Actual Communication Research Articles

Errors of Omission and Commission in Group Communication Networks

We advance a novel theory of relational schemas on how formal and informal structures in communication networks explain why members misperceive and miss the existence of communication links in their networks. To test our hypotheses based on the theory, we collected data on 23, 12-person networks (N = 212 participants), measuring both the actual communication networks (who was actually connected to whom) and perceived networks (who was perceived by others to be connected to whom). Comparing the actual and perceived networks shows that members tend to overestimate the presence of communication links between members in formal groups and informal structures (e.g., reciprocity and two-path) and underestimate the presence of links between people in different groups and people who are part of an unclosed triad. There is a clear accuracy trade-off in using relational schemas for perceptions of communication networks. These findings provide insights into how members’ perceptions of who knows whom hinder their ability to share information effectively.

Engaging Electronic Engineering Students in Realistic Engineering Problems: Calculations on the Feasibility and Implementation of Optical Communication Links

The article describes a task on the feasibility and possible options regarding the implementation of actual optical communication links. The task regards three (3) fiber-optic links with different transmission lengths and bit-rates and the students have to decide on the feasibility and implementation options for those links. The task is formulated in a problem-solving manner (in the sense that it refers to actual situations, more than one options may be considered feasible while some solutions may lead to revisiting initial calculations on the basis of new data). The basic aim of the task is the students to work on issues that most probably will encounter during their professional life and, at the same time, develop a conceptual understanding and a working knowledge on a broad range of essential concepts and notions regarding optical communication links.

The Algorithm Accuracy of Mathematical Model to Improve the Transmission Speed of E-commerce Platform

Abstract After the input signal of e-commerce network information enters the network, it will inevitably be polluted by network delay. There is an error between the network output signal obtained by the receiver and the input signal of the transmitter. We combine control combination algorithms with mathematical models. And the power spectral density function is introduced into the signal transmission mathematical model of the e-commerce platform. Experiments prove that the new model is closer to the transmission efficiency change of the actual network communication signal than the traditional model.

Application Research of Data Encryption Algorithm in Computer Security Management

In order to promote the research process of information security in the whole society, improve the safety factor of computer data communication, strengthen computer security management, the author proposes a computer data encryption strategy that combines the strong security of the 3DES encryption algorithm and the asymmetric encryption advantages of the RSA algorithm. Through the detailed analysis of DES encryption algorithm and 3DES encryption, creatively uses the RSA encryption algorithm to improve the single 3DES algorithm, consolidates the performance of the 3DES encryption algorithm to ensure data communication, and ensures the data integrity, better improve encryption performance. Experiments show that: The proposed encryption algorithm improves security performance by 10 times, and the response efficiency is only 1 ms away from other algorithms, compared with other algorithms, it has better encryption performance and is suitable for actual computer data communication scenarios. Conclusion: The encryption algorithm proposed by the author has achieved good results in terms of security performance and response efficiency, it is suitable for actual computer data security communication and can effectively improve computer security management.

Compartmental observability approach for the optimal transparency problem in multi-agent systems

This paper considers a network of agents, where each agent is assumed to take actions optimally with respect to a predefined payoff function involving the latest actions of the agent’s neighbors. Neighborhood relationships stem from payoff functions rather than actual communication channels between the agents. A principal is tasked to optimize the network’s performance by controlling the information available to each agent with regard to other agents’ latest actions. The information control by the principal is done via a compartmental observability approach, which comprises a static partitioning of agents into blocks and making the mean of agents’ latest actions within each block publicly available. While the problem setup is very general in terms of the payoff functions and the network’s performance metric, the paper has a narrower, yet still fairly general, focus to display essential characteristics of the problem and to show how it can be addressed in practice. In particular, the performance metric is assumed to be a function of the steady-state behavior of the agents. After conducting a comprehensive steady-state analysis of the network, two efficient heuristic algorithms finding nearly-optimal partitions with respect to various performance metrics are presented and validated via numerical studies.

Ассоциативный потенциал прецедентного онима Чернобыль в русской языковой картине мира

The Internet space of the beginning of the 21 st century is rich in wide selection of figurative language units, which reflect modern realities in network communication by referring to already known events, facts, and situations. The purpose of the proposed article is to identify the semantic-and-pragmatic and associative potential of the Chernobyl precedent situation onym as a component of the Russian language picture of the world. The analysis of the current Internet space allowed the authors to conclude that the frequency of appeals to the precedent toponym Chernobyl has increased in the last decade. Thus, in online media, chat rooms and blogs, parallels are drawn between the tragic events of 1986 and the alarming situations of our time. It can be noted that the image of Chernobyl is projected not only on states and political associations, but also on particular politicians, while the variety of integral components that underlie metaphorical images and scenarios attracts attention. The associative image of Chernobyl is constructed by means of linguistic units of the figurative system relevant to the representation of the disaster. Such words and constructions in media texts acquire new connotative backgrounds and have a high transformational potential. A wide range of connotatively labeled linguistic means used in modeling the image of Chernobyl is the basis for the formation of a fragment of the worldview of a modern linguistic personality reflected in actual network communication.

Разработка концепции стратегического развития районов Крайнего Севера на основе построения системы сбалансированных показателей в условиях цифровой трансформации социально-экономических процессов.

The article substantiates the need to revise the approach to developing a strategy for the socio-economic development of the regions of the Far North in order to ensure their sustainable development in the current conditions of digital transformation of the economy. The implementation of the Industry 4.0 concept requires the use of updated, adjusted methods and tools for building modern programs for the strategic development of both enterprises, industries, complexes, and regions. The adjustment of strateg management systems should take place, first of all, on the parameters of digitalization, since the timely introduction of information and communication technologies into management processes is not just a modern trend, but a necessary condition for creating and maintaining competitive advantages. Therefore, the development of a modified system of strategic management of the regions, of course, should include the conditions, opportunities and results of the application of actual and relevant information and communication technologies, as well as take into account possible risks and barriers to their implementation. Despite the different geopolitical significance of the regions, diffrent socio-economic indicators of development, climatic and sectoral features, the approach to developing the concept of socio-economic development of territories can be interpreted as universal at the conceptual level of building a common strategic map. As a tool for developing a strategy for sustainable development of the regions, a balanced scorecard was chosen, the main advantage of which is a balanced combination of both financial and non-financial indicators of the socio-economic activities of the regions. Thus, the result of the study was the development of a modified general strategic map of the balanced scorecard of the regions, the main difference of which from the existing ones lies in the possibility of taking into account the impact of the indicators of the digital transformation of the main socio-economic phenomena and processes at the regional level. The modification of the balanced scorecard consists in the formulation of a new component “digital transformation and digital security”.

Developing Digital Communication Competency in the Business Classroom

The COVID-19 pandemic has cast digital communication competency into sharp relief. Rapid virtualization of how we work and learn has highlighted this challenge for business education. In response, the business communication syllabus must evolve to include digital communication competencies. Most of our students are comfortable with computer and Internet usage. However, our research uncovers a gap between their perceived and actual digital communication competency, as well as indications of stress in their online relationships. This article offers suggestions on creating a business communication syllabus in tune with learner and business needs given that digital communication is rapidly becoming the norm.

A Deep Learning-Based User Selection Scheme for Cooperative NOMA System with Imperfect CSI

This paper studies the user selection problem for a cooperative nonorthogonal multiple access (NOMA) system consisting of a base station, a far user, and N near users. The selected near user receives its own message and assists the far user by relaying the far user’s message. Firstly, we propose a user selection strategy to maximize the selected near user’s data rate while satisfying the quality-of-service (QoS) requirement of the far user. Considering that the channel state information (CSI) of users in actual communication is usually imperfect, we then analyze the outage probability of the NOMA system based on the user selection strategy under imperfect CSI and obtain a closed-form expression. The theoretical analysis shows that the diversity order of the NOMA system under imperfect CSI is 0, which means the multiuser diversity order disappears. In order to improve the impact of imperfect CSI on system performance, we use the deep learning method to identify and classify channels of imperfect CSI and improve the accuracy of CSI. The simulation results show that the theoretical analysis of outage performance is consistent with the numerical results. Compared with the strategy without the deep learning method, the proposed deep learning-based user selection scheme significantly improves the system performance. Furthermore, we verify that our scheme recovers the diversity gain.

Power allocation in D2D enabled cellular network with probability constraints: A robust Stackelberg game approach

In this paper, we develop a Stackelberg game with a non-uniform pricing in a two-tier network environment, where multiple device-to-device (D2D) users reuse the uplink spectrum resources of the cellular user (CU). Assuming that the base station (BS) is protected by pricing the interference from D2D users, we propose a Stackelberg game framework where BS and D2D users are treated as the leader and followers respectively. Besides, the channel state information (CSI) uncertainty in the actual communication environment is considered to be suppressed the interference fluctuation effects. Specifically, the probability constraint is included to optimize the user utility. The complex probability constraint is transformed to the deterministic form of the convex second-order cone constraint by a novel method. Furthermore, a norm approximation method is proposed to reduce the computational complexity. Finally, the analytical solution of Nash equilibrium is acquired to generate the best response to the leader and all followers. Simulation results show that the proposed scheme is more effective comparing to the algorithms without the CSI probability constraint.

An Exploratory Study on the Possibility of Metaverse-based Korean Language Subject Design

The purpose of this study is to explore the possibility of developing a metaverse-based Korean language subject reflecting the changing teaching/learning environment of university and the diversified characteristics and needs of foreign learners. Even though the utilization of the so-called metaverse in the field of Korean language education is still in its early stages, target language learning based on concrete and direct experience is very effective, just as emphasized in ‘The Cone of Experience’, Thus, if students can learn the Korean language through the metaverse based on such programs as Virtual Worlds and Mirror Worlds, it would help them increase their actual communication skills to a great extent. In order to design a metaverse-based Korean language subject, this study first examined the technical types of the metaverse, and then analyzed the functions of Gather.town and ifland, both of which are currently being developed. As a result, this study revealed the possibility of conducting a Korean language subject through the metaverse platform. After arguing for the necessity of establishing a ‘contextual scenario’ based on communication tasks, this study also presented a four-step instructional model according to each scenario. In the future, there should be follow-up research that proves the effectiveness of this instructional model by applying it to the actual education field.

Rydberg atom-based AM receiver with a weak continuous frequency carrier.

We demonstrate an atom-based amplitude-modulation (AM) receiver for digital communication with a weak continuous frequency carrier using a Rydberg AC Stark effect in a vapor cell and achieve the operating carrier frequency continuously from 0.1 GHz to 5 GHz at a single Rydberg state. A strong local oscillator (LO) field ELO acts as a gain to shift the Rydberg level to a high sensitivity region, and a weak carrier field ECarr keeps the same frequency with the LO field. The digital baseband signals are encoded onto the ECarr using the amplitude modulation technique with the different modulation frequency. The response of Rydberg atom to the baseband signal is probed via a Rydberg electromagnetically induced transparency (EIT). The measured instantaneous bandwidth of the system is about 230 kHz. To demonstrate the performance of our system for an actual communication, we consider a color image as an example, the received image displays that the bit error rate (BER) is less than 5% when the maximum data transfer rate is about 238 kbps. Meanwhile, our system shows the weak carrier field of ECarr ≥ 13.52 μV/cm can be used for the practical communication with BER less than 5%. Our works break the limitation that EIT-AT based atomic receivers only operate at the near resonant frequencies of the Rydberg transitions, making this emerging of quantum technology close to the practical application with high sensitivity and broad bandwidth.

Inverse design of sub-wavelength convergent grating based on the "stepped particle swarm algorithm".

In this Letter, a novel "stepped particle swarm optimization" (SPSO) based on field intensity adjustment is proposed. After that, we used this algorithm to design a sub-wavelength converging grating that could be integrated with the detector on the back. Firstly, the advantages of reverse design in the process of two-dimensional or multi-element grating pattern design were summarized by comparing the theory of forward and reverse design. Then, the common "particle swarm optimization" (PSO) and our proposal were compared in the reverse design process; we found that the field strength at the focal point obtained by the improved algorithm was approximately twice of the conventional PSO, and the SPSO had higher computational efficiency and better global convergence. The value of the SPSO had been steadily enlarged, which significantly improved the performance of the sub-wavelength convergent grating. Finally, the property of the sub-wavelength converging grating was simulated, and we found that the grating could achieve good convergence in the focal length range of 100-150 µm, and had a strong ability to compensate for the position deviation of the incident light. In actual optical communications, this sub-wavelength converging grating will play an important role in alleviating the bandwidth and quantum efficiency, improving the performance of the detectors.

The effect of customized L1 translation on L2 production: Cognitive pathes of L2 processing in bilingual mental lexicon

Considering that L1 translation in L2 lexical encoding is commonplace in vocabulary learning in Korea, the study sought the type of L1 translation that can efficiently promote the target L2 processing. For this, 77 English majors at a university participated in two vocabulary learning conditions using ‘L1α’ (dictionary L1) and ‘L1β’ (individual learner’s own L1 reflecting his/her own speech style in actual communication) and were tested in a vocabulary production test reflecting L2 communication. Using a paired-samples t-test in a within-subjects design, the effects of the two L1 translation types were compared in the immediate and delayed posttests. The results suggested that the advantage of ‘L1β’ over ‘L1 α’ in L2 production was evident both for single-word and multi-word lexical items. The effect of ‘L1β’ was also confirmed in long-term memory. In the frame of computational models of lexical processing, the study suggested that the cognitive path ‘Concept-L1β-L2’ is more efficient than ‘Concept-L1β-L1α-L2’. Consequently, in the learning process, encoding new L2 lexical items through ‘L1 β’ rather than ‘L1α’ may be more effective to retrieve the target L2 in production. Pedagogical suggestions are discussed based on this model.

Unequal error protection transmission for federated learning

Communication has been recognized as one of the primary challenges of federated learning (FL), but the actual communication algorithm or protocol design is still rarely involved in the existing studies. In the paper, viewing the model exchange in FL as a special kind of traffic, an unequal error protection (UEP) scheme is designed based on multi-rate channel coding and multi-layer modulation for it. To answer the question of how to make error control for FL when the wireless channel is no longer simplified as a pipeline, this paper firstly theoretically analyzes the impact of transmission error on machine leanring (ML) model, which reveals that the dynamic range of the weights should be taken into consideration. Guided by the analysis, the UEP scheme is applied to FL in multiple perspectives including parameter, network and time. Furthermore, a UEP-based adaptive coding method is developed for the case with dynamic signal-to-noise ratio (SNR) to ensure faster and more stable convergence of the FL model while saving as much bandwidth as possible. Comprehensive numerical simulation on several real-world datasets verifies that the proposed UEP transmission schemes can indeed bring significant benefits in accuracy, robustness and efficiency, especially when the channel condition is poor.

PhD graduates in non-academic roles: harnessing communication knowledge to meet organizational goals

PurposePhD graduates are increasingly taking non-academic roles outside and inside universities. While effective communication is a frequently mentioned concern among employers, little is known about what actual communication PhD graduates do as part of their work. The purpose of this study is to examine the nature of work-related communication activities by PhDs in non-academic sectors.Design/methodology/approachThe conceptual framework presented in this paper focused on the intersection between individual day-to-day experience and work structures through the analytic lens of genre knowledge. Using a narrative approach, attending to both individual experience and cross-case patterns, the authors conducted semi-structured interviews with 11 PhD holders in non-academic careers. Interviews and related documents were analyzed inductively for emerging themes and deductively for cross-case patterns.FindingsIn pursuing organizational goals, PhD graduates undertook diverse writing and other communication work and developed a rich tapestry of genre knowledge. This knowledge enabled them to negotiate different encounters with specific genres, undertake new genres and mediate among different genres.Originality/valueThis study highlighted the value of framing future research around a) the intersection between individual communication experience and organizational factors; and b) the analytic lens of genre knowledge to understand how organizational roles and goals lead to diverse communication practices. As for practical implications, the organizationally bounded roles and goals influencing participants’ communication practices also hold true for those doing PhDs where success requires mastering a limited academic set of genres. While the authors cannot prepare PhD graduates for all the genres they may need, the authors could explicitly teach how genres work in the PhD context.

Machine-Learning-Based Darknet Traffic Detection System for IoT Applications

The massive modern technical revolution in electronics, cognitive computing, and sensing has provided critical infrastructure for the development of today’s Internet of Things (IoT) for a wide range of applications. However, because endpoint devices’ computing, storage, and communication capabilities are limited, IoT infrastructures are exposed to a wide range of cyber-attacks. As such, Darknet or blackholes (sinkholes) attacks are significant, and recent attack vectors that are launched against several IoT communication services. Since Darknet address space evolved as a reserved internet address space that is not contemplated to be used by legitimate hosts globally, any communication traffic is speculated to be unsolicited and distinctively deemed a probe, backscatter, or misconfiguration. Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. Mainly, we make use of six supervised machine-learning techniques, including bagging decision tree ensembles (BAG-DT), AdaBoost decision tree ensembles (ADA-DT), RUSBoosted decision tree ensembles (RUS-DT), optimizable decision tree (O-DT), optimizable k-nearest neighbor (O-KNN), and optimizable discriminant (O-DSC). We evaluate the implemented DTDS models on a recent and comprehensive dataset, known as the CIC-Darknet-2020 dataset, composed of contemporary actual IoT communication traffic involving four different classes that combine VPN and Tor traffic in a single dataset covering a wide range of captured cyber-attacks and hidden services provided by the Darknet. Our empirical performance analysis demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques, scoring a 99.50% of classification accuracy with a low inferencing overhead of 9.09 µ second. Finally, we also contrast our BAG-DT-DTDS with other existing DTDS models and demonstrate that our best results are improved by (1.9~27%) over the former state-of-the-art models.

Short-distance equivalent test of acquisition, pointing, and tracking process for space laser communication.

The function and performance of acquisition, pointing, and tracking (APT) for the space laser communication terminals must be characterized on the ground before the launching. However, short working distance causes the terminals to not be viewed as a point, leading to the challenge for the whole-process equivalent test. A novel equivalent test method is proposed, to the best of our knowledge, by using a narrow laser beam and the imaging relationship between two terminals. The communication terminal is transformed to a virtual point by establishing an imaging relationship between the antenna of the transmitter and the tracking detector of the receiver. Thus, the deviation angle between the optical axis of the receiver and the centroid line of two terminals can be measured accurately. Furthermore, the acquisition process of searching the small-size antenna in the receiver with an extensive coverage beam in the transmitter is replaced by searching a large receiver surface with a narrow beam. This point-searching-surface method is equivalent to the surface-searching-point method in actual space communication. With this method, two terminals spaced 5 m apart were employed to construct the short-distance verification platform, and the APT function verification and performance test were completed.

Detecting Physiological Needs Using Deep Inverse Reinforcement Learning

ABSTRACT Smart health-care assistants are designed to improve the comfort of the patient where smart refers to the ability to imitate the human intelligence to facilitate his life without, or with limited, human intervention. As a part of this, we are proposing a new Intelligent Communication Assistant capable of detecting physiological needs by following a new efficient Inverse Reinforcement learning algorithm designed to be able to deal with new time-recorded states. The latter processes the patient’s environment data, learns from the patient previous choices and becomes capable of suggesting the right action at the right time. In this paper, we took the case study of Locked-in Syndrome patients, studied their actual communication methods and tried to enhance the existing solutions by adding an intelligent layer. We showed that by using Deep Inverse Reinforcement Learning using Maximum Entropy, we can learn how to regress the reward amount of new states from the ambient environment recorded states. After that, we can suggest the highly rewarded need to the target patient. Also, we proposed a full architecture of the system by describing the pipeline of the information from the ambient environment to the different actors.

Текстовый блог как разновидность дружеского письма

Summary: the paper provides a comparative analysis of modern communication in networks and blogs of the information space and the genre of friendly writing in the Pushkin era. The general characteristic features of these types of text are highlighted, which are the imitation of live speech, as well as the interaction of the representative and communicative functions of communication. The paper shows the commonality of goals and communicative strategies of friendly writing and networking: self-presentation, simplified text structure, the use of elements of oral speech and its imitation. Selfpresentation both in a blog and in a friendly letter consists of two components: the actual presentation and communication. Different strategies of self-presentation create an image of the author, and communication ensures the translation of this image into the reader’s environment and forms feedback, readers’ response, allowing the author to adjust his image during communication. The paper also proves that during online communication in blogs, texts are created that transform the genre of friendly writing, including the reader’s works, his associations and cultural memory in the composition.