Miracle-dz/UniFAR-FTUs huggingface dataset:
Security and Privacy Challenges in Connected Vehicular Cloud Computing

Vehicular cloud computing (VCC) is an improvement from conventional cloud computing to new revolutionized computing services including intelligent transportation, autonomous driving and vehicle control, Internet browsing, online documentation, and infotainment. It enables vehicles to autonomously share heterogeneous computational resources while solving unanticipated critical problems dynamically. However, in a world of black hats, technology often has a dark side as well. Therefore, the VCC is limited by considerable security and privacy challenges. The special characteristics of VCC, including the multitenancy nature of clouds, intermittent wireless communications, high mobility of vehicles, and rapid resource elasticity with decentralized operations, have promoted security solutions used in vehicular ad hoc networks and conventional cloud computing to be revised. This paper first presents a state-of-the-art study of VCC that focuses on VCC architecture, its features analysis, and extensive VCC applications. Second, the proposed threats identification taxonomy and an exhaustive survey on security and privacy issues in VCC are presented under a layered approach: physical resource layer, vehicle-to-anything (V2X) network layer, and vehicular cloud layer, as well as at a complete system level. Finally, we highlight and discuss challenges and open research issues that can be considered as future research directions.
Using Asymmetric Cost Matrices to Optimize Care Management Interventions

The majority of health care expenditures are incurred by a small portion of the population. Care management or intervention programs may help reduce medical costs, especially those of extremely high-cost members. For these programs to be effective, however, the insurer must identify and select potential high-cost members to be assigned to an intervention before they incur those costs. Because high medical costs are often connected to an accident or traumatic event that cannot be anticipated, it can be difficult to predict who will be high-cost in the future. In this article, we explore the use of machine learning in predicting high-cost members. Specifically, we use the extreme gradient boosting algorithm to develop risk scores for members based on demographic, medical, and financial histories. To select members for intervention, we develop asymmetric cost matrices that account for potentially unequal savings or losses for assigning interventions to members. We show how these matrices can be reduced to a function of the expected savings per dollar of intervention, which is easily used to optimize the risk score threshold at which members are assigned an intervention. These techniques, which can be tailored to the specific needs of an insurer, may help insurers select the optimal members for intervention programs, reduce overall costs, and improve member health outcomes.
Tourist intentions to donate to non-lethal feral cat management at a potential natural World Heritage site in Japan

ABSTRACT Feral cat management needs cooperation, including financial support, from a variety of stakeholders. We used a payment card approach to investigate the willingness of tourists to donate to non-lethal feral cat management at a potential natural World Heritage site on Amami Oshima Island in Japan. We found that more than 80% of tourists intended to donate funds to non-lethal feral cat management, and the mean willingness to donate was about USD $14 (1374.1 JPY). The rate of participation in the donation was higher than that found in previous studies, and the amount of intention to donate was large enough to support non-lethal cat management. Respondent income and attitudes toward lethal options affected intentions to donate. Those who valued the island farmland also had a higher intention to donate. Our findings provide useful information for the implementation of feral cat management strategies to conserve biodiversity while minimizing conflict.
Outcomes of Operative Management of Insertional Achilles Tendinopathy in the Young vs Elderly

Background: Insertional Achilles tendinopathy (IAT) is a common cause of chronic posterior heel pain. Surgical intervention reproducibly improves patients’ pain and functional status. We hypothesized that patients older than 60 years would have similar improvements in pain and function and low rates of complications after surgery for IAT when compared to a younger cohort. Methods: Retrospective review of adult case series in patients undergoing surgical management of IAT. Patients were stratified into those 60 years and younger and those older than 60 years. Patients with prior or concomitant surgical procedures and revisions were excluded. Visual analog scale (VAS), Short Form–36 Physical Component Summary and Mental Component Summary (SF-36 PCS/MCS) scores, wound infection, and recurrence, defined as a redevelopment of heel pain in the operative extremity within 6 months, were assessed with a minimum follow-up of 12 months. Statistical analysis was performed using linear regression mixed models and χ2 analysis. Thirty-seven patients were enrolled, with 38 operative heels. The younger cohort had an average age of 49.1 (range, 26-60) years. The older group had an average age of 66.8 (range, 61-76) years. Results: VAS and SF-36 PCS scores for the entire cohort significantly improved at 6 and 12 months postoperatively (P < .001). Postoperative SF-36 MCS scores for the cohort significantly improved only at 12 months (P < .001). No significant differences between the young and elderly were seen with regard to improvements in VAS and SF-36 PCS/MCS at 6 or 12 months postoperatively. Multiple linear regression models showed no significant difference between age groups and VAS score, SF-36 PCS/MCS, or change in pain scores after controlling for comorbidities. No significant difference in overall complication rates was seen between the 2 groups (4.9% vs 29.4%, P = .104). There was 1 recurrence of heel pain in the younger group and 4 recurrences of pain in the older group (23.5%) at 6 months, of which 2 resolved at 1 year. There was 1 case of a superficial wound infection requiring antibiotics in the older cohort (5.9%). No patients required surgical revision. Conclusion: Surgical management of IAT in an older population produced similar improvements in clinical results when compared to a younger cohort, with no significant increase in postoperative complications. Level of Evidence: Level III, retrospective comparative series.
Analyzing Vegetative Cover of the Bois Brule River Watershed Re-Visited in Northwestern Wisconsin, Part I: Forest Stand Changes (1968 to 2016)

The Bois Brule River flows through some of the most diverse habitats found anywhere in Wisconsin. In 2015 and 2016, our survey teams were able to collect and analyze forest strata data from 48 of the 54 forest stands surveyed by Davidson in 1968–69 using the point-centered quarter sampling method. Stands were dominated by transitional forest of northern hardwoods, while some forest types were represented by only one stand each. Average absolute tree density decreased by 25% over the 48 years, while the tree basal area did not change much with a slight decrease of 6%. These changes were indicative of a later successional stage of these forests. Species showing significant change are Betula papyrifera, Abies balsamea and Acer rubrum. Old growth forests have entered a later stage of maturity with some Pinus strobus individuals becoming snags today and P. resinosa trees becoming larger. Our sapling surveys in these old growth forests suggest Acer rubrum and Abies balsamea could become dominant forests in the future, with little or no recruitment from the pines. Our survey depicts a forest that is trending to a later successional stage of development and recovering from the cutover from the early 20th century. Future threats to these forests include disruptions in the natural fire regime, non-native plants (e.g., Rhamnus cathartica, Lonicera spp.), insect infestations (e.g., Choristoneura fumiferana, Agrilus planipennis), excessive fragmentation from harvesting activities and climate change. These forests may exhibit some resiliency to climate change due to the watershed’s proximity to Lake Superior and the river’s deep valley.
Breaking the Curse of Nonregularity with Subagging - Inference of the Mean Outcome under Optimal Treatment Regimes

Precision medicine is an emerging medical approach that allows physicians to select the treatment options based on individual patient information. The goal of precision medicine is to identify the optimal treatment regime (OTR) that yields the most favorable clinical outcome. Prior to adopting any OTR in clinical practice, it is crucial to know the impact of implementing such a policy. Although considerable research has been devoted to estimating the OTR in the literature, less attention has been paid to statistical inference of the OTR. Challenges arise in the nonregular cases where the OTR is not uniquely defined. To deal with nonregularity, we develop a novel inference method for the mean outcome under an OTR (the optimal value function) based on subsample aggregating (subagging). The proposed method can be applied to multi-stage studies where treatments are sequentially assigned over time. Bootstrap aggregating (bagging) and subagging have been recognized as effective vari- ance reduction techniques to improve unstable estimators or classifiers (Bu hlmann and Yu, 2002). However, it remains unknown whether these approaches can yield valid inference results. We show the proposed confidence interval (CI) for the optimal value function achieves nominal coverage. In addition, due to the variance reduction effect of subag- ging, our method enjoys certain statistical optimality. Specifically, we show that the mean square error of the proposed value estimator is strictly smaller than that based on the sim- ple sample-splitting estimator in the nonregular cases. Moreover, under certain conditions, the length of our proposed CI is shown to be on average shorter than CIs constructed based on the existing state-of-the-art method (Luedtke and van der Laan, 2016) and the “oracle” method which works as well as if an OTR were known. Extensive numerical studies are conducted to back up our theoretical findings.
Complete spatial safety for C and C++ using CHERI capabilities

Lack of memory safety in commonly used systems-level languages such as C and C++ results in a constant stream of new exploitable software vulnerabilities and exploit techniques. Many exploit mitigations have been proposed and deployed over the years, yet none address the root issue: lack of memory safety. Most C and C++ implementations assume a memory model based on a linear array of bytes rather than an object-centric view. Whilst more efficient on contemporary CPU architectures, linear addresses cannot encode the target object, thus permitting memory errors such as spatial safety violations (ignoring the bounds of an object). One promising mechanism to provide memory safety is CHERI (Capability Hardware Enhanced RISC Instructions), which extends existing processor architectures with capabilities that provide hardware-enforced checks for all accesses and can be used to prevent spatial memory violations. This dissertation prototypes and evaluates a pure-capability programming model (using CHERI capabilities for all pointers) to provide complete spatial memory protection for traditionally unsafe languages. As the first step towards memory safety, all language-visible pointers can be implemented as capabilities. I analyse the programmer-visible impact of this change and refine the pure-capability programming model to provide strong source-level compatibility with existing code. Second, to provide robust spatial safety, language-invisible pointers (mostly arising from program linkage) such as those used for functions calls and global variable accesses must also be protected. In doing so, I highlight trade-offs between performance and privilege minimization for implicit and programmer-visible pointers. Finally, I present CheriSH, a novel and highly compatible technique that protects against buffer overflows between fields of the same object, hereby ensuring that the CHERI spatial memory protection is complete. I find that the byte-granular spatial safety provided by CHERI pure-capability code is not only stronger than most other approaches, but also incurs almost negligible performance overheads in common cases (0.1% geometric mean) and a worst-case overhead of only 23.3% compared to the insecure MIPS baseline. Moreover, I show that the pure-capability programming model provides near-complete source-level compatibility with existing programs. I evaluate this based on porting large widely used open-source applications such as PostgreSQL and WebKit with only minimal changes: fewer than 0.1% of source lines. I conclude that pure-capability CHERI C/C++ is an eminently viable programming environment offering strong memory protection, good source-level compatibility and low performance overheads.
Computer‐based evaluation to assess students' learning for the multiple‐choice question–based exams: CBE‐MCQs software tool

In this study, a computer‐based evaluation (CBE) software tool, namely CBE‐MCQs, is proposed to assist the instructor to evaluate and analyze traditional classroom‐based exams, which include multiple‐choice questions (MCQs) reliably, quickly, and automatically. Using the proposed CBE‐MCQs software tool while performing question analysis, the learning outcome‐based analyzes are performed by matching the learning outcomes of the courses with the exam questions. With question‐based evaluations, item analyzes such as item difficulty, item discrimination, and item distracters can be made easily. Also, with the question‐based evaluation, the instructor of the course can effectively measure the extent to which students achieve the learning outcomes of the related course. Thus, the CBE‐MCQs tool helps successful maintenance of both accreditation and training processes with the analysis it provides to the instructors. In addition, the final exam assessment of the course based on MCQs within the scope of the case study was carried out with the CBE‐MCQs software tool. Each MCQ was evaluated based on classical theory within the scope of difficulty index (P), discrimination index (D), and distracter efficiency (DE). It is observed that association between D and P was statistically significant according to Pearson correlation (r = −0.600) and χ2 (χ2 = 15.98, df = 8; α = 0.05). Also, a system usability survey of 10 statements was applied to the instructors using this software to evaluate the usability of the CBE‐MCQs software tool. As a result of a series of experimental studies and survey, the developed CBE‐MCQs software tool is evaluated as simple, effective, and satisfactory by 87.4% of the instructors.
A New Revised Efficient of VAM to Find the Initial Solution for the Transportation Problem

Transportation Problem (TP) is a very important problem which has been vastly studied in Operations Research domain. There are some classical methods to find the initial basic feasible solution (IBFS) which minimize the total shipping cost of (TP) such as north-west corner method (NWCM), minimum cost method (MCM) and Vogel’s approximation method (VAM) which the best one of them. In this paper, we suggest a new amendment to (VAM) to find (IBFS) of (TP), which is an iterative method and the results will be near the optimal solution and in some cases equal to the optimal solution. In the numerical experiences we compare the results of the new approach with other classical methods to verify the efficiency of the new method. The proposed method is very effective and well-suited for use in solving these problems of various sizes.
Content Based Image Retrieval Using Two Color Feature Extraction

Content Based Image Retrieval (CBIR) is a process to search for an image based on the content or features that are inside. Nowadays, many image retrieval applications have been made to meet the needs, so this application can provide convenience in terms of the introduction and search for an image. In this research, we used 10 different objects as image retrieval consists of Bicycle, Cow, Flower, Frangipani, Grape, Horse, Lovebird, Orange, Strawberry, Tree. These objects can be expressed in 10 classes. Our aim using these objects is viewed from the color of every object and the object of a different kind. From that point of view we built a CBIR system by utilizing the main features of the object (image). The main feature is the color feature. In this research, the main process is the extraction of color features with the color histogram and color moments. So in this research it will produce feature extraction by measuring the similarity in the library image. This measurement is done by calculating the closest distance using the euclidean distance method. The data library used in this research is 10 pieces of data, the test data is 50 pieces with 5 pieces for each of these classes. After testing using data and methods described above, the results of accuracy are obtained that the application of the color moments method gets better results than the color histogram method.
LEFT ATRIAL FUNCTION ASSESSMENT BY 2D-SPECKLE TRACKING ECHOCARDIOGRAPHY IN HEMODIALYSIS PATIENTS WITH NORMAL LEFT VENTRICULAR SYSTOLIC FUNCTION

Background: Chronic renal failure leads to structural changes and functional abnormalities in the heart, which are known as uremic cardiomyopathy. These changes may be progressive and related directly to a grossly aggravated risk of cardiovascular events. Left atrial volume estimated by echocardiography is reported to be a sensitive marker of LV diastolic dysfunction and LA function plays a central role in maintaining optimal cardiac output despite impaired LV relaxation and reduced LV compliance. Objective: The aim of the study was to investigate the regional function of the left atrial myocardium using two-dimensional speckle tracking echocardiography in hemodialysis patients with preserved left ventricular ejection fraction. Patients and methods: This study was a cross-sectional comparative study conducted on 80 individuals divided into two groups; Group (1) (the control group) consisted of 20 healthy individuals, Group (2) consisted of 60 patients on regular hemodialysis. All patients were subjected to full history taking, complete physical examination, resting 12-lead electrocardiography, left ventricular assessment by conventional echocardiography and speckle tracking echocardiography, left atrial assessment by conventional echocardiography and speckle tracking echocardiography. Results: In our study, the left atrial global longitudinal strain (LAGLS) value assessed by speckle tracking echocardiography significantly decreased in group (1) which was 34.63 ± 10.78 versus 46.76 ± 8.99 in group (2). Conclusion: The mean left atrial global longitudinal strain (LAGLS) significantly decreased in patients with ESRD on regular hemodialysis.
Modeling of Monopulse Radar Signals Reflected from Ground Clutter in a Time Domain Considering Doppler Effects

To evaluate the performance of a monopulse radar system, it is necessary to accurately model radar return signals from the ground surface in a time domain. In this paper, we propose a numerical method to model these return signals including radar radio frequency specifications, such as the pulse repetition interval, frequency, and polarization, as well as the antenna geometry, the ground clutter backscattering characteristics, and so on. The Doppler effect is also incorporated into the signal generation scheme because of the dynamics of the platform/clutter and the antenna orientation. The Doppler frequency shift caused by the ground clutter is modeled by employing the time correlation of the received signals. In some scenarios, the monopulse signals are generated and numerically examined. For real radar application, the effect of the platform’s roll stabilization on the monopulse signals is investigated based on the proposed signal generation scheme.
Exact k-Component Graph Learning for Image Clustering

The performance of graph-based clustering methods highly depends on the quality of the data affinity graph as a good affinity graph can approximate well the pairwise similarity between data samples. To a large extent, existing graph-based clustering methods construct the affinity graph based on a fixed distance metric, which is often not an accurate representation of the underlying data structure. Also, they require postprocessing on the affinity graph to obtain clustering results. Thus, the results are sensitive to the particular graph construction methods. To address these two drawbacks, we propose a k-component graph clustering (k-GC) approach to learn an intrinsic affinity graph and to obtain clustering results simultaneously. Specifically, k-GC learns the data affinity graph by assigning the adaptive and optimal neighbors for each data point based on the local distances. Efficient iterative updating algorithms are derived for k-GC, along with proofs of convergence. Experiments on several benchmark datasets have demonstrated the effectiveness of k-GC.
Predictive models for chromatin folding: connecting sequence to structure

The DNA packaged inside a nucleus shows complex structures stabilized by a host of DNAbound factors. This combination of DNA and bound factors is known as chromatin. Both the distribution of bound factors and the contacts between different locations of the DNA can be now measured on a genome-wide scale. Nevertheless, to what extent is the likelihood of contact between sites in the genome encoded by the spatial sequence of bound factors? Current approaches at addressing this question primarily use simulations of heterogeneous polymers to generate structures using the locations of bound factors. In contrast, here we develop novel predictive models for connecting chromatin sequence to structure using statistical physics, information theory and machine learning. Since our methods do not require costly polymer simulations they can quickly predict the effect on structure due to changes in the distribution of bound factors. In addition, our methods are formulated in a manner that allows us to solve the inverse problem: namely, given just structural data, predict the likely sequence of bound factors. We show that the models developed can make biologically meaningful predictions, highlighting key features of the mechanisms through which the three-dimensional conformation of DNA is coordinated by the interactions between DNA-bound factors.
Growing in the city: Urban evolutionary ecology of avian growth rates

Rapid environmental change driven by urbanization offers a unique insight into the adaptive potential of urban‐dwelling organisms. Urban‐driven phenotypic differentiation is increasingly often demonstrated, but the impact of urbanization (here modelled as the percentage of impervious surface (ISA) around each nestbox) on offspring developmental rates and subsequent survival remains poorly understood. Furthermore, the role of selection on urban‐driven phenotypic divergence was rarely investigated to date.