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As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities and lacks reasonable interpretability. Recently, a deep unfolding technique has attracted widespread concern due to the more explainable perspective and weaker data dependency. More importantly, it has been proven that deep unfolding enables convergence acceleration when applied to iterative algorithms. On this basis, we rigorously deduce an iterative sparse Bayesian learning (SBL) algorithm and construct a Bayesian deep unfolded network in a one-to-one correspondence. Moreover, the common but intractable off-grid errors, caused by grid mismatch, are directly considered in the signal model and computed in the iterative process. In addition, minimum hole array, little considered in deep unfolding, is adopted to further improve estimation performance owing to the maximized array degrees of freedom (DOFs). Extensive simulation results are presented to illustrate the superiority of the proposed method beyond other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel "double decomposition method" that employs the "cloud radiative kernel" and "cloud regime" concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies. Plain Language Summary: Climate models project that rising levels of greenhouse gas emissions will raise Earth's global mean surface air temperature. However, the amount of warming to be expected remains highly uncertain. A main underlying cause for this uncertainty is the representation of atmospheric clouds in these models. In particular, the response of clouds to global warming, known as the "cloud feedback" over the Southern Ocean has been linked to higher levels of projected warming. To address the uncertainty, we apply a novel method to two satellite data sets. This method teases apart contributions from changing cloud properties such as their horizontal spatial coverage and their ability to reflect sunlight, to the cloud feedback, and then further dissects each of these contributions into those arising from changes occurring both within groups of cloud types and changes occurring between these groups. Results suggest that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds exacerbates warming in the Southern Ocean but are partially counterbalanced by cloud thickening in part due to stronger near‐surface winds in the same region. Our novel methods and results can potentially be used to evaluate climate models and aid in tracing their shortcomings to poorly represented physical processes. Key Points: MODIS satellite observations indicate that in the Southern Ocean the interannual cloud optical depth feedback is slightly positiveMore frequently occurring thin low‐level clouds and fewer storm‐track clouds contribute to a positive feedback in the Southern OceanStrengthened near‐surface winds contribute to a negative low‐level cloud feedback in the Southern Ocean [ABSTRACT FROM AUTHOR]

For direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian learning (SBL) is highly favored by researchers owing to its excellent estimation performance. However, traditional SBL-based methods always assign Gaussian priors to parameters to be solved, leading to moderate sparse signal recovery (SSR) effects. The reason is Gaussian priors play a similar role to l 2 regularization in sparsity constraint. Therefore, numerous methods are developed by adopting hierarchical priors that are used to perform better than Gaussian priors. However, these methods are in straitened circumstances when multiple measurement vector (MMV) data are adopted. On this basis, a block-sparse SBL method (named BSBL) is developed to handle DOA estimation problems in MMV models. The novelty of BSBL is the combination of hierarchical priors and block-sparse model originating from MMV data. Therefore, on the one hand, BSBL transfers the MMV model to a block-sparse model by vectorization so that Bayesian learning is directly performed, regardless of the prior independent assumption of different measurement vectors and the inconvenience caused by the solution of matrix form. On the other hand, BSBL inherited the advantage of hierarchical priors for better SSR ability. Despite the benefit, BSBL still has the disadvantage of relatively large computation complexity caused by high dimensional matrix operations. In view of this, two operations are implemented for low complexity. One is reducing the matrix dimension of BSBL by approximation, generating a method named BSBL-APPR, and the other is embedding the generalized approximate message passing (GAMB) technique into BSBL so as to decompose matrix operations into vector or scale operations, named BSBL-GAMP. Moreover, BSBL is able to suppress temporal correlation and handle wideband sources easily. Extensive simulation results are presented to prove the superiority of BSBL over other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper discusses the influence of pH value, temperature and current density on the crystalline and amorphous structure of Ni-W alloy coatings during the electroplating process. The relationship between the corrosion resistance of Ni-W alloy coating and its crystal structure is discussed. In addition, the most common types of ternary systems and composite coatings for electrodeposited Ni-W alloy coatings are introduced. The effects of element types and contents on the hardness, wear resistance, corrosion resistance and thermal stability of Ni-W alloy coatings are analyzed. Finally, the current problems and development directions of Ni-W alloy coatings are proposed.

This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19. [ABSTRACT FROM AUTHOR]

The direction-of-arrival (DOA) estimation of wideband signals, based on sparse signal reconstruction, has recently been proposed, owing to its unique high-resolution performance. As a typical tool of sparse signal reconstruction, sparse Bayesian learning (SBL) enhances little sparsity in most works, leading to a non-robust local fitting. To significantly enhance sparsity, we proposed a novel hierarchical Bayesian prior framework, and deduced a novel iterative approach. It was discovered that the iterative approach had a lower computational complexity than the majority of current state-of-the-art algorithms. Besides, the proposed approach achieves a high angular estimation accuracy and sparsity performance, by utilizing the joint sparsity of the multiple measurement vector (MMV) models. Moreover, the approach stabilizes the estimated values between different frequencies or snapshots, so as to obtain a flat spatial spectrum. Extensive simulation results are presented, to demonstrate the superior performance of our method. [ABSTRACT FROM AUTHOR]

The research aims to study the application of remote Femto Second (FS) technology in black silicon material preparation and optical devices. Based on the principle and characteristic research of FS technology, the interaction between FS and silicon is adopted to propose a scheme for preparing black silicon material through experiments. Moreover, the experimental parameters are optimized. Then, the scheme of using the FS for etching polymer optical power splitter as a new technical means is proposed. In addition, while ensuring processing accuracy, the appropriate process parameters of laser etching photoresist are obtained. The results show that the performance of black silicon prepared with SF6 as the background gas is greatly improved in the 400-2200nm range. However, the performance of black silicon samples with the two-layer structure etched at different laser energy densities has little difference. Black silicon with a Se+Si two-layer film structure has the best optical absorption performance in the infrared range of 1100nm-2200nm. Besides, the optical absorption rate is the highest when the laser scanning rate is 0.5mm/s. In the band of >1100nm, when the maximum laser energy density is 6.5kJ/m2, the overall absorption of the etched sample is the worst. The absorption rate is the best when the laser energy density is 3.9kJ/m2. It suggests that the proper parameter selection greatly impacts the quality of the final laser-etched sample. [ABSTRACT FROM AUTHOR]

In this research, we study an effective method to encourage users to generate stronger passwords. Specifically, we propose a novel design of password strength meters that incorporates contextual information to help users digest the message generated by the password strength meter. We evaluate our design by leveraging three independent and complementary methods: a survey-based experiment using students to evaluate the saliency of our conceptual design (proof of concept), a controlled laboratory experiment conducted on Amazon Mechanical Turk to test the effectiveness of the proposed design (proof of value), and a randomized field experiment conducted in collaboration with an online forum in Asia to establish proof of use. In each study, we observe that users exposed to the proposed password strength meter are more likely to change their passwords, leading to a new password that is significantly stronger. Our findings suggest that the proposed design of augmented password strength meters is an effective method for promoting secure password behavior among end users. Our design also requires minimal computational resources and technical capabilities. Password-based authentication is the most commonly used method for gaining access to secured systems. Unfortunately, empirical evidence highlights the fact that most passwords are significantly weak, and encouraging users to create stronger passwords is a significant challenge. In this research, we propose a theoretically augmented password strength meter design that is guided by the elaboration likelihood model of persuasion (ELM). We evaluate our design by leveraging three independent and complementary methods: a survey-based experiment using students to evaluate the saliency of our conceptual design (proof of concept), a controlled laboratory experiment conducted on Amazon Mechanical Turk to test the effectiveness of the proposed design (proof of value), and a randomized field experiment conducted in collaboration with an online forum in Asia to establish proof of use. In each study, we observe the changes in users' behavior in response to our proposed password strength meter. We find that the ELM-augmented password strength meter is significantly effective at addressing the challenges of password-based authentication. Users exposed to this strength meter are more likely to change their passwords, leading to a new password that is significantly stronger. Our findings suggest that the proposed design of augmented password strength meters is an effective method for promoting secure password behavior among end users. History: Alessandro Acquisti, Senior Editor; Pallab Sanyal, Associate Editor. Funding: The authors gratefully acknowledge financial support from the Social Science and Humanities Research Council of Canada (SSHRC) [Grant 435-2018-0605]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1125. [ABSTRACT FROM AUTHOR]

Recent findings suggest that cold acclimation can enhance cold resistance in wheat. Dongnongdongmai 1 (DM1) is a winter wheat variety that can overwinter at −30 °C; however, its cold acclimation mechanism is yet to be fully elucidated. Here, we elucidated the potential mechanisms of cold acclimation in DM1 and the China Spring (CS) variety, especially the role of the antioxidant system, using transcriptome and physiological analyses. Cold stress increased H2O2 and O2− production in both varieties; however, CS had higher contents of H2O2 and O2− than DM1. Moreover, cold significantly increased ROS-scavenging activities in DM1, especially at 30 days after exposure. Gene ontology (GO) analysis showed that differentially expressed peroxidase (POD) genes were enriched in antioxidant activity, with most POD genes being significantly upregulated in DM1 under cold acclimation. Additionally, cold acclimation increased the expression of cold acclimation protein (CAP), late embryogenesis abundant protein (LEA), and cold-responsive genes in both varieties, with higher expression levels in DM1. Overall, the results showed that DM1 exhibited a higher cold tolerance than CS during cold acclimation by increasing the expression of POD genes, LEA, CAP, and cold-responsive proteins, improving the understanding of the mechanism of cold resistance in DM1. [ABSTRACT FROM AUTHOR]

Phytophthora root rot is a destructive soybean disease worldwide, which is caused by the oomycete pathogen Phytophthora sojae (P. sojae). Wall-associated protein kinase (WAK) genes, a family of the receptor-like protein kinase (RLK) genes, play important roles in the plant signaling pathways that regulate stress responses and pathogen resistance. In our study, we found a putative Glycine max wall-associated protein kinase, GmWAK1, which we identified by soybean GmLHP1 RNA-sequencing. The expression of GmWAK1 was significantly increased by P. sojae and salicylic acid (SA). Overexpression of GmWAK1 in soybean significantly improved resistance to P. sojae, and the levels of phenylalanine ammonia-lyase (PAL), SA, and SA-biosynthesis-related genes were markedly higher than in the wild-type (WT) soybean. The activities of enzymatic superoxide dismutase (SOD) and peroxidase (POD) antioxidants in GmWAK1-overexpressing (OE) plants were significantly higher than those in in WT plants treated with P. sojae; reactive oxygen species (ROS) and hydrogen peroxide (H2O2) accumulation was considerably lower in GmWAK1-OE after P. sojae infection. GmWAK1 interacted with annexin-like protein RJ, GmANNRJ4, which improved resistance to P. sojae and increased intracellular free-calcium accumulation. In GmANNRJ4-OE transgenic soybean, the calmodulin-dependent kinase gene GmMPK6 and several pathogenesis-related (PR) genes were constitutively activated. Collectively, these results indicated that GmWAK1 interacts with GmANNRJ4, and GmWAK1 plays a positive role in soybean resistance to P. sojae via a process that might be dependent on SA and involved in alleviating damage caused by oxidative stress. [ABSTRACT FROM AUTHOR]

The Haiyang-1C (HY-1C) satellite was launched on September 7, 2018. The Chinese Ocean Color and Temperature Scanner (COCTS) onboard HY-1C has the capacity for ocean color and sea surface temperature (SST) observations. The sun glint reflected on sea surface can interfere with the radiance reaching the field of view of satellite sensor, leading to the higher reflectance that is misclassified as cloud and restrictions of satellite data availability. Due to the lack of ability to avoid sun glint, the bidirectional reflectance distribution function (BRDF) model is implemented in the sun glint correction of HY-1C/COCTS visible images. The atmospheric direct transmittance is calculated by aerosol optical depth (AOD) and Rayleigh optical depth (ROD). The comparisons before and after sun glint correction of visible images at different locations and different time are discussed and analyzed. The spectra are compared before and after the sun glint correction, and it has an obvious decrease in both radiance and reflectance. The performance of the BRDF model in low, moderate, and severe glint contaminations is compared. The results indicate that the BRDF model is useful in the sun glint correction of visible bands and leading to more HY-1C/COCTS clear-sky coverage, as consequence can be applied as the preprocessing in the cloud detection and SST retrieval to improve the clear-sky coverage. [ABSTRACT FROM AUTHOR]

Objective: The goal of this study was to examine the relation between users' reported risk concerns and their choice behaviors in a mobile application (app) selection task. Background: Human users are typically regarded as the weakest link in cybersecurity and privacy protection; however, it is possible to leverage the users' predilections to increase security. There have been mixed results on the relation between users' self-reported privacy concerns and their behaviors. Method: In three experiments, the timing of self-reported risk concerns was either a few weeks before the app-selection task (pre-screen), immediately before it (pre-task), or immediately after it (post-task). We also varied the availability and placement of clear definitions and quizzes to ensure users' understanding of the risk categories. Results: The post-task report significantly predicted the app-selection behaviors, consistent with prior findings. The pre-screen report was largely inconsistent with the reports implemented around the time of the task, indicating that participants' risk concerns may not be stable over time and across contexts. Moreover, the pre-task report strongly predicted the app-selection behaviors only when elaborated definitions and quizzes were placed before the pre-task question, indicating the importance of clear understanding of the risk categories. Conclusion: Self-reported risk concerns may be unstable over time and across contexts. When explained with clear definitions, self-reported risk concerns obtained immediately before or after the app-selection task significantly predicted app-selection behaviors. Application: We discuss implications for including personalized risk concerns during app selection that enable comparison of alternative mobile apps. [ABSTRACT FROM AUTHOR]

Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service's website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker's limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice. [ABSTRACT FROM AUTHOR]

Tamarin is a popular tool dedicated to the formal analysis of security protocols. One major strength of the tool is that it offers an interactive mode, allowing to go beyond what push-button tools can typically handle. Tamarin is for example able to verify complex protocols such as TLS, 5G, or RFID protocols. However, one of its drawback is its lack of automation. For many simple protocols, the user often needs to help Tamarin by writing specific lemmas, called "sources lemmas", which requires some knowledge of the internal behaviour of the tool. In this paper, we propose a technique to automatically generate sources lemmas in Tamarin. Following the intuition of manually written sources lemmas, our lemmas try to keep track of the origin of a term by looking into emitted messages or facts. We prove formally that our lemmas indeed hold, for arbitrary protocols that make use of cryptographic primitives that can be modelled with a subterm convergent equational theory (modulo associativity and commutativity). We have implemented our approach within Tamarin. Our experiments show that, in most examples of the literature, we are now able to generate suitable sources lemmas automatically, in replacement of the hand-written lemmas. As a direct application, many simple protocols can now be analysed fully automatically, while they previously required user interaction. [ABSTRACT FROM AUTHOR]

The threat of a cryptographically relevant quantum computer contributes to an increasing interest in the field of post-quantum cryptography (PQC). Compared to existing research efforts regarding the integration of PQC into the Transport Layer Security (TLS) protocol, industrial communication protocols have so far been neglected. Since industrial cyber-physical systems (CPS) are typically deployed for decades, protection against such long-term threats is needed. In this work, we propose two novel solutions for the integration of post-quantum (PQ) primitives (digital signatures and key establishment) into the industrial protocol Open Platform Communications Unified Architecture (OPC UA): a hybrid solution combining conventional cryptography with PQC and a solution solely based on PQC. Both approaches provide mutual authentication between client and server and are realized with certificates fully compliant to the X.509 standard. We implement the two solutions and measure and evaluate their performance across three different security levels. All selected algorithms (Kyber, Dilithium, and Falcon) are candidates for standardization by the National Institute of Standards and Technology (NIST). We show that Falcon is a suitable option – especially – when using floating-point hardware provided by our ARM-based evaluation platform. Our proposed hybrid solution provides PQ security for early adopters but comes with additional performance and communication requirements. Our solution solely based on PQC shows superior performance across all evaluated security levels in terms of handshake duration compared to conventional OPC UA but comes at the cost of increased handshake sizes. In addition to our performance evaluation, we provide a proof of security in the symbolic model for our two PQC-based variants of OPC UA. For this proof, we use the cryptographic protocol verifier ProVerif and formally verify confidentiality and authentication properties of our quantum-resistant variants. [ABSTRACT FROM AUTHOR]

Objective: Evaluate the effectiveness of training embedded within security warnings to identify phishing webpages.Background: More than 20 million malware and phishing warnings are shown to users of Google Safe Browsing every week. Substantial click-through rate is still evident, and a common issue reported is that users lack understanding of the warnings. Nevertheless, each warning provides an opportunity to train users about phishing and how to avoid phishing attacks.Method: To test use of phishing-warning instances as opportunities to train users' phishing webpage detection skills, we conducted an online experiment contrasting the effectiveness of the current Chrome phishing warning with two training-embedded warning interfaces. The experiment consisted of three phases. In Phase 1, participants made login decisions on 10 webpages with the aid of warning. After a distracting task, participants made legitimacy judgments for 10 different login webpages without warnings in Phase 2. To test the long-term effect of the training, participants were invited back a week later to participate in Phase 3, which was conducted similarly as Phase 2.Results: Participants differentiated legitimate and fraudulent webpages better than chance. Performance was similar for all interfaces in Phase 1 for which the warning aid was present. However, training-embedded interfaces provided better protection than the Chrome phishing warning on both subsequent phases.Conclusion: Embedded training is a complementary strategy to compensate for lack of phishing webpage detection skill when phishing warning is absent.Application: Potential applications include development of training-embedded warnings to enable security training at scale. [ABSTRACT FROM AUTHOR]

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases. [ABSTRACT FROM AUTHOR]

In this communication, we have discussed and compared the different drying processes i.e. flat plate solar collector, open sun and mechanical dryer with the preservative solution of Sodium Benzoate. Loquats were pretreated with preservative solution and subject to the drying process. Drying kinetics in terms of moisture lost per hour and drying rate, texture and antioxidants retention were determined before and after drying. Statistical analysis of the data showed that the different drying process has significantly affected on the kinetics of loquat fruits with the confidence interval of (α ≤ 0.05), whereas the preservative solution showed a non-significant effect on texture and antioxidants retention. High moisture lost per hour (5.4%) was recorded in mechanical dryer followed by flat plate solar collector dryer (3.7 %) while minimum (1.8%) was recorded in open sun drying. Good textured dried loquat with optimum hardness (313 g) and stiffness (141 g mm−1) was found in flat plate solar collector dried samples. Similarly more antioxidants (52%) retained in samples dried with hot air using flat plate solar collector followed by the mechanical dryer (47%) while minimum (41%) was recorded in open sun drying. Although mechanical drying is fast, it does not help to maintain a good texture and more antioxidants. From the study, it was concluded that hot air drying using a flat plate solar collector is better for drying process to get valuable dried loquat with good texture and more antioxidants. [ABSTRACT FROM AUTHOR]

The evolution of surface topography of 304L stainless steel, induced by long pulse laser with a wavelength of 1070 nm and a pulse width of 10 μs and a pulse number of 400 in air, was investigated in the fluence range between around 3 J/cm2 and 210 J/cm2. Three-dimensional (3D) morphologies and two-dimensional (2D) cross-sectional profiles of microstructures at different laser fluences were compared. Furthermore, surface profiles of the microstructures were quantified by using the inner diameter, the outer diameter, the elevation of central area and the rim height thereof. The trends of the above four geometrical parameters and surface profiles suggest five laser fluence ranges, where the corresponding topographies change from bump to crater. Energy dispersive spectrometer (EDS) was used to analyze surface elements of different microstructures, and the results showed that the content of oxygen had a great influence on surface tension and surface topography. In this study, a complex picture was drawn where melting, vaporization, solidification, melt displacement, melt expulsion and plasma shielding take place at different laser fluences. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of domain highlighting in helping users identify whether Web pages are legitimate or spurious.Background: As a component of the URL, a domain name can be overlooked. Consequently, browsers highlight the domain name to help users identify which Web site they are visiting. Nevertheless, few studies have assessed the effectiveness of domain highlighting, and the only formal study confounded highlighting with instructions to look at the address bar.Method: We conducted two phishing detection experiments. Experiment 1 was run online: Participants judged the legitimacy of Web pages in two phases. In Phase 1, participants were to judge the legitimacy based on any information on the Web page, whereas in Phase 2, they were to focus on the address bar. Whether the domain was highlighted was also varied. Experiment 2 was conducted similarly but with participants in a laboratory setting, which allowed tracking of fixations.Results: Participants differentiated the legitimate and fraudulent Web pages better than chance. There was some benefit of attending to the address bar, but domain highlighting did not provide effective protection against phishing attacks. Analysis of eye-gaze fixation measures was in agreement with the task performance, but heat-map results revealed that participants' visual attention was attracted by the highlighted domains.Conclusion: Failure to detect many fraudulent Web pages even when the domain was highlighted implies that users lacked knowledge of Web page security cues or how to use those cues.Application: Potential applications include development of phishing prevention training incorporating domain highlighting with other methods to help users identify phishing Web pages. [ABSTRACT FROM AUTHOR]

The work by Harrison, Ruzzo, and Ullman (the HRU paper) on safety in the context of the access matrix model is widely considered to be foundational work in access control. In this paper, we address two errors we have discovered in the HRU paper. To our knowledge, these errors have not been previously reported in the literature. The first error regards a proof that shows that safety analysis for mono-operational HRU systems is in (\bf NP). The error stems from a faulty assumption that such systems are monotonic for the purpose of safety analysis. We present a corrected proof in this paper. The second error regards a mapping from one version of the safety problem to another that is presented in the HRU paper. We demonstrate that the mapping is not a reduction, and present a reduction that enables us to infer that the second version of safety introduced in the HRU paper is also undecidable for the HRU scheme. These errors lead us to ask whether the notion of safety as defined in the HRU paper is meaningful. We introduce other notions of safety that we argue have more intuitive appeal, and present the corresponding safety analysis results for the HRU scheme. [ABSTRACT FROM AUTHOR]