Search

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s of Science in Mathematical and Computer Science and Engineering of the Nelson Mandela African Institution of Science and Technology

ARt Image Exploration Space (ARIES) is a free, cloud-based dynamic environment offering art historians and others an extensive array of practical tools for analysing images. It is the product of a successful collaboration between art historians, librarians, computer scientists, and engineers from the Frick Art Reference Library, New York University's Tandon School of Engineering, and Brazil's Universidade Federal Fluminense. ARIES is a powerful tool for art historians, both replicating and augmenting traditional methods they have long-used to study images.1 With the advent of the prevalent use of digital photos, art historians lacked the technology capable of replacing what they had previously been able to accomplish in the analogue world. Wood Ruby and Deutch realized that art historians needed an out-of-the-box solution that didn't require extensive knowledge of other disciplines (computer science and engineering). The result of successful collaborations and a generous donation, ARIES is now available in BETA form at www.artimageexplorationspace.com.

Predicting the binding affinity between compounds and proteins with reasonable accuracy is crucial in drug discovery. Computational prediction of binding affinity between compounds and targets greatly enhances the probability of finding lead compounds by reducing the number of wet-lab experiments. Machine-learning and deep-learning techniques using ligand-based and target-based approaches have been used to predict binding affinities, thereby saving time and cost in drug discovery efforts. In this review, we discuss about machine-learning and deep-learning models used in virtual screening to improve drug-target interaction (DTI) prediction. We also highlight current knowledge and future directions to guide further development in this field.

Differential count of leukocytes plays a consequential role in the determination of diagnostic details of a patient. Conventionally, leukocyte identification is performed manually by skilled medica...

As cybersecurity issues become ever more prevalent in today’s society, it is imperative that our computer science and engineering programs train students to work in cybersecurity fields. The University of Cincinnati’s Department Electrical Engineering and Computer Science, part of a NSA Center of Excellence, is developing a new B.S. in Cybersecurity Engineering which will be coordinated with cybersecurity programs in Information Technology and in Political Science. We describe our current cybersecurity certificate progran and our proposed new B.S. in Cybersecurity Engineering, which will meet the criteria to apply for ABET EAC accreditation.

In today's society, the popularization rate of computers in the whole country and even all over the world is increasing rapidly year by year. This makes People's Daily work closely connected with computers, information technology and the Internet. As an auxiliary tool, the computer provides great convenience for the process of teaching. Intelligent tools make the learning process more convenient, so the significance of computer science and technology to computer education is huge. In the teaching process, teachers should try their best to use the advantages of computers to assist teaching, and provide students with better network learning environment and learning conditions. Different from the traditional teaching methods, the application of computer technology to education has gradually formed a new type of computer education. This has caused a great change to the current situation of China's education development, and at the same time, China's education model is also faced with various problems.

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes.

Pap smear is a microscopic screening process of cervical smear used to detect pre-cancerous and cancerous cells in the cervix region. Cell nuclei segmentation is one of the most significant task in cervical cell image analysis which aids in identifyi

Age-related Macular Degeneration (AMD) is the leading cause of legal blindness and central vision loss in the 50 above age group among both developed and developing countries. In India, the prevalence of early late ARMD is similar to that observed in

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.

The data management group at the Nanyang Technological University (dante) was founded in 2009 by the first author when the School of Computer Science and Engineering hired two young faculty members in this area. The group currently consists of three faculty members and more than twenty graduate students, research assistants, and postdocs. Our alumni include faculty members at the Chinese University of Hong Kong, National University of Singapore, University of New South Wales, and several researchers and engineers at Facebook, Google, eBay, Huawei, and other technology companies. The group's major funding is from the Ministry of Education in Singapore, National Research Foundation, and companies such as Huawei.

This presentation considers elements of Software Performance Engineering (SPE) and how they have evolved. It addresses both skills needed by practitioners and areas of research. Which topics should be covered? How can the education cover realistic systems and problems? What is the history of SPE and is it relevant today? Are these topics unique to SPE education? How should SPE education be integrated with other specialties in Computer Science and Engineering?

Research in smart homes is still quite recent; however, there is no doubt that it will become a pervasive research topic in the near future. This chapter analyzes the whole research production on smart homes indexed in the Scopus database from 1985 to 2019, yielding a total of 11,000 studies. One of the goals of this chapter is to identify the main countries and institutions that have published on this topic and what their interest has been throughout the time. Four out of 116 countries stand out in this field, namely China, USA, India, and South Korea. In terms of the main institutions, the three with the highest scientific output are Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Another aim of the chapter is to determine the research fields and subfields investigated about smart homes. The publications are mainly focused on two scientific fields, that is, computer science and engineering, accounting for 64% of the overall scientific production. In an aggregate analysis of all publications, four main clusters have been identified, namely Internet of Things, Activity Recognition, Security, and Energy.

The past 30 years have seen increased adoption of learning management systems (LMSs) in education. Several studies have investigated the usability of LMSs for students. However, very few studies have assessed the usability from the instructors’ perspective. Usability issues can pose challenges for instructors who use LMSs to create, manage and deliver courses. These challenges require instructors to spend extra time and energy on tackling the challenges rather on teaching-related tasks, which will have negative impacts on learners’ experiences and learning outcomes. This paper aims to identify usability challenges in LMSs for instructors. We used Canvas as an example and conducted user testing with 35 university instructors in computer science and engineering disciplines. Pre- and post-interviews were transcribed and analyzed together with the observation data during their use of Canvas to carry out tasks. The results show that, although Canvas has made continuous efforts to improve its usability, instructors still face some usability challenges. Instructors are a diverse user group for LMSs. Further research should consider recruiting participants from other disciplines and investigating other LMSs to identify possibilities for improving general usability of digital tools for instructors.

The aim of this paper is to develop a web application where scholars of the Faculty of Computer Science and Engineering (FINKI) at the University of Ss. Cyril and Methodius can display and share their projects and publications. Visitors can view, search through, and filter the authors, projects and publications that can be added and edited by the administrators via the administrator panel. In this paper, we first explain the type of system we are building and go through similar existing systems explaining how they work and what they offer. Then, we go through the programming languages and technologies we decided to use to develop this web application. After that, the development phase follows, where we describe each of the features we implemented. In The Final Product section we finally show images where you can see how the web application works and what it looks like. We finish the paper with a conclusion, briefly summarizing what we have achieved.

We present a Software Quality course taught in a MSc program in Computer Science and Engineering. The course takes an overview (‘breadth’) approach, reviewing the most important topics that contribute to the quality of software. The course has been taught traditionally as well as online; we discuss the advantages and disadvantages of both styles and point out what should be kept from the online experience. We also discuss the students’ evaluation and feedback.

Fifty years after the publication of the first algorithms for the automatic detection of Atrial Fibrillation (AF), this cardiac condition is still the most studied from the computer science and engineering perspectives. Machine learning techniques are widely applied to a variety of problems, including detection, characterization, prediction and simulation, in general with promising results. In the last years, the Big Data + Deep Learning binomial is getting most of the attention in academia and industry, but on many occasions this approach fails on capitalizing all the knowledge acquired in previous decades of research. This article, written as a companion to the keynote with the same title presented in the CinC 2020 conference, tries to illustrate the importance of exploiting expert knowledge and classical approaches in synergy with the most advanced deep learning methods, which by themselves have fundamental limitations. The discussion is built around the AF detection problem and the conclusions extracted from the Physionet/CinC Challenge 2017, but the main points can be relevant in other problems for which humans have a better answer than computers, and this answer can be described.

The university has been applying a teaching methodology based on continuous assessment over the last few years. The main advantage of this methodology is that students acquire the knowledge gradually, so that they consolidate it better in their memory; however, they run the risk of forgetting what was studied in the first parts of the course when they finish it. To motivate students to review what they learnt, we have implemented an augmented reality mobile application and methodology with which students review the contents of the subject through the serious game. Moreover, the game focuses on the specific difficulties of each student by reinforcing those questions that are most difficult for them. In this paper, we also present how the game has been customized for various courses from the Degree in Computer Science and Engineering at the University of Cadiz and which results were obtained.

This Research Full Paper presents our experience in analyzing and selecting block-based programming environments to support the teaching of algorithms for the students starting the introductory courses of a Computer Science major. The teaching of algorithms and programming concepts to students of the first years of Computer Science and Engineering courses has been a major challenge because students often have difficulty understanding the logic and abstraction, leading to a high dropout rate. Some strategies have been conducted to further the mission of helping students understand better those basic concepts, but this topic still remains a major problem for students in the initial grades of those courses. In previous projects developed at our university, we have already proposed the use of learning objects and gamification, with very positive results. One of the questions that arise when we adopt new teaching approaches is to know how this new path will contribute to the student’s learning. In this project, we conducted a study on eight block-based programming environments and sought to identify which aspects of those environments comply with the Computer Science reference curriculum. Our work was based on the joint task force on Computing Curricula conducted by the ACM and IEEE Computer Society CS2013 curriculum guidelines for undergraduate programs in Computer Science. We studied the virtual programming environments Alice, MIT App Inventor, Blockly Games, Code.org, Gameblox, Pencil Code, Microsoft MakeCode and Scratch. Then, we crossed the characteristics of each, identified the positive and negative points of each teaching environment in relation to the topics established by the guidelines. We have classified the main characteristics of those programming environments, establishing criteria such as: prior programming knowledge requirements; ease of interaction with users; programming language code; availability of documentation for learning; programming practices addressed by the environment; and ease of learning programming. We believe that this work can contribute to the selection process of a suitable programming environment to be adopted in an introductory course of computer programming.

Mathematical subjects studied in the Faculty of Computer Science and Engineering at Burgas Free University are focused on acquiring fundamental knowledge and developing essential skills required for future engineers. This paper looks into changing the process of assessment of students’ achievements in mathematics from closed-book exams to diversification of tasks and expected outcomes. Three aspects of assessing the mathematical knowledge in engineering programs are considered: the importance of identifying the entry level, blended learning and creating students’ portfolios with MatLab. The experience of the authors in using the tools of blended learning and the use of a software environment are presented. Some emerging problems in the educational process that need to be faced are outlined.

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last decade, and has been identified by many industries as a highly desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models; most importantly, governing how they are verified and validated, and how validation information can be transferred between models. Unlike its companion, which does not attempt detailed specification of any twin components, this paper will attempt to outline a rigorous representation theory of models, based on the introduction of two new concepts: mirrors and virtualizations. The paper is not intended as a passive wish list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science, and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework.

Stem Cells (SCs) show a wide range of applications in the treatment of numerous diseases, including neurodegenerative diseases, diabetes, cardiovascular diseases, cancer, etc. SC related research has gained popularity owing to the unique characteristics of self-renewal and differentiation. Artificial Intelligence (AI), an emerging field of computer science and engineering, has shown potential applications in different fields like robotics, agriculture, home automation, healthcare, banking, and transportation since its invention. This review aims to describe the various applications of AI in SC biology, including understanding the behavior of SCs, recognizing individual cell type before undergoing differentiation, characterization of SCs using mathematical models and prediction of mortality risk associated with SC transplantation. This review emphasizes the role of neural networks in SC biology and further elucidates the concepts of machine learning and deep learning and their applications in SC research.

Automation in education has become a major factor shaping a direct relationship between the offered automated services and the academic performance of students in universities. In this paper, we discuss a toolset that features several services aimed at improving the overall experience of students during their stay at the Computer Science and Engineering Department in the American University of Sharjah. The toolset is an attempt to replace many processes that were still, until recently, dependent on paper and pencil. The proposed transformation includes services that affect, on daily basis, the performance of students in their classes. In particular, the toolset automates services related to academic advising, course registration (planning for course sections, availability of courses and handling of waiting lists), exam scheduling (detecting conflicts), tracking attendance in courses, helping students find the proper internship program, and monitoring student progress in their study plans (admission for the second year). The toolset is made accessible through a mobile friendly web portal. Our initial evaluation, based on conducted surveys, shows appreciation from the part of students of the provided services.

The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011&ndash, 2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information.

In smart cities, the progress of technology has allowed the implementation of sensors, originating the Internet of Things (IoT) and making cities safer and more sustainable. Hence, the presence of elements that generate visual and artistic effects of IoT technology can make a great contribution to the provision of information that the urbanite needs. The aim of this study is to analyze worldwide research on the visual and artistic effects of IoT in smart cities. Bibliometric techniques were utilized on 1278 articles on this subject matter for the period of 2010&ndash, 2019 to achieve results on activity production. This has increased yearly, where in the last triennium, it has accumulated 85.21% of documents. Computer science and engineering were the most prominent subject areas where the articles were classified. The lines of research in the development of this research topic have been detected. Furthermore, the main directions for future research have also been identified. This study aims to contribute to highlighting the drivers of this field of research, in addition to providing the available information and future directions to improve academic and scientific discussion.

In this paper we report our approach and experiences concerning the introduction of Python programming language in programming-related academic curricula. Firstly we motivate our choice and approach regarding the use of Python programming language. Then we discuss the results obtained in two courses that we taught to computer science and engineering students, both with a strong focus on developing students’ practical programming skills: Algorithm Design and Artificial Intelligence. We report our approach and findings, including identified difficulties and obtained results, as well as proposed future improvements.

Due to its quantitative nature, bibliometrics is becoming increasingly popular among policy makers for academic hiring and career promotions. In this article, we quantitatively assess the impact that the granularity level in the classification of scientific areas would entail on research evaluation based on bibliometric indicators. We use as a case study the Italian national habilitation system (ASN), which classifies faculty members according to their academic discipline and relies on journal counts, citations, and h-indices as a basis for promoting tenure track researchers to associate professors and associate to full professors. The assessment checks whether the individual indicators of a researcher are above a certain threshold, e.g., the median over the population of researchers working in the same discipline. Our investigation focuses on two related, rather broad disciplines: computer science and computer engineering. We show that the ASN practice of using the same thresholds for all members of a scientific discipline can favor certain sub-communities that are characterized by higher bibliometric indicators, and disfavor others. We report evidence that up to 30% of Italian faculty members of certain sub-communities would see their indicators drop below the threshold, thus becoming not eligible for promotion, if the ASN were conducted on a more accurate, fine-grained classification. Conversely, in the same scenario, up to 11% of faculty members, in different sub-communities, would see their indicators rise above the threshold, granting them eligibility. Our data set includes 1685 authors, 89,185 distinct publications, and 262,286 author-publication pairs.

Trust has long been a focus of research in the areas of psychology and marketing. However, it has not been studied extensively in computer science and engineering. In this study, the notion of trust was investigated from the perspective of statistical analysis and automatic machine-based classification of speech. A gender-balanced database of speech samples representing individuals with low and high public trust was created and analyzed statistically to determine acoustic speech parameters correlated with trust. The effect of gender was analyzed. An automatic trust classification based on acoustic speech parameters was conducted using a neural network.

To address the computing challenge of ’big data’, a number of data-intensive computing frameworks (e.g., MapReduce, Dryad, Storm and Spark) have emerged and become popular. YARN is a de facto resource management platform that enables these frameworks running together in a shared system. However, we observe that, in cloud computing environment, the fair resource allocation policy implemented in YARN is not suitable because of its memoryless resource allocation fashion leading to violations of a number of good properties in shared computing systems. This paper attempts to address these problems for YARN. Both singlelevel and hierarchical resource allocations are considered. For single-level resource allocation, we propose a novel fair resource allocation mechanism called Long-Term Resource Fairness (LTRF) for such computing. For hierarchical resource allocation, we propose Hierarchical Long-Term Resource Fairness (H-LTRF) by extending LTRF. We show that both LTRF and H-LTRF can address these fairness problems of current resource allocation policy and are thus suitable for cloud computing. Finally, we have developed LTYARN by implementing LTRF and H-LTRF in YARN, and our experiments show that it leads to a better resource fairness than existing fair schedulers of YARN. Accepted version

Pattern recognition is a task of searching particular patterns or features in the given input. The data mining, computer networks, genetic engineering, chemical structure analysis, web services etc. are few rapidly growing applications where pattern recognition has been used. Graphs are very powerful model applied in various areas of computer science and engineering. This paper proposes a graph based algorithm for performing the graphical symbol recognition. In the proposed approach, a graph based filtering prior to the matching is performed which significantly reduces the computational complexity. The proposed algorithm is evaluated using a large number of input drawings and the simulation results show that the proposed algorithm outperforms the existing algorithms.

This research is partially supported by the National Natural Science Foundation of China (51975121, 51775112), the Guangdong Basic and Applied Basic Research Foundation (2019B1515120095), the MoST International Cooperation Program (6-14) and the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.

Since 2011 Thomas Schedel has been working as a research assistant at the Institute of Information Systems (iisys) at Hof University. In 2016 he successfully finished the Ph.D. program "Computer Science and Engineering" (CSE) at Aalborg University in Denmark. In his thesis - supervised by Henrik Legind Larsen (Aalborg University) and Claus Atzenbeck (Institute of Information Systems, Hof University) - he claims that considering temporal aspects in addition to spatial and visual properties in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance. For the purpose of providing evidence, parsers for recognizing spatial, visual and temporal object relations have been implemented and tested in a series of user surveys. It turned out, that in none of the test cases pure spatial or visual parser could outperform the spatio-temporal parser. Instead, the spatio-temporal parser was able to compensate limitations of conventional parsers. Furthermore, differences in parsing accuracy were successfully tested for statistical significance. The results indicate a non-trivial effect that is recognizable by humans. We have shown that the addition of a temporal parser shifts machine detected structures significantly closer to what knowledge workers intend to express. Due to its significant contributions to the research area of spatial hypermedia the thesis has been awarded with the Informatikpreis 2016 (given by the German Fachbereichstag Informatik, FBTI).

We report the experience of adopting an innovative technique for creating tutorial videos which complement lectures and facilitate students' learning. Our technique relies on: 1) preparing starter pages consisting of code fragments or writings/figures on a drawing tablet; 2) illustrating complex ideas on the drawing tablet; 3) recording all computer desktop activities (e.g., development of code on a programming IDE, illustration on the drawing tablet); and 4) sharing the recorded tutorial videos with students online. Our technique has been adopted in creating tutorial series for four Computer Science and Engineering courses, ranging from the first year to the third year. Analytics of these online tutorial videos is presented to show the average amount of time which each registered student spent on watching them. Course evaluation results indicate that our technique is perceived as effective for achieving the course learning outcomes. Comparison of students' performance on complex topics (arrays and loops) also indicates a positive impact of our approach.

That wasn’t a science fiction scenario. These were AI technologies that are technically feasible today and are being developed as part of computer science and engineering. Traditionally, AI (Artificial Intelligence) was understood as the simulation of intelligent human thought and action. This definition suffers from the fact that “intelligent human thinking” and “acting” are not defined. Furthermore, man is made the yardstick of intelligence, although evolution has produced many organisms with varying degrees of “intelligence”. In addition, we have long been surrounded in technology by “intelligent” systems that control our civilization independently and efficiently, but often differently from humans.

Nowadays, information technologies are used in almost every field of Computer Science and Engineering. One of the most used areas is the health sector. With the use of digital hospital systems, patient data is now stored in a computerized environment, thereby creating biomedical data sets. These datasets, which are very large in size, are very difficult to analyze and interpret by a human. The machine learning algorithms are mainly used to analyze and interpreted these data sets. In this study, the performances of 5 machine learning algorithms have been compared by employing 5 different biomedical data sets and the results obtained were compared statistically. Results reveal that the KNN algorithm performs better for small biomedical data sets, whereas the ANN algorithm performs better for large data sets in terms of classification problem for the health sector.

The activity of computer programming is a fundamental element of the software development process. Computer programming is initially taught in courses related to the area of Computer Science in basic subjects named Fundamentals of Programming, Logic of Programming and others. These subjects are considered difficult and present a high amount of withdrawal and failure. Therefore, methods and tools that assist in the teaching-learning process are necessary. This paper reports the proposition of a method and tools for mapping the evolution of programmers during the development of computer programs, which can be used as an aid in the computer programming teaching-learning process. The proposed method with respective tools, guides the collection and data analysis from events generated during computer programming and allows the data collection and consolidation for the instructor/teacher's analysis. Using the method and tools developed it was possible to carry out 5 experiments with students from technical and undergraduate courses in the area of Computer Science and Engineering in 3 different institutions. Based on the data collected in these experiments, it was possible to observe that the number of failed compilations, the average of compilation errors and the total number of compilation errors are related to the grade assigned to the solved exercise, the higher these numbers are, the lower the obtained grade is. Also, regarding the data collected in the experiments, it is possible to group the errors performed by students during the development of the programs, giving the instructor/teacher an individual and group overview regarding their difficulties. In addition, a positive impact was also observed regarding the detailed feedback to the students about the data collected during the program development. This is because in the second data collection, in comparison with the first, it is possible to observe a lower incidence of compilation errors performed during the program development process.

The paper presents the state of education in the field of computers and information technology, in the context of the evolution of higher education worldwide. The challenges faced by higher education institutions are highlighted: the massification of higher education in the field, the increased mobility of students and staff, the competition of different forms of non-formal and informal training, insufficient financial resources.

This paper describes the design of a group-based project that combines both the hardware and software disciplines of our Computer Science (CS) and Computer Engineering (CE) undergraduates. The goals for the multi-disciplinary design project (MDP) are to provide our 3rd year undergraduates with the opportunities to apply the theoretical knowledge acquired from their early years to solving a practical problem, foster a continuous improvement mindset, and experience the benefits and challenges of working collaboratively in a large team. This requires the formulation of a suitably complex problem that can integrate the skill sets from both the CE and CS programmes. It also requires suitable assessment strategies that can evaluate the students at the individual level, at the group level and at the inter-group level. This paper shares our pedagogical considerations in designing the project module, our experience gleaned from conducting the module over the last five years and some results of a recent online survey conducted to gather students’ feedback on their experience of MDP.

Quality grading is necessary for making any food product marketable. Quality parameters of food products include both Visual and Olfactory factors. The paper attempts to discuss studies conducted in the field of Computer Vision that in-turn uses Digital Image processing and Soft-computing tools in the field of Food Processing Industry. The outcome of this paper will help in understanding the application of Computer Vision in automating the inspection of food products based on various well define Quality parameters.

The process of developing, parameterizing, validating, and maintaining models occurs within a wide variety of tools, and requires significant time and resources. To maximize model utilization, models are often shared between various toolsets and experts. One common example is sharing aircraft engine models with airframers. The functionality of a given model may be utilized and shared with a secondary model, or multiple models may run collaboratively through co-simulation. There are many technical challenges associated with model sharing and cosimulation. For example, data communication between models and tools must be accurate and reliable, and the model usage must be well-documented and perspicuous for a user. This requires clear communication and understanding between computer scientists and engineers. Most often, models are developed by engineers, whereas the tools used to share the models are developed by computer scientists. Computer scientists often struggle with implementing methods for exchanging data between models while simultaneously maintaining the usability that engineers desire, and engineers often struggle with utilizing the tools that computer scientists have developed. In the process of developing tools and interfaces for model sharing and co-simulation, much is lost in the communication between computer scientists and engineers. This paper seeks to ease the communication barrier associated with model sharing and model co-simulation by clearly and simply discussing some of the commonly associated challenges and solutions, and then providing clear and concise implementation examples.

We present an active learning experience conducted on a Cryptography course within a Bachelor's Degree in Computer Science and Engineering with the goal of promoting better attitudes towards cryptography. Students were asked to create short videos that presented in an attractive way day-to-day topics or systems related with cryptography but not included in the course syllabus. Participation was optional although additional marks were assigned depending on a rubric-based assessment. Students, besides creating the videos, could participate also (or only) in evaluating some of the peer-created videos using the same rubric used by the teachers. Other active learning experiences consider student-generated videos but often topics are required to be within the course syllabus. We describe the experience design and how we have used YouTube and Microsoft Class Notebook to seamlessly deploy the experience as an extra curricular activity, with minimum teachers overhead. To measure the effect of the experience on the students attitude, we have carried on a survey (pre-test and post-test), adapted from an already existing attitudes towards statistics scale, on two student groups (intervention and control). Results show that intervention group presents higher increments in attitudes in the three analysed dimensions (usefulness, likeness, and motivation) respect to the control group. We have also conducted a satisfaction survey on intervention group, that show that a mean of 80% of students evaluate as positive (agree or strongly agree) the experience. Final grades, however, do not present significant differences.

Computer Organization and Design (COD) is a fundamentally required early-stage undergraduate course in most computer science and engineering curricula. During the two sessions (lecture and project part) of one COD course, educational platforms play an important role in cultivating students' computational thinking, especially the ability of viewing the hardware and software in a computer system as a whole (computer system thinking ability for short in this paper). In order to improve teaching quality, in this paper, we discuss the deployment of an inexpensive in-house Field Programmable Gate Array (FPGA) cloud platform, which can provide students with hardware-software co-design methodology and practice. The platform includes 32 FPGA nodes and the scale can be dynamically changed. Each cloud node is heterogeneously composed of an ARM processor and a tightly-coupled reconfigurable fabric to provide students with hands-on hardware and software programming experiences. We illustrate our efforts to make the FPGA cloud as an easy-to-use resource pool to elastically support a class with 92 undergrads via Internet access and to monitor students' experimental behaviors. We also present key insights in our teaching activities that indicate such appliance is feasible to provide practice of both basic principles and emerging co-design techniques for students. We believe that our cost-effective FPGA cloud is of significant interests to educators looking forward to improving computer system-related courses.