Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Method to Determine which Combination of Keywords Triggered Automatic Filtering of a Message

WeChat, the most popular social media platform in China, has over one billion monthly active users. China-based users of the platform are subject to automatic filtering of chat messages limiting their ability to freely communicate. WeChat is one among many Chinese Internet platforms which automatically filter content using keyword combinations, where if every keyword component belonging to a blacklisted keyword combination appears in a message then it is filtered. Discovering these sensitive combinations has previously been performed by sending messages containing potentially sensitive news articles and, if the article is filtered, attempting to isolate the triggering keyword combination from the article by sending additional messages over the platform. However, due to increasing restrictions on account registration, this testing has become decreasingly economical. In order to improve its economy, we analyzed the algorithm previously used to extract keyword combinations from news articles and found large areas of improvement in addition to subtle flaws. We evaluate multiple approaches borrowing concepts from group testing literature and present an algorithm which eliminates the aforementioned flaws and which requires on average 10.3% as many messages as the one previously used.
Percentages, Probabilities and Professions of Performance

Experimental cybersecurity publication should provide readers with a reliable report of the experimental methods, dataset(s) used and full analysis of the results to allow the readers to fully understand the capabilities and limitations of the experiment, and to compare the results to other similar tools or processes. This paper provides an example of looking at experimental results a few different ways, in an attempt to get a better understanding of the underlying processes. We encourage other authors to do the same. We conclude with some basic recommendations.
I don't own the data": End User Perceptions of Smart Home Device Data Practices and Risks

Smart homes are more connected than ever before, with a variety of commercial devices available. The use of these devices introduces new security and privacy risks in the home, and needs for helping users to understand and mitigate those risks. However, we still know little about how everyday users understand the data practices of smart home devices, and their concerns and behaviors regarding those practices. To bridge this gap, we conducted a semi-structured interview study with 23 smart home users to explore what people think about smart home device data collection, sharing, and usage practices; how that knowledge affects their perceived risks of security and privacy; and the actions they take to resolve those risks. Our results reveal that while people are uncertain about manufacturers' data practices, users' knowledge of their smart home does not strongly influence their threat models and protection behaviors. Instead, users' perceptions and concerns are largely shaped by their experiences in other computing contexts and with organizations. Based on our findings, we provide several recommendations for policymakers, researchers and designers to contribute to users' risk awareness and security and privacy practices in the smart home.
Certified Phishing: Taking a Look at Public Key Certificates of Phishing Websites

The share of phishing websites using HTTPS has been constantly increasing over the last years. As a consequence, the simple user advice to check whether a website is HTTPSprotected is no longer effective against phishing. At the same time, the use of certificates in the context of phishing raises the question if the information contained in them could be used to detect phishing websites. In this paper we take a first step towards answering this question. To this end, we analyze almost 10 000 valid certificates queried from phishing websites and compare them to almost 40 000 certificates collected from benign sites. Our analysis shows that it is generally impossible to differentiate between benign sites and phishing sites based on the content of their certificates alone. However, we present empirical evidence that current phishing websites for popular targets do typically not replicate the issuer and subject information.
Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm–1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.
Replication: No One Can Hack My Mind Revisiting a Study on Expert and Non-Expert Security Practices and Advice

A 2015 study by Iulia Ion, Rob Reeder, and Sunny Con-solvo examined the self-reported security behavior of security experts and non-experts. They also analyzed what kind of security advice experts gave to non-experts and how realistic and effective they think typical advice is. Now, roughly four years later, we aimed to replicate and extend this study with a similar set of non-experts and a different set of experts. For the non-experts, we recruited 288 MTurk participants, just as Ion et al. did. We also recruited 75 mostly European security experts, in contrast to the mostly US sample from Ion et al. Our ﬁndings show that despite the different samples and the four years that have passed, the most common pieces of expert advice are mostly unchanged, with one notable exception. In addition, we did see a fair amount of ﬂuctuation in the long tail of advice. Non-expert self-reported behavior, however, is unchanged, meaning that the gap be-tween experts and non-experts seen in Ion et al.’s work is still just as prominent in our study. To extend the work, we also conducted an A/B study to get a better understanding of one of the key questions concerning experts’ recommendations, and we identiﬁed types of advice where research by the usable security community is most sorely needed.
Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.
Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.
An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization

One of the unavoidable bottlenecks in the public application of passive signal (e.g., received signal strength, magnetic) fingerprinting-based indoor localization technologies is the extensive human effort that is required to construct and update database for indoor positioning. In this paper, we propose an accurate visual-inertial integrated geo-tagging method that can be used to collect fingerprints and construct the radio map by exploiting the crowdsourced trajectory of smartphone users. By integrating multisource information from the smartphone sensors (e.g., camera, accelerometer, and gyroscope), this system can accurately reconstruct the geometry of trajectories. An algorithm is proposed to estimate the spatial location of trajectories in the reference coordinate system and construct the radio map and geo-tagged image database for indoor positioning. With the help of several initial reference points, this algorithm can be implemented in an unknown indoor environment without any prior knowledge of the floorplan or the initial location of crowdsourced trajectories. The experimental results show that the average calibration error of the fingerprints is 0.67 m. A weighted k-nearest neighbor method (without any optimization) and the image matching method are used to evaluate the performance of constructed multisource database. The average localization error of received signal strength (RSS) based indoor positioning and image based positioning are 3.2 m and 1.2 m, respectively, showing that the quality of the constructed indoor radio map is at the same level as those that were constructed by site surveying. Compared with the traditional site survey based positioning cost, this system can greatly reduce the human labor cost, with the least external information.
Dual Frequency Orbiter-Radar System for the Observation of Seas and Tides on Titan: Extraterrestrial Oceanography from Satellite

Saturn’s largest moon, Titan, is believed to have a ~100 km thick ice shell above a global ocean of liquid water. Organic materials, including liquid hydrocarbon lakes and seas in its polar terrain, cover Titan’s surface, which makes it a world of two oceans. The RADAR instrument on board Cassini, was able to probe lakes and seas during few dedicated altimetric observations, revealing its capability to work as a sounder. Herein, we describe the design of, and scientific motivation for, a dual frequency X/Ka-band radar system that is able to investigate Titan’s subsurface liquid water ocean, as well as the depth and composition of its surface liquid hydrocarbon basins. The proposed system, which could take advantage of the telecommunications dish, can operate as a sounder, as Synthetic Aperture Radar (SAR) able to map the surface at tens meters of scale resolution, and when data are acquired from close-adjacent orbits, as a repeat-pass SAR interferometer (InSAR). The instrument, which is based on the architecture of the Cassini RADAR, can also characterize Titan’s interior by using geophysical measurements of the tidal amplitude to derive high accuracy estimates of the Love number h2 from a 1500 km circular orbit.
A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance

Today, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, such approaches are still hampered by long training times. Traditional spectral–spatial hyperspectral image classification only utilizes spectral features at the pixel level, without considering the correlation between local spectral signatures. Our article has tested a novel hyperspectral image classification pattern, using random-patches convolution and local covariance (RPCC). The RPCC is an effective two-branch method that, on the one hand, obtains a specified number of convolution kernels from the image space through a random strategy and, on the other hand, constructs a covariance matrix between different spectral bands by clustering local neighboring pixels. In our method, the spatial features come from multi-scale and multi-level convolutional layers. The spectral features represent the correlations between different bands. We use the support vector machine as well as spectral and spatial fusion matrices to obtain classification results. Through experiments, RPCC is tested with five excellent methods on three public data-sets. Quantitative and qualitative evaluation indicators indicate that the accuracy of our RPCC method can match or exceed the current state-of-the-art methods.
Biosynthesis of the human milk oligosaccharide 3‐fucosyllactose in metabolically engineered Escherichia coli via the salvage pathway through increasing GTP synthesis and β‐galactosidase modification

3‐Fucosyllactose (3‐FL) is one of the major fucosylated oligosaccharides in human milk. Along with 2′‐fucosyllactose (2′‐FL), it is known for its prebiotic, immunomodulator, neonatal brain development, and antimicrobial function. Whereas the biological production of 2′‐FL has been widely studied and made significant progress over the years, the biological production of 3‐FL has been hampered by the low activity and insoluble expression of α‐1,3‐fucosyltransferase (FutA), relatively low abundance in human milk oligosaccharides compared with 2′‐FL, and lower digestibility of 3‐FL than 2′‐FL by bifidobacteria. In this study, we report the gram‐scale production of 3‐FL using E. coli BL21(DE3). We previously generated the FutA quadruple mutant (mFutA) with four site mutations at S46F, A128N, H129E, Y132I, and its specific activity was increased by nearly 15 times compared with that of wild‐type FutA owing to the increase in kcat and the decrease in Km. We overexpressed mFutA in its maximum expression level, which was achieved by the optimization of yeast extract concentration in culture media. We also overexpressed L‐fucokinase/GDP‐ L‐fucose pyrophosphorylase to increase the supply of GDP‐fucose in the cytoplasm. To increase the mass of recombinant whole‐cell catalysts, the host E. coli BW25113 was switched to E. coli BL21(DE3) because of the lower acetate accumulation of E. coli BL21(DE3) than that of E. coli BW25113. Finally, the lactose operon was modified by partially deleting the sequence of LacZ (lacZΔm15) for better utilization of D‐lactose. Production using the lacZΔm15 mutant yielded 3‐FL concentration of 4.6 g/L with the productivity of 0.076 g·L−1·hr−1 and the specific 3‐FL yield of 0.5 g/g dry cell weight.
CCA1 and LHY contribute to nonhost resistance to Pyricularia oryzae (syn. Magnaporthe oryzae) in Arabidopsis thaliana

ABSTRACT The circadian clock enables plants to adapt to their environment and control numerous physiological processes, including plant–pathogen interactions. However, it is unknown if the circadian clock controls nonhost resistance (NHR) in plants. To find out, we analyzed microarray data with the web-based tool DIURNAL to reveal that NHR-related genes show rhythmic expression patterns in the absence of a pathogen challenge. Our clock mutant analyses found that cca1-1 lhy-11 double mutant showed compromised NHR to Pyricularia oryzae, suggesting that two components of the circadian clock, CCA1 and LHY, are involved in regulating penetration resistance in Arabidopsis thaliana. By analyzing pen2 double mutants, we revealed that CCA1 contributes to time-of-day-dependent penetration resistance as a positive regulator and that LHY regulates post-penetration resistance as a positive regulator. Taken together, our results suggest that the circadian clock regulates the time-of-day-dependent NHR to P. oryzae and thus enables A. thaliana to counteract pathogen attacks. Abbreviations: EE: evening element; ETI: effector-triggered immunity; NHR: nonhost resistance; PAMP: pathogen-associated molecular pattern; PTI: PAMP-triggered immunity; SAR: systemic acquired resistance. Graphical abstract In pen2 mutant, CCA1 contributes to time-of-day-dependent penetration resistance as a positive regulator and LHY regulates post-penetration resistance as a positive regulator. The circadian clock regulates the time-of-day-dependent NHR to P. oryzae and thus enables A. thaliana to counteract pathogen attacks.
Scaled-up direct-current generation in MoS2 multilayers-based moving heterojunctions.

Techniques for scaling up the direct-current (d.c.) triboelectricity generation in MoS2 multilayers-based Schottky nanocontacts are vital for exploiting the nanoscale phenomenon for real world applications of energy harvesting and sensing. Here, we show that the scaling up the d.c. output can be realized by using various MoS2 multilayers-based heterojunctions including metal/semiconductor (MS), metal/insulator (tens of nanometers)/semiconductor (MIS), and semiconductor/insulator (a few nanometers)/semiconductor (SIS) moving structures. It is shown that the tribo-excited energetic charge carriers can overcome the interfacial potential barrier by different mechanisms, such as thermionic emission, defect conduction, and quantum tunneling in the case of MS, MIS, and SIS moving structures. By tailoring the interface structure, it is possible to trigger electrical conduction resulting in optimized power output. We also show that the band bending in the surface charged region (SCR) of MoS2 determines the direction of the d.c. power output. Our experimental results show that engineering the interface structure opens up new avenues for developing next-generation semiconductor-based mechanical energy conversion with high performance.
Inner ear organoids: new tools to understand neurosensory cell development, degeneration and regeneration

ABSTRACT The development of therapeutic interventions for hearing loss requires fundamental knowledge about the signaling pathways controlling tissue development as well as the establishment of human cell-based assays to validate therapeutic strategies ex vivo. Recent advances in the field of stem cell biology and organoid culture systems allow the expansion and differentiation of tissue-specific progenitors and pluripotent stem cells in vitro into functional hair cells and otic-like neurons. We discuss how inner ear organoids have been developed and how they offer for the first time the opportunity to validate drug-based therapies, gene-targeting approaches and cell replacement strategies. Summary: Inner ear organoids from pluripotent stem cells and tissue-specific progenitors provide new tools to validate drug-based therapies, gene-targeting approaches and cell replacement strategies for hearing loss.
Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble

Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.