Trust and distrust in contradictory information transmission

We analyse the problem of contradictory information distribution in networks of agents with positive and negative trust. The networks of interest are built by ranked agents with different epistemic attitudes. In this context, positive trust is a property of the communication between agents required when message passing is executed bottom-up in the hierarchy, or as a result of a sceptic agent checking information. These two situations are associated with a confirmation procedure that has an epistemic cost. Negative trust results from refusing verification, either of contradictory information or because of a lazy attitude. We offer first a natural deduction system called SecureNDsim to model these interactions and consider some meta-theoretical properties of its derivations. We then implement it in a NetLogo simulation to test experimentally its formal properties. Our analysis concerns in particular: conditions for consensus-reaching transmissions; epistemic costs induced by confirmation and rejection operations; the influence of ranking of the initially labelled nodes on consensus and costs; complexity results

Inference of development activities from interaction with uninstrumented applications

Studying developers’ behavior in software development tasks is crucial for designing effective techniques and tools to support developers’ daily work. In modern software development, developers frequently use different applications including IDEs, Web Browsers, documentation software (such as Office Word, Excel, and PDF applications), and other tools to complete their tasks. This creates significant challenges in collecting and analyzing developers’ behavior data. Researchers usually instrument the software tools to log developers’ behavior for further studies. This is feasible for studies on development activities using specific software tools. However, instrumenting all software tools commonly used in real work settings is difficult and requires significant human effort. Furthermore, the collected behavior data consist of low-level and fine-grained event sequences, which must be abstracted into high-level development activities for further analysis. This abstraction is often performed manually or based on simple heuristics. In this paper, we propose an approach to address the above two challenges in collecting and analyzing developers’ behavior data. First, we use our ActivitySpace framework to improve the generalizability of the data collection. ActivitySpace uses operating-system level instrumentation to track developer interactions with a wide range of applications in real work settings. Secondly, we use a machine learning approach to reduce the human effort to abstract low-level behavior data. Specifically, considering the sequential nature of the interaction data, we propose a Condition Random Field (CRF) based approach to segment and label the developers’ low-level actions into a set of basic, yet meaningful development activities. To validate the generalizability of the proposed data collection approach, we deploy the ActivitySpace framework in an industry partner’s company and collect the real working data from ten professional developers’ one-week work in three actual software projects. The experiment with the collected data confirms that with initial human-labeled training data, the CRF model can be trained to infer development activities from low-level actions with reasonable accuracy within and across developers and software projects. This suggests that the machine learning approach is promising in reducing the human efforts required for behavior data analysis.This work was partially supported by NSFC Program (No. 61602403 and 61572426)

Seismic response mitigation of elevated tanks by HDRB and FPS isolation systems

The aim of the paper is to evaluate the effectiveness of two isolation systems for the seismic protection of elevated steel storage tanks: High Damping Rubber Bearings (HDRB) and Friction Pendulum (FPS) isolators. As a case study, an elevated tank which collapsed during the Kocaeli Earthquake in 1999 at Habas Pharmaceutics plant in Turkey has been studied. A time-history analysis is conducted using lumped mass model to demonstrate the high base shear demand and inevitable collapse of support columns due to the insufficient shear strength. A proper design of HDRB and FPS isolator and a complete non-linear analysis of the isolated tanks prove the high effectiveness of both isolation systems for reducing the response of the tank. Results revealed that the tank with the FPS provides better performance compared to HDRB in terms of the isolation displacement convective base shear demands

Seismic vulnerability mitigation of liquefied gas tanks using concave sliding bearings

The aim of this paper is to evaluate the effectiveness of a concave sliding bearing system for the seismic protection of liquefied gas storage tanks through a seismic fragility analysis. An emblematic case study of elevated steel storage tanks, which collapsed during the 1999 İzmit earthquake at Habas Pharmaceutics plant in Turkey, is studied. Firstly, a fragility analysis is conducted for the examined tank based on a lumped-mass stick model, where the nonlinear shear behaviour of support columns is taken into account by using a phenomenological model. Fragility curves in terms of an efficient intensity measure for different failure modes of structural components demonstrate the inevitable collapse of the tank mainly due to insufficient shear strength of the support columns. A seismic isolation system based on concave sliding bearings, which has been demonstrated a superior solution to seismically protect elevated tanks, is then designed and introduced into the numerical model, accounting for its non-linear behaviour. Finally, a vulnerability analysis for the isolated tank is performed, which proves a high effectiveness of the isolation system in reducing the probability of failure within an expected range of earthquake intensity levels

β-Cell Differentiation of Human Pancreatic Duct-Derived Cells After In Vitro Expansion

β-Cell replacement therapy is a promising field of research that is currently evaluating new sources of cells for clinical use. Pancreatic epithelial cells are potent candidates for β-cell engineering, but their large-scale expansion has not been evidenced yet. Here we describe the efficient expansion and β-cell differentiation of purified human pancreatic duct cells (DCs). When cultured in endothelial growth-promoting media, purified CA19-9+ cells proliferated extensively and achieved up to 22 population doublings over nine passages. While proliferating, human pancreatic duct-derived cells (HDDCs) downregulated most DC markers, but they retained low CK19 and SOX9 gene expression. HDDCs acquired mesenchymal features but differed from fibroblasts or pancreatic stromal cells. Coexpression of duct and mesenchymal markers suggested that HDDCs were derived from DCs via a partial epithelial-to-mesenchymal transition (EMT). This was supported by the blockade of HDDC appearance in CA19-9+ cell cultures after incubation with the EMT inhibitor A83-01. After a differentiation protocol mimicking pancreatic development, HDDC populations contained about 2% of immature insulin-producing cells and showed glucose-unresponsive insulin secretion. Downregulation of the mesenchymal phenotype improved β-cell gene expression profile of differentiated HDDCs without affecting insulin protein expression and secretion. We show that pancreatic ducts represent a new source for engineering large amounts of β-like-cells with potential for treating diabetes

Interacting in Desktop and Mobile Context: Emotion, Trust and Task Performance

The Personal Assistant for onLine Services (PALS) project aims at attuning the interaction with mobile services to the momentary usage context