Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to peoples productivity and wellbeing. However, we currently have limi
ted understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).
As an emerging business phenomenon especially in China, instant messaging (IM) based social commerce is growing increasingly popular, attracting hundreds of millions of users and is becoming one important way where people make everyday purchases. Suc
h platforms embed shopping experiences within IM apps, e.g., WeChat, WhatsApp, where real-world friends post and recommend products from the platforms in IM group chats and quite often form lasting recommending/buying relationships. How and why do users engage in IM based social commerce? Do such platforms create novel experiences that are distinct from prior commerce? And do these platforms bring changes to user social lives and relationships? To shed light on these questions, we launched a qualitative study where we carried out semi-structured interviews on 12 instant messaging based social commerce users in China. We showed that IM based social commerce: 1) enables more reachable, cost-reducing, and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity, and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships. We demonstrate that all these unique aspects link closely to the characteristics of IM platforms, as well as the coupling of user social and economic lives under such business model. Our study provides important research and design implications for social commerce, and decentralized, trusted socio-technical systems in general.
Linear carbon chains (LCCs) have been shown to grow inside double-walled carbon nanotubes (DWCNTs) but isolating them from this hosting material represents one of the most challenging tasks towards applications. Herein we report the extraction and se
paration of LCCs inside single-wall carbon nanotubes (LCCs@SWCNTs) extracted from a double walled host LCCs@DWCNTs by applying a combined tip-ultrasonic and density gradient ultracentrifugation (DGU) process. High-resolution transmission electron microscopy (HRTEM), optical absorption, and Raman spectroscopy show that not only short LCCs but clearly long LCCs (LLCCs) can be extracted and separated from the host. Moreover, the LLCCs can even be condensed by DGU. The Raman spectral frequency of LCCs remains almost unchanged regardless of the presence of the outer tube of the DWCNTs. This suggests that the major importance of the outer tubes is making the whole synthesis viable. We have also been able to observe the interaction between the LCCs and the inner tubes of DWCNTs, playing a major role in modifying the optical properties of LCCs. Our extraction method suggests the possibility towards the complete isolation of LCCs from CNTs.
Given a large number of low-level heterogeneous categorical alerts from an anomaly detection system, how to characterize complex relationships between different alerts, filter out false positives, and deliver trustworthy rankings and suggestions to e
nd users? This problem is motivated by and generalized from applications in enterprise security and attack scenario reconstruction. While existing techniques focus on either reconstructing abnormal scenarios or filtering out false positive alerts, it can be more advantageous to consider the two perspectives simultaneously in order to improve detection accuracy and better understand anomaly behaviors. In this paper, we propose CAR, a collaborative alerts ranking framework that exploits both temporal and content correlations from heterogeneous categorical alerts. CAR first builds a tree-based model to capture both short-term correlations and long-term dependencies in each alert sequence, which identifies abnormal action sequences. Then, an embedding-based model is employed to learn the content correlations between alerts via their heterogeneous categorical attributes. Finally, by incorporating both temporal and content dependencies into one optimization framework, CAR ranks both alerts and their corresponding alert patterns. Our experiments, using real-world enterprise monitoring data and real attacks launched by professional hackers, show that CAR can accurately identify true positive alerts and successfully reconstruct attack scenarios at the same time.
