Intelligent Services Research Articles

Price Monitoring, or Where Surveillance Studies Meets the Sociology of Price Formation

This essay stages an encounter between surveillance studies and the sociology of price and price formation. I argue that surveillance is integral to the calculative operations of retail price strategies, especially during periods of high inflation, and that these calculative operations hinge on the human infrastructure of a precarious data-collecting workforce. I present my argument through a case study of competitor price monitoring, a corporate surveillance practice in which commercial actors record, process, and transmit information on competitors’ pricing, inventory, and display strategies. While pricing intelligence services like web-scraping and dynamic pricing algorithms are typically applied in e-commerce settings, I focus here on a firm that specializes in the collection of brick-and-mortar retail price data, with special attention to the labor practices involved in the manual (in-person, device-aided) production of retail price information at the national-industrial scale. Building on auto-ethnographic observations as a price data collector as well as analysis of employee reviews of the firm on sites like Indeed and Glassdoor, I argue that surveillance studies can contribute in significant ways to our understanding of the material practices, and politics, of price and price formation.

Влияние нарушений качества рентгенографических исследований органов грудной клетки на работу врачей-рентгенологов и диагностического искусственного интеллекта.

When performing radiographic studies, quality violations and errors periodically occur, making it difficult for an image to be interpreted and described by both a radiologist and software based on artificial intelligence technology. Incorrect filling of meta-information in DICOM format headers may be a separate problem for diagnostic artificial intelligence. Purpose: determination of quality violations of the chest x-ray examinations, which most strongly impede the work of radiologists and diagnostic software based on artificial intelligence in the conditions of the Moscow city health care system. Material and methods. To study the impact of the quality of x-ray examinations on the work of a radiologist, an online survey was conducted among radiologists, employees of the Moscow reference center for radiation diagnostics. To study the impact of the quality of x-ray examinations on the work of AI-based software, an analysis of the results of processing diagnostic studies according to the modalities chest x-ray and lung fluorography for the 4th quarter of 2023 by artificial intelligence services was carried out as part of an “Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow”. Results. The survey involved 172 radiologists. The most common quality violations by doctors are incompleteness of chest coverage and asymmetric position of the patient, as well as violations of image clarity / contrast. At the same time, the incompleteness of the chest coverage causes the greatest difficulties in interpreting the study for doctors with up to 5 years of experience. For more experienced doctors, the greatest difficulties are caused by violations of the clarity and contrast of images. The main problems hindering the description of studies by AI-services are non-filling or incorrect filling of meta-information about the study stored in DICOM format, as well as violations of the patient’s laying and positioning. Conclusion. The problems we have identified indicate the need for more careful adherence to the methodology of conducting diagnostic studies, especially with regard to filling out meta-information about the study. AI-based software developers need to evaluate the performance of their solutions based on the developed testing and monitoring methodology.

Graph neural network based intelligent tutoring system: A survey

Online education is developing rapidly driven by artificial intelligence technology. The massive learning resources lead to information overload and low resource utilization. Intelligent tutoring system (ITS) plays a vital role in the education platform, providing personalized learning services for students. The data obtained from the online education platform has complex correlations, which can be potentially transformed into multi-level graph structures. In recent years, graph neural networks (GNNs) have been tried to be introduced into intelligent learning services due to their superior performance in processing graph-structured data. This paper aims to provide researchers and engineers with a general overview of modeling processes and techniques for intelligent learning services based on GNNs. Through a careful review of the advanced models published between 2019 and 2023, existing research primarily focuses on four detailed areas within the smart services scenario. The GNN models involved are systematically classified, and the principles, pioneers and variants of various models are summarized in detail. Simultaneously, this paper analyzes the applications, the specific problems to be solved, and the technologies and innovations of graph-based models in the four key areas. In addition, we examine the commonly used datasets and evaluation metrics in the field of education. Finally, the current challenges and future development trends are summarized to provide comprehensive and in-depth guidance for research in related fields.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Risks and Opportunities Brought by Artificial Intelligence Empowering the Financial Industry

Intelligent risk management and trade optimization play pivotal roles in the realm of investment banking. Leveraging AI's capabilities, institutions harness vast datasets and machine learning algorithms to detect potential risks and fine-tune trading strategies. This results in more accurate decision-making processes and better risk mitigation measures. Furthermore, AI enables personalized customer service and precise market forecasting, elevating client satisfaction levels and enabling the anticipation of market trends with greater precision. Tailored services foster stronger client relationships, driving long-term partnerships and loyalty. In addition to enhancing operational efficiency and client satisfaction, AI drives innovation and development within investment banking. This includes the introduction of intelligent advisory services and the implementation of digital perception systems, revolutionizing traditional banking practices. However, the integration of AI also presents challenges such as data privacy concerns, regulatory compliance issues, the need for talent cultivation, and organizational transformation. Looking ahead, the future of investment banking is expected to witness increased AI adoption, leading to higher levels of intelligence, more sophisticated learning techniques, and collaborative innovation across the industry. This trajectory promises to reshape the landscape of investment banking, creating new opportunities for growth and advancement.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

The increasing ubiquity of Android devices has precipitated a concomitant surge in sophisticated malware attacks, posing critical challenges to cybersecurity infrastructures worldwide. Existing models have achieved significant strides in malware detection but often suffer from high false-positive rates, lower recall, and computational delays, thus demanding a more efficient and accurate system. Current techniques primarily rely on static features and simplistic learning models, leading to inadequate handling of temporal aspects and dynamic behaviors exhibited by advanced malware. These limitations compromise the detection of modern, evasive malware, and impede real-time analysis. This paper introduces a novel framework for Android malware detection that incorporates Temporal and Dynamic Behavior Analysis using Long Short-Term Memory (LSTM) networks and Attention Mechanisms. We further propose development of an efficient Grey Wolf Optimized (GWO) Decision Trees to find the most salient API call patterns associated with malwares. An Iterative Fuzzy Logic (IFL) layer is also deployed before classification to assess the "trustworthiness" of app metadata samples. For Ongoing Learning, we propose use of Deep Q-Networks (DQNs), which helps the reinforcement learning model to adapt more quickly to changes in the threat landscapes. By focusing on crucial system calls and behavioral characteristics in real-time, our model captures the nuanced temporal patterns often exhibited by advanced malwares. Empirical evaluations demonstrate remarkable improvements across multiple performance metrics. Compared to existing models, our approach enhances the precision of malware identification by 8.5 %, accuracy by 5.5 %, and recall by 4.9 %, while also achieving an 8.3 % improvement in the Area Under the Receiver Operating Characteristic Curve (AUC), with higher specificity and a 4.5 % reduction in identification delay. In malware pre-emption tasks, our model outperforms by improving precision by 4.3 %, accuracy by 3.9 %, recall by 4.9 %, AUC by 3.5 %, and increasing specificity by 2.9 %. These gains make our framework highly applicable for real-time detection systems, cloud-based security solutions, and threat intelligence services, thereby contributing to a safer Android ecosystem.

Advanced informatic technologies for intelligent construction: A review

Since there is a growing interest in driving the digital transformation of the construction industry, the goal of this paper is to provide a full picture and understanding of the emerging research front on intelligent construction. Through the quantitative and qualitative analysis, relevant pieces of literature are thoroughly reviewed to extract useful knowledge to raise awareness of intelligent construction, resulting in the summary of research status and the determination of future needs. In light of the research findings, the rapid development of intelligent construction follows the strategies of standardization, digitization, intellectualization, collaboration, greenization, and customization, which mainly support “4S” technologies. The popular research topics covering intelligent engineering services for the project's full lifecycle (i.e., design, construction, operation, and maintenance) and even the city level have been deeply explored, which are proven to dramatically improve project efficiency, safety, and automation. Moreover, challenges exist in three potential paths for future research, including the digital twin-enabled engineering cloud platform, man-machine-environment interaction, great sustainability under carbon peaking and carbon neutrality goals. As for the practical value, a synthesized point of up-to-date reference is created to guide managers to promote intelligent construction in real-world engineering.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

LAYANAN EDUKASI PENDIDIKAN KARAKTER CERDAS PENGABDIAN KEPADA MASYARAKAT UNTUK MENANAMKAN NILAI-NILAI KARAKTER JUJUR PADA INDIVIDU

Abstract: Knowledge, attitudes and skills regarding honest character values ​​can be provided through intelligent character education educational services in a classroom format to service participants. The aim of this community service research is to provide education to the public to have knowledge, skills and attitudes regarding honest character values, including: (1) telling it as it is; (2) acting on the basis of truth; (3) defending the truth; (4) responsible; (5) fulfill obligations and receive rights; (6) open-minded; and (7) keeping promises. The location, data source, respondents and research population were all 70 GPT youth who were members of the fellowship at Mola Tumori. The research instrument was a closed questionnaire and analyzed with inferential statistics and the results were described. The results of the research concluded that the intelligent character education service, community service to instill intelligent, honest character values, is very effective in providing understanding, knowledge, attitudes and skills to service participants; the average understanding score before being given the service was in the poor category with a score of 57.53%, while after being given the service it rose to the very good category with a score of 86.15%; The increase in score obtained by each participant was at least 18% and the maximum was 41%. It is recommended that educators, counselors and religious leaders use this intelligent character education service in a class format to instill honest character values ​​in service participants. Keywords: Honest character, classic counseling

Development of Composite Intelligent Services Operating in a Heterogeneous Environment

The paper discusses methods, approaches, tools and technologies for composite service development. The expediency of applying the considered principles to create software application of so-called composite artificial intelligence (AI) is noted in order to implement the latter within the "distributed artificial intelligence as a service" (DAIaaS) paradigm. With the participation of the authors, the IACPaaS (Intelligent Applications, Control and Platform as a Service) cloud platform has been created and continues to be actively developed. It is intended for the creation, use and maintenance of multi-agent intelligent services that process ontological knowledge graphs. Currently, the platform provides such cloud service delivery models as PaaS (platform as a service), SaaS (software as a service), DaaS (data as a service) and Desktop as a Service. One of development directions is to provide support for the DAIaaS paradigm. It consists in the designing and implementation of tools that make it possible to use the platform and its services as components of composite applications (including AI ones) operating in a heterogeneous distributed computing environment. As a result, language and software tools were created that allow the development of distributed intelligent systems, some of the components of which are located on the IACPaaS platform, and the rest are located elsewhere. Depending on where the leading component is placed, certain tools and technologies implemented on the platform are used. The article also contains examples of created services for several domains (medicine, transport modeling, laser additive manufacturing) and options for using the platform.

Research on the Effect of the Empathy Ability of AI Service Robots on Consumer Well-Being: The Chain Mediating Model

Artificial intelligence (AI) service robots represent a relatively new service paradigm with the potential to raise consumer well-being and service quality. Most previous studies ignored the significance of AI-human exchange relationships in favor of exploring the effects of AI service robots on consumers from the perspective of artificial intelligence technology stimuli. This study investigated how the empathy ability of AI service robots affects consumer well-being in AI-human exchange relationships. It did this by developing a chain mediation model based on mind perception theory and social exchange theory. The results indicate that the empathy ability of AI service robots has a significant impact on consumer well-being. The empathy ability of AI service robots improves consumer well-being through consumer self-congruence and consumer warmth perception. Furthermore, consumer privacy concerns negatively moderate the chain mediation effect. This study provides suggestions on how to improve consumer well-being for marketers and developers of AI service robots.

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

Development of ancient Chinese inscription rubbings knowledge base in the context of cultural communication and art appreciation

PurposeTo promote the cultural communication and art appreciation of Inscriptions and calligraphy which are important cultural heritages in China, with a broad crowd base and educational appreciation value, by using digital humanities methods and technologies, the paper aims to discuss how to develop a new user-oriented knowledge base according to user needs for inscription rubbings research and appreciation.Design/methodology/approachThe paper investigates user needs and current problems on the basis of status quo and summation of extensive service experience; validates the rationality of requirements and user scenarios with user research methods including focus groups, interviews and behavior observations; then designs and develops the technical solution of data process, knowledge base construction and service platform through experimental research methods based on agile project management.FindingsThe paper proposes a new reading mode for browsing and appreciation inscription rubbing works, a diverse knowledge-integrated knowledge organizational structure, a systematic user experience and service guiding framework which can be widely applicable to the development of other cultural heritage knowledge bases. Apart from that, the paper puts forward intelligent service development goals for the future.Originality/valueThe paper proposes a new reading mode with the overall functions and user experience design and development for browsing and appreciation inscription rubbing works, a diverse knowledge-integrated knowledge organizational structure, a systematic user experience and service guiding framework which can be widely applicable to the development of other cultural heritage knowledge bases. Apart from that, the paper puts forward intelligent service development goals for the future.

Intelligence Oversight and Accountability in Poland: Has Anything Changed?

The years 1990 and 1991 marked the beginning of a sea change in this particular area of the functioning of the Polish state. The following phenomena were analyzed: Over three decades on from the restitution of the Polish democratic state, how have the focal issues of key intelligence oversight institutions been addressed? How successful was the political transition in the area of intelligence services? The article is composed of four main parts. The first part provides a discussion of the “special services” concept, which determines the Polish institutional framework for intelligence oversight and accountability. Subsequent sections focus on particular forms of intelligence oversight: executive control, parliamentary oversight, and independent oversight. Our focus is on the institutional dimension of state security. The article is intended as an overview of the issue and relies on traditional historical and institutionalist approaches. Our proposal is to conduct a detailed case study to present the current situation in a country in East Central Europe, with the aim of generating general findings that could be used to guide reform efforts.

In the Service of the Eastern Bloc: Czechoslovak Intelligence and the Cyprus Conflict 1974–1989

ABSTRACT Even after the division of the island in 1974, the ‘Cyprus Issue’ remained a significant determinant in the relations between Greece and Turkey. In the context of the ongoing Cold War, the Eastern Bloc sought to exploit this situation, aiming to undermine the cohesion of NATO’s southeastern wing. The primary objective of this study is to assess how one of the socialist states, Czechoslovakia, attempted to influence the Cyprus conflict between 1974 and 1989 through its foreign intelligence services. The study illustrates this through the example of the Czechoslovak residency in Nicosia, showcasing Czechoslovakia’s intelligence methods in recruiting foreign collaborators, acquiring information, and conducting intelligence operations in one of the world’s most geopolitically exposed regions.

Stepping out of the shadows: the legitimacy of the Bahamas’ NCIA

ABSTRACT The Bahamas is a relatively small archipelago challenged by a broad spectrum of diverse, complex, and consequential national security threats that extend to the ‘unknown’ of a global security threat equation. To address the threat spectrum, Bahamian policymakers incorporated the National Crime Intelligence Agency (NCIA) within the national security architecture. For various reasons, Bahamians are sceptical about, scared of, and uncomfortable with the idea or reality of the NCIA. As such, the NCIA has to pass the legitimacy test as it steps out of the shadows. This article provides a preview into the legitimacy test of The Bahamas’ professional secret intelligence service.

Towards interworking of matter and oneM2M: Design and implementation of a matter–oneM2M Interworking Proxy Entity

The new smart home protocol, Matter, has garnered worldwide attention for its ability to tackle the most significant obstacle to fully unlock the potential of smart homes: interoperability. The increasing support for Matter among numerous vendors in their products is having a noticeable impact on interoperability at the device level. This work expands the reach of Matter to the IoT platform level by integrating it with an open and standardized platform, oneM2M. One of the key strengths of oneM2M is its ability to provide an open, common, and flexible interface at the platform level, facilitating seamless interconnection across various domains. This integration pave the way to provide Matter’s smart homes user with advanced intelligence services, including AI/ML applications, and emerging concepts like the metaverse. The proposed Interworking Proxy Entity (IPE) for Matter has been successfully implemented, and multiple scenarios have been tested using both commercial and experimental Matter devices within the oneM2M ACME and Mobius platforms. The results demonstrate seamless integration of Matter devices into the oneM2M platform, enabling other applications to interact with them solely through oneM2M standard interfaces. Additionally, the IPE proves to be practical for real-world applications with negligible overhead, offering mutual benefits for both Matter and oneM2M ecosystems.

Development of an Intelligent Service Platform for a Poultry House Facility Environment Based on the Internet of Things

Development of an Intelligent Service Platform for a Poultry House Facility Environment Based on the Internet of Things

A Genetic Causal Explainer for Deep Knowledge Tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based knowledge tracing (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this paper, we propose a causal explanation method based on the genetic algorithm (GA), named Genetic Causal Explainer(GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multi-strategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.