Intelligence Work Research Articles

A work of artificial generative intelligence as an object of copyright protection

A work of artificial generative intelligence as an object of copyright protection

What should an ordinary person understand about the work of generative artificial intelligence? Proceedings of the competition "TRIZformashka-2024"

Three basic aspects of the operation of generative neural network models (generative artificial intelligence) are discussed in the article: the concept of a "token", the probabilistic nature of the generated response, and the concept of a "large model", the size of which ensures the pseudo-intelligent behavior of neural network chatbots. The issues of implementing generative models, areas and methods of their application are not discussed in principle.The materials of the "TRIZformashka-2024" competition, which was dedicated to neural network models, are provided. The fact of pseudo-intelligence of generative models is demonstrated. It turns out that a model trained on a single phrase "mama myla ramu" ("mama washed the frame") and using a context of a single letter for generation can sometimes behave as if it knows the rules of declension in the Russian language and is able to change a word by case!The concept of a "token" is considered in relation to the generation of texts, pictures and passwords. On the basis of "tokens" a practically useful method of generating passwords is built, difficult to guess, but easy to reproduce (difficult to forget).The concept of a "large model" is presented clearly and intelligibly due to its "visualization" by comparing it with physical quantities. (If one parameter of a neural network weighed one gram, then 200 freight trains would be needed to transport it. If one parameter had a length one millimeter, then the neural network would revolve around the Earth at the equator 25 times. If one second was required to learn one parameter, then it would be necessary to start training a modern neural network in the times of the Cro-Magnons.)The materials will be useful for studying generative artificial intelligence at any age.

Design and implementation of a multifunctional desktop robot for computer peripherals

Abstract As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.

Estimates of the accuracy of identification of a nonlinear dynamic system using step test signals

Machine learning (ML) has become widespread in everyday life. On its basis, programs with artificial intelligence work, on the basis of which many virtual assistants have evolved. ML plays an important role in various spheres of activity of many enterprises. ML helps to automate many processes, simplifying the functioning of the company. Named Entity Recognition (NER) models allow to automatically select and search for information according to certain criteria in mobile data extracted, for example, by the logistic method. Support for NER by Python models makes it possible to flexibly program specific requests that are generated in the forensic examination process. Open source creates a unique opportunity to continuously improve the model by training it on datasets. A powerful NER package is the spaCy framework, which helps to simplify data and extract detailed information from input data, train a model, perform model tuning, and more. spaCy is compatible with 64-bit CPython 3.7+ and runs on Unix/Linux, macOS/OS X and Windows. The latest spaCy releases are available over pip and conda.

Parallel intelligence in three decades: a historical review and future perspective on ACP and cyber-physical-social systems

Recent advances in human-in-the-loop or human-centric research have sparked a new wave of scientific exploration. These studies have enhanced the understanding of complex social systems and contributed to more sustainable artificial intelligence (AI) ecosystems. However, the incorporation of human or social factors increases system complexity, making traditional approaches inadequate for managing these complex systems and necessitating a novel operational paradigm. Over decades of work, a mature and comprehensive theory of parallel intelligence (PI) has been established. Rooted in cyber-physical-social systems (CPSS), PI adapts flexibly to various situations within complex systems through the ACP framework (Artificial systems, Computational experiments, and Parallel execution), ensuring system reliability. This paper provides a detailed review and a novel perspective on PI, beginning with the historical and philosophical origins of CPSS and proceeding to present both the fundamental framework and technological implementations of PI. PI-based Industry 5.0 is highlighted, where three pillars are adopted to help realize the supposed vision. Additionally, the paper outlines applications of PI in multiple fields, such as transportation, healthcare, manufacturing, and agriculture, and discusses the opportunities and challenges for imaginative intelligence. The continuous exploration of PI is expected to eventually facilitate the realization of “6S”-based (safe, secure, sustainable, sensitive, service, and smart) parallel ecosystems.

Deep tectonics and oil and gas prospectives of the kura depression according to geophysical data

According to the sedimentary features, the transfer of the upper, middle and lower case segments of the Kura depression is marked by oil-scientists and researchers. Structure-tectonic signs of these segments are significantly different, and are characterized by favorable conditions for the emergence, migration and gene- ration of hydrocarbons. Emigrate oil and gas products in basins created potential fields in favorable anticlinal structures, oil and gas collectors. Kur-Cabrırrı is the main oil-gas complex plieli-gas complex pliocene sediments in the Lower Kura depression in the Key-Caucasian Mmmatic basin in the Zevabadi Magmathic basin. It is of great importance in terms of monitoring the history of paleostructural development of these complexes, starting the mass generation of oil and gas, to investigate their migration directions, formation and maintenance of oil and gas fields. The official article was dedicated to the results of the complex in recent years in recent years, according to the results of regional geophysical intelligence work carried out by Azerbaijan and foreign companies. Based on a comprehensive analysis of geophysical information on regional profiles, the tectonic structure of Mesozoic-Cenozoic deposits was clarified, the conditions of sedimentation in the basins were assessed, changes in thickness were traced, and new explanations of the geological development of the territory were provided. At the same time, the boundaries of the Mugan monocline with the Jalilabad depression and the Karaja-Gyzylagad uplift zone have been clarified.

Making Emotional Intelligence Work at Work? An Experimental Intervention to Unlock Emotion Abilities

Making Emotional Intelligence Work at Work? An Experimental Intervention to Unlock Emotion Abilities

Making AI Work for Government: Critical Success Factor Analysis Using R-SWARA

This study quantifies what makes Artificial Intelligence (AI) work for government, the critical success factors (CSFs) for successful AI implementation within the Directorate General of Taxes (DGT). Analyzing factors such as technology, organization, process, and environment, the research highlights the importance of organizational readiness, strategic vision, and leadership support to drive successful AI integration within DGT. The dimension of the organization became the most critical factor, followed by technology, process, and environment. The findings offer actionable insights for DGT's decision-making processes, aiding in strategic resource allocation and tailored AI strategy refinement. Furthermore, this research is a valuable reference for other public sector organizations that aim to enhance operational efficiency through the adoption of AI. This study empowers decision makers within the DGT and the wider public sector by providing nuanced information on the critical factors that influence the successful implementation of AI, fostering improved operational efficiency and governance practices.

Optimising intelligence operations for international law enforcement: harnessing THRIVE for national intelligence model advancement

ABSTRACT THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, and Engagement) represents a decision-making framework introduced by the United Kingdom’s (UK) National Police Chiefs’ Council in 2017, with a particular focus on vulnerability. Alongside THRIVE other intelligence-led policing models such as the National Intelligence Model (NIM), have become integral to policing practices. While THRIVE is widely adopted as a primary analysis and decision-making framework in UK police services, its examination remains limited, including its impact on the NIM and its use by intelligence personnel. Interviews with 15 police personnel from operation intelligence units within a specific English service were conducted to ascertain its level of adoption. A series of Freedom of Information (FOI) requests to all 43 UK police services in England and Wales were then initiated, to understand if the THRIVE model is adopted and, if so, where within their respective units. The findings indicate widespread acceptance and integration of THRIVE among intelligence practitioners, without immediate adverse effects on the application of the NIM. The use of heuristic naturalistic decision-making processes in THRIVE assessments, suggests a need for further research. Though, there is a risk of reduced decision-making capacity among frontline intelligence workers using THRIVE within the constraints of the NIM.

Implementation of VCDLN-Learning Multiplatform Through Mobile Communication Based on Artificial Intelligence in Asia

Purpose: This research is a continuation of the previous year, namely 2022, which functions to implement VCDLN through Mobile Communication-based TVUPI media which uses Android Mobile Phones in building a distance learning system in Asia. In doing so, it is carried out based on the work of Artificial Intelligence (AI). Method: This research uses the Research and Development method through the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). Specifically, implementation involves assessments from teachers from Indonesia, Japan, and South Korea. Meanwhile, evaluation indicators use indicators from AI from Expert. Result and Conclusion: In addition, the opinions of educators from South Korea Japan, and Indonesia support the expansion of VCDLN-Learning through Mobile Communication Based on AI services to other countries. Three aspects of the AI Framework tend to get high scores, namely the Deep Learning aspect with scores of 185, 187, and 189. Then the Robotics aspect with scores of 187, 192, and 195, and the Neural Network aspect with scores of 192,196 and 198 from a maximum score of 200. Implementation of The Research: This finding is reinforced by the thought of AI experts that the VCDLN innovation applies the framework of AI, especially in building mobile communication and fast access automation systems from Big Data for Mobile VCDLN-learning in Learning Services. Originality/Value: This research contributes to existing literature regarding Virtual Community Digital Learning, Television Technology, media, and digital applications regarding mobile communication. Strengthening the contribution of theories about Artificial Intelligence technology to Digital Mobile Communications in Asia.

British surveillance of postwar Soviet radio jamming: US–UK intelligence relations and interference detection at BBC Tatsfield, 1948-1949

ABSTRACT Following World War II, the BBC, better known for radio programme broadcasts, continued its long-standing work of radio spectrum surveillance at the specialized receiving and measurement facility located at Tatsfield. Both before and after Soviet jamming of US and British Russian-language broadcasts escalated in April 1949, Tatsfield catalogued the technical characteristics of jamming signals. BBC engineers, experienced in interpreting types and sources of radio interference, issued fortnightly summaries with interpretations of these technical observations. US intelligence agencies knew of Tatsfield’s findings, but differing perspectives in Washington and London resulted in divergent responses to the technical intelligence data. With rising tensions in wider Anglo-American intelligence relations, fueled by the Soviet weapons program and security breaches, such differences presaged separate approaches to Soviet communications threats. US agencies received greatly increased funding, while the BBC’s budget struggles persisted. Even so, interference detection was a high-water mark in Britain’s early Cold War intelligence work.

FROM THE HISTORY OF THE ALASH MOVEMENT’S SOCIO-POLITICAL ACTIVITIES DURING THE CIVIL WAR

The relevance of the study is conditioned by the need to expand the amount of available information on the history of socio-political movements during the Civil War in the Russian Empire. In this regard, the purpose of this study is to reveal the essence of the Alash movement in Kazakhstan, its socio-political activities, and the consequences of the Civil War. The method of critical analysis was chosen as the primary approach, which facilitated the effective processing of archival and scientific sources of information, based on which further research was conducted. The following methods of scientific cognition were also used: historical and genetic, historical and system, diachronic analysis, heuristic, synthesis, deduction, and comparison. This study examines the establishment and activities of Alash Autonomy, analyzing archival facts and papers by Kazakh and foreign researchers. It cites eyewitness accounts, explores the political structure, evaluates leaders' actions, and explains intelligence work's consequences. It justifies changes in allies, details Alash-Soviet Russia cooperation, and describes outcomes. The materials of this research paper are of practical value for historical science due to the presented assessment of data on the Alash movement, analysis of scientific literature, observation and discussion presented in this study.

How does artificial intelligence work in organisations? Algorithmic management, talent and dividuation processes

AbstractThis article analyses the forms of dividuation workers undergo when they are linked to technologies, such as algorithms or artificial intelligence. It examines functionalities and operations deployed by certain types of Talent Management software and apps—UKG, Tribepad, Afiniti, RetailNext and Textio. Specifically, it analyses how talented workers materialise in relation to the profiles and the statistical models generated by such artificial intelligence machines. It argues that these operate as a nooscope that allows the transindividual plane to be quantified through a process known as dividuation. Finally, by way of conclusion, the notion of the dividual is presented as the logic that characterises the human–machine relationship in the case of artificial intelligence and as the horizon of what Felix Guattari called “machinic capitalism”.

Creating Engaging Chatbot Experiences by Tailoring Personalities in Text-Based Interactions

Our expressive AI software is driven by creativity fused with technology. This transformative platform allows users to generate various content easily and augment their creative capacities. Through the merger of modern human-computer interaction artificial intelligence tools, people and artificial intelligent work together to come up with creative innovations. Our goal is not only about generating content but also creating avatars that have memories to foster active involvement. Expressive AI introduces digital companions with distinct personas, which enhance interactions while offering innovative ideas. Such a journey does not just change the digital content creation field. it also demonstrates how people and smart technologies can work together and create what had never been expected in terms of intelligent creativity.

Cyber AI Research Trends

Today day to day there is an increase in cyber threat agents who are continuously coming with strategies which will help them evade the usual defences and end up obtaining or compromising vital information. AI use in monitoring tools by now can count among crucial techniques that help business entities to prevent these dangers. The propelled novel algorithms have replaced ‘human-in-the-loop’ method performing actions to assess security threats making the mechanisms much better. Such a piece dives into the area of AI robots that are utilized in threat intelligence, in order for machines to quickly discover all loop holes that attackers could use to break into a network. The application of Artificial Intelligence by businesses can in advance of the attacks, rather than as a response after the event, thwart them by the use of algorithms and predictive analytics to spot patterns of anomalies or suspicious activities. AI-powered models covering neural-network based threat identification to natural language processing belong to the spectrum of solutions implementing machine learning trained on datasets and cutting-edge algorithms. AI-based threat intelligence was presented to public as in line with practical coverage and real life issues and especially through examples. This technical issue might solve the inefficiency of identifying threats soon and thus increase their accuracy once again, in fact, it will use immediate reaction well too, like it used to. An exploration of computer ethics will touch on the topics of cyber security and data handling among others. As for their practical aspects, they have to be derived from moral righteousness founded on inner values. On the other hand, utilizing AI in threat intelligence processes calls for training of skilled workforce who will, in the long run, be able to face not only today’s cyber threats but also the future ones. In the past ten years reacting was the major issue in analysing cyber intelligence. Through the use of machine learning, enterprises across cyberspace can foresee probabilities of the most burdensome situations for them. Thus, making friendly organizations is the work of great threat intelligence in the 21st century

소위 ‘약한 인공지능(AI)’ 영역에서의 민사책임에 관한 연구

Now, the core of the discussion is how artificial intelligence (AI), as a rapidly developing advanced technology, can solve many of the most difficult problems facing humanity beyond solving severe social problems. Therefore, the true value of artificial intelligence (AI) as a cutting-edge technology is to solve huge problems facing humanity, such as social problems such as gun violence, food shortages, incurable diseases, and global warming caused by carbon emissions, and to contribute to human development. The concern, though, is that no one knows exactly how current artificial intelligence (AI) works. In other words, it is similar to developing a bomb that does not know when it will explode without a safety device.
 Already, artificial intelligence (AI) is causing damage by providing false information. So, if we don't know how to control better artificial intelligence (AI) when it appears, dangerous things can happen. Therefore, there will be a need for international norms on artificial intelligence (AI), which will be made possible by international treaties. The European Union has already enacted the AI Act as the first regulatory law on AI. This contains stronger regulations on the AI industry than in the United States or Asia. In particular, the Act stipulates the obligation to display AI-generated content. Thus, the EU's AI regulation law can be evaluated as legislation designed to ensure the safety of AI-generated products, such as cars and toys. In addition, in September 2022, the European Union (EU) issued a directive on AI accountability. It focuses on the legal principles of compensation for damages caused by artificial intelligence (AI). 
 Legislation on punitive damages for deepfake harms is required. As a civil liability, the victim's damages should be recognized not only for mental damages, but also for aggravated damages for monetary damages, if any. As such a legislative measure, so-called punitive damages should be introduced for malicious and deliberate use of artificial intelligence (AI) as a perpetrator. In the so-called risk liability of artificial intelligence (AI), the operator of artificial intelligence (AI), which has enormous profits through the operation of the risk source, will have to bear the risk liability as a no-fault liability. Korea's product liability law has embraced this principle of risk liability. In order for product liability law to apply to artificial intelligence (AI), there must be a defect in the artificial intelligence (AI) installed in the product.

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing

As one of important Edge-Cloud solutions, mobile crowd sensing (MCS) platform resides in the cloud, and recruits massive workers in the edge network to sense data, so big data can be collected to build various applications or services to consumers, which is a promising computing paradigm. There are two critical properties that are not well addressed. One is current privacy preserving (PP) scheme pays little attention to quality-ware based data collection, so malicious or low-trust workers can cause false or low-quality data attacks, which will seriously affect the foundation of data-based services. The second is the lack of an intelligent worker selection scheme so to reduce costs and improve data quality under PP condition. To address the above issues, in this paper, we first propose a big data collection framework that not only has the function of PP but also can effectively identify worker trust, which is not achieved by previous studies. In particular, the worker trust identification approach in this paper abandons the past unreasonable assumption that data quality can be identified by the obtained data of data requester (DR). Then, we propose a reinforcement-based worker selection scheme that comprehensively considers workers’ trust, workers’ quotation and workers’ locations, so as to overcome the problem of worker recruitment that cannot be quality-ware in the past location privacy protection. Theoretical analysis and massive experimental results demonstrate that the proposed strategy is better than the state-of-the-art strategies in data quality, system cost and other performance indicator

Application of artificial intelligence technology in hospital management systems

This article explores the application of artificial intelligence technology in hospital management systems, specifically using the innovative work order project as an example. The system can interact with users through intelligent AI interaction through text and voice, enabling intelligent work order repair and query processing. The project includes multiple functions such as work order form generation, attachment uploading, voice file uploading, work order query, and interface docking. In addition, this article also introduces the requirements for integrating hospital logistics business with Turing CHATGPT application scenarios, including equipment failure repair, patient satisfaction surveys, patient follow-ups, and medical staff training and learning. Through detailed descriptions of interface docking, data flow, and functions, this article highlights the potential of AI technology to enhance work efficiency and user experience in hospital management systems. Finally, this article discusses the current achievements and future development directions and calls for further innovation and progress in medical information management systems.

Data mining

Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in machine learning. Social networking is about finding patterns in user behaviour and user engagement [5][6].