Paper[edit]

Paper
edit
description
id	Vol-3194/paper74
wikidataid	Q117344963→Q117344963
title	An XAI Framework for Automated Allocation and Exploitation of Resources
pdfUrl	https://ceur-ws.org/Vol-3194/paper74.pdf
dblpUrl	https://dblp.org/rec/conf/sebd/Marcia22
volume	Vol-3194→Vol-3194
session	→

An XAI Framework for Automated Allocation and Exploitation of Resources[edit]

An XAI Framework for Automated Allocation and
Exploitation of Resources
Diego Marcia
Mathematics and Computer Science Department at Università degli Studi di Cagliari (UniCA), Italy


                                      Abstract
                                      I propose an explainable-AI framework to join high-level system specifications with the probabilistic
                                      output of Machine Learning algorithms via a formal reasoning tool. The main application guiding the
                                      planning of this research activity is an Intelligent System to manage a smart building environment while
                                      aiming at minimizing energy consumption. However, it can be generalised to allow the management of
                                      any system with well-defined sets of constraints and rules expressed in a formal language, which has to
                                      take in consideration transient user requirements expressed in Natural Language.

                                      Keywords
                                      Logic programming, probabilistic programming, machine learning, natural language interfaces




1. Introduction
Controlling a smart building environment requires having both a set of solid default behaviours,
as well as the ability to adapt to contingent changes from the former, in line with the temporary
needs of the users. Aim of my project is providing a two-level programming framework for such
systems. At the lower level, the framework should provide planners with a concise language to
define the expected components of the system, together with an objective function the system
should try to optimise. Such optimisation might be subjected to both general constraints defined
at planning time, and to transient constraints defined at run-time by the users. The handling
of the latter constitutes the higher level part of the framework, where users with no technical
expertise should be able to define short- to long-term behaviours the system is expected to
abide to, by using a Natural Language Interface (NLI). Being subject to general constraints,
the optimisation of the objective function might require planning the availability of resources
(in a smart building, these constraints can express concepts like “hot water should always be
available"). For this reason, the system is expected to be able to schedule several actions on its
components, together with forecasting the conditions it will most likely operate in the short
term. This requires the use of Machine Learning (ML) algorithms and techniques, which allow to
anticipate both the use of the available resources by the users, and the external (environmental)
conditions of operation. While Neural Networks are a current hot topic in similar tasks carried
under such uncertain conditions, they tend to act as an opaque layer between users and the

SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ diego.marcia@unica.it (D. Marcia)
€ diegomarcia.github.io (D. Marcia)
� 0000-0003-2576-8836 (D. Marcia)
                                    © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073
                                    CEUR Workshop Proceedings (CEUR-WS.org)
�governance of the system. For this reason, one of the objectives of this project, is avoiding the
use of Neural Networks to carry the planning and managing of resources, in favour of highly
explainable inferences. One of the options currently being taken into consideration for the
architecture of this explainable-AI framework, is the use of Logical Programming for governing
the behaviour of the system.


2. Overview of the framework
In its most general formulation, the framework operates on two levels: on the lower level, the
framework is geared toward Intelligent Systems (IS) developers and architects, while the higher
level is intended to make such ISs usable by end-users. However, it should be noted that domain
experts taking part in the initial planning of the system’s architecture might lack the expertise
required for the use of developer tools, so the model of interaction exposed to end users might
be re-purposed for use by such actors in the system definition phase.
The two levels have different focus. The components that make up the real-world system to
be governed, most likely sensors and actuators in an automation project, will be defined in
the lower level. A subset of these components will be used in defining an Objective Function
(OF), which the IS will try to optimise. The definition of the Objective Function will most likely
allow the use of constraints. To make a use-case example, the system architects of a Building
Automation scenario would be interested in reducing the electric energy consumption, while
ensuring hot water will always be available at the faucets. End users will most likely have little
to no knowledge of the rules governing the lower layer, and will exclusively interact with the
upper layer: to them, the IS exposes uniquely a NLI, or a similarly user-friendly interface. The
users would require the system to deviate from the baseline behaviour, to satisfy their needs.
Such requirements would be expressed in two forms: immediate commands, and scenarios. The
former are commands whose execution require an immediate and often temporary change in
the system configuration (“Open the kitchen window”), while the scenarios are commands to be
repeated with a certain periodicity, or more generally, which are not bound in the immediate
time (“Turn the AC on on afternoons”).
It is possible that transient user requirements conflict with the satisfaction of the OF and its
constraints. Two follow-ups are possible for handling this situation: either giving priority to
the user needs, in other words taking the user input as an additional constraint, or initiate a
NLI conversational session with the user instead, trying to understand if the conflict arose from
a flawed interpretation of their input, or if they wish to deviate from the default behaviour, and
to what extent.
Since the real-world system being managed has to deal with user preferences and expectations,
as well as meet some pre-defined performance criteria, end users and system maintainers
would both benefit from some degree of introspection by the IS. For this reason, the tools
and techniques falling in the domain of explainable-AI will most likely be preferred in the IS
development. Although deep neural networks can be used for the purpose of predicting user
needs in home automation scenarios [1] [2], they usually lack the aforementioned introspection
capabilities this project aims at achieving. One of the main objectives is indeed giving the
users a full track of the steps that led to a certain autonomous decision from the IS, starting
�from the end-user natural language input and the system architect optimisation objectives and
constraints.


3. Planned techniques for the lower level
The core idea of the project, is the integration of Machine Learning techniques with logic
programming in an hybrid XAI framework, with an emphasis geared toward the latter. With
this setting in mind, the developers will be provided with a Logic Programming (LP) interface
(most likely, a Prolog dialect) to express the OF and its constraints. Not only that, but the
framework itself will be centred around a reasoning engine to forecast the future state of the
components involved in the OF calculation (sensors, in a Building Automation scenario), whose
role will be the intelligent allocation of resources to satisfy the requirements. The role of the
ML techniques would be providing predictions on future states of the system, while all the
reasoning based on these values would be carried via LP techniques and tools. The developers
should in this phase provide for each component of the system, be it active or passive, the code
lambdas to interact with their low-level primitives for input/output.
It should be noted that the topic of intelligent scheduling of appliances use has been already
studied in recent years, and fully explainable methods for it have been proposed, even taking into
consideration the negative cost of energy given by photovoltaic panels [3] or in the absence of
absolute certainty over the user preferences [4], but there still is room for possible improvements,
like the integration with predictive models of cost estimation and with the detection of user
preferences, or the the improvement of scheduling flexibility.
Regarding the integration between ML techniques, or more generally probabilistic predictions of
future conditions with a LP framework, one interesting setting worth exploring is DeepProbLog
[5], which allows combining a probabilistic variant of ProLog [6] with the probabilistic outputs
of tools such as neural networks.
The LP interface is not the only possible tool for developers and architects of the IS. the first of
two alternative techniques being taken into consideration is the use of a structured DDL, which
might allow an unambiguous enumeration and description of the system components without
requiring the knowledge of a programming language. This DDL, still to be fixed, might also help
in the concise expression of the OF constraints. Another alternative is represented by the use of
an NLI, such as in the upper layer. The System architects, who might be expert in the problem
domain, are not expected to also be experts in software developing techniques: their description
of the system might therefore come in the form of semi-structured text written in a Natural
Language. From this, the framework might extract key understandings of the components
and the rules governing the planned IS, and generate code stubs for the developers. Such a
pre-processing step might also be useful in identifying inconsistencies and contradictions in
the requirements description from the architects, requiring their active disambiguation and/or
emendation, or providing developers with attention flags.
�4. Planned techniques for the upper (NLI) level
The upper level provides an interface to non-technical users of the IS. Therefore, it cannot provide
for formal programming languages, and should focus on more natural interaction models. The
most obvious choice would be a Natural Language Interface, possibly but not necessarily in the
form of a speech recognition module, which gives —especially in its conversational interaction
mode— a good compromise between the user’s spontaneous expression of their needs and the
guidance required for compliance with the IS definition. Models that try to find a compromise
between precision and user engagement have already been studied [7]. I expect the accepted
language to be a rich form of Controlled Natural Language, which will not be designed with
minimality in mind, so as to allow the user to express their inputs in more than one way.
This latter aspect is of particular interest. The IS components are enumerated and described in a
formal language, with names and actions which are bounded to specific code lambdas. Therefore,
it is a requirement that the framework’s reasoning engine operates on these specific names.
However the users, not being aware of the low-level specification, might use different and
ambiguous terms to refer to the System components. This requires the disambiguation of their
input. A natural choice for this activity would be the use of word embeddings to find the most
likely match between what the user asked to operate on and what is available to the inference
engine. This has already been applied to different domains [8]. The conversational interaction
would be effective in determining whether the user simply called the right object with the
wrong name, or asked for an action the IS is not capable of carrying. The same applies to user
requests which might conflict with the general behaviour defined by the System developers.
It should be noted that these same techniques might be used for providing the System architects
with a user-friendly interface for defining and amending their high-level specification [9].


5. Considerations on the expected milestones
At the time of writing, the PhD is in the fifth month of year one. The first implementation
step would be exploring the feasibility of the NLI, in particular with respect to the definition
of a first form of CNL and the use of word embeddings. The output of this layer would be an
intermediate form between a Natural Language and the language to be used by the inference
engine. I expect this phase to be over before the second half of Y2.
With the start of Y2, it should already be clear with language will be used in the inference phase,
together with the specification language to be used by system developers. Therefore, by the
first half of Y3, the lower level of the framework should be complete. The second half of Y3
would be devoted to integrating the two layers.


Acknowledgments
Diego Marcia’s PhD is funded via a NOP Research and Innovation 2014-2020 grant, Action IV.5
– PhD programmes on green topics http://www.ponricerca.gov.it/opportunita/react-eu-phd-
programmes-on-innovation-and-green-topics/.
�References
[1] D. Popa, F. Pop, C. Serbanescu, A. Castiglione, Deep learning model for home automation
    and energy reduction in a smart home environment platform, Neural Computing and
    Applications 31 (2019) 1317 – 1337. doi:10.1007/s00521-018-3724-6.
[2] M. Nasir, K. Muhammad, A. Ullah, J. Ahmad, S. Wook Baik, M. Sajjad, Enabling automation
    and edge intelligence over resource constraint iot devices for smart home, Neurocomputing
    491 (2022) 494–506. doi:https://doi.org/10.1016/j.neucom.2021.04.138.
[3] F. A. Qayyum, M. Naeem, A. S. Khwaja, A. Anpalagan, L. Guan, B. Venkatesh, Appliance
    scheduling optimization in smart home networks, IEEE Access 3 (2015) 2176–2190. doi:10.
    1109/ACCESS.2015.2496117.
[4] V. Nguyen, W. Yeoh, T. C. Son, V. Kreinovich, T. Le, A scheduler for smart homes with proba-
    bilistic user preferences, in: M. Baldoni, M. Dastani, B. Liao, Y. Sakurai, R. Zalila Wenkstern
    (Eds.), PRIMA 2019: Principles and Practice of Multi-Agent Systems, Springer International
    Publishing, 2019, pp. 138–152. doi:10.1007/978-3-030-33792-6_9.
[5] R. Manhaeve, S. Dumančić, A. Kimmig, T. Demeester, L. De Raedt, Neural probabilistic
    logic programming in deepproblog, Artificial Intelligence 298 (2021) 103504. URL: https:
    //www.sciencedirect.com/science/article/pii/S0004370221000552. doi:https://doi.org/
    10.1016/j.artint.2021.103504.
[6] L. De Raedt, A. Kimmig, Probabilistic (logic) programming concepts, Machine Learning
    100 (2015) 5 – 47. doi:https://doi.org/10.1007/s10994-015-5494-z.
[7] N. C. Truong, T. Baarslag, S. Ramchurn, L. Tran-Thanh, Interactive scheduling of appliance
    usage in the home, in: 25th International Joint Conference on Artificial Intelligence (IJCAI-
    16), 2016, p. 7. URL: http://eprints.soton.ac.uk/id/eprint/396670.
[8] A. Morales-Garzón, J. Gómez-Romero, M. J. Martin-Bautista, A word embedding-based
    method for unsupervised adaptation of cooking recipes, IEEE Access 9 (2021) 27389 – 27404.
    doi:10.1109/ACCESS.2021.3058559.
[9] S. Mishra, A. Sharma, Automatic word embeddings-based glossary term extraction from
    large-sized software requirements, in: N. Madhavji, L. Pasquale, A. Ferrari, S. Gnesi
    (Eds.), Requirements Engineering: Foundation for Software Quality, Springer International
    Publishing, 2020, pp. 203 – 218. doi:10.1007/978-3-030-44429-7_15.
�

An XAI Framework for Automated Allocation and Exploitation of Resources[edit]

An XAI Framework for Automated Allocation and
Exploitation of Resources
Diego Marcia
Mathematics and Computer Science Department at Università degli Studi di Cagliari (UniCA), Italy


                                      Abstract
                                      I propose an explainable-AI framework to join high-level system specifications with the probabilistic
                                      output of Machine Learning algorithms via a formal reasoning tool. The main application guiding the
                                      planning of this research activity is an Intelligent System to manage a smart building environment while
                                      aiming at minimizing energy consumption. However, it can be generalised to allow the management of
                                      any system with well-defined sets of constraints and rules expressed in a formal language, which has to
                                      take in consideration transient user requirements expressed in Natural Language.

                                      Keywords
                                      Logic programming, probabilistic programming, machine learning, natural language interfaces




1. Introduction
Controlling a smart building environment requires having both a set of solid default behaviours,
as well as the ability to adapt to contingent changes from the former, in line with the temporary
needs of the users. Aim of my project is providing a two-level programming framework for such
systems. At the lower level, the framework should provide planners with a concise language to
define the expected components of the system, together with an objective function the system
should try to optimise. Such optimisation might be subjected to both general constraints defined
at planning time, and to transient constraints defined at run-time by the users. The handling
of the latter constitutes the higher level part of the framework, where users with no technical
expertise should be able to define short- to long-term behaviours the system is expected to
abide to, by using a Natural Language Interface (NLI). Being subject to general constraints,
the optimisation of the objective function might require planning the availability of resources
(in a smart building, these constraints can express concepts like “hot water should always be
available"). For this reason, the system is expected to be able to schedule several actions on its
components, together with forecasting the conditions it will most likely operate in the short
term. This requires the use of Machine Learning (ML) algorithms and techniques, which allow to
anticipate both the use of the available resources by the users, and the external (environmental)
conditions of operation. While Neural Networks are a current hot topic in similar tasks carried
under such uncertain conditions, they tend to act as an opaque layer between users and the

SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ diego.marcia@unica.it (D. Marcia)
€ diegomarcia.github.io (D. Marcia)
� 0000-0003-2576-8836 (D. Marcia)
                                    © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073
                                    CEUR Workshop Proceedings (CEUR-WS.org)
�governance of the system. For this reason, one of the objectives of this project, is avoiding the
use of Neural Networks to carry the planning and managing of resources, in favour of highly
explainable inferences. One of the options currently being taken into consideration for the
architecture of this explainable-AI framework, is the use of Logical Programming for governing
the behaviour of the system.


2. Overview of the framework
In its most general formulation, the framework operates on two levels: on the lower level, the
framework is geared toward Intelligent Systems (IS) developers and architects, while the higher
level is intended to make such ISs usable by end-users. However, it should be noted that domain
experts taking part in the initial planning of the system’s architecture might lack the expertise
required for the use of developer tools, so the model of interaction exposed to end users might
be re-purposed for use by such actors in the system definition phase.
The two levels have different focus. The components that make up the real-world system to
be governed, most likely sensors and actuators in an automation project, will be defined in
the lower level. A subset of these components will be used in defining an Objective Function
(OF), which the IS will try to optimise. The definition of the Objective Function will most likely
allow the use of constraints. To make a use-case example, the system architects of a Building
Automation scenario would be interested in reducing the electric energy consumption, while
ensuring hot water will always be available at the faucets. End users will most likely have little
to no knowledge of the rules governing the lower layer, and will exclusively interact with the
upper layer: to them, the IS exposes uniquely a NLI, or a similarly user-friendly interface. The
users would require the system to deviate from the baseline behaviour, to satisfy their needs.
Such requirements would be expressed in two forms: immediate commands, and scenarios. The
former are commands whose execution require an immediate and often temporary change in
the system configuration (“Open the kitchen window”), while the scenarios are commands to be
repeated with a certain periodicity, or more generally, which are not bound in the immediate
time (“Turn the AC on on afternoons”).
It is possible that transient user requirements conflict with the satisfaction of the OF and its
constraints. Two follow-ups are possible for handling this situation: either giving priority to
the user needs, in other words taking the user input as an additional constraint, or initiate a
NLI conversational session with the user instead, trying to understand if the conflict arose from
a flawed interpretation of their input, or if they wish to deviate from the default behaviour, and
to what extent.
Since the real-world system being managed has to deal with user preferences and expectations,
as well as meet some pre-defined performance criteria, end users and system maintainers
would both benefit from some degree of introspection by the IS. For this reason, the tools
and techniques falling in the domain of explainable-AI will most likely be preferred in the IS
development. Although deep neural networks can be used for the purpose of predicting user
needs in home automation scenarios [1] [2], they usually lack the aforementioned introspection
capabilities this project aims at achieving. One of the main objectives is indeed giving the
users a full track of the steps that led to a certain autonomous decision from the IS, starting
�from the end-user natural language input and the system architect optimisation objectives and
constraints.


3. Planned techniques for the lower level
The core idea of the project, is the integration of Machine Learning techniques with logic
programming in an hybrid XAI framework, with an emphasis geared toward the latter. With
this setting in mind, the developers will be provided with a Logic Programming (LP) interface
(most likely, a Prolog dialect) to express the OF and its constraints. Not only that, but the
framework itself will be centred around a reasoning engine to forecast the future state of the
components involved in the OF calculation (sensors, in a Building Automation scenario), whose
role will be the intelligent allocation of resources to satisfy the requirements. The role of the
ML techniques would be providing predictions on future states of the system, while all the
reasoning based on these values would be carried via LP techniques and tools. The developers
should in this phase provide for each component of the system, be it active or passive, the code
lambdas to interact with their low-level primitives for input/output.
It should be noted that the topic of intelligent scheduling of appliances use has been already
studied in recent years, and fully explainable methods for it have been proposed, even taking into
consideration the negative cost of energy given by photovoltaic panels [3] or in the absence of
absolute certainty over the user preferences [4], but there still is room for possible improvements,
like the integration with predictive models of cost estimation and with the detection of user
preferences, or the the improvement of scheduling flexibility.
Regarding the integration between ML techniques, or more generally probabilistic predictions of
future conditions with a LP framework, one interesting setting worth exploring is DeepProbLog
[5], which allows combining a probabilistic variant of ProLog [6] with the probabilistic outputs
of tools such as neural networks.
The LP interface is not the only possible tool for developers and architects of the IS. the first of
two alternative techniques being taken into consideration is the use of a structured DDL, which
might allow an unambiguous enumeration and description of the system components without
requiring the knowledge of a programming language. This DDL, still to be fixed, might also help
in the concise expression of the OF constraints. Another alternative is represented by the use of
an NLI, such as in the upper layer. The System architects, who might be expert in the problem
domain, are not expected to also be experts in software developing techniques: their description
of the system might therefore come in the form of semi-structured text written in a Natural
Language. From this, the framework might extract key understandings of the components
and the rules governing the planned IS, and generate code stubs for the developers. Such a
pre-processing step might also be useful in identifying inconsistencies and contradictions in
the requirements description from the architects, requiring their active disambiguation and/or
emendation, or providing developers with attention flags.
�4. Planned techniques for the upper (NLI) level
The upper level provides an interface to non-technical users of the IS. Therefore, it cannot provide
for formal programming languages, and should focus on more natural interaction models. The
most obvious choice would be a Natural Language Interface, possibly but not necessarily in the
form of a speech recognition module, which gives —especially in its conversational interaction
mode— a good compromise between the user’s spontaneous expression of their needs and the
guidance required for compliance with the IS definition. Models that try to find a compromise
between precision and user engagement have already been studied [7]. I expect the accepted
language to be a rich form of Controlled Natural Language, which will not be designed with
minimality in mind, so as to allow the user to express their inputs in more than one way.
This latter aspect is of particular interest. The IS components are enumerated and described in a
formal language, with names and actions which are bounded to specific code lambdas. Therefore,
it is a requirement that the framework’s reasoning engine operates on these specific names.
However the users, not being aware of the low-level specification, might use different and
ambiguous terms to refer to the System components. This requires the disambiguation of their
input. A natural choice for this activity would be the use of word embeddings to find the most
likely match between what the user asked to operate on and what is available to the inference
engine. This has already been applied to different domains [8]. The conversational interaction
would be effective in determining whether the user simply called the right object with the
wrong name, or asked for an action the IS is not capable of carrying. The same applies to user
requests which might conflict with the general behaviour defined by the System developers.
It should be noted that these same techniques might be used for providing the System architects
with a user-friendly interface for defining and amending their high-level specification [9].


5. Considerations on the expected milestones
At the time of writing, the PhD is in the fifth month of year one. The first implementation
step would be exploring the feasibility of the NLI, in particular with respect to the definition
of a first form of CNL and the use of word embeddings. The output of this layer would be an
intermediate form between a Natural Language and the language to be used by the inference
engine. I expect this phase to be over before the second half of Y2.
With the start of Y2, it should already be clear with language will be used in the inference phase,
together with the specification language to be used by system developers. Therefore, by the
first half of Y3, the lower level of the framework should be complete. The second half of Y3
would be devoted to integrating the two layers.


Acknowledgments
Diego Marcia’s PhD is funded via a NOP Research and Innovation 2014-2020 grant, Action IV.5
– PhD programmes on green topics http://www.ponricerca.gov.it/opportunita/react-eu-phd-
programmes-on-innovation-and-green-topics/.
�References
[1] D. Popa, F. Pop, C. Serbanescu, A. Castiglione, Deep learning model for home automation
    and energy reduction in a smart home environment platform, Neural Computing and
    Applications 31 (2019) 1317 – 1337. doi:10.1007/s00521-018-3724-6.
[2] M. Nasir, K. Muhammad, A. Ullah, J. Ahmad, S. Wook Baik, M. Sajjad, Enabling automation
    and edge intelligence over resource constraint iot devices for smart home, Neurocomputing
    491 (2022) 494–506. doi:https://doi.org/10.1016/j.neucom.2021.04.138.
[3] F. A. Qayyum, M. Naeem, A. S. Khwaja, A. Anpalagan, L. Guan, B. Venkatesh, Appliance
    scheduling optimization in smart home networks, IEEE Access 3 (2015) 2176–2190. doi:10.
    1109/ACCESS.2015.2496117.
[4] V. Nguyen, W. Yeoh, T. C. Son, V. Kreinovich, T. Le, A scheduler for smart homes with proba-
    bilistic user preferences, in: M. Baldoni, M. Dastani, B. Liao, Y. Sakurai, R. Zalila Wenkstern
    (Eds.), PRIMA 2019: Principles and Practice of Multi-Agent Systems, Springer International
    Publishing, 2019, pp. 138–152. doi:10.1007/978-3-030-33792-6_9.
[5] R. Manhaeve, S. Dumančić, A. Kimmig, T. Demeester, L. De Raedt, Neural probabilistic
    logic programming in deepproblog, Artificial Intelligence 298 (2021) 103504. URL: https:
    //www.sciencedirect.com/science/article/pii/S0004370221000552. doi:https://doi.org/
    10.1016/j.artint.2021.103504.
[6] L. De Raedt, A. Kimmig, Probabilistic (logic) programming concepts, Machine Learning
    100 (2015) 5 – 47. doi:https://doi.org/10.1007/s10994-015-5494-z.
[7] N. C. Truong, T. Baarslag, S. Ramchurn, L. Tran-Thanh, Interactive scheduling of appliance
    usage in the home, in: 25th International Joint Conference on Artificial Intelligence (IJCAI-
    16), 2016, p. 7. URL: http://eprints.soton.ac.uk/id/eprint/396670.
[8] A. Morales-Garzón, J. Gómez-Romero, M. J. Martin-Bautista, A word embedding-based
    method for unsupervised adaptation of cooking recipes, IEEE Access 9 (2021) 27389 – 27404.
    doi:10.1109/ACCESS.2021.3058559.
[9] S. Mishra, A. Sharma, Automatic word embeddings-based glossary term extraction from
    large-sized software requirements, in: N. Madhavji, L. Pasquale, A. Ferrari, S. Gnesi
    (Eds.), Requirements Engineering: Foundation for Software Quality, Springer International
    Publishing, 2020, pp. 203 – 218. doi:10.1007/978-3-030-44429-7_15.
�