Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed-Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside "social bubbles." Our results could lead to a deeper understanding of how technology biases our exposure to new information.

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside “social bubbles.”

We analyze the relationship between partisanship, echo chambers, and vulnerability to online mis-information by studying news sharing behavior on Twitter. While our results confirm prior findings that online misinformation sharing is strongly correlated with right-leaning partisanship, we also uncover a similar, though weaker, trend among left-leaning users. Because of the correlation be-tween a user’s partisanship and their position within a partisan echo chamber, these types of influ-ence are confounded. To disentangle their effects, we performed a regression analysis and found that vulnerability to misinformation is most strongly influenced by partisanship for both left- and right-leaning users.

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Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization (AIM) model (Bruce & Tsotsos, 2009) quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model (Karklin & Lewicki, 2009) captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%-80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework.

Abstract-Efficient handling of faults during operation is highly dependent on the interval (latency) from the time embedded instruments detect errors to the time when the fault manager localizes the errors. In this paper, we propose a self-reconfiguring IEEE 1687 network in which all instruments that have detected errors are automatically included in the scan path. To enable self-reconfiguration, we propose a modified segment insertion bit (SIB) compliant to IEEE 1687. We provide time analyses on error detection and fault localization for single and multiple faults, and we suggest how the self-reconfiguring IEEE 1687 network should be designed such that time for error detection and fault localization is kept low and deterministic. For validation, we implemented and performed post-layout simulations for one self-reconfiguring network. We show that compared to previous schemes, our proposed network significantly reduces the fault localization time.

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This paper presents a comprehensive outlook for the current technology status and the prospective upcoming advancements. VLSI scaling trends and technology advancements in the context of sub-10-nm technologies are reviewed as well as the associated device modeling approaches and compact models of transistor structures are considered. As technology goes into the nanometer regime, semiconductor devices are confronting numerous short-channel effects. Bulk CMOS technology is developing and innovating to overcome these constraints by introduction of (i) new technologies and new materials and (ii) new transistor architectures. Technology boosters such as high-k/metal-gate technologies, ultra-thin-body SOI, Ge-on-insulator (GOI), AIII–BV semiconductors, and band-engineered transistor (SiGe or Strained Si-channel) with high-carrier-mobility channels are examined. Nonclassical device structures such as novel multiple-gate transistor structures including multiple-gate field-effect transistors, FD-SOI MOSFETs, CNTFETs, and SETs are examined as possible successors of conventional CMOS devices and FinFETs. Special attention is devoted to gate-all-around FETs and, respectively, nanowire and nanosheet FETs as forthcoming mainstream replacements of FinFET. In view of that, compact modeling of bulk CMOS transistors and multiple-gate transistors are considered as well as BSIM and PSP multiple-gate models, FD-SOI MOSFETs, CNTFET, and SET modeling are reviewed.