Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Major Security Issue That Facing Social Networks with Its Main Defense Strategies

The Social Network Service "SNS" has enabled significant advancements in a wide variety of scientific fields, and as a result, it has become an extremely popular subject in both academia and business. SNSs can be extremely beneficial to users because they eliminate economic and geographical barriers and can be used for job searching, entertainment and education. Regardless of the economic and social benefits, protecting businesses and users\u27 security and privacy remains a critical issue that must be addressed. It is critical to address and evaluate social network service challenges, as they vary according to the variety of SNS sites. Thus, by discussing SNS challenges alongside available and potential solutions, users, developers, and businesses can identify relevant and timely responses to specific threats, resulting in the best SNS-based services possible. The objective of this article is to discuss the inherent challenges of social networking sites and some critical solutions for resolving them. We extracted and analyzed seminal papers to add to the corpus of literature by focusing on several critical challenges in the social network service domain and shedding light on how these challenges affect a variety of domains, including users, sites, and business. The most frequently mentioned difficulties concerned privacy risks, anonymity risks, malware, spam, identity theft, phishing, business data, social content, technical issues, and psychological difficulties. By incorporating previously discovered solutions, this paper addressed these issues. The implications for both researchers and practitioners have been discussed

소셜 네트워크와 이커머스 플랫폼에서의 잠재 네트워크 마이닝

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2023. 2. 권태경.웹 기반 서비스의 폭발적인 발달로 사용자들은 온라인 상에서 폭넓게 연결되고 있다. 온라인 플랫폼 상에서, 사용자들은 서로에게 영향을 주고받으며 의사 결정에 그들의 경험과 의견을 반영하는 경향을 보인다. 본 학위 논문에서는 대표적인 온라인 플랫폼인 소셜 네트워크 서비스와 이커머스 플랫폼에서의 사용자 행동에 대해 연구하였다. 온라인 플랫폼에서의 사용자 행동은 사용자와 플랫폼 구성 요소 간의 관계로 표현할 수 있다. 사용자의 구매는 사용자와 상품 간의 관계로, 사용자의 체크인은 사용자와 장소 간의 관계로 나타내진다. 여기에 행동의 시간과 레이팅, 태그 등의 정보가 포함될 수 있다. 본 연구에서는 두 플랫폼에서 정의된 사용자의 행동 그래프에 영향을 미치는 잠재 네트워크를 파악하는 연구를 제시한다. 위치 기반의 소셜 네트워크 서비스의 경우 특정 장소에 방문하는 체크인 형식으로 많은 포스트가 만들어지는데, 사용자의 장소 방문은 사용자 간에 사전에 존재하는 친구 관계에 의해 영향을 크게 받는다. 사용자 활동 네트워크의 저변에 잠재된 사용자 간의 관계를 파악하는 것은 활동 예측에 도움이 될 수 있으며, 이를 위해 본 논문에서는 비지도학습 기반으로 활동 네트워크로부터 사용자 간 사회적 관계를 추출하는 연구를 제안하였다. 기존에 연구되었던 방법들은 두 사용자가 동시에 방문하는 행위인 co-visitation을 중점적으로 고려하여 사용자 간의 관계를 예측하거나, 네트워크 임베딩 또는 그래프 신경망(GNN)을 사용하여 표현 학습을 수행하였다. 그러나 이러한 접근 방식은 주기적인 방문이나 장거리 이동 등으로 대표되는 사용자의 행동 패턴을 잘 포착하지 못한다. 행동 패턴을 더 잘 학습하기 위해, ANES는 사용자 컨텍스트 내에서 사용자와 관심 지점(POI) 간의 측면(Aspect) 지향 관계를 학습한다. ANES는 User-POI 이분 그래프의 구조에서 사용자의 행동을 여러 개의 측면으로 나누고, 각각의 관계를 고려하여 행동 패턴을 추출하는 최초의 비지도학습 기반 접근 방식이다. 실제 LBSN 데이터에서 수행된 광범위한 실험에서, ANES는 기존에 제안되었던 기법들보다 높은 성능을 보여준다. 위치 기반 소셜 네트워크와는 다르게, 이커머스의 리뷰 시스템에서는 사용자들이 능동적인 팔로우/팔로잉 등의 행위를 수행하지 않고도 플랫폼에 의해 서로의 정보를 주고받고 영향력을 행사하게 된다. 이와 같은 사용자들의 행동 특성은 리뷰 스팸에 의해 쉽게 악용될 수 있다. 리뷰 스팸은 실제 사용자의 의견을 숨기고 평점을 조작하여 잘못된 정보를 전달하는 방식으로 이루어진다. 나는 이를 해결하기 위해 사용자 리뷰 데이터에서 사용자 간 사전 공모성(Collusiveness)의 가능성을 찾고, 이를 스팸 탐지에 활용한 방법인 SC-Com을 제안한다. SC-Com은 행동의 공모성으로부터 사용자 간 공모 점수를 계산하고 해당 점수를 바탕으로 전체 사용자를 유사한 사용자들의 커뮤니티로 분류한다. 그 후 스팸 유저와 일반 유저를 구별하는 데에 중요한 그래프 기반의 특징을 추출하여 감독 학습 기반의 분류기의 입력 데이터로 활용하는 방법을 제시한다. SC-Com은 공모성을 갖는 스팸 유저의 집합을 효과적으로 탐지한다. 실제 데이터셋을 이용한 실험에서, SC-Com은 기존 논문들 대비 스팸 탐지에 뛰어난 성능을 보여주었다. 위 논문에서 다양한 데이터에 대해 연구된 암시적 연결망 탐지 모델은 레이블이 없는 데이터에 대해서도 사전에 연결되었을 가능성이 높은 사용자들을 예측하므로, 실시간 위치 데이터나, 앱 사용 데이터 등의 다양한 데이터에서 활용할 수 있는 유용한 정보를 제공하여 광고 추천 시스템이나, 악성 유저 탐지 등의 분야에서 기여할 수 있을 것으로 기대한다.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63박

Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556

Social User Mining: User Profiling of Social Media Network Based on Multimedia Data Mining

In recent years, the pervasive use of social media has generated extraordinary amounts of data that has started to gain an increasing amount of attention. Each social media source utilizes different data types such as textual and visual. For example, Twitter is used to transmit short text messages, whereas Flickr is used to convey images and videos. Moreover, Facebook uses all of these data types. From the social media users’ standpoint, it is highly desirable to find patterns from different data formats. The result of the huge amount of data from different sources or types has provided many opportunities for researchers in the fields of data mining and data analytics. Not only the methods and tools to organize and manage such data have become extremely important, but also methods and tools to discover hidden knowledge from such data, which can be used for a variety of applications. For example, the mining of a user's profile on social media could help to discover any missing information, including the user's location or gender information. However, the task of developing such methods and tools is very challenging. Social media data is unstructured and different from traditional data because of its privacy settings, data noise, and large capacity of data. Moreover, combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a useful tool for discovering unknown information about the users. Minimal research has been conducted on the combination of image and text data for social user mining. To address these challenges and to discover unknown information about users, we proposed a novel mining framework for social user mining that includes: 1) a data assemble module for different media source, 2) a data integration module, and 3) mining applications. First, we introduced a data assemble module in order to process both the textual and the visual information from different media sources, and evaluated the appropriate multimedia features for social user mining. Then, we proposed a new data integration method in order to integrate the textual and the visual data. Unlike the previous approaches that used a content based approach to merge multiple types of features, our main approach is based on image semantics through a semi-automatic image tagging system. Lastly, we presented two different application as an example of social user mining, gender classification and user location

지리적 거리 정보를 활용한 가짜 팔로워 구매자 식별 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 김종권.The reputation of social media such as Twitter, Facebook, and Instagram now regard as one persons power in real-world. The person who has more friends or followers can influence more individuals. So the influence of users is associated with the number of friends or followers. On the demand of increasing social power, an underground market has emerged where a customer can buy fake followers. The one who purchase fake followers acts vigorously in online social network. Thus, it is hard to distinguish customer from celebrity or cyberstar. Nevertheless, there are unique characteristics of legitimate users that customers or fake followers cannot manipulate such as a small-world property. The small-world property is mainly qualified by the shortest-path and clustering coefficient. In the small-world network, most people are linked by short chains. Existing work has largely focused on extracting relationship features such as indegree, outdegree, status, hub, or authority. Even though these research explored the relationship features to classify abnormal users of fake follower markets, research that utilize the small-world property to detect abnormal users is not studied. In this work, we propose a model that adapt the small-world property. Specifically, we study the geographical distance for 1hop-directional links using nodes geographical location to verify whether a social graph has the small-world property or not. Motivated by the difference of distance ratio for 1hop directional links, we propose a method which is designed to generate 1hop link distance ratio and classify a node as a customer or not. Experimental results on real-world Twitter dataset demonstrates that the proposed method achieves higher performance than existing models.Chapter 1 Introduction 1 1.1 Motivations 1 1.2 Fake Follower Markets 3 1.3 Research Objectives 5 1.4 Contributions 6 1.5 Thesis Organization 8 Chapter 2 Related Work 10 2.1 Small World Phenomenon 10 2.2 Online Social Abusing Attack Detection 11 2.2.1 Contents-based Detection 12 2.2.2 Social Network-based Detection 13 2.2.3 Behavior-based Detection 5 Chapter 3 Characteristic of Customers and Fake Followers 16 3.1 Data Preparation 16 3.2 Fake Follower Properties 21 3.3 Customer Properties 26 Chapter 4 Social Relationship and Geographical Distance 29 4.1 Geographical Distance in OSNs 29 4.2 Follower Ratio 34 Chapter 5 Detecting Customers 38 5.1 Key Features for Customer Detection 38 5.2 Performance matrices 40 5.3 Experiments 41 5.4 Comparison with Baseline Method 44 5.5 Comparison with Feature-based Method 47 5.6 Impact of Balanced Dataset 49 5.7 Fake Follower Detection 50 Chapter 6 Future Work 52 6.1 The Absence of Location Information 52 6.2 Hybrid Detection Method with Link Ratio and Profile Information 54 Chapter 7 Conclusion 56 Bibliography 58 국문초록 69Docto

You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

Reality-Mining with Smartphones: Detecting and Predicting Life Events based on App Installation Behavior

Life events are often described as major forces that are going to shape tomorrow\u27s consumer need, behavior and mood. Thus, the prediction of life events is highly relevant in marketing and sociology. In this paper, we propose a data-driven, real-time method to predict individual life events, using readily available data from smartphones. Our large-scale user study with more than 2000 users shows that our method is able to predict life events with 64.5% higher accuracy, 183.1% better precision and 88.0% higher specificity than a random model on average

Improving the Performance of Recommendation on Social Network by Investigating Interactions of Trust and Interest Similarity

On the social media, lots of people share their experiences through various factors like blogs, online ratings, reviews, online polling and tweets. Study shows that the factors such as interpersonal interest and interpersonal influence from the social media which is based on the circles as well as groups of friends leads to opportunities and challenges in solving the problems on datasets. This challenge is for the Recommender System (RS) to find the solution on cold start and sparsity problems. In this paper, on the basis of the probabilistic matrix factorization, the social factors like personal interest, interpersonal influence and interpersonal interest similarity are combined into a unified personalized recommendation model. These factors can improve the associating linkage in latent space. Various datasets are used to conduct the experiments to get the results that show that the proposed model performs better than the existing approaches