CLAN: A Contrastive Learning based Novelty Detection Framework for Human Activity Recognition

In ambient assisted living, human activity recognition from time series sensor data mainly focuses on predefined activities, often overlooking new activity patterns. We propose CLAN, a two-tower contrastive learning-based novelty detection framework with diverse types of negative pairs for human activity recognition. It is tailored to challenges with human activity characteristics, including the significance of temporal and frequency features, complex activity dynamics, shared features across activities, and sensor modality variations. The framework aims to construct invariant representations of known activity robust to the challenges. To generate suitable negative pairs, it selects data augmentation methods according to the temporal and frequency characteristics of each dataset. It derives the key representations against meaningless dynamics by contrastive and classification losses-based representation learning and score function-based novelty detection that accommodate dynamic numbers of the different types of augmented samples. The proposed two-tower model extracts the representations in terms of time and frequency, mutually enhancing expressiveness for distinguishing between new and known activities, even when they share common features. Experiments on four real-world human activity datasets show that CLAN surpasses the best performance of existing novelty detection methods, improving by 8.3%, 13.7%, and 53.3% in AUROC, balanced accuracy, and [email protected] metrics respectively

Automated Contact Tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.Comment: 10 pages and 2 figure

A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space

Human activity recognition (HAR) is a key challenge in pervasive computing and its solutions have been presented based on various disciplines. Specifically, for HAR in a smart space without privacy and accessibility issues, data streams generated by deployed pervasive sensors are leveraged. In this paper, we focus on a group activity by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme which extracts causality patterns from pervasive sensor event sequences generated by a group of users to support as good recognition accuracy as the state-of-the-art graphical model. To filter out irrelevant noise events from a given data stream, a set of rules is leveraged to highlight causally related events. Then, a pattern-tree algorithm extracts frequent causal patterns by means of a growing tree structure. Based on the extracted patterns, a weighted sum-based pattern matching algorithm computes the likelihoods of stored group activities to the given test event sequence by means of matched event pattern counts for group activity recognition. We evaluate the proposed scheme using the data collected from our testbed and CASAS datasets where users perform their tasks on a daily basis and validate its effectiveness in a real environment. Experiment results show that the proposed scheme performs higher recognition accuracy and with a small amount of runtime overhead than the existing schemes

Multiple Activities of Aspartate Transcarbamoylase in Burkholderia cepacia: Requirement for an Active Dihydroorotase for Assembly into the Dodecameric Holoenzyme

The aspartate transcarbamoylase (ATCase) was purified from Burkholderia cepacia 25416. In the course of purification, three different ATCase activities appeared namely dodecameric 550 kDa holoenzyme, and two trimeric ATCases of 140 kDa (consists of 47 kDa PyrB subunits) and 120 kDa (consists of 40 kDa PyrB subunits) each. The 120 kDa PyrB polypeptide arose by specific cleavage of the PyrB polypeptide between Ser74 and Val75 creating an active polypeptide short by 74 amino acids. Both the 40 and 47 kDa polypeptides produced active trimers. To compare the enzyme activity of these trimers, an effector assay using nucleotides was performed. The 140 kDa trimer showed inhibition while the 120 kDa polypeptide showed less inhibition. To verify the composition of the pyrBC holoenzyme complex, B. cepacia dihydroorotase (DHOase, subunit size of 45 kDa) was purified by the pMAL protein fusion and purification system and holoenzyme reconstruction was performed using purified ATCase and DHOase. Both the 140 kDa and the 120 kDa trimers could produce holoenzymes of 550 kDa and 510 kDa, respectively. The reconstructed ATCase holoenzyme from cleaved ATCase showed better reconstruction compared to that from uncleaved ATCase in the conventional ATCase activity gel assay. To characterize the relationship between pyrimidine pathway and virulence factor production, motility tests and biofilm assays were conducted using pyrC- mutant. Even though no significant difference in growth rates was observed, there were significant differences between the wild type and mutant in the production of biofilm and virulence factors. This study will help us to understand the structure and regulation of ATCase holoenzyme with DHOase, and facilitate the use of B. cepacia as an applicable bio-tool. Additionally, we can potentially pursue more efficient drug targets for B. cepacia

The Responsibilities of the Director Toward Third Parties:Comparison between Korea and China

如今，股份有限公司的比重越来越大了，它的影响力也越来越大了。这意味着代表董事和业务担当董事的任务比较重要了。 在这个情况下，董事对第三者因做不法行为而造成第三者的损害，董事要承担损害赔偿责任。韩国规定在《韩国商法典》第401条，中国规定在《中国公司法》第153条。《韩国商法典》第401条规定了“董事因恶意或者重过失而懈怠该任务时，董事对第三者要承担损害赔偿责任”。《中国公司法》第153条规定了“董事、高级管理人员违反法律、行政法规或者公司章程的规定，损害股东利益的，股东可以向人民法院提起诉讼”。 董事和公司是委任关系，董事是公司的代理人，董事对公司要负担善管注意义务。如果董事懈怠该任务的话...Nowadays, both rates and influence of Corporation are increasing. This reflects the importance of many directors’ role such as CEO deciding policies. In this situation, when a director does an illegal act to third parties, he/she has to compensate for his/her illegality. This is regulated by article 401 Commercial Law in Korea and article 153 Corporation Law in China. The article 401 of Korea...学位：法学硕士院系专业：法学院_民商法学(含劳动法学、社会保障法学)学号：1292010115430

DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition

With the advancement of IoT technology, recognizing user activities with machine learning methods is a promising way to provide various smart services to users. High-quality data with privacy protection is essential for deploying such services in the real world. Data streams from surrounding ambient sensors are well suited to the requirement. Existing ambient sensor datasets only support constrained private spaces and those for public spaces have yet to be explored despite growing interest in research on them. To meet this need, we build a dataset collected from a meeting room equipped with ambient sensors. The dataset, DOO-RE, includes data streams from various ambient sensor types such as Sound and Projector. Each sensor data stream is segmented into activity units and multiple annotators provide activity labels through a cross-validation annotation process to improve annotation quality. We finally obtain 9 types of activities. To our best knowledge, DOO-RE is the first dataset to support the recognition of both single and group activities in a real meeting room with reliable annotations