Diverse Platforms Research Articles

Efficient Algorithms for Range Mode Queries in the Big Data Era

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

An agent-based model of cross-platform information diffusion and moderation

Social media platforms are highly interconnected because many users maintain a presence across multiple platforms. Consequently, efforts to limit the spread of misinformation taken by individual platforms can have complex consequences on misinformation diffusion across the social media ecosystem. This is further complicated by the diverse social structures, platform standards, and moderation mechanisms provided on each platform. We study this issue by extending our previous model of Reddit interactions and community-specific moderation measures. By adding a followership-based model of Twitter interactions and facilitating cross-platform user participation, we simulate information diffusion across heterogeneous social media platforms. While incorporating platform-specific moderation mechanisms, we simulate interactions at the user level and specify user-specific attributes. This allows practitioners to conduct experiments with various types of actors and different combinations of moderation. We show how the model can simulate the impacts of such features on discussions facilitated by Reddit and Twitter and the cross-platform spread of misinformation. To validate this model, we use a combination of empirical datasets from three U.S. political events and prior findings from user surveys and studies.

Multimodal semantic enhanced representation network for micro-video event detection

Currently, micro-videos have gained widespread acceptance as a prominent form of user-generated content across various social media platforms. Accurate event analysis of micro-videos can greatly enhance the many diverse social media platforms applications. Although some studies have shown promising results from a multimodal perspective, there is still a challenge in extracting informative cues from inaccurate modalities, particularly for text modality that is prone to inaccuracies and noise. In this paper, we propose a multimodal semantically enhanced representation network (MSERN) for micro-video event detection. To better address inaccurate and noisy text sentences, we first extract visual concepts in the form of adjective-noun pairs (ANPs), through a fine-grained common representation module, to complement the textual descriptions. To maximize the acquisition of modality-specific cues from both visual and textual modalities, we then implement a coarse-grained private representation module to ensure that private representations encompass unique facets of the modalities beyond the common perspective. Finally, because two modules will collaborate, the fine-grained common and coarse-grained private representations are integrated to ensure a reinforced micro-video representation. We evaluate our proposed method on a micro-video event detection dataset and the experimental results show a superior performance compared to the state-of-the-art methods.

English Network Teaching Model and Design of Evaluation System Based on Association Rule Algorithm

This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.

Connectivism as a deterrent pedagogical approach for blind students at Historically Disadvantaged Institutions

The educational domain, like other spheres of human endeavour, was impelled to improvise and undergo unforeseen transitions because of covid19. These transitions, among other things, affected teaching and learning processes that had to shift from physical to online mode. Dissimilar to traditional learning approaches such as cognitivism and constructivism which emphasise the cruciality of learning context, connectivism is underpinned by the notion that learning is disseminated through networks using diverse virtual learning platforms. In the case of blind students, these learning platforms require up-to-date technologies for accessibility purposes. The availability of updated technologies is an outstanding matter in most Historically Disadvantaged Institutions (HDI) of higher learning. This disadvantages the affected students in terms of academic progression. Thus, this study aims to divulge the challenges faced by blind students at HDI of higher learning through the Connectivism paradigm. A qualitative descriptive design will undergird the study. Five blind students from HDI will be purposively sampled and interviewed. The findings will be presented and analysed through Qualitative Content Analysis (QCA), and viable recommendations will be made.

The empowerment of AIGC technology in high school English education

The application of AIGC technology in high school English teaching creates a rich virtual language application environment and diverse learning platforms for students. These multimodal activities not only provide tailored learning paths but also, through in-depth data analysis and effectiveness evaluation, support the implementation of adaptive learning. While fostering independent thinking among students, they also assist in integrating English learning into their daily lives. This advancement not only optimizes the allocation of educational resources in underprivileged western regions, promoting an overall improvement in educational quality, but also confers profound significance on education.

The use of digital platforms by citizen aid actors during the Ukraine humanitarian crisis

Following the Ukraine humanitarian crisis, which was a result of the war in Ukraine, ordinary citizens stepped up to spontaneously coordinate various humanitarian responses to support affected populations. In this article such individuals who founded or coordinated this form of spontaneous humanitarian aid are referred to as citizen aid actors (CAA). This paper explores the work of citizen aid actors in Poland, a country which received many of the people displaced by the war in Ukraine. The study applies the concept of digital affordances to illustrate and argue that citizen aid actors relied on the features available on digital platforms to attain their goals. It is an outcome of a qualitative research approach where interviews, participant observation, and analysis of relevant online material about the studied citizen aid actors were used as methods of collecting data. The findings indicate that these actors used diverse digital platforms at different stages of the humanitarian aid projects, from establishment, organization and sustenance. While this study demonstrates how specific digital platforms contributed to the accomplishment of the work done by CAAs, it also highlights how problematic it can be when the actors largely rely on digital platforms. As such, potential risks associated with overreliance on digital platforms as a way of implementing projects are factored in. Ensuing concerns include the presence of online trolls, the spreading of fake news, and internet disruptions as potential obstacles for the success of the aid projects.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

“What You Post in the Group Stays in the Group”: Examining the Affordances of Bounded Social Media Places

This study focuses on the affordances of bounded social media places (BSMPs), low visibility places within social media platforms like private messaging and private groups. While researchers have focused on BSMPs within specific platforms, this study presents a systematic examination of BSMPs across multiple platforms to facilitate theoretical durability. Interviews with users of BSMPs across diverse platforms ( N = 35) reveal that BSMPs discourage the affordance of visibility as they are considered private due to visibility management mechanisms and trust in known audiences. They encourage personalization as users believe they receive relevant content from and can send relevant content to specific audiences, in the absence of algorithms. BSMPs also encourage synchronicity by facilitating continuous conversations. The strength of encouragement or discouragement of these affordances varies across different BSMPs and is informed by users’ social positions. This study therefore contributes a framework and shared terminology for future research on BSMPs across social media.

Network Traffic Classification Against Adversarial Attacks Based Deep Learning

Background: Cybersecurity is a prominent concern in today's interconnected world, encompassing both local and remote wireless and wired access across diverse communication technology platforms. It is important to recognize the threat posed by hackers who are currently compromising organizational functionalities, bypassing security measures, and stealing hypersensitive information. Common hacking techniques such as Portscan, Distributed Denial of Service (DDoS) attacks, and the use of Botnets, are frequently utilized by hackers. Materials and Methods: The authors explore the advantages of using Machine Learning (ML) to classify attacks such as Port Scanning, DDoS, Botnet, and Botnet-Attempt in a mixture of both benign and attack traffic. They use various Artificial Neural Networks (ANN) structures, each having distinct properties, to train and test on a benchmark dataset (CICIDS2017). The aim is to identify the most effective ANN model and the optimal number of input features required to classify data that contains events of Portscan, DDoS, Botnet, and Botnet-Attempt attacks. Results: Various features are used as inputs for an ML model with single and multiple hidden layers, each with different neurons, to evaluate their impact on classification accuracy using a Python language. The best accuracy obtained is 99.71%, achieved by using all features of the dataset and 4 hidden layers, while the accuracy obtained using only 7 features is 97.6%. Conclusion: ANN models can perform well in classifying network traffic against adversarial attacks by using an optimal combination of features as input and hidden layers.

Securing the Digital Landscape: Present Concerns and Hurdles in Digital Rights Management

The landscape of Digital Rights Management (DRM) is witnessing continual evolution, marked by emerging issues and challenges that demand attention and adaptation. As technology advances, DRM faces the complex task of balancing copyright protection with user rights and expectations. One prominent challenge lies in the realm of interoperability, where varying DRM systems can create barriers for users seeking seamless access to content across different platforms. Privacy concerns have also come to the forefront, as DRM systems often involve the collection of user data to enforce licensing and access controls. Striking the right balance between effective content protections and safeguarding user privacy poses a delicate challenge for DRM implementation. Furthermore, the rise of streaming services and the proliferation of diverse digital platforms increasethe demand for standardized DRM solutions. In the absence of universally accepted standards, fragmentation and compatibility issues can arise, affecting both content providers and consumers. As the digital environment undergoes globalization, the significance of legal and regulatory frameworks will grow, playing a crucial role in influencing the future of DRM. Navigating the complexities of international law and dealing with jurisdictional differences are additional barriers to the effective deployment of DRM. Essentially, the emerging issues and challenges in Digital Rights Management highlight the importance of a dynamic and collaborative approach to ensuring intellectual property protection while respecting the constantly changing demands and rights of digital content consumers.

Scrolling with Belief: Understanding the Millennial Generation’s Perception of Religious Knowledge on Social Media

In an age marked by the widespread adoption of social media, the millennial generation’s perception of religious knowledge has emerged as a prominent research area. This inquiry delves into how millennials interpret religious values in the digital realm, with a particular focus on the role of social media in shaping and disseminating these beliefs. Scholars have sought to uncover the opportunities and challenges faced by Muslim scholars as they engage in dakwah and knowledge sharing through online platforms. Additionally, the study explores the essential role played by social media in facilitating Islamic learning, especially considering unprecedented global events such as the COVID-19 endemic. These investigations collectively illuminate the complex relationship between millennials, social media, and religious values interpretation. This study uses quantitative methods and data is analyzed using SPSS software. Out of the total 150 participants, 49 were actively involved in the survey process indicate that there is a positive but weak relationship between the daily duration of surfing YouTube content and participants’ perceptions of YouTube’s influence on their belief system. While a relationship exists, its relatively weak nature suggests that the influence of time spent on YouTube on participants’ beliefs may be influenced by additional factors not considered in this analysis. However, while these studies have enriched our understanding of this dynamic intersection, further research is needed. Future inquiries should delve into specific strategies for effective dakwah on diverse social media platforms and consider the evolving nature of social media usage among millennials.

Cognitive Dissonance Equilibrium: Local Product Stigma in the "Filosofi Pete" Ad Narrative Flow

Following the 1998 reform in Indonesia, advertising has undergone significant expansion, leveraging enhanced communication accessibility and media proliferation to shape public perceptions of promoted goods. Presently, media channels are inundated with commercial campaigns showcasing both domestic and international products. Within this landscape, Tokopedia, a key player in the e-commerce sector, employs diverse media platforms such as YouTube to engage consumers amid intensifying market rivalry. This study delves into Tokopedia's approach via the "Filosofi Pete" advertisement, exploring its endeavors to combat stereotypes surrounding local products. Drawing parallels between domestic goods and petai a culturally stigmatized item the research highlights the heightened stigma amid globalization, where foreign products often enjoy elevated prestige and perceived quality standards. Through the lens of cognitive dissonance theory and Todorov's narrative analysis, the analysis reveals Tokopedia's strategic narrative framework aimed at reshaping perceptions towards local products. By dissecting the advertisement's storyline, narrative structure, and visual elements, the study unveils Tokopedia's nuanced tactics for challenging entrenched biases. Through compelling storytelling and visual imagery, Tokopedia endeavors to reframe the discourse around local products, fostering a more favorable consumer outlook. This research sheds light on the intricate interplay between advertising strategies, cultural perceptions, and consumer behavior, offering insights into how brands navigate evolving market dynamics to carve out a competitive edge.

MizBERT: A Mizo BERT Model

This research investigates the utilization of pre-trained BERT transformers within the context of the Mizo language. BERT, an abbreviation for Bidirectional Encoder Representations from Transformers, symbolizes Google’s forefront neural network approach to Natural Language Processing (NLP), renowned for its remarkable performance across various NLP tasks. However, its efficacy in handling low-resource languages such as Mizo remains largely unexplored. In this study, we introduce MizBERT , a specialized Mizo language model. Through extensive pre-training on a corpus collected from diverse online platforms, MizBERT has been tailored to accommodate the nuances of the Mizo language. Evaluation of MizBERT’s capabilities is conducted using two primary metrics: masked language modeling and perplexity, yielding scores of 76.12% and 3.2565, respectively. Additionally, its performance in a text classification task is examined. Results indicate that MizBERT outperforms both the Multilingual BERT model and the Support Vector Machine algorithm, achieving an accuracy of 98.92%. This underscores MizBERT’s proficiency in understanding and processing the intricacies inherent in the Mizo language.

Non-surgical Synergistic Interventions for the Treatment of Skin Cancer.

Skin cancer is broadly classified into two categories i.e., non-melanoma skin cancer (NMSC) and malignant melanoma (MM), with MM having a greater fatality rate than NMSC. A large number of treatment strategies currently exist for these skin cancer types, ranging from monotherapies to complex multifaceted synergistic interventions including dual therapies, trimodality therapy, and multicomponent combinations therapy. These combinatorial cancer treatments have delivered more favorable results when compared with monotherapies, and although combination treatments increase the cost of treatment, these regimens have lower side effect profiles, decreased resistance, high efficacy and an improved long-term response. Synergistic combination treatments for skin cancer are often complex, wide-ranging and encompass diverse platforms with various mechanisms of action. An understanding of the physiological potential, as well the efficacy of such treatments, is therefore vital to ensure patients receive the best possible treatment. This review therefore focuses on the current advancements and existing non-surgical combinative drug delivery methods utilized for treating skin cancer. It encompasses the diverse pharmaceutical delivery systems, clinical outcomes, and oncology strategies employed and aims to highlight the role of non-surgical combination therapies in enhancing patient compliance, reducing treatment durations, and improving overall survival rates while addressing relapses and metastasis. The promising outlook of the research being conducted in this field has also been provided, as well as the barriers to the effective treatment of this complex condition.

An energy efficient authentication scheme for cluster-based wireless IoT sensor networks

Wireless Internet of Things (IoT) sensor networks (WITSN) play a pivotal role in modern society, facilitating a myriad of applications ranging from smart homes to industrial automation. Therefore, safeguarding these networks against security threats is paramount to ensure the integrity, confidentiality, and availability of data transmitted within them. However, WITSNs face escalating security threats due to their diverse structures and platforms. Existing literature has identified these vulnerabilities but lacks comprehensive solutions to address them effectively. To bridge this gap, this paper proposes a novel security approach termed Randomized Bi-Phase Authentication Scheme (RBAS), which integrates digital watermarking techniques to fortify both external and internal network security. RBAS not only tackles data availability, confidentiality, and authenticity challenges prevalent in WITSNs but also strives to maintain a delicate equilibrium between robust security measures and energy efficiency. Key contributions of this work include the meticulous examination and validation of the integration of cyclic redundancy check (CRC) codes in IoT sensor network authentication, demonstrating their efficacy in error detection through simulations. Furthermore, RBAS employs advanced hashing, cryptography, and dynamic verification codes to ensure strong error detection and data authenticity, leveraging robust CRC codes and randomization to thwart potential attacks. The scheme's complexity acts as a deterrent against manipulation, while its cluster awareness enhances adaptability, and cryptographic principles bolster overall security. Extensive performance evaluation using the Network Simulator-2 (NS2) reveals significant benefits of RBAS, including an 8 % reduction in power consumption and a 7 % increase in network longevity. Notably, RBAS surpasses existing solutions with a 14 % reduction in dropping ratio and an 8 % decrease in latency. The success of RBAS stems from its innovative utilization of lightweight watermarking techniques and cluster-based routing, enabling proactive identification of data tampering and thereby enhancing the network's overall security posture. This pioneering work not only advances the state of the art in WITSN security but also holds profound implications for the practical deployment of secure and efficient IoT sensor networks in real-world scenarios.

Rational Design of NIR-II Emitting Conjugated Polymer Derived Nanoparticles for Image-Guided Cancer Interventions.

Due to the reduced absorption, light scattering, and tissue autofluorescence in the NIR-II (1000-1700nm) region, significant efforts are underway to explore diverse material platforms for in vivo fluorescence imaging, particularly for cancer diagnostics and image-guided interventions. Of the reported imaging agents, nanoparticles derived from conjugated polymers (CPNs) offer unique advantages to alternative materials including biocompatibility, remarkable absorption cross-sections, exceptional photostability, and tunable emission behavior independent of cell labeling functionalities. Herein, the current state of NIR-II emitting CPNsare summarized and structure-function-property relationships are highlighted that can be used to elevate the performance of next-generation CPNs. Methods for particle processing and incorporating cancer targeting modalities are discussed, as well as detailed characterization methods to improve interlaboratory comparisons of novel materials. Contemporary methods to specifically apply CPNs for cancer diagnostics and therapies are then highlighted. This review not only summarizes the current state of the field, but offers future directions and provides clarity to the advantages of CPNs over other classes of imaging agents.

Progressive Web Apps in Cross-Platform Development: A Comparative Analysis and Evaluation

The evolution of app development methodologies has been significant, yet the discussion around cross-platform development remains pertinent. Apps must seamlessly function on both Android and iOS devices, accommodating diverse hardware configurations and platform versions. The proliferation of device categories beyond smartphones and tablets adds further complexity to achieving multi-platform compatibility. Despite the presence of multi-platform frameworks supported by both practical application and research, the challenge of efficiently developing apps for various targets persists. Progressive Web Apps (PWAs) potentially offer a solution to this ongoing quest for unifying technology. This paper aims to evaluate the role of PWAs in multi-platform development, exploring their foundational aspects and assessing the current landscape of possibilities. The evaluation includes a comparative analysis conducted in two phases. In the first phase, the different application development paradigms are analyzed with their advantages and disadvantages. In the second phase, the PWAs are compared to native and cross-hybrid apps based on launch time, installation size, and Largest Contentful Paint (LCP), with PWAs outperforming in all three metrics. By examining these aspects, the paper seeks to provide a complete understanding of PWAs' potential in cross-platform development. Key Words: Progressive Web Apps, Cross-Platform Development, App Development, Web Application, Multi-Platform Compatibility, Frameworks, Comparative Analysis, Performance Evaluation, Technology Unification, Mobile Devices, Android, iOS

An Insight into Indian Payment and Settlement Systems

This review article explores the evolution, current state, and future prospects of India's payment and settlement systems. Over the past few decades, these systems have undergone significant transformations driven by technological advancements, regulatory reforms, and the burgeoning demand for efficient financial transactions. The article examines the diverse instruments and platforms that constitute India's payment infrastructure, highlighting the pivotal role of regulatory bodies in shaping its development. Furthermore, it discusses the profound impact of digital innovation on enhancing financial inclusion and accessibility. This review aims to provide a nuanced understanding of how India's payment and settlement systems have evolved to support a robust financial ecosystem.

Secure Browse: AI-Powered Phishing Defense for Browsers

Purpose: With the rising threat of phishing attacks exploiting user naivety, this report introduces a novel approach to bolster web security. Traditional rule-based systems and existing solutions fall short in addressing sophisticated phishing attempts. The proposed solution entails a Chromium-based browser extension that leverages machine learning classification techniques. Methodology: A Python web server, utilizing decision trees, k- nearest neighbors, and random forests, assesses the legitimacy of a given URL. The extension communicates with the server, providing real-time notifications to users when visiting potential phishing sites. Findings: Experimental results demonstrate the effectiveness of the ensemble model with an accuracy of 90.68%, marking a significant improvement over rule-based alternatives. Unique contribution to theory, policy and practice: Future work includes refining models, incorporating user feedback, and expanding the application to diverse platforms and contexts.