Examining Effects of Badge Repeatability and Level on Users’ Knowledge Sharing in Online Q&A Communities

This study investigates the differential effects of badge repeatability and level on users’ knowledge sharing behaviors in an online Q&A (Question & Answer) community. Drawing on reinforcement theory and attribution theory of motivation, we conjecture that nonrepeatable badges reinforce individuals’ behaviors primarily by promoting internal attributions that strengthen their self-determination motivation, while repeatable badges reinforce people’s behaviors mainly via external attributions that undermine their self-determination motivation. By using fixed-effects models to analyze a panel data, we observe that nonrepeatable badges can better motivate users to share their knowledge than repeatable badges. In addition, the results show that attaining a higher level of nonrepeatable badges is associated with an increased effect for knowledge sharing, and that attaining a higher level of repeated badges leads to a decreased effect. These findings can contribute to extant literature by offering a probable explanation regarding why some gamified awards can motivate people better than others

A Mutual Attraction Model for Both Assortative and Disassortative Weighted Networks

In most networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a Mutual Attraction Model to characterize weighted evolving networks. By introducing the initial attractiveness $A$ and the general mechanism of mutual attraction (controlled by parameter $m$), the model can naturally reproduce scale-free distributions of degree, weight and strength, as found in many real systems. Simulation results are in consistent with theoretical predictions. Interestingly, we also obtain nontrivial clustering coefficient C and tunable degree assortativity r, depending on $m$ and A. Our weighted model appears as the first one that unifies the characterization of both assortative and disassortative weighted networks.Comment: 4 pages, 3 figure

Securing Cyber-Physical Social Interactions on Wrist-worn Devices

Since ancient Greece, handshaking has been commonly practiced between two people as a friendly gesture to express trust and respect, or form a mutual agreement. In this article, we show that such physical contact can be used to bootstrap secure cyber contact between the smart devices worn by users. The key observation is that during handshaking, although belonged to two different users, the two hands involved in the shaking events are often rigidly connected, and therefore exhibit very similar motion patterns. We propose a novel key generation system, which harvests motion data during user handshaking from the wrist-worn smart devices such as smartwatches or fitness bands, and exploits the matching motion patterns to generate symmetric keys on both parties. The generated keys can be then used to establish a secure communication channel for exchanging data between devices. This provides a much more natural and user-friendly alternative for many applications, e.g., exchanging/sharing contact details, friending on social networks, or even making payments, since it doesn’t involve extra bespoke hardware, nor require the users to perform pre-defined gestures. We implement the proposed key generation system on off-the-shelf smartwatches, and extensive evaluation shows that it can reliably generate 128-bit symmetric keys just after around 1s of handshaking (with success rate >99%), and is resilient to different types of attacks including impersonate mimicking attacks, impersonate passive attacks, or eavesdropping attacks. Specifically, for real-time impersonate mimicking attacks, in our experiments, the Equal Error Rate (EER) is only 1.6% on average. We also show that the proposed key generation system can be extremely lightweight and is able to run in-situ on the resource-constrained smartwatches without incurring excessive resource consumption

General Dynamics of Topology and Traffic on Weighted Technological Networks

For most technical networks, the interplay of dynamics, traffic and topology is assumed crucial to their evolution. In this paper, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r and degree-strength correlation are all in consistence with empirical evidences.Comment: 4 pages, 4 figure

Prediction of stroke risk in patients with transient ischemic attack: ABCD score and its derived scores

Transient Ischemic Attack (TIA) is a high-risk signal of acute ischemic cerebrovascular disease, indicates a significant increase in the risk of ischemic stroke, especially within 7 days. Risk assessment and stratification are important in patient with TIA. A variety of simple prediction scales were developed based on the risk factors for stroke in patients with TIA, such as the California scale, ABCD scale, and ABCD2 scale. Among them, the ABCD scale score is used most commonly, but as its application becomes more and more common, the defects of this scale are also increasingly apparent. In recent years, some derived scales of ABCD score were introduced in order to improve the sensitivity and specificity of prediction. This article reviews the evolution, contents, characteristics, and predictive value of the ABCD score and its derived scales in the prediction of the stroke risk in patients with TIA

rac-1-(Furan-2-ylmeth­yl)-N-nitro-5-(oxolan-2-ylmeth­yl)-1,3,5-triazinan-2-imine

In the title compound C13H19N5O4, which belongs to the insecticidally active neonicotinoid group of compounds, the triazane ring exhibits a half-chair conformation. The large discrepancy between the two nitro O—N—N bond angles [116.1 (2) and 123.98 (19)°] may be attributed to intra­molecular N—H⋯O hydrogen bonding involving one of the nitro O atoms as the acceptor. The delocalization of the electrons extends as far as the nitro group, forming coplanar π-electron networks. In the crystal, inversion dimers lined by pairs of N—H⋯O hydrogen bonds occur