Social changes occur, inter-alia, by a complex combination of technological innovation, often independent and separate. This paper adopts the document analysis approach to decode how the dynamics between technology and society have evolved post-industrialization. Towards this, it maps the existing role of technology while identifying a few critical trends with the growing influence of technology on society, including the search for a universal innovation strategy, sustainability issues, and the ever-increasing dominance of technology hype. As the outcome, the study has highlighted seven factors that need to be taken into consideration while recalibrating the role of technology in society: standardization focus on technology role, enhanced adoption in allied sectors to facilitate horizontal growth of technology, causality conundrum, criticality of technology hype adjustment, approach to multi-dimensional sustainability, moving beyond market regulation towards strengthening technical regulation.
This review article critically examines the landscape of technologies to provide sustainable, equitable, inclusive, and quality online education in rural India. The focus is on understanding the challenges, opportunities, and impact of technology adoption in enhancing education outcomes in rural areas. The review encompasses a comprehensive analysis of various aspects, including access to e-learning tools, infrastructure development, teacher training, student engagement, and the transformative role of technology in reshaping traditional teaching methods. The review begins by providing an overview of the current state of education in rural India, highlighting the disparities in educational access and quality between urban and rural regions. It then delves into an in-depth exploration of the potential of technology to bridge these gaps, examining initiatives such as digital classrooms, online platforms, mobile learning apps, and digital content repositories that are being leveraged to improve educational access and outcomes in rural communities.
Intelligent reflecting surface (IRS) has appeared as an innovative green and economical technique to markedly enhance the spectrum and energy efficiency in mobile edge computing (MEC) systems. However, limited by single antenna setting, the potential of IRS-aided MEC systems has not been fully exerted. To further improve the system computational performance, the paper introduces multiple-input single-output (MISO) technology, and proposes a MISO-based MEC system with IRS. In this system, IRS is utilized to support the communication between users and a multi-antenna access point integrated with edge servers. Also, we adopt non-orthogonal multiple access (NOMA) strategy for information transmission. The computation rate is maximized to optimally design receiver beamforming, CPU frequency, transmit power, and IRS phase shifts. This formulated problem is non-convex and challenging to solve due to multi-variable coupling. To tackle it, we exploit alternating optimization manner to address the four decoupled subproblems until convergence. Simulation results highlight the superior computation rate performance of our proposed IRS-aided MISO-based MEC system.
Background: Artificial Intelligence is an evolving technology in the education sector. In India, with the prevailing digital divide among various sections of the society, it is feared that a ‘AI Divide’ would further aggravate the situation. Objectives: This paper aims to assess the perspective and level of awareness among college and university-level students in Assam, India, regarding AI and to assess the association between AI usage in academic purpose and the socio demographic characteristics of these students. Materials and Methods: Simple Random Sampling has been used for data collection from 200 respondents using a structured questionnaire. Chi square test is used for analysis. Results: Familiarity with AI and AI-related courses reveals that 14.5% is ‘not familiar at all’ and 15.5% is “very familiar” with AI and its usage. 7% have undergone AI-related courses, indicating a potential gap in AI education. Male students show a significantly higher usage of AI for academic purposes compared to female students in Assam. Urban students exhibit a significantly higher usage of AI compared to rural students. Literacy level of parents and the monthly income of parents do not show a significant association with AI usage among students in Assam. Students who are more familiar with AI tend to use it more for academic purposes. Whether students have undergone any AI related course does not significantly influence their usage of AI for academic purposes.
Visually impaired learners face significant challenges in accessing quality education due to the lack of accessible educational materials and adaptive learning environments. Traditional educational methods often fall short in addressing the unique needs of these learners, leading to a gap in educational outcomes and opportunities. UnSight aims to enhance the educational experience for visually impaired learners through a virtual education platform that integrates advanced AI technologies. This platform leverages speech and text processing, image and text integration, and voice-to-action commands to address accessibility gaps. Emphasizing adaptive learning and personalized feedback, the proposed system contributes to SDG 4: Quality Education. This paper discusses the technical architecture, standardization efforts, scalability, and ethical considerations involved in developing and deploying UnSight.
In this paper, a Praseodymium-doped fiber amplifier (PDFA) operating in O-band (1276 nm to 1356 nm) is reported. The performance is evaluated by optimizing PDF length, Praseodymium (Pr 3+ ) concentration, its doping radius along with the numerical aperture (NA), pump power and input power to the PDFA in terms of small signal gain and noise figure (NF). Small signal gain >25.23 dB has been observed for the wavelength region of 1276 nm to 1356 nm with a maximum gain of 44.56 dB at 1312 nm. The NF varying in the range of 4.1 dB to 8.2 dB has been obtained. Furthermore, the performance of the PDFA is analyzed in a coarse wavelength division multiplexing (CWDM) transmission link of a fiber-optic communication system at a data rate of 10 giga bits per second (Gbps) as a pre-amplifier based on the quality factor (Q-factor). An errorless transmission with a Q-factor >6 over 90 km of conventional single-mode fiber (SMF) has been achieved.
The paper explores the various challenges faced by farmers in the South 24 Parganas region of West Bengal. These challenges include inadequate financial support, limited technical knowledge, unsustainable farming practices, overproduction, market price fluctuations, and a lack of basic facilities and amenities. These issues have led to a decline in interest in farming and an increase in land sales among the farming community. Our study aims to propose effective strategies to empower farmers, improve financial stability, and foster community development. We conducted thorough surveys and interviews to gather data. A key contribution of our research is the proposal of a mobile application. This app facilitates direct interactions between sellers and buyers, spreads awareness, educates people about effective farming techniques, and motivates young people to pursue farming. The innovative app includes user profiles, a recommendation system for personalized product discovery, and a chat feature with real-time translation, ensuring smooth communication and efficient deal-making
This paper presents an integrated electromagnetic band gap (EBG) wearable antenna for the Internet of Things (IoT) applications. The proposed antenna is designed on Jean's substrate (is an element of(r) = 1.7, loss tangent (tan delta) = 0.085). The suggested antenna's final dimensions are 66.80 x 66.80 x 0.7 mm(3) The proposed antenna frequencyresonate at 3.53 GHz and operates in the frequency range 3.505 to 3.558 GHz. EBG's primary function is to reduce back lobe radiation in order to increase the proposed antenna's gain in the operating frequency band. After using EBG, gain is increased from 2.9 to 8.7 dBi. The radiation efficiency is 88.43 %. The bending analysis for wearable antenna at different radius is presented. An excellent consent is found between the simulated and measured outcomes, confirming that it is appropriate for IoT applications in 5G Sub-6 GHz frequency band.
Technology and our networked environment has made privacy an increasingly challenging state to achieve but in order to ensure we continue to meet the foundational standard for human rights, it is essential that privacy is elevated as a priority global discussion. We must continually review whether technology and network deliverables are meeting a privacy standard in a format accessible to all. In this paper we review whether Web 2.0 has met the required standard and if not, what impact this has had on society. From this we ask what we need to address in Web 3.0 to ensure those inadequacies do not proliferate into Web 3.0 developments. Finally, this paper offers five human rights centric privacy design principles for future development.
Quantum Communication becomes a new field of communication technologies with highly diverse applications, and much complexity, and is a promising area deserving further exploration. This paper aims to provide a complete expository of quantum communication, describing the principles that govern it, which is quite different from classical communication. However, the paper also reflects on how the industrialization of quantum communication was achieved and examples of funded projects that were developed. Quantum communication is then considered in the context of $6 G$ networks, IoT, government, and defense to show the typical use of quantum communication in future information processing and secure communication domains. The paper aims to integrate the most recent developments, difficulties, and future outlook of the subject, hence serving as a thorough manual for researchers and practitioners operating in a complicated landscape of quantum communication technologies.
The cyber threat landscape is ever evolving, and as technologies advance and grow, so do the vulnerabilities in software technologies, programming languages and software development frameworks. There is an imperative need to be able to preemptively counter emerging threats in software and applications, standalone or otherwise. There are several vulnerability intelligence tools and services available in the market, however they suffer from a single common drawback. The vulnerability intelligence they present depends on selective and even proprietary feeds of information. With software technology that is largely driven by community efforts, a much better solution is to present the vulnerability intelligence from the community driven vulnerability databases itself. Furthermore, vulnerability intelligence can also be utilized in the field of cyber forensics. Forensic investigators require a sound foundational knowledge of vulnerability insights and attack vectors to understand how an attack or an incident might have occurred. This paper presents a web-based vulnerability intelligence platform that can effectively leverage the OSV database, NVD, CAPEC and CWE to present a more comprehensive, community driven vulnerability intelligence that can not only help organizations in their vulnerability management efforts but also help cyber forensic analysts in getting relevant information about known attack methods in real time.
In this article an enhanced accuracy of hand gesture recognition using data augmentation is presented. The proposed model has base on the CNN with data augmentation to recognize static hand gestures. The model has tested on 7172 images after being trained on 27,455 images. The accuracy of the model using supplemented data was 99.76%, which is nearly greater than the accuracy of the CNN model without augmentation (86.87%).
Smart home automation systems require convenient and efficient user interface to control home appliances. Gesture recognition-based solutions offer flexibility to the users and play a crucial role in advancing human-computer interaction and immersive computing environments. This work proposes a novel solution leveraging deep learning techniques with attention mechanisms including self-attention tailored for processing 3D tensors derived from the gesture images. A set of hand gestures is defined, and the system is trained and optimized to meet the real time requirements in controlling devices. To improve the accuracy, the model is parallelly trained with dynamic learning to adaptively fuse with the classification module. The proposed modular architecture is implemented using Raspberry Pi with IoT devices for a typical home environment. The test result achieves gesture classification accuracy of98.24% and latency of about 0.2 seconds in real time control. The working model highlights a practical solution under ITU-T Recommendation J.1611 which deals with the functional requirements of a smart home and gateway.
Computing force infrastructure is a new type of information infrastructure with computing as the center and network as the foundation. The development trend of integrated computing and networking has enriched and expanded the supply, application, and service methods of computing force, playing an important role in empowering and increasing efficiency in the industry. However, it has also brought new security challenges and increased the difficulty of security protection. Based on the successful industry practice, this paper puts forward the computing force network $(C F N)$ security protection requirements for operators and industry customers. The goal of these policies is to help industry customers achieve the three synchronization of security (synchronous planning, synchronous construction, and synchronous maintenance) when building CFN, and to guide the industry to improve CFN security capabilities.
Cryptographically Relevant Quantum Computers (CRQCs) are no longer hypothetical; they present significant challenges to current IT infrastructure by potentially breaking existing encryption schemes within minutes. This paper introduces an innovative and efficient method for achieving quantum-resistant encryption through lattice-based cryptography. We specifically tackle the challenge of encrypting extremely small units of data, such as a single letter or a single-bit message, by constructing a multidimensional lattice. Our proposed technique leverages the Short Vector Problem (SVP) in lattice-based cryptography and incorporates the Learning with Errors (LWE) methodology for data encryption and decryption. We demonstrate the feasibility and robustness of this approach using a real-time messaging application that provides quantum-resistant end-to-end encryption. Our work has the potential for deployment in strategic applications, securing information from the “harvest now, decrypt later” threat, even in the presence of quantum technologies.
The earlier idea of the communication system was to focus on modulation and channel coding schemes as separate entities. However, later the perspective of coded modulation was introduced by coding theorists and system designers. In this manuscript, we propose a Low Density Parity Check (LDPC) coded and bit-interleaved precoder, which is added before a 256-Quadrature Amplitude Modulation (QAM) and Reed-Solomon (RS) coded system. This technique significantly enhances the spectral efficiency (measured in bits per two-dimensional symbol) and improves the performance of coded modulation over a fading channel. By bitwise interleaving at the encoder output, the system disperses errors, making them easier to correct, while RS codes provide additional reliability by correcting symbol errors. Consequently, the proposed method boosts both spectral efficiency and overall system reliability, ensuring high data integrity in challenging transmission environments.
The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents and developing mitigating strategies. Specifically, this study analyses existing open-access AI-incident databases through a systematic methodology and identifies nine gaps in current AI incident reporting practices. Further, it proposes nine actionable recommendations to enhance standardization efforts to address these gaps. Ensuring the trustworthiness of enabling technologies such as AI is necessary for sustainable digital transformation. Our research promotes the development of standards to prevent future AI incidents and promote trustworthy AI, thus facilitating achieving the UN sustainable development goals. Through international cooperation, stakeholders can unlock the transformative potential of AI, enabling a sustainable and inclusive future for all.
With the rapid development of technologies such as $5 G$, cloud computing, and big data, network virtualization technology has been widely applied, effectively improving the utilization of resources such as computing, storage, and network, as well as the flexibility of IT systems or networks, reducing system maintenance costs. However, at the same time, the complexity of network virtualization environment makes security threats more covert and diverse, and traditional security protection methods are difficult to cope with, which also brings many security risks. This article analyzes the current development status and related work of network virtualization, sorts out the security threats of network virtualization, summarizes the characteristics and architecture of network virtualization technology, and further introduces the security measures and typical use cases of network virtualization, which can be used to guide and promote network virtualization security.
Presented study explores the transformative potential of Generative Artificial Intelligence (AI) and RetrievalAugmented Generation (RAG) in India’s e-governance ecosystem. Employing a multi-faceted analytical approach, the research uncovers insights into AI’s growth dynamics, perceptions, and impact on service delivery and citizen engagement. The findings emphasize the need for robust security measures, ethical guidelines, and context-specific deployment strategies. By presenting a framework for sustainable AI-driven knowledge management practices, the study offers actionable recommendations to policymakers and practitioners, fostering public trust and paving the way for an efficient, citizen-centric governance model in India. This innovative research contributes significantly to the growing body of knowledge on AI-driven governance, positioning India at the forefront of this transformative journey.
Indoor localisation has gained significant attention in recent years due to its applications in various domains, including robotics, asset tracking, and location-based services. Traditional indoor localisation systems rely on several access points or infrastructure, which are sometimes costly and challenging to establish. This paper investigates the viability of using a single access point for the indoor localisation of static objects. Initially, a dataset is recorded in the laboratory with ESP32 and a smartphone. After that, the dataset is utilised to train an algorithm that can infer the user’s location from current RSSI readings. Furthermore, the RSSI single feature is converted into five statistical features: 25%,50%, and 75% of quartiles, as well as min and max. The Dempster-Shafer Theory (DST) is used to classify the different regions. The localisation algorithm used in this system maps the RSSI values to appropriate locations inside the interior environment. The algorithm shows better results than several other machine learning algorithms.
