scholarly journals Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2098 ◽  
Author(s):  
Guang Xing Lye ◽  
Wai Khuen Cheng ◽  
Teik Boon Tan ◽  
Chen Wei Hung ◽  
Yen-Lin Chen
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Early Stage ◽  
Hybrid Approach ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Heterogeneous Objects ◽  
High Satisfaction ◽  
The Social ◽  
Smart Community

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

scholarly journals Optimal Placement of Social Digital Twins in Edge IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6181
Author(s):  
Olga Chukhno ◽  
Nadezhda Chukhno ◽  
Giuseppe Araniti ◽  
Claudia Campolo ◽  
Antonio Iera ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Data Exchange ◽  
Service Discovery ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Digital Twins ◽  
The Social

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

Towards a Deep Learning-Driven Service Discovery Framework for the Social Internet of Things: A Context-Aware Approach

2021 ◽  
pp. 480-488
Author(s):  
Abdulwahab Aljubairy ◽  
Ahoud Alhazmi ◽  
Wei Emma Zhang ◽  
Quan Z. Sheng ◽  
Dai Hoang Tran
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Service Discovery ◽  
Context Aware ◽  
The Social ◽  
Social Internet Of Things

scholarly journals Personalizing trip recommendations: A framework proposal

2015 ◽  
Vol 5 (1) ◽  
pp. 24
Author(s):  
Tamer Uçar ◽  
Adem Karahoca
Keyword(s):  
Collaborative Filtering ◽  
Information Filtering ◽  
Hybrid Approach ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Future Trends ◽  
Demographic Information ◽  
Trip Planning ◽  
Hybrid Approaches ◽  
Content Based Filtering

<p>Personalized trip planning is a very common problem in tourism domain. There are several studies in this area each one of all aims to provide recommendations based on user preferences. Recommendation engines mostly use two common methods: content based filtering and collaborative filtering. As a combination of these two methods, hybrid approaches are also popular for recommendation systems.  This study provides a deep analysis about recent studies in trip recommendation domain. Applied techniques and mentioned methodologies in literature is discussed at all points. Insights about the proposed systems are provided clearly. Besides a literature survey, this study also proposes a novel travel recommender method based on a tourism datasource. A hybrid approach involving demographic, content-based and collaborative filtering techniques are proposed in order to eliminate drawbacks of each approach. Recommendations will be based on many factors including users’ demographic information, past travel locations and favorite seasons. Based on such inputs, recommender engine predicts possible travel locations along with various flight options. Possible challenges and future trends are concluded as a result of this study.</p><p> </p><p>Keywords: Recommender systems, trip recommendation, personalized recommendation, information filtering.</p>

Global Optimization With Quantum Walk Enhanced Grover Search

2014 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Early Stage ◽  
Hybrid Approach ◽  
Quantum Walk ◽  
Quantum Walks ◽  
Tunneling Effect ◽  
Grover’S Algorithm ◽  
Grover's Algorithm ◽  
Grover Search

One of the significant breakthroughs in quantum computation is Grover’s algorithm for unsorted database search. Recently, the applications of Grover’s algorithm to solve global optimization problems have been demonstrated, where unknown optimum solutions are found by iteratively improving the threshold value for the selective phase shift operator in Grover rotation. In this paper, a hybrid approach that combines continuous-time quantum walks with Grover search is proposed. By taking advantage of quantum tunneling effect, local barriers are overcome and better threshold values can be found at the early stage of search process. The new algorithm based on the formalism is demonstrated with benchmark examples of global optimization. The results between the new algorithm and the Grover search method are also compared.

Satisfaction with the Social Work Profession: A Ten-Year Analysis of Undergraduate Students' Perceptions

1997 ◽  
Vol 80 (3) ◽  
pp. 835-838 ◽  
Author(s):  
James G. Hanson ◽  
James G. McCullagh
Keyword(s):  
Social Work ◽  
Career Choice ◽  
Social Workers ◽  
Undergraduate Students ◽  
Perceived Satisfaction ◽  
High Satisfaction ◽  
The Social

A 10-yr. study of 746 social work undergraduates' perceived satisfaction with seven factors related to their career choice suggested high satisfaction with social work as a career; with the purposes and functions of social work, and the students' initial volunteer experience. There were no significant changes in satisfaction over the 10-yr. period, which findings parallel those of other studies in which similar methods have been used with practicing social workers.

scholarly journals Graph node rank based important keyword detection from Twitter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mukesh Kumar ◽  
Palak Rehan
Keyword(s):  
Real Time ◽  
English Language ◽  
Hybrid Approach ◽  
Levenshtein Distance ◽  
Social Media Networks ◽  
The Social ◽  
Keyword Detection ◽  
Product Launches ◽  
Process Detection ◽  
Good Resource

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

Antecedents of growth-oriented entrepreneurship before and during the Greek economic crisis

2017 ◽  
Vol 24 (3) ◽  
pp. 528-544 ◽  
Author(s):  
Ioannis Giotopoulos ◽  
Alexandra Kontolaimou ◽  
Aggelos Tsakanikas
Keyword(s):  
Work Experience ◽  
Early Stage ◽  
New Ventures ◽  
High Growth ◽  
Content Type ◽  
Crisis Period ◽  
Individual Level ◽  
Level Data ◽  
The Social ◽  
Before And After

Purpose The purpose of this paper is to explore potential drivers of high-growth intentions of early-stage entrepreneurs in Greece before and after the onset of the financial crisis of 2008. Design/methodology/approach To this end, the authors use individual-level data retrieved from Global Entrepreneurship Monitor annual surveys (2003-2015). Findings The results show that high-growth intentions of Greek entrepreneurs are driven by different factors in the crisis compared to the non-crisis period. Male entrepreneurs and entrepreneurs with significant work experience seem to be more likely to be engaged in growth-oriented new ventures during the crisis period. The same appears to hold for entrepreneurs who are motivated by an opportunity and also perceive future business opportunities in adverse economic conditions. On the other hand, the educational level and the social contacts of founders with other entrepreneurs are found to drive ambitious Greek entrepreneurship in the years before the crisis, while they were insignificant after the crisis outbreak. Originality/value Based on the concept of ambitious entrepreneurship, this study contributes to the literature by investigating the determinants of entrepreneurial high-growth expectations in the Greek context emphasizing the crisis period in comparison to the pre-crisis years.

scholarly journals Real-Time Primitives for CoAP: Extending the Use of IoT for Time Constraint Applications for Social Good

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1257
Author(s):  
Gabriel Eggly ◽  
Mariano Finochietto ◽  
Emmanouil Dimogerontakis ◽  
Rodrigo Santos ◽  
Javier Orozco ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Real Time ◽  
Processing Speed ◽  
Wireless Sensors ◽  
Communication Protocols ◽  
Social Good ◽  
Vulnerable Communities ◽  
The Social

Internet of Things (IoT) have become a hot topic since the official introduction of IPv6. Research on Wireless Sensors Networks (WSN) move towards IoT as the communication platform and support provided by the TCP/UDP/IP stack provides a wide variety of services. The communication protocols need to be designed in such a way that even simple microcontrollers with small amount of memory and processing speed can be interconnected in a network. For this different protocols have been proposed. The most extended ones, MQTT and CoAP, represent two different paradigms. In this paper, we present a CoAP extension to support soft real-time communications among sensors, actuators and users. The extension facilitates the instrumentation of applications oriented to improve the quality of life of vulnerable communities contributing to the social good.

scholarly journals IMPLEMENTASI SOCIETY 5.0 DALAM KEBIJAKAN DAN STRATEGI PENDIDIKAN PADA PANDEMI COVID-19

2022 ◽  
Vol 2 (1) ◽  
pp. 34-43
Author(s):  
ADITYA ZULMI RAHMAWAN ◽  
ZAENURIYAH EFFENDI
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Learning Process ◽  
Research Method ◽  
Social Sector ◽  
Scientific Journals ◽  
Artificial Intelligent ◽  
The Social ◽  
The Impact ◽  
The Internet Of Things

The COVID-19 pandemic poses problems in various sectors. The most vulnerable sector in this situation is the social sector, especially education. Problems such as the learning process make the continuity of education a concern. This is a challenge for the community in the era of society 5.0 in the hope of overcoming the problems that arise due to the Covid-19 pandemic. The use of big data, artificial intelligence, and the internet of things is an alternative effort to help deal with the impact of the pandemic in accordance with the conditions in this disruptive era. This study aims to determine the policies and strategies of society 5.0 in the learning process as an effort to handle the impact of the pandemic. This study uses a systematic review research method of literature published by scientific journals in the period January 2010 to December 2021. The data used comes from published journals related to the topics studied and from various electronic media. The results of the study can find out strategies in the learning process in the implementation of society 5.0 in policies in the field of education as an effort to deal with the impact of the covid-19 pandemic. ABSTRAKPandemi covid-19 memberikan permasalahan di berbagai sektor. Sektor yang paling rentan dalam situasi ini adalah sektor sosial terutama pada pendidikan. Permasalahan seperti proses pembelajaran membuat keberlangsungan pendidikan menuai kekhawatiran. Hal ini menjadi sebuah tantangan bagi masyarakat di era society 5.0 dengan harapan dapat mengatasi permasalahan yang timbul akibat pandemi Covid-19. Pemanfaatan big data, artificial intelligent, dan internet of things menjadi upaya alternatif dalam membantu menangani dampak pandemi yang sesuai dengan keadaan di era disruptif ini. Penelitian ini bertujuan untuk mengetahui kebijakan dan strategi society 5.0 dalam proses pembelajaran sebagai upaya penanganan dampak pandemi. Penelitian ini menggunakan metode penelitian tinjauan sistematis terhadap literatur yang diterbitkan oleh jurnal ilmiah pada periode Januari tahun 2010 hingga Desember 2021. Sumber yang digunakan berasal dari jurnal-jurnal yang sudah dipublikasikan terkait dengan topik yang dikaji dan dari berbagai media elektronik. Hasil penelitian dapat mengetahui strategi dalam proses pembelajaran dalam implementasi society 5.0 pada kebijakan di bidang pendidikan sebagai upaya menghadapi dampak pandemi covid-19.

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