Miracle-dz/UniFAR-FTUs huggingface dataset:
How do app vendors respond to subject access requests? A longitudinal privacy study on iOS and Android Apps

EU data protection laws grant consumers the right to access the personal data that companies hold about them. In a first-of-its-kind longitudinal study, we examine how service providers have complied with subject access requests over four years. In three iterations between 2015 and 2019, we sent subject access requests to vendors of 225 mobile apps popular in Germany. Throughout the iterations, 19 to 26% of the vendors were unreachable or did not reply at all. Our subject access requests were fulfilled in 15 to 53% of the cases, with an unexpected decline between the GDPR enforcement date and the end of our study. The remaining responses exhibit a long list of shortcomings, including severe violations of information security and data protection principles. Some responses even contained deceptive and misleading statements (7 to 13%). Further, 9% of the apps were discontinued and 27% of the user accounts vanished during our study, mostly without proper notification about the consequences for our personal data. While we observe improvements for selected aspects over time, the results indicate that subject access request handling will be unsatisfactory as long as vendors accept such requests via email and process them manually.
How does SARS-CoV-2 cause COVID-19?

The viral receptor on human cells plays a critical role in disease progression Viruses enter cells and initiate infection by binding to their cognate cell surface receptors. The expression and distribution of viral entry receptors therefore regulates their tropism, determining the tissues that are infected and thus disease pathogenesis. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third human coronavirus known to co-opt the peptidase angiotensin-converting enzyme 2 (ACE2) for cell entry (1). The interaction between SARS-CoV-2 and ACE2 is critical to determining both tissue tropism and progression from early SARS-CoV-2 infection to severe coronavirus disease 2019 (COVID-19). Understanding the cellular basis of SARS-CoV-2 infection could reveal treatments that prevent the development of severe disease, and thus reduce mortality.
TEA: Enabling State-Intensive Network Functions on Programmable Switches

Programmable switches have been touted as an attractive alternative for deploying network functions (NFs) such as network address translators (NATs), load balancers, and firewalls. However, their limited memory capacity has been a major stumbling block that has stymied their adoption for supporting state-intensive NFs such as cloud-scale NATs and load balancers that maintain millions of flow-table entries. In this paper, we explore a new approach that leverages DRAM on servers available in typical NFV clusters. Our new system architecture, called TEA (Table Extension Architecture), provides a virtual table abstraction that allows NFs on programmable switches to look up large virtual tables built on external DRAM. Our approach enables switch ASICs to access external DRAM purely in the data plane without involving CPUs on servers. We address key design and implementation challenges in realizing this idea. We demonstrate its feasibility and practicality with our implementation on a Tofino-based programmable switch. Our evaluation shows that NFs built with TEA can look up table entries on external DRAM with low and predictable latency (1.8-2.2 μs) and the lookup throughput can be linearly scaled with additional servers (138 million lookups per seconds with 8 servers).
Testing the effectiveness of tailored phishing techniques in industry and academia: a field experiment

Organizations are experiencing more and more sophisticated attacks specifically targeting their employees and customers. These attacks exploit tailored information on the victim or organization to increase their credibility. To date, no study has evaluated the role of 'traditional' phishing cognitive effects in these advanced settings. In this paper, we run a field experiment targeting 747 subjects employed in two organizations (a university and a large international consultancy company) to evaluate the interaction between phishing persuasion techniques and the success rate in a highly-tailored setting. For this purpose, we exploit well-established user notification methods to devise enhanced attack delivery techniques, and evaluate how such techniques affect success rate of our phishing campaigns. We find that the effect of 'traditional' attack techniques is widely mitigated in highly-tailored phishing settings, suggesting that current user training and detection techniques may be off-target for more sophisticated attacks. However, we find that the means by which the attack is delivered to the victim matter, and can greatly (up to three times) boost the effect of the base attack.
ANGEL2 is a member of the CCR4 family of deadenylases with 2′,3′-cyclic phosphatase activity

Deadenylating RNA molecules Transfer RNA (tRNA) and messenger RNA molecules often acquire a terminal 2′,3′-cyclic phosphate group when processed in the cell. These cyclic phosphates provide attachment points for tRNA ligases and must be removed to recycle tRNAs from stalled ribosomes. Pinto et al. identified a deadenylase from human tissue culture cells that can do the job. Biochemical characterization and analysis of a crystal structure reveal ANGEL2 as a 2′,3′-cyclic phosphatase with functions for RNA processing and modification. Science, this issue p. 524 The RNA phosphatase ANGEL2 affects transfer RNA processing and unconventional messenger RNA splicing in human cells. RNA molecules are frequently modified with a terminal 2′,3′-cyclic phosphate group as a result of endonuclease cleavage, exonuclease trimming, or de novo synthesis. During pre-transfer RNA (tRNA) and unconventional messenger RNA (mRNA) splicing, 2′,3′-cyclic phosphates are substrates of the tRNA ligase complex, and their removal is critical for recycling of tRNAs upon ribosome stalling. We identified the predicted deadenylase angel homolog 2 (ANGEL2) as a human phosphatase that converts 2′,3′-cyclic phosphates into 2′,3′-OH nucleotides. We analyzed ANGEL2’s substrate preference, structure, and reaction mechanism. Perturbing ANGEL2 expression affected the efficiency of pre-tRNA processing, X-box–binding protein 1 (XBP1) mRNA splicing during the unfolded protein response, and tRNA nucleotidyltransferase 1 (TRNT1)–mediated CCA addition onto tRNAs. Our results indicate that ANGEL2 is involved in RNA pathways that rely on the ligation or hydrolysis of 2′,3′-cyclic phosphates.
NUCLEOTIDE SEQUENCE VARIATIONS IN THE HSP70 GENE OF OLIVE LEAF YELLOWING-ASSOCIATED VIRUS

SUMMARY Single strand conformation pattern (SSCP) analysis of the HSP70 homologue gene of thirty isolates of Olive leaf yellowing-associated virus (OLYaV) from different geographical origins showed the existence of wide variability within this virus. This variability was clearly confirmed when multiple alignments of the HSP70 nucleotide sequences of 13 isolates that had different SSCP patterns were analysed. All isolates clustered with OLYaV-type in a phylogenetic tree constructed with the available HSP70 sequences of several members of the family Closteroviridae. The level of variability in a fragment of 383 nucleotides of the HSP70 gene ranged from 1% to 23%. Three distinct groups of isolates were identified, each of which had an internal variability lower than 10%, but showed a higher divergence from members of the other two groups (from 15 to 23%). In RT-PCR assays, primers specific for HSP70, but not those specific for the RdRp or HSP90 cistrons, amplified isolates of all groups. Whether or not the molecular differences observed in a highly conserved portion of the Closteroviridae genome (HSP70) can be indicative of the existence of more than one species within the OLYaV population remains to be ascertained.
Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing Bounding-Boxes Object Detection

Due to the shortcomings of the weakly supervised and fully supervised object detection (i.e., unsatisfactory performance and expensive annotations, respectively), leveraging partially labeled images in a cost-effective way to train an object detector has attracted much attention. In this paper, we formulate this challenging task as a missing bounding-boxes’ object detection problem. Specifically, we develop a pseudo ground truth mining procedure to automatically find the missing bounding boxes for the unlabeled instances, called pseudo ground truths here, in the training data, and then combine the mined pseudo ground truths and the labeled annotations to train a fully supervised object detector. Furthermore, we propose an incremental learning framework to gradually incorporate the results of the trained fully supervised detector to improve the performance of the missing bounding-boxes’ object detection. More importantly, we find an effective way to label the massive images with limited labors and funds, which is crucial when building a large-scale weakly/webly labeled dataset for object detection. The extensive experiments on the PASCAL VOC and COCO benchmarks demonstrate that our proposed method can narrow the gap between the fully supervised and weakly supervised object detectors, and outperform the previous state-of-the-art weakly supervised detectors by a large margin (more than 3% mAP absolutely) when the missing rate equals 0.9. Moreover, our proposed method with 30% missing bounding-box annotations can achieve comparable performance to some fully supervised detectors.
Experiences of restrictive practices: A view from people with disabilities and family carers

This research report relates for the first time the experiences of people with disabilities and afmilies in Victoria their experiences and responses to the implementation of restrictive practcies oon people with disabilities. These practices under the Victorian Disability Act 2006, permit the use of mechanical restraints and seclusion if a person is danger to self or others. The study shows how focus on the behaviour misses exploration of causes. These causes lie in people life histories of abuse, in their interactions and their environments. Much behaviour is of 'protest' and 'resistance'. Using restrictive interventions simply doubly punishes the person and creates more angst. the result it acquiescence at one extreme and cycles of more and more violent behaviour on the other. Policy and practcie needs to be cognisant of the factors that produce behaviours seen to be of concern to others if the Senior Practitioner's aim to produce dignity without restraint is to be accomplished.
Biotelemetry of White Crappies in a South Dakota Glacial Lake

Abstract Ultrasonic telemetry was used to determine monthly and diel movements of white crappies Pomoxis annularis in a South Dakota glacial lake, Sonic transmitters were surgically implanted in 37 adult white crappies (265–327 mm, total length; 315–530 g) at intervals from 10 April through 22 September 1992. From 15 April to 15 October, 1,712 locations were recorded for 34 of these fish. Movement (m/h; measured along the straight-line distance between successive locations) was significantly different among months (P < 0.05). Movement was highest in May (102.1 m/h) and significantly different from movements in April and June, The second highest amount of movement was in July (82.4 m/h). Other than in early spring, movement did not vary significantly among months. Depth of lake at white crappie locations differed significantly among months. White crappies were in significantly shallower lake areas in June and October than in August. Distance from shore differed significantly among months. White crappies we.
Probabilistic Verification of Network Configurations

Not all important network properties need to be enforced all the time. Often, what matters instead is the fraction of time / probability these properties hold. Computing the probability of a property in a network relying on complex inter-dependent routing protocols is challenging and requires determining all failure scenarios for which the property is violated. Doing so at scale and accurately goes beyond the capabilities of current network analyzers. In this paper, we introduce NetDice, the first scalable and accurate probabilistic network configuration analyzer supporting BGP, OSPF, ECMP, and static routes. Our key contribution is an inference algorithm to efficiently explore the space of failure scenarios. More specifically, given a network configuration and a property φ, our algorithm automatically identifies a set of links whose failure is provably guaranteed not to change whether φ holds. By pruning these failure scenarios, NetDice manages to accurately approximate P(φ). NetDice supports practical properties and expressive failure models including correlated link failures. We implement NetDice and evaluate it on realistic configurations. NetDice is practical: it can precisely verify probabilistic properties in few minutes, even in large networks.
Effects of video captioning on EFL vocabulary learning and listening comprehension

Abstract This study investigated how video caption type affected vocabulary learning and listening comprehension of low-intermediate Chinese-speaking learners of English. Each video was presented twice with one of the five caption types: (1) no caption (NC), (2) full caption with no audio (FCNA), (3) full caption (FC), (4) full caption with highlighted target-word (FCHTW), and (5) full caption with highlighted target-word and L1 gloss (FCL1), where gloss was presented simultaneously with full caption. The results showed that caption type did affect vocabulary learning. FCL1 facilitated the learning of both word form and meaning in a multimedia listening activity. FCHTW increased attention to word form at the expense of word meaning. Videos with either captions (FCNA) or audio (NC) were not helpful for the learning of written words, indicating that presentation of verbal information through two modalities (audio plus text) was superior over single-modality presentation. While caption type had no impact on listening comprehension, concurrent presentations of video, audio, and captions did not overload the learners in the FC condition, suggesting that selective attention might be allocated to different parts of the visual stimuli during the first and second exposure to the videos. Additionally, the presence of highlighted words and glosses in the captioning line might direct learner attention to vocabulary rather than video content.
Technology maturity for the habitable-zone exoplanet imaging observatory (HabEx) concept

HabEx Architecture A is a 4m unobscured telescope mission concept optimized for direct imaging and spectroscopy of potentially habitable exoplanets, and also enables a wide range of general astrophysics science. The exoplanet detection and characterization drives the enabling core technologies. A hybrid starlight suppression approach of a starshade and coronagraph diversifies technology maturation risk. In this paper we assess these exoplanet-driven technologies, including elements of coronagraphs, starshades, mirrors, jitter mitigation, wavefront control, and detectors. By utilizing high technology readiness solutions where feasible, and identifying required technology development that can begin early, HabEx will be well positioned for assessment by the community in 2020 Astrophysics Decadal Survey.
Classic Meets Modern: a Pragmatic Learning-Based Congestion Control for the Internet

These days, taking the revolutionary approach of using clean-slate learning-based designs to completely replace the classic congestion control schemes for the Internet is gaining popularity. However, we argue that current clean-slate learning-based techniques bring practical issues and concerns such as overhead, convergence issues, and low performance over unseen network conditions to the table. To address these issues, we take a pragmatic and evolutionary approach combining classic congestion control strategies and advanced modern deep reinforcement learning (DRL) techniques and introduce a novel hybrid congestion control for the Internet named Orca1. Through extensive experiments done over global testbeds on the Internet and various locally emulated network conditions, we demonstrate that Orca is adaptive and achieves consistent high performance in different network conditions, while it can significantly alleviate the issues and problems of its clean-slate learning-based counterparts.
Status and symptomatology of Alternaria leaf blight (Alternaria alternata) of Gerbera (Gerbera jamisonii) in Kashmir valley

Alternaria leaf blight is one of the most important diseases of gerbera (Gerbera jamisonii) worldwide. The disease was prevalent in all the flower growing areas of Kashmir valley surveyed during 2009. The overall mean disease incidence and intensity ranged from 50.7 to 67.5% and 19.2 to 28.2%, respectively. The highest disease incidence (60.72%) irrespective of phonological stages and intensity (24.96%) was recorded in district Srinagar. The lowest level of disease was in district Anantnag (56.5 and 20.0%, respectively). The pathogen associated with the disease was identified as Alternaria alternata (Fr. Keissler). In early stages of disease development, spots were circular and brown with somewhat irregular margins; the lesion later turned dark brown. Maximum disease development in the field was observed during June when spots attained a maximum size of 23 to 26 mm.
A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.
Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review

Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C- and L- bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors.
TROPOMI NO2 Tropospheric Column Data: Regridding to 1 km Grid-Resolution and Assessment of their Consistency with In Situ Surface Observations

This work presents a regridding procedure applied to the nitrogen dioxide (NO2) tropospheric column data, derived from the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI) The regridding has been performed to provide a better comparison with punctual surface observations It will be demonstrated that TROPOMI NO2 tropospheric column data show improved consistency with in situ surface measurements once the satellite retrievals are scaled to 1 km spatial sampling A geostatistical technique, i e , the ordinary kriging, has been applied to improve the spatial distribution of Level 2 TROPOMI NO2 data, which is originally sparse and uneven because of gaps introduced by clouds, to a final spatial, regular, sampling of 1 km ×1 km The analysis has been performed for two study areas, one in the North and the other in the South of Italy, and for May 2018-April 2020, which also covers the period January 2020-April 2020 of COVID-19 diffusion over the Po Valley The higher spatial sampling NO2 dataset indicated as Level 3 data, allowed us to explore spatial and seasonal data variability, obtaining better information on NO2 sources In this respect, it will be shown that NO2 concentrations in March 2020 have likely decreased as a consequence of the lockdown because of COVID-19, although the far warmest winter season ever recorded over Europe in 2020 has favored a general NO2 decrease in comparison to the 2019 winter Moreover, the comparison between NO2 concentrations related to weekdays and weekend days allowed us to show the strong correlation of NO2 emissions with traffic and industrial activities To assess the quality and capability of TROPOMI NO2 observations, we have studied their relationship and correlation with in situ NO2 concentrations measured at air quality monitoring stations We have found that the correlation increases when we pass from Level 2 to Level 3 data, showing the importance of regridding the satellite data In particular, correlation coefficients of Level 3 data, which range between 0 50–0 90 have been found with higher correlation applying to urban, polluted locations and/or cities