Chatbot systems have evolved from rule-based conversational tools to advanced data-driven systems that support more complex interaction. This paper examines this evolution and examines how advanced chatbot systems may function as System-Integrated Conversational Natural User Interfaces (System-Integrated Conversational NUIs). In this role, natural-language interaction serves as a user-facing interface to broader digital capabilities rather than only supporting conversational response generation. The paper provides a structured review of key stages in chatbot development, introduces the System-Integrated Conversational NUI as an interface-centred concept, and proposes a conceptual architecture and design principles for this expanded interface role. Key challenges and future considerations are also discussed, with particular attention to interaction quality, system complexity, responsible operation, and the coexistence of conversational and structured interface elements. This study provides a conceptual basis for analysing and designing advanced chatbot systems that support natural-language access to connected digital capabilities.

The time-evolving large graph has received attention due to it's participation in real-world applications such as social networks and PageRank calculation. It is necessary to partition a large-scale dynamic graph in a streaming manner in order to overcome the memory bottleneck while partitioning the computational load. Reducing network communication and balancing the load between the partitions are the criteria for achieving effective run-time performance in graph partitioning. Moreover, an optimal resource allocation is needed to utilise the resources while storing the graph streams into the partitions. A number of existing partitioning algorithms have been proposed to address the above problem. However, these partitioning methods are incapable of scaling the resources and handling the stream of data in real-time. In this study, we propose a dynamic graph partitioning method called Scalable Dynamic Graph Partitioner(SDP) using the streaming partitioning technique. The SDP contributes a novel vertex assigning method, communication-aware balancing method, and a scaling technique in order to produce an efficient dynamic graph partitioner. Experiment results show that the proposed method achieves up to 90% reduction of communication cost and 60%-70% balancing the load dynamically, compared with previous algorithms. Moreover, the proposed algorithm significantly reduces the execution time during partitioning.

Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction.

Image recognition tasks have gained enormous progress with a tremendous amount of training data. However, it isn't easy to obtain such training datasets that contain numerous annotated images in the domain of grocery product recognition. A small number of training data always results in a less than stellar recognition accuracy. Here we attempt to address this challenge by using generative adversarial networks (GAN), which can generate natural images for data augmentation. This paper aims to investigate the feasibility of using GAN to create synthetic training data, and thus to improve grocery product recognition accuracy. In this work, different GAN variants and image rotation are employed to enlarge the fruit datasets. Then, we train the CNN classifier using different data augmentation methods and compare the top-1 accuracy results. Finally, our experiments demonstrate that Auxiliary Classifier GAN (ACGAN) has achieved the best performance, which obtains l.26%~3.44% increase in recognition accuracy. As an additional contribution, the results show that the effectiveness of using generated data is very close to that of using real data, which in our best experimental case, are 93.85% and 94.25%, respectively.

Abstract The case‐based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer‐based domain knowledge to medical students for solving real‐world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine‐readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM‐CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM‐CD methodology, an interactive case‐based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.

With the increasing number of Internet of Things (IoT) devices, the volume and variety of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its high latency and scalability. In order to process this data in less time, fog computing has evolved as an extension to Cloud computing. In a fog computing environment, a resource monitoring service plays a vital role in providing advanced services, such as scheduling, scaling and migration. Most of the research in fog computing has assumed that a resource monitoring service is already available. Conventional methods proposed for other distributed systems may not be suitable due to the unique features of a fog environment. To improve the overall performance of fog computing and to optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a support and confidence based (SCB) technique which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a fog emulator with synthetic data. The experimental results obtained from the fog emulator show that the proposed technique consumes 19 percent lesser resources compared with the existing technique.

Challenges arise when extracurricular programs aren't aligned to the local curriculum and when courses are inflexible regarding the students learning pace. Modification of pre-existing programs can be achieved with enough domain knowledge, which can solve relevant gaps for digital technology Curriculums. The process of modifying such programs can bring to light other challenges like mixing very different technology, encouraging self-efficacy and engagement, and pacing learning to individual needs. This pilot-study highlights some of the challenges faced in using a program not designed for the local audience; the steps taken to change the program for the Australian digital technology curriculum; and identifies some areas of further research in extracurricular activity development for a digital technology curriculum.

We introduce an extension to Multiple Classification Ripple Down Rules (MCRDR), called Contextual MCRDR (C-MCRDR). We apply C-MCRDR knowledge-base systems (KBS) to the Textual Question Answering (TQA) and Natural Language Interface to Databases (NLIDB) paradigms in restricted domains as a type of spoken dialog system (SDS) or conversational agent (CA). C-MCRDR implicitly maintains topical conversational context, and intra-dialog context is retained allowing explicit referencing in KB rule conditions and classifications. To facilitate NLIDB, post-inference C-MCRDR classifications can include generic query referencing – query specificity is achieved by the binding of pre-identified context. In contrast to other scripted, or syntactically complex systems, the KB of the live system can easily be maintained courtesy of the RDR knowledge engineering approach. For evaluation, we applied this system to a pedagogical domain that uses a production database for the generation of offline course-related documents. Our system complemented the domain by providing a spoken or textual question-answering alternative for undergraduates based on the same production database. The developed system incorporates a speech-enabled chatbot interface via Automatic Speech Recognition (ASR) and experimental results from a live, integrated feedback rating system showed significant user acceptance, indicating the approach is promising, feasible and further work is warranted. Evaluation of the prototype’s viability found the system responded appropriately for 80.3% of participant requests in the tested domain, and it responded inappropriately for 19.7% of requests due to incorrect dialog classifications (4.4%) or out of scope requests (15.3%). Although the semantic range of the evaluated domain was relatively shallow, we conjecture that the developed system is readily adoptable as a CA NLIDB tool in other more semantically-rich domains and it shows promise in single or multi-domain environments.

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes.

In recent years, the data growth rate has been observed growing at a staggering rate. Considering data search as a primitive operation and to optimize this process on large volume of data, various solution have been evolved over a period of time. Other than finding the precise similarity, these algorithms aim to find the approximate similarities and arrange them into bins. Locality sensitive hashing (LSH) is one such algorithm that discovers probable similarities prior calculating the exact similarity thus enhance the overall search process in high dimensional search space. Realizing same strategy for encrypted data and that too in public cloud introduces few challenges to be resolved before probable similarity discovery. To address these issues and to formalize a similar strategy like LSH, in this paper we have formalized a technique O-Bin that is designed to work over encrypted data in cloud. By exploiting existing cryptographic primitives, O-Bin preserves the data privacy during the similarity discovery for the binning process. Our experimental evaluation for O-Bin produces results similar to LSH for encrypted data.

In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information about a large number of small frequent sub patterns. For this study we have developed a genetic based approach to find maximal frequent patterns using a user defined threshold value as a constraint. To optimize the search problems, a genetic algorithm is one of the best choices which mimics the natural selection procedure and considers global search mechanism which is good for searching solution especially when the search space is large. The use of evolutionary algorithm is also effective for undetermined solutions. Therefore, this approach uses a genetic algorithm to find maximal frequent item sets from different sorts of data sets. A low support value generates some large patterns which contain the information about huge amount of small frequent sub patterns that could be useful for mining association rules. We have applied this genetic based approach for different real data sets as well as synthetic data sets. The experimental results show that our proposed approach evaluates less nodes than the number of candidate item sets considered by Apriori algorithm, especially when the support value is set low.

Personalized healthcare envisions providing customized treatment and management plans to individuals at their doorstep. Key factors to ensure personalized healthcare is to involve with the individual in their daily life activities and process the gathered information to provide recommendations. We identified the mostly exposed domains for gathering chronic disease patients information that includes: clinical, social media, and daily life activities. Clinical data is related to the health-care of the patients while social media, sensory, and wearables data is related to the wellness data of the patients. A framework is required to monitor the health and wellness information of the patients for health and wellness analytics provisioning to the physicians for better decision making. We propose Personalized, Ubiquitous Life-care Decision Support System (PULSE); a state of the art decision support system that helps physicians and patients in life-style management of chronic disease patients such as Diabetes. The proposed approach not only utilizes clinical information but also personalized information by correlation to find hidden information using big data health analytic for improvement of life-care. PULSE provides health analytics by utilizing and processing clinical information of the patient. In the same way, it provides wellness analytics to the patients by using their social, activities, emotions and daily life information. The co-relation between clinical and personalized analytics is performed for better recommendations to the patients. This eventually results in improved life-care and healthy living of the individuals.

Summary It is a fact that the attention of research community in computer science, business executives, and decision makers is drastically drawn by big data. As the volume of data becomes bigger, it needs performance‐oriented data‐intensive processing frameworks such as MapReduce, which can scale computation on large commodity clusters. Hadoop MapReduce processes data in Hadoop Distributed File System as jobs scheduled according to YARN fair scheduler and capacity scheduler. However, with advancement and dynamic changes in hardware and operating environments, the performance of clusters is greatly affected. Various efforts in literature have been made to address the issues of heterogeneity (i.e., clusters consisting of virtual machines and machines with different hardware), network communication, data locality, better resource utilization, and run‐time scheduling. In this paper, we present a survey to discuss various research efforts made so far to improve Hadoop MapReduce scheduling. We classify scheduling algorithms and techniques proposed in the literature so far based on their addressing areas and present a taxonomy. Furthermore, we also discuss various aspects of open issues and challenges in the scheduling of MapReduce to improve its performance. Copyright © 2015 John Wiley & Sons, Ltd.

Healthcare domain is continuously growing with new knowledge emerged at different levels of clinical interest. At the same time, there is an increasing interest in the use of clinical decision support systems (CDSSs) to increase the healthcare quality and efficiency. Majorly the existing CDSSs are not designed to adapt scientific research in a well-established and automatic manner. Clinicians and researchers access the online resources on frequent basis for unmet questions during the course of patient care. They usually follow a dis-integrated approach to search for their required information from resources of their interest. Additionally, there is lack of defined mechanism to integrate the relevant knowledge for future use. To overcome the disintegrated and non-automatic approach, we introduce the concept of KnowledgeButton; a comprehensive model for evidence adaption from online credible knowledge sources in a well-defined and established manner. It saves the time of clinicians spend unnecessary in searching research evidence using disintegrated and manual mechanism. In this paper, we provide architecture design, workflows, and scenarios complemented with primary results. It covers walk-through from search query generation to evaluation of search results.

Clinical Decision Support Systems assist the physicians to make critical decisions during the diagnosis and treatment of the patients. CDSS envisions an extendable, shareable and reusable knowledge base to generate shareable guidelines and recommendations. Existing systems utilize HL7 standard Arden Syntax MLM and data model vMR schema for shareability and interoperability purpose. Understanding the Arden Syntax and vMR schema classes is tedious task for physicians, therefore an easy to use knowledge authoring environment is required to overcome the barrier of acquiring clinical knowledge. We are presenting Arden Syntax based authoring environment called Arden Syntax Studio that provides an easy to use interface to the physicians for creating shareable MLM without understanding the HL7 standard Arden Syntax. The created MLM can easily integrate with other healthcare systems by using HL7 standard data model vMR. The system hides the vMR layer from physicians by replacing vMR schema class's layer with corresponding understandable healthcare system's concepts. Therefore, the physician does not need to learn and understand complete Arden Syntax and vMR data model to create shareable rules in form of MLM. The system also provides the Intelli-sense functionality to enhance the knowledge rule creation process and reduce the possibilities of physician's errors. Furthermore, to enhance the interoperability feature our system capitalizes standard terminologies of the SNOMED CT concepts. The terminologies that are specific to organization, environment or region are handled with localized concepts in addition to SNOMED CT concepts.

Responding to social issues is very crucial because their impact can be significant to organizations or individuals. In this paper, we focus on proposing the method that identifies the personalized relevance of social issues to targets, such as individuals or organizations. To achieve this aim, we first collected trending social issues from Google Trends, micro- blog, and Internet news. Then, we obtained the well-structured document management system as a target domain that contains all activities regarding target objects. We applied the Term Frequency Inverse Document Frequency to obtain the personalized relevance weight of the social issue to a target.

In this paper we analyze the Web coverage of three search engines, Google, Yahoo and MSN. We conducted a 15 month study collecting 15,770 Web content or information pages linked from 260 Australian federal and local government Web pages. The key feature of this domain is that new information pages are constantly added but the 260 web pages tend to provide links only to the more recently added information pages. Search engines list only some of the information pages and their coverage varies from month to month. Meta-search engines do little to improve coverage of information pages, because the problem is not the size of web coverage, but the frequency with which information is updated. We conclude that organizations such as governments which post important information on the Web cannot rely on all relevant pages being found with conventional search engines, and need to consider other strategies to ensure important information can be found.