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Article in practitioner magazine After hearing from a business school professor about the phenomenon of micro-moments, we wanted to know more about how these tiny interactions can have a big impact on an organisation, affecting its people and its culture

Edge computing is attracting an increasing attention presently even though most of the building energy efficiency solutions are still using cloud computing for gathering, pre-processing and analyzing energy data. However, edge computing still requires more power in order to be used alone to meet the high computation demand of artificial intelligence based energy saving solutions. Meanwhile, a hybrid edge-cloud architecture can be the best current approach to implement energy efficiency systems. It provides end-users and utility companies with a flexible control of their energy usage footprints, minimizes the cost of cloud hosting, and improves privacy-preservation. Accordingly, in this paper, we present a novel energy efficiency system based on a hybrid edge-cloud computing architecture. To analyze energy and occupancy data collected through different smart meters and occupancy sensors, we use a micro-moment approach to cluster energy observations into different categories representing both normal and abnormal energy usage. Following, a deep micro-moments (deepM2) scheme is deployed to automate the Anomaly Detection task, where a new approach called deepM2-AD is developed. Moving forward, deepM2-AD is implemented on three different architectures, defined as edge-only, cloud-only and hybrid edge-cloud to evaluate their performance and identify their merits and demerits. Overall, the hybrid edge-cloud architecture has presented the best compromise in terms of improving the processing speed, curtailing the cost of cloud hosting, and reducing the communication latency. Therefore, it has a great potential for supporting real-time energy consumption anomaly detection applications that help in minimizing wasted energy. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Acknowledgments. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy. 2020, The Author(s). Open Access funding provided by the Qatar National Library. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). Scopus

The detection of anomalous energy usage could help significantly in signaling energy wastage and identifying faulty appliances, especially if the individual power traces are analyzed. To that end, this paper proposes a novel abnormal energy consumption detection approach at the appliance-level using autoencoder and micro-moments. Accordingly, energy usage footprints of different household appliances along with occupancy patterns are analyzed for modeling normal energy consumption behaviors, and on the flip side, detecting abnormal usage. In effect, energy micro-moments occur when end-users reflexively (i) switch on/off an appliance to start/stop an energy consumption action; (ii) increase/reduce energy consumption of a specific appliance; and (iii) enter/leave a specific room. Put differently, energy micro-moments are captured by reference to end-users' daily tasks usually performed to meet their preferences. In this regard, energy micro-moment patterns are extracted from appliance-level consumption fingerprints and occupancy data using an innovative rule-based algorithm to represent the key intent-driven moments of daily energy use, and hence model normal and abnormal behaviors. Moving forward, energy micro-moment patterns are fed into an autoencoder including 48 input/output neurons, and 4 neurons in the intermediate layer aiming at reducing the computational cost and improving the detection performance. This has helped in accurately detecting two kinds of anomalous energy consumption, i.e. "excessive consumption" and "consumption while outside", where up to 0.95 accuracy and F1 score have been achieved, for example, when analyzing microwave energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Acknowledgements. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

open access proceedings With global pollution and buildings power consumption on the rise, energy efficiency research has never been more necessary. Accordingly, data visualization is one of the most sought challenges in data analysis, especially in energy efficiency applications. In this paper, a novel micro-moment Gramian angular fields time-series transformation of energy signals and ambient conditions, abbreviated as M2 GAF, is described. The proposed tool can be used by energy efficiency researchers to yield a deeper understanding of building energy consumption data and its environmental conditions. Current results show sample G2 GAF representation for three power consumption datasets. In summary, the proposed tool can unveil novel energy time-series analysis possibilities as well as original data visualization that can yield deeper insights, and in turn, improved energy efficiency.

持続可能な都市の成立には人口流入，物流取引の活性化が不可欠であり人材育成が求められる。当該地域の魅力を若年層が認識して訴求する課題解決力，および情報発信能力が養われれば，地域の良さを再認識して留まる可能性がある。しかし，日常生活に馴染んだ観光資源を客観的に捉えて興味を抱くことは難しく，さらにSNSは拡散性に長けるが特異な表現に陥りやすく訴求力に欠く。被験者からの知見では注目を集め席巻するにはインパクトを優先し，共通言語のハッシュタグの多数利用が不可欠である。より多くのハッシュタグを考案した被験者ほど成績評価に有為性が確認できた一方，問題意識の当事者間格差は大きく，改善が必要であることが明らかとなった。, To establish a sustainable city, population inflow and the revitalization of logistics transactions are indispensable. Therefore, human resource development is required. If young people recognize the attractiveness of the area and can demonstrate problem solving and information dissemination abilities, it is possible that an area’s desirability will be recognized once more and that they will stay. However, it is difficult to objectively grasp resources that migrants require to feel familiar with everyday life and that will excite interest. SNS is prone to diffusibility and it is easy to fall into local forms of expression that lack the power to persuade more widely. Attention may be gained from those with knowledge of a subject; however, when priority is given to general sweeping, using many common hashtags is indispensable. Subjects that devised more hashtags were able to confirm the validity of grade evaluation, while the disparity between parties concerning problem awareness was large, and improvement was required.

The paper outlines the sustAGE system, a smart solution that builds upon strategic technology trends, such as Internet-of-Things, machine learning and recommender systems, to support sustainable work environments and increase wellness at work and well-being with a focus on the ageing workforce. Acknowledging the interrelation of the work and private arrays for healthy ageing, the developed solution utilizes a recommendation-based approach providing personalized warnings and preventive recommendations regarding occupational risks, as well as personalized cognitive and physical training activities for the off-work context with the overall goal of maintaining Work Ability and enabling sustainable work. The piloting of the proposed solution in two critical industrial domains provides promising results towards the use of personalized recommendation-based interventions for the working context and beyond for improving workers’ occupational safety and health, performance and general well-bei ng.

Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transforming human behavior rather than device metrics and benchmarks. In this paper, a micro-moment-based smart plug system is proposed as part of a larger multi-appliance energy efficiency program. The smart plug, which includes two sub-units: the power consumption unit and environmental monitoring unit collect energy consumption of appliances along with contextual information, such as temperature, humidity, luminosity and room occupancy respectively. The plug also allows home automation capability. With the accompanying mobile application, end-users can visualize energy consumption data along with ambient environmental information. Current implementation results show that the proposed system delivers cost-effective deployment while maintaining adequate computation and wireless performance., Comment: This paper has been accepted in SCA2020: The Fifth international conference on Smart City Applications

The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption behaviors. User energy consumption is inductively derived from the time-to-time standards of living that shape the user's everyday consumption habits. This work builds on the detection of repeated usage consumption patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators in an office environment and targets specific user habits. Using our previous research findings on the value of energy-related micro-moment recommendations, the implemented system is an integrated solution that avoids unnecessary energy consumption. With the use of a messaging API, the system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits. The solution has been implemented on the Home Assistant open source platform, which allows the definition of automations for controlling the office equipment. Experimental evaluation with several scenarios shows that the system manages first to reduce energy consumption, and second, to trigger users' actions that could potentially urge them to more sustainable energy consumption habits., Comment: This paper will appear in Communications in Computer and Information Science( CCIS) - Springer Book - [Smartgreens extension]

Since electricity consumption of households in developing countries is dramatically increasing every year, it is now more prudent than ever to utilize technology-based solutions that assist energy end-users to improve energy efficiency without affecting quality of life. User behavior is the most important factor that influences household energy consumption and recommender systems can be the technology enabler for shaping the users' behavior towards energy efficiency. The current literature mostly focuses on energy usage monitoring and home automation and fails to engage and motivate users, who are not as committed and self-motivated. In this work, we present a context-aware recommender system that analyses user activities and understands their habits. Based on the output of this analysis, the system synchronizes with the user activities and presents personalized energy efficiency recommendations at the right moment and place. The recommendation algorithm considers user preferences, energy goals, and availability in order to maximize the acceptance of a recommended action and increase the efficiency of the recommender system. The results from the evaluation on a publicly available dataset comprising energy consumption data from multiple devices shows that micro-moments repeatedly occur within user's timeline (covering more than 35% of user future activities) and can be learned from user logs. Copyright 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In recent years, in the advent of global internet availability and almost equally omnipresent mobile device usage, online user behavior together with the resulting experience has been changing and evolving at a significant pace. Users do not only spend more time online, they consult online resources and seek information for various goals and are motivated by a variety of needs: those intent-driven actions are dispersed throughout the day (or night) and integrated within their daily activities. The user behavior is determined by specific micro- moments which are truly the moments of consumer needs and therefore the moments the marketers need to be a part of. Furthermore, mobile devices and smartphones in particular, possess the ability to promptly meet the immediate needs of their users thus shaping new opportunities for marketers to capture and utilize. In order to have a better understanding of online user intention and behavioral patterns, the paper provides an insight of recent studies of user micro-moments within the digital environment. The main goal of this paper is to provide an overview of recent academic and professional papers on the changes in online user experience and behavior and suggest the theoretical background for further research. Mobile devices can directly influence user behavior and emotional states by addressing a wide variety of information needs. The implications of these findings are important as they empower the possibilities of direct marketing in the digital era.

The file attached to this record is the author's final peer reviewed version. In the twenty-first century, resilience has emerged as an important topic linked to calls for adaptability, well-being and organizational performance. Extant strategic human resource management (HRM) literature and practices have developed many insights into resilience. However, overall, they have a propensity to conceptualise resilience as being associated with ‘macro-’ and ‘extreme’ situations. This paper complements the prevailing perspective by developing a micro-focus on resilience through the conceptual framework of organizational ambidexterity surfacing under-examined individual resilience in connection with HRM practices. Methodologically, the paper adopts a qualitative approach presenting data from two illustrative contexts: an ‘everyday’ quasi-governmental institution and a prima facie ‘extreme’ pan-international military organization. Using template analysis, a number of valuable themes and similarities are identified. The findings and discussion underline the managerial challenges in handling organizational ambidextrous dynamics and tensions surrounding resilience, positive and sceptical approaches in relation to individual and organizational stances towards HRM practices. As such, the results point at value in HRM managers and practices recontextualising and appreciating ‘extremes’ and resilience more as an everyday (rather than exceptional) phenomenon wherein myriad micro-moments are highly significant in constructing and influencing macro-contexts. This also implies a need to see cynical resistance as normative rather than automatically negatively.

Worldwide demographics are changing; we are living longer and, in developed countries, the birth-rate is dropping. In this context and motivated by the challenge of sustainable ageing, this paper presents sustAGE, a multi-modal person-centred IoT platform, which integrates with the daily activities of ageing employees both at work and outside. The sensed information allows the system to assess the state of the users and context-related aspects with the aim to provide timely recommendations to support wellbeing, wellness, and productivity. Herein, we describe the use cases, outline the overall system architecture, and introduce the first prototype of the platform implemented up-to-date. Furthermore, the results from the usability evaluation conducted with real users who used the prototype for one month are presented.