UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance

Object detection has long been a topic of high interest in computer vision literature. Motivated by the fact that annotating data for the multi-object tracking (MOT) problem is immensely expensive, recent studies have turned their attention to the unsupervised learning setting. In this paper, we push forward the state-of-the-art performance of unsupervised MOT methods by proposing UnsMOT, a novel framework that explicitly combines the appearance and motion features of objects with geometric information to provide more accurate tracking. Specifically, we first extract the appearance and motion features using CNN and RNN models, respectively. Then, we construct a graph of objects based on their relative distances in a frame, which is fed into a GNN model together with CNN features to output geometric embedding of objects optimized using an unsupervised loss function. Finally, associations between objects are found by matching not only similar extracted features but also geometric embedding of detections and tracklets. Experimental results show remarkable performance in terms of HOTA, IDF1, and MOTA metrics in comparison with state-of-the-art methods

HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering

We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://github.com/VinAIResearch/HyperCUT.gitComment: Accepted to CVPR 202

Encourage Fathers' Use of Paternal Leave in Vietnam: the Labor Contract Characteristics and Fathers' Behavior

Vietnam reformed the maternity regime in 2014, in which male employees currently paying social insurance premiums whose wives give birth to children are entitled to a maternity leave of from 5 to 14 working days. This is widely regarded as a landmark reform that has shifted the Vietnam maternity regime toward a model that is more supportive of the compatibility of work and family life. In this paper, we show that fathers' leave-taking was only about 5,2% of total male employees currently paying social insurance premiums whose wives give birth, even when fathers are on leave period, they spend very little time with their children; despite drastic changes in gender norms in Vietnam, as well as short periods of leave, may have long-lasting effects on fathers’ involvement in newborn care and housework. We also find that groups of fathers, who are cadres and civil servants, as well as those with permanent working contracts profited more strongly from changing their behavior, while self-employment and temporary work lower fathers’ chances of taking leave. Finally, we make some compelling proposals to increase the number of fathers taking parental leave

Encourage Fathers' Use of Paternal Leave in Vietnam: the Labor Contract Characteristics and Fathers' Behavior

Vietnam reformed the maternity regime in 2014, in which male employees currently paying social insurance premiums whose wives give birth to children are entitled to a maternity leave of from 5 to 14 working days. This is widely regarded as a landmark reform that has shifted the Vietnam maternity regime toward a model that is more supportive of the compatibility of work and family life. In this paper, we show that fathers' leave-taking was only about 5,2% of total male employees currently paying social insurance premiums whose wives give birth, even when fathers are on leave period, they spend very little time with their children; despite drastic changes in gender norms in Vietnam, as well as short periods of leave, may have long-lasting effects on fathers’ involvement in newborn care and housework. We also find that groups of fathers, who are cadres and civil servants, as well as those with permanent working contracts profited more strongly from changing their behavior, while self-employment and temporary work lower fathers’ chances of taking leave. Finally, we make some compelling proposals to increase the number of fathers taking parental leave

Federated Few-shot Learning for Cough Classification with Edge Devices

Automatically classifying cough sounds is one of the most critical tasks for the diagnosis and treatment of respiratory diseases. However, collecting a huge amount of labeled cough dataset is challenging mainly due to high laborious expenses, data scarcity, and privacy concerns. In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns. Specifically, we formulate a new problem to tackle these challenges and adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. We illustrate the superiority of our method compared with other approaches on COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average F1-Score of 86%. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds. This new methodology is able to classify cough sounds in data-scarce situations and maintain privacy properties. The outcomes of this work can be a fundamental framework for building support systems for the detection and diagnosis of cough-related diseases.Comment: 21 pages, 5 figure

Context-aware Knowledge-based Systems: A Literature Review

Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed

Chemical constituents from the leaves of Styrax argentifolius H.L. Li and their antioxidative activity

Searching for bioactive agents from medicinal plants, the phytochemical investigation on the EtOAc extract of the Vietnamese Styrax argentifolius leaves has resulted in the isolation and structural determination of five compounds, including one nor-neolignan egonoic acid (1), one lignan (+)-pinoresinol (2), one sterol (20R)-3β-hydroxysitgmasta-5,22-dien-7-one (3), and two triterpenoids lupeol (4), and 2α,3α,24-trihydroxy-urs-12-en-28-oic acid (5). The chemical structures of these secondary metabolites were elucidated by NMR and MS spectral data. All isolated compounds were first observed in S. argentifolius species. Sterol 3 and triterpenoid 5 were detected in genus Styrax for the first time. With the IC50 value of 19.10 µg/mL, compound 2 possessed the strong activity in DPPH radical scavenging assay