Miracle-dz/UniFAR-FTUs huggingface dataset:
PSDEM: A Feasible De-Obfuscation Method for Malicious PowerShell Detection

PowerShell is so extremely powerful that we have seen that attackers are increasingly using PowerShell in their attack methods lately. In most cases, PowerShell malware arrives via spam email, using a combination of Microsoft Word documents to infect victims with its deadly payload. Nowadays, the de-obfuscation and analysis of PowerShell are still based on the manual analysis. However, as the number of malicious samples and obfuscation methods growing quickly, it is so slow that can’t satisfy the demand. In this paper, we propose a de-obfuscation method of PowerShell called PSDEM which has two layers de-obfuscation to get original PowerShell scripts. One is extracting PowerShell scripts from much obfuscated document code. The other is de-obfuscating scripts including encoding, strings manipulation and code logic obfuscation. Meanwhile, we design an automatic de-obfuscation and analysis tool for malicious PowerShell scripts in Word documents based on PSDEM. We test the performance of the tool from the accuracy of de-obfuscation and the efficiency of time, and evaluation results show that it has a satisfactory performance. PSDEM improves the efficiency and accuracy rate for analyzing malicious PowerShell Scripts in Word documents, as well as provides a path in which further analysis for security experts to get more information about attacks.
Impact of Intrauterine Growth Restriction on the Capillarization of the Early Postnatal Rat Heart

Capillarization plays a key role in the growth of the developing heart. We therefore hypothesized that impaired heart development following intrauterine growth restriction (IUGR) may arise from inadequate myocardial capillary growth. The aims of the study were to examine the effect of IUGR on the growth and diffusion radius of intramyocardial capillaries in rats at postnatal day 1. Uteroplacental insufficiency was induced in rats in late gestation (E18, term = E22) by bilateral uterine artery and vein ligation (restricted offspring N = 12; six males and six females); offspring from sham‐operated dams were used as controls (N = 10; five males and five females). At postnatal day 1, the hearts were immersion‐fixed and heart volume, capillary length density, capillary diffusion radius, and total capillary length were stereologically determined. Restricted offspring were significantly smaller at birth, with a concomitant reduction in heart volume and total myocardial capillary length compared to controls. Capillary growth was not impaired relative to heart size, with no significant differences in capillary length density or diffusion radius in the myocardium of restricted and control offspring. There were no sex differences in any of the parameters examined. In conclusion, there was no evidence to indicate that microvascular development is compromised in the heart of IUGR offspring at 1 day after birth. Total myocardial capillary length, however, was significantly reduced in the growth restricted offspring and further longitudinal studies are required to elucidate the long‐term impact, particularly following hypertrophic cardiac growth. Anat Rec, 302:1580–1586, 2019. © 2018 American Association for Anatomy
Population history provides foundational knowledge for utilizing and developing native plant restoration materials

A species’ population structure and history are critical pieces of information that can help guide the use of available native plant materials in restoration treatments and decide what new native plant materials should be developed to meet future restoration needs. In the western United States, Pseudoroegneria spicata (bluebunch wheatgrass; Poaceae) is an important component of grassland and shrubland plant communities and commonly used for restoration due to its drought resistance and competitiveness with exotic weeds. We used next‐generation sequencing data to investigate the processes that shaped P. spicata's geographic pattern of genetic variation across the Intermountain West. Pseudoroegneria spicata's genetic diversity is partitioned into populations that likely differentiated since the Last Glacial Maximum. Adjacent populations display varying magnitudes of historical gene flow, with migration rates ranging from multiple migrants per generation to multiple generations per migrant. When considering the commercial germplasm sources available for restoration, genetic identities remain representative of the wildland localities from which germplasm sources were originally developed, and they maintain high levels of heterozygosity and nucleotide diversity. However, the commercial germplasm sources represent a small fraction of the overall genetic diversity of P. spicata in the Intermountain West. Given the low migration rates and long divergence times between some pairs of P. spicata populations, using commercial germplasm sources could facilitate undesirable restoration outcomes when used in certain geographic areas, even if the environment in which the commercial materials thrive is similar to that of the restoration site. As such, population structure and history can be used to provide guidance on what geographic areas may need additional native plant materials so that restoration efforts support species and community resilience and improve outcomes.
UFO - Hidden Backdoor Discovery and Security Verification in IoT Device Firmware

Recently, the use of embedded devices such as WiFi APs, IP CAM, and drones in Internet of Things (IoT) applications has become more widespread. These embedded devices are connected to networks and are often used for critical services. Thus, they receive significant attention from hackers who attempt to find a major intrusion vector in IoT applications. Hackers focus on identifying hidden backdoors in embedded devices to gain full remote access; if they gain access, they can cause significant damage to critical infrastructures. Therefore, to improve embedded device security, this study introduces Universal Firmware vulnerability Observer (UFO); UFO is a firmware vulnerability discovery system, which can automatically perform tasks such as reversing firmware embedded filesystem, identifying vulnerability, and exploring password leaks to meet the IoT firmware security verification standards, including OWASP, UL-2900, and ICSA Labs. In addition, we design a Shell Script Dependency algorithm to help identify hidden backdoor problems by discovering suspicious shell script execution paths in the extracted firmware filesystem. We use 237 real-world embedded device firmware files to evaluate UFO. The results indicate that the effectiveness of reversing firmware binary is 96%, which is significantly higher than that of open source tools. Besides, we also conclude that 73% of firmware files contain Common Vulnerabilities and Exposures in their embedded Linux kernel, 22% of firmware files can leak login passwords, and 6% of firmware files contain hidden backdoors. Moreover, we reported hidden backdoor problems to two IoT device vendors in Taiwan and received their confirmation. UFO can be successfully used for verifying firmware security and discovering hidden backdoor threats in commercial IoT devices.
Milk Other Than Breast Milk and the Development of Asthma in Children 3 Years of Age. A Birth Cohort Study (2006–2011)

Prevalence of asthma in Australian children is amongst the highest in the world. Although breastfeeding positively influences infant immunity, early introduction of Milk Other than Breast Milk (MOTBM) may also play an important role in the development of Asthma. The aim of this study was to investigate the association between the introduction of MOTBM in the first six months after birth and the development of reported persistent asthma in 3-year olds. A sample of 1121 children was extracted from the Environments for Healthy Living longitudinal birth cohort study. Introduction of MOTBM during the first six months after birth increased almost two-fold the risk of development of persistent asthma after adjusting for other covariates (Adjusted Relative Risk (ARR): 1.71, 95% CI: 1.03–2.83, p = 0.038). This study indicates that the introduction of MOTBM in the first six months of life is a risk factor for asthma incidence among 3-year old children. This result is important in explaining the benefits of breastfeeding as part of public health interventions to encourage mothers to increase breastfeeding initiation and duration, and avoid the introduction of MOTBM in the first six months after childbirth.
Defect Detection from UAV Images Based on Region-Based CNNs

With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In computer vision and data mining communities, traditional object detection methods usually train a class-specific learner (e.g., the SVM) based on the low level features to detect the single class of images by sliding a local window. Thus, they may not suit for the UAV images with complex background and multiple kinds of interest objects. Recently, the deep convolutional neural networks (CNNs) have already shown great advances in the object detection and segmentation fields and outperformed many traditional methods which usually been employed in the past decades. In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on the region-based faster R-CNN model, we present a multi-class defects detection model for electrical equipment which is more efficient and accurate than traditional single class detection methods. Technically, we have replaced the RoI pooling layer with a similar operation in Tensorflow and promoted the mini-batch to 128 per image in the training procedure. These improvements have slightly increased the speed of detection without any accuracy loss. Therefore, the modified region-based CNN could simultaneously detect multi-class of defects of the electrical devices in nearly real time. Experimental results on the real word electrical equipment images demonstrate that the proposed method achieves better performance than the traditional object detection algorithms in defect detection.
A Fast Converging Mechanism for Load Balancing among SDN Multiple Controllers

Load balancing among multiple controllers is a critical issue in the software-defined networking (SDN), since traffic dynamics prevent control-plane loads from being evenly distributed among controllers. Load imbalance may cause some controllers to be overloaded while the other controllers are still underutilized. Though there have been several proposed solutions, theydo not consider the convergence time of load balancing. We thus propose a fast-converging loadbalancing (FCLB) mechanism that seeks to achieve fast convergence for balancing loads (i.e., assigning switches) among the SDN controllers. Fast convergence can rapidly release controllersfrom being overloaded, thereby shortly recovering network performance and preventing some unanticipated results (e.g., controller crash). Searching for the optimal solution from a large set of switch-controller combinations can lead to a large delay, which can aggravate negative impacts from overloading. We thus leverage the genetic algorithm to lind a near-optimal solutionOur simulation results show that FCLB has at least 20.7% faster convergence time than the other mechanisms while achieving better load balancing performance.
Energy-Aware Incentivized Data Dissemination via Wireless D2D Communications With Weighted Social Communities

Device-to-device (D2D) communications are featured by high energy efficiency and spectrum efficiency, which offers a promising technique for data dissemination over wireless networks. In this paper, we propose a novel solution that exploits D2D communications to enable efficient data dissemination over wireless networks. To distribute some messages to a target group of users, the base station first identifies the most influential users, called initial sources or seeds, and fulfills their requests. Then, these source devices forward their received messages to remaining users via D2D communications. To incentivize the sources in data forwarding, we propose a monetary auction-based mechanism and a moneyless matching-based mechanism, which can be activated depending on the practical application scenario. The monetary mechanism obtains the global optimal solution and achieves truthfulness, but it involves transfer of monetary rewards. In contrast, the moneyless mechanism enables a lightweight implementation, and it guarantees two-sided stability so that both sides of users in data forwarding are willing to accept the pairing result. To expedite data dissemination, we take advantage of social-awareness in a manner different from many existing approaches. We exploit weighted social relationships, which can be direct or indirect links between any two users in a connected community. To evaluate the performance of the proposed approaches, we develop novel schemes to generate and preprocess synthetic and real tracing datasets. Extensive simulation results with both synthetic and real tracing datasets demonstrate that our approaches achieve high utilities.
Top food sources of percentage of energy, nutrients to limit and total gram amount consumed among US adolescents: National Health and Nutrition Examination Survey 2011–2014

Abstract Objective To identify most commonly consumed foods by adolescents contributing to percentage of total energy, added sugars, SFA, Na and total gram intake per day. Design Data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. Setting NHANES is a cross-sectional study nationally representative of the US population. Participants One 24 h dietary recall was used to assess dietary intake of 3156 adolescents aged 10–19 years. What We Eat in America food category classification system was used for all foods consumed. Food sources of energy, added sugars, SFA, Na and total gram amount consumed were sample-weighted and ranked based on percentage contribution to intake of total amount. Results Three-highest ranked food subgroup sources of total energy consumed were: sugar-sweetened beverages (SSB; 7·8 %); sweet bakery products (6·9 %); mixed dishes – pizza (6·6 %). Highest ranked food sources of total gram amount consumed were: plain water (33·1 %); SSB (15·8 %); milk (7·2 %). Three highest ranked food sources of total Na were: mixed dishes – pizza (8·7 %); mixed dishes – Mexican (6·7 %); cured meats/poultry (6·6 %). Three highest ranked food sources of SFA were: mixed dishes – pizza (9·1 %); sweet bakery products (8·3 %); mixed dishes – Mexican (7·9 %). Three highest ranked food sources of added sugars were: SSB (42·1 %); sweet bakery products (12·1 %); coffee and tea (7·6 %). Conclusions Identifying current food sources of percentage energy, nutrients to limit and total gram amount consumed among US adolescents is critical for designing strategies to help them meet nutrient recommendations within energy needs.
Distorting Neural Representations to Generate Highly Transferable Adversarial Examples

Deep neural networks (DNN) can be easily fooled by adding human imperceptible perturbations to the images. These perturbed images are known as the `adversarial examples' that pose a serious threat to security and safety critical systems. A litmus test for the strength of adversarial examples is their transferability across different DNN models in a black box setting (i.i. when target model's architecture and parameters are not known to attacker). Current attack algorithms that seek to enhance adversarial transferability work on the decision level i.e. generate perturbations that alter the network decisions. This leads to two key limitations: (a) An attack is dependent on the task-specific loss function (e.g. softmax cross-entropy for object recognition) and therefore does not generalize beyond its original task. (b) The adversarial examples are specific to the network architecture and demonstrate poor transferability to other network architectures. We propose a novel approach to create adversarial examples that can broadly fool different networks on multiple tasks. Our approach is based on the following intuition: "Deep features are highly generalizable and show excellent performance across different tasks, therefore an ideal attack must create maximum distortions in the feature space to realize highly transferable examples". Specifically, for an input image, we calculate perturbations that push its feature representations furthest away from the original image features. We report extensive experiments to show how adversarial examples generalize across multiple networks across classification, object detection and segmentation tasks.
APOE genotypes in Lebanon: distribution and association with hypercholesterolemia and Alzheimer's disease.

AIM We first investigated the distribution of APOE genotypes in Lebanese general population and Alzheimer's disease study (ADS) groups, and compared it with 1000 genomes populations. Then, we assessed eventual association between APOE genotypes, hypercholesterolemia and Alzheimer's disease (AD). MATERIALS & METHODS This cross-sectional study was conducted on 591 individuals. Clinical and biological data were collected, DNA was extracted and genotyped using Kompetitive allele specific PCR (KASP™) and PCR-restriction fragment length polymorphis (PCR-RFLP). Results: Prevalence of APOE genotypes in Lebanon was similar to that seen in Asian populations. APOE genotypes were not associated with hypercholesterolemia. A significant difference between APOE genotypes in AD cases versus controls and versus Lebanese general population was seen. Moreover, E4 allele was approximately threefold higher in Alzheimer's disease study patients when compared with the remaining individuals. CONCLUSION We established the distribution of APOE genotypes in the Lebanon and showed that in contrast to lipid profile, E4 correlates with AD.
Time-Lapse Helical X-ray Computed Tomography (CT) Study of Tensile Fatigue Damage Formation in Composites for Wind Turbine Blades

Understanding the fatigue damage mechanisms in composite materials is of great importance in the wind turbine industry because of the very large number of loading cycles rotor blades undergo during their service life. In this paper, the fatigue damage mechanisms of a non-crimp unidirectional (UD) glass fibre reinforced polymer (GFRP) used in wind turbine blades are characterised by time-lapse ex-situ helical X-ray computed tomography (CT) at different stages through its fatigue life. Our observations validate the hypothesis that off-axis cracking in secondary oriented fibre bundles, the so-called backing bundles, are directly related to fibre fractures in the UD bundles. Using helical X-ray CT we are able to follow the fatigue damage evolution in the composite over a length of 20 mm in the UD fibre direction using a voxel size of (2.75 µm)3. A staining approach was used to enhance the detectability of the narrow off-axis matrix and interface cracks, partly closed fibre fractures and thin longitudinal splits. Instead of being evenly distributed, fibre fractures in the UD bundles nucleate and propagate locally where backing bundles cross-over, or where stitching threads cross-over. In addition, UD fibre fractures can also be initiated by the presence of extensive debonding and longitudinal splitting, which were found to develop from debonding of the stitching threads near surface. The splits lower the lateral constraint of the originally closely packed UD fibres, which could potentially make the composite susceptible to compressive loads as well as the environment in service. The results here indicate that further research into the better design of the positioning of stitching threads, and backing fibre cross-over regions is required, as well as new approaches to control the positions of UD fibres.
Pedestrian Video Data Abstraction and Classification for Surveillance System

In this study, we have developed abstracted pedestrian behavior representation and classification method for pedestrian video surveillance system. An effective intelligent surveillance system can be constructed if the high-resolution surveillance image information is efficiently summarized. The motion of the pedestrian is represented by a multi-layer grid map using a detector and a tracker. A normal pattern and anomalous pattern database were constructed and classified using the CNN classifier. With the abstracted pedestrian data and CNN network, the abnormal situation can be detected up to recall 92.0%, precision 99.9%.
On Geometric Alignment in Low Doubling Dimension

In real-world, many problems can be formulated as the alignment between two geometric patterns. Previously, a great amount of research focus on the alignment of 2D or 3D patterns, especially in the field of computer vision. Recently, the alignment of geometric patterns in high dimension finds several novel applications, and has attracted more and more attentions. However, the research is still rather limited in terms of algorithms. To the best of our knowledge, most existing approaches for high dimensional alignment are just simple extensions of their counterparts for 2D and 3D cases, and often suffer from the issues such as high complexities. In this paper, we propose an effective framework to compress the high dimensional geometric patterns and approximately preserve the alignment quality. As a consequence, existing alignment approach can be applied to the compressed geometric patterns and thus the time complexity is significantly reduced. Our idea is inspired by the observation that high dimensional data often has a low intrinsic dimension. We adopt the widely used notion “doubling dimension” to measure the extents of our compression and the resulting approximation. Finally, we test our method on both random and real datasets; the experimental results reveal that running the alignment algorithm on compressed patterns can achieve similar qualities, comparing with the results on the original patterns, but the running times (including the times cost for compression) are substantially lower.
Physical activity may compensate for prolonged TV time regarding pulse rate—a cross-sectional study

Abstract Background. Regular exercise reduces pulse rate, but it is less clear how prolonged sitting time affects pulse rate. Our hypothesis was that high physical activity could compensate for prolonged sitting time regarding the pulse rate. Methods. Regression analysis was performed on cross-sectional data including 47,457 men and women based on two Swedish cohort studies, EpiHealth (18–45 years) and LifeGene (45–75 years). Self-reported leisure time physical activity was given in five levels, from low (level 1) to vigorous (level 5), and television time was used as a proxy of sitting time. Results. A higher physical activity (level 4 compared to level 1) was associated with a lower pulse rate in middle-aged females (-2.7 beats per minute [bpm]; 95% CI -3.3 to -2.2) and males (-4.0 bpm; 95% CI -4.7 to -3.4). The relationship between physical activity and pulse rate was strongest in the young. A prolonged television time (3 h compared to 1 h per day) was associated with a slightly higher pulse rate in middle-aged females (+0.6 bpm; 95% CI +0.3 to +0.8) and males (+0.9 bpm; 95% CI +0.7 to +1.2). Among participants with a prolonged television time (3 h), those with a high physical activity (level 4) had a lower pulse rate compared to those with a low physical activity (level 1). Conclusions. A prolonged television time was associated with a high pulse rate, while high physical activity was associated with a low pulse rate. The results suggest that a high physical activity could compensate for a prolonged television time regarding pulse rate.