Techniques of Linguistic Cognitive Manipulation in the Information and Psychological Confrontation Environment

The article is devoted to the scientific understanding of the linguistic and cultural factors of shaping external ideology of governance in Ukraine during the preparation and conduct of a series of low-intensity conflicts (“color revolutions”) in 2004-2014. Using linguistic methods of semantic, structural, contextual, discourse and communicative analysis, the author studies verbal forms of linguistic cognitive manipulation with the public opinion of the Ukrainian society focusing on the techniques of polyphonic attack, conceptual modeling and reframing. Communicative strategies and methods of indoctrination of the population are viewed in the environment of the information and psychological war launched by the US-led collective West in the digital space of mass media and the worldwide web with the aim of alienating Ukraine from the geopolitical space of Russia and turning it into an antipode state of the Russian Federation. The author focuses on verbal forms and means of indoctrination identified in communicative strategies of disinformation, data blocking, falsifying truth, fake messages, tabooing of signs, distortion of the true semantic meaning and substitution of concepts that are used by subjects of information and psychological influence in the synergetic coupling of media resources of the media of Ukraine and the collective West. The author concludes that some processes are underway in the interdiscursive environment of the “post-Maidan” Ukraine that structure a new dominant information need of the society around the image of Russia as an enemy and aggressor. Russian linguistic and cultural pattern holders demonstrate speech behavior in Ukraine that has been formed under the external ideology of governance and illustrates the destructive changes in the values of Russian self-identity, which is a consequence of linguistic cognitive manipulation of the public opinion under the imposed information model of total Russophobia.

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

Illegal drug trafficking on the internet

The relevance of the study is due to the change of activity of organized criminal groups involved in the circulation of psychoactive substances towards the organization of the mentioned activity through the Internet. The purpose of this work is to investigate the current problems of combating drug trafficking by using the worldwide web through contactless sale of narcotic drugs and psychotropic substances. It is necessary to strengthen the analytical component of law enforcement activities in the detection and analysis of content on the Internet through the introduction of information technology in law enforcement activities, the dissemination of knowledge about OSINT. The materials of the article are of practical value for the higher education graduates of police specialties, police officers of the criminal block and specialists in the field of operational search activities.

A Web-based Mobile Medical Monitoring System

A hospitalwide mobile medical monitoring system is described. Telemetry devices are attached to patients to acquire, store and process continuous data about their state of health. Medical staff may examine realtime graphical information and make comparisons with historical data. Parameters may be set to cause an automatic alert to portable devices held by appropriate staff if a patient requires urgent observation. The system uses the services provided by Internet and Intranet to allow remote supervision and consultation. A hardware/software prototype has been constructed to demonstrate realtime data acquisition, wireless transmission/reception and connection to the Worldwide Web. The realtime, supervisory and remote teaching aspects of the software system are being designed and are in the process of implementation.

Worldwide antipsychotic drug search intensities: pharmacoepidemological estimations based on Google Trends data

Prescription patterns of antipsychotic drugs (APDs) are typically sourced from country-specific data. In this study, a digital pharmacoepidemiological approach was used to investigate APD preferences globally. Publicly available data on worldwide web search intensities in Google for 19 typical and 22 atypical APDs were temporally and spatially normalized and correlated with reported prescription data. The results demonstrated an increasing global preference for atypical over typical APDs since 2007, with quetiapine, olanzapine, risperidone, and aripiprazole showing the largest search intensities in 2020. Cross-sectional analysis of 122 countries in 2020 showed pronounced differences in atypical/typical APD preferences that correlated with gross domestic product per capita. In conclusion, the investigation provides temporal and spatial assessments of global APD preferences and shows a trend towards atypical APDs, although with a relative preference for typical APDs in low-income countries. Similar data-sourcing methodologies allow for prospective studies of other prescription drugs.

The new physicality of data

The mass digitisation of data arguably began in the late 1980s with the mainstream adoption of new low-cost desktop computers in the workplace and visions of a paperless office. The term reflected a focus on the digitisation of existing processes. Such comparisons continued with the arrival of the Internet and worldwide web in the mid-1990s, with visions of e-commerce replacing traditional bricks-and-mortar shops. In the first two decades of the 21st century, we have entered an era where much of the data created is now digital by design and default yet simultaneously integrated with physical objects and real-world interactions. This article explores some of the innovations made possible by this new physicality of data, from the birth of a digital twin to the death of privacy, and the growing urgency for new approaches to data governance and information lifecycle management.

The Internet of Things (IoT) and Social Interaction: Influence of Source Attribution and Human Specialization

The evolvement of IT has open new doors in connecting many devices to the worldwide web that successively produce data around the physical setting using the IoT. However, the system of message turns out to be slightly intricate in human specialization-internet of things communication for the reason that the IoT is a system including diverse objects transferring data This study examines the hypothetical pathway by which the changes in source attribution that is multiple against single and specialization that is multi-functionality against single functionality of IoT devices affect the quality of human- internet of things interaction. The result from the study obtained from 80 participants that took part in the experiment shows that multiple source attribution improves the condition of information basically for the low-involvement people supports further probes the multiple source effects. However, this study recommends improvement of attribution source and human specialization-IoT.

Multi-Purpose Dataset of Webpages and Its Content Blocks: Design and Structure Validation

The need for automated data extraction is continuously growing due to the constant addition of information to the worldwide web. Researchers are developing new data extraction methods to achieve increased performance compared to existing methods. Comparing algorithms to evaluate their performance is vital when developing new solutions. Different algorithms require different datasets to test their performance due to the various data extraction approaches. Currently, most datasets tend to focus on a specific data extraction approach. Thus, they generally lack the data that may be useful for other extraction methods. That leads to difficulties when comparing the performance of algorithms that are vastly different in their approach. We propose a dataset of web page content blocks that includes various data points to counter this. We also validate its design and structure by performing block labeling experiments. Web developers of varying experience levels labeled multiple websites presented to them. Their labeling results were stored in the newly proposed dataset structure. The experiment proved the need for proposed data points and validated dataset structure suitability for multi-purpose dataset design.

A Modified Fault Detection Bayesian Learning Model For Inter Connected Vehicle Networks

Currently an important worldwide web, The IoT represents the biggest connected vehicle network of all, but will evolve into a much larger network of connected vehicles. Though a concept promising, the combination of different enabling frequencies does pose various intrinsic and defining challenges in the form of communication systems like privacy and protection. It is also important to establish an effective and dependable strategy to access information for solutions which emerge from increasingly complex vehicle and data systems because of the proliferation of wireless medium. In this article, we provide and improve a new algorithm known as Advanced Fault Detection and Management with Bayesian Network techniques, in which we intend to locate and adjust spatial vehicle faults in real time. Often, we apply measurement method to discover the most effective fault detection methodology, which is the turning point. A sequence of recent studies illustrated findings shows that the suggested approaches include fault detection and correction utilizing tools accessible previously.