Reviewerly/exHarmony huggingface dataset:
Metamorphosis of Recommender Systems: Progressive Inclusion of Consumers

Recommender Systems (RS) are computer-based tools that use Artificial Intelligence (AI) algorithms to make product or service recommendations to users. A recommendation algorithm is usually applied to predict users’ tastes and preferences based on their behavioral characteristics. RS has gained the attention of e-retailers and managers connected to e-business. This research aims to provide a holistic and deep understanding of RS concerning its current progress and future scope. Hence, the goal of the study is to review the various trends and developments that have taken place in the field of RS in the last decade. Also, it outlines the key future scope and its application in various domains. For this purpose, a comprehensive and systematic literature review has been conducted using recently developed Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR). A total of 60 journal articles and conference proceedings published from 2010 to 2022 under top publishers have been selected. The extant literature has been scrutinized and research gaps have been identified. Furthermore, this paper also envisions the future of RS, which may broaden the horizon for new research directions in this field.
Cultural Values in Idiomatic Expression of Jambi Malay Language Bungo Malay Dialect: Antropholinguistics Perspective

This research is aimed to describe cultural values that found in the idiomatic expressions of Jambi Malay Language Bungo Malay dialect Then, the research is conducted through several steps, namely, collecting data, analyzing, and presenting the result of data analysis. The methods chosen in collecting data were observational and conversational method, by applying some techniques such as non participant observational technique, note taking technique, elicitation, and recording technique. Then, in analyzing technique, the writer used descriptive qualitative approach by using contextual analysis method in order to explain the cultural values from the expressions. Moreover, the writer used informal method for presenting the result of analysis. The result of research shows that the expressions of idiomatic from the Jambi Malay Language Bungo Malay dialect contain cultural values that have been developed in the society, especially for Jambi Malay speakers that categorized into good and bad values. Besides, the idiomatic expressions that used by Jambi Malay Language Bungo Malay dialect also contains ethics, moral, and politeness
Modeling the Barriers in Adoption of Neo Banks in India

Wave of innovative technology is fast replacing the traditional financial services by new innovative financial services provided by Neo Banks. Despite that, Indian consumers are not embracing Neo Banks at the expected rate. Using Neo Bank, people can transact easily, make fund transfer, opening of new bank account etc. 24/7 spread over the year etc. However, its effective adoption is a bottleneck because of inherent and unknown barriers. However, some attempts on the identification of these barriers have been carried out; the literature lacks a thorough investigation into the relationship and interdependencies among them. We have identified ten possible barriers via literate review and in consultation with the experts. These barriers have been analyzed using Fuzzy DEMATEL method based on the expert’s responses. Moreover, the analysis demonstrates Digital Inclusion barrier is the prominent one out of total barriers. Since it will affect the other barriers of Neo Banks adoption but it would not be affected by any other barriers it has a lesser threshold value. Hence, Digital Inclusion barrier is considered as most significant barrier which must be overcome for ensuring the effective adoption of Neo Bank.
Enhancing Customer Support Services in Banking Using Generative AI

Amid the evolving financial landscape, enhancing customer experience remains paramount for banks. One key area underpinning this experience is Customer Support Services (CSS). While the banking sector has historically employed various technological aids like Interactive Voice Response (IVR) Systems and chatbots, their rule-based nature often renders them less versatile. This research explores the potential of Generative AI in transforming Customer Support Services in banking. Unlike traditional systems, Generative AI’s ability to create unique content offers a more personalized and context-aware interaction. We have compared conventional methods with advanced Generative AI capabilities through a scenario-based approach. The findings provide insights into how Generative AI can revolutionize Customer Support Services across digital platforms, promising an enriched customer experience.
The Impact of the Metaverse on Green Marketing: A Paradigm Shift in Sustainable Consumer Behaviour

The rise of the metaverse has attracted a lot of interest as technology improvement keep transforming how we interact and engage with the virtual world. The term “metaverse” denotes an interactively simulated world in which individuals have the ability to engage with an artificially created setting along with other users in real time. The primary objective of the present study is to examine the possible implications of the metaverse on green marketing, with a special emphasis on its effects on sustainable consumption behaviour. Through a comprehensive analysis of the metaverse’s distinctive attributes, potential advantages, and inherent obstacles, our objective is to ascertain the extent to which this emerging digital realm might effectively facilitate the progress of ecologically sustainable products as well as behaviours of consumers. Furthermore, we analyze the potential implications for businesses and marketers in utilizing the metaverse as a means to augment their environmentally conscious advertising approaches.
Role of Two-Dimensional (2D) and Virtual Reality (VR) in Effectiveness of In-Game Advertising

In-game advertising technology is shifting from two-dimensional to virtual reality graphics. This study aims at examining how a digital game that is either played in a two-dimensional (2D) or head-mounted-display (HMD) virtual reality (VR) graphics is perceived and experienced by the players. More specifically the study illustrates the effect of 2D technology vs. HMD VR technology on players’ feeling of presence and brand recall. Results indicate that subjects who played HMD VR game reported higher level of presence than 2D game players. However, HMD VR players showed lower rates of brand recall than 2D players. This study provides insights for academicians as well as marketing practitioners from the perspectives of attention and elaboration considering 2D and HMD VR technology as important elements for designing effective games in the context of in-game advertising.
Factors Influencing the Readiness for Artificial Intelligence Adoption in Indian Insurance Organizations

Artificial intelligence (AI) technology is being adopted across industries. Adoption is a three-phase process- pre-adoption, adoption, and post-adoption. In this study, a systematic literature review is conducted to extract factors that influence the pre-adoption phase or readiness of the organization for adopting AI. These factors are narrowed down to 20 based on the discussion with the domain experts. These factors are mapped to Technology-Organization-Environment-Individual (T-O-E-I) framework that is derived from the technology-organization-environment (T-O-E) and human-organization-technology fit (H-O-T fit) frameworks. The experts ranked these factors independently. These rankings are used to calculate the global ranking of the factors using the Rough Stepwise Weight Assessment Ratio Analysis Method (R-SWARA), a multi-criteria decision-making (MCDM) method. The top seven factors are the following - perceived benefits of AI, AI system capabilities, data ecosystem in the organization, perceived compatibility of AI systems, perceived ease-of-use of the AI systems, IT infrastructure of the firm, and support from the top management. Sensitivity analysis shows that the ranks are robust.
A multi-scale pyramid feature fusion-based object detection method for remote sensing images

Object detection is a basic and challenging task in remote sensing image analysis that has received extensive attention in recent years. Feature fusion is one of the key steps in object detection. Most existing methods of feature fusion first complete the preliminary fusion of feature maps of different scales through ‘add’ or ‘concat’ operations, followed by using a single-scale convolution to further improve the fusion effect. However, due to the fact that multi-level features exhibit multi-scale representations, the fusion effect of existing methods is limited. To improve the efficiency of feature fusion, we propose a multi-scale pyramid feature fusion network, which performs multi-scale learning through multi-scale convolution kernels to complete multi-level feature fusion more effectively. Then we propose a lightweight decoupled head, which alleviates the conflict between the classification task and the localization task. We conducted experiments on the dataset of object detection in aerial images (DOTA) dataset and the HRSC2016 dataset to verify our proposed methods. The results show that the performance of our proposed methods is better than other existing methods, with an mAP of 73.3%, 67.6%, 65.0%, and 96.7% on the DOTA1.0, DOTA1.5, DOTA2.0, and HRSC2016 datasets, respectively. Meanwhile, the parameter quantity of the proposed model is 10.3 M, and the inference time is 5.1 ms, which meets the requirement of lightweight and ensures the timeliness of detection.
Amazon Alexa and I: Exploring Factors Affecting Usage Behaviours and Patterns Over Time

The proliferation of Voice Controlled Smart Assistants (VCSAs) is increasing, and this is expected to continue in the future. There is much promise in how the growing capabilities of these Artificial Intelligence-powered devices can affect user experiences, usage and habit yet little is known about these areas. This study aims to contribute to knowledge in these areas by utilizing an interpretive philosophy and a qualitative approach. A tri-phased data collection process was utilized which included the collation of diary data from fourteen participants over an eight-week period. This short paper provides insight into the key findings identified which highlight: how usage evolves over time, and factors that affect this evolution. The paper also discusses further steps the researchers will take to progress this study.
Practicality of Smart Apps Creator-based Instructional Media on 2D Animation Subject

Based on the results of observations at Vocational high School Pasundan 1 of Serang City (SMK 1 Pasundan Serang City), some of them do not meet the requirements for mastery learning outcomes, students find it difficult to digest animation lessons, and the teacher's limited ability to make media. Anticipating these obstacles requires an interesting new learning media that can generate students' imagination and creativity, one of which is learning media based on smart app creators. This study aims to find out how the process of developing learning media for Smart Apps Creator, to find out from the development of learning media in 2D Animation subjects that will be used in the learning process and to find out interactive learning media based on Smart Apps Creator in 2D Animation subjects at SMK 1 Pasundan which will be used for the teaching and learning process. This method used Research and Development (R&amp;D). Research through use identification of potential and problems, data collection, product design, design validation, revision, product testing, revision, trial, revision and mass production to produce a final product. The trial was conducted on 24 students of class XI SMK Pasundan 1 Serang City. The assessment of the 4 validators of the Interactive Learning Media, which is 90%, shows that the Interactive Learning Media is very valid to be used as a learning resource. The results of the assessment of 24 students that Interactive Learning Media has a practicality value of 89.30%, it can be said that the level of practicality of Interactive Learning Media is very practical and very well stated.
Blsnet: a tri-branch lightweight network for gesture segmentation against cluttered backgrounds

Abstract Hand gesture segmentation is an essential step to recognize hand gestures for human–robot interaction. However, complex backgrounds and the variety of gesture shapes cause low semantic segmentation accuracy in the existing lightweight methods because of imprecise features and imbalance between branches. To remedy the above problems, we propose a new segmentation structure for hand gestures. Based on the structure, a novel tri-branch lightweight segmentation network (BLSNet), is proposed for gesture segmentation. Corresponding to the structure parts, three branches are employed to achieve local features, boundaries and semantic hand features. In the boundary branch, to extract multiscale features of hand gesture contours, a novel multi-scale depth-wise strip convolution (MDSC) module is proposed based on gesture boundaries for directionality. For hand boundary details, we propose a new boundary weight (BW) module based on boundary attention. To identify hand location, a semantic branch with continuous downsampling is used to address complex backgrounds. We use the Ghost bottleneck as the building block for the entire BLSNet network. To verify the effectiveness of the proposed network, corresponding experiments have been conducted based on OUHANDS and HGR1 datasets, and the experimental results demonstrate that the proposed method is superior to contrast methods.
Internet of Things and Sustainability: A Literature Review

The purpose of this study is to review the literature of Internet of things research in the sustainability domain. We used keyword co-occurrence analysis to identify the newly emerging areas keeping ‘internet of things’ and ‘sustainability’ as central themes. The findings indicate energy management, smart cities, industry 4.0, artificial intelligence, smart water management, supply chain management, smart farming, and digital transformation as key emerging areas. Further, we found a strong connection between energy efficiency, IoT, and AI research. The study also highlights the usage of emerging technologies like blockchain, machine learning, digital twins, cyber-physical system, etc. contributing towards the sustainable development goals.
Trust building with artificial intelligence: comparing with human in investment behaviour, emotional arousal and neuro activities

This study investigated on people’s two-stage iterative trust building process (the initial trust stage and the iteration from feedback stage) with AI partners, and made comparisons with human partners in the aspects of investment behavior, emotional arousal and neuro activities. The experiment was designed using repeated trust games and the investment amount was transformed into the initial trust, the feedback from the partner and the final trust. Comparisons of behavior were conducted by regression analysis. The results showed that the initial trust for AI had stronger influence on the final trust than human partners, while the feedback from human during interaction had stronger influence than the feedback from AI. By emotion scales, it was found that the arousal of participants’ positive emotions was more significant when interacting with human than with AI, and the emotional arousal (both positive and negative ones) had stronger correlation with the feedback from human than from AI.. The findings indicated a stronger influence of feedback from human partners on decision making and emotional arousal than from AI partners. The fNIRS results also showed that people’s neuro activities in the PFC were stronger when making investment decisions towards human than AI partners.
Parallelising semantic checking in an IDE: A way toward improving profits and sustainability, while maintaining high-quality software development

Abstract After recent improvements brought the incremental compilation of large industrial test suites down to a few seconds, the first semantic checking of a project became one of the longest-running processes. As multi-core systems are now the standard, we derived a parallelisation using software engineering laws to improve the performance of semantic checking. Our measurements show that even an outdated laptop is fast enough for daily use. The performance improvements came without performance regressions, and we can’t expect additional massive benefits even from infinitely scaling Cloud resources. Companies should utilise cheaper machines that still o er enough performance for longer. This approach can help businesses increase profits, reduce electronic waste and promote sustainability while maintaining high-quality software development practices.
Formal modeling of multi-viewpoint ontology alignment by mappings composition

Abstract We propose a formal approach based on Bigraphical Reactive Systems (BRS) to provide a formal modeling of multi-viewpoint ontology alignment by composition systems’ structure using bigraphs their dynamic behaviors using bigraphical reaction rules. In the first phase of this approach, we address the modeling of the static structure the dynamic behavior of multi-viewpoint ontology alignment systems. We show how bigraphs enable the description of the di erent multi-view point ontology entities. Furthermore, we define a set of bigraphical reaction rules to model the dynamic nature of the alignment. We introduce composition strategies to describe multi-viewpoint ontology alignment systems’ behaviors. Then, we present a case study on which we illustrate the application of our proposed approach. Finally, we combine the logical reflection of Maude language the hierarchical structure of the BRS to provide an executable formal model for multi-viewpoint ontology alignment by composition systems.
Multi-scale attention fusion network for semantic segmentation of remote sensing images

In the realm of high-resolution remote sensing image (HRSI) segmentation, convolutional neural networks have shown their effectiveness and superiority. However, there are still two problems in the segmentation model that generally adopts the encoder-decoder structure in the face of HRSI: 1) Fusing high-level feature maps and low-level feature maps directly in the decoder will make spatial detail features easy to mask; 2) Although self-attention has been used to capture the long-distance dependence of features, the consumption of computing power and memory makes it have many restrictions in practical applications. Aiming at these two problems, this paper proposes a new HRSI segmentation model (named MLWNet). First, the introduction of the maximum pooling module improves the quality of the feature map and obtains the receptive field of the whole map and rich global semantic information. Then, based on a new linear complexity self-attention mechanism, we design a multi-scale linear self-attention module to abstract the correlation between contexts. Finally, the weighted feature fusion helps the feature map restore spatial details and refine the segmentation results. On the two HRSI datasets of ISPRS Potsdam and ISPRS Vaihingen, MLWNet achieved mIOU segmentation accuracy of 78.19% and 71.61%, respectively, which not only outperforms other mainstream segmentation models but also has only 17.423 M parameters. The segmentation model in this study has high precision and small parameters, which can provide decision information for real-time use of remote sensing images.
Hybrid of Least Significant Bits and most Significant Bits for Improving Security and Quality of Digital Image Steganography

The Security of confidential communication is protected using the most popular type of carrier to hold information known as Image steganography. The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are steganography algorithms used for information hiding in digital images both have disadvantages of Low image quality, Security, vulnerability to any small modifications, and long encoding time during message compression. To overcome this limitation, the research proposes a Secured Hybrid algorithm called S-Hybrid to combine (LSB and MSB) bits based on checking Two bits (the least significant bit and the most significant bit) of the cover images and replace them with a secret message which was implemented in Netbeans IDE. However, the S-Hybrid algorithm produced the best stego-image quality. Large cover images made the hybrid algorithm’s quality better. The proposed S-Hybrid had a lesser encoding time than the existing method having the highest compression ratio which reduces the transmission effort making the encoding time short which is correlated to the security and makes the proposed method perform better than the existing one. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work. Mean-squared error (MSE), Peak signal-to-noise ratio (PSNR), encoding time, and Compression ratio are used for performance evaluation between the proposed S-Hybrid algorithm and the existing Method after embedding messages in digital images.
Deep Learning Based on EfficientNet for Multiorgan Segmentation of Thoracic Structures on a 0.35 T MR-Linac Radiation Therapy System

The advent of the 0.35 T MR-Linac (MRIdian, ViewRay) system in radiation therapy allows precise tumor targeting for moving lesions. However, the lack of an automatic volume segmentation function in the MR-Linac’s treatment planning system poses a challenge. In this paper, we propose a deep-learning-based multiorgan segmentation approach for the thoracic region, using EfficientNet as the backbone for the network architecture. The objectives of this approach include accurate segmentation of critical organs, such as the left and right lungs, the heart, the spinal cord, and the esophagus, essential for minimizing radiation toxicity during external radiation therapy. Our proposed approach, when evaluated on an internal dataset comprising 81 patients, demonstrated superior performance compared to other state-of-the-art methods. Specifically, the results for our approach with a 2.5D strategy were as follows: a dice similarity coefficient (DSC) of 0.820 ± 0.041, an intersection over union (IoU) of 0.725 ± 0.052, and a 3D Hausdorff distance (HD) of 10.353 ± 4.974 mm. Notably, the 2.5D strategy surpassed the 2D strategy in all three metrics, exhibiting higher DSC and IoU values, as well as lower HD values. This improvement strongly suggests that our proposed approach with the 2.5D strategy may hold promise in achieving more precise and accurate segmentations when compared to the conventional 2D strategy. Our work has practical implications in the improvement of treatment planning precision, aligning with the evolution of medical imaging and innovative strategies for multiorgan segmentation tasks.