Search results for: landscape metrics

Authors: Mareye Zeleke Mekonen

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The Kafficho language has distinct issues in information retrieval because of its restricted resources and dearth of standardized methods. In this endeavor, with the cooperation and support of linguists and native speakers, we investigate the creation of information retrieval systems specifically designed for the Kafficho language. The Kafficho information retrieval system allows Kafficho speakers to access information easily in an efficient and effective way. Our objective is to conduct an information retrieval experiment using 220 Kafficho text files, including fifteen sample questions. Tokenization, normalization, stop word removal, stemming, and other data pre-processing chores, together with additional tasks like term weighting, were prerequisites for the vector space model to represent each page and a particular query. The three well-known measurement metrics we used for our word were Precision, Recall, and and F-measure, with values of 87%, 28%, and 35%, respectively. This demonstrates how well the Kaffiho information retrieval system performed well while utilizing the vector space paradigm.

Keywords: Kafficho, information retrieval, stemming, vector space

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Authors: Meduranda Marco

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This paper aimed to discuss the Integrative Assessment (IA) initiative of the Schools Division Office - Navotas City. The introduction provided a brief landscape analysis of the current state of education, the context of SDO Navotas, and the rationale for the administration of Integrative Assessment (IA) in schools. The IA methodology, procedure, and implementation activities were also shared. Feedback and reports on IA showed positive results as all schools in the Division were able to operationalize IA and consequently foster academic ease for learners and parents. Challenges met after compliance were also documented and strategies to continuously improve the Integrative Assessment process were proposed.

Keywords: distance learning assessment, integrative assessment, academic ease, learning outcomes evaluation

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Authors: Obioma Uche

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Due to the recent volatility in oil & gas prices as well as increased development of non-conventional resources, it has become even more essential to critically evaluate the profitability of petroleum prospects prior to making any investment decisions. Traditionally, simple Monte Carlo (MC) algorithms have been used to randomly sample probability distributions of economic and geological factors (e.g. price, OPEX, CAPEX, reserves, productive life, etc.) in order to obtain probability distributions for profitability metrics such as Net Present Value (NPV). In recent years, second-order MC algorithms have been shown to offer an advantage over simple MC techniques due to the added consideration of uncertainties associated with the probability distributions of the relevant variables. Here, a strategy for the application of the second-order MC technique to a case study is demonstrated to analyze its effectiveness as a tool for portfolio management.

Keywords: Monte Carlo algorithms, portfolio management, profitability, risk analysis

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Authors: Jozef Mindas, Jana Skvareninova

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Wetlands are a main natural source of methane emissions, but they also represent the important biodiversity reservoirs in the landscape. There are about 26 thousands hectares of wetlands in Slovakia identified via the wetlands monitoring program. Created database of wetlands in Slovakia allows to analyze several ecological processes including also the methane emissions estimate. Based on the information from the database, the first estimate of the methane emissions from wetlands in Slovakia has been done. The IPCC methodology (Tier 1 approach) has been used with proposed emission factors for the ice-free period derived from the climatic data. The highest methane emissions of nearly 550 Gg are associated with the category of fens. Almost 11 Gg of methane is emitted from bogs, and emissions from flooded lands represent less than 8 Gg.

Keywords: bogs, methane emissions, Slovakia, wetlands

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Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: Balázs Kulcsár

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Today, there is no part of the world that does not bear the mark of man in some way. This process seems unstoppable. So perhaps the best thing we can do is to touch that handprint gently and with the utmost care. There are multiple uses for the same piece of land, the coordination of which requires careful and sustainable spatial planning. The case study of the Rétközlake in north-eastern Hungary illustrates a habitat rehabilitation project in which a number of human uses were coordinated with the conservation and restoration of the natural environment. Today, the good condition of the habitat can only be maintained artificially, but the project has paid particular attention to finding a sustainable solution. The rehabilitation of Lake Rétköz is considered good practice in resolving land-use conflicts.

Keywords: sustainability, ecosystem service, land use conflict, landscape utilization

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Authors: Shilpa Sondkar, Maithili Patil, Atharva Potnis

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The current health care landscape desires efficiency and patient satisfaction for optimal performance. Medical appointment booking apps have increased the overall efficiency of clinics, hospitals, and e-health marketplaces while simplifying processes. These apps allow patients to connect with doctors online. Not only are mobile doctor appointment apps a reliable and efficient solution, but they are also the future of clinical progression and a distinct new stage in the patient-doctor relationship. Compared to the usual queuing method, the web-based appointment system could significantly increase patients' satisfaction with registration and reduce total waiting time effectively.

Keywords: appointment, patient, scheduling, design and development, Figma

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Authors: Janet Holland

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Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

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Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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Authors: Babatope David Omoniyi, Fiona Lalor, Sinead Furey

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This ongoing project investigates the impact of Brexit on consumer perceptions of trust in the food industry. Brexit has significantly impacted the food industry, triggering a paradigm shift in the movement of food/agricultural produce, regulations, and cross-border collaborations between Great Britain, Northern Ireland, and the Republic of Ireland. In a world where the dynamics have shifted because of regulatory changes that impact trade and the free movement of foods and agricultural produce between these three countries, monitoring and controlling every stage of the food supply chain have become challenging, increasing the potential for food fraud and food safety incidents. As consumers play a pivotal role in shaping the market, understanding any shifts in trust post-Brexit enables them to navigate the market with confidence and awareness. This study aims to explore the complexities of consumer perceptions, focusing on trust as a cornerstone of consumer confidence in the post-Brexit food landscape. The objectives include comparing trust in official controls pre- and post-Brexit, determining consumer awareness of food fraud, and devising recommendations that reflect the evidence from this primary research regarding consumer trust in food authenticity post-Brexit. The research design follows an exploratory sequential mixed methods approach, incorporating qualitative methods such as focus groups and structured interviews, along with quantitative research through a large-scale survey. Participants from UCD and Ulster University campuses, comprising academic and non-academic staff, students, and researchers, will provide insights into the impact of Brexit on consumer trust. Preliminary findings from focus groups and interviews highlight changes in labelling, reduced quantity and quality of foods in both Northern Ireland and the Republic of Ireland, fewer food choices, and increased food prices since Brexit. The study aims to further investigate and quantify these impacts through a comprehensive large-scale survey involving participants from Northern Ireland and the Republic of Ireland. The results will inform official controls and consumer-facing messaging contributing valuable insights to navigate the evolving post-Brexit food landscape.

Keywords: Brexit, consumer trust, food fraud, food authenticity, food safety, food industry

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Authors: Varsha Hemant Patil, Swati A. Bhavsar, Abolee H. Patil

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Image quality assessment plays an important role in image evaluation. This paper aims to present an investigation of classic techniques in use for image quality assessment, especially for super-resolution imaging. Researchers have contributed a lot towards the development of super-resolution imaging techniques. However, not much attention is paid to the development of metrics for testing the performance of developed techniques. In this paper, the study report of existing image quality measures is given. The paper classifies reviewed approaches according to functionality and suitability for super-resolution imaging. Probable modifications and improvements of these to suit super-resolution imaging are presented. The prime goal of the paper is to provide a comprehensive reference source for researchers working towards super-resolution imaging and suggest a better framework for measuring the performance of super-resolution imaging techniques.

Keywords: interpolation, MSE, PSNR, SSIM, super resolution

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Maari Sugawara

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This research critically engages with the erotic and political entanglements of male clientele of Dominatrices who identify as submissives (hereafter referred to as submissives) within Hong Kong's BDSM scene. Employing masochism as an analytical framework, it interrogates the intersections of capitalism, heteropatriarchy, postcolonialism, and commodified desire. BDSM (Bondage and Discipline, Dominance and Submission, Sadism and Masochism) encompasses practices that explore power, control, and subordination through both physical and psychological role-play, predicated on consent, negotiation, and boundary delineation. This makes BDSM a fertile site for examining how dominance and submission are mobilized, challenged, and reiterated. This study focuses on the dynamics between thirty male submissives and three professional Dominatrices active in Hong Kong since 2019. The predominance of male interviewees reflects the demographic reality that most clients engaging with professional Dominatrices are male. These submissives—men who willfully relinquish control—offer a critical lens for exploring how BDSM, as both practice and market, mirrors and destabilizes dominant power structures. BDSM relationships occasionally replicate the hierarchical logics of heterosexual marriage, particularly in the expectation that submissives engage exclusively with a single Dominatrix, reflecting a dynamic of devotion and fidelity akin to traditional marital structures. However, these relationships also function as counter-normative spaces where care and control are reconfigured, enabling the negotiation of alternative power configurations. By centering BDSM work rather than broader kink practices, this study foregrounds the commodification of intimacy as a key site where suppressed desires, economic forces, and political tensions converge. The submissives in this study are predominantly affluent, cisgender men, underscoring the socio-economic asymmetries in the BDSM market. Furthermore, the research examines how Hong Kong’s political turbulence—particularly the 2019 Yellow Umbrella Movement and the COVID-19 pandemic—has reverberated through the BDSM scene, reshaping the contours of desire, trust, and power in these intimate transactions. The increasing tensions with mainland China, alongside the erosion of public trust in state institutions, form a critical backdrop to this evolving landscape. Grounded in gender and sexuality theories, this research interrogates how the desires of male submissives are constructed within and resist heteronormative frameworks. BDSM practices, far from existing outside capitalist and colonial logics, often act as both a mirror and critique of these systems, revealing complex ways in which power is commodified, enacted, and contested. In their pursuit of emotional care and alternative forms of control, male submissives navigate a paradoxical terrain where their masochistic desires both subvert and perpetuate the socio-political status quo. By examining the intersections of desire, commodification, and the shifting socio-political landscape, this research provides a nuanced understanding of how BDSM functions as a site of negotiation for those navigating the turbulent crosscurrents of capitalist and colonial legacies. Ultimately, it uncovers the complex interplay between erotic practices and the structures of power and identity in a city undergoing profound transformation.

Keywords: masochism, Hong Kong, identity, BDSM, dominatrix, masculinity, gender studies

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Authors: Adhila Sandra Devy, Evangeline Syalomita Silitonga

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The landscape of corporate governance and accountability has led to the emergence of Integrated Reporting (IR) in response to the shortcomings of traditional reporting frameworks. Developed by The International Integrated Reporting Council (IIRC), IR aims to offer stakeholders a comprehensive view of a company’s performance by integrating financial and non-financial disclosures. This study analyzes literature on Integrated Reporting and Corporate Financial Performance from 2013 to 2024, employing a descriptive analysis methodology. 31 relevant articles were gathered from various sources, indicating a positive correlation between integrated reporting and financial performance, albeit without conclusive evidence of long-term impact.

Keywords: integrated reporting, corporate financial performance, corporate performance, firm performance, bibliometric analysis

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Authors: Diteboho Xaba, Kolentino Mpeta, Tlotliso Qejoe

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This paper assesses the in-sample forecasting of the South African exchange rates comparing a linear ARIMA model and a SETAR model. The study uses a monthly adjusted data of South African exchange rates with 420 observations. Akaike information criterion (AIC) and the Schwarz information criteria (SIC) are used for model selection. Mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) are error metrics used to evaluate forecast capability of the models. The Diebold –Mariano (DM) test is employed in the study to check forecast accuracy in order to distinguish the forecasting performance between the two models (ARIMA and SETAR). The results indicate that both models perform well when modelling and forecasting the exchange rates, but SETAR seemed to outperform ARIMA.

Keywords: ARIMA, error metrices, model selection, SETAR

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Authors: Purnomo Yustianto, Robin Doss, Novianto B. Kurniawan Suhardi

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Since its inception, the design activities of Service Oriented Architecture (SOA) has been guided with aspects from the Service Design Principles (SDP), such as cohesion, granularity, loose coupling, discoverability, and autonomy, etc. The goal of this paper is two folds. The first is to examine the position of SDP within the context of software quality, and the second is to reframe the aspects of SDP into a more concise terms and relations. This paper is divided into four parts, in which after the introduction, a review on related software quality is provided to determine the quality context of SDP. The third part reviews the original SDP and offers a relation model among the SDP aspects. The fourth part explores the design quality metrics available for SOA and proposes a relationship representing the design quality. Among the aspects of design principles, the cohesion and coupling aspect is determined to be the two important aspects for achieving reusability of a service.

Keywords: SOA, software quality, service design principle, reusability, cohesion, coupling

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Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: Blaise Fyama, Elie Museng, Grace Mukoma

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In this article we present a java implementation of video telephony using the SIP protocol (Session Initiation Protocol). After a functional analysis of the SIP protocol, we relied on the work of Italian researchers of University of Parma-Italy to acquire adequate libraries for the development of our own communication tool. In order to optimize the code and improve the prototype, we used, in an incremental approach, test techniques based on a static analysis based on the evaluation of the complexity of the software with the application of metrics and the number cyclomatic of Mccabe. The objective is to promote the emergence of local start-ups producing IP video in a well understood local context. We have arrived at the creation of a video telephony tool whose code is optimized.

Keywords: static analysis, coding complexity metric mccabe, Sip, uml

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Tugba Altin

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In an era marked by pronounced climate change consequences, communities are observed to confront traumatic events that yield both tangible and intangible repercussions. Such events not only cause discernible damage to the landscape but also deeply affect the intangible aspects, including emotional distress and disruptions to cultural landscapes. The Lytton Creek Fire of 2021 serves as a case in point. Beyond the visible destruction, the less overt but profoundly impactful disturbance to place attachment (PA) is scrutinized. PA, representing the emotional and cognitive bonds individuals establish with their environments, is crucial for understanding how such events impact cultural identity and connection to the land. The study underscores the significance of addressing both tangible and intangible traumas for holistic community recovery. As communities renegotiate their affiliations with altered environments, the cultural landscape emerges as instrumental in shaping place-based identities. This renewed understanding is pivotal for reshaping adaptation planning. The research advocates for adaptation strategies rooted in the lived experiences and testimonies of the affected populations. By incorporating both the tangible and intangible facets of trauma, planning efforts are suggested to be more culturally attuned and emotionally insightful, fostering true resonance with the affected communities. Through such a comprehensive lens, this study contributes enriching the climate change discourse, emphasizing the intertwined nature of tangible recovery and the imperative of emotional and cultural healing after environmental disasters. Following the pronounced aftermath of the Lytton Creek Fire in 2021, research aims to deeply understand its impact on place attachment (PA), encompassing the emotional and cognitive bonds individuals form with their environments. The interpretive phenomenological approach, enriched by a hermeneutic framework, is adopted, emphasizing the experiences of the Lytton community and co-researchers. Phenomenology informed the understanding of 'place' as the focal point of attachment, providing insights into its formation and evolution after traumatic events. Data collection departs from conventional methods. Instead of traditional interviews, walking audio sessions and photo elicitation methods are utilized. These allow co-researchers to immerse themselves in the environment, re-experience, and articulate memories and feelings in real-time. Walking audio facilitates reflections on spatial narratives post-trauma, while photo voices captured intangible emotions, enabling the visualization of place-based experiences. The analysis is collaborative, ensuring the co-researchers' experiences and interpretations are central. Emphasizing their agency in knowledge production, the process is rigorous, facilitated by the harmonious blend of interpretive phenomenology and hermeneutic insights. The findings underscore the need for adaptation and recovery efforts to address emotional traumas alongside tangible damages. By exploring PA post-disaster, the research not only fills a significant gap but advocates for an inclusive approach to community recovery. Furthermore, the participatory methodologies employed challenge traditional research paradigms, heralding potential shifts in qualitative research norms.

Keywords: wildfire recovery, place attachment, trauma recovery, cultural landscape, visual methodologies

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Authors: Ng'etich K. Stephen

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Abstract—Globally analytics in basketball has advanced tremendously in the last decade. Organizations are leveraging the insights to improve team and player performance and, in the long run, generate revenue out of it. Due to limited basketball game-related statistics in African competitions, teams are unaware of how they compete with other continental basketball teams. The purpose of this study is to evaluate the regional difference in basketball game-related statistics between African teams that played in the newly formed league, the basketball African league and the European league. The basketball African league, a competition created through the partnership between NBA and FIBA, offers a good starting point since it has valuable basketball metrics to analyze. This study sought to use multivariate linear discriminant analysis to identify the game-related statistics that discriminate the teams in Euro league and the basketball African league.

Keywords: basketball africa league, basketball, euroleague, fiba, africa

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Authors: C. Jameke

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During the cognitivist era in cognitive psychology, a theory of internal rules and symbolic representations was posited as an account of human cognition. This type of cognitive architecture had its heyday during the 1970s and 80s, but it has now been largely abandoned in favour of subsymbolic architectures (e.g. connectionism), non-representational frameworks (e.g. dynamical systems theory), and statistical approaches such as Bayesian theory. In this presentation I describe this changing landscape of research, and comment on the increasing influence of neuroscience on cognitive psychology. I then briefly review a few recent developments in connectionism, and neurocomputation relevant to cognitive psychology, and critically discuss the assumption made by some researchers in these frameworks that higher-level aspects of human cognition are simply emergent properties of massively large distributed neural networks

Keywords: connectionism, emergentism, postocgnitivist, representations, subsymbolic archiitecture

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Authors: Jonathan W. Z. Lim, Vrizlynn L. L. Thing

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Cybercrimes are on the rise, in part due to technological advancements, as well as increased avenues of exploitation. Sophisticated threat actors are leveraging on such advancements to execute their malicious intentions. The increase in cybercrimes is prevalent, and it seems unlikely that they can be easily eradicated. A more serious concern is that the community may come to accept the notion that this will become the trend. As such, the key question revolves around how we can reduce cybercrime in this evolving landscape. In our paper, we propose to build a systematic framework through the lens of a cyber threat actor. We explore the motivation factors behind the crimes and the crime stages of the threat actors. We then formulate intervention plans so as to discourage the act of committing malicious cyber activities and also aim to integrate ex-cyber offenders back into society.

Keywords: crime motivations, crime prevention, cybercrime, ex-cyber criminals

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Authors: Gertrude I Hewapathirana, Mohamed M. Moustafa, Michel G. Zaitouni

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The purposes of this research are: (a) to provide an epistemological and ontological understanding of the WLB theory, practice, and research to illuminate how the WLB evolved between 2000 to 2020 and (b) to analyze peer-reviewed research to identify the gaps, hotspots, underlying dynamics, theoretical and thematic trends, influential authors, research collaborations, geographic networks, and the multidisciplinary nature of the WLB theory to guide future researchers. The research used four-step bibliometric network analysis to explore five research questions. Using keywords such as WLB and associated variants, 1190 peer-reviewed articles were extracted from the Scopus database and transformed to a plain text format for filtering. The analysis was conducted using the R version 4.1 software (R Development Core Team, 2021) and several libraries such as bibliometrics, word cloud, and ggplot2. We used the VOSviewer software (van Eck & Waltman, 2019) for network visualization. The WLB theory has grown into a multifaceted, multidisciplinary field of research. There is a paucity of research between 2000 to 2005 and an exponential growth from 2006 to 2015. The rapid increase of WLB research in the USA, UK, and Australia reflects the increasing workplace stresses due to hyper competitive workplaces, inflexible work systems, and increasing diversity and the emergence of WLB support mechanisms, legal and constitutional mandates to enhance employee and family wellbeing at multilevel social systems. A severe knowledge gap exists due to inadequate publications disseminating the "core" WLB research. "Locally-centralized-globally-discrete" collaboration among researchers indicates a "North-South" divide between developed and developing nations. A shortage in WLB research in developing nations and a lack of research collaboration hinder a global understanding of the WLB as a universal phenomenon. Policymakers and practitioners can use the findings to initiate supporting policies, and innovative work systems. The boundary expansion of the WLB concepts, categories, relations, and properties would facilitate researchers/theoreticians to test a variety of new dimensions. This is the most comprehensive WLB landscape analysis that reveals emerging trends, concepts, networks, underlying dynamics, gaps, and growing theoretical and disciplinary boundaries. It portrays the WLB as a universal theory.

Keywords: work-life balance, co-citation networks; keyword co-occurrence network, bibliometric analysis

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Authors: Gagandeep Kaur

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Cloud computing uses various computing resources such as CPU, memory, processor etc. which is used to deliver service over the network and is one of the emerging fields for large scale distributed computing. In cloud computing, execution of large number of tasks with available resources to achieve high performance, minimal total time for completion, minimum response time, effective utilization of resources etc. are the major research areas. In the proposed research, an algorithm has been proposed to achieve high performance in load balancing and resource utilization. The proposed algorithm is used to reduce the makespan, increase the resource utilization and performance cost for independent tasks. Further scheduling metrics based on algorithm in cloud computing has been proposed.

Keywords: resource utilization, response time, load balancing, performance cost

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Authors: Jesus David Argueta Moreno, Justa Rufina Martel, Edith Gabriela Carrasco

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The Central American Coffee small, medium, and large corporations search for excellence, sustainability, and continuous improvement, triggers in a still unknown scale the Local expansion, crusading, and franchising strategies towards a more suitable commercial opportunity, where the dynamics of the Central American business displacement can be explained through the markets permeability traits. By considering the previously mentioned, the present study aims to evaluate the franchising potentialities offered by Central American Coffee business scenario, in order to explain dynamics of the business demography phenomena and its relevance on the Central American competitiveness landscape.

Keywords: competitiveness, franchising, business demography, Central American Coffee

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Authors: Charles Ruffner, Michael Manton, Gintautas Kibirkstis, Gediminas Brazaitas, Vitas Marozas, Ekaterine Makrickiene, Rutile Pukiene, Per Angelstam

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In dynamic boreal forests, fire is an important natural disturbance, which drives regeneration and mortality of living and dead trees, and thus successional trajectories. However, current forest management practices focusing on wood production only have effectively eliminated fire as a stand-level disturbance. While this is generally well studied across much of Europe, in Lithuania, little is known about the historic fire regime and the role fire plays as a management tool towards the sustainable management of future landscapes. Focusing on Scots pine forests, we explore; i) the relevance of fire disturbance regimes on forestlands of Lithuania; ii) fire occurrence in the Dzukija landscape for dry upland and peatland forest sites, and iii) correlate tree-ring data with climate variables to ascertain climatic influences on growth and fire occurrence. We sampled and cross-dated 132 Scots pine samples with fire scars from 4 dry pine forest stands and 4 peatland forest stands, respectively. The fire history of each sample was analyzed using standard dendrochronological methods and presented in FHAES format. Analyses of soil moisture and nutrient conditions revealed a strong probability of finding forests that have a high fire frequency in Scots pine forests (59%), which cover 34.5% of Lithuania’s current forestland. The fire history analysis revealed 455 fire scars and 213 fire events during the period 1742-2019. Within the Dzukija landscape, the mean fire interval was 4.3 years for the dry Scots pine forest and 8.7 years for the peatland Scots pine forest. However, our comparison of fire frequency before and after 1950 shows a marked decrease in mean fire interval. Our data suggest that hemi-boreal forest landscapes of Lithuania provide strong evidence that fire, both human and lightning-ignited fires, has been and should be a natural phenomenon and that the examination of biological archives can be used to guide sustainable forest management into the future. Currently, fire use is prohibited by law as a tool for forest management in Lithuania. We recommend introducing trials that use low-intensity prescribed burning of Scots pine stands as a regeneration tool towards mimicking natural forest disturbance regimes.

Keywords: biodiversity conservation, cultural burning, dendrochronology, forest dynamics, forest management, succession

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Khethiwe Mtshali, Zamantungwa T. H. Khumalo, Stanford Kwenda, Ismail Arshad, Oriel M. M. Thekisoe

Abstract:

Ecologically, the gut, mammary glands and bloodstream consist of distinct microbial communities of commensals, mutualists and pathogens, forming a complex ecosystem of niches. The by-products derived from these body sites i.e. faeces, milk and blood, respectively, have many uses in rural communities where they aid in the facilitation of day-to-day household activities and occasional rituals. Thus, although livestock rearing plays a vital role in the sustenance of the livelihoods of rural communities, it may serve as a potent reservoir of different pathogenic organisms that could have devastating health and economic implications. This study aimed to simultaneously explore the microbial profiles of corresponding faecal, milk and blood samples from lactating cows using 16S rRNA metagenomic sequencing. Bacterial communities were inferred through the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline coupled with SILVA database v138. All downstream analyses were performed in R v3.6.1. Alpha-diversity metrics showed significant differences between faeces and blood, faeces and milk, but did not vary significantly between blood and milk (Kruskal-Wallis, P < 0.05). Beta-diversity metrics on Principal Coordinate Analysis (PCoA) and Non-Metric Dimensional Scaling (NMDS) clustered samples by type, suggesting that microbial communities of the studied niches are significantly different (PERMANOVA, P < 0.05). A number of taxa were significantly differentially abundant (DA) between groups based on the Wald test implemented in the DESeq2 package (Padj < 0.01). The majority of the DA taxa were significantly enriched in faeces than in milk and blood, except for the genus Anaplasma, which was significantly enriched in blood and was, in turn, the most abundant taxon overall. A total of 30 phyla, 74 classes, 156 orders, 243 families and 408 genera were obtained from the overall analysis. The most abundant phyla obtained between the three body sites were Firmicutes, Bacteroidota, and Proteobacteria. A total of 58 genus-level taxa were simultaneously detected between the sample groups, while bacterial signatures of at least 8 of these occurred concurrently in corresponding faeces, milk and blood samples from the same group of animals constituting a pool. The important taxa identified in this study could be categorized into four potentially pathogenic clusters: i) arthropod-borne; ii) food-borne and zoonotic; iii) mastitogenic and; iv) metritic and abortigenic. This study provides insight into the microbial composition of bovine faeces, milk, and blood and its extent of overlapping. It further highlights the potential risk of disease occurrence and transmission between the animals and the inhabitants of the sampled rural community, pertaining to their unsanitary practices associated with the use of cattle by-products.

Keywords: microbial profiling, 16S rRNA, NGS, feces, milk, blood, lactating cows, small-scale farmers

Procedia PDF Downloads 112

Authors: David A. Wallace, Salakjit J. Nilboworn

Abstract:

Secure and reliable transmission and distribution of electrical power is crucial in today’s ever-increasing demand for electricity. Traditional methods of protecting the electrical grid have relied on relaying systems receiving voltage and current inputs from inductive instruments transformers (IT). This method has provided robust and stable performance throughout the years. Today with the advent of new non-conventional transformers (NCIT) and sensors, the electrical landscape is changing. These new systems have to ability to provide the same electrical performance as traditional instrument transformers with the added features of data acquisition, communication, smaller footprint, lower cost and resistance to GMD/GIC events.

Keywords: non-conventional instrument transformers, digital substations, smart grids, micro-grids

Procedia PDF Downloads 82