Computer Sciences Commons^™

On A Multiserver Queueing System With Customers’ Impatience Until The End Of Service Under Single And Multiple Vacation Policies, Mokhtar Kadi, Amina A. Bouchentouf, Lahcene Yahiaoui

Applications and Applied Mathematics: An International Journal (AAM)

This paper deals with a multiserver queueing system with Bernoulli feedback and impatient customers (balking and reneging) under synchronous multiple and single vacation policies. Reneged customers may be retained in the system. Using probability generating functions (PGFs) technique, we formally obtain the steady-state solution of the proposed queueing system. Further, important performance measures and cost model are derived. Finally, numerical examples are presented.

Multilayer Security Of Rgb Image In Discrete Hartley Domain, Umar H. Mir, Deep Singh, D. C. Mishra, Parveiz N. Lone

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we present RGB image encryption and decryption using random matrix affine cipher (RMAC) associated with discrete Hartley transform (DHT) and random matrix shift cipher (RMSC). The parameters in RMAC and RMSC phases act as two series of secret keys whose arrangement is imperative in the proposed algorithm. The computer simulations with results and examples are given to analyze the efficiency of the proposed approach. Further, security analysis and comparison with the prior techniques successfully supports the robustness and validation of the proposed technique.

Lightgwas: A Novel Machine Learning Procedure For Genome-Wide Association Study, Ambrozio Bruno, Luca Longo, Lucas Rizzo

Articles

This paper proposes a novel machine learning procedure for genome-wide association study (GWAS), named LightGWAS. It is based on the LightGBM framework, in addition to being a single, resilient, autonomous and scalable solution to address common limitations of GWAS implementations found in the literature. These include reliance on massive manual quality control steps and specific GWAS methods for each type of dataset morphology and size. Through this research, LightGWAS has been contrasted against PLINK2, one of the current state-of-the-art for GWAS implementations based on general linear model with support to firth regularisation. The mean differences measured upon standard classification metrics, …

A Comparative Analysis Of Rule-Based, Model-Agnostic Methods For Explainable Artificial Intelligence, Giulia Vilone, Lucas Rizzo, Luca Longo

Articles

The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are usually not explicit within a model. Scholars have proposed several methods for extracting rules from data-driven machine-learned models. However, limited work exists on their comparison. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by four post-hoc rule extractors by employing …

Exploring The Potential Of Defeasible Argumentation For Quantitative Inferences In Real-World Contexts: An Assessment Of Computational Trust, Lucas Rizzo, Pierpaolo Dondio, Luca Longo

Articles

Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy …

Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher

Conference papers

Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the …

Image Encryption Using Gingerbreadman Map And Rc4a Stream Cipher, Abdul Gaffar, A. B. Joshi,, Dhanesh Kumar

Applications and Applied Mathematics: An International Journal (AAM)

Day to day increasing flow of sensitive or confidential information, such as images, audio, video, etc., over unsecured medium (like Internet) has motivated more concentration for concrete crypto algorithms. In this paper, an image encryption algorithm based on a permutation and substitution cipher has been proposed. In permutation stage, image pixels are shuffled using gingerbreadman map while in substitution stage, pixels are bit-wise XOR-ed with the keystream generated using RC4A (Rivest Cipher 4A) stream cipher algorithm. For the proposed scheme, statistical analyses, like histogram, adjacent pixels correlation coefficient, and information entropy are given. Security analyses, like key sensitivity, occlusion analysis …

Analysis Of Map/Ph/1 Queueing Model With Breakdown, Instantaneous Feedback And Server Vacation, G. Ayyappan, K. Thilagavathy

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we analyze a single server queueing model with feedback, a single vacation under Bernoulli schedule, breakdown and repair. The arriving customers follow the Markovian Arrival Process (MAP) and service follow the phase-type distribution. When the server returns from vacation, if there is no one present in the system, the server will wait until the customer’s arrival. When the service completion epoch if the customer is not satisfied then that customer will get the service immediately. Under the steady-state probability vector that the total number of customers are present in the system is probed by the Matrix-analytic method. …

Proactive Management In Academic Libraries: Promoting Improved Communication And Inclusion Of Academic Librarians And Archivists In Cybersecurity Policy Creation, Paul J. Luft

Theses and Dissertations

Although increasing cybersecurity threats continue in libraries, not many studies are available which examine surrounding cybersecurity policies. Even less has been done on specific types of libraries such as academic and archives. When it comes to academic libraries, cybersecurity policies take a top-down approach to managing and creating policies. The problem is that both academic libraries and archives are unique areas within a university setting. Some of the general policies do not always handle specific issues dealt with in an academic library or archives. This paper investigates if an actual gap or void in policy exists which could create issues …

Exploring Information For Quantum Machine Learning Models, Michael Telahun

Electronic Theses and Dissertations

Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state …

Neural Network Development In An Artificial Intelligence Gomoku Program, David Garcia

Theses and Dissertations

The game of Gomoku, also called Five in a Row, is an abstract strategy board game. The Gomoku program is constructed upon an algebraic monomial theory to aid values for each possible move and estimate chances for the artificial intelligence program to accomplish a winning path for each move and rounds. With the utilization of the monomial theory, winning configurations are successfully converted into monomials of variables which are represented on board positions. In the artificial intelligence program, an arduous task is how to perform the present configuration of the Gomoku game along with the past moves of the two …

Reinforcement Learning Environment For Orbital Station-Keeping, Armando Herrera Iii

Theses and Dissertations

In this thesis, a Reinforcement Learning Environment for orbital station-keeping is created and tested against one of the most used Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). This thesis also explores the foundations of Reinforcement Learning, from the taxonomy to a description of PPO, and shows a thorough explanation of the physics required to make the RL environment. Optuna optimizes PPO's hyper-parameters for the created environment via distributed computing. This thesis then shows and analysis the results from training a PPO agent six times.

Artificial Intelligence In A Main Warehouse In Panasonic: Los Indios, Texas, Edison Antonio Trejo Hernandez

Theses and Dissertations

The Panasonic Company warehouse is located in Los Indios Texas. The warehouse presents the limitation of the great distances between its headquarters and the Main Warehouse for supplying the branches and main customers, which requires a considerable amount of time to maintain effective communication in the inventory area. In addition, during an online review, it can be confirmed that the website is disabled, contradicting its corporate policy.

The structure of the thesis proposal is arranged in four chapters from the Introduction, Statement of the Problem and Purposes; Previous Studies and Definition of the literature; the Research Methodology and the resources …

Teaching Applications And Implications Of Blockchain Via Project-Based Learning: A Case Study, Kevin Mentzer, Mark Frydenberg, David J. Yates

Information Systems and Analytics Department Faculty Journal Articles

This paper presents student projects analyzing or using blockchain technologies, created by students enrolled in courses dedicated to teaching blockchain, at two different universities during the 2018-2019 academic year. Students explored perceptions related to storing private healthcare information on a blockchain, managing the security of Internet of Things devices, maintaining public governmental records, and creating smart contracts. The course designs, which were centered around project-based learning, include self-regulated learning and peer feedback as ways to improve student learning. Students either wrote a research paper or worked in teams on a programming project to build and deploy a blockchain-based application using …

How Do Monetary Incentives Influence Prosocial Fundraising? An Empirical Investigation Of Matching Subsidies On Crowdfunding, Zhiyuan Gao

Dissertations and Theses Collection (Open Access)

Monetary incentives, such as matching subsidies, are widely used in traditional fundraising and crowdfunding platforms to boost funding activities and improve funding outcomes. However, its effectiveness on prosocial fundraising is still unclear from both theoretical (Bénabou and Tirole, 2006; Frey, 1997; Meier, 2007a) and empirical studies (Ariely et al., 2009; Karlan and List, 2007; Rondeau and List, 2008). This dissertation aims to examine the effectiveness of matching subsidies on prosocial fundraising in the crowdfunding context. Specifically, I study how the presence of matching subsidies affects overall funding outcomes and funding dynamics in the online prosocial crowdfunding environment.

The first essay …

In-Depth Analysis Of College Students’ Data Privacy Awareness, Vernon D. Andrews

Theses and Dissertations

While attending university, college students need to be aware of issues related to data privacy. Regardless of the age, gender, race, or education level of college students, every student can relate to the advancement of the internet within the last decades. The internet has completely transformed the way the world receives and stores messages. Positively, the internet allows messages to be sent across the globe within the fraction of a second and provides students with access to potentially unlimited information on a variety of subjects. On the downside, there are issues on the internet that can cause more harm than …

Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of …

Secure Answer Book And Automatic Grading, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In response to the growing need to perform assessments online, we have developed a secure answer book, as well as a tool for automatically grading it for our course on spread- sheet modeling. We applied these techniques to a cohort of about 160 students who took the course last term. In this paper, we describe the design, implementation and the techniques employed to enhance both the security of the answer book and the ease, accuracy and consistency of grading. In addition, we summarize the experience and takeaways, both from the instructor and the student perspectives. Although the answer book and …

Smartfuzz: An Automated Smart Fuzzing Approach For Testing Smartthings Apps, Lwin Khin Shar, Nguyen Binh Duong Ta, Lingxiao Jiang, David Lo, Wei Minn, Kiah Yong Glenn Yeo, Eugene Kim

Research Collection School Of Computing and Information Systems

As IoT ecosystem has been fast-growing recently, there have been various security concerns of this new computing paradigm. Malicious IoT apps gaining access to IoT devices and capabilities to execute sensitive operations (sinks), e.g., controlling door locks and switches, may cause serious security and safety issues. Unlike traditional mobile/web apps, IoT apps highly interact with a wide variety of physical IoT devices and respond to environmental events, in addition to user inputs. It is therefore important to conduct comprehensive testing of IoT apps to identify possible anomalous behaviours. On the other hand, it is also important to optimize the number …

Renewal Of An Information Systems Curriculum To Support Career Based Tracks: A Case Study, Swapna Gottipati, Venky Shankararaman, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

The pace at which technology redefines traditional job functions is picking up rapidly. This trend is triggered particularly by advances in analytics, security, cloud computing, Artificial Intelligence and big data. The purpose of this paper is to present a case study on our approach to renewing an undergraduate IS Major curriculum to align with the needs of the industry. We adopt a survey based approach to study Information Systems (IS) graduate skills requirements and re-design the curriculum framework for the IS program at our school. The paper describes in detail the process, the redesigned IS curriculum, the impact of the …

Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua

Research Collection School Of Computing and Information Systems

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view. It is worth noting that the contribution …

Causal Intervention For Weakly-Supervised Semantic Segmentation, Zhang Dong, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels --- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding context, e.g., the correct image-level classification of "horse'' and "person'' may be not only due to the recognition of each instance, but also their co-occurrence context, making the model inspection (e.g., CAM) hard to distinguish between the boundaries. Inspired by this, we propose a structural causal model to analyze the causalities among images, contexts, and …

A Bert-Based Dual Embedding Model For Chinese Idiom Prediction, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all …

Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao

Research Collection School Of Computing and Information Systems

Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …

A Social Network Analysis Of Jobs And Skills, Derrick Ming Yang Lee, Dion Wei Xuan Ang, Grace Mei Ching Pua, Lee Ning Ng, Sharon Purbowo, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this study, we analyzed job roles and skills across industries in Singapore. Using social network analysis, we identified job roles with similar required skills, and we also identified relationships between job skills. Our analysis visualizes such relationships in an intuitive way. Insights derived from our analyses are expected to assist job seekers, employers as well as recruitment agencies wanting to understand trending and required job roles and skills in today’s fast changing world.

Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of …

Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types …

Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Darshana Rathnayake, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera

Research Collection School Of Computing and Information Systems

Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency--vs.--accuracy tradeoff by exploiting cross-modal dependencies -- i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We …