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Investors face similar macroeconomic risks and opportunities regardless of their individual investment preferences. To best navigate growth and inflation concerns, the authors propose building macro factor–mimicking portfolios diversified across asset classes and style factors. They focus on the macro factors growth, inflation, and defensive. Their approach allows for shaping the macroeconomic risk exposure of a given portfolio by applying systematic macro factor completion to effectively address specific economic outcomes. Key Findings ▪ Asset class and style factor returns have clear sensitivities to salient macro factors that can be utilized to build out diversified macro factor-mimicking portfolios. ▪ The authors demonstrate the use of macro factor-mimicking portfolios in a macro factor completion framework to purposefully manage a given portfolio’s macro factor risk exposure. ▪ Important use cases are investors’ positioning in light of negative growth and/or rising inflation scenarios.

A new generation of cellular technology has been deployed every decade since the 1980s. We are presently amid the global commercial deployment by Mobile Network Operators (MNOs) of the fifth generation (5G) of cellular technology; and concurrently, a flurry of research is engaged in designing the next generation (6G). This offers an opportune time to assess the techno-economics research literature over the past decade that informed 5G’s development lifecycle from standardization to deployment. Our goal is to provide a retrospective review of this literature and offer recommendations for how the methodologies employed in this style of model-based research (theoretical and empirical) may be improved to best contribute to the design of Next Generation (Next-G) wireless technologies. Our review categorizes the prior research into eight key research themes including use cases, technologies, modeling techniques, financial metrics, open science practices, business model evaluation, spatial focus and data visualization. Our review of the prior literature leads us to conclude that the quality and impact of techno-economic assessment studies directed at designing Next-G (or 6G) technologies would be improved if researchers are: (i) more transparent about network quality of service assumptions, (ii) openly share input data, code, and results, (iii) focus on total cost analysis, with special attention paid to (iv) ensuring the affordability of 6G technologies, and, (v) actively take account of developments emerging from other modeling efforts to maximize the potential for mutual benefits.

“Reading” is the primary technology investment arbitration practitioners use to engage with party submissions, prior awards, investment treaties and all other written materials relevant to the arbitration. Yet, an alternative and complementary approach to parse investor-state arbitration (ISA) texts is rapidly emerging. In what is variably known as “text mining”, “text analytics”, or “computational analysis”, algorithms rather than humans are beginning to digest written materials to extract relevant insights. What this algorithmic approach lacks in nuance (computers are bad at understanding text), it makes up for in efficiency (computers excel at crunching numbers). This chapter targeted at arbitration professionals introduces the methodologies underpinning text analytics, explains and evaluates their use cases in investment arbitration, and assesses their promises and limitations.

Learning Factories are research and learning environments that demonstrate new concepts and technologies for the industry in a practical environment. The interaction between physical and virtual components is a central aspect. The mediation and presentation usually occur directly in the learning factory and are thus limited in time and concerning the user group. A learning factory- on-demand- can be provided by dividing and virtualizing the individual components via containers and microservices. This enables both local operation and operation hybrid cloud or cloud systems. Physical components can be mapped either through standardized interfaces or suitable emulators. Using the example of the Learning Factory at Reutlingen University (Werk150), it will be shown how different use cases can be made available utilizing software-based orchestration, thus promoting broader and more independent teaching.

If algorithms are to be the policy analysts of the future, the policy metrics they produce will require careful validation. This paper introduces a new dataset that assists in the creation and validation of automated policy metrics. It presents a corpus of laws that have been redrafted to improve readability without changing content. The dataset has a number of use cases. First, it provides a benchmark of how expert legislative drafters render texts more readable. It thereby helps test whether off-the-shelf readability metrics such as Flesch-Kincaid pick up readability improvements in legal texts. It can also spur the development of new readability metrics tailored to the legal domain. Second, the dataset helps train policy metrics that can distinguish policy form from policy substance. A policy text can be complex because it is poorly drafted or because it deals with a complicated substance. Separating form and substance creates more reliable algorithmic descriptors of both.

The rise of 5th-Generation (5G) technology has provided great potential for the development of manufacturing industry. How to apply 5G technology in manufacturing industry has become a current hot topic. With the growth of personalized demands and the shortening of product life cycles, the flexibility of production model for Industry 4.0-oriented manufacturing has become an inevitable trend, and manufacturers need to be capable to quickly build or reconfigure new production systems to respond to market changes. Plug-and-play (PnP) of functional units is the basic guarantee to realize the rapid set-up and reconfiguration of production systems, where the construction of the Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS) is one of the key problems to be solved. Therefore, the use of 5G technology to realize the agile and real-time transmission of large amounts of data, combined with the standard modular design to achieve the PnP of functional units, and construct IIOT and CPS, will be able to fully stimulate the development potential of future manufacturing industry. The Advanced Manufacturing Technology Center (AMTC) at Tongji University (Shanghai, China) carried out Learning Factory (LF) design and implementation of 5G-based PnP application, in order to provide application scenarios for the rapid establishment and operation of production systems. In addition, the AMTC developed use cases on demand for research, and cultivate students` knowledge and skills related to 5G and PnP in manufacturing industry. This paper introduces the environment and training process design of the LF in detail, as well as the current progress of implementation case in AMTC.

The COVID-19 pandemic has largely disrupted the way of retailing. Retailers with only brick-mortar are forced to adopt online mode to sustain their business, while online retailers started equipping them with sophisticated smart technologies like AI (Artificial Intelligence), ML (Machine Learning), etc. to attract, engage and transact with their customers. This paper enumerates few use cases of technologies in the retailing sector during the COVID-19 crisis. Online impulse buying is generating a good chunk of revenue, and all the retailers are making effort to lure customers for impulse buying. Due to lockdown and physical distancing people, started buying more on e-commerce portals and so the importance of impulse buying also heightened for the retailers. This paper also focuses on market trends that may intensify the role of technologies in retailing post-COVID-19 pandemic crisis. The fear of potential new strains of the virus is posing uncertainty about the rebounding of the economy and changes in the preferences of the individual. To combat these challenges technology is expected to play a vital role. The various advantages and challenges/barriers of probable technology adoption in the retail sector are also discussed. This paper will add to the literature of online retailing and give practical insights to the retailers in adopting adequate technology.

Policy issues • AI systems in the context of employment are intrusive and have negative impacts on workers. The proposed Regulation fails to address the specificity of AI uses in employment, including platform work. An ad hoc directive on AI in employment is therefore necessary. • As a consequence of focusing the regulatory approach on highrisk applications, the majority of use cases are considered lowrisk, not subject to any evaluation and de facto authorised. • High-risk uses are permitted subject to compliance with specific requirements and an ex ante conformity assessment based on internal control checks. This approach stacks the deck in favour of tech providers when the priority of this Regulation should have been to protect EU citizens and workers’ rights.

The majority of countries are currently struggling to achieve sustainable levels of recycling and waste treatment, particularly relating to household waste, and this area is in urgent need of new solutions. In general, the waste management sector has struggled with low consumer trust, fraud, manipulation, significant manual processes, and low levels of information and control. Here we propose a hybrid blockchain solution called a Polkadot parachain. Polkadot is a blockchain technology that allows for the development of a network of blockchains, each called a parachain that can be customised to the business needs of a given application. This solution provides the cost benefits, scalability, and control of a permissioned blockchain while providing the security, verifiability, and trust of a public blockchain. The solution combines three typically separate blockchain use cases: supply chain tracking, incentivisation through a payment system, and gamification to achieve a complete solution for waste management. We provide a detailed discussion on the design of this blockchain solution with the use of blockchain functionality assessed against the criteria and development approaches found in the literature. Finally, we demonstrate how such a blockchain can be implemented with the Substrate blockchain development framework and detail a pilot project where this system will be implemented.

Various assistive systems for supporting manual assembly tasks in industry have been proposed within the last years. These systems range from simple work instructions on screens, to animated mixed reality and in-situ projected hints directly onto the work-piece. Several design approaches exist for these systems. Commonly, assistive systems are tested and evaluated using abstract use cases with widely available building blocks (e.g. Duplo or Lego). These approaches work to test simple picking and placing tasks, but cannot reproduce the complexity of real assembly, which may include the tightening of screws, adding components from specific directions or attaching cables in a predefined way. Other evaluation approaches make use of realistic work processes from real industrial contexts. However, these evaluations are less controllable and hardly replicable. Our work presents a realistic and easyto-recreate approach to evaluate assistive systems for manual assembly tasks. It comprises a 3D-modelled object that considers several procedures and aspects of manual assembly and, as an abstract assembly object, helps to improve the usability of laboratory developed assistive systems. The model itself is developed to be 3D printed and uses standardized components to make it easily available for research and learning factories around the globe. With this approach, we want to support researchers in comparing and evaluating assistive systems for industrial assembly tasks by providing a realistic and easy-to-use test setup with high internal and external validity.

Process mining has now become a standard in many companies as part of process control in various areas. Its application in the context of ensuring good corporate governance has also been discussed by previous papers for specific areas. This paper integrates the possible approaches to use process mining in the context of assurance activities against the background of the three-lines-of-defense model. By using specific use cases, the areas of application and the challenges of process mining for assurance are further discussed.

Blockchain is claimed to be a liberating technology that can upend current social structures through its decentralized, immutable ledger. In reality, however, blockchain is not absolutely decentralized, trustless, or transparent. Rather, blockchain is a general technology with a wide variety of applications, many of which are sustaining and incorporative of existing models. Blockchain artwork title registry is one of the use cases that can improve market transparency and efficiency without disrupting current market structure or eliminating intermediaries. Moreover, privacy and transparency are not incompatible. As a tamper-proof ledger that does not disclose a record’s content, blockchain can actually increase protections for privacy. The adoption of such a blockchain-based registry is the foundation for the long-term development of other blockchain applications in the art market, such as artist resale royalties and fractional ownership. Therefore, the registry is a conservative use case of this novel technology, with the potential to promote wider blockchain adoption in, and interoperability across, the art world. Through literature review and interviews, this essay explores blockchain’s implication for the art market, examines blockchain’s potentials and constraints, and proposes a starting point for the art world to reach consensus.

We develop a taxonomy – Oxford Venture Ecosystem Taxonomy (OVET) -- to classify technology startup ventures along nine dimensions: (1) the area of work, (2) purpose of technology use, (3) technology stack, (4) platform business model, (5) type of clients, (6) value capture strategy, (7) founder and funder characteristics, (8) geographical footprint, and (9) funding cycle. This paper provides a theory and method for developing taxonomies, emphasizing the importance of clarifying the purpose for which a taxonomy is used and the determination of the appropriate level of abstraction. This approach is then applied to develop the OVET taxonomy in the context of specific sectors with AI use cases. We illustrate this application in four sectors, namely fintech, healthtech, lawtech, and proptech. In the last section, we discuss how such a taxonomy, enabling classifications and analytics, could provide valuable insights and improve the quality of decision-making by venture founders, investors, policy makers and other stakeholders in the venture ecosystem.

Use of patent data by academics is widespread, especially in scholarship related to innovation and technology management. However, even with increased online access to patent databases, patent searches at scale are still challenging. We present Patentopia, a simple online tool extracting information from the PatentsView portal linked to the United States Patent and Trademark Office. In this note, we describe the tool's operation, use cases, and envisioned usage.

Due to its interactive and intuitive nature, Virtual Reality has great potential for training of industrial assembly procedures. Training in a virtual environment is potentially easier, safer and cheaper than real-world training and it allows fast switching between training scenarios. A lot of research has been done in this area, mostly conducting evaluations in laboratory settings with abstract use cases. This paper describes our implementation of a virtual training application, which was developed for a real, complex assembly in cooperation with an industry partner. The application features a guided training, which allows users to practice the assembly of complex products step by step in a virtual environment. An interactive exploded view enables the exploration of individual parts in context to the model. In addition, a sectional view tool reveals the internal structure of an assembly. The application offers collaborative tools for trainers to monitor and support the progress. A content authoring tool allows the creation and modification of training scenarios. First feedback from a qualitative study shows positive feedback from users. They appreciated the intuitive use of visual and haptic feedback for interaction. Our tests also reveals that some steps are challenging to train virtually and therefore certain simplifications are necessary. VR will not replace traditional training, but the tools described in this paper may provide a useful addition to current solutions in industry and learning factories.

In this paper, we explore new and traditional approaches to measuring population density, and ways in which density information has frequently been used by humanitarian, private-sector and government actors to advance a range of private and public goals. We explain how new innovations are leading to fresh ways of collecting data—and fresh forms of data—and how this may open up new avenues for using density information in a variety of contexts. Section III examines one particular example: Facebook’s High-Resolution Population Density Maps (also referred to as HRSL, or high resolution settlement layer). This recent initiative, created in collaboration with a number of external organizations, shows not only the potential of mapping innovations but also the potential benefits of inter-sectoral partnerships and sharing. We examine three particular use cases of HRSL, and we follow with an assessment and some lessons learned. These lessons are applicable to HRSL in particular, but also more broadly. We conclude with some thoughts on avenues for future research.

Companies are employing various data mining techniques to find solutions to pervasive business problems. All the functions in an organization are finding their own specific use cases to apply data mining models and benefit from it. Most common data mining models focus on prediction, classification or regression. Marketing as a discipline has numerous applications for these data mining models in forecasting, cross selling problems. With limited marketing budgets and numerous marketing activities, it becomes extremely crucial to segregate which activities to focus more. Though there are numerous ways to measure and analyze performance of various marketing activities and tactics, the decision is not as straight forward due to attribution issues. There has been some research on measuring performances of marketing activities but there is a void in research done on sequencing marketing activities and tactics. Marketing activities and tactics if sequenced based on data mining models would yield a better return on the marketing bucks invested. Our research focusses on utilizing decision trees to create the best marketing activity sequence. We also explain the methodology to generate the most effective marketing activity sequence.

Blockchain-based technologies are starting to permeate every aspect of the Canadian legal landscape. Blockchain is often solely associated with cryptocurrencies, such as Bitcoin; however, it has numerous applications beyond virtual currencies. Blockchain technology has the potential to provide numerous opportunities to those providing legal services, notably through smart contracts. However, some legal uncertainty remains with respect to smart contracts. The legality of smart contracts and their place of operation under Canadian law has yet to be researched. Potential risks for clients, as well as for lawyers—especially with respect to their ethical and professionalism duties—also need to be researched and highlighted. This paper begins by providing an overview of smart contracts, including a brief history of the development of smart contracts, followed by a discussion of the underpinnings of blockchain-based applications, and a presentation of some use cases. After forming an understanding of smart contracts, the paper addresses the important technical and legal limitations that smart contracts face. Since blockchain-based smart contracts rely on blockchain technology and digitalization, this paper explains how smart contracts can only be as “smart” as its developer, and the computers running them. This paper then transitions into a discussion of how the advent of cryptocurrencies and smart contracts relaunched decades-old debates about how society should regulate technology. Although we argue that legal frameworks must evolve with society, we will highlight that we do not need to rewrite or create new approaches to each technological innovation. Subsequently, this paper provides an overview of smart contracts under Canadian contract law. As it currently stands, academics have not fully addressed the question of whether smart contracts conform to our traditional contract law framework. This paper will address the legal status and remaining questions regarding the legal status of smart contracts and the enforceability of smart contract performance. While there may be many barriers to the adoption of any new technologies, legal uncertainty—and therefore legal risks for clients and lawyers—should not be one of them. The key findings below present an overview of the academic research on the key legal questions facing smart contracts in Canada. The key findings are followed by an overview of some of the preliminary use cases of smart contracts in the legal field.

Blockchain serves as shared ledger for many cryptocurrencies such as ethereum and bitcoin. Blockchains are now paving way for healthcare, real estate and other applications. In this paper, we described the state-of-the art of blockchain technology by untangling the complexity of blockchain systems. We also have defined the prerequisites for blockchain to work, the process of adding a new entry in blockchain though various consensus algorithms, security factors and the system requirements of blockchain systems. This paper focuses on explaining every working aspect of blockchain systems and how it can transform human lives by mentioning some latest and exciting real time use cases of blockchains.

Blockchain is a distributed ledger technology which provides a way to afford more transparency to our everyday systems by exposing all the interactions of the system as a bunch of transactions. Efficient and Effective Governance – this is the need of the day for any country or organization. This not only solves the current problems but also sets a clear path towards progress and development. Unfortunately, this might not be the case always. Progressive countries are always on the hunt to find technological solutions to their everyday problems. This paper discusses how Blockchain Technology might be able to tackle these problems and push the systems in the right direction. We discuss four problem use cases – intergovernmental trade, land registration, voting and Dubai smart city, and present how blockchain is working towards the problems surrounding them. Finally we look at some problems from the Indian perspective that could be addressed using the Distributed Ledger technology.

The digital transformation is ubiquitous and pushing the case further for ERP companies to incorporate more machine learning algorithms in order to drive intelligent real-time decision-making capabilities. ERP systems have started incorporating machine learning use cases powered by the huge enterprise data, cloud and compute capabilities. However, privacy of data remains a challenge. Data privacy is at the core of a machine learning model that is trained on sensitive information. Not just for profit businesses, but even academic endeavors in the field of medicine cannot progress if they can’t access sensitive medical information in a privacy preserved format. Ramifications of applying a ML model without even fully understanding what is happening inside its hidden layers can be disastrous and the resulting perils can lead to legal consequences. Therefore, Privacy preserving AI techniques started evolving in last few years. The privacy preserving AI field is still growing and there is an understanding gap in organizations and individuals, which makes privacy breach or compromise a pervasive business challenge. This paper focuses on what are key challenges for ERP companies as far as Training machine learning models on their enterprise data is concerned. And how can these challenges be overcome by applying data anonymization and differential privacy techniques.

The physical world is measured by sensors and translated into processible data, and data has to be translated into commands to be executed by actuators. Due to the growing interest in IoT, the number of platforms designed to support IoT has risen considerably. As a result of different approaches, standards, and use cases, there is a wide variety and heterogeneity of IoT platforms. This leads to difficulties in comprehending, selecting, and using appropriate platforms. This paper gives a detailed review on wide variety and heterogeneity of iot platforms.

We investigate the technical problem of community and clique detection in investor network. We introduce a pruning technique for investor clique enumeration based on specific economic and financial contexts. It can be incorporated into the central clique solver to reduce the computing time and improve the clique-based community detection method. Furthermore, we present several use cases in the study on investor behavior.

Artificial Intelligence (AI)-driven IT aims to describe the penetration of machine intelligence solutions in the IT department, what focus currently exists and what future developments are expected. It furthermore aims to provide an overview over existing utilities for organizations’ IT and what could be challenges when implementing AI in IT. By using a state-of-the-art approach, this work elaborates about existing solutions in practice and about selected scientific research found in connection with them. For analysis of use cases, the IT infrastructure library (ITIL) framework is used. Additionally, to aid and complete analysis’ findings, expert interviews have been conducted and evaluated with employees of Capgemini Invent, a major IT-consulting firm. This work shows that a substantial amount of AI solutions is already existing for various areas of an IT operating model. By connecting use case analysis and evaluation of expert interviews, among other results, a clear focus area for AI solutions was found in the area of IT operations.

The current COVID-19 pandemic had a big impact on the internet usage. Many big IT infrastructure providing giants have to perform the migration of the networking infrastructure for "work from home" scheme. The new challenging use cases where the provisioning of solutions such as switching to open-source platforms from proprietary devices requires attention. The maximum usage of a VPN for teleconferencing and “work from home” scheme caused a huge surge on the internet. This situation forced all the IT infrastructure providers to switch towards Software-Defined Network (SDN), Hybrid Software-Defined Network (HSDN), and Software-Defined Wide-Area Networks (SD-WAN). This paper focuses on the new areas and use cases generated due to the COVID-19 pandemic crisis.

Data has evolved immensely in recent years, in type, volume and velocity. There are several frameworks to handle the big data applications. The project focuses on the Lambda Architecture proposed by Marz and its application to obtain real-time data processing. The architecture is a solution that unites the benefits of the batch and stream processing techniques. Data can be historically processed with high precision and involved algorithms without loss of short-term information, alerts and insights. Lambda Architecture has an ability to serve a wide range of use cases and workloads that withstands hardware and human mistakes. The layered architecture enhances loose coupling and flexibility in the system. This a huge benefit that allows understanding the trade-offs and application of various tools and technologies across the layers. There has been an advancement in the approach of building the LA due to improvements in the underlying tools. The project demonstrates a simplified architecture for the LA that is maintainable.

Supply chain has various use cases and it’s a huge area where it involved lot of operations in Packings, Manufacturing, Shipping and Picking. Human-Computer Speech is getting vitality as a technique of PC collaboration. There has been a later upsurge in talk-based look engines and colleagues, for example, Alexa, Siri, Google Home and Cortana. NLP methodologies, for example, NLTK for Python can be associated with dismember talk, and intelligently responses can be found by arranging an engine to supply fitting human like responses. This kind of program is known as a Chatbot, which is the focal point of this contemplate. We accept that a coordinated and effective inventory network framework will convey consistent worth, helping customers to convey the correct item or administration at the correct cost. It has been demonstrated certain that the inventory network represents 80 percent of the complete operational expense, and thus, making efficiencies on this front is at the center of improved benefit and business advantage for any maker.

The EU Taxonomy is the first standardised and comprehensive classification system for sustainable economic activities. It covers activities responsible for up to 80 percent of EU greenhouse gas emissions and may play an important role in channelling investments into low-carbon technologies by helping investors to make informed decisions. However, especially in transition sectors much depends on the stringency of the technical performance thresholds that the Taxonomy applies to economic activities that are not yet “green”. This paper shows that for several sectors, the thresholds are not yet on track to support the transition towards climate neutrality. To this end, we analyse a large-scale public consultation with detailed responses to the specific thresholds from a variety of stakeholders. Two distinct use cases of the Taxonomy complicate the use of a single threshold for emission-intensive sectors: For new investments, criteria need to be stricter than for current activities of companies. We also argue that for the sectors not covered by the Taxonomy, there is a need to differentiate between low-emissions activities and high-emission activities that are incompatible with a low-carbon future.

This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events., To be presented at the 6th International Symposium in Computational Economics and Finance in Paris, October 29-31, 2020

Climate-related financial risks might have the potential to trigger the next systemic financial crisis, as recently stated by the Bank for International Settlements. In consequence, understanding these so-called Green Swan risks should be a key priority in financial decision-making and supervision. However, a systematic approach and a comprehensive theory on climate-adjusted financial risk metrics is still missing. This study is a first step to fill this gap, with a focus on transition risks. Drawing on insights from climate science, economics and finance research, we derive a set of important criteria to ensure that climate risk tools provide high quality, comparable, and decision-relevant results. We then use a sample of 16 climate transition risk tools and conduct two lines of research: First, by aid of descriptive analysis, we assess the tools’ coverage of risk sources and financial assets, their inputs (i.e. underlying climate scenarios), and their outputs (i.e. climate-adjusted financial metrics). Second, we use the previously defined criteria for an in-depth analysis of the quality, comparability and decision-relevance of the tools. The results will be presented at the individual tool level, and at the meta level. Based on the results of our descriptive and criteria-based analysis, we derive potential next steps for tool provider, conclusions for potential tool users, and guidelines for supervisory authorities. The analysis could be used as starting point for building a comprehensive theory of meaningful climate-related financial risk indicators, aid practitioners to select the tools best suited to their needs and use cases, and inform regulatory processes on financial climate risk assessment principles.

In the FinTech world, 2020 could be called the year of stablecoins, which are crypto assets with a stable value. Stablecoins were analyzed by various intergovernmental organizations, regulators and scholars. Brightest minds around the world are still trying to understand what the legal nature of various types of stablecoins is and how to regulate stablecoins without hindering innovation. In the UK and EU, regulators are considering whether stablecoins such as LibraCoin are electronic money (e-money) and should be regulated accordingly. In their analysis regulators and scholars concluded that some stablecoins could indeed be considered e-money. The author of this Working Paper found that most legal analyses were done in abstract and were not dedicated to specific stablecoins and the context in which they exist. This Working Paper address this gap by analyzing concrete stablecoins such as LibraCoin, USDT, PAXG, DAI and the context in which they exist. One of the aims of the Working Paper is to create a general framework that can be used to analyse any existing stablecoin project under the UK and EU e-money legislation. This Working Paper defines nine types of stablecoin while explaining and applying the e-money test to these types of stablecoins. Unsurprisingly, this Working Paper reveals that most stablecoins analyzed are not e-money. Some stablecoins such as USDT most likely can be considered as e-money in the EU and UK. However, functions and use cases for USDT are quite different from the use cases of e-money as we know it. USDT and other stablecoins are extensively used in the world of Decentralized Finance (DeFi), and regulators should take into consideration the context surrounding stablecoins when deciding whether to apply the e-money framework to them.

The economical of cloud computing as well as its compelling scaling provides it with an advancement motorist for small firms, especially in the establishing world. Cloud deployed venture source preparation, supply chain control apps, customer connection administration functions, medical apps and mobile functions possess the prospective to get to countless users. Within this paper, our experts check out the various principles associated with cloud computing. Leveraging our adventures on a variety of clouds, our company take a look at clouds from specialized, and also service aspects. Our experts highlight some of the possibilities in cloud computing, underscoring the relevance of clouds as well as presenting why that modern technology should be successful. This paper explains in detail about characteristics, applications and use cases of cloud computing.

Risk assessments have been critical missions for all kinds of operations management. Disruptive applications are developed to assess risks more precisely with the adoption of advanced technologies. Such a paradigm shift is ongoing in various industries, especially the auto insurance business. Contrasted with a common practice mainly based on static demographics, the mobility-based risk assessment predicts risks using dynamic driving data obtained via IoT-enabled telematics. This study proposes a novel solution to learn about drivers’ risks. We specifically consider two aspects of mobility, namely trait and trajectory, monitored by GPS, on-board diagnostics, and in-vehicle cameras in a real-time manner. Traits refer to the distinctive driving behaviors (styles) of drivers, while trajectories consist of the vehicle motion sequences along with the contextual factors on trips. We select the features extracted from the two to optimize a predictive model that assesses risks at both trip and driver levels. Using the fine-granular driving data and crash reports, we find that behavioral traits play a significant role in predicting crashes. We also notice that the risks of drivers are heterogeneous and could evolve over time. In a series of empirical experiments, the proposed solution outperforms the current practice and the alternative methods considered by prior literature. We show that the mobility-based models are consistently superior to demographic-based ones commonly adopted by practitioners. Quantitatively, our approach improves recall (precision) of logistic regression, support vector machine, and random forests by 43.45% (41.82%), 29.46% (27.87%), and 26.24% (25.27%), respectively. Last, the proposed solution is more robust and computationally efficient to unusual use cases (i.e., skewed and small training samples). The findings provide several managerial implications and a blueprint for the auto insurance industry to operationalize IoT-enabled risk assessments in the coming era of 5G communication.

Artificial learning is playing a very important role in supporting emerging and existing technologies present around us. In the areas of learning and tutoring from a person to a researcher, it plays a key role in the field of knowledge due to its advancement. The adaptive e-learning is providing personalized and advanced learning system to the learners. Why this is important? This is because it facilitates the capacity of a person which provides more accuracy in reading and understanding in more detail. To find the most appropriate personalized learning material for a learner is difficult on the net. Artificial Intelligence-based E-Learning is a platform that possesses the ability to perform different tasks that require human intelligence. It helps in analyzing and creating different solutions to human-related problems, speech recognition, software problems. Due to its diversifying nature in different fields here it is in much demand for the process of e-learning. Artificial Neural Networks is making everything easily available and in finding their own personalized learning environment through innumerable resources by suggesting to users depending on their interests and in taking less amount of time and effort. In this paper, I will discuss the importance of artificial neural networks in E-Learning personalization along with its advantages and disadvantages. Also, I will be discussing some of the use cases and the importance of artificial neural networks as Neural Networks, personalization, artificial intelligence, applications.

Inspired by the idea that digital text can support examination of the sociomaterial world, we present a novel computational approach, guided by assemblage theory, to understand the emergence of assemblages of personal automation practices. We overlay assemblage theory on machine learning models for text analysis, clustering, and dimensional reduction to turn these computational models into vehicles for operationalizing the possibility space, points of attraction, and other key assemblage theory constructs. We also use an inductive analysis to deepen the insights from our computational findings. We demonstrate our approach for visualization and discovery on a unique data set of conditional text-based rules of the form “if this, then that,” defining Internet-connected applets that consumers can run to automate events in their daily lives. We uncover 127 personal automation practices and interpret the material roles of their components, as well as the expressive roles that emerge in the realized possibility space. We also show how the full possibility space provides a dynamic dashboard that reifies both what is, as well as what could be. Our results can stimulate additional research and encourage practitioners to look beyond use cases to those underlying points of attraction driving consumers’ deeper needs to automate their lives.

Digital Twin (DT) is a virtual representation, which integrates sensor data, physical models, historical data, and etc. to emulate the life of its physical counterpart. Although, existing studies have shown many use cases in health monitoring, decision-making support, and life-cycle management, the development of the DT remains complex. Meanwhile, there is a limited number of studies that investigate a generic and detailed method to develop the DT. Therefore, the aim of this paper is to present a step by step guideline that allows a developer to identify and to populate the requirements for developing the DT as well as to corroborate that the DT fulfil its missions. A case study of a modified mobile asset illustrated how the guideline was applied and how the DT purpose was validated through an empirical test. Further studies will investigate the attributes of the scalability and the flexibility of the proposed framework to pave a way for a common approach for the DT development and evaluation.

The proposed work describes the design of a multimedia platform managing users and implementing cybersecurity. The paper describes in details the use cases of the whole platform embedding Big Data and artificial intelligence (AI) engine predicting network attacks. The platform has been tested by Tree Ensemble algorithm classifying and predicting anomalous server logs of possible attacks. The data logs are collected in Cassandra Big Data System enabling the AI training model. The work has been developed within the framework of a research industry project.

The following research investigates the application of security tokenization to commercial real estate assets. Primary research through interviews was conducted to uncover some of the most salient use cases and blockchain benefits for the space. The report explores three domains of blockchain application to real estate: (1) the application of blockchain to securities issuance and trading, (2) the application of blockchain to the real estate investment value chain, and (3) the application of blockchain to the representation of the physical assets themselves. Overall, we find that the value creation provided by tokenization can come in several layers, with some standalone benefits emerging by applying tokenization to each of the three domains in isolation. However, significant synergies can arise from combining these layers. As integration increases, additional features become possible. Our conclusion offers a general framework that can be used to perform future research on the tokenization of other types of assets and their related securities.

This paper discusses a software architecture methodology, called Elemental Software Architecture, which is derived from two other existing methodologies: Juval Lowy’s Method and Robert Martin’s Clean Architecture. Juval Lowy’s Method decomposes requirements into core use case and further decomposes these use cases using volatility decomposition into layers that contain Clients, Managers, Engines, Resource Accessors and Resources. Robert Martin’s Clean Architecture decomposes requirements into use cases that are further decomposed into different parts using the SOLID principles to make system resilient against changes in requirements and scaling. Similarities exist between two methodologies, and both can be mapped to common elements. The methodology proposed in this paper uses these common elements and a decomposition method to decompose the requirements of a system into these common elements.

Distributed ledger technology (DLT), including blockchain, enables secure processing of transactions between untrustworthy parties in a decentralized system. However, DLT is available in different designs that exhibit diverse characteristics. Moreover, DLT characteristics have complementary and conflicting interdependencies. Hence, there will never be an ideal DLT design for all DLT use cases; instead, DLT implementations need to be configured to contextual requirements. Successful DLT configuration requires, however, a sound understanding of DLT characteristics and their interdependencies. In this manuscript, we review DLT characteristics and organize them into six groups. Furthermore, we condense interdependencies of DLT characteristics into trade-offs that should be considered for successful deployment of DLT. Finally, we consolidate our findings into DLT archetypes for common design objectives, such as security, usability, or performance. Our work makes extant DLT research more transparent and fosters understanding of interdependencies and trade-offs between DLT characteristics.

Recent years have seen rapid growth in IS scholarship addressing the efficacy and utility of blockchain technology in trade-processing and financial services. Numerous IS scholars encourage applied research into the ostensible synergies between the nascent technology and its various use cases. Yet the vast majority of published research approaches the issue from a purely contemplative or theoretical perspective. Addressing this gap in the IS literature, we apply the design science research methodology in the construction a software artefact for the abstract representation of physical assets in the form of blockchain tokens, a process colloquially referred to as tokenization. We present the final iteration of the artefact, evaluating our results against the requirements collected through the design search process. This informs a rigorous evaluation of the conceptual limitations of blockchain-based software artefacts. We conclude that, provided the aforementioned requirements are adequately observed within the design search process, blockchain technology can indeed improve post-trade processing.

The written word is the oldest and most common type of data. Today, mass literacy and cheap technology allow for greater word output per capita than ever before in human history. To keep pace, companies and scholars increasingly depend on automated analyses — not only of what people say (content) but also how they feel (sentiment). This makes it pertinent to understand the accuracy of these automated analyses. While information systems research has produced remarkable leaps of progress, the emphasis has been on innovation rather than evaluation. From an applied perspective, it is not clear whether leaderboard results for selected problems generalize across data sets and domains. In this article, we focus on sentiment analysis methods and assess performance across applications by combining a meta-analysis of 216 comparative computer science publications on 271 unique data sets with experimental evaluations of novel language models. To the best of our knowledge, this constitutes the most comprehensive assessment of sentiment analysis accuracy to date. We find that method choice explains only 10% of the variance in accuracy. Controlling for contextual factors such as data set and paper characteristics increases explanatory power to over 75%, suggesting differences across research problems matter. We find that accuracy of sentiment analysis can indeed approach 95% but can also fall below 50%. This shows that more nuanced benchmarks, rather than best attainable values for selected use cases, are more meaningful for an applied audience. We compute benchmark values that take both methodological choices and application context into account.

Few blockchain centric projects have gone beyond their white paper or proofs-of-concept. While many have fallen below expectations and failed to address the fundamental issues of scalability, privacy, and trust distribution, there are a few “imperfect” projects that are making an impact on society. We describe the lessons learned from three projects and highlight their “improvisions” in achieving their vision of serving the underserved, and identify areas of possible improvements. Our research has shown that mass adoption of blockchain technology will accelerate in financial industry and supply chain with private permissioned blockchains, but these e-inclusion projects using “Inclusive” Blockchain will take a longer time with OnChain/OffChain complexities. A long-term view is needed to build a Noah’s Ark as the rush to build the Tower of Babel to harness short term gain may not bring net benefits to the economy and society.

With the exponential advancements in technology each year, a plethora of applications have been developed ranging from the most basic to the most advanced use cases. The amelioration in the smartphone industry has proven to be a major factor in the rapid development of mobile applications. The time and cost required to build apps increases due to the presence of multiple platforms, resulting in different programming for each. Hence, to mitigate these challenges, the solution was found in hybrid applications. To join this race of the digital world, a cross-platform application was developed for college canteen, which administered the users to place orders efficiently through a mobile application. Concurrently, this expunged the canteen problem of crowd management by notifying the customers after order completion, and assisted the staff to become dexterous with the use of this technology, thereby reducing manual labour. This paper discusses the technologies used, comparative studies and results obtained after the development of the prototype of the proposed system.

Internet of Things (IoT) envisions inter-connection among the locations and objects of our everyday lives including computers, vehicles, factories, smart cities, and electrical grids, using ordinary set of protocols and technologies that are used for inter-networking. Using host-to-host communication paradigms such as Internet Protocol (IP), for realizing this vision in real-time makes the situation more complex due to highly heterogeneous and resource-constrained devices that utilize multiple interfaces and security regimes for communication. Moreover, data exchange and security management are statistically independent of the context of the application. Concept of Named Data Networking (NDN) can address the basic cause of the aforementioned challenges and thus, can help in achieving the vision of IoT in a more secure, reliable, straightforward, and innovation-friendly manner. In this regard, this article discusses the concept of NDN in integration with IoT for easy network configuration, and compare and analyze the same with IP-based communication. Moreover, this paper explores versions of NDN simulator ‘ndnSIM’ for quantifying the effects of the various use cases.

Detecting text from natural images is an ongoing field of research. In this paper, we propose a text-extraction and detection algorithm pipeline for obtaining information about a particular book by using computer vision. Features of the book such as its reviews, rating and, the price can be displayed to the end user, thus helping people make an informed decision about the book on which they are going to spend time reading. The text detection algorithm uses edge-enhanced Maximally Stable External Region for identifying the text-blob segments accompanied by various non-text area filtering algorithms to find the bounding boxes. These bounding boxes are then chained together and undergo OCR, performed by the Tesseract engine. The results of the extracted text are further improved by performing post-processing NLP techniques such as domain-based OCR and typo correction. The method proposed in this paper has extended use cases in different areas of text detection from natural images.

This study looks generally at how regtech is evolving globally and more specifically at how central banks in developing countries are contemplating the use of regtech, the potential use cases, and any impact on financial inclusion. Our research involved desktop research and interviews with central bank officials, providers, donors and consultants involved in global regtech initiatives. We provide examples of regtech use by regulators in India, Mexico, Nigeria, Nepal and Philippines as well as a compendium of the types of technologies that are being used and could be potentially used as regtech.

Gathering information from the distributed Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as they are either synthetic, too small in size and complexity or do not provide means for a fine-grained evaluation. We propose LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. We evaluate state-of-the-art SPARQL endpoint federation approaches on this benchmark with respect to their query runtime, triple pattern-wise source selection, number of endpoints requests, and result completeness and correctness. Our evaluation results suggest that the performance of current SPARQL query federation systems on simple queries (in terms of total triple patterns, query result set sizes, execution time, use of SPARQL features etc.) does not reflect the systems performance on more complex queries. Moreover, current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets. This work was supported by the BMWi project SAKE. We are especially thankful to Helena Deus (Foundations Medicine, Cambridge, MA, USA) and Shanmukha Sampath (Democritus University of Thrace, Alexandroupoli, Greece) for providing real use case large data queries. peer-reviewed 2020-01-12

Since its first widespread implementation in 2009, distributed ledgers in general, and blockchain technology in particular, have rapidly become a part of the FinTech vernacular. In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional methods of verifying and settling transactions. In so doing, we discuss current and potential use cases of this technology and provide a business-oriented framework for proper as well as improper implementations and applications of blockchains and distributed ledgers.

The Internet of Things (IoT) requires pervasive collection and linkage of user data to provide personalised experiences based on potentially invasive inferences. Consistent identification of users and devices is necessary for this functionality, which poses risks to user privacy. The General Data Protection Regulation (GDPR) contains numerous provisions relevant to these risks, which may nonetheless be insufficient to ensure a fair balance between users’ and developers’ interests. A three-step transparency model is described based on known privacy risks of the IoT, the GDPR’s governing principles, and weaknesses in its relevant provisions. Eleven ethical guidelines are proposed for IoT developers and data controllers on how information about the functionality of the IoT should be shared with users above the GDPR’s legally binding requirements. Two use cases demonstrate how the guidelines apply in practice: IoT in public spaces and connected cities, and connected cars.