SlideRocket was an online presentation platform that let users create, manage, share and measure presentations. SlideRocket was provided via a SaaS model. The company was acquired by VMware in April 2011, who sold it to ClearSlide, a similar SaaS application, in March 2013. It is no longer offering independent signups, as the platform is being integrated into ClearSlide. == History == SlideRocket was founded in Jan 2006, and launched as a private beta in March 2008 at the Under The Radar Spring event. A public beta was announced in September 2008 followed shortly by public release on October 28, 2008. SlideRocket is most commonly credited with inventing the PResuMÉ or Presentation Résumé in early 2009. On April 26, 2011, SlideRocket was acquired by VMware. On March 5, 2013, VMware sold SlideRocket to ClearSlide. SlideRocket is based in San Francisco.
Commission on Enhancing National Cybersecurity
The President's Commission on Enhancing National Cybersecurity is a Presidential Commission formed on April 13, 2016, to develop a plan for protecting cyberspace, and America's economic reliance on it. The commission released its final report in December 2016. The report made recommendations regarding the intertwining roles of the military, government administration and the private sector in providing cyber security. Chairman Donilon said of the report that its coverage "is unusual in the breadth of issues" with which it deals. == Recommendations == The report made sixteen major recommendations with fifty-three specific action items broadly grouped under six areas: Protecting the information and digital infrastructure Investing in the secure growth of information and digital infrastructure Consumer information access Building the cybersecurity workforce Building a secure governmental cybersecurity framework Keeping interconnectivity open, fair, competitive, and secure The Commission found that strong authentication systems were mandatory for adequate cybersecurity, not just for the government, but for all commercial systems, and private individuals. The commission also stressed remote identity proofing and security for the Internet of things (IoT). Finding that technicians who know cybersecurity and can protect systems are few and in short supply, the commission recommended nationally supported training programs to produce an adequate workforce, as well as increasing the level of expertise in the existing workforce. The Commission highlighted the importance of partnerships between government and the private sector as a powerful tool for encouraging the technology, policies and practices we need to secure and grow the digital economy. (page 2) Some criticised the commission's work as lacking an understanding of cybersecurity and not being cognizant of "cyber reality" and the cost of some of the action items, but others found the report constructive and meaningful. == Commission members == The initial members of the Commission are: Tom Donilon, former Assistant to the President and National Security Advisor (Chair) Sam Palmisano, former CEO of IBM (Vice Chair) General Keith Alexander, CEO of IronNet Cybersecurity, former Director of the National Security Agency and former Commander of U.S. Cyber Command Annie Antón, Professor and Chair of the School of Interactive Computing at Georgia Tech. Ajay Banga, President and CEO of MasterCard Steven Chabinsky, General Counsel and Chief Risk Officer of CrowdStrike Patrick Gallagher, Chancellor of the University of Pittsburgh and former Director of the National Institute of Standards and Technology Peter Lee, Corporate Vice President, Microsoft Research Herbert Lin, Senior Research Scholar for Cyber Policy and Security at the Stanford Center for International Security and Cooperation and Research Fellow at the Hoover Institution Heather Murren, former member of the Financial Crisis Inquiry Commission and co-founder of the Nevada Cancer Institute Joe Sullivan, Chief Security Officer of Uber and former Chief Security Officer of Facebook Maggie Wilderotter, Executive Chairman of Frontier Communications == Follow-on == Incoming President Trump has indicated that he wants a full review of U.S. cyber protection policy. == Notes and references ==
Living lab
The concept of the living lab has been defined in multiple ways. A definition from the European Network of Living Labs (ENoLL) is used most widely, describing them as "user-centred open innovation ecosystems” that integrate research and innovation through co-creation in real-world environments.[1] Emerging at the intersection of ambient intelligence research and user experience methodologies in the late 1990s, the concept was pioneered at the Massachusetts Institute of Technology (MIT) as a way to study human interaction with new technologies in natural settings. Over time, living labs have evolved beyond their origins as controlled research environments, becoming dynamic platforms for participatory design, collaborative experimentation, and iterative innovation across various domains, including urban development, healthcare, sustainability, and digital technology. Characterized by principles such as real-world experimentation, active user involvement, and multi-stakeholder collaboration, living labs enable the continuous adaptation and validation of solutions in everyday contexts. Today, they are implemented globally, supported by networks like the European Network of Living Labs (ENoLL), and increasingly recognized as vital tools for addressing local and global transformation agendas. == Background == The term "living lab" has emerged in parallel from the ambient intelligence (AmI) research communities context and from the discussion on experience and application research (EAR). The emergence of the term is based on the concept of user experience and ambient intelligence. The term dates back to the late 1990s when Professor William J. Mitchell, Kent Larson, and Alex (Sandy) Pentland at the Massachusetts Institute of Technology were credited with first exploring the concept of a living laboratory. It was first associated with MIT's Media Lab as a concept for studying real-life contexts, where they described a living lab as a controlled environment designed to test new information and communication technology (ICT) innovations in a simulated home setting. This was also when some of the key characteristics often assigned to living labs today began to take shape. They argued that a living lab represents a user-centric research methodology for sensing, prototyping, validating and refining complex solutions in multiple and evolving real-life contexts. Research on living labs has expanded since the 1990s, especially in the 2010s, with growing interest in co-creation and participatory design. Particularly in Europe, the living lab evolved into a model that focused on studying user interactions with technology in real-world environments. This shift was influenced by earlier experiences in participatory design and social experiments with ICT. As interest grew, the term began to encompass a broader array of initiatives and projects, leading to variations in its interpretation and implementation. Today, living labs are used in various fields, such as technology, healthcare, and urban sustainability, showing a transition from a narrow focus on their role as controlled environments to a more wide-ranging understanding of collaborative innovation addressing real societal challenges, while also being referred to with various descriptions and definitions available from different sources. == Description == The ENoLL definition that refers to living labs as "user-centred open innovation ecosystems” that integrate research and innovation through co-creation in real-world environments is the most widely accepted description of living labs in academic literature. In simple terms, living labs can be described as an organization or experimental space, that can be both virtually or physically located, bringing different stakeholders from research, business, government, and citizens together to design and test solutions to be implemented in a real world environment. A common definition for the living lab term still does not exist to this day, which is due to the fact that living labs are interpreted and implemented across different contexts and can cover a wide range of activities and organizations, leading to different understandings of how living labs should function. Living labs also often operate in various territorial contexts (e.g. city, agglomeration, region, campus), and can vary in their methodological approach integrating concurrent research and innovation processes within a public-private-people partnership. Despite these variations, common characteristics include user-centricity, real-world experimentation, multi-stakeholder collaboration, and iterative innovation processes. The systematic user co-creation approach refers to integrating research and innovation processes through the co-creation, exploration, experimentation and evaluation of innovative ideas, scenarios, concepts and related technological artefacts in real life use cases. Such use cases involve user communities, not only as observed subjects but also as a source of creation. This approach allows all involved stakeholders to concurrently consider both the global performance of a product or service and its potential adoption by users. This consideration may be made at the earlier stage of research and development and through all elements of the product life-cycle, from design up to recycling. User-centred research methods, such as action research, community informatics, contextual design, user-centered design, participatory design, empathic design, emotional design, and other usability methods, already exist but fail to sufficiently empower users for co-creating into open development environments. More recently, the Web 2.0 has demonstrated the positive impact of involving user communities in new product development (NPD) such as mass collaboration projects (e.g. crowdsourcing, Wisdom of Crowds) in collectively creating new contents and applications. Real-world experimentation emphasizes conducting activities in real-life settings to ensure that the results of the projects and solutions are applicable to actual market conditions. Multi-stakeholder collaboration refers to an approach that involved various stakeholders, such as users, businesses, researchers, and government entities, working together towards a common goal. This is an important characteristics of living lab because collaboration of these diverse groups allows for exchange of ideas and perspectives, which are thought to enhance innovation processes. Iterative innovation processes involve a cyclical method of developing products or services, where stages such as research, development, testing, and implementation are revisited multiple times based on feedback and evaluation. This process allows for continuous improvement of the innovation, product, or service being developed. In particular, the ongoing involvement of the user creates feedback mechanisms that are ultimately key to successful development and implementation of products and services. A living lab is not similar to a testbed as its philosophy is to turn users, from being traditionally considered as observed subjects for testing modules against requirements, into value creation in contributing to the co-creation and exploration of emerging ideas, breakthrough scenarios, innovative concepts and related artefacts. Hence, a living lab rather constitutes an experiential environment, which could be compared to the concept of experiential learning, where users are immersed in a creative social space for designing and experiencing their own future. Living labs could also be used by policy makers and users/citizens for designing, exploring, experiencing and refining new policies and regulations in real-life scenarios for evaluating their potential impacts before their implementations. == European Network of Living Labs (ENoLL) == The European Network of Living Labs (ENoLL) is an international, non-profit, independent association of certified living labs, which popularized the living lab concept in the aim to increase user involvement in innovation. Formed in November 2006 under the guidance of the Finnish European Presidency, ENoLL is composed of a variety of stakeholders, including municipalities and research institutes, businesses, and users. Its primary role is to support the collaboration among living labs across Europe and includes many living labs focused on user-driven innovation across sectors. ENoLL focuses on facilitating knowledge exchange, joint actions and project partnerships among its historically labelled +/- 500 members, influencing EU policies, promoting living labs and enabling their implementation worldwide. ENoLL serves as a platform for linking living labs around the globe, which enables knowledge sharing and collaborative learning among diverse cultural environments. Membership to the platform is open to organizations worldwide, and ENoLL has expanded beyond Europe to include global members. ENoLL follows an application and accreditation pro
Ontology alignment
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. == Computer science == For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007), there are three major dimensions for similarity: syntactic, external, and semantic. Coincidentally, they roughly correspond to the dimensions identified by Cognitive Scientists below. A number of tools and frameworks have been developed for aligning ontologies, some with inspiration from Cognitive Science and some independently. Ontology alignment tools have generally been developed to operate on database schemas, XML schemas, taxonomies, formal languages, entity-relationship models, dictionaries, and other label frameworks. They are usually converted to a graph representation before being matched. Since the emergence of the Semantic Web, such graphs can be represented in the Resource Description Framework line of languages by triples of the form
Systematic review
A systematic review is a scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. A systematic review extracts and interprets data from published studies on the topic (in the scientific literature), then analyzes, describes, critically appraises and summarizes interpretations into a refined evidence-based conclusion. For example, a systematic review of randomized controlled trials is a way of summarizing and implementing evidence-based medicine. Systematic reviews, sometimes along with meta-analyses, are generally considered the highest level of evidence in medical research. While a systematic review may be applied in the biomedical or health care context, it may also be used where an assessment of a precisely defined subject can advance understanding in a field of research. A systematic review may examine clinical tests, public health interventions, environmental interventions, social interventions, adverse effects, qualitative evidence syntheses, methodological reviews, policy reviews, and economic evaluations. Systematic reviews are closely related to meta-analyses, and often the same instance will combine both (being published with a subtitle of "a systematic review and meta-analysis"). The distinction between the two is that a meta-analysis uses statistical methods to induce a single number from the pooled data set (such as an effect size), whereas the strict definition of a systematic review excludes that step. However, in practice, when one is mentioned, the other may often be involved, as it takes a systematic review to assemble the information that a meta-analysis analyzes, and people sometimes refer to an instance as a systematic review, even if it includes the meta-analytical component. An understanding of systematic reviews and how to implement them in practice is common for professionals in health care, public health, and public policy. Systematic reviews contrast with a type of review often called a narrative review. Systematic reviews and narrative reviews both review the literature (the scientific literature), but the term literature review without further specification refers to a narrative review. == Characteristics == A systematic review can be designed to provide a thorough summary of current literature relevant to a research question. A systematic review uses a rigorous and transparent approach for research synthesis, with the aim of assessing and, where possible, minimizing bias in the findings. While many systematic reviews are based on an explicit quantitative meta-analysis of available data, there are also qualitative reviews and other types of mixed-methods reviews that adhere to standards for gathering, analyzing, and reporting evidence. Systematic reviews of quantitative data or mixed-method reviews sometimes use statistical techniques (meta-analysis) to combine results of eligible studies. Scoring levels are sometimes used to rate the quality of the evidence depending on the methodology used, although this is discouraged by the Cochrane Library. As evidence rating can be subjective, multiple people may be consulted to resolve any scoring differences between how evidence is rated. The EPPI-Centre, Cochrane, and the Joanna Briggs Institute have been influential in developing methods for combining both qualitative and quantitative research in systematic reviews. Several reporting guidelines exist to standardise reporting about how systematic reviews are conducted. Such reporting guidelines are not quality assessment or appraisal tools. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement suggests a standardized way to ensure a transparent and complete reporting of systematic reviews, and is now required for this kind of research by more than 170 medical journals worldwide. The latest version of this commonly used statement corresponds to PRISMA 2020 (the respective article was published in 2021). Several specialized PRISMA guideline extensions have been developed to support particular types of studies or aspects of the review process, including PRISMA-P for review protocols and PRISMA-ScR for scoping reviews. A list of PRISMA guideline extensions is hosted by the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. However, the PRISMA guidelines have been found to be limited to intervention research and the guidelines have to be changed in order to fit non-intervention research. As a result, Non-Interventional, Reproducible, and Open (NIRO) Systematic Reviews was created to counter this limitation. For qualitative reviews, reporting guidelines include ENTREQ (Enhancing transparency in reporting the synthesis of qualitative research) for qualitative evidence syntheses; RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) for meta-narrative and realist reviews; and eMERGe (Improving reporting of Meta-Ethnography) for meta-ethnograph. Developments in systematic reviews during the 21st century included realist reviews and the meta-narrative approach, both of which addressed problems of variation in methods and heterogeneity existing on some subjects. == Types == There are over 30 types of systematic review and Table 1 below non-exhaustingly summarises some of these. There is not always consensus on the boundaries and distinctions between the approaches described below. === Scoping reviews === Scoping reviews are distinct from systematic reviews in several ways. A scoping review is an attempt to search for concepts by mapping the language and data which surrounds those concepts and adjusting the search method iteratively to synthesize evidence and assess the scope of an area of inquiry. This can mean that the concept search and method (including data extraction, organisation and analysis) are refined throughout the process, sometimes requiring deviations from any protocol or original research plan. A scoping review may often be a preliminary stage before a systematic review, which 'scopes' out an area of inquiry and maps the language and key concepts to determine if a systematic review is possible or appropriate, or to lay the groundwork for a full systematic review. The goal can be to assess how much data or evidence is available regarding a certain area of interest. This process is further complicated if it is mapping concepts across multiple languages or cultures. As a scoping review should be systematically conducted and reported (with a transparent and repeatable method), some academic publishers categorize them as a kind of 'systematic review', which may cause confusion. Scoping reviews are helpful when it is not possible to carry out a systematic synthesis of research findings, for example, when there are no published clinical trials in the area of inquiry. Scoping reviews are helpful when determining if it is possible or appropriate to carry out a systematic review, and are a useful method when an area of inquiry is very broad, for example, exploring how the public are involved in all stages systematic reviews. There is still a lack of clarity when defining the exact method of a scoping review as it is both an iterative process and is still relatively new. There have been several attempts to improve the standardisation of the method, for example via a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline extension for scoping reviews (PRISMA-ScR). PROSPERO (the International Prospective Register of Systematic Reviews) does not permit the submission of protocols of scoping reviews, although some journals will publish protocols for scoping reviews. == Stages == While there are multiple kinds of systematic review methods, the main stages of a review can be summarised as follows: === Defining the research question === Some reported that the 'best practices' involve 'defining an answerable question' and publishing the protocol of the review before initiating it to reduce the risk of unplanned research duplication and to enable transparency and consistency between methodology and protocol. Clinical reviews of quantitative data are often structured using the mnemonic PICO, which stands for 'Population or Problem', 'Intervention or Exposure', 'Comparison', and 'Outcome', with other variations existing for other kinds of research. For qualitative reviews, PICo is 'Population or Problem', 'Interest', and 'Context'. === Searching for sources === Relevant criteria can include selecting research that is of good quality and answers the defined question. The search strategy should be designed to retrieve literature that matches the protocol's specified inclusion and exclusion criteria. The methodology section of a systematic review should list all of the databases and citation indices that were searched. The titles and abstracts of identified articles can be checked against predetermined criteria for eligibility and r
Sentence embedding
In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE), which demonstrated performance improvements in downstream text classification tasks. == Applications == In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval-augmented generation. Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization. == Evaluation == A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.
Reservoir sampling
Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1}