2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献

{"title":"Balance Graphs: An Aid for Studying Convolutional Neural Networks","authors":"Lyell Embery, Eva Ignatious, S. Azam, Miriam Jonkman, Friso De Boer","doi":"10.1109/SNPD54884.2022.10051785","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051785","url":null,"abstract":"Deep Learning Neural Networks offer a powerful tool to process visual data and to make decisions, but a limitation is its black box nature which offers low transparency to human examiners. This hurdle presents a particular challenge in using Neural Networks in safety-critical systems, which require high performance and transparency such as medical diagnosis of life-threatening diseases. This paper seeks to build and test a neural network for grading gliomas of comparable function and complexity to others in the field, then to apply Data Visualisation techniques to render the internal workings of the NN more understandable to a human observer. The purpose is to develop a system that can classify brain tumors into low-grade gliomas (LGG) and high-grade gliomas (HGG), to aid with diagnosis and prognosis The Brain Tumor Segmentation Challenge 2020 (BraTS2020) data set was used, with data categorised based on a combination of grade assigned in BraTS2020, and the labels in the segmentation data. As some categories are over-represented, methods were employed to ensure a better balance between different categories. Data augmentation was used to expand the limited number of scans in the BraTS2020. A 3D convolution neural network (CNN) was constructed to grade gliomas. With the method developed in this paper, an accuracy of 94.1% was achieved. A newly devised method to visually represent the weights of a convolution is explored. These graphs, called ‘weight graphs’ allow convolutions to be condensed into a visual medium. The weight graph is designed for easy visual interpretation of the weights assigned within a particular convolution. To overcome the limitations of weight graphs, an alternate graph was devised, called a balance graph, because it shows the overall balance of weights in a kernel, allowing for a quick impression of what effect a single kernel has. It is demonstrated that Balance Graphs improve the accessibility and transparency of the of the weights in convolution layers.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134503347","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}

{"title":"Goal-Driven Context-Aware Service Recommendation for Mashup Development","authors":"Xihao Xie, Jia Zhang, R. Ramachandran, Tsengdar J. Lee, Seungwon Lee","doi":"10.1109/SNPD54884.2022.10051805","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051805","url":null,"abstract":"As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected up to the current step as well as the mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of this approach.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":" 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113946295","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}

{"title":"The Role of Organizational Factors and Trust on FinTech Adoption in Indian Financial Organizations","authors":"L. Grandhi, S. Wibowo, Marilyn A. Wells, S. Grandhi","doi":"10.1109/SNPD54884.2022.10051816","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051816","url":null,"abstract":"Trust is a crucial factor in technology adoption decisions made by organizations. Understanding its importance is critical for accelerating the adoption of financial technologies (FinTech). Earlier studies investigated the significance of trust in new technologies and the subsequent technology adoption decisions. However, these were limited to studying the benefits of FinTech, and not the factors enabling trust in FinTech and the subsequent adoption decision. This research-in-progress paper aims to investigate the role of organizational factors in enabling trust in FinTech to support FinTech adoption among Indian financial organizations. This study adopts a quantitative method for data collection. The initial data collected from Indian financial organizations indicate the importance of senior management support, organization size and competence in enabling trust in FinTech. The role of organizational factors in enabling trust in FinTech and the subsequent adoption decision will be then assessed using the structural model.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022041","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}

{"title":"Modified Coronavirus Herd Immunity Optimizer for Permutation Scheduling Problems","authors":"Yuqing Gao, Ruey-Maw Chen","doi":"10.1109/SNPD54884.2022.10051774","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051774","url":null,"abstract":"The permutation flow shop scheduling problem (PFSSP) is well-applied in the industry, which is confirmed to be an NP-Hard optimization problem, and the objective is to find the minimum completion time (makespan). A modified coronavirus herd immunity optimizer (CHIO) with a modified solution update is suggested in this work. Meanwhile, the simulated annealing strategy is used on the updating herd immunity population to prevent trapping on local optima, and an adjusted state mechanism is involved to prevent fast state change/ convergence. Nine instances of different problem scales on the FPSSP dataset of Taillard were tested. The experimental results show that the proposed method can find the optimal solutions for the tested instances, with ARPDs no more than 0.1, indicating that the proposed method can effectively and stably solve the PFSSP.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127907416","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}

{"title":"Classification and Characterization of Memory Reference Behavior in Machine Learning Workloads","authors":"Seok-Kyung Kwon, H. Bahn","doi":"10.1109/SNPD54884.2022.10051800","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051800","url":null,"abstract":"With the recent penetration of artificial intelligence (AI) technologies into many areas of computing, machine learning is being incorporated into modern software design. As the in-memory data of AI workloads increasingly grows, it is important to characterize memory reference behaviors in machine learning workloads. In this paper, we perform a characterization study for memory references in machine learning workloads as the learning types (i.e., supervised vs. unsupervised) and the problem domains (i.e., classification, regression, and clustering) are varied. From this study, we uncover the following five characteristics. First, machine learning workloads exhibit significantly different memory reference patterns from traditional workloads, but they are similar regardless of learning types and problem domains. Second, in all workloads, memory reads and writes continue to appear for a wide range of memory addresses, but there is a specific time period where only reads appear. Third, among references to memory areas (i.e., code, data, heap, stack, library), library accounts for about 90% of total memory references. Fourth, there is a low popularity bias between memory pages referenced in machine learning workloads, especially for writes. Fifth, when estimating the likelihood of re-referencing, temporal locality is dominant in top 100 memory pages, but access frequency provides better information after that ranking. It is expected that the characterization of memory references conducted in this paper will be helpful in the design of memory management policies for machine learning workloads.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134030299","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}

{"title":"Quantum Mechanics of Theorem of Bayes Modeled by Machine Learning Principles","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD54884.2022.10051776","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051776","url":null,"abstract":"A theory consisting in quantum mechanics and theorem of Bayes, is presented. In essence, the Bayes probability has been built from two subspaces. While in one some quantum measurements are done, in the another it is seen that the probabilities acquire their highest values. Thus, the validity of a prior probability makes sense is there is a clear difference between the done measurement of probability amplitude. Thus, the principles of machine learning compacted in the criteria of Tom Mitchell have been employed. The simulations have shown that the size of space has direct impact on the prior probability that presumably would get low values of probability in a limited subspace. These values have turned out to be strongly correlated to the times in which measurements are done in a big space. Therefore, it is evident the prospective applicability of this novel approach in all those scenarios that require of a quantum measurement in separated times.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"34 Suppl 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123466637","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}

{"title":"IoT and Computer Vision Based Aquaponics System","authors":"Nazib Ahmad, M. Hasan, Mhamudur Rohomun, Raihana Irin, R. Rahman","doi":"10.1109/SNPD54884.2022.10051814","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051814","url":null,"abstract":"A farming method known as aquaponics aims to be a more effective response to the world's worries about food production and scarcity. In order to produce plants or vegetables in addition to fish to meet the rising global demand for food, this technique combines aquaculture (fish farming) and aquaponics (plants grown without soil). This technique or system promises to utilize less water, fertilizer, pesticides, and space while yet producing as much as conventional farming. The challenge is making it more usable and scalable for commercial and public use. To overcome such challenges, this technique must be intelligent and automated by rigorous sensing, monitoring, and system control. Our current research aims to develop a practical smart IoT-based aquaponics system. It offers a workable solution and ideas for further investigation while also looking for gaps in the earlier investigations.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126145903","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}

{"title":"Theory and Simulation of Multipurpose Antenna for Detection and Degrading of Viruses in Times of Pandemic","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD54884.2022.10051813","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051813","url":null,"abstract":"This paper present a theoretical model that aims to minimize the capabilities of viruses in public places through engineered electromagnetic fields. Thus, the modeling of antenna based at the infinitesimal dipole is used. In addition fields and directivity at the far field region are calculated. This proposal empathizes the fact that the radiated energy will affect the spike protein of viruses. In this manner the functionality of virus as to produce infection would be minimized. Simulations of the radiate electric field are presented.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238497","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}

{"title":"Attention Mechanism, Linked Networks, and Pyramid Pooling Enabled 3D Biomedical Image Segmentation","authors":"Pooja Ravi, Srijarko Roy, Indira Dutta, Kottilingam Kottursamy","doi":"10.1109/SNPD54884.2022.10051771","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051771","url":null,"abstract":"We present an approach to detect and segment tumorous regions of the brain by establishing three varied segmentation architectures for multiclass semantic segmentation along with data-specific customizations like residual blocks, soft attention mechanism, pyramid pooling, linked architecture, and 3D compatibility to work with 3D brain MRI images. The proposed segmentation architectures namely, Attention Residual U-Net 3D (ARU-Net 3D), LinkNet 3D and PSPNet 3D, segment the MRI images and successfully isolate three classes of tumors. By assigning pixel probabilities, each of these models differentiates between pixels belonging to tumorous and non-tumorous regions of the brain. By experimenting and observing the performance of each of the three architectures using metrics like Dice Loss and Dice Score, on the BraTS2020 dataset, we successfully establish the following validation scores: 0.6488, 0.6485, and 0.6501 for the ARU-Net, LinkNet, and PSPNet 3D architectures respectively. Code has been made available at: https://github.com/indiradutta/BrainTumorSeg-III-D.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881127","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}

{"title":"Deep Learning Used to Detect Gear Inspection","authors":"Jiao Jian, Chuin-Mu Wang","doi":"10.1109/SNPD54884.2022.10051817","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051817","url":null,"abstract":"Automatic gear defect detection equipment is relatively expensive, so small and medium-sized enterprises cannot afford the cost of such equipment. Therefore, most companies still use manual inspection methods for gear defect detection. Manual inspection methods not only take a long time but also has uneven detection quality. This paper proposes to use AI technology to build a cheap and fast gear defect detection method. And this method is used to complete the detection of gear tooth profile defects, tooth pitch defects and central hole defects. The method proposed in this paper is divided into four steps. In the first step, the ResNet model [1] is used to classify whether the gear image is complete or not. In the second step, the YOLOv4 model [2] is used to find the rectangular area of the tooth shape and tooth pitch in the image and cut it out. The third step is to use the UNet model [3] to segment the tooth profile and pitch profile, and calculate the area occupied by the profile. Finally, whether the difference from the average area is too large is used as the basis for judging whether the gear is defective. In the experiment result, 186 gear images are used for detection, and the obtained accuracy is about 91%. This result in addition to verifying the feasibility of the proposed method, it is also found that the proposed method can quickly and accurately detect gear defects that are difficult to judge by human eyes.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134138963","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}