Intelligent Communication Research Articles

YAPAY ZEKÂ İLETİŞİMİNDE CHATGPT: İNSANLAŞAN DİJİTALLEŞMENİN GELECEĞİ

Uzun süredir iletişim alanında dijitalleşme, yeni bilgisayar teknolojilerin kullanımı ve web 2.0 gibi kavramlar tartışılırken artık günümüzde yapay zekâ temelli teknolojiler de yeni bir tartışma konusu olarak literatürdeki yerini almıştır. Dünyada AI, (Yapay Zekâ), IoT (Nesnelerin İnterneti) ve blockchain teknolojisi gibi yeni nesil bilgi ve iletişim teknolojilerinin yaygınlaşmasıyla teknolojik ve endüstriyel devrim hız kazanmış ve yapay zekâ kavramı hükümet, şirketler ve akademiden büyük ilgi görmüştür. Yapay zekâ, robotların birbirleriyle ve insanlarla ilişkili görevlerini yerine getirebilme yeteneği olarak ifade edilmekte; bu nedenle robotik ve yapay zekanın gelecekte günlük hayatımızın neredeyse her alanına dahil olması beklenmektedir. Özellikle yapay zekâ temelli üretilen bazı programlar günlük kullanımda bireylerin iletişimsel süreçlerini, çalışma hayatlarını ve toplumsal etkileşimlerini etkileyeceği öngörülmektedir. Bu programların başında ise, son dönemlerde sıkça tartışılan ve üzerine araştırmalar yapılan ChatGPT (Chatbot Generative Pre-trained Transformer) programı gelmektedir. Bu program, sohbet şeklinde etkileşim kurabilme özelliği ile ön plana çıkmaktadır. Bu doğrultuda çalışmada, yapay zekâ ve iletişim ilişkisi, ChatGPT uygulaması özelinde iletişimin geleceği konusunda kuramsal, betimleyici ve metodolojik bir çerçeve sunulması amaçlanmaktadır. Ayrıca, iletişimsel anlamda yeni bir alan açan bu programın yararlarına, sakıncalarına, iletişim alanı üzerindeki etkilerine ve 2023 sonrasında beklenen muhtemel değişikliklere odaklanılmaktadır. Ve son olarak biz yazarlar olarak bu dönemi; “İnsanlaşan Dijitalleşme Çağı” olarak adlandırarak alana yeni bir katkı sunmayı amaçlıyoruz.

Artificial intelligence and human communication: A systematic literature review

The advancement of artificial intelligence has enhanced communication and contributed to the flow of information in recent years. This review attempts to report on the findings of a systematic literature review on artificial intelligence and human communication. The review focused on four databases— Elsevier, Google Scholar, Oxford and Sage. In order to refine the results, inclusion and exclusion criteria were defined with the purpose of eliminating all publications which came across as clearly irrelevant. After applying the protocol and the analysis model, a corpus of 21 papers was obtained between May and June, 2023. The results show that with the increasing availability of monitoring data and the recent advances in computing platforms, artificial intelligence has become a tool for network operators to automate communication. In addition, artificial intelligence-based technologies such as machine learning, human machine communication and computer mediated communication have demonstrated superhuman capabilities in solving some real world problems.

Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks.

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22% at the cell-edge and 1.5% at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively.

Remote Bridge Inspection and Actual Bridge Verification Based on 4G/5G Communication Environments

The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment.

Retracted: Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

Retracted: Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

Beam‐Steering Metadevices for Intelligent Optical Wireless‐Broadcasting Communications

High‐performance beam‐steering devices play a crucial role for optical wireless communication (OWC), which can meet the demand for increasing number of wireless mobile devices and emerging high‐speed multimedia applications. Conventional beam‐steering optical components like spatial light modulator and digital micromirror device are limited to realize a small steering angle (typ. several degrees), thus dramatically limits the spatial scope of OWC. Herein, a beam‐steering metadevice utilizing an ultracompact metasurface assisted with a spatial light modulator is presented, which can significantly increase the beam‐steering angle without at the cost of complicated optical setup like conventionally used angle magnifier. Based on the actively tunable beam‐steering metadevice, an intelligent bidirectional optical broadcasting communication system is designed and experimentally demonstrated, which exhibits nine broadcasting areas covering a large field‐of‐view of 20° × 20°, with user‐defined dynamic beam‐steering ability in each area, and each user has a data rate of 10 Gbps for both upstream and downstream transmission. The proposed metadevices can meet the demand of intelligent optical wireless communication which requires both high‐performance and low‐cost for practical purpose, under currently available technical architectures.

The impact of network social presence on live streaming viewers’ social support willingness: a moderated mediation model

With the accelerating development of social networks and the popularization of intelligent personal communication devices, live streaming has provided fluid experiences in time and space for the Chinese people, especially during the COVID-19 pandemic. Live streaming has enabled the real-time communication and interaction between viewer and live host, and has created a range of live hosts and new forms of business models due to the affordance of virtual currencies and gift reward mechanisms featured on live streaming platforms. Based on a questionnaire survey of 515 live viewers, this study examines the impact of the viewers’ network social presence on social support willingness and analyzes the roles of parasocial interaction and emotional response. The study reveals that network social presence has a direct positive impact on emotional, instrumental, and economic support willingness. Additionally, parasocial interaction plays a mediating role in the impact of network social presence on emotional, instrumental, and economic support willingness. Furthermore, the higher the degree of emotional response, the stronger the mediating effect of parasocial interaction on the relationship between network social presence and instrumental support willingness. Findings shed light on the potential intermediate mechanism and the boundary conditions of the influence of network social presence on the social support willingness of viewers, providing new insights on promoting the relationships between live hosts and viewers on live broadcast platforms.

Emotional Intelligence (EI) – The Need of the Hour

Objective: To explore the significance of emotional intelligence (EI) in communication and interpersonal interactions. Theoretical framework: Understanding the concept of emotional intelligence and its relevance to effective leadership and workplace dynamics. Method: Examining the relationship between emotional intelligence, leadership success, and employee productivity. Results and conclusion: Highlighting the importance of emotional intelligence for leaders and its positive impact on employee satisfaction and productivity. Implications of the research: Identifying the practical implications of emotional intelligence in fostering a respectful and productive work environment. Originality/value: Highlighting the unique contribution of emotional intelligence to leadership effectiveness and its impact on employee well-being and organizational outcomes.

UKSG 2023: scholarly communication for the information community

PurposeIt reports new research and innovative ideas and technology presented at the UKSG Conference held in Glasgow, Scotland. The report is to disseminate outcomes from the conference plenaries, lightning talks, breakout sessions and exhibitions. Topics like open access, global equity, artificial intelligence and scholarly communication, research archiving and preservation among others are described as presented during the conference.Design/methodology/approachThis paper uses the viewpoint method in reporting a United Kingdom Serials Group (UKSG) experience of a conference held in April, 2023 in Glasgow, Scotland.FindingsThe scholarly community debated much that is trending on various topics and concluded that a lot needs to be done in collaborations, open access, research into new ways of dealing with contemporary issues and new technologies.Originality/valueThe paper presents the issues discussed at the UKSG Conference 2023 have not been presented before. Current trends are brought to the fore.

Spread spectrum modulation recognition based on phase diagram entropy

Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System.

The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers' mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G's many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3GPP standards.

TS-WHT: A Two-Step Walsh–Hadamard Transform Approach for Blind Error Correcting Code Classification

Blind classification of error-correcting codes is essential for intelligent wireless communication, cognitive radio, and non-cooperative communication. The object of this paper is to investigate the differences in the Walsh spectrum between linear block code, convolutional code, and Turbo code. Based on these differences, a two-step Walsh Hadamard transform (TS-WHT) strategy is proposed for error-correcting code blind classification. Further, according to the detection theory, the peak detection threshold is detailed and optimized with the cyclic feature of CRC codes. The results indicate that when employing the optimized threshold, probabilities of correct classification are superior for linear block codes and convolutional codes. Moreover, with the help of the fault-tolerant feature of the WHT technique, the classification accuracy of the proposed method is substantially better than existing approaches under low signal-to-noise ratio(SNR) conditions.

Intelligent covert communication design for cooperative cognitive radio network

Intelligent covert communication design for cooperative cognitive radio network

Space-Efficient Storage Structure of Blockchain Transactions Supporting Secure Verification

The rapid growth of the blockchain size is a major bottleneck hindering its implementations in data-heavy applications. Current efforts improve the distributed storage ways and transactions' storage mechanisms of blockchain, however, the blockchain distribution and integrity are destroyed. Simplified Payment Verification (SPV) is closely related to blockchain storage, but the current solutions did not explore the privacy-preserving SPV. In this paper, we propose a new storage structure for blockchain transactions, called Coloring Index (CI), to reduce the blockchain's space occupation. Specifically, we devise an index building algorithm to simply calculate the indices of transactions for the sake of information concealing. By improving the Coloring Embedder for multi-sets query, we can store the indices into the Coloring Embedder to achieve the structured storage of transactions with small space occupation. Using CI, SPV query proceeds without revealing the user's address, thereby achieving secure data sharing in applications such as the intelligent vehicles' communications and distributed IoT. We prove CI's security against malicious full nodes when establishing possible connections between the address and the user. The experiments show that blockchain systems using our CI store one time more transactions than Merkle tree and half more than Bloom filter.

CHANGES IN EMOTIONAL INTELLIGENCE OF STUDENTS ENROLLED IN THE EMOTIONAL INTELLIGENCE AND SOCIAL COMMUNICATION COURSE

During the Fall semester of the school year 2021-2022, three concurrent Emotional Intelligence and Social Communications (EISC) courses were conducted to assist students’ personal development. Using a mixed-method approach, this descriptive study explores changes in the EI levels of students attending these courses. After conducting the survey, which revealed a significant mean difference of 0.12 (95% CI [0.003, 0.237]) between during-course and post-course scores, the researchers employed purposive sampling to select interview participants with the most increased, most decreased, and nearly unchanged levels of EI. The interview data indicated that most interviewees experienced an increase in the ability to consider other perspectives and some in the ability to regulate emotions. On the other hand, emotion management ability seemed to have experienced the slightest improvement. Additionally, the analysis of 20 learner’s reflections showed that learners indicating the most recurrent ideas are learners’ realisations about self (personal shortcomings, past unfortunate events, existing helpful habits/accomplishment, or intentions of future change/improvement) and statements about benefits of mindfulness (increased perception or relief from unpleasant emotions).

Digital Ethics: Dilemma or Opportunityin Intelligent Higher Education Organizations

The purpose of this essay is to generate thinking on digital ethics as an essential agent to promote values, principles, and practices in higher education organizations and to stimulate healthy and safe digital behaviors in the people involved in the educational act, for an appropriate use of Information and Communication Technologies (ICT). For this purpose, an analysis of the relationship between ICT and ethics is presented, which will constitute an opportunity to establish human relations in universities based on respect, justice, inclusion, transparency, and responsibility, for the benefit of a complex, technologically active, and impacted society by the COVID-19 coronavirus disease pandemic. It begins with a technological description of the global and local reality, then some theoretical and practical elements related to intelligent organizations, Information and Communication Technologies (ICT), are explained. In addition, digital ethics is approached from a theoretical approach, considering whether this is a dilemma or an opportunity to generate change from the perspective of intelligent organizations. Finally, a proposal is provided with guidelines to promote digital ethics in universities that contribute to a healthy coexistence focused on the common good, with the use of digital technologies.

An end-to-end screen shooting resilient blind watermarking scheme for medical images

The rapid progress in artificial intelligence and network communication has led to the development of advanced regional medical systems, revolutionizing the way hospitals and patients connect. However, the security of these systems is threatened by the potential breach of medical data, especially sensitive medical images. Existing watermarking schemes have focused on optimizing either imperceptibility or robustness, creating a fundamental trade-off between these two aspects that poses a significant challenge in the field. To address this challenge and ensure the confidentiality and integrity of medical data, this paper proposes an end-to-end screen shooting resistant blind watermarking scheme for medical images. The proposed method offers a robust and reliable solution for the secure transmission and storage of medical images, particularly in the face of screen shooting attacks. To enhance both security and imperceptibility, the scheme employs a novel encryption technique based on BCH codes, resulting in a trinary sequence for watermark images. These watermarks are then embedded in the spatial domain, effectively protecting against unauthorized access while preserving the integrity of the original image. To further enhance robustness, the method introduces a neural network structure specifically designed to resilient screen shooting attacks. By simulating the screen shooting process and incorporating various distortions, this network combines the strengths of residual networks and generative adversarial networks. Moreover, the proposed method introduces a novel loss function that exploits the unique characteristics of the image transformation domain, optimizing both imperceptibility and robustness. In the subsequent experiment, the watermarked images achieved satisfactory results with the highest PSNR and SSIM values reaching 62.563 dB and 0.9999, respectively. The proposed scheme provides an effective solution to protect the privacy and copyright of medical images, effectively addressing a critical security concern prevalent in regional healthcare systems.

Anti-Jamming Communication Using Slotted Cross Q Learning

Most of the existing intelligent anti-jamming communication algorithms model sensing, learning, and transmission as a serial process, and ideally assume that the duration of sensing and learning timeslots is very short, almost negligible. However, when the jamming environment changes rapidly, the sensing and learning time can no longer be ignored, and the adaptability of the wireless communication system to the time-varying jamming environment will be significantly reduced. To solve this problem, this paper proposes a parallel Q-learning (PQL) algorithm. In the case of long sensing and learning time, by modeling sensing, learning, and transmission as parallel processes, the time that the transmitter remains silent during sensing and learning is reduced. Aiming at the situation that the PQL algorithm is susceptible to jamming when the jamming changes faster, this paper proposes an intelligent anti-jamming algorithm for wireless communication based on Slot Cross Q-learning (SCQL). In the case of rapid change of jamming channel, the system can sense and learn the jamming patterns in multiple successive jamming periods at the same time in the same timeslot, and use multiple Q-tables to learn the jamming patterns in different jamming periods, so as to achieve the effect of reliable communication in the environment with rapid change of jamming. The simulation results show that the jamming collision rate of the proposed algorithm under the condition of intelligent blocking jamming is equivalent to that of the traditional Q-learning (QL), but the timeslot utilization rate is higher. Compared with PQL, the proposed algorithm has the same slot utilization and lower jamming collision rate. Compared with random frequency hopping (RFH) anti-jamming, the proposed algorithm not only has higher timeslot utilization, but also has lower jamming collision rate.

Optimizing horizontal scalability in cloud computing using simulated annealing for Internet of Things

The Internet of Things (IoTs) is a technology that connects sensor devices to the Internet to enable smarter and more intelligent communication. Today, many industries are using various IoT devices to create smart and intelligent environments. However, the sudden increase in demand has created a major challenge for IoT connections, known as scalability. Scalability refers to increasing and expanding the number of internet-connected devices for a specific application. To address this issue, we propose simulated annealing-based horizontal scaling to achieve faster and more efficient scaling to accommodate IoT devices. We explore different horizontal scaling methods and propose a Markov chain process to model the scaling. We then use simulated annealing to optimize the scaling visualized by the Markov chain process. Our goal is to focus on the flexible nature of horizontal scalability for adding various IoT devices and resources as needed. We have compared our proposed horizontal scalability optimization with vertical scalability, which has a built-in feature of elasticity. We have evaluated several parameters, such as cost, service rate, and transfer rate, and found that our proposal outperforms existing methods.

Embodied, Intelligent Communication for Multi-Agent Cooperation

High-performing human teams leverage intelligent and efficient communication and coordination strategies to collaboratively maximize their joint utility. Inspired by teaming behaviors among humans, I seek to develop computational methods for synthesizing intelligent communication and coordination strategies for collaborative multi-robot systems. I leverage both classical model-based control and planning approaches as well as data-driven methods such as Multi-Agent Reinforcement Learning (MARL) to provide several contributions towards enabling emergent cooperative teaming behavior across both homogeneous and heterogeneous (including agents with different capabilities) robot teams.