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Today, machine learning is now a part of many aspects of our life. Many people are aware of machine learning, even though it is known by various names – artificial intelligence, deep learning, deep neural networks (DNN), etc. Through the increasing utilization of machine learning, there becomes a point in which we need more portable applications that perform nearly as well as a lab environment which brings an attention to various pruning or network reduction techniques for reducing the computational workloads and network size. In this paper, we focus on performance characterization of knowledge distillation (KD) which is to transfer a learning experience from a teacher network to a student network. For the characterization, we explore the effects of knowledge distillation on two different architectures of various sizes to gain insight into the resulting student models. Each network used in this exploration belongs to a class of machine learning that processes images, called convolutional neural networks (CNN). The implications based on KD performance characterization include: (i) overall results of the KD method were less accurate than the teacher network; (ii) information from well-trained larger network is effectively transferred to smaller network quickly. The accuracy is converged earlier and flattened out to a plateau. For this behavior to be more beneficial, we should apply the weight decay at an earlier epoch and possibly more often; and (iii) smaller scale network should not be a teacher for larger scale network which will causes the degradation on accuracy.

The article considers a matrix memory element, which is a storage matrix built using diodes based on complex semiconductors. A decoder device using memory cells based on Cu2Se diodes, which provides the necessary signal based on various combinations of input signals. MD (memory device) can find application in information processing systems when creating a storage device with the required capacity and capable of working in the interval from −60 °C to +125 °C. As a multifunctional device, the requirement for the electoral device is that it is advantageous in terms of energy saving and for the same reason, the decoders are built on the elements with the lowest energy consumption. Matrix memory element can be used built using diodes based on complex semiconductors and matrix memory device can be used in information distribution and processing networks, computer technology, automation systems and in other areas of technology. In information processing systems when creating a memory device with the required capacity. Based on the abovementioned, the devices are modern, economical, highly technological for the development and applicable to modern automation devices and switching equipment.

The vibro-tactile P300 based Brain-Computer Interface is an interesting tool for severe impaired patients which cannot communicate using the muscular and visual vias. In this study we presented an improved tactile BCI for binary communication that reduces the wrong answers by adding a threshold to the decision value to achieve a valid answer, otherwise, the BCI gives an indecisive answer to the question. The threshold is calculated using a statistical test on the EEG, data recorded during the question to the patient. In total 7 tactile stimulators were placed on different parts of the subject’s body. We tried the new BCI with 4 healthy subjects and using the statistical test they were able to get no wrong answers after 10 questions asked to each participant. The spelling accuracy and information transfer rate with and without statistical test are presented as well as examples of event related potentials.

This study aims to figure out the feasibility of the current information transfer approach to the passengers and clarify the accuracy of information when passing to the passengers. This study focused on Taipei metro’s information board and aimed to analyze the information’s accuracy and feasibility and understand if the information is legible to the subject with their reviews. This study conducted several investigations, including: 1. Record the behavior and process of drawing; 2. Experiment with static image and motion video; 3. Survey research of Likert scale, usability test, semantic difference, and mental load; 4. Semi-structured interview. This study helps to understand the ease of using the Taipei Metro information display and explore the factors and behavioral differences that affect users’ search for information. This study found that: 1. The presentation of the background color of the information block; 2. Specification of information arrangement and number; 3. Identify two trains going to different destinations; 4. The status indication of train information; 5. The dynamic switching effect of different information, all of the above, will affect the reading comprehension of the test participants in searching for information. We will analyze the experimental results and summarize the user requirements to provide suggestions for future design optimization.

In brief, augmented reality can be defined as a new “environment”, which is created by simultaneously transferring virtual data such as sound, image, graphic produced in virtual environment to real environment.It is seen that the augmented reality has great potential in idea generation, development, presentation to users and information transfer as a representation (presentation) tool with its current applications. Accordingly, the purpose of this study is to evaluate the use of AR technology in cultural heritage areas, to study application examples in archaeological sites and to examine the potential contributions that different uses can provide. Within the framework of this research, AR application is planned to be developed for the ancient city of Alexandria Troas, one of the important cities of the Troas region, located in Canakkale. With the application created, it is aimed to understand the value of the historical artifacts and structures unearthed during the excavation, to transfer them to the people who will experience the area, and to keep the city alive within the framework of virtual heritage values.

Apparently, two-thirds of our planet is covered by water bodies, of which oceanic coverage deeply impacts terrestrial life. Oceans form a majestic resource of most of the amazing marine creatures and natural resources, especially mineral oil. This makes underwater monitoring one of the most demanding areas to explore. For monitoring the oceanic layer, a specialized form of underwater vehicle network is employed using underwater drones for acoustic monitoring. The underwater drones are autonomously operated and remotely tracked using onshore and offshore base stations. The underwater research draws eminence from a wide variety of application-based research, including collaborative undersea exploration, naval surveillance and other oceanographic mining and engineering tasks. Underwater communication confronts unique challenges that involve severe bandwidth constraints, high bit error rates, variable propagation delays and limited spatial correlation. Such constraints mandate the use of sound waves for information transfer that further requires complex signal processing at receiving ends. Owing to such complex capabilities, acoustic sensors are quite expensive and therefore, sparsely deployed. Moreover, oceanic tides and drifts results in high vulnerability for acoustic devices towards failure due to fouling and corrosion underwater. Our research is focused on presenting surveys of architecture and applications of underwater drones for acoustic sensor networks abided by significant characteristics and challenges.

The study deals with the application of intelligent management technologies to support communicative mechanisms in the modern production environment based on the analytical tools which apply the cognitive approach, namely, the semantic modelling. It supports the application of the situation analysis for strategic control based on the principle of management metafunctions. The article presents the semantic model of a discrete situational network in the form of a network graph. It is designed to contribute to the implementation of the communicative strategies to facilitate the decision-making under digitalization. The analytical procedure also designed for decision-making on municipal level that is important under tough budget constraints conditioned by the COVID-19 pandemic. The main result of the study is the semantic model of the communicative mechanism of the production system. The proposed intelligent solutions are risk-sensitive because it accounts for the significant exposures. The systematization of the order of interaction under implementation of the cyber-physical systems, production cooperation and communication within the specialization network chain allow to apply the system analysis to the communicative processes that enable information transfer. The article is designed to contribute to the system theory and methodology by application of logical-linguistic analysis and modeling as well as by combining adaptive management and semiotic approach in application to the communicative mechanism of production systems.

The Echo State Network reservoir computing model is used in many applications. The original equations have a form of ‘instantaneous’ information flow. Here we suggest using a more physically realistic form of the equations, which takes into account the time needed for information to flow and to be processed. We demonstrate the effect of this change on a timeseries task, sunspot prediction, on a dynamical system emulation task, NARMA10, and on formulating ‘reservoir of reservoir’ equations.

Using the example of psychoanalysis, this chapter shows that the claim that cognitive neuroscience provides an empirical foundation for psychological theories is untenable.

Event-Related Potentials (ERP) detection is a latent problem in the clinical, neuroscience, and engineering fields. It is an open challenge that contributes to achieving more accurate and adaptable Brain-Computer Interfaces (BCI). The state-of-the-art typically uses simple classifiers based on Discriminant Analysis due to their little computational demand. Some more recent approaches have started using Deep Learning techniques, but these do not provide any temporal information and rarely focus on detecting the P300 at sample level in electroencephalography (EEG) signals, which would improve the Information Transfer Rate in BCIs. In other research areas, recurrent neural networks have shown high performance in those tasks that require online responses. We propose a new methodology, based on Long-Short Term Memory networks, in a sample level forecast to predict the P300 signal continuously. We get a slight improvement concerning the standard procedure, typically Bayesian Linear Discriminant Analysis, and we also show that the model predicts the occurrence of the P300 ERP at sample level in EEG signals. This brings us the possibility of evaluating the inherent variation between subjects. Our approach contributes to more agile and adaptable BCIs development, going further in the real-life usage of BCIs.

Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.

Absence of human in-charge in fully autonomous vehicles (FAVs) impose multi-pronged in-cabin monitoring in real-time to monitor any irregular situation. Moreover, occupant’s visual information (such as video, images) transmission from in-cabin of vehicle to the data center and control-room is required for various in-cabin monitoring tasks. However, this information transfer may cause a substantial threat to the occupants. Though applying some patch on face of the occupants protects personal information however, simultaneously it deteriorates the important facial information which is very crucial in monitoring various tasks such as emotion detection. Therefore, a human-computer-interaction (HCI) is required to manage this trade-off between facial information and facial anonymization without any important information loss. Also, this HCI based in-cabin monitoring system should be implicit to retain the users trust in the intelligent transportation system (ITS). In this paper, we have proposed generative adversarial network (GAN) based in-cabin monitoring approach for emotion detection in FAV. In this method, we proposed to generate an artificial (virtual) face having real-facial expressions to provide facial anonymity to the occupants while monitoring FAV cabin. Therefore, this method provides anonymous facial information which is essentially required in detection and monitoring tasks to avoid irregular situations. We have used both publicly available and our own in-cabin dataset to assess our proposed approach. Our experiments show satisfactory results.

RFID is the emerging technology in the human achievement in terms of security and authentication. The contact less communication with intervention of digital media enables this technology for sophisticated use in every human computer interaction. The passive tag based RFID enables a cost effective, less maintenance system for information transfer and user identification. This paper is to automate the identification of users(patients) at health care centers to ensure proper health maintenance and drug distribution. It can be applied to reduce the crowd and maintain social distance by developing good interactive system at health care centers.

Several developments in recent years have highlighted the urgent need for an increase in sustainable behavior. In an effort to systematically achieve global human well-being and create a more sustainable and equal world by 2030, the United Nations passed the Sustainable Development Goals in 2015. In this chapter, we discuss several different approaches for Information Systems (IS) to contribute to these goals. We present ideas and first results from different research approaches in which we analyze how immersive systems, in particular, are capable of addressing challenges regarding awareness, motivation, information transfer, and educating citizens to act in a sustainable manner. We conclude that the main features of immersive systems are particularly suited to approach challenges related to sustainable behavior and attitude change. With this chapter, we hope to inspire further research efforts on how IS can make a contribution to a more sustainable world.

Communication is a fundamental ability of humans, and much of our daily energy is used in producing, sharing, receiving, and understanding information and messages. As part of our capacities as information communicators and receivers, we often infer and evaluate the mental states of those people with whom we are interacting. Here we describe research from social psychology, communication, behavioral economics, and neuroscience that highlights the role of mentalizing in communication and decision-making more broadly. In this chapter, we give particular focus to the neuroscientific evidence, which shows that the mentalizing network, a set of cortical brain regions thought to preferentially process social information, is commonly activated by communicators and audiences, and facilitates successful information transfer between communicators and receivers. We close with future directions for research in psychology and neuroscience that further elucidates the role of mentalizing in communication and decision-making.

Today, digital assistants can support students during their studies. A quick and easily useable and accessible information transfer, individually tailored to the students’ needs is required. Individual educational biographies and an increasing number of students require individual information provision and advice. Research on digital assistance systems has increased dramatically over the past decade. We focus on the individual digital study assistant (IDSA) field with its functionalities embedded in a typical student life cycle (SLC). In order to determine the status quo of DSA, we conduct a literature review with a focus on their functionalities. One research finding indicates that the DSA field generates a wide range of DSA functionalities. We structured them developing a morphological box. Finally, we discuss a further research agenda for the development, adaption, introduction, and success of IDSA.

The article describes the characteristic features of implementing the NET Linx open network architecture in the Control Logix system. We analyzed how the main types of networks in this communication protocol architecture work in the standard three-tier model of an industrial enterprise’s supervisory control and data acquisition system. We used the 1756-A4 chassis-based Control Logix 1756 platform with the 1756-ENBT, 1756-CNB, 1756-DNB Series C switching modules and the 1756-L55 processor as a study and analysis model. This configuration allows transferring data using the Ethernet IP, Control Net, and Device Net protocols belonging to the operating control, automatic control, and input/output tiers, respectively. End-to-end information transfer from the automatic control tier to the input/output tier as well as the characteristic features of data transformation upon transfer from one tier to another, the impact of these transformations on the data transfer delay time and the bandwidth are of major scientific interest. The latter parameters were given special attention, because the system’s response time and delay stability are the key parameters of operation of the supervisory control and data acquisition system on the whole.

This paper focuses on specific aspects of human-computer communication, i.e., the interculturality in information and communication technologies in the global world of interconnectedness. The current world of communication can be described by two aspects, namely, the vast proportion of communication is done electronically and it is more or less always intercultural because it is conducted through modern electronic means of communication (emails, social media, presentations, eLearning, blended learning, mLearning, etc.) in the global world of interconnectedness (the World Wide Web). These two aspects are at the foundation of basically all processes both IT and business and must not be neglected. The paper attempts to highlight the fact that a vast part of information can be lost during the information transfer through communication, and it is due to the aforementioned combination of interculturality and interconnectedness. The research was conducted into the awareness of these two intertwined aspects in foreign (Asian) and Czech university students of business and IT. The results clearly show the lack of information and knowledge about the current situation.

In this paper substantiates the need to develop a new design of the receiving device to increase the speed of information transfer between motile objects. This is especially true for cases of large distances in poor climatic conditions. In such a situation, the power of the transmitted signal decreases with increasing distance to the receiver. In this case, due to the small signal-to-noise ratio, information may be lost, in addition, it takes longer to process. This reduces the speed of information transfer. In the work, a new method is developed for constructing a receiver for m-ary radio communication system, which allows for the exchange of information between motile objects. Various methods for processing phase-shifted complex signals in various reception options (with a high level of interference, medium and low) are considered. It has been established that the most appropriate is the use of non-optimal incoherent processing of m-ary phase-shifted complex signals in matched filters and correlators. In addition, the work presents the results of studies of various operating modes of the developed receiving device. The directions of further research and development to improve the development characteristics are identified.

With twenty-first-century medicine, we can care for more patients in more places and in more ways because of the capability provided by digitalized radiographic images, the electronic medical record, and videoconferencing via the Internet. Using these resources is called telemedicine or telehealth. Telehealth uses synchronous (real-time) or asynchronous (delayed) information transfer and communication to provide medical care that is beyond telephone calls, email messages, or fax transmission.

With the explosive growth of Internet of Things data, the storage of large amounts of data has become a problem that plagues people. These IoT devices generally do not have powerful computing and storage capabilities. Most of them assume the role of data collection and information transfer, which also brings challenges to data storage. And a large proportion of the large amount of data is the user’s privacy data, so under the premise of storing a large amount of data, we must also ensure the security of the data. In view of the above problems, this article proposes an effective IoT data storage model based on secret sharing, which effectively guarantees the security of IoT data, and proposes a secure distributed storage of IoT data based on blockchain. This solution has the advantages of a complete blockchain. Under the supervision of the edge computing nodes of the entire network, it can ensure that each transaction data is not tampered with, and it can also obtain complete original data.

Modern science is increasingly called on to produce societally relevant and usable knowledge to tackle global challenges. Academics respond by conducting research projects that transcend the boundaries of single disciplines and institutions by actively engaging with non-academic actors. Such institutional arrangements open up entirely new perspectives for science communication and the problem-solving of real-world issues. However, they also call for elaborate management tasks and demand learning processes on the part of all those involved. This chapter introduces transfer and implementation (T&I) as a conceptual pair of terms to grasp the challenges, without compromising the opportunities of this new research mode. In doing so, this chapter discusses contemporary approaches of science knowledge transfer, and promotes a notion that prioritises knowledge transfer over information transfer through artefacts. After reframing T&I as a management area in research projects, I present three strategic policy pathways for sustainable land management.

There has never been such a rich offer in terms of ERP solutions that customers can choose from because the problem of knowledge has never been emphasized in the past. An ERP system in line with the company’s business is metamorphosed into a knowledge management tool that improves information transfer and generates “knowledge”. On the other hand, major decisions are rarely simple, and the best alternative can befall only after careful deliberation. In the present research we endeavor to provide a model of multiple-criteria decision-making (MCDM) in the selection of ERP applications. We illustrate and apply a MCDM technique, namely analytical hierarchy process (AHP) to assist SMEs to select the most appropriate ERP. We formulate an AHP decision model and apply it to a hypothetical case study to demonstrate the feasibility of the choice to select the most appropriate ERP software for a specific SME. We believe that our work could become a didactic source of inspiration for teaching decision making techniques to young people and students. The application of the proposed model indicates that it can be applied to improve decision-making processes and condense the time interval needed for ERP selection. Our model can be considered good practice and identified with the know-what and know-who component of a KM model.