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Introduction Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients with kidney stones. There are many imaging options for diagnosing and managing kidney stone disease, and CT imaging is the preferred method. Objectives Radiologists need to manually analyse large numbers of CT slices to diagnose kidney stones, and this process is laborious and time-consuming. This study used deep automated learning (DL) algorithms to analyse kidney stones. The primary purpose of this study is to classify kidney stones accurately from CT scans using deep learning algorithms. Methods The Inception-V3 model was selected as a reference in this study. Pre-trained with other CNN architectures were applied to a recorded dataset of abdominal CT scans of patients with kidney stones labelled by a radiologist. The minibatch size has been modified to 7, and the initial learning rate was 0.0085. Results The performance of the eight models has been analysed with 8209 CT images recorded at the hospital for the first time. The training and test phases were processed with limited authentic recorded CT images. The outcome result of the test shows that the Inception-V3 model has a test accuracy of 98.52 % using CT images in detecting kidney stones. Conclusions The observation is that the Inception-V3 model is successful in detecting kidney stones of small size. The performance of the Inception-V3 Model is at a high level and can be used for clinical applications. The research helps the radiologist identify kidney stones with less computational cost and disregards the need for many experts for such applications.

Since 1973, 535 specific claims valued at more than $6 billion have been settled between the Government of Canada and First Nations governments for outstanding treaty obligations. Critics of specific land claims point to the absence of statistical evidence that shows a positive impact on First Nations economies and characterize specific claims as a multi-billion-dollar liability for Canadian taxpayers. This research shows that the economic benefits of specific claims are being lost to First Nations economies through high rates of economic leakage, especially in cases in which large proportions of the settlement funds are disbursed on a per capita basis. Collaborating with the Little Red River Cree Nation (LRRCN) in Alberta (a recent recipient of a $239 million settlement), we use household expenditure data, band-owned businesses’ financial statements, and band administration audit reports to estimate their rate of economic leakage and the economic impact of their specific claims settlement. Results indicate that the economic leakage rate for the LRRCN is 83.5 percent. Using household expenditure data and input–output models, we estimate the economic impact of the LRRCN settlement. Assuming a 100 percent per capita disbursal of the funds, the settlement would contribute $275–$339 million in provincial output, $172–$212 million in gross domestic product, and $110–$127 billion in labour income, and it would create 2,393–2,714 full-time jobs. The results of this research may be of value to First Nations leaders in making decisions concerning the distribution and investment of specific claims settlements in the future.

Enterobius vermicularis (pinworm) infectious disease is small in size and needs careful analysis. This disease is usually seen in children and causes serious damage if early signs are not detected. Enterobius vermicularis is one of the most common helminth infections in the world. Humans are the only natural host of this parasitic disease. living in crowded environments is a risk factor for transmission. In this study, the deep learning method was applied to the small-scale pinworm disease that could be overlooked with a bare eye examination. The deep learning method(ResU-Net and U-Net) is trained with 365 Images to have high accuracy detection of the infection. The method was tested with 56 Images and the highest achievement dice score was 0.95%. This work has a unique future based on a segmentation mask that produces immediate visualization. The method is superior to the existing models. The segmentation mask method produces less error compared with the classical methods and uses less human resources, time and cost.

In this study we investigate the investment behaviour of the First Nation governments (FNGs) (N=68) in Saskatchewan, Canada. FNGs invest revenues into First Nation-owned businesses or through joint...

Crowding-out effects of tobacco consumption refers to the situation in which tobacco consumption reduces a household's spending on other commodities. In this paper, we test for the crowding-out effects of recreational tobacco consumption on food expenditures among on-reserve Indigenous communities in Canada.We use data from household expenditure surveys (N=469) to investigate tobacco consumption behaviour of on-reserve households of six First Nation communities in Canada. Using conditional Engel curves, we estimate the crowding-out effects of spending on tobacco on budget share of other expenditure categories with a focus on food expenditures.Our analysis showed that crowding-out effects of consuming tobacco on consumption of goods and services are minimal with an estimated impact of -0.00004 on the budget share of food expenditures in the remaining budget excess of expenditures on tobacco. While crowding-out effects are not statistically significant in general, the budget share of store-bought food expenditures is significantly lower (i.e. 5%) among tobacco consumer households.This study sheds new light on some of the indirect impacts of recreational tobacco use and Indigenous food insecurity in Canada. This study is the first attempt to test for crowding-out effects of tobacco consumption among Indigenous populations in Canada. Our findings are important and highlight areas for substantial improvements in health and well-being outcomes given the alarming rates of food insecurity experienced by Indigenous communities in Canada.RéSUMé: OBJECTIF: L’effet d’éviction de la consommation de tabac désigne la situation dans laquelle cette consommation réduit les dépenses d’un ménage pour d’autres produits. Dans cet article, nous analysons l’effet d’éviction exercé par l’usage récréatif du tabac sur les dépenses alimentaires dans les communautés autochtones du Canada vivant dans les réserves. MéTHODE: Nous utilisons les données d’enquêtes sur les dépenses des ménages (N = 469) pour étudier le comportement de consommation de tabac des ménages de six communautés des Premières nations du Canada vivant dans des réserves. À l’aide de courbes d’Engel conditionnelles, nous estimons l’effet d’éviction des dépenses pour le tabac sur la part du budget des autres catégories de dépenses, en particulier les dépenses alimentaires. RéSULTATS: Selon notre analyse, l’effet d’éviction de la consommation de tabac sur la consommation de produits et services est minime, avec un impact estimatif de -0,00004 sur la part du budget des dépenses alimentaires dans l’excédent budgétaire restant des dépenses pour le tabac. Bien que l’effet d’éviction ne soit pas significatif dans l’ensemble, la part du budget des dépenses consacrée aux aliments achetés au magasin est sensiblement (5 %) moins élevée dans les ménages qui consomment du tabac. CONCLUSION: Cette étude apporte un nouvel éclairage sur certaines incidences indirectes de l’usage récréatif du tabac et sur l’insécurité alimentaire dans les populations autochtones du Canada. Elle représente une première tentative d’analyser l’effet d’éviction de la consommation de tabac dans les populations autochtones du Canada. Nos constatations sont importantes et font ressortir des aspects nécessitant des améliorations substantielles dans les résultats de santé et de bien-être, vu les taux alarmants d’insécurité alimentaire dans les communautés autochtones du Canada.

There are several network protocols available to enable efficient data transmission between the source and the destination. Mobile ad-hoc networks (MANETs) are usually small, battery-operated mobile devices or nodes. The communication between the nodes does not have a fixed infrastructure. This work aims to use the available resources at the maximum level at the minimum executable period. This work uses an improved algorithm for the Mobile Ad hoc Networks (MANETs) routing protocol to optimize the number of hop count without considering the usual parameters such as the energy involved in the process. The performance has further increased by combining the scheduling and the optimal route selection algorithms. The success of the overall routing process relies on a combinational work of balanced minimum execution time scheduling and the optimum route selecting algorithms. The introduced balanced-minimum execution time provides two advantages. It increases the scheduling performance and provides load balance on the network. The combined optimum route selection algorithm uses varying network parameters of the link quality, mobility, and end-to-end delay at the optimization process. The superiority of the method presented has been proven by the results obtained in the simulation.

In today's web, it is not uncommon for web applications to take a complete URL as input from users. Usually, once the web application receives a URL, the server opens a connection to it. However, if the URL points to an internal service and the server still makes the connection, the server becomes vulnerable to Server-Side Request Forgery (SSRF) attacks. These attacks can be highly destructive when they exploit internal services. They are equally destructive and need much less effort to succeed if the server is hosted in a cloud environment. Therefore, with the growing use of cloud computing, the threat of SSRF attacks is becoming more serious. In this paper, we present a novel defense approach to protect internal services from SSRF attacks. Our analysis of more than 60 SSRF vulnerability reports shows that developers' awareness about this vulnerability is generally limited. Therefore, coders usually have flaws in their defenses. Even when these defenses have no flaws, they are usually still affected by important security and functionality limitations. In this work, we develop a prototype based on the proposed approach by extending the functionality of a popular reverse proxy application and deploy a set of vulnerable web applications with that prototype. We demonstrate how SSRF attacks on these applications, with almost no loss of performance, are prevented.

Growing numbers of advanced malware-based attacks against governments and corporations, for political, financial and scientific gains, have taken security breaches to the next level. In response to such attacks, both academia and industry have investigated techniques to model and reconstruct these attacks and to defend against them. While such efforts have been all useful in mitigating the effects of modern attacks, automated malware code reuse inspection and campaign attribution have received less attention.

Nowadays, the whole world is deadly involved with Coronavirus disease. The superpowers are placed at the top of the list of countries affected by this deathly virus. The main objective of this work is to compare totally fifteen countries with the highest number of populations infected with the disease to identify the countries which keep their healthcare superior to their economic aspects. In this research, fuzzy. PROMETHEE method was employed for the evaluation of management and prevention of this deadly disease caused by coronavirus (covid-19) in most infected countries taking into consideration several parameters such as total cases, total deaths, total recovered, active cases, critical and serious cases, total test and population. According to the outcome of the results, Germany was found to appear as the first country in the PROMETHEE Rainbow which was capable of managing to prevent and control its population against covid-19 infection, caring about healthcare more than the economy or military power, compared to that of other countries under study.

It has been suggested that provincial and national multipliers may provide incorrect estimates of the economic impacts when examining distinct communities. Using data collected from a comprehensive survey of household spending on two First Nations in Saskatchewan, Canada, we use Input-Output models to refine regional multipliers for these distinct populations. We also estimate the rate of economic leakage and the economic impacts of First Nation spending. Results indicate that economic leakage rates for First Nation economies is roughly 90 percent; meaning that 90 cents of every dollar spent by First Nations for goods and services occurs off-reserve. Using our new multipliers, we find that First Nation spending contributes over $741 million to Saskatchewan’s GDP, creates approximately 11,244 full-time jobs, and leads to an estimated increase of over $462 million in labor force income for the province. If policy makers intend to build on-reserve economies, strategies must be found to recapture off-reserve spending by providing comparable on-reserve goods and services. In the absence of on-reserve economic development, First Nation economic growth will likely remain stagnant with few wealth generating opportunities and lower standards of living for First Nation members.

Assigning family labels to malicious apps is a common practice for grouping together malware with identical behavior. However, recent studies show that apps labeled as belonging to the same family do not necessarily behave similarly: one app may lack or have extra capabilities compared to others in the same family, and, conversely, two apps labeled as belonging to different families may exhibit close behavior. To reveal these inconsistencies, this paper presents AndrEnsemble, a characterization system for Android malware families based on ensembles of sensitive API calls extracted from aggregated call graphs of different families. Our method has several advantages over similar characterization approaches, including a greater reduction ratio with respect to original call graphs, robustness against transformation attacks, and flexibility to be applied at different granularity levels. We experimentally validate our approach and discuss three specific use cases: mobile ransomware, SMS Trojans and banking Trojans. This left us with some interesting findings. First of all, malicious operations in these types of malware are not necessarily exercised by using several sensitive API calls all together. Second, SMS Trojans have larger ensembles of API calls compared to the other types. Last but not least, we identified several samples with identical ensembles though being labeled as part of different families.

Obfuscation techniques modify an app's source (or machine) code in order to make it more difficult to analyze. This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case malware. Since malware analysis often requires considerable resource investment, detecting the particular obfuscation technique used may contribute to apply the right analysis tools, thus leading to some savings. In this paper, we propose ANDRODET, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation. ANDRODET leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner. We compare our results with a batch learning algorithm using a dataset of 34,962 apps from both malware and benign apps. Experimental results show that online learning approaches are not only able to compete with batch learning methods in terms of accuracy, but they also save significant amount of time and computational resources. Particularly, ANDRODET achieves an accuracy of 92.02% for identifier renaming detection, 81.41% for string encryption detection, and 68.32% for control flow obfuscation detection, on average. Also, the overall accuracy of the system when apps might be obfuscated with more than one technique is around 80.66%. (C) 2018 The Authors. Published by Elsevier B.V. This work has been partially supported by MINECO grantTIN2016-79095-C2-2-R (SMOG-DEV) and CAM grant S2013/ICE-3095 (CIBERDINE), co-funded with European FEDER funds. Furthermore, it has been partially supported by the UC3M’sgrant Programa de Ayudas para la Movilidad

Abstract – In the latter half of 2015, a survey looking at attitudes and beliefs about creativity was distributed on the campus of the University of Saskatchewan. Over 2000 responses were gathered, including more than 200 in the College of Engineering. Initial quantitative results from this study were reported in 2016 in Neufeld et al [2]. In terms of the methods used in the study, as discussed in Neufeld et al [2], an online pilot survey was distributed to students and faculty from a variety of the Colleges at the University. Survey questions probed respondents’ affinity for creativity, their personality characteristics, their opinions on state, trait and skill-based viewpoints on creativity, and demographic details. The first part of the survey was a validated Creative Attitudes and Values measurement tool (part of the Runco Creativity Assessment Battery (rCAB)© 2012), as discussed in Acar and Runco [1]. This tool consists of 25, 5-point Likert scale items. Of these 25 items, 15 and 10 were indicative and contraindicative items, respectively. Contraindicative items were reverse coded so that they could be used along with the indicative ones. Both past research and our results showed good inter-item reliability scores for this measurement tool. In Neufeld et al [2] we presented results covering all of the closed-form, quantitative questions along with some correlational calculations with the rCAB scores. The focus of the current paper is on the qualitative results, as well as on a factor analysis of the rCAB questions. The factor analysis was quite successful. We used SPSS and forced a correlation of items, reducing to three factors. We have just over 29% of variance accounted for, with 10% non-redundant residuals. We have strong anti-correlation between one factor and the other two, and no correlation between the other two. These results will be compared to those of the rCAB authors [3]. As for the qualitative data, we asked several open-ended questions to probe how respondents defined creativity, whether they regarded it as a positive behavior, as well as how they felt about creativity in terms of it being a skill, trait and/or state. For example, pairs of questions asked when creativity is difficult and easy, when it should and should not be used, and when it grows and diminishes. For each of the 9 questions that had open-ended answers, concepts were extracted from individual responses. Concepts were then grouped into themes. Themes and concepts were compared across questions and were aligned. Responses were then coded for concepts and themes. At this point, the text data could be quantitatively examined. This paper presents those results, and discusses the implications of the concepts, themes, and their statistics for how we talk about creativity, and how we can teach it. Comparisons will be made between the results from engineering students and staff versus non-engineers. This paper completes the first level of evaluation of the results of this initial survey focused on attitudes and beliefs about creativity. Future work will focus on examining correlations between the results of different questions, including the rCAB scores.

Wireless Sensor Networks (WSNs) are attracting attention from the research community. One of the key issues is to provide them with privacy protection. In recent years, a huge amount of contributions has been focused on this area. Surveys and literature reviews have also been produced to give a systematic view of the different approaches taken. However, no previous work has focused on privacy models, that is, the set of assumptions made to build the approach. In particular, this paper focuses on this matter by studying 41 papers of the last 5 years. We highlight the great differences appearing among related papers that could make them incompatible to be applied simultaneously. We propose a set of guidelines to build comprehensive privacy models so as to foster their comparability and suitability analysis for different scenarios. This work was supported by the MINECO Grant TIN2013-46469-R (Security and Privacy in the Internet of You (SPINY)) and the CAM Grant S2013/ICE-3095 (Cybersecurity,Data, and Risks (CIBERDINE)), which is cofunded by EuropeanFunds (FEDER). Furthermore, J.M. de Fuentes and L. González-Manzano were also partially supported by the Programa de Ayudas a la Movilidad of Carlos III University of Madrid.

Security and reliability of information technologies have emerged as major concerns nowadays. Risk assessment, an estimation of negative impacts that might be imposed to a network by a series of potential sources, is one of the main tasks to ensure the security and is performed either statically or dynamically. Static risk assessment cannot satisfy the requirements of real-time and ubiquitous computing networks as it is pre-planned and does not consider upcoming changes such as the creation of new attack strategies. However, dynamic risk assessment (DRA) considers real-time evidences, being capable of diagnosing abnormal events in changing environments. Several DRA approaches have been proposed recently, but it is unclear which technique fits best into IT scenarios with different requirements. Thus, this chapter introduces recent trends in DRA, by analyzing 27 works and proposes a decision guide to help IT managers in choosing the most suitable DRA technique considering three illustrative scenarios – regular computer networks, internet of things, and industrial control systems.

Information flows in Android can be effectively used to give an informative summary of an application’s behavior, showing how and for what purpose apps use specific pieces of information. This has been shown to be extremely useful to characterize risky behaviors and, ultimately, to identify unwanted or malicious applications in Android. However, identifying information flows in an application is computationally highly expensive and, with more than one million apps in the Google Play market, it is critical to prioritize applications that are likely to pose a risk. In this work, we develop a triage mechanism to rank applications considering their potential risk. Our approach, called TRIFLOW, relies on static features that are quick to obtain. TRIFLOW combines a probabilistic model to predict the existence of information flows with a metric of how significant a flow is in benign and malicious apps. Based on this, TRIFLOW provides a score for each application that can be used to prioritize analysis. TRIFLOW also provides an explanatory report of the associated risk. We evaluate our tool with a representative dataset of benign and malicious Android apps. Our results show that it can predict the presence of information flows very accurately and that the overall triage mechanism enables significant resource saving. This work was supported by the MINECO grants TIN2013-46469-R and TIN2016-79095-C2-2-R, and by the CAM grant S2013/ICE-3095.

Is creativity important in engineering design? If it is, then why do most undergraduate engineering programs spend so little time teaching creativity? And therefore, as a result of our programs, do our students emerge more creative, less creative or no different compared to when they arrived? If creativity is worth developing, can we accurately measure it in our students, and can we enhance it systematically?These were some of the questions that motivated the initiation of a creativity research program in the College of Engineering at the University of Saskatchewan. The assumption was that creativity is important in engineering, especially in design. The intent was to understand how we could assess creativity in our students and then enhance it.The focus of this initial study is a precursor to many of these more applied questions. We had students and faculty from a variety of Colleges, including Engineering, answer an online survey that probed attitudes towards creativity, respondent personality characteristics, opinions regarding conditional influences on creativity, and potential demographic factors influencing the creativity of individuals. As well, we employed a validated creativity attitudes and beliefs measurement tool (rCAB) as an accepted benchmark for assessment.The survey included both closed- and open-ended questions. The results from some of the open-ended questions have been analyzed to determine emerging groups of similar types of answers, and then efforts have been made to relate the groups in a meaningful framework.The results for the Engineering students are emphasized, but they are also compared with students and faculty from other Colleges. Closed questions were analyzed using inferential statistical tests (distributions, means, standard deviations, t-tests, ANOVA, Cronbach’s alpha), while the open-ended responses are compared more qualitatively when they cannot be quantified easily.The survey went through ethics approval and was distributed in the latter half of the Fall 2015 term.

A new image encryption scheme, based on a total shuffling and parallel encryption algorithm is proposed in this paper. Two chaotic systems have been used in the encryption algorithm to confuse the re- lationship between the plain-image and the cipher- image. To make the encryption procedure more con- fusing and complex, the plain-image is first divided into 4 sub-images and then the position of each sub- image is changed pseudo-randomly according to a lo- gistic map. Next, a total shuffling matrix is used to shuffle the position of pixels in the whole image and then sub-images are encrypted simultaneously in a parallel manner. The experimental results on USC data base demonstrate that the proposed encryption algo- rithm has a low time complexity and has the advan- tages of large key space and high security. Moreover, the robustness of this locally encryption method is much more in contrast with other encryption schemes and the distribution of gray values has a random-like behavior in the encrypted image.

With widespread use of internet based services and applications, there is a real need for ensuring the reliability and security of data transmitted over this platform. One of the major tools to guarantee the security of information is data encryption. However, the proposed encryption methodology is usually expected to have some key characteristics to be applicable in computer networks. A suitable encryption algorithm should be fast, reliable, and, more importantly, it should impose less metadata to the original data. Aiming at these characteristics, an asymmetric encryption system has been proposed recently based on Boolean product of matrices. This cryptosystem is analyzed in this paper from another point of view. Here, it is shown that Boolean information is really sensitive, and a minor change in binary data might lead to a significant change in the retrieved values through the decryption procedure. This issue becomes even more critical when the application area is a computer network in which numerous incidents may happen to data before they reach the desired destination; for instance, different kinds of noises are capable of changing the originality of information. Simulations and experiments conducted in this paper show that this fast cryptosystem is not a suitable tool to ensure the security of data in computer networks.

Given an undirected graph with a set of vertices and edges, vertex coloring, a well-known classical optimization problem in graph theory, consists of partitioning all vertices into independent sets and assigning unique colors to adjacent vertices with an effort to use the least number of colors. This paper presents a new algorithm based on artificial bee colony for solving the aforementioned problem. The proposed algorithm is evaluated on the DIMACS challenging benchmarks and computational results show that the presented method achieves highly competitive results. The outstanding outcomes of our algorithm are related to its computational time and also the number of colors it uses. Our simulations show that the computational time is considerably short and also the number of colors for vertex coloring is optimal in most of the cases.

The resource-constrained project scheduling problem (RCPSP) includes activities which have to be scheduled due to precedence and resource restrictions such that an objective is satisfied. There are several variants of this problem currently, and also different objectives are considered with regards to the specific applications. This paper tries to introduce a new multi-agent learning algorithm (MALA) for solving the multi-mode resource-constrained project scheduling problem (MMRCPSP), in which the activities of the project can be performed in multiple execution modes. This work aims to minimize the total project duration which is referred to its makespan. The experimental results show that our method is a new one for this specific problem and can outperform other algorithms in different areas.