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Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling.

This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases.

This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes.

The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies.

As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.

The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

Training is one of the key dimensions of internal marketing. Virtual reality (VR), a computer technology that replicates an environment (real or imagined) and simulates a user’s physical presence in that environment to allow for user interaction, offers unique opportunities from a training perspective, such as allowing users to improve their skills without the consequence of failing real customers or the need to be in the real environment physically. This study aims to focus on comparing the effectiveness of VR hospitality training with that of real-world hospitality training.

This study adopts situated cognition theory to empirically test the effect of the awareness of contextual variables (social interaction, location and task) on learning and compare learning outcomes between tourism training in VR and real-world experimental settings.

Results indicate that location and task awareness enhance cognitive absorption, but social awareness does not influence cognitive absorption. There is no significant difference between training in real-world and VR environments. Finally, cognitive absorption has a positive effect on mental model change (the learning outcome).

This result advances the theoretical understanding on the significance of learning context by applying situated cognition theory in hospitality training and has significant implications for training that aims for rigor and efficiency within cost, location and time constraints.

培训是内部营销的关键维度之一。 虚拟现实 (VR), 一种复制某种环境（真实的或想象的）并模拟在该环境中实质性存在的用户的计算机技术并且允许用户交互, 从培训的角度来看提供独特的机会, 例如允许用户在没有培训的情况下提高他们的技能避免承担损失真实客户的后果或需要实际身处真实环境中。 本研究的重点是比较 VR 酒店培训与现实世界的招待培训的效果。

本研究采用情境认知理论对情境变量（社交互动、位置和任务）的认识对学习和比较 VR 和现实世界实验环境中的旅游培训之间的学习成果进行实证检验。

结果表明, 位置和任务意识增强了认知吸收, 但社会意识不影响认知吸收。 在现实世界和 VR 环境中的训练之间没有显着差异。 最后, 认知吸收对心理模式改变（学习结果）有显著积极作用。

该结果通过在酒店培训中应用情境认知理论促进了对学习情境重要性的理论理解, 并对旨在成本、地点和时间限制内实现严谨和高效的培训具有重要意义。

Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.

Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k‐Nearest Neighbor approaches.

WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k‐Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k‐Nearest Neighbor approach.

A new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).

The purpose of this paper is to investigate whether it is possible to identify different types of sadistic offenders within a sample of sexual homicide offenders (SHOs).

The study addresses this research question through the use of two-step hierarchal cluster analysis and binary logistic regression utilizing a sample of 350 cases of sexual homicide from Canada.

Results from cluster analysis show that three groups emerge: a non-sadistic group, a mixed group that show evidence of some sadistic behavior and a sadistic group that have high levels of sadistic behavior. Additionally, the sadistic cluster was more likely to destroy or remove evidence at the crime scene than the mixed and non-sadistic cluster and was more likely to leave the victim’s body at a deserted location than the non-sadistic cluster.

This is the first study to examine the dimensionality of sadism within a sample of SHOs.

Emergency managers face coordinative challenges that require a high degree of mobility, flexibility and the ability to interpret heterogeneous, location-dependent information of various sources and quality. Recent information and communication technology-driven developments like crowdsourcing or social networks have opened up new organizational possibilities for emergency managers. To make quick but solid decisions, and improve the coordination of activities performed by crowdsourcees during disaster response, the authors suggest the use of collaborative features from crowdsourcing and inherent availability of resources from social network effects. In this paper, the idea of considering collaboration and crowdsourcing as drivers for flexibility in the design of business processes in the context of emergency management is prepared, the meaning of location-dependent tasks for volunteers is investigated, and the added value of social network effects is substantiated.

This paper is part of an ongoing research project in the field of crowdsourcing. It represents conceptual work that builds on relevant literature.

In terms of emergency management, the paper sheds light on what emergency managers may consider when coordinating activities performed by volunteers and how they may benefit from social network effects. Furthermore, it is shown how they can exploit information using collaboration-based and tournament-like crowdsourcing, how they can benefit from invoking additional resources using weak ties from social networks, and how visualization of information may support decision-making.

Exemplary applications to exploit crowdsourcing and social network effects to support improvisation and to respond flexibly in disaster response are given.

This paper suggests novel collaborative approaches to support emergency managers in their decision-making. Based on social network analysis, the value of weak ties is elaborated, and based on a taxonomy from crowdsourcing, distinct collaborative alternatives are developed and proposed for application in emergency management.

Location awareness is essential to decisions pertinent to tracking and progress reporting, as well as to safety in construction projects. However, these applications have been mostly limited to the outdoor environment, where satellites for positioning information are in view. Recent studies on indoor location sensing systems are now overcoming this limitation and offering significant potential on construction practices, and radio frequency identification (RFID) is the most widely utilised technology for such application. The purpose of this paper is to address a wide range of protocols that are vital for RFID deployment for indoor construction. The paper identifies deployment settings to provide data acquisition with higher accuracy for indoor location sensing in construction.

A computational platform was designed to assess and evaluate the most suitable condition related to deployment of reference tags in construction. In this platform, a number of protocols and parameters are presented and their performance is evaluated. The evaluation scenarios were performed on a construction facility in Montreal, as well as in a controlled lab environment. The computational platform used for the study comprises the use of passive reference RFID tags and K Nearest Neighbour algorithm (K‐NN) for course‐grained detection of target's location and its classification into pre‐defined zone areas.

The studies resulted in a number of observations, findings, and lessons learned for RFID deployment in construction. The results indicate that: the speed of the reader is in direct relationship with the detection error rate; zone configuration effectiveness is in direct relationship with the deployed RFID read‐range; error rate on the controlled environment is significantly lower than rates in construction site; and stationary reader performs better than moving reader.

The paper's findings are expected to be of considerable value to researchers and practitioners involved in the utilisation of RFID technology in construction. The paper provides a set of helpful protocols for the deployment of passive RFIDs for automated onsite management of construction operations.

Location‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.

This paper includes an overview of prediction techniques and reviews several location‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.

A new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.

This overview provides a broad background for future research in prediction.

Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.