Reviewerly/exHarmony huggingface dataset:
From Paper to Pixels: An Investigation of the Feasibility and Acceptability of QR Code Attendance-Taking in A Higher Education Setting

The advancement of information technology has benefited education, industry, health, and government by boosting communication, knowledge exchange, and productivity. According to research, QR codes can make learning and reference simple and enjoyable. QR codes are popular two-dimensional barcodes because they are inexpensive, simple to create, and can carry information. These attributes make QR codes ideal for applications in education, especially for attendance-taking, an essential aspect of academic assessment in higher education. While this practice is often done manually, it could be time-consuming and disruptive to the students during final examinations. This study investigates the feasibility and acceptability of adopting QR codes for attendance-taking during final examinations in a higher education and the perceived usefulness and attitude toward using this approach. Results indicate that the QR code scanning practice was feasible and acceptable to students. Besides, using QR code scanning for attendance eliminates the need for paper-based sheets, thereby reducing waste and, in alignment with the goal of SDG 12, encourages more sustainable consumption patterns through paper usage reduction and wastage.
"Project Driven Practical Teaching Reform Based on U+ EEC - Taking the Course \"Engineering Training II\" as an Example"

In order to promote the construction of emerging engineering practice courses, the article proposes to use the U+ Engineering Education Cloud (U+ EEC) platform to carry out project driven teaching, and combs its process in detail. Firstly, taking "Engineering Training II" training course as an example, this paper uses OBE achievement oriented concept to reconstruct the teaching design, and reversely designs the curriculum outline according to the graduation requirements. Then, combined with the CDIO Engineering Education Concept, each link of project driven teaching is designed. With the help of U+ EEC, project management and process evaluation are carried out to form the "1+8" project driven teaching mode. Finally, through the data analysis of the degree of achievement of the curriculum objectives and the questionnaire survey, the teaching reform methods proposed in this paper are effective and have great promotion value.
Promoting educational innovation in engineering through the design of a student centered and technology mediated formative proposal

The article presents the results of a research on an innovative and student-centered training proposal for the course "Computer Project Management" at the Minuto de Dios UNIMINUTO University Corporation. The objective is to design a proposal that adjusts to the needs of the students, focusing on the development of problem-solving skills in context. The study uses a mixed design, collecting and analyzing quantitative and qualitative data. It is divided into two phases: characterization and redesign of the course. The research highlights the importance of characterization to identify areas for improvement in pedagogical practice, as well as the potential of problem solving and ICT mediation in the development of disciplinary competencies.
Project oriented teaching approach of decentralised applications for undergraduate students

The emergence of new technologies and software engineering methodologies in the decentralised Web, referred to as Web 3.0, often requires using new approaches to supplement conventional development concepts such as object-oriented programming and design patterns. Therefore, it is essential to understand the blockchain fundamentals and modular development of distributed systems built from multiple components. This paper proposes a project-oriented teaching approach of smart contracts-based distributed applications (dApps) integrated into existing (Web 2.0) Web applications. First, we present the main overview of the Web programming course for a bachelor study program, prior knowledge requirements, and the characteristics of the lectures. Second, we define the dApp functional requirements for student projects that intend to consolidate the dApp theoretical concepts with a practical approach to complement the Web 3.0 concepts. The study of our teaching approach is based on an experiment where we thoroughly analyse performance on the course level, projects and individual students. The results indicate a high correlation between students' performance in Web 2.0 and Web 3.0 development, and a higher degree of iterative process is also associated with better-performing students. Moreover, the student project outcomes also provide a basis for possible future course improvements.
Educational Technology in the Post-Pandemic Era: Current Progress, Potential, and Challenges

Information technology has been significantly developed during the past decades. Teachers leverage various technologies to enhance teaching experience and knowledge delivery. Students explore diverse knowledge from numerous learning resources on the Web. Furthermore, the COVID-19 pandemic accelerates processing technology integration in education. During the pandemic, Zoom and Google Classroom were widely used for course content delivery, and they are still presenting their values in the post-pandemic era. Some other applications are also continuously adopted when in-person teaching and learning have been resumed. In this paper, a literature review-based approach is applied to summarize recent achievements in educational technology and present several promising directions for the future of education, including artificial intelligence-powered content generation and metaverse. A roadmap is proposed to illustrate the current and future progress of education technology. Meanwhile, safeguards are also provided to guide schools and parents to be aware of the potential hazards during technology use at the K-12 education level.
Automatic topic terms identification from OER

Currently, there are valuable educational materials in digital format need to be analyzed automatically about covered topics. OER metadata about covered topics is essentially required by learners to build effective learning pathways towards their individual learning objectives. Using natural language processing techniques, topic terms can be automatically identified from metadata OER. Topic modeling allows identifying topics automatically from a set of documents. Based on the LDA model, we propose an automatic topic terms identification from OERs. A total of 4142 OER about “Information Technology” were collected from SkillsCommons. Finally, to identify the best experiment, we used the values of coherence and the distance inter-topic. Results reveal that discovered topics can help to improve the accessibility, discoverability, and usefulness of open educational resources. In addition to supporting the development of more effective and efficient teaching and learning practices.
Research on the digital literacy model construction and practice of higher vocational students in the digital era

Entering the digital age, digital literacy has become one of the essential knowledge and key skills for talents. First of all, this paper sorts out the research status and the existing framework of digital literacy at home and abroad, conducts comparative analysis and research, puts forward the index reference frame, and preliminarily constructs the evaluation index of digital literacy of higher vocational students. Then, the evaluation index of digital literacy is verified by factor analysis, and the data results prove that the evaluation index constructed in this paper has certain rationality. On this basis, the weight of the evaluation index is calculated, and thus the formal digital literacy evaluation index of higher vocational students is obtained. Finally, according to the digital literacy evaluation index of higher vocational students' digital literacy level questionnaire, the Su Xichang area vocational students digital literacy level is analyzed, further verify the higher vocational students' digital literacy components and the rationality and scientific evaluation model, to enhance the level of digital education in higher vocational colleges to provide certain reference.
Learning User Interface Design for Smart Learning Environment

This paper describes the design for a learning user interface in a smart learning environment that combines smartwatches, smart glasses, and other devices in addition to smartphones and tablets. To understand the learning status of learners in smart learning environment, we consider a method for estimating the learner's state by focusing on learning logs and biometric data during online learning. In this paper, we also present implementation of a function to monitor the learner's faces captured by the front camera of a tablet used as a learning terminal in a smart learning environment. We examine how learners' faces can be captured using this monitoring function and discuss how these data can be used in a smart learning environment.
Referring Image Segmentation Using Text Supervision

Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring texts used in RIS already provide sufficient information to localize the target object. Hence, we propose a novel weakly-supervised RIS framework to formulate the target localization problem as a classification process to differentiate between positive and negative text expressions. While the referring text expressions for an image are used as positive expressions, the referring text expressions from other images can be used as negative expressions for this image. Our framework has three main novelties. First, we propose a bilateral prompt method to facilitate the classification process, by harmonizing the domain discrepancy between visual and linguistic features. Second, we propose a calibration method to reduce noisy background information and improve the correctness of the response maps for target object localization. Third, we propose a positive response map selection strategy to generate high-quality pseudo-labels from the enhanced response maps, for training a segmentation network for RIS inference. For evaluation, we propose a new metric to measure localization accuracy. Experiments on four benchmarks show that our framework achieves promising performances to existing fully-supervised RIS methods while outperforming state-of-the-art weakly-supervised methods adapted from related areas. Code is available at https://github.com/fawnliu/TRIS.
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

Federated learning (FL) is a privacy-enhanced distributed machine learning framework, in which multiple clients collaboratively train a global model by exchanging their model updates without sharing local private data. However, the adversary can use gradient inversion attacks to reveal the clients’ privacy from the shared model updates. Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and in-dependently distributed) data. Moreover, we theoretically derive a convergence guarantee to FedAvg with uniformly perturbed local learning rates. Therefore, by perturbing the learning rate of each client with random noise, we propose a learning rate perturbation (LRP) defense against gradient inversion attacks. Specifically, for classification tasks, we adapt LPR to ada-LPR by personalizing the expectation of each local learning rate. The experiments show that our defenses can well enhance privacy preservation against existing gradient inversion attacks, and LRP outperforms 5 baseline defenses against a state-of-the-art gradient inversion attack. In addition, our defenses only incur minor ac-curacy reductions (less than 0.5%) of the global model. So they are effective in real applications.
Extensible and Efficient Proxy for Neural Architecture Search

Efficient or near-zero-cost proxies were proposed recently to address the demanding computational issues of Neural Architecture Search (NAS) in designing deep neural networks (DNNs), where each candidate architecture network only requires one iteration of backpropagation. The values obtained from proxies are used as predictions of architecture performance for downstream tasks. However, two significant drawbacks hinder the wide adoption of these efficient proxies: (1) they are not adaptive to various NAS search spaces; and (2) they are not extensible to multi-modality downstream tasks. To address these two issues, we first propose an Extensible proxy (Eproxy) that utilizes self-supervised, few-shot training to achieve near-zero costs. A key component to our Eproxy’s efficiency is the introduction of a barrier layer with randomly initialized frozen convolution parameters, which adds non-linearities to the optimization spaces so that Eproxy can discriminate the performance of architectures at an early stage. We further propose a Discrete Proxy Search (DPS) method to find the optimized training settings for Eproxy with only a handful of benchmarked architectures on the target tasks. Our extensive experiments confirm the effectiveness of both Eproxy and DPS. On the NDS-ImageNet search spaces, Eproxy+DPS achieves a higher average ranking correlation (Spearman ρ = 0.73) than the previous efficient proxy (Spearman ρ = 0.56). On the NAS-Bench-Trans-Micro search spaces with seven tasks, Eproxy+DPS delivers comparable performance with the early stopping method (146× faster). For the end-to-end task such as DARTS-ImageNet-1k, our method delivers better results than NAS performed on CIFAR-10 while only requiring one GPU hour with a single batch of CIFAR-10 images. Our code is available at https://github.com/leeyeehoo/GenNAS-Zero.
Building Vision Transformers with Hierarchy Aware Feature Aggregation

Thanks to the excellent global modeling capability of attention mechanisms, the Vision Transformer has achieved better results than ConvNet in many computer tasks. However, in generating hierarchical feature maps, the Transformer still adopts the ConvNet feature aggregation scheme. This leads to the problem that the semantic information of the grid area of image becomes confused after feature aggregation, making it difficult for attention to accurately model global relationships. To address this, we propose the Hierarchy Aware Feature Aggregation framework (HAFA). HAFA enhances the extraction of local features adaptively in shallow layers where semantic information is weak, while is able to aggregate patches with similar semantics in deep layers. The clear semantic information of the aggregated patches, enables the attention mechanism to more accurately model global information at the semantic level. Extensive experiments show that after using the HAFA framework, significant improvements have been achieved relative to the baseline models in image classification, object detection, and semantic segmentation tasks.
Calibrating Panoramic Depth Estimation for Practical Localization and Mapping

The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate depth estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information. While panoramic images can easily capture the surrounding context from commodity devices, the estimated depth shares the limitations of conventional image-based depth estimation; the performance deteriorates under large domain shifts and the absolute values are still ambiguous to infer from 2D observations. By taking advantage of the holistic view, we mitigate such effects in a self-supervised way and fine-tune the network with geometric consistency during the test phase. Specifically, we construct a 3D point cloud from the current depth prediction and project the point cloud at various viewpoints or apply stretches on the current input image to generate synthetic panoramas. Then we minimize the discrepancy of the 3D structure estimated from synthetic images without collecting additional data. We empirically evaluate our method in robot navigation and map-free localization where our method shows large performance enhancements. Our calibration method can therefore widen the applicability under various external conditions, serving as a key component for practical panorama-based machine vision systems.
Exploring Temporal Concurrency for Video-Language Representation Learning

Paired video and language data is naturally temporal concurrency, which requires the modeling of the temporal dynamics within each modality and the temporal alignment across modalities simultaneously. However, most existing video-language representation learning methods only focus on discrete semantic alignment that encourages aligned semantics to be close in the latent space, or temporal context dependency that captures short-range coherence, failing in building the temporal concurrency. In this paper, we propose to learn video-language representations by modeling video-language pairs as Temporal Concurrent Processes (TCP) via a process-wised distance metric learning framework. Specifically, we employ the soft Dynamic Time Warping (DTW) to measure the distance between two processes across modalities and then optimize the DTW costs. Meanwhile, we further introduce a regularization term that enforces the embeddings of each modality approximating a stochastic process to guarantee the inherent dynamics. Experimental results on three benchmarks demonstrate that TCP stands as a state-of-the-art method for various video-language understanding tasks, including paragraph-to-video retrieval, video moment retrieval, and video question-answering. Code is available at https: //github.com/hengRUC/TCP.
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution

Omnidirectional images (ODIs) have become increasingly popular, as their large field-of-view (FoV) can offer viewers the chance to freely choose the view directions in immersive environments such as virtual reality. The Möbius transformation is typically employed to further provide the opportunity for movement and zoom on ODIs, but applying it to the image level often results in blurry effect and aliasing problem. In this paper, we propose a novel deep learning-based approach, called OmniZoomer, to incorporate the Möbius transformation into the network for movement and zoom on ODIs. By learning various transformed feature maps under different conditions, the network is enhanced to handle the increasing edge curvatures, which alleviates the blurry effect. Moreover, to address the aliasing problem, we propose two key components. Firstly, to compensate for the lack of pixels for describing curves, we enhance the feature maps in the high-resolution (HR) space and calculate the transformed index map with a spatial index generation module. Secondly, considering that ODIs are inherently represented in the spherical space, we propose a spherical resampling module that combines the index map and HR feature maps to transform the feature maps for better spherical correlation. The transformed feature maps are decoded to output a zoomed ODI. Experiments show that our method can produce HR and high-quality ODIs with the flexibility to move and zoom in to the object of interest. Project page is available at http: //vlislab22.github.io/OmniZoomer/.
Trajectory Unified Transformer for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is an essential link to understanding human behavior. Recent work achieves state-of-the-art performance gained from hand-designed post-processing, e.g., clustering. However, this post-processing suffers from expensive inference time and neglects the probability that the predicted trajectory disturbs downstream safety decisions. In this paper, we present Trajectory Unified TRansformer, called TUTR, which unifies the trajectory prediction components, social interaction, and multimodal trajectory prediction, into a transformer encoder-decoder architecture to effectively remove the need for post-processing. Specifically, TUTR parses the relationships across various motion modes using an explicit global prediction and an implicit mode-level transformer encoder. Then, TUTR attends to the social interactions with neighbors by a social-level transformer decoder. Finally, a dual prediction forecasts diverse trajectories and corresponding probabilities in parallel without post-processing. TUTR achieves state-of-the-art accuracy performance and improvements in inference speed of about 10× - 40× compared to previous well-tuned state-of-the-art methods using post-processing.
Adaptive Illumination Mapping for Shadow Detection in Raw Images

Shadow detection methods rely on multi-scale contrast, especially global contrast, information to locate shadows correctly. However, we observe that the camera image signal processor (ISP) tends to preserve more local contrast information by sacrificing global contrast information during the raw-to-sRGB conversion process. This often causes existing methods to fail in scenes with high global contrast but low local contrast in shadow regions. In this paper, we propose a novel method to detect shadows from raw images. Our key idea is that instead of performing a many-to-one mapping like the ISP process, we can learn a many-to-many mapping from the high dynamic range raw images to the sRGB images of different illumination, which is able to preserve multi-scale contrast for accurate shadow detection. To this end, we first construct a new shadow dataset with ~ 7000 raw images and shadow masks. We then propose a novel network, which includes a novel adaptive illumination mapping (AIM) module to project the input raw images into sRGB images of different intensity ranges and a shadow detection module to leverage the preserved multi-scale contrast information to detect shadows. To learn the shadow-aware adaptive illumination mapping process, we propose a novel feedback mechanism to guide the AIM during training. Experiments show that our method outperforms state- of-the-art shadow detectors. Code and dataset are available at https://github.com/jiayusun/SARA.
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Compared to 2D transformations, the exploration of 3D viewpoint invariance deserves more attention for its greater practical significance. Motivated by the success of adversarial training in promoting model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve viewpoint robustness of common image classifiers. By regarding viewpoint transformation as an attack, VIAT is formulated as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on a new attack GMVFool, while the outer minimization trains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case adversarial viewpoint distributions. To further improve the generalization performance, a distribution sharing strategy is introduced leveraging the transferability of adversarial viewpoints across objects. Experiments validate the effectiveness of VIAT in improving the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.