scholarly journals An assessment of the translation-specificity of over-represented multi-word patterns in Swedish fiction texts translated from English

2021 ◽  
Vol 1 ◽  
Author(s):  
P-O Nilsson
Keyword(s):  
Data Driven ◽  
The Status ◽  
Word Pattern

This paper discusses in what sense an over-represented multi-word pattern in a corpus of translated texts can be said to be typical of translated text. The purpose of the discussion is to assess, from a quantitative as well as from a qualitative perspective, the status of translational collocation data retrieved through data-driven methods from a comparable and parallel aligned cor¬pus of English and Swedish original and translated texts. The study focuses on the explicitation of clausal relations in translations from English into Swedish. In some cases, lexical and grammatical contrast lead to explicita¬tion, but in others explicitation is due to different factors.

scholarly journals Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

2020 ◽  
Vol 12 (3) ◽  
pp. 495
Author(s):  
Alessia Goffi ◽  
Gloria Bordogna ◽  
Daniela Stroppiana ◽  
Mirco Boschetti ◽  
Pietro Alessandro Brivio
Keyword(s):  
Remote Sensing ◽  
Geographic Information ◽  
Data Driven ◽  
Local Context ◽  
Contributing Factors ◽  
Optical Images ◽  
Environmental Status ◽  
The Status ◽  
Status Indicator

The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts’ interpretations to define the contributing factors from which partial evidence of the environmental status are computed by processing multispectral images. Furthermore, it computes an environmental status indicator (ESI) map by aggregating the partial evidence degrees through a learning mechanism, exploiting volunteered geographic information (VGI). The approach is capable of capturing the specificities of local context, as well as to cope with the subjectivity of experts’ interpretations. The proposal is applied to map the status of standing water areas (i.e., water bodies and rivers and human-driven or natural hazard flooding) using multispectral optical images by ESA Sentinel-2 sources. VGI comprises georeferenced observations created both in situ by agronomists using a mobile application and by photointerpreters interacting with a geographic information system (GIS) using several information layers. Results of the validation experiments were performed in three areas of Northern Italy characterized by distinct ecosystems. The proposal showed better performances than traditional methods based on single spectral indexes.

A Survey of P2P Data-Driven Live Streaming Systems

2011 ◽  
pp. 295-310
Author(s):  
Fabio Pianese
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Data Driven ◽  
Live Streaming ◽  
Traditional Concept ◽  
Internet Measurement ◽  
Systems Software ◽  
The Status ◽  
Research Problems ◽  
Streaming Systems

Data-driven peer-to-peer live streaming systems challenge and extend the traditional concept of overlay for application-layer multicast data distribution. In such systems, software nodes propagate individually-named, ordered segments of the stream (called chunks) by independently conducting exchanges with their neighboring peers. Chunk exchanges are solely based on information that is available locally, such as the status of a node’s receive buffer and an approximate knowledge of the buffer contents of its neighbors. In this Chapter, we motivate and retrace the emergence of P2P data-driven live streaming systems, describe their internal data structures and fundamental mechanisms, and provide references to a number of known analytical bounds on the rate and delay that can be achieved using many relevant chunk distribution strategies. We then conclude this survey by reviewing the deployment status of the most popular commercial systems, the results from large-scale Internet measurement studies, and the open research problems.

THE MORALISATION OF PREDICTIVITY IN THE AGE OF DATA-DRIVEN SURVEILLANCE

2020 ◽  
Author(s):  
Sun-ha Hong
Keyword(s):  
Emerging Technologies ◽  
Human Subjects ◽  
Rapid Expansion ◽  
Data Driven ◽  
Surveillance Systems ◽  
Public And Private ◽  
The Public ◽  
The Status ◽  
The Many ◽  
Machine Readable

This paper argues that emerging technologies of datafication are intensifying a moralisation of predictivity. On one hand, this describes the growing pressure to quantify and predict every kind of social problem. Reluctance to adopt emerging technologies of surveillance is construed as abdication of a moral responsibility via negligence to inevitable progress. On the other hand, it describes the corresponding demand that human subjects learn to live in more predictable and machine-readable ways, adapting to the flaws and ambiguities of imperfect technosystems. This argument echoes that of Joseph Weizenbaum (1976), a pioneer of early AI research and the inventor of the ELIZA chatbot: that well in advance of machines fully made in our image, it is the human subjects that are asked to render themselves more compatible and legible to those machines. Drawing from a book-length research project into the public presentation of surveillance technologies, I show how messy data, arbitrary classifications, and other uncertainties become fabricated into the status of reliable predictions. Specifically, the bulk of the presentation will examine the rapid expansion of counter-terrorist surveillance systems in 2010’s America. All in all, the moralisation of predictivity helps suture the many imperfections of data-driven surveillance, and provide justificatory cover for their breakneck expansion across the boundaries of public and private. They perpetuate the normative expectation that what can be predicted must be, and what needs to be predicted surely can be. In the process, spaces for human discretion, informal norms, and sensitivity to human circumstance are being squeezed out.

scholarly journals Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

2021 ◽  
Author(s):  
I-Chun Sun ◽  
Renchi Cheng ◽  
Kuo-Shen Chen
Keyword(s):  
Artificial Intelligence ◽  
Performance Index ◽  
Prediction Accuracy ◽  
Training Data ◽  
Data Driven ◽  
Data Types ◽  
Physical Knowledge ◽  
Status Monitoring ◽  
The Status ◽  
Data Driven Approach

Abstract The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.

scholarly journals The Essential Relationship between Information Ethics and Artificial Intelligence

2021 ◽  
Vol 29 ◽  
Author(s):  
Coetzee Bester ◽  
Rachel Fischer
Keyword(s):  
Information Ethics ◽  
Data Driven ◽  
Machine Ethics ◽  
Robot Ethics ◽  
Ethical Concerns ◽  
Data Ethics ◽  
Academic History ◽  
The Status ◽  
Knowledge Claims

This article rethinks the position of Information Ethics (IE) vis-à-vis the growing discipline of the ethics of AI. While IE has a long and respected academic history, the discipline of the ethics of AI is much younger. The scope of the latter discipline has exploded in the last decade in sync with the explosion of data driven AI. Currently, the ethics of AI as a discipline can be said to have sub-divided at least into machine ethics, robot ethics, data ethics, and neuro ethics. The argument presented here is that ethics of AI can from one perspective be viewed as a sub-discipline of IE. IE is at the heart of ethical concerns about the potential de-humanising impact of AI technologies, as it addresses issues relating to communication, the status of knowledge claims, and the quality of media-generated information, among many others. Perhaps the single most concerning ethical concern in the context of data-driven AI technology is the rise of new social narratives that threaten humans’ special sense of agency and, and this is firstly an IE concern. The article thus argues for the independent position of IE as well as for its position as the core, over-arching discipline, of the ethics of AI.

scholarly journals Optical inline monitoring of the burnish surface in the punching process

2021 ◽  
Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer
Keyword(s):  
Quantitative Description ◽  
Quality Parameters ◽  
Data Driven ◽  
Cutting Surface ◽  
The Status ◽  
Punching Process ◽  
Images Processing ◽  
Triangulation Sensor ◽  
Single Part

AbstractPunching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.

Towards Data Driven Stamping Process Modelling by Optical Inline Monitoring of Cutting Surface Parameters

2021 ◽  
Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer
Keyword(s):  
Quantitative Description ◽  
Quality Metrics ◽  
Process Modelling ◽  
Data Driven ◽  
Cutting Surface ◽  
Image Capturing ◽  
The Status ◽  
Data Driven Approach ◽  
Cut Surface

Abstract Stamping is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. For this reason, a highly efficient process is crucial. The cutting process is sensitive to a multitude of parameters. A process that is not correctly adjusted is subject to considerable wear and therefore not efficient. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a cutting part, which in turn requires a quantitative description of the quality of a part. A data driven approach allows to meet this challenge and quantify these influences. We developed an optical inline monitoring system, which consists of a image capturing, triangulation and image processing, that is capable of deriving quality metrics from 2D images and triangulation data of the cutting surface, directly inside the machine and without affecting the process. We identify features that can be automatically turned into quality metrics, like fraction of the burnish surface or the cut surface inclination. As an application, we show that the status of tool wear can be inferred by monitoring the burnish surface, with immediate consequences for predictive maintenance. Furthermore, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data driven process modelling and ultimately the implementation of intelligent stamping machines.

scholarly journals New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 27-41 ◽  
Author(s):  
Kenneth R. Koedinger ◽  
Emma Brunskill ◽  
Ryan S.J.d. Baker ◽  
Elizabeth A. McLaughlin ◽  
John Stamper
Keyword(s):  
System Development ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Learning Rule ◽  
Data Driven ◽  
Educational Technologies ◽  
Tutoring Systems ◽  
The Status ◽  
Markov Decision ◽  
Process Classification

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

scholarly journals What’s the Problem with Learning Analytics?

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Neil Selwyn
Keyword(s):  
Political Economy ◽  
Public Education ◽  
Learning Analytics ◽  
Status Quo ◽  
Data Driven ◽  
Vulnerable Groups ◽  
The Political ◽  
Economic Factors ◽  
The Status

This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective — positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning analytics to entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning “data economy.” In light of these charges, the article briefly considers some possible areas of change. These include the design of analytics applications that are more open and accessible, that offer genuine control and oversight to users, and that better reflect students’ lived reality. The article also considers ways of rethinking the political economy of the learning analytics industry. Above all, learning analytics researchers need to begin talking more openly about the values and politics of data-driven analytics technologies as they are implemented along mass lines throughout school and university contexts.

scholarly journals Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study (Preprint)

2020 ◽  
Author(s):  
Haridimos Kondylakis ◽  
Cristian Axenie ◽  
Dhundy (Kiran) Bastola ◽  
Dimitrios G Katehakis ◽  
Angelina Kouroubali ◽  
...  
Keyword(s):  
Focus Group ◽  
Patient Engagement ◽  
Cancer Care ◽  
Data Driven ◽  
Practical Implementation ◽  
Focus Group Study ◽  
Patient Journey ◽  
Horizon 2020 ◽  
Cancer Management ◽  
The Status

BACKGROUND The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. OBJECTIVE This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects. METHODS Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. RESULTS Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. CONCLUSIONS Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

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