Hidden Two-Stream Collaborative Learning Network for Action Recognition

The two-stream convolutional neural network exhibits excellent performance in the video action recognition.The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames,to train a model each,and to finally integrate the outputs of the two models.Nevertheless,the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition,and the temporal and the spatial streams are just simply fused at the ends,with one stream failing and the other stream succeeding.We propose a novel hidden two-stream collaborative(HTSC)learning network that masks the steps of extracting the optical flow in the network and greatly speeds up the action recognition.Based on the two-stream method,the two-stream collaborative learning model captures the interaction of the temporal and spatial features to greatly enhance the accuracy of recognition.Our proposed method is highly capable of achieving the balance of efficiency and precision on large-scale video action recognition datasets.

A Sentinel-Based Peer Assessment Mechanism for Collaborative Learning

This paper introduces a novel mechanism to improve the performance of peer assessment for collaborative learning.Firstly,a small set of assignments which have being pre-scored by the teacher impartially,are introduced as“sentinels”.The reliability of a reviewer can be estimated by the deviation between the sentinels’scores judged by the reviewers and the impartial scores.Through filtering the inferior reviewers by the reliability,each score can then be subjected into mean value correction and standard deviation correction processes sequentially.Then the optimized mutual score which mitigated the influence of the subjective differences of the reviewers are obtained.We perform our experiments on 200 learners.They are asked to submit their assignments and review each other.In the experiments,the sentinel-based mechanism is compared with several other baseline algorithms.It proves that the proposed mechanism can effectively improve the accuracy of peer assessment,and promote the development of collaborative learning.

An Investigation of a Collaborative Learning Framework in Classroom Teaching——an action research

This essay which is dedicated into doing action research explores the difficulties in conducting the collaborative learning in a student-centered classroom teaching,mostly based on teachers’reflection.This essay adapts a framework of conducting,monitoring and evaluating collaborative learning into specific teaching context and points out research areas in the next step of action research.

Trustworthy decentralized collaborative learning for edge intelligence:A survey

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources.decentralized collaborative learning(DCL)is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data.DCL has a wide range of applications in various domains,such as smart city and autonomous driving.However,DCL faces significant challenges in ensuring its trustworthiness,as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality,undermine learning reliability or violate data privacy.Therefore,it is crucial to design DCL in a trustworthy manner,with a focus on security,robustness,and privacy.In this survey,we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects:security,robustness,and privacy.We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of trustworthy DCL(TDCL).Finally,we highlight open challenges and future directions for advancing TDCL research and practice.

Using Agents in Web-Based Constructivist Collaborative Learning System

Web-based learning systems are one of the most interesting topics in the area of the application of computers to education. Collaborative learning, as an important principle in constructivist learning theory, is an important instruction mode for open and distance learning systems. Through collaborative learning, students can greatly improve their creativity, exploration capability, and social cooperation. This paper used an agent-based coordination mechanism to respond to the requirements of an efficient and motivating learn-ing process. This coordination mechanism is based on a Web-based constructivist collaborative learning system, in which students can learn in groups and interact with each other by several kinds of communica-tion modes to achieve their learning objectives efficiently and actively. In this learning system, artificial agents represent an active part in the collaborative learning process; they can partially replace human in-structors during the multi-mode interaction of the students.

Collaborative Learning Method for Natural Image Captioning

We propose a collaborative learning method to solve the natural image captioning problem.Numerous existing methods use pretrained image classification CNNs to obtain feature representations for image caption generation,which ignores the gap in image feature representations between different computer vision tasks.To address this problem,our method aims to utilize the similarity between image caption and pix-to-pix inverting tasks to ease the feature representation gap.Specifically,our framework consists of two modules:1)The pix2pix module(P2PM),which has a share learning feature extractor to extract feature representations and a U-net architecture to encode the image to latent code and then decodes them to the original image.2)The natural language generation module(NLGM)generates descriptions from feature representations extracted by P2PM.Consequently,the feature representations and generated image captions are improved during the collaborative learning process.The experimental results on the MSCOCO 2017 dataset prove the effectiveness of our approach compared to other comparison methods.

Operation and Consideration of a Pipe Corrosion Inspection System Based on Human-in-the-Loop Machine Learning

The aim of this study is to improve the efficiency of external corrosion inspection of pipes in chemical plants.Currently,the preferred method involves manual inspection of images of corroded pipes;however,this places significant workload on human experts owing to the large number of required images.Furthermore,visual assessment of corrosion levels is prone to subjective errors.To address these issues,we developed an AI(artificial intelligence)-based corrosion-diagnosis system(AI corrosion-diagnosis system)and implemented it in a factory.The proposed system architecture was based on HITL(human-in-the-loop)ML(machine learning)[1].To overcome the difficulty of developing a highly accurate ML model during the PoC(proof-of-concept)stage,the system relies on cooperation between humans and the ML model,utilizing human expertise during operation.For instance,if the accuracy of the ML model was initially 60%during the development stage,a cooperative approach would be adopted during the operational stage,with humans supplementing the remaining 40%accuracy.The implemented system’s ML model achieved a recall rate of approximately 70%.The system’s implementation not only contributed to the efficiency of operations by supporting diagnosis through the ML model but also facilitated the transition to systematic data management,resulting in an overall workload reduction of approximately 50%.The operation based on HITL was demonstrated to be a crucial element for achieving efficient system operation through the collaboration of humans and ML models,even when the initial accuracy of the ML model was low.Future efforts will focus on improving the detection of corrosion at elevated locations by considering using video cameras to capture pipe images.The goal is to reduce the workload for inspectors and enhance the quality of inspections by identifying corrosion locations using ML models.

Solving the Pedagogical Dilemma by Implementing Collaborative Leaning Mode in College ELT:Peer Interaction and Group Cooperation

This paper mainly seeks to discuss what factors contribute to students' passive involvement in English classes. It also explores whether it would be possible to solve the above pedagogical dilemmas through implementing collaborative learning mode in ESL. After analyzing the data collected from case study, classroom observation and post-hoc interviews, researcher discovers that collaborative learning mode exerts a considerable positive influence on reducing students' learning anxiety and rebuilding their self-confidence. In addition, peer interaction and group collaboration are highly motivating and effective for ESL learners at college level.

Federated Learning on Multimodal Data:A Comprehensive Survey

With the growing awareness of data privacy,federated learning(FL)has gained increasing attention in recent years as a major paradigm for training models with privacy protection in mind,which allows building models in a collaborative but private way without exchanging data.However,most FL clients are currently unimodal.With the rise of edge computing,various types of sensors and wearable devices generate a large amount of data from different modalities,which has inspired research efforts in multimodal federated learning(MMFL).In this survey,we explore the area of MMFL to address the fundamental challenges of FL on multimodal data.First,we analyse the key motivations for MMFL.Second,the currently proposed MMFL methods are technically classified according to the modality distributions and modality annotations in MMFL.Then,we discuss the datasets and application scenarios of MMFL.Finally,we highlight the limitations and challenges of MMFL and provide insights and methods for future research.

A Critical Analysis of Group Work in Chinese University EFL Classes

In the past few years,group activities have been warmly welcomed by EFL(English as a foreign language)classes in Chi-na,but few studies have discussed it from a critical perspective.Thus,this study explores from students’experiences what and whythe strengths and weaknesses of group work in actual contexts appear.The findings reveal that Chinese learners regard group workas an effective interactive activity for language,personal,and emotional development.However,the possible pitfalls,such as pseu-dogroups,unequal participation,groupthink,L1(first language)influence may prevent learners from obtaining the benefits of CL(collaborative learning).Finally,some suggestions are given,with the purpose of creating a more suitable facilitative EFL classroomenvironment for future Chinese EFL teaching and learning.

Collaborative eye tracking based code review through real-time shared gaze visualization

Code review is intended to find bugs in early development phases,improving code quality for later integration and testing.However,due to the lack of experience with algorithm design,or software development,individual novice programmers face challenges while reviewing code.In this paper,we utilize collaborative eye tracking to record the gaze data from multiple reviewers,and share the gaze visualization among them during the code review process.The visualizations,such as borders highlighting current reviewed code lines,transition lines connecting related reviewed code lines,reveal the visual attention about program functions that can facilitate understanding and bug tracing.This can help novice reviewers to make sense to confirm the potential bugs or avoid repeated reviewing of code,and potentially even help to improve reviewing skills.We built a prototype system,and conducted a user study with paired reviewers.The results showed that the shared real-time visualization allowed the reviewers to find bugs more efficiently.

IN2CLOUD: A novel concept for collaborative management of big railway data

In the EU Horizon 2020 Shift2Rail MultiAnnual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called"IN2CLOUD," which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other(including domain knowledge and experience)but do not want to share their raw data or information.IN2CLOUD will help the movement of railway industry systems from "local" to "global" optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology(IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.

The Organizer Dilemma: Outcomes from a Collaboration Exercise

In crisis management, cross-sector collaboration exercises are perceived as improving preparedness and develop team-integration efforts. However, studies show that exercises may tend to produce results with limited learning and usefulness. The purpose of this nonexperimental, survey-based study was to measure the difference in perceived exercise effect between participants belonging to the exercise planning organizations and participants belonging to other participating groups. Surveys were distributed and collected from participants in a 2017 chemical oil-spill exercise set off the southern coast of Norway. The target population was operational staff,excluding exercise management and directing staff. The sample population consisted of operatives associated with the exercise organizer organization and others belonging to external public and nongovernmental emergency organizations. The data collection instrument was the 'Collaboration, Learning, and Utility Scale’’(CLU-scale). Findings indicated that the levels of CLU were higher among external participants than among those individuals who belong to the exercise planning organizations. This study recommends the development and adoption of a national maritime collaboration exercise framework. A practical implication is a recommendation to evaluate exercises to secure the outcome regarding collaboration skill using the same instrument.