Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

A Menu Planning Model Using Hybrid Genetic Algorithm And Fuzzy Reasoning: A Study On Malaysian Geriatric Cancer Patients

Dewasa ini, terdapat banyak model perancangan menu yang menyediakan nasihat umum kepada pelanggan di pasaran. Namun, penyelesaian yang dijana daripada model ini biasanya sangat subjektif dan sukar untuk diwakili secara sistematik. Oleh itu, pemakanan yang betul bagi warga tua adalah penting untuk mengekalkan kesihatan dan kesejahteraan. Kajian ini menghasilkan model perancangan menu berasaskan ontologi menggunakan algoritma genetik hibrid dan penaakulan kabur terhadap pesakit kanser geriatrik di Malaysia. Kajian ini adalah bertujuan untuk mengemukakan perwakilan pelan diet berdasarkan ontologi pelan diet; mereka bentuk enjin perancangan dengan mengintegrasikan algoritma genetik dengan pencarian setempat untuk memperbaiki pelan menu; membangunkan pelan menu untuk pesakit tersebut dengan menggunakan mekanisme penaakulan kabur. Dengan tujuan untuk merancang menu yang sihat kepada pesakit, ontologi digunakan untuk mengklasifikasikan nutrien, jenis makanan, struktur pemakanan dan profil peribadi. Selain itu, algoritma genetik hibrid (HGA) digunakan untuk memastikan bahawa perancangan menu dapat memenuhi semua objektif dan kekangan yang telah ditetapkan. Tambahan pula, kawalan logik kabur (FLC) diaplikasikan dalam pemodelan fungsi keahlian set kabur bagi menganggarkan keperluan pemakanan. Nowadays, there are many diet recommendation models in the market that provide general advice to the clients. However, the generated menu plan from these models are usually very subjective and difficult to be represented systematically. Thus, proper nutrition for the elderly is important to maintain health and well-being, which can lead to fulfilling and independent lives. This research presents a study on ontology-based menu planning model using hybrid genetic algorithm and fuzzy reasoning for Malaysian geriatric cancer patients. The proposed work aims to produce a diet plan representation based on diet plan ontology; design a planning engine by integrating genetic algorithm with local search technique to enhance menu planning; and develop a menu planning approach to cater for Malaysian geriatric cancer patients using fuzzy reasoning mechanism. With the aim of planning healthy menu to patients, ontology is used to classify nutrients, food groups, meal structure and personal profile. Following that, hybrid genetic algorithm (HGA) is employed to ensure that the constructed menu satisfies all the objectives and predefined constraints. Furthermore, a fuzzy logic control (FLC) was applied in the modeling of membership functions of fuzzy sets for estimating nutrition needs

Biodiversity-Health-Sustainability Nexus in Socio-Ecological Production Landscapes and Seascapes (SEPLS)

This is an open access book. It is a compilation of case studies that provide useful knowledge and lessons that derive from on-the-ground activities and contribute to policy recommendations, focusing on the interlinkages between biodiversity and multiple dimensions of health (e.g., physical, mental, and spiritual) in managing socio-ecological production landscapes and seascapes (SEPLS). This book provides insights on how SEPLS approaches can contribute to more sustainable management of natural resources, achieving global biodiversity and sustainable development goals, and good health for all. It is also expected to offer useful knowledge and information for an upcoming three-year thematic assessment of “the interlinkages among biodiversity, water, food, and health” (the so-called “nexus assessment”) by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). The book begins with an introductory chapter followed by eleven case study chapters demonstrating the nexus between biodiversity, health, and sustainable development, and then a synthesis chapter clarifying the relevance of the case study findings to policy and academic discussions. It will be of interest to scholars, policymakers, and professionals in the field related to sustainable development

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation