Within the field of context-awarecomputing, some researchers investigate how communities of mobile users could be facilitated (e.g., Brown, 1996; Bur- rell & Gay, 2001; Salber, Siewiorek & Smailagic, 2001). The Stick-e document system, for instance, allows a user to place messages or Stick-e notes at various positions in space (Brown, 1996). These are detected, retrieved, and viewed by others, with location-sensing technology on a Palmtop, whenever this loca- tion is visited. The physical location of a stored message, however, can be ex- tended to include other types of desirable context features, such as attaching notes about people or objects detected within a building (using active badges), triggering notes when specific states have been reached (e.g., temperature thresholds), and allowing users to create their own context-triggering condi- tions. In other work, Burrell and Gay (2001) described an application called Graffiti that is designed to allow a community of users to collectively specify what is relevant and useful about a location, situation, and identity. Posting electronic notes consisting of contextual knowledge (beyond what the system was able of detecting) would allow other users to receive information relating to that context.
In this study, a context-aware inpatient management system was implemented to manage inpatients health conditions in real time using wearable devices equipped with sensor technologies and using data on the patient activity statuses, location information, etc. For this purpose, the current status of IoT-based healthcare systems and safety control management status of medical institutions were surveyed, and the problems of the patient management systems were analyzed. Research was done on patient management systems necessary for medical institutions through preceding research, and experiments regarding new ICT technology applied context-awarecomputing technologies. Medical institutions‟ health care for inpatient recovery has been done through a medical staff-oriented system, and patient health care was instructed to be managed by patients or caregivers. In addition, patient safety management methods were performed in a passive way through document management for patients and caregivers. Recently, as the need for patient safety management rises along with requirements and interests on service quality improvement, there have also been increasing attempts to create medical cost reduction and service enhancement using IoT technology. In this study, a prototype using BLE Beacon-based context-aware
Abstract —Present approaches utilizing awareness of context specialize on their unique domain of em- ployment. Although similarities between those ap- proaches exist, the concepts and systems utilized in this context are vastly heterogeneous. Our goal is the abstraction of the current situation in context- awarecomputing and the development of generalized concepts for approaching the development of context- aware systems. We put our focus on ubiquitous com- puting and mobile services, since those are especially suitable for employing awareness of contextual infor- mation. This paper presents the ﬁrst step in this en- deavor, consisting of an in-depth survey of context- awarecomputing and the presentation of our future plans to proceed in this project.
Abstract: With the increasing number of vehicle and traffic jams, the urban-traffic management is becoming a serious issue. In this article, we propose novel four-tier architecture for urban-traffic management with the convergence of vehicle ad hoc networks (VANETs), 5G wireless network, software-defined network (SDN), and mobile-edge computing (MEC) technologies. The proposed architecture provides better communication and rapider responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly cut down the time for rescue. Key technologies with respect to vehicle localization, data pre-fetching, traffic lights control, and traffic prediction are also discussed. Obviously, the novel architecture shows noteworthy potential for alleviating the traffic congestion and improving the efficiency of urban-traffic management.
The performance of the proposed ContextAware handover decision algorithm and Class Aware Load Balancing algorithm have been evaluated with the scenario shown in figure 3. The overall system throughput for different data rates are shown in figure 9, which shows that Average best effort based handover has outperformed the RSS based handover techniques in providing higher system throughput, and increment becomes more prominent at higher data rate.
functionality to build and interact with distributed application by sending eXtensible Markup Language (XML) message.But security management is a difficult work of balancing security and usability. This paper present a context-aware system for user access model. Context-awarecomputing system successfully undertaking by sensor data. The main objective of the contextaware system is to find and identify the client. To distributing personal information between different devices need privacy support. By introducing new access control model for accessing resource is needed. This paper proposes an overview of the context-aware access control.
(4) The routing algorithm EPGR (Energy Prediction and Geographical Routing) given in the reference  cannot reflect most WSN nodes’ different working states practically with the node’s states designed in it. And its designing is complicated and poorly representational; The algorithm DMMDR (Dynamic Multifactor Markov Decision Routing) given in the reference  designs the sensor nodes into different states, but the topology variety’s uncertain and frequency in the practical deployment of WSN will make the scale of the “states” here instable and uncertain. The DMMDR discusses the WSN topology problem with the topological structure only and is lack of the application rationality; the model CTMPC (Context-based Triggered Task Model in Pervasive Computing) given in the reference  is an application representation of the context-awarecomputing. But this model and other similar research thoughts given by the academia are only settling in the theoretical reasoning level and the qualitative plan level. They are unable to deploy quantificationally and standardly in the concrete application as the superficial research level of the context-awarecomputing. The application of the context-awareness in the pervasive computing environment in the future must merge with mathematics tool so that the integrated indices of the WSN routing technology can be improved better; the algorithm MAFZP (Mobility Aware Fast Zoned Protocol) given in the reference  does not make full use of the context-awarecomputing with the far-fetched designing model. It cannot reflect the context relationship of the nodes’ movement well and be lack of the technical interfusion in the details. So it cannot be considered as a representative designing of the context-awareness core thought merging with the concrete application deeply for the lower specialty exertion of the context-awarecomputing.
In a user-centric system, smart objects communicate with user’s personal devices such as mobile phone or tablet. User’s devices are the main conduit between the smart environment and an information system that can store and process data. In an intelligent system based on smart objects, context is very important to interpret the data and take some intelligent actions. A medical sensor reading may be abnormal if a user is in a sitting position but same reading is okay if the user is running. A reading of body temperature in an air conditioned room should have a different meaning than a reading under the sun. ContextAwarecomputing  is about collection of implicit context and use of this context for intelligent decision making. Decision supports systems are needed to take actions based on the data in case of emergency or some abnormal and life threatening pattern in sensor reading.
The second category is “adapting to context”. It includes context-aware applications which dynamically change or adapt their behaviour based on the context of the application and the user (Schilit, Theimer and Welch, 1993; Brown, Bovey and Chen, 1997; Ward, Jones and Hopper, 1997; Abowd et al., 1998, 1999; Kortuem, Segall and Bauer, 1998; Salber et al., 1999). Ryan et al. (1998) define them as applications that monitor input from environmental sensors and allow users to select from a range of physical and logical contexts according to their current interests or activities. This category is slightly more restrictive than the previous one by identifying the method in which applications act upon context. Brown (1998) defines context-aware applications as applications that automatically provide information and take action according to the user’s present context as detected by sensors. He also adopts a narrow view of context-awarecomputing by stating that these actions can take the form of presenting information to the user, executing a program according to context or configuring a graphical layout according to context.
As the framework described provides a flexible tool for post-processing of GPS measurements, some occasions require real-time error correction. One of these situations is considered with asphalt paving projects, whereby machine operators must respond during the construction to the measured data observed with the GPS devices. This situation does not provide a large unlabeled data set that can be used to develop the dynamically established classification model that is able to classify based on the context of the complete data set.
File systems have been at the heart of modern practical computing since its inception. The hierarchical ‘tree’ model that we are so used to has proved surprisingly durable. The first useful UNIX file systems were based on this notion of organising files using folders, or directories, which could be recursively nested and in which non-directory files themselves could be located at any level. This model has been used in a similar fashion by rival PC oper- ating system vendors and others and, as such, has allowed for a certain level of compatibility between such systems. This approach has lasted so long because it has proved so suitable to monolithic, centralized computer systems that, of necessity, traditionally dominated the area. Subsequent advances in network technology has not yet forced a radical redesign of this paradigm that has enjoyed widescale deployment. This section describes the desirable characteristics of file systems, and how their design and interworking has responded to the network age.
The notion of context is all the more important when resolving entities in personal communications such as email. Personal communication often con- tains unqualified entity mentions. For example, an email from Ken Lay to Jeff Skilling might men- tion Andy with no other indication that the person mentioned is Andrew Fastow. A traditional entity linking system will fail miserably here; the mention Andy is simply too ambiguous out of context. Email- specific linkers often rely on access to the commu- nications graph to resolve such mentions. The com- munications graph is important mainly because it of- fers a guess at the discourse context shared between the author of a communication and its recipient(s).
Keys Required More Global Information Another com- mon interesting phenomenon appears in all the global-based approaches is that the weights of global context vectors for keys are usually higher than that for queries, especially in the mid-level layers. We believe this is caused by the dif- ferent usage of query and key. Considering the normaliza- tion in sof tmax function (Equation 3) which is effected on the keys, each key should consider its relationships to other items. This is why the keys require more semantic informa- tion in SANs. The results in Table 3 show that self-attention network indeed benefits more from incorporating global in- formation into keys than that of queries. However, it should be noted that enhancing the queries with context representa- tions can further improves the performance.
Discourse relation: for both source and tar- get components, we extract discourse relations between context sentences, and within the cov- ering sentence. We also extract discourse rela- tions between each pair of source context sen- tence and target context sentence. Each relation defines a boolean feature. We extract both Penn Discourse Treebank (PDTB) relations (Prasad et al., 2008) and Rhetorical Structure Theory Dis- course Treebank (RST-DTB) relations (Carlson et al., 2001) using publicly available discourse parsers (Ji and Eisenstein, 2014; Wang and Lan, 2015). Each PDTB relation has sense label de- fined in a 3-layered (class, type, subtype), e.g., CONTINGENCY.Cause.result. While there are only four semantic class labels at the class-level which may not cover well different aspects of ar- gumentative relation, subtype-level output is not available given the discourse parser we use. Thus, we use relations at type-level as features. For RST- DTB relations, we use only relation labels, but ig- nore the nucleus and satellite labels of components as they do not provide more information given the component order in the pair. Because tempo- ral relations were shown not helpful for argument mining tasks (Biran and Rambow, 2011; Stab and Gurevych, 2014b), we exclude them here.
GANs, which estimate generative models by simultane- ously training two adversarial models, were first introduced by Goodfellow et al.  for image generation. Radford et al.  further developed a more stable set of architectures for training GANs, called deep convolutional GANs (DCGAN). Recently, GAN has been widely applied to image generation , image transformation , image completion , and texture syn- thesis . Context Encoder  uses a novel channel-wise fc layer for feature learning but keeps the traditional fc layer for semantic inpainting. Yeh et al.  employed GAN with both a perceptual loss and a contextual loss to solve inpainting. Notice that the perceptual loss in  is essentially an adver- sarial loss and the contextual loss which only considers the context (excluding the synthesized region). Yang et al.  con- ducted online optimization upon a pretrained inpainting model primarily inherited from Context Encoder. The optimization is too expensive for real-time or interactive applications. Common disadvantages exist in these GAN-based approaches. First, the fc layer in the encoder–decoder framework cannot preserve accurate spatial information. Second, the discrimi- nator in current GANs only evaluates the synthesized region but not the semantic and appearance consistency between the predicted region and the image context.
CoBrA  is a design supported broke Agent to support the event of context-aware applications in AN intelligent house. The broker is AN autonomous agent that manages and controls the context model of a selected domain. It runs on a fervent laptop (server) with powerful resources. The broker agent encompasses a bedded design containing the subsequent components: context information, context ratiocinator engine, context acquisition module and privacy management module. The broker agent collects context from devices, alternative agents and sensors of its close surroundings and makes their fusion in a very coherent model which is able to be shared among devices and their corresponding agents. elapid uses metaphysics for context description that permits a decent reasoning and a far better sharing of discourse data. It uses a centralized model for the storage and also the process of context so as to avoid wasting the restricted resources of mobile devices and uses a confidentiality policy for the user. The design needs a fervent server for the broker that will
Starting from the above considerations, we designed and built Inbooki, a system to develop and read e- books that take into account the context of the readers and adapt the content and the flow to it. So, they can be considered context-aware e-books. The contribution of this paper is the detailed description of Inbooki. Some similar approaches exist, but, as we explain the the next section, they are limited because either they are bound to a specific field or they exploit little context information; instead, our approach aims at being more general and at considering many sources of information. Inbooki enables writers to structure their book taking into consideration different aspects of the context where readers will actually read the e-book. The resulting e-books are called immersive-books (shortly in-books), meaning that the readers are “immersed” in the context where they read the e-books.
In this chapter, we argue that IDS and IPS should dynamically adapt the parallelization and separation of rules based on the observed traffic on the network and the input rules database. That is, all IDS and IPS workloads are not the same, and systems should adapt to the environment in which they are placed to effectively trade-off memory requirements for run-time rule evaluation. To demonstrate this idea, we have developed an adaptive algorithm that systematically profiles the traffic and the input rules to determine a high performance and memory efficient packet inspection strategy that matches the workload. To effectively use memory for high performance, the rules are separated into groups by values of protocol fields and then these rule groups are chosen to be maintained in memory following a simple idea of “the rule groups that have a large number of rules and match the network traffic only a few times should be separated from others.” This idea follows our observation that if rules with value v for a protocol field are grouped separately from others, then for any packet that does not have value v for the protocol field, we can quickly reject all those rules, and if only a few packets have that value, then those rules will be rejected most of the time. Therefore, our workload-aware scheme aims to determine a small number of effective groups for a given workload.
Contextaware E-Learning systems provide learning content according to a learner’s context. In order to determine a learner’s context, the parameters that constitute the context and the values of these parameters in the current learner’s situation have to be found. There are several existing contextaware E-Learning systems and each of these are taking care of some of the context parameters - like learning styles, learner preferences etc. But, a standardized static context model that helps to capture a learner’s context in its entirety is not available. This paper proposes a static context model that helps to capture a learner’s context. The static context model is developed by consolidating the various context parameters used in the existing contextaware E-Learning systems and organizing them into an appropriate structure. The structure of the static context model along with the parameters that constitute the context is explained in the paper
based on this idea (Gooding and Kochmar, 2019). We extend the implementation of a sequence la- beller by Rei (2017), 1 which achieves state-of-the- art results on a number of NLP tasks. The de- sign of this architecture is highly suitable for CWI as: (1) it uses bi-directional long short-term mem- ory units (BiLSTM) (Hochreiter and Schmidhu- ber, 1997), which allow the system to learn about both the left and right context of a target word; (2) the context is combined with both word and character-level representations (Rei et al., 2016) which helps capture complexity due to rare char- acter sequences as well as morphological struc- ture; (3) this architecture uses a language mod- elling objective, which enables the model to take one of the highly informative complexity factors of word frequency into account.