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Business Model Canvas Should Pay More Attention to the software Startup Team

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Business Model Canvas Should Pay More Attention to the Softw...

Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Kai-Kristian Kemell Atte Elonen Mari Suoranta Anh Nguyen-Duc Juan Garbajosa Rafael Chanin Jorge Melegati Usman Rafiq Abdullah Aldaeej Nana Assyne Afonso Sales Sami Hyrynsalmi Juhani Risku Henry Edison Pekka Abrahamsson Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyvaskyla Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team- related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups.

关键词： Business software Interviews Tools Task analysis software engineering Ontologies

11,700 km Transmission at 4.8 bit/4D-sym via four-dimensional geometrically-shaped polarization-ring-switching modulation

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arXiv 2019年

作者： Heide, Sjoerd Vander Chen, Bin Hout, Menno Vanden Liga, Gabriele Koonen, Ton Hafermann, Hartmut Alvarado, Alex Okonkwo, Chigo Department of Electrical Engineering Eindhoven University of Technology Netherlands Mathematical and Algorithmic Sciences Lab Paris Research Center Huawei Technologies France SASU France School of Computer Science and Information Engineering Hefei University of Technology China

Using a novel geometrically-shaped four-dimensional modulation format, we transmitted 11×200 Gbit/s DWDM at 4.8 bit/4D-sym over 7,925 km and 11,700 km using EDFA-only and hybrid amplification, respectively. A rea... 详细信息

关键词： Modulation

GRIP： A Group Recommender Based on Interactive Preference Model

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Journal of computer Science & technology 2018年第5期33卷 1039-1055页

作者： Bo-Han Li An-Man Zhangi Wei Zheng Shuo Wani Xiao-Lin Qin Xue Li Hai-Lian Yin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210016 China Jiangsu Easymap Geographic Information Technology Corp. Ltd. Yangzhou 225000 China School of Information Technology and Electrical Engineering the University of Queensland Brisbane QLD 4072 Australia Unmanned Aerial Vehicle Research Institute Nanjing University of Aeronautics and Astronautics Nanjing 211106 China

Numerous applications of recommender systems can provide us a tool to understand users. A group recommender reflects the analysis of multiple users＇ behavior, and aims to provide each user of the group with the things they involve according to users＇ preferences. Currently, most of the existing group recommenders ignore the interaction among the users. However, in the course of group activities, the interactive preferences will dramatically affect the success of recommenders. The problem becomes even more challenging when some unknown preferences of users are partly influenced by other users in the group. An interaction-based method named GRIP （Group Recommender Based on Interactive Preference） is presented which can use group activity history information and recommender post-rating feedback mechanism to generate interactive preference parameters. To evaluate the performance of the proposed method, it is compared with traditional collaborative filtering on the MovieLens dataset. The results indicate the superiority of the GRIP recommender for multi-users regarding both validity and accuracy.

关键词： attribute space continuity collaborative filtering group recommender interactive preference

Deep Bilateral Retinex for Low-Light Image Enhancement

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arXiv 2020年

作者： Liang, Jinxiu Xu, Yong Quan, Yuhui Wang, Jingwen Ling, Haibin Ji, Hui School of Computer Science and Engineering South China University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information China Tencent AI Lab Shenzhen China Department of Computer Science Stony Brook University Strony BrookNY United States Department of Mathematics National University of Singapore Singapore

Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low-light image enhancement is about improving the visibility of low-light images. As the measurement noise in low-light images is usually significant yet complex with spatially-varying characteristic, how to handle the noise effectively is an important yet challenging problem in low-light image enhancement. Based on the Retinex decomposition of natural images, this paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The basic idea is to train a neural network to generate a set of pixel-wise operators for simultaneously predicting the noise and the illumination layer, where the operators are defined in the bilateral space. Such an integrated approach allows us to have an accurate prediction of the reflectance layer in the presence of significant spatially-varying measurement noise. Extensive experiments on several benchmark datasets have shown that the proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

Experimental demonstration of eight-dimensional modulation formats for long-haul optical transmission

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arXiv 2019年

作者： Van Der Heide, Sjoerd Chen, Bin Van Den Hout, Menno Hafermann, Hartmut Koonen, Ton Alvarado, Alex Okonkwo, Chigo Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Computer Science and Information Engineering Hefei University of Technology China Mathematical and Algorithmic Sciences Lab Paris Research centre Huawei Technologies France SASU France

Two novel 5.5 bit/4D modulation formats are experimentally demonstrated to transmit over 9680 km of SSMF. A reach increase of 29.4 % over the 6 bit/4D PM-8QAM is shown. Furthermore, increased nonlinear tolerance with ... 详细信息

关键词： Quadrature amplitude modulation

Towards fair and decentralized privacy-preserving deep learning with blockchain

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arXiv 2019年

作者： Lyu, Lingjuan Yu, Jiangshan Nandakumar, Karthik Li, Yitong Ma, Xingjun Jin, Jiong Research School of Computer Science Australian National University ACT2601 Australia Faculty of Information Technology Monash University ClaytonMO Australia IBM Singapore Lab 018983 Singapore School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

In collaborative deep learning, current learning frameworks follow either a centralized architecture or a distributed architecture. Whilst centralized architecture deploys a central server to train a global model over the massive amount of joint data from all parties, distributed architecture aggregates parameter updates from participating parties' local model training, via a parameter server. These two server-based architectures present security and robustness vulnerabilities such as singlepoint-of-failure, single-point-of-breach, privacy leakage, and lack of fairness. To address these problems, we design, implement, and evaluate a purely decentralized privacy-preserving deep learning framework, called DPPDL. DPPDL makes the first investigation on the research problem of fairness in collaborative deep learning, and simultaneously provides fairness and privacy by proposing two novel algorithms: initial benchmarking and privacy-preserving collaborative deep learning. During initial benchmarking, each party trains a local Differentially Private Generative Adversarial Network (DPGAN) and publishes the generated privacy-preserving artificial samples for other parties to label, based on the quality of which to initialize local credibility list for other parties. The local credibility list reflects how much one party contributes to another party, and it is used and updated during collaborative learning to ensure fairness. To protect gradients transaction during privacy-preserving collaborative deep learning, we further put forward a three-layer onionstyle encryption scheme. We experimentally demonstrate, on benchmark image datasets, that accuracy, privacy and fairness in collaborative deep learning can be effectively addressed at the same time by our proposed DPPDL framework. Moreover, DPPDL provides a viable solution to detect and isolate the cheating party in the system. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

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arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

Diffusive topological transport in spatiotemporal thermal lattices

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arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

Risk Management in Agile software Development: A Survey

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Risk Management in Agile Software Development: A Survey

Frontiers of information technology (FIT)

作者： Muhammad Hammad Irum Inayat Maryam Zahid Software Engineering and Automation Lab (SEAL) School of Computing National University of Computer and Emerging Sciences (NUCES) Islamabad Pakistan Capital University of Science and Technology (CUST) Islamabad Pakistan

ISBN: (数字)9781728166254

ISBN: (纸本)9781728166261

In agile software development, some of the formal project management activities are neglected due to heavy documentation and planning. Agile methodologies usually focus on rapid development over hefty documentation which is contrary to the nature of the risk management process. Literature studies show agile methods are used for the development safety critical systems as well. For that ignoring risk management process is inevitable. This intrigued us to explore more on the risk management practices followed in agile methods following industry. To further the investigations on industrial practices regarding risk management, in this paper we explore on (i) the risks faced by the agile practitioners, and (ii) the mitigation strategies used. For this, we conducted an online survey with 54 agile practitioners. The results showed that the project deadlines and varying requirements are the two most commonly faced risks by the practitioners. Furthermore, the results also revealed that risk management strategies are being followed to some extent, but usually in a non-systematic way.

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