Realistic Communication Research Articles

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.

Deriving human neural responses to natural speech is now possible, but the responses to male- and female-uttered speech have been shown to differ. These talker differences may complicate interpretations or restrict experimental designs geared toward more realistic communication scenarios. This study found that when a male talker and a female talker had the same fundamental frequency, auditory brainstem responses (ABRs) were very similar. Those responses became smaller and later with increasing fundamental frequency, as did click ABRs with increasing stimulus rates. Modeled responses suggested that the speech and click ABR differences were reasonably predicted by peripheral and brainstem processing of stimulus acoustics.

A frequency-agile retrodirective tag for large-scale sub-terahertz data backscattering

Backscattering is a promising power-efficient communication technique providing sustainable wireless links with a low carbon footprint. This approach is a critical enabler for dense IoT networks, which are forecast to grow to 41 billion by 2025. However, existing backscatter designs are limited to the sub-6 GHz bands or narrowband operation in the millimeter-wave regime; therefore, they fail to concurrently support many interference-free low-power users. Enabling a frequency-agile wideband backscatter design in the sub-terahertz offers a two-pronged advantage for densely deployed backscatter networks: spatial reuse enabled by directionality and frequency multiplexing enabled by the large available bandwidth. We present the first sub-THz backscatter architecture that operates above 100 GHz. Our design relies on a detailed understanding of reciprocity in leaky-wave devices and offers a realistic joint localization and communication protocol for sub-THz backscatter networks.

EFFECTIVENESS OF DUOLINGO IN FOREIGN LANGUAGE LEARNING

This study aims to explore the efficiency of Duolingo as a language learning tool, with a particular focus on its gamified nature, adaptive learning technology and strategic use of notifications. In this regard, the study employed qualitative research design wherein two semi-structured interviews were held. Specifically, these participants were housewife who had been using Duolingo daily for almost 37 days to learn English and a student who was intermittently using it to learn Turkish. In conclusion, these findings show that apart from being interactive and engaging, Duolingo significantly improves vocabulary and grammar skills. Furthermore, such game-like features as rewards, competitions and streak counts act as strong incentives for continued usage. Additionally, customized messages delivered in real time can help in bringing back users into the application and ensuring daily routine learning is maintained. Nevertheless, despite its pros there are several cons that have been found; they include restriction of lives in free version and lack of practice realistic communication scenarios. Accordingly, researchers recommend introducing artificial intelligence for improved speaking practice and enhancing the number of lives to further improve the functionality of DuoLingo.com. These facts help give more insights on how digital tools can support language acquisition across diverse contexts. Key words: Duolingo, Language Learning, Gamification, Vocabulary, Grammar and Motivation.

REPRODUCTIVE HEALTH IN TRANS AND GENDER-DIVERSE PATIENTS: Psychosocial counseling of transgender and gender-diverse individuals in fertility and reproductive medicine: a narrative review.

Transgender and gender-diverse (TGD) people have similar desires for parenting as cisgender individuals but are likely to face greater barriers in accessing fertility treatment than their cisgender peers. Mental health professionals are well-positioned to advocate for and support TGD individuals seeking fertility care through pre-fertility treatment implications counseling regarding the psychosocial aspects of fertility treatment and family building. Transgender and gender-diverse (TGD) individuals experience significantly greater all-cause mortality and mental health disparities compared to their cisgender peers. Gender-affirming hormone therapy (GAHT) is a safe and effective treatment option for gender dysphoria that dramatically improves psychosocial health outcomes but may adversely impact fertility. Medical society guidelines recommend medical fertility preservation (FP) counseling and pre-fertility treatment psychoeducational implications consultation from qualified, reproductive mental health professionals (MHPs) for TGD individuals pursuing FP or third-party reproductive treatment. However, sparse literature exists specific to the structure of mental health psychoeducational consultation for TGD individuals pursuing FP. This narrative review highlights important areas for discussion in pre-fertility treatment mental health consultations. Results indicate that implications counseling should be conducted by an MHP with specialized training in reproductive mental health with TGD populations to reduce the risk of harm and promote successful emotional navigation of fertility treatment. Such counseling should be psychoeducational and not gatekeeping in nature and may include consideration of the psychosocial (e.g. emotional, relational, ethical, spiritual, social) risks and benefits of various family-building options. During these consultations, TGD individuals can explore their hopes and fears related to fertility and future family-building plans and discuss realistic treatment expectations, individual strengths, coping and communication strategies, and identify key support network members who may aid in navigating the fertility treatment process. MHPs can provide referrals to appropriate resources if necessary to help TGD individuals navigate treatment while coping with psychological symptoms and promoting behavior change.

Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

A Distributed Task Allocation Method for Multi-UAV Systems in Communication-Constrained Environments

This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.

Deriving human neural responses to natural speech is now possible, but the responses to male- and female-uttered speech have been shown to differ. These talker differences may complicate interpretations or restrict experimental designs geared toward more realistic communication scenarios. This study found that when a male and female talker had the same fundamental frequency, auditory brainstem responses (ABRs) were very similar. Those responses became smaller and later with increasing fundamental frequency, as did click ABRs with increasing stimulus rates. Modeled responses suggested that the speech and click ABR differences were reasonably predicted by peripheral and brainstem processing of stimulus acoustics.

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions

Abstract This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.

A deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants

AbstractVirtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a novel prediction model is introduced that enhances the prediction accuracy by integrating long short‐term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub‐prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.

Ownership of Humans

Animals and species live by behavior, they possess instincts to survive. People live by their own products. Depending on natural conditions and social circumstances, people have different ways of living, ways of living, and lifestyles. A way of life is a way of making products that ensure survival and the maintenance of the species. The characteristic of this product is direct, realistic, inevitable, and natural human communication. Communicating in nature, using products as brokers, is social. Society is a product prepared for people to enjoy and satisfy. Humans do not exist, society is meaningless. The existence of society is rich so that people can live based on each other's abilities and needs. Social satisfaction makes different ways of living in the process of development. The way of life makes products not only in tools, machines, equipment, robots, computers, AI, labor, plant care, and animal domestication, but also performs the basic function of survival, maintain the race and educate fellow human beings. But there is no product as good as people. Humans are both subjects and human products. Humans are both the subject of perception and the object of perception. Mutual ownership between people in family, religion, state, company, bank by tradition, canon law, law, charter, money. This ownership is both a product of necessity and a product of freedom in the process of development.

Emergent behaviours of a non-abelian quantum synchronisation model over the unitary group

Abstract We introduce a new non-abelian quantum synchronisation model over the unitary group, represented as a gradient flow, where state matrices asymptotically converge to a common one up to phase translation. We provide a sufficient framework leading to quantum synchronisation based on Riccati-type differential inequalities. In addition, uniform time-delayed interaction is considered for modelling realistic communication, and we demonstrate that quantum synchronisation is persistent when a small time delay is allowed. Finally, numerical simulation is performed to visualise qualitative behaviours and support theoretical results.

DigiHuman: A Conversational Digital Human with Facial Expressions

Recently, Artificial Intelligence (AI)-powered chatbots and virtual humans have assumed significant roles in various domains due to their ability to interact with users and perform tasks based on their intended purpose. Virtual humans have received considerable attention in various industries due to their lifelike human appearance, behaviour, and ability to convey emotions, especially in virtual reality contexts. Conversely, chatbots are finding use in a wide range of applications and represent a promising feature of human-computer interaction due to their efficient communication with humans. Therefore, this study aims to develop a real-time chatbot that can effectively convey emotions through facial expressions, thereby promoting realistic communication. To achieve this, several advanced AI models were employed to address different aspects, including speech recognition, emotion synthesis, and response generation. The methodology, models used, components, and results are explained in detail, and the results of the user study are also presented.

Cyber Security for IEEE 802.1 Time Sensitive In-Vehicle Networking: Recent Advances and Impact Analysis of DoS Attacks

Dedicated computers control the operation of Cyber Physical Systems such as in-vehicle architectures with the help of sensors and actuators. Securing the real-time communication technologies for in-vehicle platforms attracts the attention of the research community. IEEE 802.1 Time Sensitive Networking (TSN) task group targets the standardization of Ethernet-based deterministic communication technologies due to its high bandwidth and low cost. IEEE P802.1DG working group specifies the profile for secure and real-time automotive embedded systems relying on TSN, which is envisioned to be widely used in future in-vehicle platforms. In this paper, we present an elaborate review of the research work on the security of in-vehicle communication networks with TSN support, and define various Denial of Service (DoS) attack scenarios targeting the real-time traffic in in-vehicle networks. We evaluate the impact of our attack scenarios on the performance of two different realistic in-vehicle communication networks with varying sizes. Experimental results show that DoS attacks can lead to severe consequences disrupting the healthy operation of safety-critical functions in a TSN-compliant in-vehicle network.

Investigation of virtual reality multimedia interaction technology based on wireless network

<p>In recent years, with the continuous development of wireless communication and virtual reality technology, multimedia interaction technology has received more and more attention. However, due to the limitations of bandwidth, delay, packet loss, and other problems in wireless networks, multimedia interaction technology also faces many challenges. In this paper, the virtual reality multimedia interaction technology will be studied in combination with a wireless network, in which the virtual environment is first constructed. Then, the interaction is carried out through data transmission and user interaction, and then the existing virtual reality multimedia interaction is optimized by using a data compression algorithm. In order to test the effectiveness and performance of virtual reality multimedia interaction under different network environments and determine the optimal network environment, this paper compares the multimedia interaction effect under three network environments using a wireless network, mobile network, and Bluetooth network as the research object, with download speed, loading speed, image quality, smoothness and real-time as the test variables, and 10 VR software as the constraints. The research results indicated that, under the same other conditions, taking real-time performance as an example, the delay time and feedback speed of wireless networks were between 26 ms–37 ms and 105 KB/s–115 KB/s. The delay time and feedback speed of mobile networks (3G/4G/5G, generation) were between 66 ms–75 ms and 46 KB/s–55 KB/s, while the delay time and feedback speed of Bluetooth networks were between 120 ms–130 ms and 25 KB/s–35 KB/s; this indicated that VR multimedia interaction technology in the wireless network had better performance. Virtual reality multimedia interaction technology based on wireless networks is a promising high technology that can bring users a more realistic, vivid, and intuitive communication and entertainment experience. At the same time, it will expand the application scenarios and promote the development and progress of the technology.</p>

Heuristic Scheduling Algorithm for Workflow Applications in Cloud-fog Computing Based on Realistic Client Port Communication

Heuristic Scheduling Algorithm for Workflow Applications in Cloud-fog Computing Based on Realistic Client Port Communication

Towards Emotion-Aware Agents for Improved User Satisfaction and Partner Perception in Negotiation Dialogues

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">emoticon</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lexical</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contextual</i> variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.

Designing high-speed GEO-to-Moon optical wireless communication links

Moon exploration and colonization will strongly rely upon high-speed optical wireless communications over a huge distance (around 400,000 km). Here, we introduce a holistic model, including the propagation issues as well as the realistic communication limitations and the expected practical limitations. We focus on the communication issues in a link connecting a GEO satellite to a fixed Moon optical station: here, we consider the challenging 10 Gbit/s transmission rate, and we assume an optical pre-amplified receiver, which is today the only option for high speed; we then select three different modulation formats, with corresponding implementations and increasing complexity and performance. Under these assumptions, we estimate the common operating conditions at the forward error correction threshold and point out the role of the transmitter and receiver telescopes: for practical size (e.g., 1 m), relevant limitations arise, which we combine with the typical photons-per-bit sensitivity values in an optical link. We find that all considered modulation formats can be used at 10 Gbit/s, although with different margins and hardware requirements, particularly considering the size of the optical antennas. We then extend the analysis to higher rates, up to 40 Gbit/s. This work can open the way to the realization of optical wireless communication systems to/from the future Moon Village.

Identifying fake vs. real communication records: A case study

Records of communication often play an important role in many criminal inquiries giving insight into existing and alleged relationships. The forensic analysis of digital devices can provide such information, however in some cases, screen captured records may be all that is available. In these instances, it is necessary to evaluate the authenticity of this information given the availability of free to use communication record mockup services that can be used to create realistic looking, but fictitious communication records. This work seeks to ascertain whether freely available communication record mockup services pose a threat to law enforcement officers in terms of not being able to distinguish a communication record mockup from a genuine communication record screen capture. An evaluation of communication record mockup services for creating WhatsApp, iMessage and Twitter mockups are identified and their ability to create realistic communication record mockups is evaluated. The results of these tests are provided and discussed, and 41 communication record mockups are supplied forming one of the first datasets to support those conducting communication record authenticity checks.

Examining the exposure-reception-retention link in realistic communication environments via VR and eye-tracking: The VR billboard paradigm.

Exposure is key to message effects. No effects can ensue if a health, political, or commercial message is not noticed. Yet, existing research in communication, advertising, and related disciplines often measures 'opportunities for exposure' at an aggregate level, whereas knowing whether recipients were 'actually exposed' to a message requires a micro-level approach. Micro-level research, on the other hand, focuses on message processing and retention, takes place under highly controlled laboratory conditions with forced message exposure, and largely ignores how recipients attend selectively to messages under more natural conditions. Eye-tracking enables us to assess actual exposure, but its previous applications were restricted to screen-based reading paradigms lacking ecological validity or field studies that suffer from limited experimental control. Our solution is to measure eye-tracking within an immersive VR environment that creates the message delivery and reception context. Specifically, we simulate a car ride down a highway alongside which billboards are placed. The VR headset (HP Omnicept Pro) provides an interactive 3D view of the environment and holds a seamlessly integrated binocular eye tracker that records the drivers' gaze and detects all fixations on the billboards. This allows us to quantify the nexus between exposure and reception rigorously, and to link our measures to subsequent memory, i.e., whether messages were remembered, forgotten, or not even encoded. An empirical study shows that incidental memory for messages differs based on participants' gaze behavior while passing the billboards. The study further shows how an experimental manipulation of attentional demands directly impacts drivers' gaze behavior and memory. We discuss the large potential of this paradigm to quantify exposure and message reception in realistic communication environments and the equally promising applications in new media contexts (e.g., the Metaverse).