Reviewerly/exHarmony huggingface dataset:
Digital Enhancement: A Drawbridge or a Detachment of the Digital Divide

Mizoram, one of the smallest states in India, has a very dynamic and flexible culture. The internet reached the state during the late 1990s; the general public started accessing it by the early 2000s. Digital enhancement, facilitated by technological devices, is becoming a reality in various professions as well as daily life. The problem of the digital divide—tangible availability of computers and internet connections as well as issues of content, language, skills and social resources—is yet challenging to eradicate. Whilst a group called the ‘choose nots’ are not interested in engaging with technology, several questions arise—Will digital know-how be useful in bridging the gap of the digital divide? Can digital understanding outdo the divide demarcated by the native–immigrant separation? Or does digital enhancement widen the gap by endowing the ‘haves’ while the ‘have-nots’ keep adding numbers to the ‘digital left behind’? This study focuses on the influence of the internet on Mizo culture, nature of the digital divide, and traces the internet history of Mizoram while trying to map the perceived difference in communication behaviour before and after the existence of the internet among digital immigrants.
AI in Visual Communication

Although the advent of AI-powered design tools has improved productivity and made graphic design more accessible, some designers worry that their skills will become obsolete. This shifting context raises questions about the evolving roles of designers and poses a threat to the profession's long-term viability. Following the former argument, authors in this chapter use the Google's “Human-Centered Machine Learning” (HCML) initiative, where people's problems and requirements must come first whenever ML is considered in the design of a product. It suggests that UX designers should begin with a focus on user demands, look at innovative ML solutions, and employ user-centered methodologies like prototyping and Wizard of Oz studies. This approach simplifies the creation of trustworthy, accessible, and user-centric AI systems by putting a premium on continuous adaptation, precise labeling, and collaborative engagement between UX and engineering. Because of the rapid changes occurring in the industry, UX designers will evolve into curators of AI-generated content. A seamless integration of human intuition with AI talents is still necessary to meet user expectations, assure trust, and counteract prejudice. Finally, the chapter stresses the importance of human-centered design even in the age of AI.
SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine Intelligence

Video recommendation ensures that viewers get content more relevant to their choices and taste. With the aggregation of a diverse variety of content on video streaming platforms, there is a need to improve the existing recommendation model to increase accuracy in predicting the best content analogous to past choices of the user. The model proposed in this paper employs semantic similarity to generate recommendations based on metadata and annotations. The semantic inference-based model ensures the incorporation of cognitive knowledge of user inputs and past activities, yielding better insights into user preferences. The model utilizes web-sourced metadata to augment its dataset with supplementary information. The metadata is classified using RNN and is stored in a semantic network for feature selection. Topic modeling on query terms allows the discovery of hidden topics in the user query and the use of a knowledge store like DBpedia allows for enriching the query terms based on the topics discovered. The video dataset is categorized using a combination of Random Forest and decision tree-bagging techniques, which are based on the extracted features of the videos. The proposed SemVidRec model is compared with other video recommendation models like VAVR, PVRRC, CCVR, etc. SemVidRec was discovered to outperform the baseline models based on several performance metrics, achieving an accuracy of 96.45%, precision of 95.43%, and an nDCG value of 0.97.
Metaverse‐based decentralised autonomous organisation in energy systems

Abstract Developing renewable energy generation (REG)‐rich power systems could contribute to achieving carbon neutrality. To ensure the secure and economic operation of power systems with high penetration of renewable energy, it is necessary to solve the problem of inefficient utilisation of demand‐side resources by the current electricity market mechanism. The metaverse, an emerging technology attracting widespread attention, is expected to efficiently solve this problem. The metaverse can be regarded as a virtual‐real interactive economic system built on advanced technologies such as blockchain, artificial intelligence, extended reality, avatars, and decentralised autonomous organisations (DAO). This paper first briefly introduces the concept, architecture, technologies, and features of the metaverse. Then, a metaverse‐based DAO for energy systems is proposed and the corresponding business model is explored. The Energy DAO utilises algorithms and user consensus combined with smart contracts to solidify organisational operation rules. In this way, it organises users to directly participate in multiple types of electricity markets and carbon markets, as well as behavioural data production and transactions. Finally, an Energy DAO example for demand‐side sources demonstrates how the Energy DAO could solve the problems of information asymmetry, information opacity, and incentive incompatibility in electricity market mechanisms.
AI for Designers

This chapter’s in-depth investigation will take you to the center of the field of ML. See how three key algorithms work to solve real-world problems, such as classifying snake species, forecasting student grades, and even challenging human champions at strategic games like Go. Learn why collaborative design is so crucial to the future of human–machine interaction and why designers need to adapt to new tools in order to create engaging user experiences. Explore the fascinating world of real estate categorization in the dynamic cities of Delhi and Mumbai. Look at how this ML model has progressed over time; it can now tell the difference between houses of different heights and prices per square foot by using decision trees to create accurate forecasts. However, you must steer clear of overfitting, a situation in which model complexity increases at the expense of its predictive power. This insightful research delves into the mystery of AI-caused unease, providing a window into the human psyche in the process. It gets to the heart of human-centered design by highlighting the critical visual and linguistic features that reduce anxiety. Confront the uncanny valley and the need for thorough user testing to guide AI systems towards meeting human expectations and alleviating any concerns they may inspire. Finally, this chapter concludes with the goal to experience the future of AI-infused experiences by delving into the fascinating realm where technology and people meet.
Unidimensional Continuous Variable Quantum Key Distribution under Fast Fading Channel

Abstract In this paper, the performance of a unidimensional continuous variable quantum key distribution protocol is analyzed using Gaussian modulated coherent state under fast fading channel. In the fast fading channel, both parties are connected in free space, resulting in a harsh propagation environment and pollution caused by atmospheric turbulence. This pollution makes it difficult for the communicators to determine the channel transmittance, which can only be estimated based on a probability distribution. To address this, only one modulator is used to code, which demonstrates performance equivalent to that of the symmetric modulated Gaussian coherent state protocol. The security of the protocol in the face of collective attacks is analyzed and it is proved that it can accept a certain amount of excessive channel noise while simplifying operations. Simultaneously, the impact of finite‐key length effects on the protocol is analyzed. It is also demonstrated that this protocol can reduce costs within an acceptable range of performance losses, making it more practical.
Enhancing Mitotic Cell Segmentation: A Transformer Based U-Net Approach

Mitosis segmentation plays a vital role in early cancer detection, facilitating the accurate identification of dividing cells in histopathology images. Manual mitosis counting is time-consuming and subjective, prompting the need for automated approaches to improve efficiency and accuracy. In this study, we have developed a transformer-based U-Net model that combines the effectiveness of transformers which were originally designed for natural language processing (NLP) tasks, with the efficiency of the U-Net architecture to effectively capture both high-level and low-level features in histopathology images. We train and evaluate the model on the GZMH dataset and compare its performance against other deep models such as U-Net, U-Net++ and Mobilenetv2-based U-Net. The results demonstrate that transformer-based U-Net model is better in terms of accuracy, recall, precision, F1-score and Dice coefficient. This study represents a significant advancement in mitosis segmentation, contributing to improved cancer detection and prognosis.
Adaptive enhancement method of irregular low-pixel architectural design image based on lightness component

This study explores adaptive enhancement for irregular, low-pixel architectural design images, focusing on lightness components. Utilizing a median filter and wavelet threshold method removes image noise, followed by grayscale conversion in HSV space. Applying Retinex theory generates the initial enhanced image, further improved through wavelet transform image fusion technology, resulting in the final enhanced architectural design image. Experimental results demonstrate the method's effectiveness in adapting and enhancing irregular, low-pixel architectural design images, achieving notably improved outcomes. The proposed approach successfully extracts and utilizes valuable image information, enhancing overall image quality. This study found that the proposed method can effectively extract and better utilize useful information in images, achieving more ideal image enhancement effects.
Multi-Level Cloud Datacenter Security Using Efficient Hybrid Algorithm

Security is currently the main boundary for cloud-based administrations. It is not adequate to just consolidate the cloud by adding a couple of additional controls or component answers for your current organization security programming. Businesses must utilize both virtual and physical information center security frameworks to keep them secure. The objective is to defend it from dangers that may jeopardize the secrecy, judgment, or openness of mental property or commerce data resources. These are the fundamental central focuses of all assigned attacks, and in this way, they require a high degree of security. Hundreds to thousands of physical and virtual servers are partitioned up into information centers agreeing to sort applications, information classification zones, and other criteria. To protect applications, frameworks, information, and clients, information center security takes on the workload over physical information centers and multi-cloud situations. It also applies to open cloud data centers. All server ranches ought to protect their applications and data from a rising number of refined threats and around-the-world ambushes. Each organization is at risk of assault, and numerous organizations have been compromised without being mindful of it. An evaluation of your resources and business necessities is important to improve a spotless way to deal with your way of life and cloud security technique. To deal with a strong mixture of multi-cloud wellbeing program, you should lay out perceivability and control. You can consolidate incredible controls, organize responsibility dispersion, and lay out fantastic gambles on the board with the assistance of safety items and experts.
Entangled-Based Quantum Wavelength-Division-Multiplexing and Multiple-Access Networks

This paper investigates the mathematical model of the quantum wavelength-division-multiplexing (WDM) network based on the entanglement distribution with the least required wavelengths and passive devices. By adequately utilizing wavelength multiplexers, demultiplexers, and star couplers, N wavelengths are enough to distribute the entanglement among each pair of N users. Moreover, the number of devices employed is reduced by substituting a waveguide grating router for multiplexers and demultiplexers. Furthermore, this study examines implementing the BBM92 quantum key distribution in an entangled-based quantum WDM network. The proposed scheme in this paper may be applied to potential applications such as teleportation in entangled-based quantum WDM networks.
Understanding a virtual heritage site through the lens of telepresence and virtual destination image

The experience of heritage sites through digital technology is an increasingly important topic for researchers and practitioners. Drawing upon telepresence theory, this study explores how a visitor's destination image is formed and its impact on satisfaction in a virtual heritage site experience. The survey results show that telepresence, cognitive image, affective image, and satisfaction are positively correlated. Furthermore, the relationship between telepresence and satisfaction was mediated by the destination image in the virtual heritage site experience. This research extends telepresence theory by testing its impact on destination image in a virtual tourism experience as well as providing some meaningful practical insights for heritage site marketing managers on virtual heritage experiences.
An improved DeepLabv3+ lightweight network for remote-sensing image semantic segmentation

Abstract To improve the accuracy of remote-sensing image semantic segmentation in complex scenario, an improved DeepLabv3+ lightweight neural network is proposed. Specifically, the lightweight network MobileNetv2 is used as the backbone network. In atrous spatial pyramid pooling (ASPP), to alleviate the gridding effect, the Dilated Convolution in original DeepLabv3+ network is replaced with the Hybrid Dilated Convolution (HDC) module. In addition, the traditional spatial mean pooling is replaced by the strip pooling module (SPN) to improve the local segmentation effect. In the decoder, to obtain the rich low-level target edge information, the ResNet50 residual network is added after the low-level feature fusion. To enhance the shallow semantic information, the efficient and lightweight Normalization-based Attention Module (NAM) is added to capture the feature information of small target objects. The results show that, under the INRIA Aerial Image Dataset and same parameter setting, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) are generally best than DeepLabv3+ , U-Net, and PSP-Net, which are respectively improved by 1.22%, − 0.22%, and 2.22% and 2.17%, 1.35%, and 3.42%. Our proposed method has also a good performance on the small object segmentation and multi-object segmentation. What’s more, it significantly converges faster with fewer model parameters and stronger computing power while ensuring the segmentation effect. It is proved to be robust and can provide a methodological reference for high-precision remote-sensing image semantic segmentation.
Singular and Multimodal Techniques of 3D Object Detection: Constraints, Advancements and Research Direction

Two-dimensional object detection techniques can detect multiscale objects in images. However, they lack depth information. Three-dimensional object detection provides the location of the object in the image along with depth information. To provide depth information, 3D object detection involves the application of depth-perceiving sensors such as LiDAR, stereo cameras, RGB-D, RADAR, etc. The existing review articles on 3D object detection techniques are found to be focusing on either a singular modality (e.g., only LiDAR point cloud-based) or a singular application field (e.g., autonomous vehicle navigation). However, to the best of our knowledge, there is no review paper that discusses the applicability of 3D object detection techniques in other fields such as agriculture, robot vision or human activity detection. This study analyzes both singular and multimodal techniques of 3D object detection techniques applied in different fields. A critical analysis comprising strengths and weaknesses of the 3D object detection techniques is presented. The aim of this study is to facilitate future researchers and practitioners to provide a holistic view of 3D object detection techniques. The critical analysis of the singular and multimodal techniques is expected to help the practitioners find the appropriate techniques based on their requirement.
A Multi-View Integrated Ensemble for the Background Discrimination of Semi-Supervised Semantic Segmentation

The key to semi-supervised semantic segmentation is to assign the appropriate pseudo-label to the pixels of unlabeled images. Recently, various approaches to consistency-based training and the filtering of reliable pseudo-labels have shown remarkable results. Nonetheless, there are still issues to be addressed. We find that recent approaches have specific problems in common. In pseudo-labels for training unlabeled images, we confirm that false foreground class pseudo-labels are mostly caused by background class confusion, not confusion between different foreground classes. To solve this problem, we propose a foreground and background discrimination model for semi-supervised semantic segmentation. Our proposed model is trained using a novel approach called multi-view integrated ensemble (MVIE) via output perturbation. Experimental results in various partition protocols show that our approach outperforms the existing state of the art (SOTA) in binary prediction on unlabeled data, and the segmentation model trained with the help of our model outperforms existing models.
AAHEG: Automatic Advanced Heap Exploit Generation Based on Abstract Syntax Tree

Automatic Exploit Generation (AEG) involves automatically discovering paths in a program that trigger vulnerabilities, thereby generating exploits. While there is considerable research on heap-related vulnerability detection, such as detecting Heap Overflow and Use After Free (UAF) vulnerabilities, among contemporary heap-automated exploit techniques, only certain automated exploit techniques can hijack program control flow to the shellcode. An important limitation of this approach is that it cannot effectively bypass Linux’s protection mechanisms. To solve this problem, we introduced Automatic Advanced Heap Exploit Generation (AAHEG). It first applies symbolic execution to analyze heap-related primitives in files and then detects potential heap-related vulnerabilities without a source code. After identifying these vulnerabilities, AAHEG builds an exploit abstract syntax tree (AST) to identify one or more successful exploit strategies, such as fast bin attack and Safe-unlink. AAHEG then selects exploitable methods via an abstract syntax tree (AST) and performs final testing to produce the final exploit. AAHEG chose to generate advanced heap-related exploits because the exploits can bypass Linux protections. Basically, AAHEG can automatically detect heap-related vulnerabilities in binaries without source code, build an exploit AST, choose from a variety of advanced heap exploit methods, bypass all Linux protection mechanisms, and generate final file-form exploit based on pwntools which can pass local and remote testing. Experimental results show that AAHEG successfully completed vulnerability detection and exploit generation for 20 Capture The Flag (CTF) binary files, 11 of which have all protection mechanisms enabled.
Do LIS experts select more appropriate journals than journal finders? A study about LIS journals?

The primary aim of the present study is to provide a comparative-analytical analysis of the proposed results of the Manuscript Matcher with the responses of Library and Information Science experts to select the most appropriate journal for manuscript submission. This study is a kind of applied research conducted using the survey-analytical method with a comparative approach. Data were collected using a researcher-made questionnaire. The designed questionnaire was sent to 38 Library and Information Science (LIS) experts. Moreover, snowball sampling was employed to select a sample of 38 people. Twelve articles published in 12 WOSCC-indexed LIS journals were randomly selected for the Manuscript Matcher analysis. Abstracts and bibliographic data of the articles were sent to the experts. Moreover, the abstracts and titles of the 12 articles were entered in the manuscript matcher, and the results were analyzed and compared with those obtained from experts' opinions. Frequency distribution, percentage, chi-squared (χ 2 ) test, and SPSS software (version 26) were employed for data analysis. More than 70% of the statistical population were assistant professors, 36.8% of experts had more than 40 published articles, 5% had received more than 300 citations, and 2.6% of LIS experts had an h-index above 20. Compared with 62% congruent responses of experts in the target journal selection, the Manuscript Matcher showed an inferior performance of 53% congruent responses. Furthermore, no significant relationship was found between the number of citations and the experts’ h-indexes with the rate of their congruencies in selecting the target journal. Experts were more successful than Manuscript Matcher in selecting the target journal. However, the Manuscript Matcher included comprehensive coverage of publications that can facilitate the selection of a journal for researchers due to the limited knowledge of all journal finders and the time-consuming distinct search processes.
Predicting merge conflicts considering social and technical assets

Abstract Concurrent contributions to a code base may introduce merge conflicts. Whereas merge conflicts are easy and common to introduce, resolving them is a difficult, time-consuming, and often error-prone task. Previous research concentrated on the emergence of merge conflicts considering technical assets in their analyses and often ignored the social perspective (e.g., developer roles). Our goal is to understand and predict merge conflicts considering social and technical assets. We devise three models for predicting merge conflicts based on common measures used by developers. The first model focuses on the social assets, the second on technical assets, and the third on technical and social assets. To evaluate our predictors, we report on a large-scale empirical study analyzing the histories of 66 real-world software systems. Specifically, we categorize developers into top or occasional contributors at project and merge-scenario level. We found that top contributors at project level and occasional contributors at merge-scenario level cause more merge conflicts than the other roles. Hence, the coordination of top contributors at project level and occasional contributors at merge-scenario level is a good starting point to minimize the occurrence of merge conflicts (especially because when these two developers work on the source branch, the chances of merge conflicts are 32.31%). Overall, we show that predicting merge conflicts incorporating developer roles is possible in practice with high accuracy (0.92) and recall (1.00) when combining technical and social assets, which is vital information to guide improvements on speculative merging techniques.
AI for Computational Vision, Natural Language Processing, and Geoinformatics

The rapid development of artificial intelligence technology has had a huge impact on the fields of computer vision, natural language processing, and geographic information applications [...]