Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.

The integration of quantum computing with knowledge graphs presents a transformative approach to intelligent information processing that enables enhanced reasoning, semantic understanding, and large-scale data inference. This study introduces a Quantum Knowledge Graph (QKG) framework that combines Neo4j’s LLM Knowledge Graph Builder with Quantum Natural Language Processing (QNLP) to improve the representation, retrieval, and inference of complex knowledge structures. The proposed methodology involves extracting structured relationships from unstructured text, converting them into quantum-compatible representations using Lambeq, and executing quantum circuits via Qiskit to compute quantum embeddings. Using superposition and entanglement, the QKG framework enables parallel relationship processing, contextual entity disambiguation, and more efficient semantic association. These enhancements address the limitations of classical knowledge graphs, such as deterministic representations, scalability constraints, and inefficiencies in the capture of complex relationships. This research highlights the importance of integrating quantum computing with knowledge graphs, offering a scalable, adaptive, and semantically enriched approach to intelligent data processing.

Molecular Dynamics (MD) simulation is a fundamental exploring approach for numerous scientific fields, including but not limited to drug discovery, biology, material science, and chemistry. Unfortunately, large-scale MD simulation generally requires long-period processing, even in resource-intensive supercomputers. Parallel computing is the primary methodology to accelerate MD simulation. However, parallel computing has a low theoretical improvable ceiling for MD simulation because the atom statuses of one timestep depend on its previous timestep. To unlock the full potential of MD simulation in drug discovery and biology, we propose an AccelMD framework for reducing processing time and computing costs. AccelMD is an AI-enabled framework that accelerates MD simulation while maintaining high predictive accuracy. In order to extend AccelMD's applicability and flexibility, we also developed an extra preprocessing stage, which allows the framework to adapt to various protein inputs automatically. Compared to conventional MD, the AccelMD framework achieves 47.59 X speedup on average (max: 63.78 X, min: 30.61 X). Regarding the prediction accuracy of protein structures, the average Mean Absolute Error (MAE) and TMScore values of AccelMD are 0.0058 and 0.99980, respectively. Our empirical experiments exihibit the robustness of AccelMD framework for the study of complex, long-timescale molecular interactions.

Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools' curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning.

Traditional Knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum Natural Language Processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j’s ability to analyze context-rich data, improving tasks like entity extraction and knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, context-aware NLP applications.

Machine learning has been successfully applied to big data analytics across various disciplines. However, as data is collected from diverse sectors, much of it is private and confidential. At the same time, one of the major challenges in machine learning is the slow training speed of large models, which often requires high-performance servers or cloud services. To protect data privacy while still allowing model training on such servers, privacy-preserving machine learning using Fully Homomorphic Encryption (FHE) has gained significant attention. However, its widespread adoption is hindered by performance degradation. This paper presents our experiments on training models over encrypted data using FHE. The results show that while FHE ensures privacy, it can significantly degrade performance, requiring complex tuning to optimize.

Deep learning (DL) has attracted interest in healthcare for disease diagnosis systems in medical imaging analysis (MedIA) and is especially applicable in Big Data environments like federated learning (FL) and edge computing. However, there is little research into mitigating the vulnerabilities and robustness of such systems against adversarial attacks, which can force DL models to misclassify, leading to concerns about diagnosis accuracy. This paper aims to evaluate the robustness and scalability of DL models for MedIA applications against adversarial attacks while ensuring their applicability in FL settings with Big Data. We fine-tune three state-of-the-art transfer learning models, DenseNet121, MobileNet-V2, and ResNet50, on several MedIA datasets of varying sizes and show that they are effective at disease diagnosis. We then apply the Fast Gradient Sign Method (FGSM) to attack the models and utilize adversarial training (AT) and knowledge distillation to defend them. We provide a performance comparison of the original transfer learning models and the defended models on the clean and perturbed data. The experimental results show that the defensive techniques can improve the robustness of the models to the FGSM attack and be scaled for Big Data as well as utilized for edge computing environments.

Residue Number Systems (RNS) demonstrate the fascinating potential to serve integer addition/ multiplication-intensive applications. The complexity of Artificial Intelligence (AI) models has grown enormously in recent years. From a computer system’s perspective, ensuring the training of these large-scale AI models within an adequate time and energy consumption has become a big concern. Matrix multiplication is a dominant subroutine in many prevailing AI models, with an addition/multiplication-intensive attribute. However, the data type of matrix multiplication within machine learning training typically requires real numbers, which indicates that RNS benefits for integer applications cannot be directly gained by AI training. The state-of-the-art RNS real-number encodings, including floating-point and fixed-point, have defects and can be further enhanced. To transform default RNS benefits to the efficiency of large-scale AI training, we propose a low-cost and high-accuracy RNS fixed-point representation: Single RNS Logical Partition (S-RNS-Logic-P) representation with Scaling-down Postprocessing Multiplication (SD-Post-Mul) . Moreover, we extend the implementation details of the other two RNS fixed-point methods: Double RNS Concatenation and S-RNS-Logic-P representation with Scaling-down Preprocessing Multiplication . We also design the architectures of these three fixed-point multipliers. In empirical experiments, our S-RNS-Logic-P representation with SD-Post-Mul method achieves less latency and energy overhead while maintaining good accuracy. Furthermore, this method can easily extend to the Redundant Residue Number System to raise the efficiency of error-tolerant domains, such as improving the error correction efficiency of quantum computing.

In the realm of precision agriculture, a crucial element involves the precise quantification or estimation of seedlings, fruits, and other agricultural produce on expansive multi-acre farms at various stages of cultivation. With the advent of unmanned aerial vehicles (UAVs), capturing images of watermelon fields has become a straightforward task. These images can be subsequently processed, segmented, and categorized to determine the total count of watermelons. Currently, conventional methods are employed to address this challenge, but they have their limitations. The field has benefited from the evolution of machine learning, which has the potential to streamline the process. Nevertheless, the training phase is intricate, and achieving a valuable model can be demanding. This research delves into an examination and presentation of the existing pre-trained models for image processing in this context.

As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token.

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.

The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills.

The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network Algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%.

The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.

This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.

Data science, machine learning, and distributed computational models have evolved dramatically over the last decade. Cloud and cluster computing is full-fledged and ready for processing big data. Data driven research and decision have become the trend in multiple disciplines. However, very few organizations have experienced the full impact or competitive advantage from their advanced data analytics initiatives despite significant investments in data science and machine learning. There are a number of issues resulting in such a phenomenon including difficult to maintain and configure a cluster, complex transition from a platform to another, sophisticated programming interfaces to machine learning libraries, network congestion, and most importantly lake of well-trained personnel to sanitize and analyze data. We propose a flexible heterogeneous computing cluster with off-the-shelf computers and a Blockly programming interface for multidisciplinary users such as cybersecurity ana-lyst, biologist, geologist, musician, and choreographer.

In this paper, we present a portable labware on Google CoLab for Scalable Machine Learning (SML) with PySpark for facilitating research in Science and Engineering (SML4SE) applications. This will allow researchers to access, share, collaborate, and practice hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning concepts and getting more experience in hands-on problem-solving skills for big data analytics.

As Computer Science (CS) is moving into K-12 education, we are facing a severe shortage of CS teachers and two-thirds of teachers do not have a solid CS background in the States. CS is an ever-changing field that makes it harder for updating teachers' knowledge. To produce CS teachers, universities will have to create or revise their teacher education programs to accommodate CS. In addition to the need for certified CS teachers, one additional aspect has reached a critical need situation: the professional development (PD) of existing CS teachers, who are typically non-CS graduates or certified in non-CS subjects. Therefore, there is an urgent need to build new programs and start a community and update knowledge of both the latest technology and teaching pedagogies. For example, information security has become a national concern, and data analytics with machine learning is prevalent due to big data collected daily and maturity of artificial intelligence. Yet little curriculum is developed based on the aforementioned two important subjects for K-12 CS curriculum, and often teachers' lack of knowledge in these subjects. There is a demand about the particular learning needs of existing CS teachers in these areas. Well-established curriculum and well-trained teachers will be critical as the CS teaching work force expands significantly in coming years. In this project, we report and share our experience in recruiting, developing programs, and retaining K-12 CS teachers.

New cyberattacks grow in an exponential rate. However, the state-of-the-art in cyber defense efforts cannot keep pace with the sophisticated cybersecurity threats and attacks. The industry relies excessively on anti-virus software which is effective for known malware signatures, but not sustainable for the massive amount of malware samples released daily, and its inefficiency in handling cyberattacks such as zero-day or polymorphic/metamorphic malware. Machine learning (ML) has been extensively studied with a good performance in identifying and extracting knowledge from a big dataset. This can be an alternative to the signature-based approach. Currently, many schools offer courses on topics in ML and cybersecurity (MLC), individually. It is believed that cybersecurity professionals require a deep understanding on ML in order to cope with the growing cyberattacks. Research has shown that authentic learning will create an engaging and motivating learning environment in learning emerging technologies. We propose to develop cyber workforce using authentic learning on machine learning in cybersecurity through a set of real-world cybersecurity learning modules, including a Pre/Lab/Post model focusing on real-world problem solving skills. Our goal is to integrate developed resources into existing curriculum, disseminate the resources through webinars and faculty workshops, and assess the effectiveness of authentic learning of MLC for fostering cybersecurity workforce.

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder’s, particularly, end user’s security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI.

Machine Learning (ML) analyze, and process data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect, prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum but ML for security is not well presented there. We design and develop case-study based portable labware on Google CoLab for ML to cybersecurity so that students can access, share, collaborate, and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem solving skills.

We developed a hands-on lab for students to learn macro malware detection using decision trees on an open sourced data analytics software, HPCC, running on a virtual machine. The labware along with required software and documents are freely available at our project website. Our design is low cost, customizable and can be easily replicated to other institutions. Our experience shows that students have difficulties in configuring software environment for the lab. The virtual machine approach allows pre-configurations for datasets and required software installations and setting to facilitate students' learning and instructors' teaching. Since students are working on malware, the virtual machine provides an isolated network environment for testing without affecting normal operating network. This lab design can also be integrated into online courses by simply downloading and installing the customized virtual machine.

A set of Information Assurance and Security hands-on learning modules is developed and open to the public. Topics include networking security, database security, defensive programming, web security, system fundamentals, mobile security, malware detection using Machine learning, and big data analytics on network intrusion detection. The design follows hands-on case- based pedagogical model, which yields a satisfaction rate up to 92.5% for self-learners.

Machine Learning (ML) analyzes, and processes data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect and prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum, but ML for cybersecurity is not well presented there. We design and develop case-study based portable labware on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem solving skills.

The goal of this project is to timely identify and characterize hot topics discussed on the Chinese microblogging website, Sina Weibo. First, we develop a subject-oriented crawler to retrieve Weibo posts. Second, a probabilistic topic model is applied to the dataset to identify prominent topics and keywords. A comparison on hot topics, Word Cloud, and Latent Dirichlet Allocation results are discussed, followed by future research directions.

Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.

Social networking media, such as Twitters, Facebook, and Chinese Weibo, has become a major means for people to deliberately express their ideas, thoughts, and views about everything. A huge amount of posts in these social media are issued and viewed by the general public daily. Evidently the social networking media can directly affect people's perceptions on a specific topic. Those data can be used to valuable information that will help organizations to understand what thetrends or sentiments are. Most of the research efforts in social networking data analytics are conducted in English-based social networking media. Research on Chinese social networking media receives relatively little attention. In this paper, we examine the key problems in this field, focus particularly on the characteristics of new vocabulary, emotion expressions, and hierarchical structures in Chinese “Weibo”. Associated theoretical and technological methods to address these problems are reviewed and discussed.

The maturity of the Big Data analytics allows enterprises to enhance their business processes using a data-driven approach. From retailers, E-commerce, risk analysis to pharmaceuticals, Bioinformatics, healthcare informatics, and others, business owners are thinking about mining business data into information for making better decisions to drive business value. However, the lack of reverse engineers and data analysts makes it harder for enterprises to apply Big Data analytics in their business data with a goal to enhance their operational processes. Ultimately, these processes can be automated to reduce human errors. In this paper, we study an empirical dataset containing real world service desk tickets using machine learning with a goal to automate and improve business processes, which will benefit to enterprises and customers in increasing quality of service.

Insecure data storage may open a door to malicious malware to steal users' and system sensitive information. These problems may due to developer negligence or lack of security knowledge. Android developers use various storage methods to store data. However, Attackers have attacked these vulnerable data storage. Although the developers have modified the apps after knowing the vulnerability, the user's personal information has been leaked and caused serious consequences. As a result, instead of patching and fixing the vulnerability, we should conduct proactive control for secure Android data storage. In this paper, we analyzed Android external storage vulnerability and discussed the prevention solutions to prevent sensitive information in external storage from disclosure.

The security threats to mobile application are growing explosively. Mobile app flaws and security defects could open doors for hackers to easily attack mobile apps. Secure software development must be addressed earlier in the development lifecycle rather than fixing the security holes after attacking. Early elimination of possible security vulnerability will secure our software, and mitigate the security risk threats by potential malicious attacking. However, many software developer professionals lack the necessary security knowledge and skills at the development stagef. In this paper we explore the Android common IPC vulnerability and present our developed IPC flaw detectors with open source FindSecurityBugs plugin in Android Studio IDE.

As mobile computing is now becoming more and more popular, the security threats to mobile applications are also growing explosively. Mobile app flaws and security defects could open doors for hackers to break into them and access sensitive information. Most vulnerabilities should be addressed in the early stage of mobile software development. However, many software development professionals lack awareness of the importance of security vulnerability and the necessary security knowledge and skills at the development stage. The combination of the prevalence of mobile devices and the rapid growth of mobile threats has resulted in a shortage of secure software development professionals. Many schools offer mobile app development courses in computing curriculum; however, secure software development is not yet well represented in most schools' computing curriculum. This paper addresses the needs of authentic and active pedagogical learning materials for SSD and challenges of building Secure Software Development (SSD) capacity through effective, engaging, and investigative approaches. In this paper, we present an innovative authentic and active SSD learning approach through a collection of transferrable learning modules with hands-on companion labs based on the Open Web Application Security Project (OWASP) recommendations. The preliminary feedback from students is positive. Students have gained hands-on real world SSD learning experiences with Android mobile platform and also greatly promoted self-efficacy and confidence in their mobile SSD learning.

The security threats to mobile application are growing explosively. Mobile app flaws and security defects could open doors for hackers to easily attack mobile apps. Secure software development must be addressed earlier in the development lifecycle rather than fixing the security holes after attacking. Early eliminating against possible security vulnerability will help us increase the security of our software, and militate the consequence of damages of data loss caused by potential malicious attacking. However, many software developer professionals lack the necessary security knowledge and skills at the development stage and Secure Mobile Software Development (SMSD) is not yet well represented in current computing curriculum. In this paper we present a static security analysis approach with open source FindSecurityBugs plugin for Android Studio IDE. We categorized the common mobile vulnerability for developers based on OWASP mobile security recommendations and developed detectors to meet the SMSD needs in industry and education.

The majority of malicious mobile attacks take advantage of vulnerabilities in mobile software (applications), such as sensitive data leakage, unsecured sensitive data storage, data transmission, and many others. Most of these vulnerabilities can be detected by analyzing the mobile software. In this paper, we describe a tainted dataflow approach to detect mobile software security vulnerability, particularly, SQL Injection.

Macro malware in Microsoft (MS) Office files has long persisted as a cybersecurity threat. Though it ebbed after its initial rampages around the turn of the century, it has reemerged as threat. Attackers are taking a persuasive approach and using document engineering, aided by improved data mining methods, to make MS Office file malware appear legitimate. Recent attacks have targeted specific corporations with malicious documents containing unusually relevant information. This development undermines the ability of users to distinguish between malicious and legitimate MS Office files and intensifies the need for automating macro malware detection. This study proposes a method of classifying MS Office files containing macros as malicious or benign using the K-Nearest Neighbors machine learning algorithm, feature selection, and TFIDF where p-code opcode n-grams (translated VBA macro code) compose the file features. This study achieves a 96.3% file classification accuracy on a sample set of 40 malicious and 118 benign MS Office files containing macros, and it demonstrates the effectiveness of this approach as a potential defense against macro malware. Finally, it discusses the challenges automated macro malware detection faces and possible solutions.

Support Vector Machines (SVMs) are well-known tools for the task of big data text classification. This research studies the effects of omitting non-polar training samples on the performance of a SVM-based binary text classifier. The classifier operates on a large corpus of Amazon product review text bodies, sampled from various product categories and predicts the polarity of reviews. Our results show that training on a smaller, more concise training set of only 1 and 5-star reviews offers similar performance to training on a much larger dataset that includes 1, 2, 4, and 5-star reviews, without sacrificing much in terms of precision or recall. These results hold true using both count-based and TF-IDF vectorization methodologies. The technique of training on the poles is demonstrated to offer efficient means to build a high-performing SVM-based review sentiment polarity classifier, especially in cases where labeled data may not be readily available and training time is constrained.

Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.

As mobile computing is becoming more and more popular, the security threats to mobile applications are simultaneously increasing explosively. Most malicious activities hack the user's private information, such as contact and location information, hijack the user's transactions and communications, and exploit the confidential enterprise data stored in mobile databases or in cache on mobile devices. Database security is one of the most important security areas to be addressed. Many schools are integrating database security topics into database and cybersecurity education. This paper addresses the needs for pedagogical learning materials for database security education and the challenges of building database security capacity through effective, engaging, and investigative learning approaches, through transferrable and integratable mobile-based learning modules with hands-on companion labs based on the OWASP recommendations, such as input validation, data encryption, data sharing, auditing, and others. The primary goal of this learning approach is to create a motivating learning environment that encourages and engages all students in database security concepts and practices learning. The preliminary feedback from students was positive. Students gained hands-on real world learning experiences on Mobile Database Security (MDS) with Android mobile devices, which also greatly promoted students' self-efficacy and confidence in their mobile security learning.

The ubiquitous advance of technology used on the Internet, computers, smart phones and tablets has been conducive to the creation and proliferation of cyber threats resulting in attacks that have grown exponentially. Consequently, anti-virus companies and researchers have developed new approaches for dealing with discovering and classifying malware. Among these, machine learning and big data technologies have been used for feature extraction, detection, and clustering of cyber threats. In this paper a dataset of malware and clean files (goodware) was created and analyzed from the static and dynamic features provided by the online framework VirusTotal. The purpose is to select the smallest number of features that keep classification accuracy as high as possible in order to decrease the use of resources for monitoring as well as extracting features and the time for detection. In this research, it was found that “9” features are enough to distinguish malware from “goodware” files with an accuracy of 99.60%. Selecting the most representative features for malware detection relies on the possibility of creating an embedded program that monitors the processes executed by the operating system (OS) and looks for the characteristics that match malware behavior. In addition, classification algorithms such as Random Forest (RF), Support Vector Machine (SVM) and Neural Networks (NN) were used in a novel combination that not only showed an increase in accuracy, but also in the training speed from hours to just minutes. Finally, the trained model (which was trained with a dataset of malware samples seen before September 2015) was tested on a new dataset of malware samples seen by first time between October 2015 and June 2016 and showed that the model is still effective for detection of unseen malware files.

Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation and entrance into the workforce. Moreover, there is a little room for adding additional courses into existing STEM curriculum. Therefore, we propose to transform STEM core courses by integrating IoT-based learning framework into their corresponding lab projects. The design challenges of the new learning framework is summarized in the paper. Subsequently, we propose the effective learning approaches to address those challenges. Moreover, in this paper, we present a case study by incorporating IoT-based learning framework into a Software Engineering (SWE) embedded system analysis & design course. Specifically, we introduce a lab development kit composed of Raspberry Pi/Arduino boards and a set of sensors with Zigbee supporting to provide wireless communication in the class lab section. We adopt module design method to design the course labware. Well-developed modules are presented and one sample module is illustrated in the paper. The labware is evaluated through survey questions. The majority of the students provided positive feedback and enjoyed the IoT-based lab development kit.

In this paper we present an innovative authentic learning approach of mobile security through real-world-scenario case studies. in computing field. The primary goal of this learning approach is to create an engagement and motivating learning environment that encourages all students in learning emerging technologies such as mobile software developments and its security concepts and practices. This approach provides students with hands-on laboratory practices on real-world mobile app developments and security. The laboratory consists of multiple modules covering Mobile App development, mobile security on cryptography, secure programming, network, database, Web app, mobile malware and spyware analysis. Each topic consists of a series of progressive sub-labs: a pre-lab, lab activities, and a student add-on post-lab. The preliminary feedback from students is positive. Students have gained hands-on real world experiences on mobile security with Android mobile devices, which also greatly promoted students' self-efficacy and confidences in their mobile security learning.

The popularity and the large market share of mobile devices such as smart phones and tablets have had a significant impact on our daily lives. The full-fledged computing platforms render a ubiquitous means to deliver information and knowledge. This project aims to utilize such platforms to enhance computer science education, especially in curricula such as programming, mobile application development and information assurance and security. Meanwhile, there is an urgent need to produce more qualified computer professionals in mobile application development and information assurance and security to meet the workforce shortage. In this paper, we describe the design and implementation of our hands-on pedagogical model for learning information assurance and security based on mobile devices. Not only is this model feasible, but also it is affordable. The lab ware consists of hands-on real-world relevant self-contained learning modules, and is designed to be ready for wide scale implementation.

Mobile computing has become extremely popular in our daily lives [1] and hence it is an important platform for software developers. Mobile platforms enable students to learn in a modern context when they are used as teaching tools in Computer Science (CS) or Information Technology (IT) education. Meanwhile, mobile security is an important topic in security curricula partly due to the popularity of consumer mobile devices and a shift in computing landscape towards mobile devices' apps development. Due to the rapid demand and popularity of mobile devices [2-3], the security of mobile computing is vital to the growing army of users and for the future of our social, economic and political systems. Hence, it is important and needed to develop hands-on learning materials on mobile security that produce a well-educated and trained workforce, who understands the many concerns of security, privacy, integrity and reliability, and is able to develop strong protection mechanisms and apply fundamental principles and best practices in solving real world problems [4-6]. Many colleges and universities are developing and offering courses on mobile programming and development. However, pedagogical materials based on real world applications running on a portable isolated network are still in infant stage.

This paper presents a new inexpensive portable learning platform for information assurance and security (IAS) education. Unique features such as affordable settings, portability, and isolated network are discussed. This project is developed based on open sourced or free software to minimize the implementation cost. A dedicated repository that hosts our findings and deliverables is freely available online. This portable learning platform makes it possible to offer hands-on lab intensive curriculum online and to enhance research productivity on IAS.

This paper is addressing the challenges of incorporating networking and security concepts into effective teaching and learning platform (PLab) that highlights real-world technical issues. PLab is an innovative portable learning platform that allows network applications to be safely tested. The isolated network without the need for a server promotes learning at anytime and anywhere. The strong connection between academic subjects and reality and digital-native students' everyday lives engages students in learning this emerging field, and better prepares students for the high industrial demands in mobile application development workforce. Moreover, this pedagogical model will help faculty develop expertise in the latest development of networking and security. The modular labware is designed to offer faculty the flexibility to implement it in many existing courses. Curricular materials will be delivered on Google cloud for its sustainability. This "ready-to-adopt" model will greatly save resources and time for enhancing modern networking and security education to meet the emerging workforce.

To enhance the Information Assurance and Security in computer science (CS) education and to prepare students for the challenges of security issues, this paper presents our ongoing work on developing mobile-based hands-on security learning modules that will be integrated into existing courses in CS curriculum.

To overcome shortcomings in existing embedded system education, this paper presents an ongoing work on developing a novel Android-based Mobile Sensory System Lab ware that is portable, affordable, and easily adoptable. The proposed lab ware can facilitate authentic and creative learning of embedded systems and provide extensive hands-on experience using mobile embedded platforms. Preliminary feedback from students is positive and encouraging.

This paper presents a new online collaboration system for capstone projects and other project-oriented courses. Unique features such as project grading, social media integration, and document review are proposed. This project is developed based on the open sourced project management system, Redmine. The Ruby on Rails in Redmine provides a fast and effortless development tool for added features. Our modified Redmine will be replicated freely elsewhere.

The paper presents our preliminary work on developing a remote hardware development lab using mixed reality technology (mRLab). The mRLab allows a group of students to remotely and simultaneously work on a VHDL project with an FPGA development board. All lab objects are created in 3D and an interactive layer on the 3D objects is added to host learning information along with caveats to assist learning.

Advanced mobile devices such as smartphones have become a part of daily lives for digital native students today. We believe that the integration of mobile computing into computer science (CS) curricula with smart mobile devices based learning tools will connect the education to leading-edge information technologies, motivate and engage students in CS learning, innovate CS education, fill in the gap between academia and industry, and help faculty build strong expertise on modern mobile computing. Meanwhile, the hands-on experience with mobile computing will promote student learning with a better understanding of the fundamental CS concepts. In this paper, we propose to design and develop a labware based on Android-enabled smartphones to introduce mobile computing knowledge and practice into the CS curriculum, covering a wide range of CS subjects.

Advanced mobile devices such as smart phones and tablets have become a part of daily lives for digital native students today. The integration of mobile computing with these devices into multiple courses in computer science (CS) curriculum will connect the education to leading-edge information technologies, motivate and engage students in CS learning, fill in the gap with IT industry needs, and help faculties build strong expertise on modern mobile computing. Meanwhile, the hands-on experience with mobile computing will supply the students with a better understanding of the classical CS concepts. In this paper, we propose to develop a cross-curriculum labware based on the Android platform to introduce the knowledge and practice of a wide range of CS subjects with smart phones and tablets.

Many scientific or engineering applications involve matrix operations, in which reduction of vectors is a common operation. If the core operator of the reduction is deeply pipelined, which is usually the case, dependencies between the input data elements cause data hazards. To tackle this problem, we propose a new reduction method with low latency and high pipeline utilization. The performance of the proposed design is evaluated for both single data set and multiple data set scenarios. Further, QR decomposition is used to demonstrate how the proposed method can accelerate its execution. We implement the design on an FPGA and compare its results to other methods.

Hardware accelerators such as FPGAs and GPUs in heterogeneous systems are being increasingly important for many applications. For high performance computing, the flexibility and efficiency of FPGA makes it an attractive alternative to other approaches. However, for applications that have to decompose large matrices, not many scalable solutions can be found on FPGAs. In this paper, we propose a scalable QR matrix decomposer on FPGAs based on the latest advances in tiled matrix decomposition algorithms for high performance linear algebra. The proposed design can decompose a matrix of size limited only by off-chip memory and has the potential to achieve high performance.

This paper presents a new simple low-cost portable energy efficient device for teaching operating systems. This new technology offers a variety of curricular components including an open sourced small OS for proof of concepts and is suitable for project oriented curriculum, especially for online courses. This device has a potential to be reused in other computing courses. Its inexpensive characteristics promote broad adoption especially in resource limited institutions.

Green computing has been an active research area which studies an efficient use of computing resources. It is a growing import subject that creates an urgent need to train next generation computer scientists or practitioners to think “green.” However, green computing has not yet been well taught in computer science (CS) or computer engineering programs (CE) programs, partly due to the lack of rooms to add a new course to those programs. Presented in this paper is an effort to reform core concepts of CS/CE to inculcate green computing in subjects such as algorithms, and operating systems.

Many scientific or engineering applications perform reduction of sets of sequential data streams. If the core operator of the reduction is deeply pipelined, dependencies between the input data elements cause data hazards in the pipeline. To tackle this problem, we propose a multiple set variable length reduction design with low latency and high pipeline utilization in this paper. We prove the buffer size and execution time bounds, and then show its performance on practical multiple data set scenarios. We apply the proposed method to the Householder QR decomposition and compare its performance to other methods with superior results. The proposed design is implemented on FPGAs with resource usage and performance presented.

Green computing has become an important subject due to global warming caused by man-made greenhouse gas emissions. While green computing is an active research area, it is still of infancy in computer science (CS) education. In this regard, we propose to bring green computing into CS capstone curriculum. This integration culminates in a portable and affordable platform, a portable Wireless Sensor Network (WSN) in a Box (WinBox) with emerging ZigBee wireless technology. Both instructors and students benefit from this innovation. It allows an instructor to sponsor a realistic, significant, and feasible semester-long project. It provides students a challenge to integrate their learnt principles and theories to analyze, design, implement a product-oriented project creatively. Students are well prepared for the professional workforce by gaining hands-on experience from developing a real-world product. Our publicly accessible "learning by doing" pedagogical model promotes lifelong learning.

The rapid growth of embedded systems results in a great demand of professionals for embedded software design and development. However, embedded system education is not well represented in the current Computer Science (CS) academic programs. Existing embedded systems curricula heavily rely on a significant investment in lab resources and a high knowledge base requirement for instructors (e.g., faculty whose expertise is not specifically in this area). To overcome these difficulties, we have developed an online embedded system courseware with hands-on labs in an inexpensive and portable development box. The portable and modular design of this courseware provides a "ready-to-be-adopted" model and will save resources and time in broadening embedded system education.

We have developed an innovative online tool for teaching introductory programming courses with the goals of engaging students in learning to program and increasing students' learning confidence. This system provides an online runtime environment that supports Java online compilation, execution, and verification with either Java console, GUI, or Web-based programs. In addition, the system can also support a synchronous, in-class, and instructor-led setting with instant assessment as well as an asynchronous, off-class, self-paced, and independent-study supplemental environment.

Many scientific applications involve reduction or accumulation operations on sequential data streams. Examples such as matrix-vector multiplication include multiple inner product operations on different data sets. If the core operator of the reduction is deeply pipelined, which is usually the case, dependencies between the input data cause data hazards in the pipeline and ask for a proper design. In this paper, we propose a modified design of the reduction operation based on Sips and Lin's method. We analyze the performance of the proposed design to prove the correctness of the timing and demonstrate its performance against previous methods.

With their expressiveness and simplicity, Perl compatible regular expressions (PCREs) have been adopted in mainstream signature based network intrusion detection systems (NIDSs) to describe known attack signatures, especially for polymorphic worms. NIDSs rely on an underlying string matching engine that simulates PCREs to inspect each network packet. PCRE is a superset of traditional regular expressions, and provides advanced features. However, this pattern matching becomes a performance bottleneck of software-based NIDSs, causing a big portion of their execution time to be dedicated to payload inspection, which results in an unacceptable packet drop rate. The penetration of these unexamined packets creates a security hole in such systems. Over the past decade, hardware acceleration for the pattern matching has been studied extensively and a marginal performance has been achieved. Among hardware approaches, FPGA-based acceleration engines provide great flexibility because new signatures can be compiled and programmed into their reconfigurable architecture. As more and more malicious signatures are discovered, it becomes harder to map a complete set of malicious signatures specified in PCREs to an FPGA chip. One of the space consuming components is the counter used in the constrained repetitions for PCREs. Therefore, we propose a space efficient SelectRAM counter for PCREs that use counting. The design takes advantage of the basic components contained in a configurable logic block, and thus optimizes space usage. A set of basic PCRE blocks has been built in hardware to implement PCREs. Experimental results show that the proposed scheme outperforms existing designs by at least fivefold.

Matrix decomposition applications that involve large matrix operations can take advantage of the flexibility and adaptability of reconfigurable computing systems to improve performance. The benefits come from replication, which includes vertical replication and horizontal replication. If viewed on a space-time chart, vertical replication allows multiple computations executed in parallel, and horizontal replication renders multiple functions on the same piece of hardware. In this paper, the reconfigurable architecture that supports replications for matrix decomposition applications on reconfigurable computing systems is described, and issues including the comparison of algorithms on the system and data movement between the internal computation cores and the external memory subsystem are addressed. A prototype of such a system is implemented to prove the concept. It is expected to improve the performance and scalability of matrix decomposition involving large matrices.

Java has been introduced to the mobile/wireless handsets and Java enabled handsets are now prevalent in the market, showing their successful launch. A variety of attractive services have been developed and promise a lot of fun to our daily lives, which are however targeted to the high end products that are very expensive. We focus on economic types with similar performance to the high end products. Memory compression is a key technique to achieve the goal and is integrated into a Java runtime environment.

QR decomposition, especially through the means of Householder transformation, is often used to solve least squares problems. A matrix to be decomposed with this method is usually very large, often large enough that it is not able to fit into the main memory of a workstation, let alone the internal memory of an FPGA nowadays. Efficient out-of-core algorithms have been developed to address the factorization of large matrices. This paper describes the application of variants of Householder QR decomposition on FPGA-based systems. More specifically, issues on applying out-of-core algorithms to the relatively small internal memory architecture of FPGA's are investigated.

This paper introduces a novel FPGA-based signature match co-processor architecture serving as the core of a hardware-based network intrusion detection system (NIDS). The signature match co-processor architecture is based on the shift-or algorithm. The architecture is comprised of simple shift registers, or-gates, and ROMs where patterns are stored. As compared with related work, experimental results show that the proposed work achieves higher throughput and less hardware resource in the FPGA implementations of NIDS systems

With the appearance of multi-gigabit network infrastructure, a typical network intrusion detection system (NIDS) has to cope with the network speed. By examining each packet flowing through a network segment, suspicious packets are detected and reported to assure security. Up to 57% of the execution time in a NIDS is found to compare string against a predefined/known pattern. It is hard to implement a multi-gigabit performance NIDS without hardware support. This paper proposes a very high speed string matching algorithm which can be easily implemented into FPGAs. The parallel matching design takes a segment of text from the payload of a packet and detects all possible tokens including those crossing text segment boundaries. Simulation results show a throughput of 23.43 Gbps with a moderate operating frequency of 366.2 MHz

Java technology has been integrated into mobile/wireless computing because of its rich support to portability (cross-platform nature), reusability (development libraries), and short-time development. Given a small handset, we are now able to access almost all types of services on Internet: mobile commerce, telephony, email, Web service, global position systems, and MP3 music. We, however, still suffer from its small memory limitation and power-hungry feature. We introduce a hardware compression technique to Java-enabled mobile/wireless devices and provide small memory demand. Experiment results show 25% to 50% memory saving.

Java possesses many advantages for embedded system development, including fast product deployment, portability, security, and a small memory footprint. As Java makes inroads into the market for embedded systems, much effort is being invested in designing real-time garbage collectors. The proposed garbage-collected memory module, a bitmap-based processor with standard DRAM cells is introduced to improve the performance and predictability of dynamic memory management functions that include allocation, reference counting, and garbage collection. As a result, memory allocation can be done in constant time and sweeping can be performed in parallel by multiple modules. Thus, constant time sweeping is also achieved regardless of heap size. This is a major departure from the software counterparts where sweeping time depends largely on the size of the heap. In addition, the proposed design also supports limited-field reference counting, which has the advantage of distributing the processing cost throughout the execution. However, this cost can be quite large and results in higher power consumption due to frequent memory accesses and the complexity of the main processor. By doing reference counting operation in a coprocessor, the processing is done outside of the main processor. Moreover, the hardware cost of the proposed design is very modest (about 8000 gates). Our study has shown that 3-bit reference counting can eliminate the need to invoke the garbage collector in all tested applications. Moreover, it also reduces the amount of memory usage by 77 percent.

The Active Memory System - a garbage collected memory module - was introduced as a way to provide hardware support for garbage collection in embedded systems. The major component in the design was the Active Memory Processor (AMP) that utilized a set of bit-maps and a combinational circuit to perform mark-sweep garbage collection. The design can achieve constant time for both allocation and sweeping. In this paper two enhancements are made to the design of AMP so that it can perform one-bit reference counting that postpones the need to perform garbage collection. Moreover, a caching mechanism is also introduced to reduce the hardware cost of the design. The experimental results show that the proposed modification can reduce the number of garbage collection invocations by 76%. The speed-up in marking time can be as much as 5.81. With the caching mechanism, the hardware cost can be as small as 27 K gates and 6 KB of SRAM.

One major problem of using Java in real-time and embedded devices is the non-deterministic turnaround time of dynamic memory management systems (memory allocation and garbage collection). For the allocation, the nondeterminism is often contributed by the time to perform searching, splitting, and coalescing. For the garbage collection, the turnaround time is usually determined by the size of the heap, the number of live objects, the number of object collected, and the amount of garbage collected Even with the current state-of-the-art garbage collectors (generational and incremental schemes), they may or may not guarantee the worst case latency. Moreover such schemes often prolong overall garbage collection time. In this paper, the performance analysis of the proposed Active Memory Module (AMM) for embedded systems is presented Unlike the software counterparts, the AMM can perform a memory allocation in a predictable and hounded fashion (14 cycles). Moreover it can also yield a bounded sweeping time regardless of the number of live objects or heap size. By utilizing the proposed system, the overall speed-up can be as high as 23% over the JDK 1.2.2 running in classic mode.

Recent advances in software engineering, such as graphical user interfaces and object-oriented programming, have caused applications to become more memory intensive. These applications tend to allocate dynamic memory prolifically. Moreover, automatic dynamic memory reclamation (garbage collection, GC) has become a popular feature in modern programming languages. As a result, the time consumed by dynamic storage management can be up to one-third of the program execution time. This illustrates the need for a high-performance memory management scheme. This paper presents a top-level design and evaluation of the proposed instruction extensions to facilitate heap management.

In the last thirty years, several dynamic memory management schemes have been proposed. Such schemes include first fit, best fit, segregated fit, and buddy systems. Because the performance (speed and memory utilization) of each scheme differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. In this paper, a quantitative simulator for dynamic memory management and memory tracing techniques are presented. This simulator receives dynamic memory management traces and performs allocations according to schemes (first fit, best fit, buddy systems, and segregated fit) defined by the user. At the end of each simulation run, different performance metrics are reported to the users. By using this approach, software engineers can evaluate system performance and decide which algorithm is the most suitable for their applications.

Recently, most research efforts on garbage collection have concentrated on reducing pause times. However, very little effort has been spent on the study of garbage collection efficiency, especially generational garbage collection which was introduced as a way to reduce garbage collection pause times. In this paper a detailed study of garbage collection efficiency in generational schemes is presented. The study provides a mathematical model for the efficiency of generation garbage collection. Additionally, important issues such as write-barrier overhead, pause times, residency, and heap size are also addressed. We find that generational garbage collection often has lower garbage collection efficiency than other approaches (e.g. mark-sweep, copying) due to a smaller collected area and write-barrier overhead.