Computer Sciences Commons^™

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation …

Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao

Research Collection School Of Computing and Information Systems

Compiler provenance is significant in investigating the source-level indicators of binary code, like development-environment, source compiler, and optimization settings. Not only does compiler provenance analysis have important security applications in malware and vulnerability analysis, but it is also very challenging to extract useful artifacts from binary when high-level language constructs are missing. Previous works applied machine-learning techniques to predict the source compiler of binaries. However, most of the work is done on the binaries compiled on Linux operating system. We highlight the importance and need to explore Windows compilers and the complicated binaries compiled on the latest versions of these …

Diaasq: A Benchmark Of Conversational Aspect-Based Sentiment Quadruple Analysis, Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji

Research Collection School Of Computing and Information Systems

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark …

Contrastive Video Question Answering Via Video Graph Transformer, Junbin Xiao Xiao, Pan Zhou, Angela Yao, Yicong Li, Richang Hong, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT’s uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the …

A Unified Multi-Task Learning Framework For Multi-Goal Conversational Recommender Systems, Yang Deng, Wenxuan Zhang, Weiwen Xu, Wenqiang Lei, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Learning To Ask Clarification Questions With Spatial Reasoning, Yang Deng, Shuaiyi Li, Wai Lam

Research Collection School Of Computing and Information Systems

Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational …

Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Research Collection School Of Computing and Information Systems

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity …

Marketing In Metaverse: Sales-Avatar Visual Features On Customer Trust, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The metaverse wave is sweeping across the world. Virtual shopping promises a bright future and has the potential to provide customers with an immersive and realistic shopping experience anytime and anywhere. Salesavatars are critical in customer trust development in virtual shopping in the metaverse by directly interacting with customers. Considering the virtual characteristics of the metaverse and the anonymity of salesavatars, this research investigates the effect of salesavatars' genders and ages on customers' trust in the metaverse through the lens of first impression formation. Two dimensions of facial first impression, perceived facial trustworthiness and perceived facial dominance, will be studied …

Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le

Research Collection School Of Computing and Information Systems

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …

Conference Report On 2022 Ieee Symposium Series On Computational Intelligence (Ieee Ssci 2022), Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao

Research Collection School Of Computing and Information Systems

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.

Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …

Proactive Conversational Agents In The Post-Chatgpt World, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

ChatGPT and similar large language model (LLM) based conversational agents have brought shock waves to the research world. Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to user queries. This limits their capacity to understand the users and the task better and to offer recommendations based on a broader context than a given conversation. Proactiveness is still missing in these agents, including their ability to initiate a conversation, shift topics, or offer recommendations that take into account a more …

Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis …

Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

The use of invariants in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While …

A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng

Research Collection School Of Computing and Information Systems

Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact …

Duplicate Bug Report Detection: How Far Are We?, Ting Zhang, Donggyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan, Bowen Xu, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant …

Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller

Research Collection School Of Computing and Information Systems

Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

Theme Park Visitors Prefer Human-Like Robots In Customer Service Interactions, Ady Milman, Asli D.A. Tasci

Rosen Research Review

Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. UCF Rosen College of Hospitality Management researchers, Dr. Ady Milman and Dr. …

Robust And Parallel Segmentation Model (Rpsm) For Early Detection Of Skin Cancer Disease Using Heterogeneous Distributions, Nancy Zreika, Ali El-Zaart, Abdallah El Chakik

BAU Journal - Science and Technology

Melanoma is the most common dangerous type of skin cancer; however, it is preventable if it is diagnosed early. Diagnosis of Melanoma would be improved if an accurate skin image segmentation model is available. Many computer vision methods have been investigated, yet the problem of finding a consistent and robust model that extracts the best threshold value, persists. This paper suggests a novel image segmentation approach using a multilevel cross entropy thresholding algorithm based on heterogeneous distributions. The proposed strategy searches the problem space by segmenting the image into several levels, and applying for each level one of the three …

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Optimization Techniques For Machine Learning Inference And Near Memory Image Processing In Hardware For Highly Constrained Iot Edge Nodes, Rajeev Joshi

USF Tampa Graduate Theses and Dissertations

The growing demand for fast and energy-efficient hardware for resource-constrained Internet of Things (IoT) edge devices has highlighted the limitations of conventional computing architectures. This research focuses on addressing the demand for fast, optimized, and energy-efficient machine learning inference engines as well as image processing in IoT edge applications. In this work, we address three challenging research problems and devise efficient solutions. Our investigation involved comprehensive exploration and analysis, leading to the proposal of effective approaches for overcoming these demanding issues. Through our work, we contribute novel solutions that offer improved efficiency and effectiveness in handling these research problems.

First, …

Multimodal Assessment Of Human Behavior With Applications In Analysis Of Autism Spectrum Disorder, Sk Rahatul Jannat

USF Tampa Graduate Theses and Dissertations

Human behavior analysis is an important application area of artificial intelligence. Usingdata collected from sensors (e.g., cameras), human behavior including, but not limited to, emotions, expressions, body movement, and eye gaze are analyzed. This analysis is useful in applications in diverse fields ranging from healthcare, defense, education, and marketing. Considering this, in this dissertation, we have proposed new approaches to emotion and expression recognition, subject identification, and classification of autism spectrum disorder. More specifically, this dissertation has the following contributions: 1) A new approach for multimodal ubiquitous emotion recognition is proposed; 2) Investigation into the impact of large expression variations …

Roboboits: A Simulation-Based Tutoring System To Support Ai Education Through Robotics, Sara Guerreiro-Santalla, Helen Crompton, Francisco Bellas

STEMPS Faculty Publications

This paper presents a novel tutoring system to educate pre-university students about AI, a key issue to develop AI in Education for Sustainable Society. With the aim of following a learning-by-doing approach to AI, we decided to focus on robotics as the main application domain for the students’ activities. Specifically, the tutoring system is based on the Robobo educational robot, and its simulation environment. A prototype version of the tutoring system, called RoboboITS, has been released and tested in two in-person sessions with 17 students in a secondary school at Virginia (USA), leading to and promising outcomes for future development.

Application Of Agile And Simulation Approaches For The Maximal Benefits Of Reduced Turnaround Time From The Point Of Care Testing, Yusta W. Simwita, Berit I. Helgheim

Tanzania Journal of Science

This paper uses simulation to explore improvement opportunities in the orthopaedic care process. Secondly, this study used a combination of simulation and agile strategies to explore agile operational plans that can be adopted  to maximize the benefits of reduced turnaround time. Data used for this study was collected by observing the entire orthopaedic care process. A total of 635 observations were obtained, with 179 out of these observations undergoing the whole care process. The actual collected patient data were compared with the simulation output to validate the developed simulation. Different scenarios were developed to test operational plans for the maximal …

Lightweight And Effective Website Fingerprinting Over Encrypted Dns, Yong Shao, Kenneth Hernandez, Kia Yang, Eric Chan-Tin, Mohammed Abuhamad

Computer Science: Faculty Publications and Other Works

The DNS over HTTPS (DoH) protocol is implemented to improve the original DNS protocol that uses unencrypted DNS queries and responses. With the DNS traffic, an eavesdropper can easily identify websites that a user is visiting. In order to address this concern of web privacy, encryption is used by performing a DNS lookup over HTTPS. In this paper, we studied whether the encrypted DoH traffic could be exploited to identify websites that a user has visited. This is a different type of website fingerprinting by analyzing encrypted DNS network traffic rather than the network traffic between the client and the …

Product Costing And Pricing In Small And Medium Enterprises In Tanzania, Cosmas F Kindole

Tanzania Journal of Engineering and Technology (TJET)

metalwork small and medium enterprises located in Dar es Salaam with the aim of establishing methods used by SMEs in Tanzania in setting selling prices of their products. The study was necessitated by the apparent discrepancy of prices for similar products from SMEs. The approach used to gather information was mainly through direct interview and the use of questionnaires. Direct observation and literature review were also used to establish the whole costing and pricing practices in metal manufacturing SMEs. The findings of the study established that product selling practices of the surveyed SMEs were based on instinct and appearance of …

Assessment On Usage Of The Library Website Resources By Students In The Information Studies Programme At The University Of Dar Es Salaam, Hosea N. Mungwabi

Tanzania Journal of Engineering and Technology (TJET)

The study investigated students' usage of electronic resources available on the UDSM Library's website. Specifically, the study determined whether information studies students were aware of the electronic resources, how frequently they used them, and whether their demographic characteristics influenced their usage. The study also examined the students' satisfaction with the functional and non functional attributes of the website resources. A mixed-methods research design combining both qualitative and quantitative data collection methods was used. The sample consisted of 118 student respondents who were selected using a stratified random sampling technique. The data were collected through questionnaires and interviews with class representatives …