Junhee Lee, Flavius Frasincar, Maria Mihaela Truşcă
The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.
{"title":"DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment Classification","authors":"Junhee Lee, Flavius Frasincar, Maria Mihaela Truşcă","doi":"10.1145/3626307.3626309","DOIUrl":"https://doi.org/10.1145/3626307.3626309","url":null,"abstract":"The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135588889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amal Guittoum, François Aïssaoui, Sébastien Bolle, Fabienne Boyer, Noel De Palma
IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.
{"title":"Leveraging Semantic Technologies for Collaborative Inference of Threatening IoT Dependencies","authors":"Amal Guittoum, François Aïssaoui, Sébastien Bolle, Fabienne Boyer, Noel De Palma","doi":"10.1145/3626307.3626310","DOIUrl":"https://doi.org/10.1145/3626307.3626310","url":null,"abstract":"IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135588898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The question of how a given knowledge base can be modified such that certain unwanted consequences are removed has been investigated in the area of ontology engineering under the name of repair and in the area of belief change under the name of contraction. Whereas in the former area the emphasis was more on designing and implementing concrete repair algorithms, the latter area concentrated on characterizing classes of contraction operations by certain postulates they satisfy. In the classical setting, repairs and contractions are subsets of the knowledge base that no longer have the unwanted consequence. This makes these approaches syntax-dependent and may result in removal of more consequences than necessary. To alleviate this problem, gentle repairs and pseudo-constractions have been introduced in the respective research areas, and their connections have been investigated in recent work. Optimal repairs preserve a maximal amount of consequences, but they may not always exist. We show that, if they exist, then they can be obtained by certain pseudo-contraction operations, and thus they comply with the postulates that these operations satisfy. Conversely, under certain conditions, pseudo-contractions are guaranteed to produce optimal repairs. Recently, contraction operations have also been defined for concepts rather than for whole knowledge bases. We show that there is again a close connection between such operations and optimal repairs of a restricted form of knowledge bases.
{"title":"Relating Optimal Repairs in Ontology Engineering with Contraction Operations in Belief Change","authors":"Franz Baader","doi":"10.1145/3626307.3626308","DOIUrl":"https://doi.org/10.1145/3626307.3626308","url":null,"abstract":"The question of how a given knowledge base can be modified such that certain unwanted consequences are removed has been investigated in the area of ontology engineering under the name of repair and in the area of belief change under the name of contraction. Whereas in the former area the emphasis was more on designing and implementing concrete repair algorithms, the latter area concentrated on characterizing classes of contraction operations by certain postulates they satisfy. In the classical setting, repairs and contractions are subsets of the knowledge base that no longer have the unwanted consequence. This makes these approaches syntax-dependent and may result in removal of more consequences than necessary. To alleviate this problem, gentle repairs and pseudo-constractions have been introduced in the respective research areas, and their connections have been investigated in recent work. Optimal repairs preserve a maximal amount of consequences, but they may not always exist. We show that, if they exist, then they can be obtained by certain pseudo-contraction operations, and thus they comply with the postulates that these operations satisfy. Conversely, under certain conditions, pseudo-contractions are guaranteed to produce optimal repairs. Recently, contraction operations have also been defined for concepts rather than for whole knowledge bases. We show that there is again a close connection between such operations and optimal repairs of a restricted form of knowledge bases.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135588901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.
{"title":"Block-RACS: Towards Reputation-Aware Client Selection and Monetization Mechanism for Federated Learning","authors":"Zahra Batool, Kaiwen Zhang, Matthew Toews","doi":"10.1145/3626307.3626311","DOIUrl":"https://doi.org/10.1145/3626307.3626311","url":null,"abstract":"Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135588907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.
{"title":"Identifying and Categorizing Challenges in Large-Scale Agile Software Development Projects: Insights from Two Swedish Companies","authors":"Hina Saeeda, Muhammad Ovais, Ahmad","doi":"10.1145/3610019.3610021","DOIUrl":"https://doi.org/10.1145/3610019.3610021","url":null,"abstract":"We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49431639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is to design a data processing pipeline featuring a high-accuracy Graph Convolutional Network (GCN) based political camp classifier, a botnet detection algorithm, and a robust measure of botnet effects. Our experiments reveal that while the pro-Russian botnet contributes significantly to network polarization, the pro-Ukrainian botnet contributes with moderating effects. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.
{"title":"Identifying Behavioral Factors Leading to Differential Polarization Effects of Adversarial Botnets","authors":"Yeonjung Lee, M. Ozer, S. Corman, H. Davulcu","doi":"10.1145/3610409.3610412","DOIUrl":"https://doi.org/10.1145/3610409.3610412","url":null,"abstract":"In this paper, we utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is to design a data processing pipeline featuring a high-accuracy Graph Convolutional Network (GCN) based political camp classifier, a botnet detection algorithm, and a robust measure of botnet effects. Our experiments reveal that while the pro-Russian botnet contributes significantly to network polarization, the pro-Ukrainian botnet contributes with moderating effects. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44011215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is to design a data processing pipeline featuring a high-accuracy Graph Convolutional Network (GCN) based political camp classifier, a botnet detection algorithm, and a robust measure of botnet effects. Our experiments reveal that while the pro-Russian botnet contributes significantly to network polarization, the pro-Ukrainian botnet contributes with moderating effects. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.
{"title":"Identifying Behavioral Factors Leading to Differential Polarization Effects of Adversarial Botnets","authors":"Yeonjung Lee","doi":"10.1145/3610019.3610022","DOIUrl":"https://doi.org/10.1145/3610019.3610022","url":null,"abstract":"In this paper, we utilize a Twitter dataset collected between December 8, 2021 and February 18, 2022, during the lead-up to the 2022 Russian invasion of Ukraine. Our aim is to design a data processing pipeline featuring a high-accuracy Graph Convolutional Network (GCN) based political camp classifier, a botnet detection algorithm, and a robust measure of botnet effects. Our experiments reveal that while the pro-Russian botnet contributes significantly to network polarization, the pro-Ukrainian botnet contributes with moderating effects. To understand the factors leading to these different effects, we analyze the interactions between the botnets and the users, distinguishing between barrier-crossing users, who navigate across different political camps, and barrier-bound users, who remain within their own camps. We observe that the pro-Russian botnet amplifies the barrier-bound partisan users within their own camp most of the time. In contrast, the pro-Ukrainian botnet amplifies the barrier-crossing users on their own camp alongside themselves for the majority of the time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44132003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.
{"title":"Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis","authors":"S. Sako","doi":"10.1145/3610019.3610020","DOIUrl":"https://doi.org/10.1145/3610019.3610020","url":null,"abstract":"Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47321661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.
{"title":"Identifying and Categorizing Challenges in Large-Scale Agile Software Development Projects: Insights from Two Swedish Companies","authors":"Hina Saeeda, M. Ahmad, Tomas Gustavsson","doi":"10.1145/3610409.3610411","DOIUrl":"https://doi.org/10.1145/3610409.3610411","url":null,"abstract":"We conducted a case study to examine the challenges encountered in large-scale agile development (LSAD) within two Swedish software companies. While agile methodologies have proven successful in small and medium-sized projects, their implementation in large-scale software development projects can be problematic. To identify these challenges, we employed thematic analysis, which revealed a total of 26 distinct challenges. These challenges were categorized into three main themes: Processes and practices, Teams, and Organizational-level challenges in LSAD. By recognizing and addressing these challenges, projects operating in similar contexts can synchronize their activities and harness the advantages of agile methodologies at a large scale. The article delves into comprehensive discussions on these challenges, offering valuable insights and directions for future research endeavors.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42979558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanapol Phungtua-Eng, S. Sako, Yushi Nishikawa, Yoshitaka Yamamoto
Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.
{"title":"Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis","authors":"Thanapol Phungtua-Eng, S. Sako, Yushi Nishikawa, Yoshitaka Yamamoto","doi":"10.1145/3610409.3610410","DOIUrl":"https://doi.org/10.1145/3610409.3610410","url":null,"abstract":"Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42206751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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