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.

Cloud-based large language models (LLMs) and their variants have significantly influenced real-world applications. Deploying smaller models (i.e., small language models (SLMs)) on edge devices offers additional advantages, such as reduced latency and independence from network connectivity. However, edge devices’ limited computing resources and constrained energy budgets challenge efficient deployment. This study evaluates the power efficiency of five representative SLMs — Llama 3.2, Phi-3 Mini, TinyLlama, and Gemma 2 on Raspberry Pi 5, Jetson Nano, and Jetson Orin Nano (CPU and GPU configurations). Results show that Jetson Orin Nano with GPU acceleration achieves the highest energy-to-performance ratio, significantly outperforming CPU-based setups. Llama 3.2 provides the best balance of accuracy and power efficiency, while TinyLlama is well-suited for low-power environments at the cost of reduced accuracy. In contrast, Phi-3 Mini consumes the most energy despite its high accuracy. In addition, GPU acceleration, memory bandwidth, and model architecture are key in optimizing inference energy efficiency. Our empirical analysis offers practical insights for AI, smart systems, and mobile ad-hoc platforms to leverage tradeoffs from accuracy, inference latency, and power efficiency in energy-constrained environments.

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.

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.

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.

The Internet of Things (IoT) has been the key to many advancements in next-generation technologies for the past few years. With a conceptual grouping of ecosystem elements such as sensors, actuators, and smart objects connected together, complex operations like environmental monitoring, intelligent transport systems, smart buildings, and smart cities are able to be performed. Edge computing technology extends the reach/scope of IoT ecosystems, offering robust and powerful computational capabilities by connecting multiple devices through the Internet. Unfortunately, this form of computation comes with a significant drawback with strict energy constraints and low power efficiency, which highly limits its potential and usage. In this paper, we present some of the challenges in the planning of energy-efficient IoT edge devices and discuss some of the recent research efforts that proposed promising solutions that address these challenges. Specifically, we first analyze the challenges and reasons for improving the energy consumption of edge platforms and IoT devices. Next, we perform case studies that outline the energy-saving techniques in smart grids, smart cities, electric vehicles (EV), smart home devices, and Virtual Reality and Augmented Reality (VR/AR). We further discuss different approaches such as computation offloading, edge devices hardware and software designs, and a number of algorithms that help reduce energy consumption. Finally, we outline possible future directions and our vision of improving energy efficiency on edge platforms.

Recently, with the advent of the Internet of everything and 5G network, the amount of data generated by various edge scenarios such as autonomous vehicles, smart industry, 4K/8K, virtual reality (VR), augmented reality (AR), etc., has greatly exploded. All these trends significantly brought real-time, hardware dependence, low power consumption, and security requirements to the facilities, and rapidly popularized edge computing. Meanwhile, artificial intelligence (AI) workloads also changed the computing paradigm from cloud services to mobile applications dramatically. Different from wide deployment and sufficient study of AI in the cloud or mobile platforms, AI workload performance and their resource impact on edges have not been well understood yet. There lacks an in-depth analysis and comparison of their advantages, limitations, performance, and resource consumptions in an edge environment. In this paper, we perform a comprehensive study of representative AI workloads on edge platforms. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate three categories (i.e., classification, image-to-image, and segmentation) of the most popular and widely used AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. We find that interaction between hardware and neural network models incurs non-negligible impact and overhead on AI workloads at edges. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workloads and their algorithms for edge platforms, and users need to select appropriate workload, model, and algorithm based on requirements and characteristics of edge environments.

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.

Computer network and security courses are essential parts of college computer science curriculum, and hands-on networking experience is well recognized as an effective approach to help students better understand the concepts of network and security. However, existing networking labs are usually server-based and relatively cumbersome, which require a certain level of specialty and resource to set up and maintain the lab environment. Many universities and colleges lack the resources and build-ups in this field and have difficulty providing students with hands-on practice labs. We propose pedagogically- effective affordable, easily adoptable, sustainable, anywhere and anytime accessible mobile device-based labware for security infused networking teaching and hands-on learning. In this paper, we present our mobile device-based hands-on labware (series of lab modules) for computer network and security learning, which is guided by authentic learning principles to immerse students in a real world relevant learning environment. We have been using this labware in teaching computer network, mobile security, and wireless network classes. The student feedback shows that students can learn more effectively when they have hands-on authentic learning experiences.

A NoSQL, also called a "Non-Relational" or "Not only SQL," database system provides an approach to data management and database design for very large sets of distributed data and real-time web applications. A NoSQL database system is also a popular data storage for information retrieval because it supports better scalability, availability, and faster data access while comparing with traditional relational database management systems (RDBMS). What the RDBMS data needs is predictable as its data is stored in structured tables by defining the relationship between the different columns. In contrary the data in NoSQL databases does not need to be stored in a structured or fixed fashion. When performance and real-time access are more concerned than consistency, such as indexing and retrieving large numbers of records, NoSQL databases are more suitable than relational databases. With their obvious advantages in better performance, scalability, and flexibility, NoSQL databases have been adopted lately by many small businesses as they are moving their increasing business data into the clouds. However, the research on the security of a specific NoSQL database system or NoSQL database systems in general is very limited. Although there are many storage advantages in NoSQL databases, the need of quick and easy access to data has been seriously affected by the security issue of NoSQL databases. This paper examines the maturity of security measures for MongoDB, a typical NoSQL database system, with aspects in both attack and defense at the code level. The experimental testing on NoSQL injections is performed with JavaScript and PHP. After the demonstration on how a server-side JavaScript injection attack against a NoSQL database system reveals the customer's private data, two methods are discussed in preventing this type of security problems from happening. It is believed that our study will help database developers not only realizing that NoSQL database systems are not designed with security as a priority but also learning how to build a security layer to their organizations' NoSQL applications to avoid NoSQL injections.

Nowadays many data mining algorithms focus on clustering methods. There are also a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this paper, we present a cluster-outlier iterative detection algorithm, tending to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. Experimental results demonstrate the advantages of our approach.

Visualization enables us to find structures, features, patterns and relationship in a dataset by presenting the data in various graphical forms with possible interactions. Recent development of DNA microarray technology can be used to measure the expression levels of thousands of genes simultaneously. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analysis and visualize these fast growing data. In this paper, we present VizCluster, an interactive visualization approach to cluster analysis, and its application on microarray data. VizCluster combines the merits of both high dimensional scatter-plot and parallel coordinates. Integrated with useful features, it can give a simple, fast, intuitive and yet powerful view of the data set. VizCluster supports three major analyzing modes: cluster/class discovery, class prediction, and class assessment. Its primary applications are the classification of samples on microarray datasets. The experiments are based on gene expression data from a study of multiple sclerosis and leukemia patients.