Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and ...inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures.
Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only ...limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.
In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of ...heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.
The increasing growth of online social networks has drawn researchers' attention to link prediction and has been adopted in many fields, including computer sciences, information science, and ...anthropology. The link prediction in attributed networks is a new challenge in this field, one of the interesting topics in recent years. Nodes are also accompanied in many real-world systems by various attributes or features, known as attributed networks. One of the newest methods of link prediction is embedding methods to generate the feature vector of each node of the graph and find unknown connections. The DeepWalk algorithm is one of the most popular graph embedding methods that capture the network structure using pure random walking. The present paper seeks to present a modified version of deep walk based on pure random walking for solving link prediction in the attributed network, which will be used for both network structure and node attributes, and the new random walk model for link prediction will be introduced by integrating network structure and node attributes, based on the assumption that two nodes on the network will be linked since they are nearby in the network, or connected for the reason of similar attributes. The results indicate that two nodes are more probable to establish a link in the case of possessing more structure and attribute similarity. In order to justify the proposal, the authors carry out many experiments on six real-world attributed networks for comparison with the state-of-the-art network embedding methods. The experimental results from the graphs indicate that our proposed approach is more capable compared to other link prediction approaches and increases the accuracy of prediction.
The representational basis for downstream natural language processing tasks is word embeddings, which capture lexical semantics in numerical form to handle the abstract semantic concept of words. ...Recently, the word embeddings approaches, represented by deep learning, has attracted extensive attention and widely used in many tasks, such as text classification, knowledge mining, question-answering, smart Internet of Things systems and so on. These neural networks-based models are based on the distributed hypothesis while the semantic association between words can be efficiently calculated in low-dimensional space. However, the expressed semantics of most models are constrained by the context distribution of each word in the corpus while the logic and common knowledge are not better utilized. Therefore, how to use the massive multi-source data to better represent natural language and world knowledge still need to be explored. In this paper, we introduce the recent advances of neural networks-based word embeddings with their technical features, summarizing the key challenges and existing solutions, and further give a future outlook on the research and application.
Optimized IoT service placement in the fog Skarlat, Olena; Nardelli, Matteo; Schulte, Stefan ...
Service oriented computing and applications,
12/2017, Letnik:
11, Številka:
4
Journal Article
Recenzirano
Odprti dostop
The Internet of Things (IoT) leads to an ever-growing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but ...feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fog-based computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.
The customer churn prediction (CCP) is one of the challenging problems in the telecom industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to ...predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration using gravitational search algorithm. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, naive bayes, support vector machine, random forest, decision trees, etc. on train set as well as boosting and ensemble techniques are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyperparameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve. It was found that Adaboost and XGboost Classifier gives the highest accuracy of 81.71% and 80.8% respectively. The highest AUC score of 84%, is achieved by both Adaboost and XGBoost Classifiers which outperforms over others.
The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to ...undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.
The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial ...transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.
Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. ...This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.
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