Multi-disciplinary Pathways to Computing:A Scalable and Col aborative Approach to Capitalize on the Demand for Computer Science Education

The number of students demanding computer science(CS)education is rapidly rising,and while faculty sizes are also growing,the traditional pipeline consisting of a CS major,a CS master’s,and then a move to industry or a Ph.D.program is simply not scalable.To address this problem,the Department of Computing at the University of Illinois has introduced a multidisciplinary approach to computing,which is a scalable and collaborative approach to capitalize on the tremendous demand for computer science education.The key component of the approach is the blended major,also referred to as“CS+X”,where CS denotes computer science and X denotes a non-computing field.These CS+X blended degrees enable win-win partnerships among multiple subject areas,distributing the educational responsibilities while growing the entire university.To meet the demand from non-CS majors,another pathway that is offered is a graduate certificate program in addition to the traditional minor program.To accommodate the large number of students,scalable teaching tools,such as automatic graders,have also been developed.

Panel Discussion on“Development Trends of Computer Science in the New Era”

At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.

Laboratory or Department?Exploration and Creation in Computer Science and Technology

In the very beginning,the Computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the university.As computer science developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to progress.Therefore,it is necessary to reinvigorate the relationship between computer science and other academic disciplines and celebrate exploration and creativity in research.To do this,the structures of the academic department have to act as supporting scaffolding rather than barriers.Some examples are given that show the efforts being made at the University of Cambridge to approach this problem.

Recent Development of Computer Science Education in USA

Computer science(CS)is a discipline to study the scientific and practical approach to computation and its applications.As we enter into the Internet era,computers and the Internet have become intimate parts of our daily life.Due to its rapid development and wide applications recently,more CS graduates are needed in industries around the world.In USA,this situation is even more severe due to the rapid expansions of several big IT related companies such as Microsoft,Google,Facebook,Amazon,IBM etc.Hence,how to effectively train a large number of

An Effective and Secure Quality Assurance System for a Computer Science Program

Improving the quality assurance (QA) processes and acquiring accreditation are top priorities for academic programs. The learning outcomes (LOs)assessment and continuous quality improvement represent core components ofthe quality assurance system (QAS). Current assessment methods suffer deficiencies related to accuracy and reliability, and they lack well-organized processes forcontinuous improvement planning. Moreover, the absence of automation, andintegration in QA processes forms a major obstacle towards developing efficientquality system. There is a pressing need to adopt security protocols that providerequired security services to safeguard the valuable information processed byQAS as well. This research proposes an effective methodology for LOs assessment and continuous improvement processes. The proposed approach ensuresmore accurate and reliable LOs assessment results and provides systematic wayfor utilizing those results in the continuous quality improvement. This systematicand well-specified QA processes were then utilized to model and implement automated and secure QAS that efficiently performs quality-related processes. Theproposed system adopts two security protocols that provide confidentiality, integrity, and authentication for quality data and reports. The security protocols avoidthe source repudiation, which is important in the quality reporting system. This isachieved through implementing powerful cryptographic algorithms. The QASenables efficient data collection and processing required for analysis and interpretation. It also prepares for the development of datasets that can be used in futureartificial intelligence (AI) researches to support decision making and improve thequality of academic programs. The proposed approach is implemented in a successful real case study for a computer science program. The current study servesscientific programs struggling to achieve academic accreditation, and gives rise tofully automating and integrating the QA processes and adopting modern AI andsecurity technologies to develop effective QAS.

Forecasting Budget Estimated Using Time-Series—Case Study on College of Computer Science and Information Technology

The need for information systems in organizations and economic units increases as there is a great deal of data that arise from doing many of the processes in order to be addressed to provide information that can bring interest to multi-users, the new and distinctive management accounting systems which meet in a manner easily all the needs of institutions and individuals from financial business, accounting and management, which take into account the accuracy, speed and confidentiality of the information for which the system is designed. The paper aims to describe a computerized system that is able to predict the budget for the new year based on past budgets by using time series analysis, which gives results with errors to a minimum and controls the budget during the year, through the ability to control exchange, compared to the scheme with the investigator and calculating the deviation, measurement of performance ratio and the expense of a number of indicators relating to budgets, such as the rate of condensation of capital, the growth rate and profitability ratio and gives a clear indication whether these ratios are good or not. There is a positive impact on information systems through this system for its ability to accomplish complex calculations and process paperwork, which is faster than it was previously and there is also a high flexibility, where the system can do any adjustments required in helping relevant parties to control the financial matters of the decision-making appropriate action thereon.

Prerequisite Relations among Knowledge Units:A Case Study of Computer Science Domain

The importance of prerequisites for education has recently become a promising research direction.This work proposes a statistical model for measuring dependencies in learning resources between knowledge units.Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’understanding of the material.The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit.To help understand the complexity of the inner concepts themselves,WordNet is included as an external knowledge base in thismodel.The goal is to develop a model that will enable instructors to evaluate whether or not a learning regime has hidden relationships which might hinder students’ability to understand the material.The evaluation,employing three textbooks,shows that the proposed model succeeds in discovering hidden relationships among knowledge units in learning resources and in exposing the knowledge gaps in some knowledge units.

The Future of Computer Science

It's a great pleasure for me to be here today and have this opportunity to talk to you about my view of the future of computer science, because I think this is a very important time for those of you, the students. What I like to do is I like

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Introduction to the Special Issue on ComputerModeling for Smart Cities Applications

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies

Rapid advancement in science and technology has seen computer network technology being upgraded constantly, and computer technology, in particular, has been applied more and more extensively, which has brought convenience to people’s lives. The number of people using the internet around the globe has also increased significantly, exerting a profound influence on artificial intelligence. Further, the constant upgrading and development of artificial intelligence has led to the continuous innovation and improvement of computer technology. Countries around the world have also registered an increase in investment, paying more attention to artificial intelligence. Through an analysis of the current development situation and the existing applications of artificial intelligence, this paper explicates the role of artificial intelligence in the face of the unceasing expansion of computer network technology.

Axonal Conduction Velocity: A Computer Study

This paper derives rigorous statements concerning the propagation velocity of action potentials in axons. The authors use the Green’s function approach to approximate the action potential and find a relation between conduction velocity and the impulse profile. Computer simulations are used to bolster the analysis.

Editorial: Moving Forward to Respond to Rapid Changes of Computer Science and Technology

Since its inaugural issue in 1986,the Journal of Computer Science and Technology(JCST)has been the premier English journal of China Computer Federation(CCF),serving international readers and authors by disseminating scholarly and technical papers under a rigorous review process.

The evolving profile and role of computer science

Technological innovation ushered in the computer era, and, after a few years of tutelage by established disciplines, computer science emerged as an independent discipline. In the subsequent decades computer science developed its special identity, sharing the dual character of engineering and mathematics. This evolution is revisited here based on my personal experience. In my view, the notion of computational model has been the enabler of extraordinary creativity, and at the same time the source of critical reflection two decades ago. However, capitalizing on a vibrant technology, computer science is reinventing itself as the indispensable enabler of applications. This is a crucial profile that calls for a pedagogical adaptation, where the notion of model morphs from means to end.

Overview of the Third Global Forum

Computer science continues to grow at a rapid pace,raising the issue of how universities can best adapt to this trend.At the third Global Forum on the Development of Computer Science(GFDCS),five heads of departments of computer science from Asia,Europe,and North America came together to exchange ideas under the theme Challenges and Opportunities of Computer Science in the New Era.Through the discussions,a number of new challenges were explored,including how to meet the growing demand for computer science education,how to manage increased teaching loads,how to foster collaboration between computer science and other disciplines,how to raise ethical awareness,and how to support new“transdisciplinary”modes of education and research.At the same time,there was a consensus on the need to strengthen the role of computer science in other departments,the importance of industrial collaboration,and the need for more scalable approaches to teaching.The evolving role of computer science within the context of broader science was also discussed.

Collaborative Assessments in Computer Science Education: A Survey

As computer science enrollments continue to surge, assessments that involve student collaboration may play a more critical role in improving student learning. We provide a review on some of the most commonly adopted collaborative assessments in computer science, including pair programming, collaborative exams, and group projects. Existing research on these assessment formats is categorized and compared. We also discuss potential future research topics on the aforementioned collaborative assessment formats.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.