Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Efficient Labeling of Network Incident Datasets Using Tcpreplay and Snort

Research on network intrusion detection (NID) requires a large amount of traffic data with reliable labels indicating which packets are associated with particular network attacks. In this paper, we implement a prototype of an automated system to create labeled packet datasets for NID research. In this paper, we implement a prototype of an automated system to assign labels to packet datasets for NID research. By re-transmitting pre-captured packet data in a controlled network environment pre-installed with a network intrusion detection system, the system automatically assigns labels to attack packets within the packet data. In the feasibility study, we investigate factors that may influence the detection accuracy of the attacking packets and show an example using the prototype to label a packet file. Finally, we show an efficient way to locate the packets associated with issued NID alerts using this prototype.
Real time lane detection model based on Lightweight

Many cars now have functions to assist the driver in driving, such as keeping the lane. This function can keep the vehicle in a proper position between lanes. This function can ensure the safety of the vehicle. The traditional lane detection method relies on image processing and has poor robustness. At the same time, due to the need for post-processing technology, it will generate a lot of time-consuming calculations, which is not conducive to use in changing road scenes. Based on this, this paper proposes a two-stage lane line detection method based on deep neural network, which divides lane detection into two stages, lane edge segmentation and lane line classification. In the first stage, a semantic segmentation network based on a lightweight network performs pixelated segmentation on the edge of lane lines. In the second stage, a long-term storage network based on GRU is used to detect lane lines based on lane edges. Practice has proved that the lane line detection method proposed in this paper is very reliable in various driving scenarios. At the same time, due to the use of various lightweight networks, the network model in this paper can be deployed on vehicle systems with powerful real-time performance.
Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT42, and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.
Code Specialization through Dynamic Feature Observation

Modern software (both programs and libraries) provides large amounts of functionality, vastly exceeding what is needed for a single given task. This additional functionality results in an increased attack surface: first, an attacker can use bugs in the unnecessary functionality to compromise the software, and second, defenses such as control-flow integrity (CFI) rely on conservative analyses that gradually lose precision with growing code size. Removing unnecessary functionality is challenging as the debloating mechanism must remove as much code as possible, while keeping code required for the program to function. Unfortunately, most software does not come with a formal description of the functionality that it provides, or even a mapping between functionality and code. We therefore require a mechanism that-given a set of representable inputs and configuration parameters-automatically infers the underlying functionality, and discovers all reachable code corresponding to this functionality. We propose Ancile, a code specialization technique that leverages fuzzing (based on user provided seeds) to discover the code necessary to perform the functionality required by the user. From this, we remove all unnecessary code and tailor indirect control-flow transfers to the minimum necessary for each location, vastly reducing the attack surface. We evaluate Ancile using real-world software known to have a large attack surface, including image libraries and network daemons like nginx. For example, our evaluation shows that Ancile can remove up to 93.66% of indirect call transfer targets and up to 78% of functions in libtiff's tiffcrop utility, while still maintaining its original functionality.
Security Threats from Bitcoin Wallet Smartphone Applications: Vulnerabilities, Attacks, and Countermeasures

Nowadays, Bitcoin is the most popular cryptocurrency. With the proliferation of smartphones and the high-speed mobile Internet, more and more users have started accessing their Bitcoin wallets on their smartphones. Users can download and install a variety of Bitcoin wallet applications (e.g., Coinbase, Luno, Bitcoin Wallet) on their smartphones and access their Bitcoin wallets anytime and anywhere. However, it is still unknown whether these Bitcoin wallet smartphone applications are secure or if they are new attack surfaces for adversaries to attack these application users. In this work, we explored the insecurity of the 10 most popular Bitcoin wallet smartphone applications and discovered three security vulnerabilities. By exploiting them, adversaries can launch various attacks including Bitcoin deanonymization, reflection and amplification spamming, and wallet fraud attacks. To address the identified security vulnerabilities, we developed a phone-side Bitcoin Security Rectifier to secure Bitcoin wallet smartphone application users. The developed rectifier does not require any modifications to current wallet applications and is compliant with Bitcoin standards.
Towards the Synthesis of Coherence/Replication Protocols from Consistency Models via Real-Time Orderings

This work focuses on shared memory systems with a read-write interface (e.g., distributed datastores or multiprocessors). At the heart of such systems resides a protocol responsible for enforcing their consistency guarantees. Designing a protocol that correctly and efficiently enforces consistency is a very challenging task. Our overarching vision is to automate this task. In this work we take a step towards this vision by establishing the theoretical foundation necessary to automatically infer a protocol from a consistency specification. Specifically, we propose a set of mathematical abstractions, called real-time orderings (rt-orderings), that model the protocol. We then create a mapping from consistency guarantees to the minimal rt-orderings that enforce the guarantees. Finally, we informally relate the rt-orderings to protocol implementation techniques. Consequently, rt-orderings serve as an intermediate abstraction between consistency and protocol design, that enables the automatic translation of consistency guarantees into protocol implementations.
Prognostic Value of the CLIF‐C AD Score in Patients With Implantation of Transjugular Intrahepatic Portosystemic Shunt

Prognostic assessment of patients with liver cirrhosis allocated for implantation of a transjugular intrahepatic portosystemic shunt (TIPS) is a challenging task in clinical practice. The aim of our study was to assess the prognostic value of the CLIF‐C AD (Acute Decompensation) score in patients with TIPS implantation. Transplant‐free survival (TFS) and 3‐month mortality were reviewed in 880 patients who received de novo TIPS implantation for the treatment of cirrhotic portal hypertension. The prognostic value of the CLIF‐C AD score was compared with the Model for End‐Stage Liver Disease (MELD) score, Child‐Pugh score, and albumin‐bilirubin (ALBI) score using Harrell’s C concordance index. The median TFS after TIPS implantation was 40.0 (34.6‐45.4) months. The CLIF‐C AD score (c = 0.635 [0.609‐0.661]) was superior in the prediction of TFS in comparison to MELD score (c = 0.597 [0.570‐0.623], P = 0.006), Child‐Pugh score (c = 0.579 [0.552‐0.606], P < 0.001), and ALBI score (c = 0.573 [0.545‐0.600], P < 0.001). However, the CLIF‐C AD score did not perform significantly better than the MELD‐Na score (c = 0.626 [0.599‐0.653], P = 0.442). There were no profound differences in the scores’ ranking with respect to indication for TIPS implantation, stent type, or underlying liver disease. Subgroup analyses revealed that a CLIF‐C AD score >45 was a predictor of 3‐month mortality in the supposed low‐risk group of patients with a MELD score ≤12 (14.7% vs. 5.1%, P < 0.001). Conclusion: The CLIF‐C AD score is suitable for prognostic assessment of patients with cirrhotic portal hypertension receiving TIPS implantation. In the prediction of TFS, the CLIF‐C AD score is superior to MELD score, Child‐Pugh score, and ALBI score but not the MELD‐Na score.
Serum Bile Acid, Vitamin E, and Serotonin Metabolites Are Associated With Future Liver‐Related Events in Nonalcoholic Fatty Liver Disease

Identifying patients at higher risk for poor outcomes from nonalcoholic fatty liver disease (NAFLD) remains challenging. Metabolomics, the comprehensive measurement of small molecules in biological samples, has the potential to reveal novel noninvasive biomarkers. The aim of this study was to determine if serum metabolite profiles in patients with NAFLD associate with future liver‐related events. We performed a retrospective single‐center cohort study of 187 participants with biopsy‐proven NAFLD. Metabolomic analysis was performed on serum using ultrahigh performance liquid chromatography/tandem mass spectrometry and gas chromatography/mass spectrometry. We identified liver‐related events (variceal bleeding, ascites, spontaneous bacterial peritonitis, hepatic encephalopathy, hepatocellular carcinoma, hepatopulmonary or hepatorenal syndrome) by manual chart review between index biopsy (2007‐2013) and April 1, 2018. Generalized linear models and Cox proportional hazards models were used to test the association of metabolites with liver‐related events and time to first liver‐related event, controlling for covariates and fibrosis stage. Over a mean ± SD follow‐up of 6.9 ± 3.2 years, 11 participants experienced 22 liver‐related events. Generalized linear models revealed 53 metabolites significantly associated with liver‐related events (P < 0.05). In Cox proportional hazards modeling, 69 metabolites were significantly associated with time to future liver‐related events (P < 0.05), seven of which met the false discovery rate threshold of 0.10: vitamin E metabolites gamma‐carboxyethyl‐hydroxychroman (gamma‐CEHC) and gamma‐CEHC glucuronide; primary bile acid metabolite taurochenodeoxycholate; serotonin metabolite 5‐hydroxyindoleacetate; and lipid metabolites (i) 2‐hydroxyglutarate, (ii) 3beta,17beta‐diol disulfate 1, and (iii) eicosenoyl sphingomyelin. Conclusion: Metabolites of a primary bile acid, vitamin E, and serotonin were associated with future liver‐related events. Our results suggest metabolite pathways may be useful for predicting which patients with NAFLD are at higher risk for hepatic decompensation.
Lipoprotein Insulin Resistance Index Reflects Liver Fat Content in Patients With Nonalcoholic Fatty Liver Disease

The recently developed lipoprotein insulin resistance index (LP‐IR) incorporates lipoprotein particle numbers and sizes and is considered to reflect both hepatic and peripheral IR. As tissue IR is a strong component of nonalcoholic fatty liver disease (NAFLD) pathogenesis, we aimed to assess the degree by which LP‐IR associates with hepatic fat content. This was a single‐center retrospective analysis of patients with NAFLD. LP‐IR, the homeostasis model assessment of insulin resistance (HOMA‐IR), and adipose tissue IR (Adipo‐IR) were measured simultaneously. Liver fat content was estimated by FibroScan controlled attenuated parameter. Associations were assessed using Spearman’s correlation and multivariate linear regression. The study included 61 patients. LP‐IR was correlated with HOMA‐IR (ρ = 0.30; P = 0.02), typically thought to reflect hepatic IR, but not with Adipo‐IR (ρ = 0.15; P = 0.25). Liver fat content was significantly associated with Adipo‐IR (ρ = 0.48; P < 0.001), LP‐IR (ρ = 0.35; P = 0.005), and to a lesser degree with HOMA‐IR (ρ = 0.25; P = 0.051). The association of liver fat with LP‐IR was limited to patients without diabetes (ρ = 0.60; P < 0.0001), whereas no association was seen in those with diabetes. In a multivariate model, Adipo‐IR, LP‐IR, and diabetes were independently associated with liver fat and together explained 35% of the variability in liver fat. Conclusion: LP‐IR is a reasonable measure of IR in non‐diabetic patients with NAFLD and is associated with hepatic fat content. Although adipose tissue is the major contributor to liver fat, the additional contribution of nonadipose tissues can be easily estimated using LP‐IR.
Visual–Spatial Ability Predicts Academic Achievement Through Arithmetic and Reading Abilities

This study aimed to investigate how visual–spatial ability predicted academic achievement through arithmetic and reading abilities. Four hundred and ninety-nine Chinese children aged from 10.1 to 11.2 years were recruited and measured visual–spatial, arithmetic, and reading abilities. Their mathematical and Chinese language academic achievements were collected for two consecutive school years, respectively, during the same year as cognitive tests and 1 year after the cognitive tests. Correlation analysis indicated that visual–spatial, arithmetic, and reading abilities and academic achievements were significantly correlated with each other. The structural equation modelling analyses showed that there were two paths from visual–spatial ability to academic achievement: a major path mediated by arithmetic ability and a minor serial mediation path from visual–spatial ability to arithmetic ability to reading ability, then to academic achievement. Results shed light on the importance of visual–spatial ability in education.
National Early Career Transplant Hepatologist Survey: Compensation, Burnout, and Job Satisfaction

Despite the growth of transplant hepatology as a subspecialty over the past decade, data on professional roles and compensation models remain lacking. Furthermore, the prevalence of physician burnout and job satisfaction are unknown in this profession. We aimed to conduct a comprehensive assessment of early career transplant hepatologists to fill these voids in knowledge and to inform current and future transplant hepatologists. An online survey designed to quantify clinical and nonclinical roles, compensation and structure, job satisfaction, and burnout was sent to 256 early career transplant hepatologists. Respondents were divided into three practice settings: university hospital clinical (n = 79), non–university hospital clinical (n = 35), and research (n = 25). The median age of respondents was 38 (interquartile range [IQR] 36‐40) years, and 44% were women. The median half‐days/week spent in clinic was 4 (IQR 3‐6) and in endoscopy was 1 (IQR 1‐2). Most of the respondents provided inpatient care (88%) for a median of 9 (IQR 6.5‐10) weeks/year. The median base compensation was $300,000 (IQR US $263,750‐$326,250), and most (76%) had salary‐based compensation. Although only 8% of respondents were dissatisfied with their position, the prevalence of burnout was high at 35%. Conclusion: This survey is a comprehensive assessment focusing on early career transplant hepatologists, is reflective of the current training paradigm and practice of transplant hepatology, and provides transparency to guide professional negotiations and empower both trainees pursuing careers in transplant hepatology and early career transplant hepatologists.
3D Reconstruction of Coastal Cliffs from Fixed-Wing and Multi-Rotor UAS: Impact of SfM-MVS Processing Parameters, Image Redundancy and Acquisition Geometry

Monitoring the dynamics of coastal cliffs is fundamental for the safety of communities, buildings, utilities, and infrastructures located near the coastline. Structure-from-Motion and Multi View Stereo (SfM-MVS) photogrammetry based on Unmanned Aerial Systems (UAS) is a flexible and cost-effective surveying technique for generating a dense 3D point cloud of the whole cliff face (from bottom to top), with high spatial and temporal resolution. In this paper, in order to generate a reproducible, reliable, precise, accurate, and dense point cloud of the cliff face, a comprehensive analysis of the SfM-MVS processing parameters, image redundancy and acquisition geometry was performed. Using two different UAS, a fixed-wing and a multi-rotor, two flight missions were executed with the aim of reconstructing the geometry of an almost vertical cliff located at the central Portuguese coast. The results indicated that optimizing the processing parameters of Agisoft Metashape can improve the 3D accuracy of the point cloud up to 2 cm. Regarding the image acquisition geometry, the high off-nadir (90°) dataset taken by the multi-rotor generated a denser and more accurate point cloud, with lesser data gaps, than that generated by the low off-nadir dataset (3°) taken by the fixed wing. Yet, it was found that reducing properly the high overlap of the image dataset acquired by the multi-rotor drone permits to get an optimal image dataset, allowing to speed up the processing time without compromising the accuracy and density of the generated point cloud. The analysis and results presented in this paper improve the knowledge required for the 3D reconstruction of coastal cliffs by UAS, providing new insights into the technical aspects needed for optimizing the monitoring surveys.
UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

In this paper, we investigate the problem of aligning multiple deployed camera into one united coordinate system for cross-camera information sharing and intercommunication. However, the difficulty is greatly increased when faced with large-scale scene under chaotic camera deployment. To address this problem, we propose a UAV-assisted wide area multi-camera space alignment approach based on spatiotemporal feature map. It employs the great global perception of Unmanned Aerial Vehicles (UAVs) to meet the challenge from wide-range environment. Concretely, we first present a novel spatiotemporal feature map construction approach to represent the input aerial and ground monitoring data. In this way, the motion consistency across view is well mined to overcome the great perspective gap between the UAV and ground cameras. To obtain the corresponding relationship between their pixels, we propose a cross-view spatiotemporal matching strategy. Through solving relative relationship with the above air-to-ground point correspondences, all ground cameras can be aligned into one surveillance space. The proposed approach was evaluated in both simulation and real environments qualitatively and quantitatively. Extensive experimental results demonstrate that our system can successfully align all ground cameras with very small pixel error. Additionally, the comparisons with other works on different test situations also verify its superior performance.
S-NPP VIIRS Day Night Band On-Board Solar Diffuser Calibration Validation Using the Scheduled Lunar Collections

Similar to the Reflective Solar Band (RSB) calibration, Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) on-board calibration of Day Night Band (DNB) is based on the Solar Diffuser (SD) observations in the Low Gain State (LGS). DNB has a broad spectral response covering a wavelength range roughly from 500 nm to 900 nm with a large dynamic range from three different gain states called High Gain State (HGS), Mid Gain State (MGS), and LGS. The calibration of MGS and HGS is also dependent on the LGS gain estimation with the gain ratios for each gain state. Over the lifetime of S-NPP VIIRS operations, the LGS gains have been derived from the on-board SD observations since its launch in October 2011. In this study, the lifetime LGS gains are validated by the lunar calibration coefficients (defined as F-factors) using a lunar irradiance model called Global Space-based Inter-Calibration System (GSICS) Implementation of RObotic Lunar Observatory (ROLO) (GIRO). Using the moon as an independent on-orbit calibration source, the S-NPP VIIRS DNB on-board SD based radiometric calibration is validated by the lunar F-factors within two percent of the lunar F-factors in terms of the standard deviation in the long-term trends over nine years of the S-NPP VIIRS operation.
Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter

Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions.