Determining Trust Scope Attributes Using Goodness of Fit Test: A Survey

Indonesian, as one of the countries with high number of internet users has the potential to serve as the place with great information resources. However, these resources must be accompanied by the availability of dependable information. Information trustworthiness can be obtained by assessing the confidence level (trust) of the source of information. This can be determined by using trust scope attributes. Hence, in this study, we intended to establish the trust scope attributes by means of utilizing the ones contained in the User Profile provided by social media; in this case Facebook, Google+, Twitter, and Linkedin. We carried out the research by conducting four stages namely data collection, attributes grouping, attribute selection, and surveys. A survey was then distributed to 257 randomly selected respondents (divided into two clusters: civilians and military officers) to seek for their opinions in terms of what attributes were considered to be crucial in defining the believability of an information source. Chi-square Goodness of fit Test was conducted to compare observed data with data we would expect to obtain. The results of the research suggested that there was similar judgment in terms of dictating source of information trustworthiness chosen by the research participants with the attributes provided by trust scope category. In this research, both civilians and military officer clusters concurrently perceived that educational background was the most dependable attribute

Entity Recognition of User Profile on Twitter

Atribut trust scope sebagai atribut untuk menentukan tingkat kepercayaan sumber informasi, akan diisi dengan data yang terdapat pada user profile Twitter yang dikenal sebagai Bio Twitter. Hanya saja, data tersebut harus sesuai dengan karakteristik dan fungsi dari masing-masing atribut trust scope, seperti atribut pendidikan harus diisi dengan informasi yang berkaitan dengan latar belakang pendidikan dari pemilik profil tersebut. Untuk mendapatkan data yang sesuai dengan atribut, kami melakukan named entity recognition, yang merupakan salah satu kegiatan pada proses ekstraksi informasi. Oleh karena itu, paper ini menjelaskan hasil proses pengenalan entitas yang dilakukan terhadap data yang terdapat pada user profile. Perangkat lunak yang digunakan untuk mengenali data sebagai entitas adalah IndonesiaNetagger. IndonesiaNettagger, merupakan perangkat lunak untuk mengenali entitas yang ditulis dalam bahasa Indonesia. Kami melakukan penelitian dalam empat tahap, yaitu pengenalan entity dengan data Bio twitter yang asli,identifikasi kesalahan proses pengenalan, formalisasi data dan pengujian pengenalan entitas akhir. Hasil penelitian menunjukkan keberhasilan sebagai berikut; entitas Person dikenali dengan benar adalah sebesar 71% dari total data entitas yang tersedia, entitas Organization dikenali dengan benar sebasar 50%, entitas Position 20% dikenali denganbenar, dan 50% entitas Location dikenali dengan benar.Trust scope attribute as an attribute to determine the level of trust resources, will be filled with the data contained in the user profile of Twitter –one of social media- known as Bio Twitter. However, these data should be in accordance with the characteristics and functions of each attribute, such as education attribute must be filled in with the information relating to the educational background of the owner of the profile. To obtain the data corresponding to the trust scope attributes, we perform named entity recognition, which is one of the activities in the process of information extraction. Therefore, this paper describes the results of the entity recognition process performed on data contained in the user profile. Software used to recognize the data as an entity is IndonesiaNetagger, which is to perform entity recognition that written in Indonesian language. The software recognizes only five entities namely Person, Organization, Location, Position and Other. We carried out the research by conducting four stages namely entity recognition-with original data-Bio Twitter, error identification, formalizing data, and final test. The results show the success of entity recogniton as follow; Person entity is recognized correctly by 71% of the total data available, the entity Organization recognized correctly by 50%, 20% Position entity recognized correctly, and 50% recognized correctly as Location entity

Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

Sebuah Survey: Tingkat Kepercayaan Pengguna Terhadap Informasi di Sosial Media

Abstract Information trustworthiness can be obtained based on the confidence level (trust) or reputation of the source of information. Nowadays, most people use information derived from social media, however finding reliable source of information can be troublesome. This paper discusses the results of determining the level of trust of certain information presented in social media. The media used as the source of information in this research were Facebook, Google+, Twitter, and LinkedIn. This research is a descriptive study, which is used to recognize behavior of social media users toward the trust level of the sources of information. Respondents involved in this study were divided into two clusters: Civilians and Military officers to seek for their opinion in terms of which social media that have trustworthy information. Data used to support this research was gathered through administering a survey. Survey distribution process was conducted by creating personally-administered questionnaire survey questions distributed directly to respondents. This kind of survey is quite sufficient for a limited survey purpose. Confidence level was measured using graphical and numerical measurements, and equipped with a chi-squared test hypothesis. Based on data analysis process, it was found that Twitter and Google+ chosen to be the most trustworthy source of information. Key word : information trust level;  graphical measurement; numerical measurement; chi-squared test hypothesis Abstrak Informasi yang dipercaya dapat diperoleh berdasarkan pada kepercayaan yang dimiliki oleh sumber informasi atau reputasi sumber informasi. Saat ini, banyak pengguna informasi menggunakan informasi yang berasal dari sosial media, akan tetapi mendapatkan informasi yang sumber informasinya dapat dipercaya masih belum diketahui. Paper ini membahas hasil penentuan tingkat kepercayaan informasi yang terdapat pada media sosial.  Media sosial yang digunakan sebagai sumber informasi pada penelitian ini adalah Facebook, Google+, Twitter, and LinkedIn. Penelitian ini merupakan penelitian deskriptif untuk mengetahui perilaku pengguna sosial media terhadap tingkat kepercayaan sumber informasi. Responden yang terlibat dalam penelitian ini dibagi dua kelompok yaitu kelompok Sipil dan kelompok Militer, untuk mendapatkan pilihan atas media sosial dengan informasi yang dapat dipercaya..  Data yang digunakan untuk mendukung penelitian ini diperoleh melalui survey. Penyebaran survey dilakukan dengan menggunakan pertanyaan yang dibuat sendiri sesuai dengan kebutuhan penelitian dan langsung disebar kepada responden. Survey ini cukup baik untuk survey yang terbatas. Tingka kepercayaan sosial media yang diberikan oleh pengguna menggunakan pengukuran grafis dan numerik, serta dilengkapi dengan uji hipotesis chi-kuadrat. Berdasarkan proses analisa data yang dilakukan, diperoleh bahwa media sosial Twitter dan Google+ adalah sumber informasi yang dipercaya. Kata kunci : tingkat kepercayaan informasi;  pengukuran grafis; pengukuran numerik; uji hipotesa chi-kuadra

Machine Learning for Internet of Things Data Analysis: A Survey

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Next Generation Internet of Things – Distributed Intelligence at the Edge and Human-Machine Interactions

This book provides an overview of the next generation Internet of Things (IoT), ranging from research, innovation, development priorities, to enabling technologies in a global context. It is intended as a standalone in a series covering the activities of the Internet of Things European Research Cluster (IERC), including research, technological innovation, validation, and deployment.The following chapters build on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT–EPI), the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects, presenting global views and state-of-the-art results regarding the next generation of IoT research, innovation, development, and deployment.The IoT and Industrial Internet of Things (IIoT) are evolving towards the next generation of Tactile IoT/IIoT, bringing together hyperconnectivity (5G and beyond), edge computing, Distributed Ledger Technologies (DLTs), virtual/ andaugmented reality (VR/AR), and artificial intelligence (AI) transformation.Following the wider adoption of consumer IoT, the next generation of IoT/IIoT innovation for business is driven by industries, addressing interoperability issues and providing new end-to-end security solutions to face continuous treats.The advances of AI technology in vision, speech recognition, natural language processing and dialog are enabling the development of end-to-end intelligent systems encapsulating multiple technologies, delivering services in real-time using limited resources. These developments are focusing on designing and delivering embedded and hierarchical AI solutions in IoT/IIoT, edge computing, using distributed architectures, DLTs platforms and distributed end-to-end security, which provide real-time decisions using less data and computational resources, while accessing each type of resource in a way that enhances the accuracy and performance of models in the various IoT/IIoT applications.The convergence and combination of IoT, AI and other related technologies to derive insights, decisions and revenue from sensor data provide new business models and sources of monetization. Meanwhile, scalable, IoT-enabled applications have become part of larger business objectives, enabling digital transformation with a focus on new services and applications.Serving the next generation of Tactile IoT/IIoT real-time use cases over 5G and Network Slicing technology is essential for consumer and industrial applications and support reducing operational costs, increasing efficiency and leveraging additional capabilities for real-time autonomous systems.New IoT distributed architectures, combined with system-level architectures for edge/fog computing, are evolving IoT platforms, including AI and DLTs, with embedded intelligence into the hyperconnectivity infrastructure.The next generation of IoT/IIoT technologies are highly transformational, enabling innovation at scale, and autonomous decision-making in various application domains such as healthcare, smart homes, smart buildings, smart cities, energy, agriculture, transportation and autonomous vehicles, the military, logistics and supply chain, retail and wholesale, manufacturing, mining and oil and gas