Xu Li is a Chinese computer scientist and co-founder and current CEO of SenseTime, an artificial intelligence (AI) company. Xu has led SenseTime since the company's incorporation and helped it independently develop its proprietary deep learning platform. == Education and research == Xu obtained both his bachelor's and master's degrees in computer science from Shanghai Jiao Tong University. He received his doctorate in computer science from the Chinese University of Hong Kong. Xu has published more than 50 papers at international conferences and in journals in the field of computer vision and won the Best Paper Award at the international conference on Non-Photorealistic Rendering and Animation (NPAR) 2012 and the Best Reviewer Award at the international conferences Asian Conference on Computer Vision ACCV 2012 and International Conference on Computer Vision (ICCV) 2015. He has three algorithms that have been included into the visual open-source platform OpenCV, and his "L0 Smoothing" algorithm garnered the most citations in research papers over a span of five years (2011–2015) within the ACM Transactions on Graphics (TOG), a scientific journal that Thomson Reuters InCites has placed first among software engineering journals. == Career == Previously, Xu worked at Lenovo Corporate Research & Development. He was also a visiting researcher at Motorola China R&D Institute, Omron Research Institute, and Microsoft Research. == Selected publications == Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Li Xu, Jiahao Pang, Qiong Yan, Yu-wing Tai, "Accurate Single Stage Detector Using Recurrent Rolling Convolution", (CVPR), 2017. Jimmy SJ. Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan, "Look, Listen and Learn – A Multimodal LSTM for Speaker Identification", The 30th AAAI Conference on Artificial Intelligence (AAAI), 2016 Jimmy SJ. Ren, Li Xu, Qiong Yan, Wenxiu Sun, "Shepard Convolutional Neural Networks" Advances in Neural Information Processing Systems (NIPS), 2015. Xiaoyong Shen, Chao Zhou, Li Xu, Jiaya Jia, "Mutual-Structure for Joint Filtering" International Conference on Computer Vision (ICCV), (oral presentation), 2015. Jianping Shi, Qiong Yan, Li Xu, Jiaya Jia, "Hierarchical Image Saliency Detection on Extended CSSD" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jianping Shi, Xin Tao, Li Xu, Jiaya Jia, "Break Ames Room Illusion: Depth from General Single Images" ACM Transactions on Graphics (TOG), (Proc. ACM SIGGRAPH ASIA2015). Yongtao Hu, Jimmy SJ. Ren, Jingwen Dai, Chang Yuan, Li Xu, Wenping Wang, "Deep Multimodal Speaker Naming" ACM International Conference on Multimedia (MM), 2015. Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia "Deep Edge-Aware Filters" International Conference on Machine Learning (ICML), 2015. Jianping Shi, Li Xu, Jiaya Jia "Just Noticeable Defocus Blur Detection and Estimation" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu "Handling Motion Blur in Multi-Frame Super-Resolution" IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Xiaoyong Shen, Qiong Yan, Li Xu, Lizhuang Ma, Jiaya Jia"Multispectral Joint Image Restoration via Optimizing a Scale Map" IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015. Jimmy SJ. Ren, Li Xu, "On Vectorization of Deep Convolutional Neural Networks for Vision Tasks" AAAI Conference on Artificial Intelligence (AAAI), 2015. == Awards and honors == Xu was ranked 7th in Fortune magazine's 2018 edition of its 40 Under 40. He was also named "China's Outstanding AI Industry Leader" by The Economic Observer, received the "Innovative Business Leader" Award under NetEase's "Future Technology Talent Awards", and was honored as Sina's "2017 Top Ten Economic Figures". In 2018, Xu was named EY's "Entrepreneur of the Year China" in the Technology category.
Textual case-based reasoning
Textual case-based reasoning (TCBR) is a subtopic of case-based reasoning, in short CBR, a popular area in artificial intelligence. CBR suggests the ways to use past experiences to solve future similar problems, requiring that past experiences be structured in a form similar to attribute-value pairs. This leads to the investigation of textual descriptions for knowledge exploration whose output will be, in turn, used to solve similar problems. == Subareas == Textual case-base reasoning research has focused on: measuring similarity between textual cases mapping texts into structured case representations adapting textual cases for reuse automatically generating representations.
Software requirements
Software requirements for a system are the description of what the system should do, the service or services that it provides and the constraints on its operation. The IEEE Standard Glossary of Software Engineering Terminology defines a requirement as: A condition or capability needed by a user to solve a problem or achieve an objective A condition or capability that must be met or possessed by a system or system component to satisfy a contract, standard, specification, or other formally imposed document A documented representation of a condition or capability as in 1 or 2 The activities related to working with software requirements can broadly be broken down into elicitation, analysis, specification, and management. Note that the wording Software requirements is additionally used in software release notes to explain, which depending on software packages are required for a certain software to be built/installed/used. == Elicitation == Elicitation is the gathering and discovery of requirements from stakeholders and other sources. A variety of techniques can be used such as joint application design (JAD) sessions, interviews, document analysis, focus groups, etc. Elicitation is the first step of requirements development. == Analysis == Analysis is the logical breakdown that proceeds from elicitation. Analysis involves reaching a richer and more precise understanding of each requirement and representing sets of requirements in multiple, complementary ways. Requirements Triage or prioritization of requirements is another activity which often follows analysis. This relates to Agile software development in the planning phase, e.g. by Planning poker, however it might not be the same depending on the context and nature of the project and requirements or product/service that is being built. == Specification == Specification involves representing and storing the collected requirements knowledge in a persistent and well-organized fashion that facilitates effective communication and change management. Use cases, user stories, functional requirements, and visual analysis models are popular choices for requirements specification. == Validation == Validation involves techniques to confirm that the correct set of requirements has been specified to build a solution that satisfies the project's business objectives, and to detect and correct errors in the requirements before implementation. == Management == Requirements change during projects and there are often many of them. Management of this change becomes paramount to ensuring that the correct software is built for the stakeholders. == Tool support for Requirements Engineering == === Tools for Requirements Elicitation, Analysis and Validation === Taking into account that these activities may involve some artifacts such as observation reports (user observation), questionnaires (interviews, surveys and polls), use cases, user stories; activities such as requirement workshops (charrettes), brainstorming, mind mapping, role-playing; and even, prototyping; software products providing some or all of these capabilities can be used to help achieve these tasks. There is at least one author who advocates, explicitly, for mind mapping tools such as FreeMind; and, alternatively, for the use of specification by example tools such as Concordion. Additionally, the ideas and statements resulting from these activities may be gathered and organized with wikis and other collaboration tools such as Trello. The features actually implemented and standards compliance vary from product to product. === Tools for Requirements Specification === A Software requirements specification (SRS) document might be created using general-purpose software like a word processor or one of several specialized tools. Some of these tools can import, edit, export and publish SRS documents. It may help to make SRS documents while following a standardised structure and methodology, such as ISO/IEC/IEEE 29148:2018. Likewise, software may or not use some standard to import or export requirements (such as ReqIF) or not allow these exchanges at all. === Tools for Requirements Document Verification === Tools of this kind verify if there are any errors in a requirements document according to some expected structure or standard. === Tools for Requirements Comparison === Tools of this kind compare two requirement sets according to some expected document structure and standard. === Tools for Requirements Merge and Update === Tools of this kind allow the merging and update of requirement documents. === Tools for Requirements Traceability === Tools of this kind allow tracing requirements to other artifacts such as models and source code (forward traceability) or, to previous ones such as business rules and constraints (backwards traceability). === Tools for Model-Based Software or Systems Requirement Engineering === Model-based systems engineering (MBSE) is the formalised application of modelling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases. It is also possible to take a model-based approach for some stages of the requirements engineering and, a more traditional one, for others. Very many combinations might be possible. The level of formality and complexity depends on the underlying methodology involved (for instance, i is much more formal than SysML and, even more formal than UML) === Tools for general Requirements Engineering === Tools in this category may provide some mix of the capabilities mentioned previously and others such as requirement configuration management and collaboration. The features actually implemented and standards compliance vary from product to product. There are even more capable or general tools that support other stages and activities. They are classified as ALM tools.
GCube system
gCube is an open source software system specifically designed and developed to enact the building and operation of a Data Infrastructure providing their users with a rich array of services suitable for supporting the co-creation of Virtual Research Environments and promoting the implementation of open science workflows and practices. It is at the heart of the D4Science Data Infrastructure. == Overview == It is primarily organised in a number of web service called to offer functionality supporting the phases of knowledge production and sharing. In addition, it consists of a set of software libraries supporting service development, service-to-service integration, and service capabilities extension, and a set of portlets dedicated to realise user interface constituents facilitating the exploitation of one or more services. It is designed and conceived to enact system of systems. In fact, its gCube services rely on standards and mediators to interact with other services as well as are made available by standard and APIs to make it possible for clients to use them. For instance, the DataMiner service implements the Web Processing Service protocol to facilitate clients to execute processes. The set of components dealing with Identity and Access Management rely on Keycloak and federates other IDMs thus making the overall Authentication and the Authorization management compliant with open standards such as OAuth2, User-Managed Access (UMA), and OpenID Connect (OIDC)protocols. The Catalogue relies on DCAT, OAI-PMH, and Catalogue Service for the Web to collect contents from other catalogues and data sources and offers its content by DCAT, OAI-PMH, and a proprietary REST API (gCat REST API). Its Continuous Integration/Continuous Delivery pipeline implemented by Jenkins represents an innovative approach to software delivering conceived to be scalable and easy to maintain and upgrade at a minimal cost. == History == gCube has been developed in the context of the D4Science initiative with the support of several EU projects.
Douglas Parkhill
Douglas F. Parkhill is a Canadian technologist and former research minister, best known for his pioneering work on what is now called cloud computing, and his work on Canada's Telidon videotex project. He started working at the Canadian ministry of Communications (now part of the Department of Trade and Industry) in 1969, having previously worked at the Mitre Corporation. He was responsible for many activities in communications satellites, computer communications, command and control systems and telecommunications. He was winner of the Treasury Board of Canada Secretariat's Outstanding Achievement award in 1982, the Conestoga shield for services to government and industry in computer communications research and development, the Touche Ross award for Telidon development. He was an author of several publications including the 1966 book, The Challenge of the Computer Utility. In the book, Parkhill thoroughly explored many of the modern-day characteristics of cloud computing (elastic provisioning through a utility service) as well as the comparison to the electricity industry and the use of public, private, government and community forms. The book won the McKinsey Foundation award for distinguished contributions to management literature. He worked with Dave Godfrey, the Canadian writer and novelist on a later book Gutenberg two about the social and political meaning of computer technology. He was in charge of research at the Federal Department of Communications at the time when the department was funding development of the Telidon videotext system, was heavily involved in promoting the system, and had overall control of the program. In a radio broadcast in 1980, he outlined some of the potential of the system, from financial information, to theatre reservations, with the ability to pay and print out tickets from the system. He later documented the history of the Telidon project, and the history of videotext in general. == Publications == The Challenge of the Computer Utility, Addison-Wesley, 1966, ISBN 0-201-05720-4 edited with Dave Godfrey, Gutenberg Two: The New Electronics and Social Change, Press Porcepic, 1979, ISBN 0-88878-191-1 The Beginning of a Beginning. Ottawa; Department of Communications, 1987. A history of the Telidon project.
Hyperparameter optimization
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. The objective function takes a set of hyperparameters and returns the associated loss. Cross-validation is often used to estimate this generalization performance, and therefore choose the set of values for hyperparameters that maximize it. == Approaches == === Grid search === The traditional method for hyperparameter optimization has been grid search, or a parameter sweep, which is simply an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm. A grid search algorithm must be guided by some performance metric, typically measured by cross-validation on the training set or evaluation on a hold-out validation set. Since the parameter space of a machine learner may include real-valued or unbounded value spaces for certain parameters, manually set bounds and discretization may be necessary before applying grid search. For example, a typical soft-margin SVM classifier equipped with an RBF kernel has at least two hyperparameters that need to be tuned for good performance on unseen data: a regularization constant C and a kernel hyperparameter γ. Both parameters are continuous, so to perform grid search, one selects a finite set of "reasonable" values for each, say C ∈ { 10 , 100 , 1000 } {\displaystyle C\in \{10,100,1000\}} γ ∈ { 0.1 , 0.2 , 0.5 , 1.0 } {\displaystyle \gamma \in \{0.1,0.2,0.5,1.0\}} Grid search then trains an SVM with each pair (C, γ) in the Cartesian product of these two sets and evaluates their performance on a held-out validation set (or by internal cross-validation on the training set, in which case multiple SVMs are trained per pair). Finally, the grid search algorithm outputs the settings that achieved the highest score in the validation procedure. Grid search suffers from the curse of dimensionality, but is often embarrassingly parallel because the hyperparameter settings it evaluates are typically independent of each other. === Random search === Random Search replaces the exhaustive enumeration of all combinations by selecting them randomly. This can be simply applied to the discrete setting described above, but also generalizes to continuous and mixed spaces. A benefit over grid search is that random search can explore many more values than grid search could for continuous hyperparameters. It can outperform Grid search, especially when only a small number of hyperparameters affects the final performance of the machine learning algorithm. In this case, the optimization problem is said to have a low intrinsic dimensionality. Random Search is also embarrassingly parallel, and additionally allows the inclusion of prior knowledge by specifying the distribution from which to sample. Despite its simplicity, random search remains one of the important base-lines against which to compare the performance of new hyperparameter optimization methods. === Bayesian optimization === Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian optimization builds a probabilistic model of the function mapping from hyperparameter values to the objective evaluated on a validation set. By iteratively evaluating a promising hyperparameter configuration based on the current model, and then updating it, Bayesian optimization aims to gather observations revealing as much information as possible about this function and, in particular, the location of the optimum. It tries to balance exploration (hyperparameters for which the outcome is most uncertain) and exploitation (hyperparameters expected close to the optimum). In practice, Bayesian optimization has been shown to obtain better results in fewer evaluations compared to grid search and random search, due to the ability to reason about the quality of experiments before they are run. === Gradient-based optimization === For specific learning algorithms, it is possible to compute the gradient with respect to hyperparameters and then optimize the hyperparameters using gradient descent. The first usage of these techniques was focused on neural networks. Since then, these methods have been extended to other models such as support vector machines or logistic regression. A different approach in order to obtain a gradient with respect to hyperparameters consists in differentiating the steps of an iterative optimization algorithm using automatic differentiation. A more recent work along this direction uses the implicit function theorem to calculate hypergradients and proposes a stable approximation of the inverse Hessian. The method scales to millions of hyperparameters and requires constant memory. In a different approach, a hypernetwork is trained to approximate the best response function. One of the advantages of this method is that it can handle discrete hyperparameters as well. Self-tuning networks offer a memory efficient version of this approach by choosing a compact representation for the hypernetwork. More recently, Δ-STN has improved this method further by a slight reparameterization of the hypernetwork which speeds up training. Δ-STN also yields a better approximation of the best-response Jacobian by linearizing the network in the weights, hence removing unnecessary nonlinear effects of large changes in the weights. Apart from hypernetwork approaches, gradient-based methods can be used to optimize discrete hyperparameters also by adopting a continuous relaxation of the parameters. Such methods have been extensively used for the optimization of architecture hyperparameters in neural architecture search. === Evolutionary optimization === Evolutionary optimization is a methodology for the global optimization of noisy black-box functions. In hyperparameter optimization, evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm. Evolutionary hyperparameter optimization follows a process inspired by the biological concept of evolution: Create an initial population of random solutions (i.e., randomly generate tuples of hyperparameters, typically 100+) Evaluate the hyperparameter tuples and acquire their fitness function (e.g., 10-fold cross-validation accuracy of the machine learning algorithm with those hyperparameters) Rank the hyperparameter tuples by their relative fitness Replace the worst-performing hyperparameter tuples with new ones generated via crossover and mutation Repeat steps 2-4 until satisfactory algorithm performance is reached or is no longer improving. Evolutionary optimization has been used in hyperparameter optimization for statistical machine learning algorithms, automated machine learning, typical neural network and deep neural network architecture search, as well as training of the weights in deep neural networks. === Population-based === Population Based Training (PBT) learns both hyperparameter values and network weights. Multiple learning processes operate independently, using different hyperparameters. As with evolutionary methods, poorly performing models are iteratively replaced with models that adopt modified hyperparameter values and weights based on the better performers. This replacement model warm starting is the primary differentiator between PBT and other evolutionary methods. PBT thus allows the hyperparameters to evolve and eliminates the need for manual hypertuning. The process makes no assumptions regarding model architecture, loss functions or training procedures. PBT and its variants are adaptive methods: they update hyperparameters during the training of the models. On the contrary, non-adaptive methods have the sub-optimal strategy to assign a constant set of hyperparameters for the whole training. === Early stopping-based === A class of early stopping-based hyperparameter optimization algorithms is purpose-built for large search spaces of continuous and discrete hyperparameters, particularly when the computational cost to evaluate the performance of a set of hyperparameters is high. Irace implements the iterated racing algorithm, that focuses the search around the most promising configurations, using statistical tests to discard the ones that perform poorly. Another early stopping hyperparameter optimization algorithm is successive halving (SHA), which begins as a random search but periodically prunes low-performing models, thereby focusing computational resources on more promising models. Asynchronous successive halving (ASHA) further improves upon SHA's resource utilization profile by removing the need to synchronously evaluate a
Tradeshift
Tradeshift is a cloud based business network and platform for purchase-to-pay automation, supply chain payments, marketplaces, virtual cards and supply chain financing. Its 2018 round of funding, led by Goldman Sachs, raised US$250 million at a valuation of $1.1 billion, giving the company unicorn status. Tradeshift is headquartered in San Francisco, California and has offices in London, Copenhagen, Bucharest and Kuala Lumpur. Tradeshift has reprocessed over $1 trillion USD through transactions on its network. == History == Tradeshift was founded in 2010 by Christian Lanng, Mikkel Hippe Brun, and Gert Sylvest. Inspiration for Tradeshift came after they created the world's first large scale peer-to-peer infrastructure for an e-business called NemHandel. The founders also had leading roles (Governing board member, Technical Director) in the European Commission project PEPPOL inside the European Union. In 2010, the Tradeshift platform launched in May in Copenhagen. Tradeshift won the European Startup Awards in the category of "Best Business or Enterprise Startup." In 2011, Tradeshift made its app marketplace available. In 2012, Tradeshift moved their headquarters from Copenhagen to San Francisco. In 2013, Tradeshift opened an R&D center in Suzhou, China. Tradeshift opened an additional office in London. And LATAM e-invoicing capabilities were added through partnership with Invoiceware. In 2014, Tradeshift expanded with offices in Tokyo, Paris, and Munich. The EU Commission officially approved the Universal Business Language (UBL) data format – a format Tradeshift supports – as eligible for referencing in tenders from public administrations. In 2015, Tradeshift won the Circulars "Digital Disruptor" Award at the WEF conference in Davos, Switzerland. Tradeshift also acquired product information management company Merchantry, and launched e-procurement and supplier risk management solutions. In 2016, Tradeshift acquired Hyper Travel and secured a $75 million series-D round funding. In 2017, Tradeshift acquired IBX Business Network and launches Tradeshift Ada. In 2018, Tradeshift secured a $250 million series-E round funding. and launched Blockchain Payments, the latter as part of Tradeshift Pay. In December 2018 Tradeshift acquired Babelway, an online B2B integration platform. The acquisition added three new office locations to Tradeshift (Salt Lake City, Louvain-la-neuve, Belgium, Cairo Egypt). In Q3 2018, Tradeshift reported year-over-year revenue growth of 400%, new bookings growth of 284%, and gross merchandise volume (GMV) growth of 262%. New total contract value also grew by US$47 million. Additionally, it added 27 new customers including Hertz, Shiseido, ECU and multiple Fortune 500 companies. In July 2023, HSBC and Tradeshift announced an agreement to launch a new, jointly owned business focused on the development of embedded finance solutions and financial services apps. As part of the agreement, HSBC made a $35 million investment into Tradeshift and joined its board. The agreement was part of a funding round which is expected to raise a minimum of $70 million from HSBC and other investors. The new joint venture will allow HSBC and Tradeshift to deploy a range of digital solutions across Tradeshift and other platforms. This includes payment and fintech services embedded into trade, e-commerce and marketplace experiences. In September 2023, CEO Lanng was fired for "gross misconduct on multiple grounds," including "allegations of sexual assault and harassment." Tradeshift was alleged to have fired his accuser after she complained to the company's human resources department, its co-founders and members of its board of directors about his abuse. == Financials == The company's valuation as of May 2018 was $1.1 billion. Tradeshift is now considered a unicorn, and, according to Bloomberg, will not need any further funding. Jan 14, 2020, Tradeshift announced that they had raised $240 million in Series F finance. == Acquisitions == In 2015, Tradeshift acquired product information management company Merchantry. Merchantry is a retail product information management (PIM) software for multi-vendor ecommerce retailers. In 2016, Tradeshift acquired Hyper Travel. Hyper Travel is a travel management service that allows customers to access travel agents via its native messaging apps, SMS, and email. In 2017, Tradeshift acquired IBX Group. In 2018, Tradeshift acquired Babelway, an online B2B integration platform.