Reviewerly/exHarmony huggingface dataset:
Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net

Cardiac segmentation and motion estimation are two important tasks for the assessment of cardiac structure and function. Studies have demonstrated deep learning segmentation methods considering the valuable dynamics of the heart have more robust and accurate segmentations than those treating each frame independently. The former methods require annotations of all frames for supervised training, while only end-systolic (ES) and end-diastolic (ED) frames are commonly labeled. The issue has been addressed by integrating motion estimation into the segmentation framework and generating annotations for unlabeled frames with the estimated motion. However, the current pair-wise registration method with the ED frame as the template image may result in inaccurate motion estimation for systolic frames. We therefore, propose to use a group-wise registration network where the template image is learned implicitly for optimal registration performance, with the assumption that more accurate motion estimation leads to improved segmentation performance. Specifically, a recurrent U-Net based network is employed for joint optimization of group-wise registration and segmentation of the left ventricle and myocardium, where the dynamic information is utilized for both tasks with the recurrent units. In addition, an enhancement mask covering the heart is generated with the segmentation masks, which is expected to improve the registration performance by focusing the motion estimation on the heart. Experimental results in a cardiac cine MRI dataset including normal subjects and patients show that the group-wise registration significantly outperforms the pair-wise registration which translates to more accurate segmentations. The effectiveness of the proposed enhancement mask is also demonstrated in an ablation study.
Improving PAI Online Learning Outcomes with Bookwidgets as a Media during the Covid-19 Pandemic

Islamic Religious Education (PAI) learning that conducted online during the Covid-19 pandemic had an impact on low learning outcomes. Organizing effective learning online was a challenge for Islamic Education teachers. After Islamic Education teachers learn and use Book widgets as a learning medium, the attendance and learning outcomes of students increase significantly. This study aims to determine the increase in attendance and learning outcomes of students in online Islamic Education learning specifically about fasting topic with the Book widgets learning media. The subjects in this study were 47 students of Islamic education and class VII-a and VII-e students, consisting of 22 male students and 25 female students. The classroom action research method was carried out in two cycles. Data collection techniques were carried out by writing techniques and interviews. The collected data were then analysed using descriptive analysis techniques by comparing the results between the initial data, cycle I and cycle II. The results of this study indicated a significant increase in attendance and learning outcomes of students. The attendance of students was 84% initially, to 96% in cycle I, and 100% in cycle II. The average learning outcomes of students in pre-cycle 76 became 87 in cycle I, and 96 in cycle II.
Fully Understanding Generic Objects: Modeling, Segmentation, and  Reconstruction

Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation.
Jigsaw Clustering for Unsupervised Visual Representation Learning

Unsupervised representation learning with contrastive learning achieved great success. This line of methods duplicate each training batch to construct contrastive pairs, making each training batch and its augmented version forwarded simultaneously and leading to additional computation. We propose a new jigsaw clustering pretext task in this paper, which only needs to forward each training batch itself, and reduces the training cost. Our method makes use of information from both intra- and inter-images, and outperforms previous single-batch based ones by a large margin. It is even comparable to the contrastive learning methods when only half of training batches are used. Our method indicates that multiple batches during training are not necessary, and opens the door for future research of single-batch unsupervised methods. Our models trained on ImageNet datasets achieve state-of-the-art results with linear classification, outperforming previous single-batch methods by 2.6%. Models transferred to COCO datasets outperform MoCo v2 by 0.4% with only half of the training batches. Our pretrained models outperform supervised ImageNet pretrained models on CIFAR-10 and CIFAR-100 datasets by 0.9% and 4.1% respectively. Code is available at https://github.com/Jia-Research-Lab/JigsawClustering
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal  Action Localization

The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foreground snippets or frames that contribute to the video-level classification task. This strategy frequently confuse context with the actual action, in the localization result. Separating action and context is a core problem for precise WS-TAL, but it is very challenging and has been largely ignored in the literature. In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization. It consists of two branches (i.e., the Foreground-Background branch and the Action-Context branch). The Foreground- Background branch first distinguishes foreground from background within the entire video while the Action-Context branch further separates the foreground as action and context. We associate video snippets with two latent components (i.e., a positive component and a negative component), and their different combinations can effectively characterize foreground, action and context. Furthermore, we introduce extended labels with auxiliary context categories to facilitate the learning of action-context separation. Experiments on THUMOS14 and ActivityNet v1.2/v1.3 datasets demonstrate the ACSNet outperforms existing state-of-the-art WS-TAL methods by a large margin.
Practical dynamic group signatures without knowledge extractors

A dynamic group signature ( $$\mathcal {DGS}$$ ) allows a user to generate a signature on behalf of a group, while preserving anonymity. Although many existing $$\mathcal {DGS}$$ schemes have been proposed in the random oracle model for achieving efficiency, their security proofs require knowledge extractors that cause loose security reductions. In this paper, we first propose a new practical $$\mathcal {DGS}$$ scheme whose security can be proven without knowledge extractors in the random oracle model. Moreover, our scheme can also be proven in the strong security model where an adversary is allowed to generate the group managers’ keys maliciously. The efficiency of our scheme is comparable to existing secure $$\mathcal {DGS}$$ schemes in the random oracle model using knowledge extractors. The security of our scheme is based on a new complexity assumption that is obtained by generalizing the Pointcheval–Sanders (PS) assumption. Although our generalized PS (GPS) assumption is interactive, we prove that, under the (2,1)-discrete logarithm assumption, the new GPS assumption holds in the algebraic group model.
Scalable probabilistic truss decomposition using central limit theorem and H-index

Truss decomposition is a popular notion of hierarchical dense substructures in graphs. In a nutshell, k-truss is the largest subgraph in which every edge is contained in at least k triangles. Truss decomposition aims to compute k-trusses for each possible value of k. There are many works that study truss decomposition in deterministic graphs. However, in probabilistic graphs, truss decomposition is significantly more challenging and has received much less attention; state-of-the-art approaches do not scale well to large probabilistic graphs. Finding the tail probabilities of the number of triangles that contain each edge is a critical challenge of those approaches. This is achieved using dynamic programming which has quadratic run-time and thus not scalable to real large networks which, quite commonly, can have edges contained in many triangles (in the millions). To address this challenge, we employ a special version of the Central Limit Theorem (CLT) to obtain the tail probabilities efficiently. Based on our CLT approach we propose a peeling algorithm for truss decomposition that scales to large probabilistic graphs and offers significant improvement over state-of-the-art. We also design a second method which progressively tightens the estimate of the truss value of each edge and is based on h-index computation. In contrast to our CLT-based approach, our h-index algorithm (1) is progressive by allowing the user to see near-results along the way, (2) does not sacrifice the exactness of final result, and (3) achieves all these while processing only one edge and its immediate neighbors at a time, thus resulting in smaller memory footprint. We perform extensive experiments to show the scalability of both of our proposed algorithms.
Implementing Face-To-Face Driver Approach to Teaching a Professionally Oriented Foreign Language to Non-linguistic Students

The paper is focused on one of the models of blended learning and its application in foreign language teaching. The Face-to-Face Driver model involves using various electronic resources, creating online courses as a backup tool in teaching a foreign language; at the same time, the main emphasis is placed on practical classroom activities. Electronic resources act as additional material for consolidating and expanding knowledge on a particular topic of face-to-face studies. Such course organization meets the requirements of teaching a professionally oriented foreign language, allowing students to master professional vocabulary in order to be engaged in professional communication. Implementing the Face-to-Face Driver model helps to eliminate such difficulties as multi-level groups and a limited number of classroom hours. The paper describes the design of the blended Business English course specifically created on the basis of this model for teaching undergraduate students majoring in economics; it also provides data on the experiment conducted in pilot study groups. The results of the final testing conducted in the pilot and control groups showed that the students receiving tuition under the Face-to-Face Driver model coped with the test more successfully than the students under traditional training, proving the efficiency of blended leaning compared to conventional methodology.
From Offline Learning to the Future: Subjective Assessment and Learning Outcomes when Using Different Formats

The article analyzes the perception and attitude of students of different generations and areas of training to different learning formats: online, offline and blended. The object of the study were students of three different faculties of a technical university. The subject of the study was a comparison of the assessments given by students to the characteristics of the learning process when conducting it in different formats: satisfaction with the educational results obtained, the manifestation of the state of monotony, the severity of the pleasure received, the information load. The academic performance of students in studying in different formats was analyzed separately. Hypotheses were tested regarding a more positive attitude of students to offline learning compared to online and mixed, as well as the absence of links between gender, age, direction of training and preference for a particular format. The results of the study showed that the marks of trainees practically do not change when changing formats, but subjectively trainees evaluate the learning process in an online format rather negatively: the pleasure from the learning process decreases, monotony increases, the degree of satisfaction with learning results decreases. The mixed learning format is subjectively perceived by the trainees more positively than online, and subjective assessments are more correlated with offline assessments. The conclusion is made about the formalization of the learning process during the transition from offline to online, about the difficulty of transferring and changing personal meanings, the formation of personal knowledge in an online format, which are the main criteria for learning. It is pointed out that it is necessary to search for a “formula” for blended learning and the development of learning technologies.
Online Platforms as a Resource for Teaching Professional Terminology

The article discusses principles of using online platforms in terminology learning while teaching English for professional purposes, e.g. English for power engineering specialties. The main goal of the work is to consider opportunities provided by the Internet platform Proprofs.com considered as a learning tool allowing teachers to replace the routine process of mechanical memorization of professional terms by creative work with the game elements. The application of inductive method (eliciting students’ answers, selection and analysis of the teaching material starting from the elementary and continuing with more complicated one) makes it possible to show the variety of techniques used in online platforms, and significant motivating potential of the learning resource. The research experiment included both ascertaining and formative assessment. The respondents in experimental and control groups were from IT department. Our experiment presupposed application of Proprofs.com in experimental group for the purposes of learning specific terms related to IT sphere, whereas in control group the traditional teacher-centered methods were used. Six vocabulary games designed by the author have been placed on the platform Proprofs.com. To sum up, educational games can be beneficial for both students and teachers. In gamification, rules and rewards can stick to higher school programs; also, vocabulary games (the ready-made ones, and created personally by teachers) can be used during the lesson as well as for homework and for students’ self-study, and the application of those platforms is not a time-consuming process. One of the benefits of online platforms is a variety of brain games that a teacher can create.
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot  Learning

The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i.e. learning to transfer in few-shot scenario.) or meta-learning. However, as the base classes have no overlap to the novel set, simply transferring whole knowledge from base data is not an optimal solution since some knowledge in the base model may be biased or even harmful to the novel class. In this paper, we propose to transfer partial knowledge by freezing or fine-tuning particular layer(s) in the base model. Specifically, layers will be imposed different learning rates if they are chosen to be fine-tuned, to control the extent of preserved transferability. To determine which layers to be recast and what values of learning rates for them, we introduce an evolutionary search based method that is efficient to simultaneously locate the target layers and determine their individual learning rates. We conduct extensive experiments on CUB and mini-ImageNet to demonstrate the effectiveness of our proposed method. It achieves the state-of-the-art performance on both meta-learning and non-meta based frameworks. Furthermore, we extend our method to the conventional pre-training + fine-tuning paradigm and obtain consistent improvement.
Introducing EIDU’s Solver Platform: Facilitating Open Collaboration in AI to Help Solve the Global Learning Crisis

EIDU provides child-focused learning content along with digitally supported structured pedagogy programmes for teachers and is being rolled out to all public pre-primary schools across four counties in Kenya. EIDU is content-agnostic, allowing any provider to integrate new content into the platform, with the choice and order of content for each individual child optimised through personalisation algorithms. Autonomous digitised assessment tools enable real-time learning measurement which can be fed back to content providers and researchers, facilitating continuous improvement cycles. EIDU is providing open access to its platform to facilitate collaboration in the personalisation space with 'the Solver Platform'. Researchers will have access to a vast, anonymised learning dataset to train and develop personalisation algorithms. These algorithms can be deployed onto the platform using a plug-in system and will automatically be evaluated and selected based on their measured learning impact, always ensuring the safety of learners comes first.
Comparing Few-Shot Learning with GPT-3 to Traditional Machine Learning Approaches for Classifying Teacher Simulation Responses

Teacher educators use digital clinical simulations (DCS) to provide improvisation opportunities within low-stakes classroom environments. In this study, we experimented with GPT-3 and few-shot learning to examine if it could be used with open-text DCS responses. We found that GPT-3 performed substantially worse than traditional machine learning (ML) models even on the same-sized training sets. However, the performance of GPT-3 decreased only marginally compared to traditional ML models with a training set of 20 examples (−0.06). Traditional ML models generally performed well and in some cases had similar performance to the human baseline. Future research will examine whether changes to labeling procedures or fine-tuning with existing data can improve the performance of GPT-3 with DCSs.
Embodied Agents to Scaffold Data Science Education

Arguing and working with data has become commonplace in several study domains. One way to immerse students in hands-on exploration with data is to provide them with problem-solving environments, for example jupyter notebooks, which can scaffold students' reasoning and bring them closer to disciplinary ways of thinking. Although the intrinsic affordances of jupyter notebooks (e.g., interaction with multiple data representations, automation of procedural task aspects) allow students to engage in rich learning experiences, students lack crucial social scaffolding that directly targets the process of learning. We are developing an AIED infrastructure EASEx for use in higher education contexts that brings in the affordances of embodied pedagogical agents to significantly advance educational practice by scaffolding students in a personalized manner as they work through problems using jupyter notebooks.
AI-Enabled Personalized Interview Coach in Rural India

Soft skills are one of the highly-valued skills integral in one's personal as well as professional life. Recent advancements in technology and the penetration of the internet enable new ways to build systems to identify and help learners who need assistance in improving their soft skills. This paper presents the design, development and evaluation of a prototype intelligent system for training youth in rural areas. The system assesses a personal interview of a learner using their audio/video captured from a mobile phone or web camera. Verbal and non-verbal cues are evaluated using speech processing, computer vision and natural language processing and a detailed feedback is generated using Natural language generation. The real-world data used for training the machine learning models for the system were collected from rural areas in India and were annotated by reliable experts. We validated the performance of the system on an out-of-sample data-set which included 100 interviews collected by piloting the application.
Use of Functional Ink in a Smart Tag for Fast-Moving Consumer Goods Industry

Abstract In the fast-moving consumer goods (FMCG) industry, current labelling solutions have challenges to meet the track &amp; trace requirements. Currently, FMCG items use mainly paper-based self-adhesive labels with traditional barcodes. These labels are low priced and technically easy to produce and deploy. The shift towards advanced solutions, like radio frequency identification (RFID) or near field communication (NFC) tags, still does not offer a good enough cost/benefit ratio. These advanced solutions have a high unit price or require costly changes in production lines. Still, the industry recognizes the possibilities of smart tags. Recent research has shown that functional inks can operate as cheap sensors. However, more research is needed to take functional inks into the operational FMCG environment. This paper presents one technical solution for an FMCG smart tag. The proposed smart tag builds on traditional QR-Code and Datamatrix markers, printed with standard inks. However, it also has functional ink embedded inside the marker as a sensor. This research experiments how embedding impacts the overall performance of the smart tag decoding. And if the CIEDE2000 color difference algorithm can calculate the state of the sensor. Three different parameter combinations, CIEDE2000(1, 1, 1), CIEDE2000(2, 1, 1), CIEDE2000(2.76, 1.58, 1), and their accuracy are compared. Experiments show that the proposed approach does not negatively affect the decoding performance. And that a color comparison can detect sensor states, especially when the functional ink has high enough color intensity. Between different parameters, CIEDE2000(2.76, 1.58, 1) performed best, especially in the low-intensity test. However, some future research needs to address absolute color value detection and the accuracy of color recognition; especially when if the color has low intensity.