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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the spectrum of skin diseases, based on their nature, poses significant barriers to health that affect individuals internationally. thus, basic procedures such as accurate and early diagnosis are paramount to successful treatment. this paper presents a hybrid model called Convolutional Neural Network and Random Forest (CNNRF) for accurately diagnosing five different types of skin diseases: Melanoma, Benign nevus, Basal cell carcinomas 5CC, Dermatofibroma, vascular lesions. Resizing, normalization techniques, and data augmentation were applied to preprocess the dataset, including annotated photos of skin lesions. the CNN-RF method is based on Convolutional Neural Networks (CNNs) feature extraction and classification provided by Random Forests (RFs). Based on the results of the experiments, it was established that the proposed CNN-RF model achieved greater classification accuracy of 94% compared to the CNN model with $\mathbf{9 1 \%}$ and the RF model with $\mathbf{8 8 \%}$. Moreover, for the same experiment, the CNN-RF model established an accuracy of 93 per cent, recall of 94 per cent, and $\mathbf{F 1}$ score of 94 per cent. the model’s performance was demonstrated and analyzed using a confusion matrix and classification reports. the accuracy and recall rates were very high for this type of skin lesion, specifically up to 95% accuracy and 96% recall for Dermatofibroma and Vascular Lesions. Nonetheless, this research asserts the reliability of the proposed CNN-RF model as a feasible instrument for diagnosing skin diseases automatically, as well as with superior precision and stability. thus, the findings hold significant implications for clinical practice alerts, as the model can assist dermatologists in providing timely identification to improve the quality of treatment for their clients. Future work will focus on expanding the dataset and exploring other types of compound models to increase the accuracy of the diagnosis. this work contributes to the advancement of

关键词： Radio frequency Analytical models Accuracy Dermatology Feature extraction Skin Convolutional neural networks Lesions thermal stability Diseases

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A Multi-objective Task Offloading Strategy for Workflow Applications in Mobile Edge-Cloud computing 11th

A Multi-objective Task Offloading Strategy for Workflow Appl...

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11th International symposium on Parallel architectures, Algorithms and Programming, PAAP 2020

作者： Gao, Yongqiang Yan, Dandan Inner Mongolia University Hohhot China

ISBN: (纸本)9789811600098

Prompted by the remarkable progress in both edge computing and cloud computing, mobile edge-cloud computing has become a promising computing paradigm, where mobile users may take advantages of the low-latency property of edge computing and the rich-resource capacity of cloud computing to provide a high quality of service for mobile applications. In mobile edge-cloud computing, a main challenge is how to efficiently offload workflow tasks in order to reduce energy consumption, and improve performance and reliability. In this paper, we formulate workflow offloading into a constrained multi-objective optimization problem and develop a hybrid algorithm involving differential evolution algorithm, artificial bee colony optimization and decoding heuristic to explore the optimal strategy of task offloading for workflow applications in mobile edge-cloud computing. the effectiveness of our strategy is evaluated by extensive simulations using real-world workflows. the results show that our strategy performs better than the state-of-the-art methods applied to similar problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Evaluating the Efficacy of CNN-RF Models in Gym Chest Exercise Detection

Evaluating the Efficacy of CNN-RF Models in Gym Chest Exerci...

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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Rajeev Kumar Chauhan Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Electronics and Communication Engineering Department IIMT College of Engineering Greater Noida Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the introduction of the demand for personalized fitness plans and the requisite accuracy for recording actions in training sessions, there is a growing need to implement thorough machine learning models. this research builds a hybrid system of the two pattern recognition algorithms, namely CNN-Random Forest (CNN-RF), to classify gym chest training motions. the model was trained and validated on complete images, which were 1,970. the dataset had individuals drawing attention to all types of training-related workout activities, such as push-ups and bench presses. the CNN-RF model used CNNs’ superior spatial feature extraction and R.F.’s powerful classification ability, allowing the model to identify and classify the plant species. Among the many features considered when evaluating a model, accuracy proved to be its most vital point, amounting to 94.32746%. Data analysis showed that the precision parameters of the surveillance system dropped from 93.73% to 98%, with a maximum accuracy of 98% achieved in Class 5 and the cable crossing area simultaneously. the recall metric demonstrated a low divergence, meaning the model needed some modification. It was revealed via an analysis of the confusion matrix that the model presented a percentage of accuracy quite at a very high level on the probable outcomes. On the contrary, sometimes the model is away from precise species classification, including Classes 3 and 4. the model’s stable performance with classes and instances is shown by macro and weighted average accuracy of 94% over 92% in each category. this indicates that the hybrid approach effectively manages the class imbalance problem.

关键词： Training Analytical models Accuracy Surveillance Training data Feature extraction Data models Real-time systems Recording Sports

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An Overlapping Local Community Detection Algorithm Based on Node Transitivity and Modularity Density 15th

An Overlapping Local Community Detection Algorithm Based on ...

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15th CCF Conference on computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Huang, Xintong Wu, Ling Guo, Kun College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

ISBN: (纸本)9789811625398

the local community detection (LCD) method can discover the local community structure in which the seed node is located. Compared with global community detection, local community detection is characterized by its low cost and high efficiency. However, most existing LCD methods only return a non-overlapping community. Individuals in the real world may participate in multiple communities, which can only be discovered by using overlapping local community detection methods. In this study, an overlapping local community detection algorithm based on modularity and node transitivity. First, the scope and structure information of the overlapping communities are obtained according to the node transitivity. Second, NMF is used to obtain the number of overlapping communities. Finally, the local modularity density based on edge weights is used to refine the detected local communities. the experimental results validate the high performance of our method to the other method in comparison. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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On the Use of Evolutionary Coupling for Software architecture Recovery

On the Use of Evolutionary Coupling for Software Architectur...

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Software Engineering symposium (UYMS), Turkish National

作者： Abdullah Saydemir Muhammed Esad Simitcioglu Hasan Sozer Ozyegin University Istanbul Turkey

ISBN: (纸本)9781665410717

Software architecture documentation can be partially obtained automatically by means of software architecture recovery tools. these tools mainly cluster software modules to provide a high level structural organization of these modules. they use dependency graphs as input. these graphs reflect various types of coupling among software modules. In this paper, we present an empirical evaluation of using evolutionary coupling as a complementary source of information for software architecture recovery. We use 3 open source projects as subject systems. We derive inter-module dependencies for these systems based on various levels of evolutionary coupling among their modules. We investigate the accuracy of software architecture recovery when input dependency graphs are extended with these additional dependencies. Results show that involving evolutionary coupling in the process can increase the accuracy of architecture recovery by up to 40%.

关键词： Couplings Software architecture computer architecture Organizations Documentation Software

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Video7 Extended architecture: Project Design and Statistical Analysis 19th

Video7 Extended Architecture: Project Design and Statistical...

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19th symposium on high-performance computing Systems (WSCAD)

作者： Sampaio, Vanderson S. de O. L. de Macedo, Douglas D. J. Britto, Andre Fed Univ Santa Catarina UFSC Florianopolis SC Brazil Fed Univ Sergipe UFS Sao Cristovao SE Brazil

ISBN: (纸本)9783030410506;9783030410490

the increasing number of both digital audio and video, brings up the necessity of appropriate tools for the storage and management of those kind of data. As options for the storage, there are nonrelational databases (NoSQL). the diversity of existing systems provokes the interest in proposing an architecture for the management of that content, in different types of databases. this work deepens the Video7 architecture for storing and retrieving streaming audio and video files stored in non-relational key-value, tabular, document databases. Based on the architecture and suggested project design, a tool was implemented that makes use of the Apache HBase, Apache Cassandra, Project Voldemort, Redis and MongoDB databases, and is subjected to stressful routines. the purpose of stress routines is to measure insertion and queries times, in addition to their transfer rates in response to requests to a media server. the kruskal-wallis test was used to validated the measurements. Redis database presents better performance in the submitted routines, while Project Voldemort and Apache Cassandra perform poorly than other databases.

关键词： Non-relational database performance analysis Streaming stored media high performance I/O file systems

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Adaptive Total Variation Constraint Hypergraph Regularized NMF and Its Application on Single-Cell RNA-Seq Data 15th

Adaptive Total Variation Constraint Hypergraph Regularized N...

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15th International Conference on Bio-Inspired computing: theories and Applications, BIC-TA 2020

作者： Zhu, Ya-Li Wu, Ming-Juan Wang, Chuan-Yuan Hu, Yue Liu, Jin-Xing School of Computer Science Qufu Normal University Rizhao China

ISBN: (纸本)9789811613531

Single-cell RNA sequencing (scRNA-seq) data provides a whole new view to study the development of disease and cell differentiation. As the explosive increment of scRNA-seq data, effective models are demanded to mining the intrinsic biological information. In this paper, we propose a novel nonnegative matrix factorization (NMF) method for clustering and gene co-expression network analysis, termed Adaptive Total Variation Constraint Hypergraph Regularized NMF (ATV-HNMF). Based on the gradient information, ATV-HNMF can adaptively select the different schemes to denoise in the cluster or preserve the cluster boundary information between clusters. Besides, ATV-HNMF incorporates hypergraph regularization which can consider high-order relationships between cells, which is helpful to reserve the intrinsic structure of the original data space. Experiments show that the performance on clustering outperforms other compared methods. And the related genes mined by co-expression network construction are consistent with the previous research, which illustrates that our model is effective and useful. © 2021, Springer Nature Singapore Pte Ltd.

关键词： RNA

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Neocortex and Bridges-2: A high performance AI+HPC Ecosystem for Science, Discovery, and Societal Good 7th

Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem...

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7th Latin American high performance computing Conference, CARLA 2020

作者： Buitrago, Paola A. Nystrom, Nicholas A. Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783030680343

Artificial intelligence (AI) is transforming research through analysis of massive datasets and accelerating simulations by factors of up to a billion. Such acceleration eclipses the speedups that were made possible though improvements in CPU process and design and other kinds of algorithmic advances. It sets the stage for a new era of discovery in which previously intractable challenges will become surmountable, with applications in fields such as discovering the causes of cancer and rare diseases, developing effective, affordable drugs, improving food sustainability, developing detailed understanding of environmental factors to support protection of biodiversity, and developing alternative energy sources as a step toward reversing climate change. To succeed, the research community requires a high-performance computational ecosystem that seamlessly and efficiently brings together scalable AI, general-purpose computing, and large-scale data management. the authors, at the Pittsburgh Supercomputing Center (PSC), launched a second-generation computational ecosystem to enable AI-enabled research, bringing together carefully designed systems and groundbreaking technologies to provide at no cost a uniquely capable platform to the research community. It consists of two major systems: Neocortex and Bridges-2. Neocortex embodies a revolutionary processor architecture to vastly shorten the time required for deep learning training, foster greater integration of artificial deep learning with scientific workflows, and accelerate graph analytics. Bridges-2 integrates additional scalable AI, high-performance computing (HPC), and high-performance parallel file systems for simulation, data pre- and post-processing, visualization, and Big Data as a Service. Neocortex and Bridges-2 are integrated to form a tightly coupled and highly flexible ecosystem for AI- and data-driven research. © 2021, Springer Nature Switzerland AG.

关键词： Big data

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A Study of APIs for Graph Analytics Workloads 16

A Study of APIs for Graph Analytics Workloads

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16th IEEE International symposium on Workload Characterization (IISWC)

作者： Lee, Hochan Wong, David Hoang, Loc Dathathri, Roshan Gill, Gurbinder Jatala, Vishwesh Kuck, David Pingali, Keshav Univ Texas Austin Austin TX 78712 USA Intel Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781728176451

Traditionally, parallel graph analytics workloads have been implemented in systems like Pregel, GraphLab, Galois, and Ligra that support graph data structures and graph operations directly. An alternative approach is to express graph workloads in terms of sparse matrix kernels such as sparse matrix-vector and matrix-matrix multiplication. An API for these kernels has been defined by the GraphBLAS project. the SuiteSparse project has implemented this API on shared-memory platforms, and the LAGraph project is building a library of graph algorithms using this API. How does the matrix-based approach perform compared to the graph-based approach? Our experiments on a 56 core CPU show that for representative graph workloads, LAGraph/SuiteSparse solutions are 5x slower on the average than Galois solutions. We argue that this performance gap arises from inherent limitations of a matrix-based API: regardless of which architecture a matrixbased algorithm is run on, it is subject to the same inherent limitations of the matrix-based API.

关键词： graph analytics performance study Graph-BLAS high performance computing

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ADeLe: A description language for approximate hardware

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FUTURE GENERATION computer SYSTEMS-thE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 245-258页

作者： Felzmann, Isaias B. Susin, Matheus M. Duenha, Liana Azevedo, Rodolfo Wanner, Lucas F. Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Fed Mato Grosso do Sul Fac Comp FACOM Campo Grande MS Brazil

Approximate computing techniques enable significant improvements in energy efficiency by producing potentially incorrect outputs for a small subset of inputs of a given application. Approximations introduced at the hardware level, in particular, may be applicable in multiple scenarios and offer high power savings. Integrating and evaluating approximate hardware designs within an architecture remains, however, a challenging problem. this paper presents ADeLe, a modeling language designed to facilitate the description, configuration, and integration of approximate hardware units into processors. the ADeLe framework offers a reduced-effort design flow by modeling approximations at a high level of abstraction for automatic injection into a processor model for architectural simulation. Approximations in ADeLe are implemented as snippets of code that affect instructions in the target architecture by replacing or augmenting their functional behavior, according to user-defined policies. ADeLe supports both stochastic and deterministic approximation models. the software under test has full control of the simulation by allowing or avoiding that approximations happen at any given time in executions. Energy is automatically accounted for based on tailorable models that consider the potential instantaneous power savings offered by the approximations activated in the system. ADeLe was embedded in a generic and flexible verification framework that allows for easy evaluation of the energy-quality trade-off of designs in applications. We demonstrate both the language capabilities and its framework by representing two usage scenarios, in which we select approximation techniques from related literature and inject them into various applications. First, we show how ADeLe changes the behavior of specific instructions with 6 alternate designs for 2 functional units. Next, we employ an approximation technique that affects memory and show how it explores an energy-quality trade-off in the e

关键词： Approximate computing Energy efficiency Architectural simulation Modeling language

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