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2022

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Articles 2641 - 2670 of 3613

Full-Text Articles in Computer Sciences

Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng Feb 2022

Crowdtc: Crowd-Powered Learning For Text Classification, Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng

Research Collection School Of Computing and Information Systems

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform …

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The Data Analytics And The Science Revolution, Leila Halawi, Amal Clarke, Kelly George Feb 2022

The Data Analytics And The Science Revolution, Leila Halawi, Amal Clarke, Kelly George

Publications

This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they …

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Knowledge Graph Embedding By Normalizing Flows, Changyi Xiao, Xiangnan He, Yixin Cao Feb 2022

Knowledge Graph Embedding By Normalizing Flows, Changyi Xiao, Xiangnan He, Yixin Cao

Research Collection School Of Computing and Information Systems

A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory. Our model can incorporate existing models (i.e., generality), ensure the computation is tractable (i.e., efficiency) and enjoy the expressive power of complex random variables (i.e., expressiveness). The core idea is that we embed entities/relations as elements of a symmetric group, i.e., permutations of a set. Permutations of different sets can reflect different properties of embedding. And the …

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Collaborative Curating For Discovery And Expansion Of Visual Clusters, Duy Dung Le, Hady W. Lauw Feb 2022

Collaborative Curating For Discovery And Expansion Of Visual Clusters, Duy Dung Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

In many visually-oriented applications, users can select and group images that they find interesting into coherent clusters. For instance, we encounter these in the form of hashtags on Instagram, galleries on Flickr, or boards on Pinterest. The selection and coherence of such user-curated visual clusters arise from a user’s preference for a certain type of content as well as her own perception of which images are similar and thus belong to a cluster. We seek to model such curation behaviors towards supporting users in their future activities such as expanding existing clusters or discovering new clusters altogether. This paper proposes …

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Emerging App Issue Identification Via Online Joint Sentiment-Topic Tracing, Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu Feb 2022

Emerging App Issue Identification Via Online Joint Sentiment-Topic Tracing, Cuiyun Gao, Jichuan Zeng, Zhiyuan Wen, David Lo, Xin Xia, Irwin King, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Millions of mobile apps are available in app stores, such as Apple’s App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging …

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Broken External Links On Stack Overflow, Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang, Ying Zou, Ahmed E. Hassan, Shanping Li Feb 2022

Broken External Links On Stack Overflow, Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang, Ying Zou, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2 percent of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of …

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Privacy In Blockchain Systems, Murat Osmanoğlu, Ali̇ Aydin Selçuk Feb 2022

Privacy In Blockchain Systems, Murat Osmanoğlu, Ali̇ Aydin Selçuk

Turkish Journal of Electrical Engineering and Computer Sciences

Privacy of blockchains has been a matter of discussion since the inception of Bitcoin. Various techniques with a varying degree of privacy protection and complexity have been proposed over the past decade. In this survey, we present a systematic analysis of these proposals in four categories: (i) identity, (ii) transaction, (iii) consensus, and (iv) smart contract privacy. Each of these categories have privacy requirements of its own, and various solutions have been proposed to meet these requirements. Almost every technique in the literature of privacy enhancing technologies have been applied to blockchains: mix networks, zero-knowledge proofs, blind signatures, ring signatures, …

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Permissioned Blockchain Based Remote Electronic Examination, Öznur Kalkar, İsa Sertkaya Feb 2022

Permissioned Blockchain Based Remote Electronic Examination, Öznur Kalkar, İsa Sertkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Recent coronavirus pandemic transformed almost all aspects of daily life including educational institutions and learning environments. As a result, this transformation brought remote electronic examination (shortly e-exam) concepts back into consideration. In this study, we revisit secure and privacy preserving e-exam protocol proposals and propose an e-exam protocol that utilizes decentralized identity-based verifiable credentials for proof of authentication and public-permissioned blockchain for immutably storing records. In regard to the previously proposed e-exam schemes, our scheme offers both privacy enhancement and better efficiency. More concretely, the proposed solution satisfies test answer authentication, examiner authentication, anonymous marking, anonymous examiner, question secrecy, question …

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Data Immutability And Event Management Via Blockchain In The Internet Of Things, Hakan Altaş, Gökhan Dalkiliç, Umut Can Çabuk Feb 2022

Data Immutability And Event Management Via Blockchain In The Internet Of Things, Hakan Altaş, Gökhan Dalkiliç, Umut Can Çabuk

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet of things (IoT) is the key enabler of the smart systems used in many areas, from agriculture to aviation, industrial automation to autonomous vehicles. Most IoT deployments employ cost-efficient lightweight devices with limited resources (e.g., bandwidth, energy, storage). Although an IoT network must be built in its simplest form, engineers include more sophisticated devices like gateways and servers to provide web-based services and benefit from cloud systems. So, although the nodes can be widely distributed geographically or topologically, the system becomes centralized, which causes bottlenecks and single-points-of-failure. Furthermore, providing data integrity, nonrepudiation, and event management becomes tricky. In …

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What Really Matters?: Characterising And Predicting User Engagement Of News Postings Using Multiple Platforms, Sentiments And Topics, Kholoud K. Aldous, Jisun An, Bernard J. Jansen Feb 2022

What Really Matters?: Characterising And Predicting User Engagement Of News Postings Using Multiple Platforms, Sentiments And Topics, Kholoud K. Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

This research characterises user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes, comments, cross-platform posting). We find that sentiments and topics differ by both news outlets and social media platforms, and both sentiments and topics by the four levels of user engagement expression. Finally, we predict a volume of four user engagement levels for given news content, …

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Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer Feb 2022

Understanding Deep Learning - Challenges And Prospects, Niha Adnan, Fahad Umer

Department of Surgery Docs

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. …

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Provably Weak Instances Of Plwe Revisited, Again, Katherine Mendel Feb 2022

Provably Weak Instances Of Plwe Revisited, Again, Katherine Mendel

CSB and SJU Distinguished Thesis

Learning with Errors has emerged as a promising possibility for postquantum cryptography. Variants known as RLWE and PLWE have been shown to be more efficient, but the increased structure can leave them vulnerable to attacks for certain instantiations. This work aims to identify specific cases where proposed cryptographic schemes based on PLWE work particularly poorly under a specific attack.

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Storing Data Once In M-Trees And Pm-Trees: Revisiting The Building Principles Of Metric Access Methods, Humberto Razente, Maria Camila N. Barioni, Yasin N. Silva Feb 2022

Storing Data Once In M-Trees And Pm-Trees: Revisiting The Building Principles Of Metric Access Methods, Humberto Razente, Maria Camila N. Barioni, Yasin N. Silva

Computer Science: Faculty Publications and Other Works

Since the introduction of the M-tree, a fundamental tree-based data structure for indexing multidimensional information, several structural enhancements have been proposed. One of the most effective ones is the use of additional global pivots that resulted in the PM-tree. These two indexing structures, however, can store the same data element in multiple nodes. In this article, we revisit both the M-tree and the PM-tree to propose a new construction algorithm that stores data elements only once in the tree hierarchies. The main challenge to accomplish this, is to properly select data elements when an inner node split is needed. To …

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Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi Feb 2022

Early Fire Detection: A New Indoor Laboratory Dataset And Data Distribution Analysis, Amril Nazir, Husam Mosleh, Maen Takruri, Abdul Halim Jallad, Hamad Alhebsi

All Works

Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal …

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A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah Feb 2022

A Novel Approach To Face Pattern Analysis, Shashi Bhushan, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, Ahed Abugabah

All Works

Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real‐time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, …

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Combatting Digital Addiction: Current Approaches And Future Directions, Deniz Cemiloglu, Mohamed Basel Almourad, John Mcalaney, Raian Ali Feb 2022

Combatting Digital Addiction: Current Approaches And Future Directions, Deniz Cemiloglu, Mohamed Basel Almourad, John Mcalaney, Raian Ali

All Works

In recent years, the notion of digital addiction has become popular. Calls for solutions to combat it, especially in adolescents, are on the rise. Whilst there remains debate on the status of this phenomenon as a diagnosable mental health condition; there is a need for prevention and intervention approaches that encourage individuals to have more control over their digital usage. This narrative review examines digital addiction countermeasures proposed in the last ten years. By countermeasures, we mean strategies and techniques for prevention, harm reduction, and intervention towards addictive digital behaviours. We include studies published in peer-reviewed journals between 2010 and …

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New-Media Advertising And Retail Platform Openness, Jianqing Chen, Zhiling Guo Feb 2022

New-Media Advertising And Retail Platform Openness, Jianqing Chen, Zhiling Guo

Research Collection School Of Computing and Information Systems

We recently have witnessed two important trends in online retailing: the advent of new media (e.g., social media and search engines) makes advertising affordable for small sellers, and large online retailers (e.g., Amazon and JD.com) opening their platforms to allow even direct competitors to sell on their platforms. We examine how new-media advertising affects retail platform openness. We develop a game-theoretic model in which a leading retailer, who has both valuation and awareness advantages, and a third-party seller, who sells an identical product, engage in price competition. We find that the availability of relatively low-cost advertising through new media plays …

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Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan Feb 2022

Including Everyone, Everywhere: Understanding Opportunities And Challenges Of Geographic Gender-Inclusion In Oss, Gede Artha Azriadi Prana, Denae Ford, Ayushi Rastogi, David Lo, Rahul Purandare, Nachiappan Nagappan

Research Collection School Of Computing and Information Systems

The gender gap is a significant concern facing the software industry as the development becomes more geographically distributed. Widely shared reports indicate that gender differences may be specific to each region. However, how complete can these reports be with little to no research reflective of the Open Source Software (OSS) process and communities software is now commonly developed in? Our study presents a multi-region geographical analysis of gender inclusion on GitHub. This mixed-methods approach includes quantitatively investigating differences in gender inclusion in projects across geographic regions and investigate these trends over time using data from contributions to 21,456 project repositories. …

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Post2vec: Learning Distributed Representations Of Stack Overflow Posts, Bowen Xu, Thong Hoang, Abhishek Sharma, Chengran Yang, Xin Xia, David Lo Feb 2022

Post2vec: Learning Distributed Representations Of Stack Overflow Posts, Bowen Xu, Thong Hoang, Abhishek Sharma, Chengran Yang, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Past studies have proposed solutions that analyze Stack Overflow content to help users find desired information or aid various downstream software engineering tasks. A common step performed by those solutions is to extract suitable representations of posts; typically, in the form of meaningful vectors. These vectors are then used for different tasks, for example, tag recommendation, relatedness prediction, post classification, and API recommendation. Intuitively, the quality of the vector representations of posts determines the effectiveness of the solutions in performing the respective tasks. In this work, to aid existing studies that analyze Stack Overflow posts, we propose a specialized deep …

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A Deep Dive Into The Impact Of Covid-19 On Software Development, Paulo Anselmo Da Mota Silveira Neto, Umme Ayda Mannan, Eduardo Santana De Almeida, Nachiappan Nagappan, David Lo, Pavneet Singh Kochhar, Cuiyun Gao, Iftekhar Ahmed Feb 2022

A Deep Dive Into The Impact Of Covid-19 On Software Development, Paulo Anselmo Da Mota Silveira Neto, Umme Ayda Mannan, Eduardo Santana De Almeida, Nachiappan Nagappan, David Lo, Pavneet Singh Kochhar, Cuiyun Gao, Iftekhar Ahmed

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic is considered as the most crucial global health calamity of the century. It has impacted different business sectors around the world and software development is not an exception. This study investigates the impact of COVID-19 on software projects and software development professionals. We conducted a mining software repository study based on 100 GitHub projects developed in Java using ten different metrics. Next, we surveyed 279 software development professionals for better understanding the impact of COVID-19 on daily activities and wellbeing. We identified 12 observations related to productivity, code quality, and wellbeing. Our findings highlight that the impact …

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Modeling Functional Similarity In Source Code With Graph-Based Siamese Networks, Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare Feb 2022

Modeling Functional Similarity In Source Code With Graph-Based Siamese Networks, Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, Rahul Purandare

Research Collection School Of Computing and Information Systems

Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) …

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Field Study In Deploying Restless Multi-Armed Bandits: Assisting Non-Profits In Improving Maternal And Child Health, Aditya Mate, Lovish Madan, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, Milind Tambe Feb 2022

Field Study In Deploying Restless Multi-Armed Bandits: Assisting Non-Profits In Improving Maternal And Child Health, Aditya Mate, Lovish Madan, Aparna Taneja, Neha Madhiwalla, Shresth Verma, Gargi Singh, Aparna Hegde, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work to assist non-profits that employ automated messaging programs to deliver timely preventive care information to beneficiaries (new and expecting mothers) during pregnancy and after delivery. Unfortunately, a key challenge in such information delivery programs is that a significant fraction of beneficiaries drop out of the program. Yet, non-profits often have limited health-worker resources (time) to place crucial service calls for live interaction with beneficiaries to prevent such engagement drops. To assist non-profits in optimizing …

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Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen Feb 2022

Automated Reverse Engineering Of Role-Based Access Control Policies Of Web Applications, Ha Thanh Le, Lwin Khin Shar, Domenico Bianculli, Lionel C. Briand, Cu Duy Nguyen

Research Collection School Of Computing and Information Systems

Access control (AC) is an important security mechanism used in software systems to restrict access to sensitive resources. Therefore, it is essential to validate the correctness of AC implementations with respect to policy specifications or intended access rights. However, in practice, AC policy specifications are often missing or poorly documented; in some cases, AC policies are hard-coded in business logic implementations. This leads to difficulties in validating the correctness of policy implementations and detecting AC defects.In this paper, we present a semi-automated framework for reverse-engineering of AC policies from Web applications. Our goal is to learn and recover role-based access …

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Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu Feb 2022

Understanding In-App Advertising Issues Based On Large Scale App Review Analysis, Cuiyun Gao, Jichuan Zeng, David Lo, Xin Xia, Irwin King, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Context: In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app quality and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. Objective: Towards tackling the challenge, we conduct a study on analyzing user concerns about in-app advertisement. Method: Specifically, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues …

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Multiscale Generative Models: Improving Performance Of A Generative Model Using Feedback From Other Dependent Generative Models, Changyu Chen, Avinandan Bose, Shih-Fen Cheng, Arunesh Sinha Feb 2022

Multiscale Generative Models: Improving Performance Of A Generative Model Using Feedback From Other Dependent Generative Models, Changyu Chen, Avinandan Bose, Shih-Fen Cheng, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative models (GANs in particular) for providing high-fidelity simulation of real-world systems. However, such generative models are often monolithic and miss out on modeling the interaction in multi-agent systems. In this work, we take a first step towards building multiple interacting generative models (GANs) that reflects the interaction in real world. We build and analyze a hierarchical set-up where a higher-level GAN is conditioned on the output of multiple lower-level GANs. We present a technique of using feedback …

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Simultaneous Material Identification And Target Imaging With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang Feb 2022

Simultaneous Material Identification And Target Imaging With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang

Research Collection School Of Computing and Information Systems

Material identification and target imaging play an important role in many applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commodity Radio-Frequency IDentification (RFID) devices. The key intuition is that different materials and/or target sizes cause different amounts of phase and RSS (Received Signal Strength) changes, when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system, including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the …

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Deep Graph-Level Anomaly Detection By Glocal Knowledge Distillation, Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den Hengel Feb 2022

Deep Graph-Level Anomaly Detection By Glocal Knowledge Distillation, Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by …

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Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Syed Ibrahim Hassan, Dympna O'Sullivan, Susan Mckeever, Kieran Feighan, David Power, Ray Mcgowan Feb 2022

Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Syed Ibrahim Hassan, Dympna O'Sullivan, Susan Mckeever, Kieran Feighan, David Power, Ray Mcgowan

Articles

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This paper proposes an automatic patch detection system using object detection technique. To our knowledge, this is the first time state-of-the-art object detection models Faster RCNN, and SSD MobileNet-V2 have been used to detect patches inside images acquired by LCMS. Results show that the object detection model can successfully detect patches …

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Learning Fruit Class From Short Wave Near Infrared Spectral Features, An Ai Approach Towards Determining Fruit Type, Ayesha Zeb, Waqar Shahid Qureshi, Abdul Ghafoor, Dympna O'Sullivan Feb 2022

Learning Fruit Class From Short Wave Near Infrared Spectral Features, An Ai Approach Towards Determining Fruit Type, Ayesha Zeb, Waqar Shahid Qureshi, Abdul Ghafoor, Dympna O'Sullivan

Conference papers

This paper analyzes the potential of using shortwave NIRS (near-infrared spectroscopy) for fruit classification problems. The research focuses on O-H and C-H overtone features of fruit and its correlation with NIRS and therefore opens a new dimension of fruit classification problems using NIRS. Eleven fruits, which include apple, cherry, hass, kiwi, grapes, mango, melon, orange, loquat, plum, and apricot, were used in this study to cover physical characteristics such as peel thinness, pulp, seed thickness, and size. NIR spectral data is collected using the industry-standard F-750 fruit quality meter (wavelength range 300-1100nm) for all fruit mentioned above. Different shallow machine …

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Linked Data Quality Assessment: A Survey, Aparna Nayak, Bojan Bozic, Luca Longo Feb 2022

Linked Data Quality Assessment: A Survey, Aparna Nayak, Bojan Bozic, Luca Longo

Conference papers

Data is of high quality if it is fit for its intended use in operations, decision-making, and planning. There is a colossal amount of linked data available on the web. However, it is difficult to understand how well the linked data fits into the modeling tasks due to the defects present in the data. Faults emerged in the linked data, spreading far and wide, affecting all the services designed for it. Addressing linked data quality deficiencies requires identifying quality problems, quality assessment, and the refinement of data to improve its quality. This study aims to identify existing end-to-end frameworks for …

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