Miracle-dz/UniFAR-FTUs huggingface dataset:
Can We Trust the Phone Vendors? Comprehensive Security Measurements on the Android Firmware Ecosystem

Android is the most popular smartphone platform with over 85% market share. Its success is built on openness, and phone vendors can utilize the Android source code to make customized products with unique software/hardware features. On the other hand, the fragmentation and customization of Android also bring many security risks that have attracted the attention of researchers. Many efforts were put in to investigate the security of customized Android firmware. However, most of the previous works focus on designing efficient analysis tools or analyzing particular aspects of the firmware. There still lacks a panoramic view of Android firmware ecosystem security and the corresponding understandings based on large-scale firmware datasets. In this work, we made a large-scale comprehensive measurement of the Android firmware ecosystem security. Our study is based on 8,325 firmware images from 153 vendors and 813 Android-related CVEs, which is the largest Android firmware dataset ever used for security measurements. In particular, our study followed a series of research questions, covering vulnerabilities, patches, security updates, and pre-installed apps. To automate the analysis process, we designed a framework, AndScanner+, to complete firmware crawling, firmware parsing, patch analysis, and app analysis. Through massive data analysis and case explorations, several interesting findings are obtained. For example, the patch delay and missing issues are widespread in Android firmware images, say 31.4% and 5.6% of all images, respectively. The latest images of several phones still contain vulnerable pre-installed apps, and even the corresponding vulnerabilities have been publicly disclosed. In addition to data measurements, we also explore the causes behind these security threats through case studies and demonstrate that the discovered security threats can be converted into exploitable vulnerabilities. There are 46 new vulnerabilities found by AndScanner+, 36 of which have been assigned CVE/CNVD IDs. This study provides much new knowledge of the Android firmware ecosystem with a deep understanding of software engineering security practices.
An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction

Abstract A seasonally independent index for monitoring the Madden–Julian oscillation (MJO) is described. It is based on a pair of empirical orthogonal functions (EOFs) of the combined fields of near-equatorially averaged 850-hPa zonal wind, 200-hPa zonal wind, and satellite-observed outgoing longwave radiation (OLR) data. Projection of the daily observed data onto the multiple-variable EOFs, with the annual cycle and components of interannual variability removed, yields principal component (PC) time series that vary mostly on the intraseasonal time scale of the MJO only. This projection thus serves as an effective filter for the MJO without the need for conventional time filtering, making the PC time series an effective index for real-time use. The pair of PC time series that form the index are called the Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2). The properties of the RMM series and the spatial patterns of atmospheric variability they capture are explored. Despite the fact that RMM1 and RMM.
A study of the effect of school management upon 4th and 8th grade math and reading results in relation to market driven theory

v List of Tables viii Chapter 1: Introduction 1 Statement of the Problem 2 Research Question 4 Purpose and Significance 4 Conceptual Framework 5 Operational Definitions 6 Assumptions and Limitations 10 Delimitations 10 Chapter 2: Literature Review 12 Theory Underlying This Study 22 Summary 33 Chapter 3: Methodology 35 Introduction 35 Population 35 Research Design 36 Instrumentation and Materials 37 Variables of the Study 37 Data Analysis 38 Summary 39 Chapter 4: Results 40 Running Head: Study of the Effect of School Management. vi
Formation Tracking of Mobile Robots Under Obstacles Using Only an Active RGB-D Camera

This article attempts to achieve leader-follower formation tracking for mobile robots under obstacles with only one RGB-D camera on the follower. In contrast, most existing research employs at least two sensors, including a camera and a LiDAR. However, the limited field of view of RGB-D cameras introduces three significant challenges: 1) intermittent observation of the leader, 2) partial detection of obstacles, and 3) conflicting observations of the leader and obstacles. To overcome these difficulties, we first propose a controller that enables the follower to achieve continuous observation and formation tracking of the leader without obstacles and depth images. To address the partial detection of obstacles, we design a rotating device that allows the camera to actively extend its observation range and resolve conflicts with leader detection. Based on camera feedback and the proposed controller, a multiobjective controller is presented that enables the follower to achieve formation tracking and obstacle avoidance simultaneously, without requiring communication or leader velocity. Simulations and experiments are conducted to demonstrate the feasibility of our methods.
Entropy Reduction Using Hybrid Algorithm

Data mining, C-Means clustering is well known for its efficiency proved good for large data sets. The aim of every clustering algorithm is to group the similar data items while ungroup the dissimilar items. C-Means clustering algorithm has the opposite principle as fuzzy clustering algorithm has i.e. in C-Means every point has belonging to clusters while in fuzzy clustering, they belong to only one cluster. Clustering is a supervised learning algorithm. Clustering dispersion called entropy factor is the disorderness that occur after the clustering process. Less entropy leads to good clustering. Clustering with C-mean results in unlabeled data. I present a clustering algorithm called C-Means. Then unlabeled data is matched through neural classifier. Neural Network is the classification function to distinguish between members of the two classes in the training data. For classification we use Neural Network as they can recognize the patterns.
Integrating robotics into wildlife conservation: testing improvements to predator deterrents through movement

Background Agricultural and pastoral landscapes can provide important habitat for wildlife conservation, but sharing these landscapes with wildlife can create conflict that is costly and requires managing. Livestock predation is a good example of the challenges involving coexistence with wildlife across shared landscapes. Integrating new technology into agricultural practices could help minimize human-wildlife conflict. In this study, we used concepts from the fields of robotics (i.e., automated movement and adaptiveness) and agricultural practices (i.e., managing livestock risk to predation) to explore how integration of these concepts could aid the development of more effective predator deterrents. Methods We used a colony of captive coyotes as a model system, and simulated predation events with meat baits inside and outside of protected zones. Inside the protected zones we used a remote-controlled vehicle with a state-of-the art, commercially available predator deterrent (i.e., Foxlight) mounted on the top and used this to test three treatments: (1) light only (i.e., without movement or adaptiveness), (2) predetermined movement (i.e., with movement and without adaptiveness), and (3) adaptive movement (i.e., with both movement and adaptiveness). We measured the time it took for coyotes to eat the baits and analyzed the data with a time-to-event survival strategy. Results Survival of baits was consistently higher inside the protected zone, and the three movement treatments incrementally increased survival time over baseline except for the light only treatment in the nonprotected zone. Incorporating predetermined movement essentially doubled the efficacy of the light only treatment both inside and outside the protected zone. Incorporating adaptive movement exponentially increased survival time both inside and outside the protected zone. Our findings provide compelling evidence that incorporating existing robotics capabilities (predetermined and adaptive movement) could greatly enhance protection of agricultural resources and aid in the development of nonlethal tools for managing wildlife. Our findings also demonstrate the importance of marrying agricultural practices (e.g., spatial management of livestock at night) with new technology to improve the efficacy of wildlife deterrents.
Mechanisms of shape-based spatial learning

The ability to navigate to important locations is fundamental to both human and non-human animals. The experiments presented in this thesis were designed to address three key predictions generated from the model of navigation proposed by Miller and Shettleworth (2007, 2008, 2013): First, cue competition effects should be observed between local geometric information and landmarks; Second, the attention paid to geometric and non-geometric cues within an environment should not be modifiable; Third, organisms should not learn about a global representation of the shape of the environment. The results of the blocking experiments reported in Chapter 2 demonstrate that local geometric cues compete with non-geometric cues for control over navigational behaviour, in a manner consistent with the Miller-Shettleworth model. The intradimensional-extradimensional shift and learned predictiveness effects reported in Chapters 3 and 4, respectively, are not consistent with the notion that the attention paid to geometric and non-geometric cues is fixed. The experiments reported in Chapter 5 provide core evidence that humans encode a global representation of the shape of the environments in which they navigate, a result that is also not consistent with the Miller-Shettleworth model. These results suggest that, at best, the model proposed by Miller and Shettleworth (2007, 2008, 2013) provides an incomplete explanation for spatial learning behaviour. In order to account for the data reported in Chapters 3 and 4, it is necessary for the Miller-Shettleworth model to permit changes in the attention paid to navigational stimuli. Additionally, in order to account for the data presented in Chapter 5, it appears necessary to assume that humans encode a global Euclidean representation of the shape of the environments in which they navigate. The challenge for future work will be to determine the precise manner in which multiple representations of environmental geometry support effective navigation.
Distributions of Nutrients in Relation to Phytoplankton Community Heterogeneity in the Makassar Strait, Indonesia

The Monitoring of Indonesia Throughflow (MITF) with scientific cruise across the Indonesian waters that are connecting the islands of Kalimantan and Sulawesi or strait of Makassar. This cruise carried out in August 2015 in an area between 2° and 4° South and 118° and 119° East. This cruise aims to characterize the concentration of nutrients and abundance of phytoplankton groups as well as explain their distribution based on the environmental conditions. Nutrient concentrations consisting of Inorganic Dissolved Nitrogen (DIN), Inorganic Dissolved Phosphate (DIP) and Inorganic Dissolved Silicate (DSi) were measured at seven depth layers, i.e. 10, 30-60, 100-150, 300, 500, 750 and 1000 meters. The study showed that in the transitional season (August), the upper layer of study sites were characterized by salinity between 34.05 and 34.76 PSU, temperature between 26.85 and 29.88 °C, and relatively high nutrient concentration, i.e. DIN = 8.91 - 36.74 μM, DIP = 0.02 - 0.16 μM, and DSi= 31.88 - 249.49 μM. The phytoplankton community groups found were Cyanophyta, Bacillariophyta (Diatom), and Dinophyta, where the highest abundance was Cynophyta for the genus Trichodesmium sp. The composition of phytoplankton community in the surface layer is strongly influenced by sea surface temperature, salinity and nutrient concentration. We found that nutrient utilization in the surface layer is relatively high.
Characterisation of β glucosidase from wood rotting fungi to facilitate efficient plant biomass biodegradation for biofuel production.

Mrunalini B R. β-glucosidases catalyzes hydrolysis of cellobiose and cellodextrins releasing glucose as main product which is used in food, pharmaceutical and biofuel industries, it is an important component of the cellulase complex, it not only hydrolyzes cellobiose and short-chain cello oligosaccharides to glucose, also removes inhibitory effect of cellobiose on β-1, 4-endoglucanase and exoglucanase, thereby increasing overall rate of cellulose biodegradation. Rotten wood samples were collected for isolation and identification of different fungi. 5 fungal isolates of the 20, showed higher cellulose degrading ability which were identified as Pisolithusspp. , Phanerochaetespp. , Syncephalastrumspp. , Aspergillusspp. and Nigrosporaspp. Phanerochaete spp. showed highest ability to degrade cellulose with least cellulose concentration of 22μg/ml and 10μg/ml, in biomass production. Glucose urea media supported more fungal growth than Potato dextrose broth. Nigrosporaspp. showed 560μg/ml of protein content. The β-glucosidase activity (pNPG and salicin as substrates) results indicated that Pisolithus spp. exhibited highest enzyme activity i.e., 237 and 300 μg/ml/min respectively and showed highest alcohol production of 290μl/gm of fungal mat. Therefore, if the fungi are appropriately screened and purified for β-glucosidase, they can be potentially improved for bio-conversion of cellulose to glucose and facilitate efficient plant biomass biodegradation for biofuel production. Copy Right, IJAR, 2016,. All rights reserved.
Extraction and generalisation of category-level information during visual statistical learning in autistic people

Background We examined whether information extracted during a visual statistical learning task could be generalised from specific exemplars to semantically similar ones. We then looked at whether performance in autistic people differed to non-autistic people during a visual statistical learning task and specifically examined whether differences in performance between groups occurred when sequential information was presented at a semantic level. We did this by assessing recall performance using a two-alternative forced choice paradigm after presenting participants with a sequence of naturalistic scene images. Methods 125 adult participants (61 participants with an autism diagnosis and 64 non-autistic controls) were presented with a fast serial presentation sequence of images and given a cover task to avoid attention being explicitly drawn to patterns in the underlying sequences. This was followed by a two-alternative forced choice task to assess participants’ implicit recall. Participants were presented with 1 of 3 unique versions of the task, in which the presentation and assessment of statistical regularities was done at either a low feature-based level or a high semantic-based level. Results Participants were able to generalise statistical information from specific exemplars to semantically similar ones. There was an overall significant reduction in visual statistical learning in the autistic group but we were unable to determine whether group differences occurred specifically in conditions where the learning of semantic information was required. Conclusions These results provide evidence that participants are able to extract statistical information that is presented at the level of specific exemplars and generalise it to semantically similar contexts. We also showed a modest but statistically significant reduction in recall performance in the autistic participants relative to the non-autistic participants.
Exploring experiences engaging in exercise from the perspectives of women living with HIV: A qualitative study

Objectives To explore experiences engaging in exercise from the perspectives of women living with HIV, specifically, i) nature and extent of exercise, ii) components that characterize exercise experiences, iii) facilitators and barriers, and iv) strategies for uptake and sustainability of exercise. Design Qualitative descriptive study involving online semi-structured interviews. Recruitment We recruited women living with HIV from a specialty hospital, community-based organization, and medical clinic in Toronto, Canada. Participants Ten women living with HIV who may or may not have engaged in exercise. Data collection Using a semi-structured interview guide, we asked participants to describe their experiences with, facilitators and barriers to, and strategies to facilitate uptake of exercise. We electronically administered a demographic questionnaire to describe personal, HIV and physical activity characteristics of participants. We conducted a descriptive thematic analysis with the interview data, and descriptive analysis (medians, frequencies, percentages) of questionnaire responses. Results Women characterized their experiences with exercise with six intersecting components: (1) culture, (2) gender, (3) HIV-related stigma, (4) episodic nature of HIV, (5) sense of belonging, and (6) perceptions of exercise. Facilitators to exercise included: aspirations to achieve a healthy lifestyle, using exercise as a mental diversion, having an exercise companion, and receiving financial support from community-based organizations to facilitate engagement. Barriers to exercise included: limited resources (lack of mental-health support and fitness resources in the community), financial limitations, time and gym restrictions, and cold winter weather conditions. Strategies to facilitate uptake of exercise included: creating social interactions, provision of online exercise classes, raising awareness and education about exercise, and offering practical support. Conclusions Experiences with exercise among women living with HIV were characterized by intersecting personal and environmental contextual components. Results may help inform tailored implementation of exercise rehabilitation programs to enhance uptake of exercise and health outcomes among women living with HIV.
Regulatory NK cells in autoimmune disease

NK cells are defined as the major components of the immunological network which exerts defense against tumors and viral infections as well as regulation of innate and adaptive immunity, shaped through interaction with other cells like T cells. According to the surface markers, NK cells can be divided into CD56dim NK and CD56bright NK subsets. CD56bright NK cells usually are known as regulatory NK cells. Once the immune system loses its self-tolerance, autoimmune diseases develop. NK cells and their subsets can be altered during autoimmune diseases, indicative of their prominent regulatory roles and even pathological and protective functions in autoimmune disorders. In this regard, activation of CD56bright NK cells can suppress activated autologous CD4+ T cells and subsequently prevent the initiation of autoimmunity. In this review article, we summarize the roles of regulatory NK cells in autoimmune disease occurrence which needs more research to uncover their exact related mechanism. It seems that targeting NK cells can be a promising therapeutic platform against autoimmune diseases.
Patterns of overlapping habitat use of juvenile white shark and human recreational water users along southern California beaches

Juvenile white sharks (JWS) of the Northeastern Pacific population are present in nearshore southern California waters and form mixed size class (~1.5–3 m) aggregations for weeks to months, often within 500 m of shore. These nearshore beach habitats are heavily used for human recreation (e.g., surfing, swimming, body boarding, wading, and standup paddleboarding) and the amount of spatio-temporal overlap between JWS and humans is currently unknown. Increases in human population and the Northeastern Pacific population of white sharks have raised concern over human beach safety. To determine spatio-temporal JWS-human overlap at various spatial scales (e.g., across the entire southern California coastline, across different distances from shore, and within specific beach locations), 26 beach locations across southern California were surveyed monthly resulting in 1644 aerial drone surveys between January 2019 to March 2021. Thirteen environmental variables were assessed to predict when spatio-temporal overlap between JWS and water users was highest. Coast-wide distribution of JWS was clumped, limiting human-shark co-occurrence to specific locations, with 1096 of 1204 JWS observations occurring at Carpinteria and Del Mar Beach locations. Nearshore distribution indicated JWS are often close enough to the wave break to interact with some water users (median = 101 m, range = 2–702 m), although JWS had the most spatial overlap with stand-up paddlers. Daily human-shark co-occurrence was 97% at beaches where JWS aggregations had formed, and human activity showed high spatial overlap at shark aggregation sites. Although there is higher seasonal human-shark spatio-temporal overlap where aggregations form in southern California, the number of unprovoked shark bites across southern California is extremely low. This study provides evidence that high human-shark spatio-temporal overlap does not lead to an increased bite frequency in southern California, and there are a number of possible explanations as to why JWS are not biting water users despite daily encounters.
Anti-inflammatory, immunomodulatory and anti-oxidant effects of Ocimum basilicum L. and its main constituents: A review

Ocimum basilicum L. (O. basilicum) is an ornamental and therapeutic plant with various pharmacological effects and medical applications. In this article, detailed information on the anti-oxidant, immunomodulatory, and anti-inﬂammatory properties of O. basilicum and its main constituents was provided. The literature survey of the different databases until the end of November 2021 was explored on the immunomodulatory, anti-inﬂammatory and anti-oxidant effects of the herb and its constituents. The plant and its constituents showed diverse pharmacological effects including immunomodulatory, anti-inflammatory and anti-oxidant properties by improving of the inflammatory mediators including interleukin (IL)-10, IL-4, tumor necrosis factor-alpha (TNF-α), interferon gamma (IFN-γ), nitric oxide (NO), serum levels of IFN-γ, IL10 and IL-4, Ig. G, Ig. M and phospholipase A2 (PLA2), immunoglobulin E (Ig. E), total protein (TP), oxidant and anti-oxidant markers. O. basilicum and its main constituents therefore, could be effective on the treatment of diseases associated with inflammation, immune dysregulation and oxidative stress. The present review article provides readers with organized information about the anti-oxidant, immunomodulatory, and anti-inﬂammatory properties of O. basilicum.
Invited Perspective: Measuring Biomarkers of Exposure in Target Organs—What Is Needed to Move Forward?

Russ Hauser1,2,3 and Lidia Mínguez-Alarcón1,4 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, Massachusetts, USA Channing Division of Network Medicine, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts, USA