Reviewerly/exHarmony huggingface dataset:
Continual Learning on Noisy Data Streams via Self-Purified Replay

Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a replay-based continual learning framework that simultaneously addresses both catastrophic forgetting and noisy labels for the first time. Our solution is based on two observations; (i) forgetting can be mitigated even with noisy labels via self-supervised learning, and (ii) the purity of the replay buffer is crucial. Building on this regard, we propose two key components of our method: (i) a self-supervised replay technique named Self-Replay which can circumvent erroneous training signals arising from noisy labeled data, and (ii) the Self-Centered filter that maintains a purified replay buffer via centrality-based stochastic graph ensembles. The empirical results on MNIST, CIFAR-10, CIFAR-100, and WebVision with real-world noise demonstrate that our framework can maintain a highly pure replay buffer amidst noisy streamed data while greatly outperforming the combinations of the state-of-the-art continual learning and noisy label learning methods.
Acute Effects of Virtual Reality Exercise Biking on College Students’ Physical Responses

Purpose: Current evidence regarding the use of commercially available immersive virtual reality (VR) headsets and compatible VR exercise apparatus in the promotion of individuals' physical activity (PA) is lacking. This exploratory study investigated the acute effects of a VirZoom VR exercise bike (vBike) on college students' physical responses compared to an exergaming bike (eBike) and a traditional stationary exercise bike (tBike). Method: Forty-eight college students (34 females, Xage = 23.58 ± 3.39; XBMI = 23.81 ± 3.57) completed three separate 20-minute cycling sessions on the vBike, eBike, and tBike in a counterbalanced order. Outcomes included rating of perceived exertion (RPE), assessed at 4-minute intervals throughout each cycling session, and overall PA, operationalized as pedal revolution counts (PRC) during each session. Results: Linear models indicated significant group differences between exercise modalities for RPE (F (2,138) = 14.76-27.30, all p < .001, η2 = 0.17-0.27) and PRC (F (2,138) = 74.862, p < .001, η2 = 0.48). Pairwise comparisons revealed that RPE was reported significantly higher at each time point (i.e., 4th minute, 8th minute, 12th minute, 16th minute, and 20th minute) during the tBike session vs. the vBike and eBike sessions (p < .001) and that participants had higher PRC during the vBike session vs. the eBike and tBike sessions (p < .001). Conclusion: A commercially available VR exercise bike would trigger greater overall PA as compared to exergaming and traditional cycling while being perceived less intense. Future intervention studies conducted in real-world settings to determine cause and effect relationship are warranted.
SANet: Statistic Attention Network for Video-Based Person Re-Identification

Capturing long-range dependencies during feature extraction is crucial for video-based person re-identification (re-id) since it would help to tackle many challenging problems such as occlusion and dramatic pose variation. Moreover, capturing subtle differences, such as bags and glasses, is indispensable to distinguish similar pedestrians. In this paper, we propose a novel and efficacious Statistic Attention (SA) block which can capture both the long-range dependencies and subtle differences. SA block leverages high-order statistics of feature maps, which contain both long-range and high-order information. By modeling relations with these statistics, SA block can explicitly capture long-range dependencies with less time complexity. In addition, high-order statistics usually concentrate on details of feature maps and can perceive the subtle differences between pedestrians. In this way, SA block is capable of discriminating pedestrians with subtle differences. Furthermore, this lightweight block can be conveniently inserted into existing deep neural networks at any depth to form Statistic Attention Network (SANet). To evaluate its performance, we conduct extensive experiments on two challenging video re-id datasets, showing that our SANet outperforms the state-of-the-art methods. Furthermore, to show the generalizability of SANet, we evaluate it on three image re-id datasets and two more general image classification datasets, including ImageNet. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://vipl.ict.ac.cn/resources/codes/code/SANet_code.zip</uri> .
Biclustering Collaborative Learning for Cross-Domain Person Re-Identification

In the cross-domain person re-identification (ReID) method based on clustering, the performance of the model depends heavily on the quality of the information it obtains from clustering. To improve the reliability of the clustering information obtained by the model, we propose a biclustering collaborative learning (BCL) framework derived from an identity disentanglement adaptation network (IDA-Net). IDA-Net encodes the identity and style of the input image and transfers the style on the premise of maintaining identity consistency. By comparing the clustering results obtained on the same dataset before and after the transfer process, BCL can select hard samples with higher confidence for model optimization. In each iteration, we design a conditional batch hard triplet loss to optimize the two networks. Extensive experiments on large-scale datasets (Maket1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of BCL over the state-of-the-art methods.
Reactive Cooperative Manipulation based on Set Primitives and Circular Fields

This paper addresses the problem of real-time planning in constrained dual-arm manipulation scenarios. Our proposed coupling leverages manipulability information of the cooperative bimanual task-space to a vector-field based planner by means of a repulsive circulatory field, while geometric primitives in Spin(3)⋉ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> are explored for flexible task relaxation. Furthermore, the circular field informs the cooperative framework about the safety boundaries which are in turn used to further relax motion constraints within a collision-free ball in Cartesian space. This builds a funnel along the trajectory which can be directly tracked through the proposed switching of task-primitive-priorities. The switching strategy follows an approach that ensures robustness to chattering and continuity in the joint-space. Experiments verify that our framework can run within the inner control loop of Franka Emika Panda robots.
A novel approach to investigate the effects of global warming and exchange rate on the solar power plants

The present study proposes novel approaches to concepts such as risk assessment and investment management in investments in solar power plants. In this context, 11 cities in Turkey have been taken into consideration for solar energy investment. Global warming is also one of the current issues on the World's agenda. The monthly impact of global warming has been investigated for 5 years using a deep learning model, and the effects of global warming on cost have been determined within the scope of this study. Concerning the outputs of the designed deep learning-based temperature-forecasting model, it was observed that the gain values might decrease between 5.98% and 3.74% due to the possible global warming effects in 2025–2030. Also, changes in the US$/TL ratio put solar energy investors in a difficult position. The Turkish government issued a Presidential Decree numbered 3453 on January 30, 2021, to offer a solution to this situation. The profit-loss situation of solar energy investments was investigated with the issued support policy and fixed price-purchasing policy. It has been observed that the fixed price policy is not sustainable and the new support system partially protects the solar energy investor. • Antalya city is a more suitable location for solar energy investment. • The electricity selling price varied between US$0.1 and US$0.14. • The global warming effect is investigated with a deep learning-based model. • The gain values might decrease between 5.98% and 3.74% due to global warming.
QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users

The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.
Accessibility Guidelines for VR Games - A Comparison and Synthesis of a Comprehensive Set

Increasing numbers of gamers worldwide have led to more attention being paid to accessibility in games. Virtual Reality offers new possibilities to enable people to play games but also comes with new challenges for accessibility. Guidelines provide help for developers to avoid barriers and include persons with disabilities in their games. As of today, there are only a few extensive collections of accessibility rules on video games. Especially new technologies like virtual reality are sparsely represented in current guidelines. In this work, we provide an overview of existing guidelines for games and VR applications. We examine the most relevant resources, and form a union set. From this, we derive a comprehensive set of guidelines. This set summarizes the rules that are relevant for accessible VR games. We discuss the state of guidelines and their implication on the development of educational games, and provide suggestions on how to improve the situation.
SimulSLT

Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years. However, the existing sign language translation methods need to read all the videos before starting the translation, which leads to a high inference latency and also limits their application in real-life scenarios. To solve this problem, we propose SimulSLT, the first end-to-end simultaneous sign language translation model, which can translate sign language videos into target text concurrently. SimulSLT is composed of a text decoder, a boundary predictor, and a masked encoder. We 1) use the wait-k strategy for simultaneous translation. 2) design a novel boundary predictor based on the integrate-and-fire module to output the gloss boundary, which is used to model the correspondence between the sign language video and the gloss. 3) propose an innovative re-encode method to help the model obtain more abundant contextual information, which allows the existing video features to interact fully. The experimental results conducted on the RWTH-PHOENIX-Weather 2014T dataset show that SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model while maintaining low latency, which proves the effectiveness of our method.
A semantic model for enterprise application integration in the era of data explosion and globalisation

This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as knowledge). The salient features of the model are: (1) separation of business functions from applications and enterprises, (2) three-layer architecture of the model (conceptual or semantic level, external or application level, internal or realisation level), and (3) integration of structured, semi-structured and non-structured data. To our best knowledge, the existing model or solution to EAI does not hold all the three features. A case study is presented to illustrate how the model works. The model can be used by an individual enterprise or a group of enterprises that form a network, e.g., a holistic supply chain network.
A Verifiable Symmetric Searchable Encryption Scheme Based on the AVL Tree

Abstract Verifiable symmetric searchable encryption is a keyword search technology that supports verification of search results. Many schemes improve search performance by dividing each keyword label into segments and storing them in a Trie-tree at the expense of high storage. And the index will degenerate into a linear linked list when all keyword labels have the same prefix except for the last segment. But it will greatly affect the search efficiency. In this paper, we propose a verifiable symmetric searchable encryption scheme based on the AVL Tree (abbreviated as VSSE-AVL), which uses complete keyword labels to build the index. Compared with the Trie-tree index, VSSE-AVL not only balances storage and search performance, but also avoids degradation. To verify the correctness and completeness of empty search results, we store path information in each leaf node and node with only one child node. Considering the substitution attack, we bind the file identifier and the file so that the client will find out once the server returns inconsistent search results. Rigorous security analysis shows VSSE-AVL satisfies privacy and verifiability. Compared with the verifiable SSE-2 with the same security, the experimental evaluation shows that our proposed scheme performs better on storage, search and verification.
Progressive Semantic Matching for Video-Text Retrieval

Cross-modal retrieval between texts and videos is important yet challenging. Until recently, previous works in this domain typically rely on learning a common space to match the text and video, but it is difficult to match due to the semantic gap between videos and texts. Although some methods employ coarse-to-fine or multi-expert networks to encode one or more common spaces for easier matching, they almost directly optimize one matching space, which is challenging, because of the huge semantic gap between different modalities. To address this issue, we aim at narrowing semantic gap by a progressive learning process with a coarse-to-fine architecture, and propose a novel Progressive Semantic Matching (PSM) method. We first construct a multilevel encoding network for videos and texts, and design some auxiliary common spaces, which are mapped by the outputs of encoders in different levels. Then all the common spaces are jointly trained end to end. In this way, the model can effectively encode videos and texts into a fusion common space by a progressive paradigm. Experimental results on three video-text datasets (i.e., MSR-VTT, TIGF and MSVD) demonstrate the advantages of our PSM, which achieves significant performance improvement compared with state-of-the-art approaches.
Nonlinear RANSAC with crossline correction: An algorithm for vision-based curved cable detection system

Accurate curved line shape object detection is important for assistant Autonomous Underwater Vehicles)AUVs( to complete underwater cable inspection tasks. In this article an algorithm based on adaptive canny's edge detection and an improved random sample consensus (RANSAC) algorithm, called Crossline Correction Non-linear RANSAC (CCNL-RANSAC), is proposed to detect curved line object. The algorithm is featured on adaptive threshold in the canny's operator to improve correctness of edge detection, preliminary check and cross line correction introduced in a nonlinear RANSAC method to locate cables with better success rate and less execution time. Comprehensive underwater cable detection experiments were carried out by a range-gated imaging system in a big water tank. With the proposed CCNL-RANSAC algorithm, we increased success rate to 96% ~99% in line detection for the images of curved cable taken as far as 21 m long in clear water. The average execution time is reduced from 202 ms (using standard RANSAC) to 88 ms on average using CCNL-RANSAC, with 80% time reduction at most. With the help of CCNL-RSNSAC algorithm proposed in the paper, vision based AUVs is able to make auto inspection of curved cables and pipeline underwater, which will make the periodical checking of those marine structures more efficiently.
Multimodal Entity Linking: A New Dataset and A Baseline

In this paper, we introduce a new Multimodal Entity Linking (MEL) task on the multimodal data. The MEL task discovers entities in multiple modalities and various forms within large-scale multimodal data and maps multimodal mentions in a document to entities in a structured knowledge base such as Wikipedia. Different from the conventional Neural Entity Linking (NEL) task that focuses on textual information solely, MEL aims at achieving human-level disambiguation among entities in images, texts, and knowledge bases. Due to the lack of sufficient labeled data for the MEL task, we release a large-scale multimodal entity linking dataset M3EL (abbreviated for MultiModal Movie Entity Linking). Specifically, we collect reviews and images of 1,100 movies, extract textual and visual mentions, and label them with entities registered in Wikipedia. In addition, we construct a new baseline method to solve the MEL problem, which models the alignment of textual and visual mentions as a bipartite graph matching problem and solves it with an optimal-transportation-based linking method. Extensive experiments on the M3EL dataset verify the quality of the dataset and the effectiveness of the proposed method. We envision this work to be helpful for soliciting more research effort and applications regarding multimodal computing and inference in the future. We make the dataset and the baseline algorithm publicly available at https://jingrug.github.io/research/M3EL.
"What is the Topic of the Group, Please?" On Migration, Care and the Challenges of Participation in Design

An increasing number of people around the world are forced to leave their homes due to the climate catastrophe, regional conflict or poverty. In their new host countries however, refugees and migrants are frequently met by a wide range of challenges, including wider societal participation. The difficulties migrants and refugees face have also increasingly become the topic of HCI and CSCW work. In this paper we report on a three year-long project, involving refugees, migrants and activist supporters in a co-design project to develop tools that aid the process of resettling. Several aspects have challenged equal participation in the project, including divergent motives, unequal power distribution and cultural heterogeneity. Despite these challenges the project outcomes are in use and maintained beyond the project runtime through voluntary actors. We reflect on this discrepancy between process and outcome drawing on the concept of care.
Data-Driven Kalman-Based Velocity Estimation for Autonomous Racing

Real-time velocity estimation is a core task in autonomous driving, which is carried out based on available raw sensors such as wheel odometry and motor currents. When the system dynamics and observations can be modeled together as a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. This work proposes to estimate the velocity using a hybrid data-driven (DD) implementation of the KF for non-linear systems, coined KalmanNet. KalmanNet integrates a compact recurrent neural network in the flow of the classical KF, retaining low computational complexity, high data efficiency, and interpretability, while enabling operation in non-linear SS models with partial information. We apply KalmanNet on an autonomous racing car as part of the Formula Student (FS) Driverless competition. Our results demonstrate the ability of KalmanNet to outperform a state-of-the-art implementation of the KF that uses a postulated SS model, while being applicable on the vehicle control unit used by the car.
Quality Assessment of End-to-End Learned Image Compression

Recently, learning-based lossy image compression has achieved notable breakthroughs with their excellent modeling and representation learning capabilities. Comparing to traditional image codecs based on block partitioning and transform, these data-driven approaches with artificial-neural-network (ANN) structures bring significantly different distortion patterns. Efficient objective image quality assessment (IQA) measures play the key role in quantitative evaluation and optimization of image compression algorithms. In this paper, we construct a large-scale image database for quality assessment of compressed images. In the proposed database, 100 reference images are compressed to different quality levels by 10 codecs, involving both traditional and learning-based codecs. Based on this database, we present a benchmark for existing IQA methods and reveal the challenges of IQA on learning-based compression distortions. Furthermore, we develop an objective quality assessment framework in which a self-attention module is adopted to leverage multi-level features from reference and compressed images. Extensive experiments demonstrate the superiority of our method in terms of prediction accuracy. The subjective and objective study of various compressed images also shed lights on the optimization of image compression methods.
Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss

In modern agriculture, measuring phenotypic traits helps breeders monitor plant growth, increase yield, and provide food, feed, and fiber. Traditional phenotyping requires intensive manual work, partially being intrusive. In this paper, we investigate the challenge of measuring phenotypic traits in an automated fashion through mobile robots operating in field environments. In particular, we want to measure plants from images acquired by mobile robots instead of using data from a static scanning environment. We propose to use a differentiable rendering approach to deform a generic 3D template of a plant to fit the observation recorded by a robot while ensuring a coherent deformation of the plant template. The experiments presented in this paper suggest that our approach allows for 3D reconstruction of different plant species at different growth stages using single images. From that model, we can compute important phenotypic traits, such as the leaf area index.