroke. This type of search system works best when the query ambiguity is low, the catalog is limited, and users know what they are looking for. However, Spotify's catalog is large and diverse, leading some users to struggle when formulating search intents. Query suggestions can be a powerful tool that helps users to express intents and explore content from the long-tail of the catalog. In this paper, we explain how we introduce query suggestions in Spotify's instant search system--a system that connects hundreds of millions of users with billions of items in our audio catalog. Specifically, we describe how we: (1) generate query suggestions from instant search logs, which largely contains in-complete prefix queries that cannot be directly applied as suggestions; (2) experiment with the generated suggestions in a specific UI feature, Related Searches; and (3) develop new metrics to measure whether the feature helps users to express search intent and formulate exploratory queries.
Development of science classes that introduce cutting-edge science and technology

본 연구의 목적은 가까운 미래에 활용될 것으로 예상되는 드론과 MBL 및 BYB와 같은 첨단 과학기술이 도입된 실험기자재를 활용한 과학교육 콘텐츠를 개발하고 그 가능성을 타진해 보는 것이다. 본 연구에는 사범대학교 3학년 학생 14명이 참가했다. 본 연구 결과는 다음과 같다. 첫째, 드론과 MBL, BYB를 활용한 수업은 2015개정 과학과 교육과정에서 이런 첨단 과학기기를 활용하여 적용할 수 있는 부분이 많이 있지만 모든 탐구활동을 첨단 과학기기를 활용해서 탐구활동을 할 필요는 없다. 둘째, 본 연구에서 활용한 첨단 과학기기는 각각의 장점과 한계가 분명히 있다. 첨단 과학기기를 활용한 수업은 아직 콘텐츠가 많지 않기 때문에 시범적으로 활용되는 경우가 많으며 아직 교사와 학생들에게도 생소한 실험 장비로 느껴지고 있다. 셋째, 본 연구에 참여한 예비교사들은 첨단 과학기기의 사용에 대해 긍정적인 생각을 가지고 있었으며 앞으로 현장 교사가 되었을 때 이러한 첨단 과학기기의 활용이 일상화 될 것으로 생각하면서 이러한 경험을 소중하게 생각하고 있었다. 이와 같이 첨단 과학기기를 활용한 수업은 긍정적인 측면을 많이 포함하고 있지만 모든 측면에서 전통적인 실험을 대체해야 한다는 생각을 재고할 필요가 있다. 즉, 긍정적인 측면과 부정적인 측면이 있기 때문에 수업에서는 수업 목표 달성에 필요한 긍정적인 측면을 찾아 첨단 과학기기 혹은 전통적인 실험 빙밥을 선택해야할 것이다.The purpose of this study is to develop science education contents using the experimental equipment equipped with cutting-edge science technologies such as drones, MBL and BYB, and to explore their possibilities in science classroom. In this study, 14 pre-science teachers in the third year of the College of Education participated. The results of this study are as follows; First, classes using drones, MBL, and BYB have many parts that can be applied using these advanced scientific devices in the 2015 revised science curriculum, but it is not necessary to conduct all inquiry activities using advanced scientific experimental devices. Second, the advanced scientific instruments used in this study clearly have their own strengths and limitations. Classes using state-of-the-art scientific devices are often used on a trial basis because there are not many contents yet, and they are still felt as unfamiliar experimental equipment to teachers and students. Third, pre-service teachers who participated in this study had positive thoughts about the use of high-tech scientific equipment, and valued this experience, thinking that the use of such high-tech scientific equipment would be commonplace when they became field teachers in the future. Classes using advanced scientific equipment like this have many positive aspects, but you may think that you need to reconsider the idea that traditional experiments should be replaced in all aspects. In other words, since there are positive and negative aspects, the class will have to find the positive aspect necessary to achieve the class goal and choose the high-tech scientific device or the traditional experimental device.
Mixup-based Unified Framework to Overcome Gender Bias Resurgence

Unwanted social biases are usually encoded in pretrained language models (PLMs). Recent efforts are devoted to mitigating intrinsic bias encoded in PLMs. However, the separate fine-tuning on applications is detrimental to intrinsic debiasing. A bias resurgence issue arises when fine-tuning the debiased PLMs on downstream tasks. To eliminate undesired stereotyped associations in PLMs during fine-tuning, we present a mixup-based framework Mix-Debias from a new unified perspective, which directly combines debiasing PLMs with fine-tuning applications. The key to Mix-Debias is applying mixup-based linear interpolation on counterfactually augmented downstream datasets, with expanded pairs from external corpora. Besides, we devised an alignment regularizer to ensure original augmented pairs and gender-balanced counterparts are spatially closer. Experimental results show that Mix-Debias can reduce biases in PLMs while maintaining a promising performance in applications.
A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System

Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). It consists of two basic modules including 1) General Ranking Model (GRM), which is model-agnostic and can be implemented as any ranking models; and 2) Popularity Debias Module (PDM), which estimates the impact of the competitiveness and popularity of candidate items on the CTR, by utilizing the feedback of cold-start users to re-weigh the loss in GRM. MDTF seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods.
Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Many recommender models have been proposed to investigate how users interact with items which are represented in diverse modalities. In real scenarios, different modalities reveal different aspects of item attributes and usually possess different importance to user purchase decisions. However, it is difficult for models to figure out users' true preference towards different modalities since there exists strong statistical correlation between different modalities. Even worse, the strong statistical correlation might mislead models to learn the spurious preference towards inconsequential modalities. As a result, when data (modal features) distribution shifts, the learned spurious preference might not guarantee to be as effective on inference as on the training set.
Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity

With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.
BKD: A Bridge-based Knowledge Distillation Method for Click-Through Rate Prediction

Prediction models for click-through rate (CTR) learn feature interactions underlying user behaviors, which are crucial in recommendation systems. Due to their size and complexity, existing approaches have a limited range of applications. In order to decrease inference delay, knowledge distillation techniques have been used in recommendation systems. Due to the student model's lower capacity, the knowledge distillation process is less effective when there is a significant difference in the complexity of the network architecture between the teacher model and the student model.
LibVQ: A Toolkit for Optimizing Vector Quantization and Efficient Neural Retrieval

Vector quantization is one of the critical techniques which enables dense retrieval for realtime applications. The recent study shows that vanilla vector quantization methods, like those implemented by FAISS [8], are lossy and prone to limited retrieval performances when large acceleration ratios are needed [14, 16, 18]. Besides, there have also been multiple algorithms which make the retriever and VQ better collaborated to alleviate such a loss. On top of these progresses, we develop LibVQ, which optimizes vector quantization for efficient dense retrieval. Our toolkit is highlighted for three advantages. 1. Effectiveness. The retrieval quality can be substantially improved over the vanilla implementations of VQ. 2. Simplicity. The optimization can be conducted in a lowcode fashion, and the optimization results can be easily loaded to ANN indexes to support downstream applications. 3. Universality. The optimization is agnostic to the embedding's learning process, and may accommodate different input conditions and ANN back-ends with little modification of the workflow. LibVQ may also support rich applications beyond dense retrieval, e.g., embedding compression, topic modeling, and de-duplication. In this demo, we provide comprehensive hand-on examples and evaluations for LibVQ. The toolkit is publicly released at: https://github.com/staoxiao/LibVQ/tree/demo.
Interactive Recommendation System for Meituan Waimai

As the largest local retail & instant delivery platform in China, Meituan Waimai has deployed a personalized recommender system on server and recommend nearby stores to users through APP homepage. To capture real-time intention of users and flexibly adjust the recommendation results on the homepage, we further add an interactive recommender system. The existing interactive recommender systems in the industry mainly capture intention of users based on their feedback on a specific UI of questions. However, we find that it will undermine use fluency and increase use complexity by rashly inserting a new question UI when users browse the homepage. Therefore, we develop an Embedded Interactive Recommender System (EIRS) that directly infers users' intention according to their click behaviors on the homepage and dynamically inserts a new recommendation result into the homepage1. To demonstrate the effectiveness of EIRS, we conduct systematic online A/B Tests, where click-through & conversion rate of the inserted EIRS result is 132% higher than that of the initial result on the homepage, and the overall gross merchandise volume is effectively enhanced by 0.43%.