Abstract: Cloud computing is one of the most significant technologies that the world deals with, in different sectors with different purposes and capabilities. The cloud faces various challenges in securing data from unauthorized access or modification. Consequently, security risks and levels have greatly increased. Therefore, cloud service providers (CSPs) and users need secure mechanisms that ensure that data are kept secret and safe from any disclosures or exploits. For this reason, CSPs need a number of techniques and technologies to manage and secure access to the cloud services to achieve security goals, such as confidentiality, integrity, identity access management (IAM), etc. Therefore, this paper will review and explore various access controls implemented in a cloud environment that achieve different security purposes. The methodology followed in this survey was conducting an assessment, evaluation, and comparison between those access controls mechanisms and technologies based on different factors, such as the security goals it achieves, usability, and cost-effectiveness. This assessment resulted in the fact that the technology used in an access control affects the security goals it achieves as well as there is no one access control method that achieves all security goals. Consequently, such a comparison would help decision-makers to choose properly the access controls that meet their requirements.
Abstract: In Liberia, many of the agricultural households cultivate cassava for either sustenance purposes, or to generate farm income. Many of the concentrated cassava farmers reside in Nimba, a north-eastern County that borders two other economies: the Republics of Cote D’Ivoire and Guinea. With a high demand for cassava output and products in emerging Asian markets coupled with an objective of the Liberia agriculture policies to increase the competitiveness of valued agriculture crops; there is a need to examine the level of resource-use efficiency for many agriculture crops. However, there is a scarcity of information on the efficiency of many agriculture crops, including cassava. Hence the study applying an output-oriented method seeks to assess the economic efficiency of cassava farmers in Nimba County, Liberia. A multi-stage sampling technique was employed to generate a sample for the study. From 216 cassava farmers, data related to on-farm attributes, socio-economic and institutional factors were collected. The stochastic frontier models, using the Translog functional forms, of production and revenue, were used to determine the level of revenue efficiency and its determinants. The result showed that most of the cassava farmers are male (60%). Many of the farmers are either married, engaged or living together with a spouse (83%), with a mean household size of nine persons. Farmland is prevalently obtained by inheritance (95%), average farm size is 1.34 hectares, and most cassava farmers did not access agriculture credits (76%) and extension services (91%). The mean cassava output per hectare is 1,506.02 kg, which estimates average revenue of L$23,551.16 (Liberian dollars). Empirical results showed that the revenue efficiency of cassava farmers varies from 0.1% to 73.5%; with the mean revenue efficiency of 12.9%. This indicates that on average, there is a vast potential of 87.1% to increase the economic efficiency of cassava farmers in Nimba by improving technical and allocative efficiencies. For the significant determinants of revenue efficiency, age and group membership had negative effects on revenue efficiency of cassava production; while farming experience, access to extension, formal education, and average wage rate have positive effects. The study recommends the setting-up and incentivizing of farmer field schools for cassava farmers to primarily share their farming experiences with others and to learn robust cultivation techniques of sustainable agriculture. Also, farm managers and farmers should consider a fix wage rate in labor contracts for all stages of cassava farming.
Abstract: Nowadays, dialogue systems increasingly become the
way for humans to access many computer systems. So, humans
can interact with computers in natural language. A dialogue
system consists of three parts: understanding what humans say in
natural language, managing dialogue, and generating responses in
natural language. In this paper, we survey deep learning based
methods for dialogue management, response generation and dialogue
evaluation. Specifically, these methods are based on neural network,
long short-term memory network, deep reinforcement learning,
pre-training and generative adversarial network. We compare these
methods and point out the further research directions.
Abstract: Alice Munro is a Canadian short-story writer who has been regarded as one of the greatest writers of fiction. Owing to her great contribution to fiction, she was the first Canadian woman and the only short-story writer ever to be rewarded the Nobel Prize for Literature in 2013. Her literary works include collections of short stories and one book published as a novel. Her stories concentrate on the human condition and the human relationships as seen through the lens of daily life. The setting in most of her stories is her native Canada- small towns much similar to the one where she grew up. Her writing style is not only realistic but is also characterized by autobiographical, historical and regional features. The aim of this research is to analyze one of the key stylistic devices often adopted by Munro in her fictions: the juxtaposition of the past and the present, with reference to the title story in Munro's short story collection Too Much Happiness. The story under exploration is a brief biography of the Russian Mathematician and novelist Sophia Kovalevsky (1850 – 1891), the first woman to be appointed as a professor of Mathematics at a European University in Stockholm. Thus, the story has a historical protagonist and is set on the European continent. Munro dramatizes the severe historical and cultural constraints that hindered the career of the protagonist. A pragmatic stylistic framework is being adopted and the qualitative analysis is supported by textual reference. The stylistic analysis reveals that the juxtaposition of the past and the present is one of the distinctive features that characterize the author; in a typical Munrovian manner, the protagonist often moves between the units of time: the past, the present and, sometimes, the future. Munro's style is simple and direct but cleverly constructed and densely complicated by the presence of deeper layers and stories within the story. Findings of the research reveal that the story under investigation merits reading and analyzing. It is recommended that this story and other stories by Munro are analyzed to further explore the features of her art and style.
Abstract: With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.
Abstract: This study aims to characterize the physicochemical aspects of the soils of southeastern Chapada Diamantina - Bahia related to the phytophysiognomies of this area, rupestrian field, small savanna (savanna fields), small dense savanna (savanna fields), savanna (Cerrado), dry thorny forest (Caatinga), dry thorny forest/savanna, scrub (Carrasco - ecotone), forest island (seasonal semi-deciduous forest - Capão) and seasonal semi-deciduous forest. To achieve the research objective, soil samples were collected in each plant formation and analyzed in the soil laboratory of ESALQ - USP in order to identify soil fertility through the determination of pH, organic matter, phosphorus, potassium, calcium, magnesium, potential acidity, sum of bases, cation exchange capacity and base saturation. The composition of soil particles was also checked; that is, the texture, step made in the terrestrial ecosystems laboratory of the Department of Ecology of USP and in the soil laboratory of ESALQ. Another important factor also studied was to show the variations in the vegetation cover in the region as a function of soil moisture in the different existing physiographic environments. Another study carried out was a comparison between the average soil moisture data with precipitation data from three locations with very different phytophysiognomies. The soils found in this part of Bahia can be classified into 5 classes, with a predominance of oxisols. All of these classes have a great diversity of physical and chemical properties, as can be seen in photographs and in particle size and fertility analyzes. The deepest soils are located in the Central Pediplano of Chapada Diamantina where the dirty field, the clean field, the executioner and the semideciduous seasonal forest (Capão) are located, and the shallower soils were found in the rupestrian field, dry thorny forest, and savanna fields, the latter located on a hillside. As for the variations in water in the region's soil, the data indicate that there were large spatial variations in humidity in both the rainy and dry periods.
Abstract: The identity of a neighborhood is inevitably shaped by the architecture and the people of that place. Conventionally the streets within each neighborhood served as a semi-public-private extension of the private living spaces. The street as a design element formed a hybrid condition that was neither totally public nor private, and it encouraged social interactions. Thus through creating a sense of community, one of the most basic human needs of belonging was achieved. Similar to major global cities, Tehran has undergone serious urbanization. Developing into a capital city of high rises has resulted in an increase in urban density. Although allocating more residential units in each neighborhood was a critical response to the population boom and the limited land area of the city, it also created a crisis in terms of social communication and place attachment. District 10 in Tehran is a neighborhood that has undergone the most urban transformation among the other 22 districts in the capital and currently has the highest population density. This paper will explore how the active streets in district 10 have changed into their current condition of high rises with a lack of meaningful social interactions amongst its inhabitants. A residential building can be thought of as a large group of people. One would think that as the number of people increases, the opportunities for social communications would increase as well. However, according to the survey, there is an indirect relationship between the two. As the number of people of a residential building increases, the quality of each acquaintance reduces, and the depth of relationships between people tends to decrease. This comes from the anonymity of being part of a crowd and the lack of social spaces characterized by most high-rise apartment buildings. Without a sense of community, the attachment to a neighborhood is decreased. This paper further explores how the neighborhood participates to fulfill ones need for social interaction and focuses on the qualitative aspects of alternative spaces that can redevelop the sense of place attachment within the community.
Abstract: Thanks to the development of online technology and social networks, people are able to communicate as well as learn. WhatsApp is a popular social network which is growingly gaining popularity. This app can be used for communication as well as education. It can be used for instructor-learner, learner-learner, and learner-content interactions; however, very little knowledge is available on these potentials of WhatsApp. The current study was undertaken to investigate university students’ perceptions of WhatsApp used as a tool for instructor-learner dialogue, learner-content dialogue, and learner-learner dialogue. The study adopted a survey approach and distributed the questionnaire developed by Google Forms to 54 (11 males and 43 females) university students. The obtained data were analyzed using SPSS version 20. The result of data analysis indicates that students have positive attitudes towards WhatsApp as a tool for Instructor-Learner Dialogue: it easy to reach the lecturer (4.07), the instructor gives me valuable feedback on my assignment (4.02), the instructor is supportive during course discussion and offers continuous support with the class (4.00). Learner-Content Dialogue: WhatsApp allows me to academically engage with lecturers anytime, anywhere (4.00), it helps to send graphics such as pictures or charts directly to the students (3.98), it also provides out of class, extra learning materials and homework (3.96), and Learner-Learner Dialogue: WhatsApp is a good tool for sharing knowledge with others (4.09), WhatsApp allows me to academically engage with peers anytime, anywhere (4.07), and we can interact with others through the use of group discussion (4.02). It was also found that there are significant positive correlations between students’ perceptions of Instructor-Learner Dialogue (ILD), Learner-Content Dialogue (LCD), Learner-Learner Dialogue (LLD) and WhatsApp Application in classroom. The findings of the study have implications for lectures, policy makers and curriculum developers.
Abstract: Over the past 20 years, internet has seen continual advancement and with the advent of online technology, various types of web-based games have been developed. Games are frequently being used among different age groups from baby boomers to generation Z. Games are not only used for entertainment but also utilized as a learning approach transmitting education to a level that is more interesting and effective for students. One of the popular web-based education games is Kahoot with growing popularity and usage, which is being used in different fields of studies. However, little knowledge is available on university students’ perception of Kahoot environment and application for learning subjects. Hence, the objective of the current study is to investigate students’ perceptions of Kahoot application and environment as a learning tool. The study employed a survey approach by distributing Google Forms –created questionnaire, with high level of reliability index, to 62 students (11 males and 51 females). The findings show that students have positive attitudes towards Kahoot application and its environment for learning. Regarding Kahoot application, it was indicated that activities created using Kahoot are more interesting for students, Kahoot is useful for collaborative learning, and Kahoot enhances interest in learning lesson. In terms of Kahoot environment, it was found that using this application through mobile is easy for students, its design is simple and useful, Kahoot-created activities can easily be shared, and the application can easily be used on any platform. The findings of the study have implications for instructors, policymakers and curriculum developers.
Abstract: Pedestrians are the fourth group among road traffic
users that most suffer accidents. Their death rate is even higher than
the motorcyclists group. This gives motivation for the development
of an urban vehicle capable of complying with the United Nations
Economic Commission for Europe pedestrian regulations. The
conceptual vehicle is capable of transporting two passengers and
small parcels for 100 km at a maximum speed of 90 km/h. This paper
presents the design of this vehicle using the finite element method
specially in connection with frontal crash test and car to pedestrian
collision. The simulation is based in a human body FE.
Abstract: We design and implement a precise model of string
operations using finite state machine transformers and state
transformers to approximate the values string variables can take
throughout the execution of the program.We use our model to analyze
Android program string variables. Our experimental results show that
our string analysis is very efficient at detecting the contextual effect
of string operations on the string variables. Our model proved to be
very useful when it came to verifying statements about the string
variables of the program.
Abstract: The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.
Abstract: Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.
Abstract: Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.
Abstract: Repairable items inventory system is a management tool used to incorporate all information concerning inventory levels and movements for repaired and new items. This paper presents development of an effective simulation model for managing the inventory of repairable items for a production system where production lines send their faulty items to a repair shop considering the stochastic failure behavior and repair times. The developed model imitates the process of handling the on-hand inventory of repaired items and the replenishment of the inventory of new items using Economic Order Quantity and Reorder Point ordering policy in a flexible and risk-free environment. We demonstrate the appropriateness and effectiveness of the proposed simulation model using an illustrative case problem. The developed simulation model can be used as a reliable tool for estimating a healthy on-hand inventory of new and repaired items, backordered items, and downtime due to unavailability of repaired items, and validating and examining Economic Order Quantity and Reorder Point ordering policy, which would further be compared with other ordering strategies as future work.
Abstract: This paper provides guidelines for what constitutes a knowledge worker. Many graduates from non-managerial domains adopt, at some point in their professional careers, management roles at different levels, ranging from team leaders through to executive leadership. This is particularly relevant for professionals from an engineering background. Moving from a technical to an executive-level requires an understanding of those behaviour management techniques that can motivate and support individuals and their performance. Further, the transition to management also demands a shift of contextual enablers from tangible to intangible resources, which allows individuals to create new capacities, competencies, and capabilities. In this dynamic process, the knowledge worker becomes that key individual who can help members of the management board to transform information into relevant knowledge. However, despite its relevance in shaping the future of the organization in its transition to the knowledge economy, the role of a knowledge worker has not yet been studied to an appropriate level in the current literature. In this study, the authors review both the contextual enablers and behaviour outputs related to the role of the knowledge worker and relate these to their ability to deal with everyday management issues such as knowledge heterogeneity, varying motivations, information overload, or outdated information. This study highlights that the aggregate of capacities, competences and capabilities (CCCs) can be defined as knowledge structures, the study proposes several contextual enablers and behaviour outputs that knowledge workers can use to work cooperatively, acquire, distribute and knowledge. Therefore, this study contributes to a better comprehension of how CCCs can be managed at different levels through their contextual enablers and behaviour outputs.
Abstract: In Multi-Effect Distillation (MED) desalination evaporators, the scale deposit outside the tubes presents a barrier to heat transfers reducing the global heat transfer coefficient and causing a decrease in water production; hence a loss of efficiency and an increase in operating and maintenance costs. Scale removal (by acid cleaning) is the main maintenance operation and constitutes the major reason for periodic plant shutdowns. A better understanding of scale deposition mechanisms will lead to an accurate determination of the variation of scale thickness around the tubes and an improved accuracy of the overall heat transfer coefficient calculation. In this paper, a coupled heat transfer-calcium carbonate scale deposition model on a horizontal tube bundle is presented. The developed tool is used to determine precisely the heat transfer area leading to a significant cost reduction for a given water production capacity. Simulations are carried to investigate the influence of different parameters such as water salinity, temperature, etc. on the heat transfer.
Abstract: With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.
Abstract: Parkinson's disease (PD) is a heterogeneous disorder with common age of onset, symptoms, and progression levels. In this paper we will solve analytically the PD model as a non-linear delay differential equation using the steps method. The step method transforms a system of delay differential equations (DDEs) into systems of ordinary differential equations (ODEs). On some numerical examples, the analytical solution will be difficult. So we will approximate the analytical solution using Picard method and Taylor method to ODEs.
Abstract: Analysis of stresses for an infinitesimal tetrahedron leads to a situation where we obtain a cubic equation consisting of three stress invariants. This cubic equation, when solved for a definite condition, gives the principal stresses directly without requiring any cumbersome and time-consuming trial and error methods or iterative numerical procedures. Since the failure criterion of different materials are generally expressed as functions of principal stresses, an attempt has been made in this study to incorporate the solutions of the cubic equation in the form of principal stresses, obtained for a definite condition, into some of the established failure theories to determine their modified descriptions. It has been observed that the failure theories can be represented using the quadratic stress invariant and the orientation of the principal plane.