با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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21 |
Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions
تجزیه و تحلیل احساسات در توییت های خطاب به یک حساب توییتر خاص دامنه: مقایسه عملکرد مدل و توضیح پذیری پیش بینی ها-2021 Many institutions and companies find it valuable to know how people feel about their ventures; hence, scientific
research in sentiment analysis has been intensely developed over time. Automated sentiment analysis can be
considered as a machine learning (ML) prediction task, with classes representing human affective states. Due to
the rapid development of ML and deep learning (DL), improvements in automatic sentiment analysis perfor-
mance are achieved almost every year. Since 2013, Semantic Evaluation (SemEval) has hosted a worldwide
community-acknowledged competition that allows for comparisons of recent innovations. The sentiment analysis
tasks focus on assessing sentiment in Twitter posts authored by various publishers and addressing multiple
subjects. Our study aimed to compare selected popular and recent natural language processing methods using a
new data set of Twitter posts sent to a single Twitter account. For improved comparability of our experiments
with SemEval, we adopted their metrics and also deployed our models on data published for SemEval-2017. In
addition, we investigated if an unsupervised ML technique applied for the detection of topics in tweets can be
leveraged to improve the predictive performance of a selected transformer model. We also demonstrated how a
recent explainable artificial intelligence technique can be used in Twitter sentiment analysis to gain a deeper
understanding of the models’ predictions. Our results show that the most recent DL language modeling approach
provides the highest quality; however, this quality comes at reduced model transparency. keywords: پردازش زبان طبیعی | یادگیری عمیق | تجزیه و تحلیل احساسات | فراگیری ماشین | توضیح پذیری | توییتر | Natural language processing | Deep learning | Sentiment analysis | Machine learning | Explainability | Twitter |
مقاله انگلیسی |
22 |
Corporate cleaner production strategy development and environmental management accounting: A contingency theory perspective
توسعه استراتژی تولید پاک کننده شرکت و مدیریت حسابداری محیط زیست: دیدگاه نظریه احتمالی-2021 Despite the popularity of environmental management accounting as an approach to support corporate cleaner
production measures, so far, how the environmental management accounting implementation differs according
to the stage of cleaner production strategy development is largely unknown. This study thus sought to identify
how the uses of environmental management accounting and information characteristics vary among organiza-
tions at different stages of cleaner production strategy development. Drawing on the contingency theory view of
environmental management accounting system sophistication, cleaner production strategy development stages,
and environmental management accounting uses, it developed an analytical framework. Based on eighteen case
studies of business in Sri Lanka, the study analyzed the different domain-based and functional uses of envi-
ronmental management accounting and their characteristics according to their cleaner production strategy
development (i.e., reactive, preventive and proactive stages). Overall, the study found that environmental
management accounting uses to be limited and fragmented in organizations at the reactive and preventive stages
except for using environmental management accounting for cost savings and efficiency improvements. However,
the findings suggest that as and when organizations progress into higher levels of cleaner production strategy
development, there is a relatively high level of use of environmental management accounting in terms of inte-
grative tools, and for control and stewardship purposes. keywords: تولید پاک کننده | نظریه احتمالی | پایداری شرکت | حسابداری مدیریت محیط زیست | سری لانکا | Cleaner production | Contingency theory | Corporate sustainability | Environmental management accounting | Sri Lanka |
مقاله انگلیسی |
23 |
Breaking the barriers between intelligence, investigation and evaluation: A continuous approach to define the contribution and scope of forensic science
شکستن موانع بین هوشمندی ، تحقیق و ارزیابی: رویکردی مداوم برای تعریف سهم و دامنه علم پزشکی قانونی-2020 Forensic science has been evolving towards a separation of more and more specialised tasks, with
forensic practitioners increasingly identifying themselves with only one sub-discipline or task of forensic
science. Such divisions are viewed as a threat to the advancement of science because they tend to polarise
researchers and tear apart scientific communities. The objective of this article is to highlight that a piece
of information is not either intelligence or evidence, and that a forensic scientist is not either an
investigator or an evaluator, but that these notions must all be applied in conjunction to successfully
understand a criminal problem or solve a case.
To capture the scope, strength and contribution of forensic science, this paper proposes a progressive
but non-linear continuous model that could serve as a guide for forensic reasoning and processes. In this
approach, hypothetico-deductive reasoning, iterative thinking and the notion of entropy are used to
frame the continuum, situate forensic scientists’ operating contexts and decision points. Situations and
examples drawn from experience and practice are used to illustrate the approach.
The authors argue that forensic science, as a discipline, should not be defined according to the context it
serves (i.e. an investigation, a court decision or an intelligence process), but as a general, scientific and
holistic trace-focused practice that contributes to a broad range of goals in various contexts. Since
forensic science does not work in isolation, the approach also provides a useful basis as to how forensic
scientists should contribute to collective and collaborative problem-solving to improve justice and
security. Keywords: Crime | Decision points | Entropy | Hypothetico-deductive reasoning | Model |
مقاله انگلیسی |
24 |
Knowledge restructuring through case processing: The key to generalize expertise development theory across domains?
تجدید ساختار دانش از طریق پردازش پرونده: کلید تعمیم نظریه توسعه تخصص در دامنه ها؟-2020 In many domains evidence exists that expertise development goes along with the adaptation of
cognitive structures and processes. Whilst it is generally assumed that expertise and its acquisition is domain-specific, there are nevertheless similarities across domains that may evoke
comparable processes and lead to similar cognitive restructuring. The “Knowledge Restructuring
through Case Processing” (KR-CP) theory is proposed as a domain-general framework that takes
into account similarities and differences between domains in order to explain corresponding
processes and performances of professionals in different domains. The KR-CP theory is based on
the assumption that dealing with complex cases plays a major role in many professional domains
and allows for cognitive adaptations to routine as well as novel situations. The focus of this
review is to investigate the capacity of this assumption to explain expertise development in
multiple domains. Starting from the domain of medicine, in which such outcomes have been
extensively studied, three further domains are analysed. Evidence is reviewed from counselling
and psychotherapy, business management, and law. Thereby specific methodological complications emerge concerning the criteria for expert selection, the definition of levels of expertise, or
the degree of authenticity of participants tasks. Nevertheless, direct and strong indications for
restructuring knowledge into scripts and macro-concepts could be identified in all three domains.
To further substantiate the KR-CP theory, studies are needed that explicitly address the comparison of case processing in different domains.
Keywords: Case processing | Domain-specificity | Generality of expertise | Knowledge restructuring | Levels of expertise | Running head | Knowledge restructuring through case | processing |
مقاله انگلیسی |
25 |
Optical dromions, domain walls and conservation laws with Kundu–Mukherjee–Naskar equation via traveling waves and Lie symmetry
حرکتهای نوری ، دیوارهای دامنه و قوانین حفاظت از معادله Kundu-Mukherjee-Naskar از طریق امواج مسافرتی و تقارن دروغ-2020 This paper secures optical dromions, domain walls and conservation laws for Kundu–Mukherjee–Naskar equation.
Three integration approaches are applied. These are traveling wave hypothesis, method of undetermined
coefficients and Lie symmetry. Finally, multiplier approach yielded two conservation laws supported by the
model. These are power and Hamiltonian that are expressed in quadratures. Keywords: Dromions | Kundu–Mukherjee–Naskar equation | Lie symmetry | Conservation laws |
مقاله انگلیسی |
26 |
DABGEO: A reusable and usable global energy ontology for the energy domain
DABGEO: یک هستی شناسی انرژی قابل استفاده مجدد و قابل استفاده در جهان برای حوزه انرژی-2020 The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy
management applications to perform a large-scale energy management. Thus, there is the need for
a global ontology that provides common vocabularies to represent the energy subdomains. A global
energy ontology must provide a balance of reusability–usability to moderate the effort required to
reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology
for the energy domain that provides a common representation of energy domains represented by
existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for
specific energy management applications. In contrast to previous global energy ontologies, it follows
a layered structure to provide a balance of reusability–usability. In this work, we provide an overview
of the structure of DABGEO and we explain how to reuse it in a particular application case. In
addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of
reusability–usability. Keywords: Ontology | Energy domain | Ontology reusability | Ontology usability |
مقاله انگلیسی |
27 |
The future is in the past: A framework for the Marketing-Entrepreneurship Interface (MEI)
آینده در گذشته است: چارچوبی برای رابط بازاریابی-کارآفرینی (MEI)-2020 In this article, we review milestones in the 30+ year history of conceptualizations of the Marketing- Entrepreneurship Interface (MEI) in order to develop an overdue unified framework. This framework finally achieves the original mission of the “Charleston Summit” - to create a research framework for the MEI. We update the 4-perspectives view proposed by Hansen and Eggers (2010). In particular, we retain the first per- spective, which is the commonalities between the domains of marketing and entrepreneurship, as is. In updating the perspectives, we define the fourth perspective as entrepreneurial and SME marketing and we combine the second and third perspectives into a single framework. We review some major concepts within the two domains and provide numerous suggestions for future research that emerge from the framework. Our conceptualization of the MEI creates a vast array of research possibilities for MEI scholars old and new. Keywords: Marketing – Entrepreneurship Interface | MEI history | MEI models/frameworks | MEI domain |
مقاله انگلیسی |
28 |
Monolithic convex limiting for continuous finite element discretizations of hyperbolic conservation laws
Monolithic convex limiting for continuous finite element discretizations of hyperbolic conservation laws-2020 Using the theoretical framework of algebraic flux correction and invariant domain preserving schemes, we introduce
a monolithic approach to convex limiting in continuous finite element schemes for linear advection equations, nonlinear
scalar conservation laws, and hyperbolic systems. In contrast to flux-corrected transport (FCT) algorithms that apply limited
antidiffusive corrections to bound-preserving low-order solutions, our new limiting strategy exploits the fact that these solutions
can be expressed as convex combinations of bar states belonging to a convex invariant set of physically admissible solutions.
Each antidiffusive flux is limited in a way which guarantees that the associated bar state remains in the invariant set and
preserves appropriate local bounds. There is no free parameter and no need for limit fluxes associated with the consistent mass
matrix of time derivative term separately. Moreover, the steady-state limit of the nonlinear discrete problem is well defined and
independent of the pseudo-time step. In the case study for the Euler equations, the components of the bar states are constrained
sequentially to satisfy local maximum principles for the density, velocity, and specific total energy in addition to positivity
preservation for the density and pressure. The results of numerical experiments for standard test problems illustrate the ability
of built-in convex limiters to resolve steep fronts in a sharp and nonoscillatory manner. Keywords: Hyperbolic conservation laws | Positivity preservation | Invariant domains | Finite elements | Algebraic flux correction | Convex limiting |
مقاله انگلیسی |
29 |
The AI techno-economic complex System: Worldwide landscape, thematic subdomains and technological collaborations
سیستم پیچیده فنی اقتصادی هوش مصنوعی : چشم انداز جهانی ، زیر دامنه های موضوعی و همکاری های فناوری-2020 Artificial intelligence (AI) is playing a major role in the new paradigm shift occurring across the
technological landscape. After a series of alternate seasons starting in the 60s, AI is now experiencing
a new spring. Nevertheless, although it is spreading throughout our economies and societies
in multiple ways, the absence of standardised classifications prevents us from obtaining a
measure of its pervasiveness. In addition, AI cannot be identified as part of a specific sector, but
rather as a transversal technology because the fields in which it is applied do not have precise
boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon
by investigating economic agents’ involvement in industrial activities aimed to supply
AI-related goods and services, and AI-related R&D processes in the form of patents and publications.
In order to conduct this extensive analysis, we use a complex systems approach through
the agent-artifact space model, which identifies the core dimensions that should be considered.
Therefore, by considering the geographic location of the involved agents and their organisation
types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview
of the worldwide presence of agents, (ii) investigate the patterns in which AI technological
subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, composition,
and topology of the AI R&D collaboration network. Based on a unique data collection of
multiple micro-based data sources and supported by a methodological framework for the analysis
of techno-economic segments (TES), we capture the state of AI in the worldwide landscape in the
period 2009–2018. As expected, we find that major roles are played by the US, China, and the
EU28. Nevertheless, by measuring the system, we unveil elements that provide new, crucial information
to support more conscious discussions in the process of policy design and
implementation. Keywords: Artificial intelligence | Complex systems | Agent-artifact space | Natural language processing | Semantic analysis | Network analysis |
مقاله انگلیسی |
30 |
Incorporating domain knowledge into reinforcement learning to expedite welding sequence optimization
تلفیق دانش دامنه ای به یادگیری تقویتی برای تسریع در بهینه سازی توالی پیوند-2020 Welding Sequence Optimization (WSO) is very effective to minimize the structural deformation, however
selecting proper welding sequence leads to a combinatorial optimization problem. State-of-the-art algorithms
could take more than one week to compute the best sequence for an assembly of eight weld beads which is
unrealistic for the early stages of Product Delivery Process (PDP). In this article, we develop and implement
a novel Reinforcement Q-learning algorithm for WSO where structural deformation is used to compute
reward function. We utilize a thermo-mechanical Finite Element Analysis (FEA) to predict deformation.
The exploration–exploitation dilemma has been tackled by domain knowledge driven ????-greedy algorithm
into Q-RL which helps to expedite the WSO and we call this novel algorithm as DKQRL. We run welding
simulation experiment using well-known Simufact® software on a typical widely used mounting bracket which
contains eight welding beads. DKQRL allows the reduction of structural deformation up to ∼71% and it
substantially speeds up the computational time over Modified Lowest Cost Search (MLCS), Genetic Algorithm
(GA), exhaustive search, and standard RL algorithm. Results of welding simulation demonstrate a reasonable
agreement with real experiment in terms of structural deformation. Keywords: Welding sequence optimization | FEA based welding simulation | Reinforcement learning | Structural deformation | Residual stress | Artificial intelligence | Machine learning |
مقاله انگلیسی |