Nanopublications LDF server

Matches in Nanopublications for { ?s <http://purl.org/dc/terms/abstract> ?o ?g. }

• access.2023.3269660 abstract "Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as identifying dominant topics in scientific research. However, works addressing such problems focus on identifying static topics, providing snapshots that cannot show how those topics evolve. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers. After that, we enforce data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called “hot topics” in arbitrary instants and how they correlate." assertion.
• access.2023.3269660 abstract "Topic modeling comprises a set of machine learning algorithms that allow topics to be extracted from a collection of documents. These algorithms have been widely used in many areas, such as identifying dominant topics in scientific research. However, works addressing such problems focus on identifying static topics, providing snapshots that cannot show how those topics evolve. Aiming to close this gap, in this article, we describe an approach for dynamic article set analysis and classification. This is accomplished by querying open data of notable scientific databases via representational state transfers. After that, we enforce data management practices with a dynamic topic modeling approach on the associated metadata available. As a result, we identify research trends for a given field at specific instants and the referred terminology trends evolution throughout the years. It was possible to detect the associated lexical variation over time in published content, ultimately determining the so-called “hot topics” in arbitrary instants and how they correlate." assertion.
• gigabyte.99 abstract "In China, 65 types of venomous snakes exist, with the Chinese Cobra Naja atra being prominent and a major cause of snakebites in humans. Furthermore, N. atra is a protected animal in some areas, as it has been listed as vulnerable by the International Union for Conservation of Nature. Recently, due to the medical value of snake venoms, venomics has experienced growing research interest. In particular, genomic resources are crucial for understanding the molecular mechanisms of venom production. Here, we report a highly continuous genome assembly of N. atra, based on a snake sample from Huangshan, Anhui, China. The size of this genome is 1.67 Gb, while its repeat content constitutes 37.8% of the genome. A total of 26,432 functional genes were annotated. This data provides an essential resource for studying venom production in N. atra. It may also provide guidance for the protection of this species." assertion.
• DS-230059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-240059 abstract "Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey's divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case." assertion.
• DS-170001 abstract "Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled “Big Data to Knowledge (BD2K).” The main emphasis of the more than $200M allocated to that program has been on “Big Data;” the “Knowledge” component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application." assertion.
• DS-170001 abstract "Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled “Big Data to Knowledge (BD2K).” The main emphasis of the more than $200M allocated to that program has been on “Big Data;” the “Knowledge” component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application." assertion.
• DS-170001 abstract "Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled “Big Data to Knowledge (BD2K).” The main emphasis of the more than $200M allocated to that program has been on “Big Data;” the “Knowledge” component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application." assertion.
• DS-170001 abstract "Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled “Big Data to Knowledge (BD2K).” The main emphasis of the more than $200M allocated to that program has been on “Big Data;” the “Knowledge” component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application." assertion.
• DS-170002 abstract "Research on international conflict has mostly focused on explaining events such as the onset or termination of wars, rather than on trying to predict them. Recently, however, forecasts of political phenomena have received growing attention. Predictions of violent events, in particular, have been increasingly accurate using various methods ranging from expert knowledge to quantitative methods and formal modeling. Yet, we know little about the limits of these approaches, even though information about these limits has critical implications for both future research and policy-making. In particular, are our predictive inaccuracies due to limitations of our models, data, or assumptions, in which case improvements should occur incrementally. Or are there aspects of conflicts that will always remain fundamentally unpredictable? After reviewing some of the current approaches to forecasting conflict, I suggest avenues of research that could disentangle the causes of our current predictive failures." assertion.
• DS-170002 abstract "Research on international conflict has mostly focused on explaining events such as the onset or termination of wars, rather than on trying to predict them. Recently, however, forecasts of political phenomena have received growing attention. Predictions of violent events, in particular, have been increasingly accurate using various methods ranging from expert knowledge to quantitative methods and formal modeling. Yet, we know little about the limits of these approaches, even though information about these limits has critical implications for both future research and policy-making. In particular, are our predictive inaccuracies due to limitations of our models, data, or assumptions, in which case improvements should occur incrementally. Or are there aspects of conflicts that will always remain fundamentally unpredictable? After reviewing some of the current approaches to forecasting conflict, I suggest avenues of research that could disentangle the causes of our current predictive failures." assertion.
• DS-170002 abstract "Research on international conflict has mostly focused on explaining events such as the onset or termination of wars, rather than on trying to predict them. Recently, however, forecasts of political phenomena have received growing attention. Predictions of violent events, in particular, have been increasingly accurate using various methods ranging from expert knowledge to quantitative methods and formal modeling. Yet, we know little about the limits of these approaches, even though information about these limits has critical implications for both future research and policy-making. In particular, are our predictive inaccuracies due to limitations of our models, data, or assumptions, in which case improvements should occur incrementally. Or are there aspects of conflicts that will always remain fundamentally unpredictable? After reviewing some of the current approaches to forecasting conflict, I suggest avenues of research that could disentangle the causes of our current predictive failures." assertion.
• DS-170003 abstract "Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment." assertion.
• DS-170003 abstract "Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment." assertion.
• DS-170003 abstract "Data science is a young and rapidly expanding field, but one which has already experienced several waves of temporarily-ubiquitous methodological fashions. In this paper we argue that a diversity of ideas and methodologies is crucial for the long term success of the data science community. Towards the goal of a healthy, diverse ecosystem of different statistical models and approaches, we review how ideas spread in the scientific community and the role of incentives in influencing which research ideas scientists pursue. We conclude with suggestions for how universities, research funders and other actors in the data science community can help to maintain a rich, eclectic statistical environment." assertion.
• a97f-egyk abstract "The Data Citation Principles cover purpose, function and attributes of citations. These principles recognize the dual necessity of creating citation practices that are both human understandable and machine-actionable." assertion.
• FAIRsharing.yknezb abstract "DataCite is a leading global non-profit organisation that provides persistent identifiers (DOIs) for research data. Their goal is to help the research community locate, identify, and cite research data with confidence. They support the creation and allocation of DOIs and accompanying metadata. They provide services that support the enhanced search and discovery of research content. They also promote data citation and advocacy through community-building efforts and responsive communication and outreach materials. DataCite gathers metadata for each DOI assigned to an object. The metadata is used for a large index of research data that can be queried directly to find data, obtain stats and explore connections. All the metadata is free to access and review. To showcase and expose the metadata gathered, DataCite provides an integrated search interface, where it is possible to search, filter and extract all the details from a collection of millions of records." assertion.
• sciencedb.00012. abstract "Archival Resource Keys (ARKs) are another widely used persistent identifier, supported by the California Digital Library, in collaboration with DuraSpace. ARKs work similarly to DOIs, but are more permissive in design." assertion.
• sciencedb.00013. abstract "N2T.net (Name-to-Thing) is a 'resolver', a kind of web server that stores little content itself and usually forwards incoming requests to other servers. Similar to URL shorteners like bit.ly, N2T serves content indirectly." assertion.
• sciencedb.00013. abstract "N2T.net (Name-to-Thing) is a 'resolver', a kind of web server that stores little content itself and usually forwards incoming requests to other servers. Similar to URL shorteners like bit.ly, N2T serves content indirectly." assertion.
• sciencedb.00014. abstract "EZID is a service and a platform provided by the California Digital Library for creating, registering, and managing persistent identifiers for scholarly research and cultural heritage objects, including but not limited to articles, datasets, images, and specimens." assertion.
• FAIRsharing.hFLKCn abstract "The digital object identifier (DOI) system originated in a joint initiative of three trade associations in the publishing industry (International Publishers Association; International Association of Scientific, Technical and Medical Publishers; Association of American Publishers). The system was announced at the Frankfurt Book Fair 1997. The International DOI Foundation (IDF) was created to develop and manage the DOI system, also in 1997. The DOI system was adopted as International Standard ISO 26324 in 2012. The DOI system implements the Handle System and adds a number of new features. The DOI system provides an infrastructure for persistent unique identification of objects of any type. The DOI system is designed to work over the Internet. A DOI name is permanently assigned to an object to provide a resolvable persistent network link to current information about that object, including where the object, or information about it, can be found on the Internet. While information about an object can change over time, its DOI name will not change. A DOI name can be resolved within the DOI system to values of one or more types of data relating to the object identified by that DOI name, such as a URL, an e-mail address, other identifiers and descriptive metadata. The DOI system enables the construction of automated services and transactions. Applications of the DOI system include but are not limited to managing information and documentation location and access; managing metadata; facilitating electronic transactions; persistent unique identification of any form of any data; and commercial and non-commercial transactions. The content of an object associated with a DOI name is described unambiguously by DOI metadata, based on a structured extensible data model that enables the object to be associated with metadata of any desired degree of precision and granularity to support description and services. The data model supports interoperability between DOI applications. The scope of the DOI system is not defined by reference to the type of content (format, etc.) of the referent, but by reference to the functionalities it provides and the context of use. The DOI system provides, within networks of DOI applications, for unique identification, persistence, resolution, metadata and semantic interoperability." assertion.
• DSP4FAIR abstract "Data Stewardship Plan (DSP) templates prompt users to consider various issues but typically have no requirements for actual implementation choices. But as FAIR methodologies mature the DSP will become a more directive “how to” manual for making data FAIR." assertion.
• DSP4FAIR abstract "Data Stewardship Plan (DSP) templates prompt users to consider various issues but typically have no requirements for actual implementation choices. But as FAIR methodologies mature the DSP will become a more directive “how to” manual for making data FAIR." assertion.
• DS-230058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-170011 abstract "Artificial intelligence will play an increasingly more prominent role in scientific research ecosystems, and will become indispensable as more interdisciplinary science questions are tackled. While in recent years computers have propelled science by crunching through data and leading to a data science revolution, qualitatively different scientific advances will result from advanced intelligent technologies for crunching through knowledge and ideas. We propose seven principles for developing thoughtful artificial intelligence, which will turn intelligent systems into partners for scientists. We present a personal perspective on a research agenda for thoughtful artificial intelligence, and discuss its potential for data science and scientific discovery." assertion.
• DS-170011 abstract "Artificial intelligence will play an increasingly more prominent role in scientific research ecosystems, and will become indispensable as more interdisciplinary science questions are tackled. While in recent years computers have propelled science by crunching through data and leading to a data science revolution, qualitatively different scientific advances will result from advanced intelligent technologies for crunching through knowledge and ideas. We propose seven principles for developing thoughtful artificial intelligence, which will turn intelligent systems into partners for scientists. We present a personal perspective on a research agenda for thoughtful artificial intelligence, and discuss its potential for data science and scientific discovery." assertion.
• DS-170011 abstract "Artificial intelligence will play an increasingly more prominent role in scientific research ecosystems, and will become indispensable as more interdisciplinary science questions are tackled. While in recent years computers have propelled science by crunching through data and leading to a data science revolution, qualitatively different scientific advances will result from advanced intelligent technologies for crunching through knowledge and ideas. We propose seven principles for developing thoughtful artificial intelligence, which will turn intelligent systems into partners for scientists. We present a personal perspective on a research agenda for thoughtful artificial intelligence, and discuss its potential for data science and scientific discovery." assertion.
• DS-170011 abstract "Artificial intelligence will play an increasingly more prominent role in scientific research ecosystems, and will become indispensable as more interdisciplinary science questions are tackled. While in recent years computers have propelled science by crunching through data and leading to a data science revolution, qualitatively different scientific advances will result from advanced intelligent technologies for crunching through knowledge and ideas. We propose seven principles for developing thoughtful artificial intelligence, which will turn intelligent systems into partners for scientists. We present a personal perspective on a research agenda for thoughtful artificial intelligence, and discuss its potential for data science and scientific discovery." assertion.
• DS-170004 abstract "Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists’ toolbox." assertion.
• DS-170005 abstract "The majority of economic sectors are transformed by the abundance of data. Smart grids, smart cities, smart health, Industry 4.0 impose to domain experts requirements for data science skills in order to respond to their duties and the challenges of the digital society. Business training or replacing domain experts with computer scientists can be costly, limiting for the diversity in business sectors and can lead to sacrifice of invaluable domain knowledge. This paper illustrates experience and lessons learnt from the design and teaching of a novel cross-disciplinary data science course at a postgraduate level in a top-class university. The course design is approached from the perspectives of the constructivism and transformative learning theory. Students are introduced to a guideline for a group research project they need to deliver, which is used as a pedagogical artifact for students to unfold their data science skills as well as reflect within their team their domain and prior knowledge. In contrast to other related courses, the course content illustrated is designed to be self-contained for students of different discipline. Without assuming certain prior programming skills, students from different discipline are qualified to practice data science with open-source tools at all stages: data manipulation, interactive graphical analysis, plotting, machine learning and big data analytics. Quantitative and qualitative evaluation with interviews outlines invaluable lessons learnt." assertion.
• DS-170006 abstract "Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future." assertion.
• DS-170007 abstract "In modern machine learning, raw data is the preferred input for our models. Where a decade ago data scientists were still engineering features, manually picking out the details we thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal more ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of expressing heterogeneous knowledge naturally in various domains, in as usable a form as possible, and satisfying as many use cases as possible. In this position paper, we argue that the knowledge graph is a suitable candidate for this data model. We further describe current research and discuss some of the promises and challenges of this approach." assertion.
• DS-170008 abstract "Modern biomedical research is complex and requires a cross section of experts collaborating using multi-, inter-, or transdisciplinary approaches to address scientific questions. Known as team science, such approaches have become so critical it has given rise to a new field – the science of team science. In biomedical research, data scientists often play a critical role in team-based collaborations. Integration of data scientists into research teams has multiple advantages to the clinical and translational investigator as well as to the data scientist. Clinical and translational investigators benefit from having an invested dedicated collaborator who can assume principal responsibility for essential data-related activities, while the data scientist can build a career developing tools that are relevant and data-driven. Participation in team science, however, can pose challenges. One particular challenge is the ability to appropriately evaluate the data scientist’s scholarly contributions, necessary for promotion. Only a minority of academic health centers have attempted to address this challenge. In order for team science to thrive on academic campuses, leaders of institutions need to hire data science faculty for the purpose of doing team science, with novel systems in place that incentivize the data scientist’s engagement in team science and that allow for appropriate evaluation of performance. Until such systems are adopted at the institutional level, the ability to conduct team science to address modern biomedical research with its increasingly complex data needs will be compromised. Fostering team science on campuses by putting supportive systems in place will benefit not only clinical and translational investigators as well as data scientists, but also the larger academic institution." assertion.
• DS-240063 abstract "Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions." assertion.
• DS-240063 abstract "Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions." assertion.
• DS-240063 abstract "Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions." assertion.
• DS-240063 abstract "Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-170010 abstract "Various approaches and systems have been presented in the context of scholarly communication for what has been called semantic publishing. Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.
• DS-220058 abstract "Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers." assertion.