Context Manager Research Articles

Using Generative AI to help with statistical test selection and analysis

One of the most common questions that students ask statistics advisors is ‘What test should I do?’ This paper explores the use of generative AI chatbots, specifically ChatGPT, as a tool to assist students, in particular those with limited experience in statistics, in selecting appropriate statistical tests for their analyses. Traditional methods, such as flowcharts and online test selectors, require at least a basic understanding of measurement scales and research design, which can be an issue for many students who have limited exposure to statistics on their courses. This research focuses on developing and refining prompts to guide ChatGPT in providing accurate and relevant statistical test recommendations. A hypothetical scenario was used to test the effectiveness of various prompts, ranging from simple, naïve questions to more sophisticated ones utilising specific prompt patterns, such as the ‘context manager’ and ‘flipped interaction.’ These patterns were selected to enhance the chatbot’s responses and ensure the relevance and accuracy of the test suggestions. The findings suggest that while AI chatbots like ChatGPT can be a valuable resource for students, their effectiveness is highly dependent on the quality of the prompts used. The paper concludes with a discussion on the potential of these AI tools in educational settings, acknowledging the limitations of current technology and suggesting directions for future research and development.

Learning to Copy Coherent Knowledge for Response Generation

Knowledge-driven dialog has shown remarkable performance to alleviate the problem of generating uninformative responses in the dialog system. However, incorporating knowledge coherently and accurately into response generation is still far from being solved. Previous works dropped into the paradigm of non-goal-oriented knowledge-driven dialog, they are prone to ignore the effect of dialog goal, which has potential impacts on knowledge exploitation and response generation. To address this problem, this paper proposes a Goal-Oriented Knowledge Copy network, GOKC. Specifically, a goal-oriented knowledge discernment mechanism is designed to help the model discern the knowledge facts that are highly correlated to the dialog goal and the dialog context. Besides, a context manager is devised to copy facts not only from the discerned knowledge but also from the dialog goal and the dialog context, which allows the model to accurately restate the facts in the generated response. The empirical studies are conducted on two benchmarks of goal-oriented knowledge-driven dialog generation. The results show that our model can significantly outperform several state-of-the-art models in terms of both automatic evaluation and human judgments.

Improving Memory Exploitation in M-health Applications through Context-Aware System

INTRODUCTION: The mobile application requires some information about its environment in order to the adaptation of its behaviour according to this information in a transparent manner. However, such a task usually needs the resource consumption of mobile devices.OBJECTIVES: the main goal of this work was the design of the context aware infrastructure for the healthcare applications embedded on mobile devices. This last aims to the integration of the context awareness into the execution environment and providing a adequate methodology of the generic context manager, with the maintenance of low level the resource consumption.METHODS: This collected the context information (medical data) thanks to the component (Context Element Monitor). When the components set acquire values of different elements context which belong the same class, these components run in the same time. it is mean the execution of its in a group way. From the state of monitors (ACTIVE or DISABLED), our system creates the new context situation. The mobile application is structurally an assembly of services. The adaptation methodology revolves around the execution manager of services according to the new created context situation. This approach ensures the best use of resources depending on the characterization of context and reduces the amount of contextual information in the adaptation processes.RESULTS: We evaluated a memory space in three cases, since it is the most resource that is affected in the field of the mobile health (mobile patient monitoring application). For this, we estimated the consumed memory at two levels: the first is the management module "Context Manager" and the second level considers the adaptation procedure. In the Context Manager, the first and the third case were the exploited memory space was almost identical, Knowing that its consumption is varied between 1556 and 2026 KB. But in the second case, it increased and become varying between 2424 and 6848 KB. the other hand, in the adaptation mechanism, it is necessary a huge space of a memory for managing services more than 40000 kb.CONCLUSION: Our own approach of context manager and adaptation can open the new way in the m-health applications development. This allows these applications that can exploit the great quantity and the different type of a medical data with the best use of the memory space.

AMBLE: A Context-Aware Mobile Learning Framework

INTRODUCTION: Mobile Learning is a new pivotal learning trend nowadays. With the increasing use of sophisticated smartphones equipped with augmented reality supporting tools and sensors, mobile learning platforms are expected to deliver tailor-made and customized learning elements to learners. Context-awareness is regarded as the fundamental approach or workaround to lift this learning style to distribute adaptive and personalized learning elements in mobile devices. OBJECTIVES: The main priority in mobile learning is to make learning elements as flexible as possible using different forms of context data to extend the natural adaptation capabilities in mobile devices in order to engage learners in extremely rich environments. METHODS: In this paper, A context-aware MoBile LEarning framework is proposed, namely the AMBLE framework. It processes contextual data at four distinct levels namely: Sensing Layer, Adaptation Layer, Context Processing Layer, and Application Layer to perform adaptation of learning contents based on the actual environment and conditions of the learner. RESULTS: Partial implementation of the proposed framework has the potential to capture and represent the physical context information that may be used to perform a dynamic adaptation of learning contents and thus significantly improve the mobile learning experiences. Extra work is expected regarding the implementation of the other layers and components of the framework including the user model, context manager, and the adaptation engine. CONCLUSION: The AMBLE framework proposes some relevant content adaptations with some positive results. As future works, new forms of user-context adaptation synthesized with other extracted data sets of contextual information will be used to establish and align relevant dynamic adaptation and personalization of learning contents.

The Effect of Supply Chain Integration on Supply Chain Performance

The study investigated the effect of supply chain integration on the performance of supply chains. Specifically, the study investigated the effect of information, operational and relational integration of supply chains and how they affect the overall performance of the supply chain. The study administered 1500 questionnaires to some selected supply chain and information systems specialists and based on the responses received, 400 questionnaires were fully completed and which was used in the analysis of the study. Based on the findings, it was found that information and operation integration had a positive effect on supply chain performance whiles relational integration has a negative effect on supply chain performance. The result implies that within the Ghanaian context managers should invest more in information and operation integration in order to achieve positive outcomes in their supply chain whiles relationship integration should be done with more caution because of its underlying elements of trust and long-term commitments.

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The semantic denitions of compound Python programming language operators, for which we have expanding applications in application projects, are explained in the article. The denitions are written in the basic initial form, which is primary for the study of programming and for the construction of algorithms for data processing. On the basis of semantic denitions, syntax denitions are formed, which form the correct subset of the general Python syntax, and can be supplemented by extension operations without modication of the base part. The method of operational-functional denition of semantics is presented, which allows to rebuild syntax denitions so as to preserve the basic semantics of individual constructions of the language and to minimize the length of output by grammatical denitions startdef=>+"example". For this purpose we use basically three methods: reduction of complete grammatical rules; substitution of denitions of non-terminals on the right side of the syntax; adding non-strictly dened terminals with reference to previously dened non-terminals. Semantics models are built on the classication of operations and data conversion functions. Models are treated as universal algebras U(A) =< M;Ω >, where M is a nonempty set (quantities, memory cells, structures, operators), and Ω is a set of operations (possibly partial) on the set M, including the signature. We dene semantics models in two groups: 1)models based on valid Python operations; 2)models based on Python operators and control structures. For each group of models of algebra semantics has its own peculiarities. Semantics models are dened for the following objects: numeric types, assignment, data system, print, input; conditional expressions, conditional operator if; loop operators while, for; try exception control operator; operator with context managers.

Control as a Service Architecture to Support Context-aware Control Application Development

Taking advantage of IoT, services and Cloud technologies, the development of Cloud of Things (CoT) changes the way control application are engineered and developed moving from a dedicated design and development of control applications to a Control as a Service vision. This vision requires developing a new architecture to connect physical and logical objects as well as integrating basic control patterns to organize a consistent control service orchestration. To fit this challenge, we propose a multi-layer Control as a Service architecture to describe control systems in a holistic way. The integration of a context manager, analysing continuously the system environment as well as the control system behaviour is used to monitor and orchestrate on the fly the deployment of context-aware control services. Thanks to this context dependent orchestrator, adaptive control system can be implemented.

The Effect of Supply Chain Integration on Supply Chain Performance

The study investigated the effect of supply chain integration on the performance of supply chains. Specifically, the study investigated the effect of information, operational and relational integration of supply chains and how they affect the overall performance of the supply chain. The study administered 1500 questionnaires to some selected supply chain and information systems specialists and based on the responses received, 400 questionnaires were fully completed and which was used in the analysis of the study. Based on the findings, it was found that information and operation integration had a positive effect on supply chain performance whiles relational integration has a negative effect on supply chain performance. The result implies that within the Ghanaian context managers should invest more in information and operation integration in order to achieve positive outcomes in their supply chain whiles relationship integration should be done with more caution because of its underlying elements of trust and long-term commitments.

Critical issues for employees in inter-municipal health care services: a multiple case study

BackgroundTraditional, hierarchical government structures have recently been challenged by increased complexity, fragmented services and heavy public demand. When healthcare services become fragmented and decentralised, they require redesign. Inter-municipal cooperation is a strategy to deal with current challenges and future demographic changes. Few studies exist that can help us conceptualize challenges regarding employment in this context and inform managers in the involved municipalities. This study aims to identify critical issues for employees in inter-municipal health care services and to elaborate on how and why these issues are experienced.MethodsA multiple qualitative case study was conducted with data from interviews, observation studies, a participant workshop and inter-municipal healthcare service project documents and reports. The study involved two districts in Norway and six cases including 17 informants. First, a within-case analysis was conducted for all cases; second, a cross-case analysis was conducted in each district to examine replication, contrasts and extension to emergent findings; and, eventually, replicated findings in Districts 1 and 2 were analysed across districts.ResultsThree critical issues were identified: support, differences, and geographical distances. Employees working in teams experienced fewer challenges than did those working as isolated individuals.ConclusionsCritical issues for employees represent an important aspect of inter-municipal cooperation, and additional research should be undertaken to inform future policy and practice.

Real-Time Anomaly Detection in Elderly Behavior with the Support of Task Models

With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.

Model of a Context-Aware Middleware for Mobile Workers

With the development of Internet of things and Web of things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services which meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service which is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent observation channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates’ results while preserving the reactivity of the self-adaptive system.

A Data Model for Context Aware System on Smart Phone

스마트 폰 기술이 빠르게 발전함으로써, 유비쿼터스 컴퓨팅의 핵심 기술인 사용자 상황인지 기술이 실현 가능하게 되었다. 본 연구에서는 스마트폰에서 사용자의 상황인지 시스템을 위한 데이터 모델을 제안한다. 본 연구의 스마트폰에서의 상황인지 시스템은 센서, 센서 데이터 관리자, 로그데이터관리자, 컨텍스트 관리자, 서비스제공자 등 5가지의 서브시스템으로 구성되는데, 본 연구에서 이러한 5개의 서브시스템에서 관리되는 데이터를 정의하고 모델링함으로서, 스마트폰에서의 상황인지 시스템을 개발하기 위한 프레임워크 및 개발환경 구축에 토대가 될 것이다. 또한, 향후 본 연구를 기반으로 스마트폰에서의 다양한 사례를 이용하여 실험함으로써, 개인화 서비스를 위한 상황인지 모델을 개발하는데 연구를 집중할 계획이다.

From Ambient Sensing to IoT-based Context Computing: An Open Framework for End to End QoC Management.

Quality of Context (QoC) awareness is recognized as a key point for the success of context-aware computing. At the time where the combination of the Internet of Things, Cloud Computing, and Ambient Intelligence paradigms offer together new opportunities for managing richer context data, the next generation of Distributed Context Managers (DCM) is facing new challenges concerning QoC management. This paper presents our model-driven QoCIM framework. QoCIM is the acronym for Quality of Context Information Model. We show how it can help application developers to manage the whole QoC life-cycle by providing genericity, openness and uniformity. Its usages are illustrated, both at design time and at runtime, in the case of an urban pollution context- and QoC-aware scenario.

A survey on addressing privacy together with quality of context for context management in the Internet of Things

Making the Internet of Things (IoT) a reality will contribute to extend the context-aware ability of numerous sensitive applications. We can foresee that the context of users will include not only their own spatio-temporal conditions but also those of the things situated in their ambient environment and at the same time, thanks to the IoT, those that are located in other remote spaces. Consequently, next-generation context managers have to interact with the IoT underlying technologies and must, even more than before, address both privacy and quality of context (QoC) requirements. In this article, we show that the notions of privacy and QoC are intimately related and sometimes contradictory and survey the recent works addressing them. Current solutions usually consider only one notion, and very few of them started to bridge privacy and QoC. We identify some of the remaining challenges that next-generation context managers have to deal with to favour users’ acceptability by providing both the optimal QoC level and the appropriate privacy protection.

An Infrastructure to Enable Lightweight Context-Awareness for Mobile Users

Mobile phones enable us to carry out a wider range of tasks every day, and as a result they have become more ubiquitous than ever. However, they are still more limited in terms of processing power and interaction capabilities than traditional computers, and the often distracting and time-constricted scenarios in which we use them do not help in alleviating these limitations. Context-awareness is a valuable technique to address these issues, as it enables to adapt application behaviour to each situation. In this paper we present a context management infrastructure for mobile environments, aimed at controlling context information life-cycle in this kind of scenarios, with the main goal of enabling application and services to adapt their behaviour to better meet end-user needs. This infrastructure relies on semantic technologies and open standards to improve interoperability, and is based on a central element, the context manager. This element acts as a central context repository and takes most of the computational burden derived from dealing with this kind of information, thus relieving from these tasks to more resource-scarce devices in the system.

Task-Centered Context Manager for Customer Relationship Management Systems

In the customer relationship management (CRM) environment, agents tend to multitask. But the current CRM systems are not task-centered and therefore hinder the agent’s multitasking performance. We proposed the task-centered context manager, which better matches the agent’s mental model by focusing on the tasks that an agent is working on. The task-centered context manager automatically creates contexts, each for a separate task. It provides a flexible context switching mechanism for agents to easily go back and forth between tasks. And its priority-driven interface helps agents focus on important tasks. A formative usability evaluation indicated that our task-centered context manager was very well received by participating agents as evidenced by the overall System Usability Scale score of 88.5.

Open framework for distributed context management in ubiquitous environments

Most previous research into ubiquitous computing has focused on small and ad hoc environments with proprietary infrastructures and protocols. Consequently, context-aware services are usually developed without considering interoperability among different systems, devices and services. This work presents an open framework for ubiquitous environments, with a distributed context management architecture and a communication model based on standard protocols. With this framework, a smart device can search for context-aware services and a service can be easily deployed via standard protocols and distributed context managers. A prototype of the framework has been implemented to demonstrate its feasibility.

Estimating User Preferences by Managing Contextual History in Context Aware Systems

Context awareness enables smart service discovery and adaptation for mobile and wireless hosts. The contextual data is acquired from sensors present in the smart space, which may be absent. The inherent noisy nature of wireless environments does not guarantee the gathering of correct data. A history module is thus required in conjunction with existing context-aware systems that overcomes these limitations by predicting the data. We present a modular approach that when coupled with existing context managers will be able to provide user preference on the basis of usage history.

TPM context manager and dynamic configuration management for trusted virtualization platform

It is absolutely critical that trusted configuration management which significantly affects trust chain establishment, sealing storage and remote attestation, especially in trusted virtualization platform like Xen whose system configuration changes easily. TPM (trusted platform module) context manager is presented to carry out dynamic configuration management for virtual machine. It manages the TPM command requests and VM (virtual machine) configurations. The dynamic configuration representation method based on Merkle hash tree is explicitly proposed against TCG (trusted computing group) static configuration representation. It reflects the true VM status in real time even if the configuration has changed, and it eliminates the invalidation of configuration representation, sealing storage and remote attestation. TPM context manager supports TCG storage protection, remote attestation etc, which greatly enhances the security on trusted virtualization platform.

Dynamic context management for pervasive applications

AbstractPervasive computing aims at providing services for human beings that interact with their environment (encompassing objects and people who reside in it). Pervasive computing applications must be able to take into account the context in which users evolve, for example, physical location, social or hierarchical position, current tasks as well as related information. These applications have to deal with the dynamic integration in the environment of new, and sometimes unexpected, elements (users or devices). In turn, the environment has to provide context information to newly designed applications. This requires a framework which is open, dynamic and minimal. We describe an architecture in which context information is distributed in the environment and context managers use semantic Web technologies in order to identify and characterize available resources. The components in the environment maintain their own context expressed in RDF (Resource Description Framework) and described through OWL ontologies. They may communicate this information to other components, obeying a simple protocol for identifying them and determining the information they can provide. We show how this architecture allows introducing new devices and new applications without interrupting what is working. In particular, the openness of ontology description languages makes possible the extension of context descriptions and ontology matching helps dealing with independently developed ontologies.