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Towards a Model: Examining the Positive Associations of Warmth, Competence, and Familiarity with Musicians' Attitudes Towards AI
This study investigates attitudes towards AI musicians through a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. Data analysis focuses on the interplay between Anthropomorphism Degree (AD), Listening Type (LT), Warmth (W), Competence (C), Attitude (A), and Familiarity (F). The sample comprises 211 valid responses from college students, exploring perceptions via a questionnaire. Results indicate significant positive associations between attitudes towards AI and Competence, Familiarity, and Warmth. However, predictive validity analysis suggests caution in relying solely on the PLS-SEM model. Importance-Performance Analysis (IPMA) highlights competence as the primary influencer of attitudes towards AI, emphasizing its critical role over Warmth and Familiarity. This study contributes to understanding the nuanced dimensions of human interactions with AI musicians. 2024 IEEE. -
Towards a Smarter Connected Society by Enhancing Internet Service Providers' QoS metrics using Data Envelopment Analysis
This paper analyses wireline broadband Quality of Service (QoS) metrics of India's small and medium Internet Service Providers (ISPs). Key Performance Indicators (KPIs) used in this analysis are - Fault repair (>90% in 1 working day and >=99% in 3 working days), Response time to customer for voice-to-voice operator assistance (in 60 sec. >60% and in 90 sec. >90%), Broadband connection speed from ISP to node (Download speed) and Service availability/uptime. Benchmarks are arrived at, using the Slack Based Measure (SBM) in Data Envelopment Analysis (DEA). Twenty Decision Making Units (DMUs - ISPs) were used in the analysis with eight of them needing to improve their QoS on some of the mentioned parameters. Relative benchmark providers for all providers needing improvement with their weightage are found and optimal targets by each QoS metric is mathematically arrived at. The Electrochemical Society -
TOWARDS A SUSTAINABLE FUTURE: Need for Posthuman Ethics in the Anthropocene Era
Ethics, which constitutes principles that guide human conduct, deserves particular attention in this era of the Anthropocene, when human actions greatly influence ecology. Renaissance humans have hegemonised humanistic ethics of living and interacting with the world since the Enlightenment. While exalting human exceptionalism, humanism has relegated all other forms of existence to a subservient taxonomy, categorising them as raw material for human empowerment. The self-exalted autonomous subject, homo sapiens, faces the threat of extinction in the wake of unprecedented and violent ecological reactions. The exponential growth of the agency of intelligent machines also calls to question the autonomous human agency propagated by humanism. A paradigm shift is the moments need; this paper suggests posthuman ethics as an alternative. The new worldview, post-humanism, places the homo sapiens in relation to the rest of the universe. Philosophical post-humanism, proposed by Francesca Ferrando, foregrounds posthuman ethics that are post-humanistic, post-anthropocentric, and post-dualistic. They form a roadmap towards a sustainable future. 2023 Journal of Dharma: Dharmaram Journal of Religions and Philosophies. -
Towards a theory of well-being in digital sports viewing behavior
Purpose: Social television (Social TV) viewing of live sports events is an emerging trend. The realm of transformative service research (TSR) envisions that every service consumption experience must lead to consumer well-being. Currently, a full appreciation of the well-being factors obtained through Social TV viewing is lacking. This study aims to gain a holistic understanding of the concept of digital sports well-being obtained through live Social TV viewing of sports events. Design/methodology/approach: Focus group interviews were used to collect data from the 40 regular sports viewers, and the qualitative data obtained is analyzed thematically using NVivo 12. A post hoc verification of the identified themes is done to narrow down the most critical themes. Findings: The exploration helped understand the concept of digital sports well-being (DSW) obtained through live Social TV sports spectating and identified five critical themes that constitute its formation. The themes that emerged were virtual connectedness, vividness, uncertainty reduction, online disinhibition and perceived autonomy. This study defines the concept and develops a conceptual model for DSW. Research limitations/implications: This study adds to the body of knowledge in TSR, transformative sport service research, digital customer engagement, value co-creation in digital platforms, self-determination theory and flow theory. The qualitative study is exploratory, with participants views based on a single match in one particular sport, and as such, its findings are restrained by the small sample size and the specific sport. To extend this studys implications, empirical research involving a larger and more diversified sample involving multiple sports Social TV viewing experiences would help better understand the DSW concept. Practical implications: The research provides insights to Social TV live streamers of sporting events and digital media marketers about the DSW construct and identifies the valued DSW dimensions that could provide a competitive advantage. Originality/value: To the best of the authors knowledge, the exploration is the first attempt to describe the concept of DSW and identify associated themes. 2021, Emerald Publishing Limited. -
Towards a Tribal Literary Criticism in India: Engaging Northeast Tribal Voices in English Literature
This article formulates and applies a tribal literary criticism to the tribal voices of Northeast India articulated in English literature. It moves beyond prevailing literary paradigms that have traditionally marginalised indigenous worldviews and viewpoints by adopting indigenous-tribal epistemologies and a decolonial approach. By applying tribal knowledge through close readings of selected tribal literary texts produced by tribal writers from the Northeastern regions of India, the study explores key concepts such as community, land, identity, and ecology, rooted in the tribal holistic worldview of the God-world-human continuum. Relying on tribal worldviews, oral traditions, memories, storytelling, and lived experiences, indigenous-tribal interpretative tools offer alternative frameworks. Furthermore, the study validates tribal ways of knowing and expands the field of literary criticism by including diverse epistemic traditions. 2026, Penerbit Universiti Kebangsaan Malaysia. All rights reserved. -
Towards an Epistemology of Reading: Defining the Process of Reading in Modern Terms
The chaotic space caused by information explosion in present times has made the process and purpose of reading to be always questioned. Technological advancement has made reading appear as a mere mockery at the very outset. But the world still prioritizes knowledge that is acquired through observation, valuation and interpretation. At the time of Big Data, there still persists a sense of agency to define a given information as episteme. The present essay emphasizes on looking at reading as a modern phenomenon by presupposing the epistemological presence at the centre of any rational pursuit. Based on the Kantian precepts on enlightenment, the paper attempts to understand this presence of knowledge by delving into the major disciplines of modern philosophy that help in observing, valuing and interpreting the act of reading in present times. More than laying terms for defining the text within the modern space, the study essentializes reading in a virtually driven algorithmic world. AesthetixMS 2021 -
Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting
In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. -
Towards Automated and Optimized Security Orchestration in Cloud SLA
In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreementinspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability. 2023 International Journal on Recent and Innovation Trends in Computing and Communication. All rights reserved. -
Towards Computation Offloading Approaches in IoT-Fog-Cloud Environment: Survey on Concepts, Architectures, Tools and Methodologies
The Internet of Things (IoT) provides communication and processing power to different entities connected to it, thereby redefining the way objects interact with one another. IoT has evolved as a promising platform within short duration of time due to its less complexity and wide applicability. IoT applications generally rely on cloud for extended storage, processing and analytics. Cloud computing increased the acceptance of IoT applications due to enhanced storage and processing. However, the integration does not offer support for latency-sensitive IoT applications. The latency-sensitive IoT applications had greatly benefited with the introduction of fog/edge layer to the existing IoT-Cloud architecture. The fog layer lies close to the edge of the network making the response time better and reducing the delay considerably. The three-tier architecture is still in its earlier phase and needs to be researched further. This paper addresses the offloading issues in IoT-Fog-Cloud architecture which helps to evenly distribute the incoming workload to available fog nodes. Offloading algorithms have to be carefully chosen to improve the performance of application. The different algorithms available in literature, the methodologies and simulation environments used for the implementation, the benefits of each approach and future research trends for offloading are surveyed in this paper. The survey shows that the offloading algorithms are an active research area where more explorations have to be done. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards connected government services: A cloud software engineering framework
Cloud computing technologies are being used highly successfully in large-scale businesses. Therefore, it is useful for governments to adopt cloud-driven multi-channel, and multiple devices to offer their services such as e-tax, e-vote, e-health, etc. Since these applications require open, flexible, interoperable, collaborative, and integrated architecture, service-oriented architecture approach can be usefully adopted to achieve flexibility and multi-platform and multi-channel integration. However, its adoption needs to be systematic, secure, and privacy-driven. In this context, micro services architecture (MSA), a direct offshoot of SOA, is also a highly attractive mechanism for building and deploying enterprise-scale applications. This chapter proposes a systematic framework for cloud e-government services based on the cloud software engineering approach and suggests a cloud adoption model for e-government, leveraging the benefits of MSA patterns. The proposed model is based on a set of evaluated application characteristics that, in turn, support emerging IT-based technologies. 2021 by IGI Global. All rights reserved. -
Towards developing an automated technique for glaucomatous image classification and diagnosis (AT-GICD) using neural networks
Glaucoma is the eye defect that has become the second leading cause of blindness worldwide and also stated as incurable, may cause complete vision loss. The earlier diagnosis of glaucoma in Human Eye is a great confrontation and very important in present scenario, for providing efficient and appropriate treatments to the persons. Though there is much advancement in Ocular Imaging that affords methods for earlier detection, the appropriate results can be obtained by integrating the data from structural and functional evaluations. With that note, this paper involves in developing automated technique for glaucomatous image classification and diagnosis (AT-GICD). The model considers both the textural and energy features for effectively diagnosing the defect. Image Segmentation is processed for obtaining the exact area of optic nerve head; histogram gradient based conversion is employed for enhancing the fundus image features. Further, Wavelet Energy features are extracted and applied to the artificial neural networks (ANN) for classifying the NORMAL and GLAUCOMA images. The Accuracy rate based comparison with other existing models is carried out for evidencing the effectiveness of the proposed model in glaucomatous image classification. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Towards Net-Zero Hotels: AI-Enabled Energy Transition and Demand Response Models
This paper presents an AI and machine learning-driven approach to support energy transition and demand-side management in hotel operations, aiming for net-zero energy goals in hot and arid regions. The proposed framework leverages predictive modeling to forecast HVAC energy consumption based on key environmental and operational factors such as temperature, humidity, occupancy, and thermostat settings. Among the tested models, LightGBM demonstrated superior performance in terms of accuracy and computational efficiency. Additionally, a demand response strategy was simulated, where small thermostat adjustments during high-load periods led to significant energy savings without affecting guest comfort. The results highlight the effectiveness of integrating AI-powered decision systems into hospitality energy management, contributing to climate-resilient, sustainable hotel infrastructure. 2025 IEEE. -
Towards Optimal ?-Binding Functions of (2K1?K2)-Free Graphs and (P3?K1)-Free Graphs
A function f:N?R is called a ?-binding function for a hereditary family G of graphs, if ?(G)?f(?(G)) for every G?G where ?(G) and ?(G) denote the chromatic number and clique number respectively. In his influential work, Gya?fa? (1987) showed that the family of (2K1?K2)-free graphs and the family of (P3?K1)-free graphs are ?-bounded. Randerath and Schiermeyer (2004) improved the ?-binding functions of both these classes to x+12. In this paper, we further improve the ?-binding function of both these classes to x22 for x?3. Furthermore, we obtain a tight chromatic bound for (P3?K1)-free graphs with clique number 4. The Author(s), under exclusive licence to Springer Nature Japan KK 2025. -
Towards resilience: navigating local knowledge in flood risk management strategies in Majuli Island, Assam
Scientific knowledge of climate change and its latent effects is important. But it often lacks the expertise of local communities. This paper emphasises the importance of understanding local knowledge within the dynamics of vulnerability and resilience. It also offers insights into the applicability of these knowledge systems, providing valuable lessons and challenges on local knowledge in flood risk management. The study was conducted within a qualitative framework, utilising a case study design, in Majuli Island. Data were collected through 20 key informant interviews. Findings reveal that the diverse dimensions of local knowledge among Indigenous communities strengthen mitigation, coping, and adaptation strategies, enabling them to endure recurring floods. The evidence presented can guide government and non-governmental organisations (NGOs) in Majuli in integrating local knowledge into their interventions. By documenting and critically analysing existing practices, this paper adds to the growing literature on local knowledge in disaster research and practice. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Towards Smarter Transit Systems: An Artificial Intelligence based IoT Approach
Transportation today is paramount, and difficulties such as unreliable bus schedules and overcrowding are still found due to inadequate managerial practices. While cities are confronted with rapid urbanization and population growth, public transit remains a strong reliance of the middle class, especially in India. Individuals are subsequently subjected to overcrowded, and unreliable modes of transit, which lead them to seek private solutions that ultimately leads to increased private vehicle usage, which is directly related to more congestion and pollution. Therefore, utilising an IoT/machine learning based solution which provides commuters with updated bus locations and occupancy via their mobile phones to make more informed travel decisions, thus reducing wait times is proposed. Accurately tracking the buses via gps, is beneficial for providing timely information, where sensors are used for estimating occupancy based on passenger counts. The traffic prediction provided to users is generated from a Random Classifier machine learning model that would otherwise improve commuting efficiency and urban mobility. The model is found to have 98% accurate on cross-validation and 99% on test data, while the average F1-score over various traffic situations is 0.99. The described solution assists transit users by providing up to date service information improving the passengers quality of travel, heightened their sense of safety, and creates a more integrated urban experience, which promotes long-term sustainable development to meet the interconnectedness challenges cities confront with rapid urban expansion. 2025 IEEE. -
Towards Smarter Warehouse Layouts: Simulation-Driven Insights on Congestion and Forklift Flow Patterns
In high-mix, make-to-order warehouse environments, slotting decisions in constrained warehouse settings affect flow dynamics, yet their behavioral implications remain underexplored. This study employs discrete event simulation (DES) using the software FlexSim to evaluate three slotting strategies: baseline reflecting current operations, a benchmark informed by heuristic frequency-based clustering, and a proposed layout based on a learned configuration within a forklift-operated warehouse characterized by narrow aisles, unidirectional traffic, and spatial contention. Instead of emphasizing throughput alone, the study takes a closer look at how forklifts operate on a day-to-day basis, specifically how their time is divided between active movement, waiting due to blockages, and idling with no task assigned. Each strategy was tested over 20 simulation replications. Notably, the proposed layout cut blocked time by more than 30% and allowed forklifts to remain idle (and ready) more often, without reducing overall utilization. These patterns held consistently across runs. Statistical analysis confirmed that the differences in forklift behavior were significant, and Levenes tests showed that performance didnt become more erratic. These findings demonstrate that the improvements are systematic, not random. The work presents a simulation-based method for diagnosing layout effectiveness by looking at behavior, not just outputs, connecting slotting choices to real operational flow and system stability. This approach supports more resilient warehouse designs in settings with limited space and high product mix. 2025 IEEE. -
Towards sustainable business: Review of sentiment analysis to promote business and well-being
Sustainability in business is expected considering the growth in the long run. Sustainable development goals are important for our sustainability on this planet. In case of a business, it is essential to ensure sustainable processes and sustainability of the existing customers. Sustainable customers can in turn contribute to improving the process by providing constructive suggestions to the business. This paper is an attempt to review sentiment analysis techniques to improve the customer experience of a business. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Towards Sustainable Finance: Understanding Green Banking Adoption Among Indian Young Adults
This study explores the factors influencing the adoption of green banking among young adults in India, drawing on the Theory of Planned Behaviour. Key determinants include environmental concern, social factors, perceived behavioural control, subjective norms, and attitude toward behaviour. The moderating role of effort expectancy is also examined. Based on data from respondents aged 1835, the findings provide practical insights for banks to develop targeted strategies that encourage green banking practices among youth. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Towards Sustainable Living through Sentiment Analysis during Covid19
Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. The Electrochemical Society -
Towards sustainable resource management: A short and long-run dynamics of mineral production on ecological footprint
The effect of mineral production on ecological footprint is examined in this study while controlling for economic growth, renewable energy consumption, and trade openness as additional determinants for Pakistan. On the empirical front, the study uses the Dynamic Autoregressive Distributed Lag (DYNARDL) simulations for the data collected between 1990 and 2021. The result portrays movement to the long-run equilibrium relationship when considering the ecological footprint as the outcome variable amidst mineral production, economic growth, renewable energy consumption, and trade openness as the covariates. Further, the finding shows temporal dynamics of mineral production on environmental quality with a short-term degradation versus long-term amelioration, which suggests that mineral production can be conducted more sustainably over time with an implication towards taking measures such as technological advancements, improved efficiency, and better waste management practices. Additionally, it failed to find evidence for the conventional Environmental Kuznets Curve, implying a need for policy reevaluation, reassessment of economic development models and accounting for environmental externalities in economic decision-making. Besides, as expected, the outcome demonstrates that using renewable energy lowers the ecological footprint both in long and short terms, which indicates that utilization of renewable energy sources reduces reliance on fossil fuels, resulting in decreased environmental degradation, thereby fostering the need for emphasis on the importance of continued technological innovation in renewable energy technologies to reduce the ecological footprint further. Moreover, it shows that trade openness improves the environmental quality in the short run (worsens it in the long run), thereby highlighting that trade openness may lead to short-term environmental benefits by promoting cleaner technologies and increasing resource efficiency. However, in the long term, trade openness can exacerbate environmental degradation due to economic priorities often taking precedence over environmental concerns. 2024 Elsevier Ltd