Reviewerly/exHarmony huggingface dataset:
A<sup>2</sup>GSTran: Depth Map Super-resolution via Asymmetric Attention with Guidance Selection

Currently, Convolutional Neural Network (CNN) has dominated guided depth map super-resolution (SR). However, the inefficient receptive field growing and input-independent convolution limit the generalization of CNN. Motivated by vision transformer, this paper proposes an efficient transformer-based backbone A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GSTran for guided depth map SR, which resolves the above intrinsic defect of CNN. In addition, state-of-the-art (SOTA) models only refine depth features with the guidance which is implicitly selected without supervision. So, there is no explicit guarantee to mitigate the artifacts of texture copying and edge blurring. Accordingly, the proposed A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GSTran simultaneously solves two sub-problems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., guided monocular depth estimation and guided depth SR, in separate branches. Specifically, the explicit supervision upon monocular depth estimation lifts the efficiency of guidance selection. The feature fusion between branches is designed via bi-directional cross attention. Moreover, since guidance domain is defined in high resolution (HR), we propose asymmetric cross attention to maintain the guidance information via pixel unshuffle instead of pooling which has unequal channel number to depth features. Based on the supervisions to depth reconstruction and guidance selection, the final depth features are refined by fusing the output features of the corresponding branches via channel attention to generate the HR depth map. Sufficient experimental results on synthetic and real datasets for multiple scales validate our contributions compared with SOTA models. The code and models are public via https://github.com/alex-cate/Depth_Map_Super-resolution_via_Asymmetric_Attention_with_Guidance_Selection
Motives for Getting Higher Education in Different Cultures (China, Africa, Russia)

The chapter presents the results of a cross-cultural study of the motives for obtaining higher education by students from different countries, including Russia, China, Chad, and Cameroon. The sample consisted of 400 students from various higher educational institutions studying various disciplines. The obtained results allowed the authors to analyze both differences and similarities in the motives for getting higher education caused by sociocultural factors. The critical features in the formation of motivation for learning, the motivation for achieving success in different educational systems in different cultures are revealed. The author argues that the formation of learning motives to a greater extent depends on the motivational-background and national-psychological characteristics of students from different countries.
Automatic Punctuation Verification of School Students’ Essay in Portuguese

Textual production is a key activity at different levels of education. The analysis of essays encompasses several criteria, such as lexical and syntactic errors, cohesion, and coherence. Within these criteria, how the students include punctuation (i.e., final mark and comma) could influence the quality of the final production. Thus, the literature has proposed several approaches to verifying punctuation correction in students’ essays for English. However, despite the advancements in natural language processing models for other languages, there is a significant gap concerning punctuation verification. Therefore, this paper proposed a new approach based on state-of-the-art language models to develop a punctuation prediction method for Portuguese. The proposed model was applied to evaluate the textual productions of students in Brazilian public schools. Finally, the results of this study and its practical implications for educational settings are further discussed.
EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images

Abstract The accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet.
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation

In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors (e.g., rain streaks) or artificially attack factors (e.g., adversarial attack). Whereas, most existing methods are designed to address a single degradation factor and are tailored to specific application scenarios. In this work, we present the first attempt to improve the robustness of semantic segmentation tasks by simultaneously handling different types of degradation factors. Specifically, we introduce the Preprocessing Enhanced Adversarial Robust Learning (PEARL) framework based on the analysis of our proposed Naive Adversarial Training (NAT) framework. Our approach effectively handles both rain streaks and adversarial perturbation by transferring the robustness of the segmentation model to the image derain model. Furthermore, as opposed to the commonly used Negative Adversarial Attack (NAA), we design the Auxiliary Mirror Attack (AMA) to introduce positive information prior to the training of the PEARL framework, which improves defense capability and segmentation performance. Our extensive experiments and ablation studies based on different derain methods and segmentation models have demonstrated the significant performance improvement of PEARL with AMA in defense against various adversarial attacks and rain streaks while maintaining high generalization performance across different datasets. The source codes are available at https://github.com/JiaoXianghao/PEARL.
A Contrastive Learning Framework for Dual-Target Cross-Domain Recommendation

Cross-Domain Recommendation (CDR) is proposed to address the long-standing data sparsity problem in recommender systems (RSs). Traditional CDR only leverages relatively richer information from an auxiliary domain to improve the performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users to achieve knowledge transfer between domains. We argue that the existing methods face two challenges: (1) how to learn more representative user and item embeddings in each domain, and (2) in the case of a small number of common users in real-world datasets, how to achieve better knowledge transfer. To address these challenges, in this paper, we propose a contrastive learning (CL) framework, called CL-DTCDR. In CL-DTCDR, we first design a CL task in each domain to learn more representative user and item embeddings. Then, we further construct positive pairs of the user and her/his most similar user between domains to optimize user embeddings. By two CL tasks, CL-DTCDR effectively improves performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that CL-DTCDR significantly outperforms the state-of-the-art approaches.
DCEL: Deep Cross-modal Evidential Learning for Text-Based Person Retrieval

Text-based person retrieval aims at searching for a pedestrian image from multiple candidates with textual descriptions. It is challenging due to uncertain cross-modal alignments caused by the large intra-class variations. To address the challenge, most existing approaches rely on various attention mechanisms and auxiliary information, yet still struggle with the uncertain cross-modal alignments arising from significant intra-class variation, leading to coarse retrieval results. To this end, we propose a novel framework termed Deep Cross-modal Evidential Learning (DCEL), which deploys evidential deep learning to consider the cross-modal alignment uncertainty. Our DCEL model comprises three components: (1) Bidirectional Evidential Learning, which models alignment uncertainty to measure and mitigate the influence of large intra-class variation; (2) Multi-level Semantic Alignment, which leverages a proposed Semantic Filtration module and image-text similarity distribution to facilitate cross-modal alignments; (3) Cross-modal Relation Learning, which reasons about latent correspondences between multi-level tokens of image and text. Finally, we integrate the advantages of the three proposed components to enhance the model to achieve reliable cross-modal alignments. Our DCEL method consistently outperforms more than ten state-of-the-art methods in supervised, weakly supervised, and domain generalization settings on three benchmarks: CUHK-PEDES, ICFG-PEDES, and RSTPReid.
V2Depth: Monocular Depth Estimation via Feature-Level Virtual-View Simulation and Refinement

Due to the lack of spatial cues giving merely a single image, many monocular depth estimation methods have been developed to leverage stereo or multi-view images to learn the spatial information of a scene in a self-supervised manner. However, these methods have limited performance gain since they are not able to exploit sufficient 3D geometry cues during inference, where only monocular images are available. In this work, we present V2Depth, a novel coarse-to-fine framework with Virtual View feature simulation for supervised monocular Depth estimation. Specifically, we first design a virtual-view feature simulator by leveraging the technique of novel view synthesis and contrastive learning to generate virtual view feature maps. In this way, we explicitly provide representative spatial geometry for subsequent depth estimation in both the training and inference stages. Then we introduce a 3DVA-Refiner to iteratively optimize the predicted depth map. During the optimization process, 3D-aware virtual attention is developed to capture the global spatial-context correlations to maintain the feature consistency of different views and estimation integrity of the 3D scene such as objects with occlusion relationships. Decisive improvements over state-of-the-art approaches on three benchmark datasets across all metrics demonstrate the superiority of our method.
Mixture-of-Experts Learner for Single Long-Tailed Domain Generalization

Domain generalization (DG) refers to the task of training a model on multiple source domains and test it on a different target domain with different distribution. In this paper, we address a more challenging and realistic scenario known as Single Long-Tailed Domain Generalization, where only one source domain is available and the minority class in this domain has an abundance of instances in other domains. To tackle this task, we propose a novel approach called Mixture-of-Experts Learner for Single Long-Tailed Domain Generalization (MoEL), which comprises two key strategies. The first strategy is a simple yet effective data augmentation technique that leverages saliency maps to identify important regions on the original images and preserves these regions during augmentation. The second strategy is a new skill-diverse expert learning approach that trains multiple experts from a single long-tailed source domain and leverages mutual learning to aggregate their learned knowledge for the unknown target domain. We evaluate our method on various benchmark datasets, including Digits-DG, CIFAR-10-C, PACS, and DomainNet, and demonstrate its superior performance compared to previous single domain generalization methods. Additionally, the ablation study is also conducted to illustrate the inner workings of our approach.
Reparo: QoE-Aware Live Video Streaming in Low-Rate Networks by Intelligent Frame Recovery

Live video streaming has grown dramatically in recent years. A key challenge is achieving high video quality of experience (QoE) in low-rate networks. To tackle this problem, recent streaming approaches strategically drop video frames, thus reducing the bandwidth required. However, these methods are usually designed for video on demand (VoD) services and perform poorly in live video streaming. In this paper, we design a new live video streaming approach, Reparo, which aims to improve users' QoE in low-rate networks. On the upload client side, Reparo discards video frames such that they are never encoded or transmitted. To decide which frames should be dropped, we design a real-time Video Frame Discarding (VFD) model, which strives to minimize the impact on video quality while maximizing bandwidth savings. To complement this, Reparo further proposes a modified adaptive bitrate algorithm and two encoding modes, targeting low-frame-rate encoding. On the server side, Reparo then recovers the dropped frames using a lightweight Video Frame Interpolation Deep Neural Network (VFI-DNN). Experimental results show that, compared with vanilla DASH, Reparo reaches an SSIM gain of 0.018, or reduces bandwidth consumption by 30.86%. With an average bandwidth of 0.974Mbps, it improves QoE by 18.13% on average compared to DASH.
NarSUM '23: The 2nd Workshop on User-Centric Narrative Summarization of Long Videos

With video capture devices becoming widely popular, the amount of video data generated per day has seen a rapid increase over the past few years. Browsing through hours of video data to retrieve useful information is a tedious and boring task. Video Summarization technology has played a crucial role in addressing this issue. It is a well-researched topic in the multimedia community. However, the focus so far has been limited to creating summary to videos which are short (only a few minutes). This workshop aims to call for researchers on relevant background to focus on novel solutions for user-centric narrative summarization of long videos. This workshop will also cover important aspects of video summarization research like what is "important" in a video, how to evaluate the goodness of a created summary, open challenges in video summarization, etc.
An open-source natural language processing toolkit to support software development: addressing automatic bug detection, code summarisation and code search

<ns3:p>This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project – acronym for “DEveloper COmpanion for Documented and annotatEd code Reference” – (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package – “Activities for the developer” – intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.</ns3:p>
Research on the Reform and Development of Physical Education Teaching in Colleges and Universities Based on Information Technology

The widespread usage of information technology in today's world demonstrates its numerous practical benefits. The enhancement of higher education teaching standards, the development of athletic talent, and the overall growth of students are all factors in the reform and development of physical education instruction in colleges and universities. To achieve the innovation of teaching methods and the long-term development of college physical education teaching, the reform of college physical education teaching based on information technology must incorporate information technology into all elements of college physical education teaching. The article begins by outlining the impact of information technology on the reform of physical education instruction in colleges. It then provides strategies for utilizing information technology to improve and develop physical education instruction in colleges and breathe new life into it.
Securing RFID-Based Attendance Management Systems: An Implementation of the AES Block Cipher Algorithm

The main feature of Radio Frequency IDentification (RFID) technology is its ability to locate, track and monitor people or objects. RFID Architecture consists of two significant components: a tag and a reader. RFID is prone to major security and privacy attacks. This research aims to demonstrate the importance of encryption in securing RFID-based attendance management systems, specifically focusing on using the Advanced Encryption Standard (AES) block cipher. The application of the cipher algorithm in encrypting the RFID tag Unique Identifier (UID) and the reader will be implemented to show how it can enhance the security of RFID tags. The proposed approach ensures secure data transmission, preventing unauthorized access and ensuring the confidentiality and integrity of attendance data. The research provides insights into the importance of encryption in securing RFID-based systems, which is critical in protecting sensitive data and ensuring the privacy and security of individuals.
Digit-Size Selection for FPGA Implementation of Generic Digit-Serial Multiplication Over GF(2m)

Multiplications over the Galois field GF <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2^{m})$</tex> are widely used for the realization of point operations in the elliptic curve cryptosystem (ECC). Implementation of these multipliers for resource-constrained applications possesses a significant research challenge. Efficient implementation of field multiplication is therefore an important area of research. We present here the design of generic digit-serial multipliers for GF <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(2^{m})$</tex> and coded those using VHDL for different values of field order and different digit-sizes. We have synthesized the design using Xilinx ISE Design Suite version 14.4 and estimated the FPGA resource consumption such as the number of slice LUTs and slice registers, as well as, the minimum clock period required for different digit sizes for three different values of field order, such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m=64,193$</tex> , and 239. Besides, we have estimated the minimum computational delay and slice-delay product for different digitsizes and different field orders in order to determine the digit size to be used for the minimum usage of LUT slices, the minimum delay, and the minimum slice-delay product. Interestingly, we find that for the digit-size, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w=16$</tex> the computational delay is the lowest for different field orders. It is observed that the number of slices and slice-delay product are minimum in the case of bitserial architectures for different values of the field order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> .
AutoML for On-Sensor Tiny Machine Learning

Sensors with embedded machine learning core (MLC) enable ultra-low-power, low latency, and intelligent inferences at the extreme edge. However, deploying performant machine learning (ML) models within the resource bounds of MLC is challenging. This paper presents an automatic ML framework that trains and deploys ML models on the MLC embedded in the iNEMO inertial measurement units. Given a labeled inertial sensor dataset, the framework automatically selects the best set of features, filters, and window size to apply to the dataset from a search space of supported MLC parameters. The framework then trains a decision tree or random forest within the resource bounds of the MLC and generates the register configuration file to deploy the trained ML model on sensor with MLC capability. For human activity recognition, the framework achieves 93% test accuracy under 1 mW of power, which is 41× lower than an ARM Cortex-M4 implementation. The framework also enables on-sensor fall detection with 95% test accuracy under 0.3 mW of power.
Experiencing Visual Captions: Augmented Communication with Real-time Visuals using Large Language Models

We demonstrate Visual Captions, a real-time system that integrates with a video conferencing platform to enrich verbal communication. Visual Captions leverages a fine-tuned large language model to proactively suggest visuals that are relevant to the context of the ongoing conversation. We implemented Visual Captions as a user-customizable Chrome plugin with three levels of AI proactivity: Auto-display (AI autonomously adds visuals), Auto-suggest (AI proactively recommends visuals), and On-demand-suggest (AI suggests visuals when prompted). We showcase the usage of Visual Captions in open-vocabulary settings, and how the addition of visuals based on the context of conversations could improve comprehension of complex or unfamiliar concepts. In addition, we demonstrate three approaches people can interact with the system with different levels of AI proactivity. Visual Captions is open-sourced at https://github.com/google/archat.
XR and AI: AI-Enabled Virtual, Augmented, and Mixed Reality

This workshop aims to unite experts and practitioners in XR and AI to envision the future of AI-enabled virtual, augmented, and mixed reality experiences. Our expansive discussion includes a variety of key topics: Generative XR, Large Language Models (LLMs) for XR, Adaptive and Context-Aware XR, Explainable AI for XR, and harnessing AI to enhance and prototype XR experiences. We aim to identify the opportunities and challenges of how recent advances of AI could bring new XR experiences, which cannot be done before, with a keen focus on the seamless blending of our digital and physical worlds.