Alvin Chin

Reflective X Ray Optics

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Martin Atzmueller

Osnabrück University

Jilei Tian

Tsinghua University

Christoph Trattner

University of Bergen

Bin Xu

Tsinghua University

Mark Chignell

University of Toronto

Bin Guo

Northwestern Polytechnical University

Zhiwen Yu

Northwestern Polytechnical University

Andreas Hotho

University of Würzburg

Daqing Zhang

University of Science and Technology Liaoning

Denis Helić

Graz University of Technology

Cooperative Institutions

Tsinghua University

Chinese Academy of Sciences

Kentucky Educational Television

Beijing University of Posts and Telecommunications

University of Toronto

Hudson Institute

Kerckhoff Klinik

University of Science and Technology of China

University of New Orleans

University of Chinese Academy of Sciences

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Community-Driven Adaptation: Automatic Content Adaptation in Pervasive Environments

Iqbal Mohomed Alvin Chin Jim Chengming Cai Eyal de Lara

Mobile devices are increasingly being used to access Web content but lack the resources for proper presentation to the user. To address this problem, content is typically adapted to be more suitable for a mobile environment. Community-driven adaptation (CDA) is a novel approach to automatic content adaptation for mobile devices that adapts content based on feedback from users. CDA groups users into communities based on common characteristics, and assumes that users of the same community have similar adaptation requirements. CDA learns how to adapt content by observing how members of a community alter adapted content to make it more useful to them. Experiments that consider the idealized case, where all users perform the same task, show that CDA can reduce wastage of network bandwidth by up to 90% and requires less user interaction to correct bad adaptation decisions compared with existing approaches to automatic content adaptation.

Content Adaptation

User-Generated Content

Web content

10.1109/mcsa.2004.10

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Citations (20)

Optimizing taxiing with detours by using traffic dynamics and driving habits

Huihui Chen Bin Guo Zhiwen Yu Alvin Chin Chao Chen

Traffic jams in cities decrease the efficiency of taxiing, thus lower the profit of per unit time of taxi drivers. This paper proposes an app running on drivers' smartphones that recommends detour routes in order to optimize the taxiing performance. We formulate the problem to maximize the profit of a driver with constraints on charging the passenger according to the normal route, and saving the taxiing time. Considering the driving habits, we propose a solution to search the optimal route based on the information of the traffic dynamics and fuel consumption estimation.

10.1145/2968219.2971446

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LeXiang: Share the Joy of Reliving Activities from Offline to Online

Alvin Chin Wei Wang Xia Wang

Many people share photos and their locations using applications such as Foursquare, Facebook and Twitter when attending an event or activity, as a means for promoting an activity, for conversation and as an archive to create a sense of community. People find events to attend using applications such as Event Brite and Meet Up, and exchange contact information using applications such as Whats App or We Chat. The problem exists in that the content, contact and context captured in the activity are managed by separate applications, which makes it cumbersome to find and share. How can we record, share and connect easily in the offline activity to relive that activity experience online? We create an application called LeXiang to allow users to easily create ad-hoc activities in real time, record all the participants that actually attended, and record and share content from the activity for reliving the experience. We explain our concept of an ephemeral social network, study how people find and record activities, and then describe our user interface to achieve this.

Social Activity

Social network (sociolinguistics)

Ephemeral key

Interface (matter)

10.1109/ithings.2014.72

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Connected and Autonomous Driving

IT Professional (2018)

Jilei Tian Alvin Chin Halim Yanıkömeroğlu

The articles in this special section examine the market for autonomous and connected vehicles. The automotive industry is transitioning toward connected, autonomous, and electric vehicles; offering contextual, intelligent, and personal consumer experiences; and innovating disruptive ecosystems and business models such as car sharing and hailing services. All of these are primarily driven by fast-moving technology and innovations from 5G communication, cloud and edge computing platforms to artificial intelligence and big data. These changes are predicted to dominate our lives in about 10 years, leading to significant consumer and business values, including improved traffic and safety, smart journey management, cleaner environments, intelligent and personal mobility experiences, and competitive cost management. Similar to companies like Apple, Google, and Baidu heavily investing in connected and autonomous vehicles, car manufacturers are moving to be more like software companies in their use of big data, machine learning, and artificial intelligence to understand the driving context and consumer behavior. These days, a car is not just for going from point A to point B; rather, it delivers a holistic experience that revolves around our daily activities, with the future being the autonomous vehicle.

10.1109/mitp.2018.2876928

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User Mobility for Opportunistic Ad-Hoc Networking

Jing Su Alvin Chin A. Popivanova Ashvin Goel Eyal de Lara

As mobile devices become increasingly pervasive and commonly equipped with short-range radio capabilities, we observe that it might be possible to build a network based only on pair-wise contact of users. By using user mobility as a network transport mechanism, devices can intelligently route latency-insensitive packets using power-efficient short-range radio. Such a network could provide communication capability where no network infrastructure exists, or extend the reach of established infrastructure. To collect user mobility data, we ran two user studies by giving instrumented PDA devices to groups of students to carry for several weeks. We evaluate our work by providing empirical data that suggests that it is possible to make intelligent routing decisions based on only pair-wise contact, without previous knowledge of the mobility model or location information.

Mobility model

Delay-tolerant networking

10.1109/mcsa.2004.29

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Hypertext 2007 conference review

ACM SIGWEB Newsletter (2008)

Alvin Chin James Goulding

From September 10 to 12, 2007, over 100 attendees convened in Manchester, England, travelling from all over Europe, as well as the far climes of North America, Asia and Australia. Unlike many visitors to Manchester they weren't here to witness the city's much heralded football team, but had gathered instead at the University of Manchester for the 18th International Conference on Hypertext and Hypermedia (Hypertext 2007). Here they would discuss recent innovations in hypertext, whose most famous form exists as the World Wide Web, and assess the challenges and opportunities in the latest groundbreaking research. Traditionally the success of the Hypertext conference series has been attributed to its immense diversity, and this year was no different, with papers being divided into 5 varied tracks: Hypertext and the Person; Hypertext and Society; Practical Hypertext; Hypertext Culture and Communication; and Hypertext Models and Theory. The conference was a vibrant affair that featured 16 full papers and 7 short papers (with a 29% overall acceptance rate), posters, demos, keynotes, panels, Birds-of-a-Feather (BOFs) and social events. However, what characterized this year's conference the most was an underlying sense of reintegration, a rejoining of disparate trends in hypertext to common goals. And as such a lot of unity and camaraderie too.

Hypertext

10.1145/1377501.1377503

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International Workshop on Modeling Social Media (MSM 2018) Chairs' Welcome & Organization

Martin Atzmueller Alvin Chin Christoph Trattner

Bienvenue! It is our great pleasure to welcome you to the WWW 2018 International Workshop on Modeling Social Media (MSM'2018) - Applying Machine Learning and AI for Modeling Social Media. This is our 9th edition of our workshop. Social networks such as Facebook, Twitter, and LinkedIn have paved the way for generating huge amount of diverse, streaming bit data in a short period of time. Such social media data require the application of big data analytics to produce meaningful information to both information consumers and data generators. Machine learning and AI techniques are particularly effective in situations where deep and predictive insights need to be uncovered from such social media data sets that are large, diverse and fast changing. The workshop aims to address machine learning and AI methods, frameworks, algorithms, and the applications and evaluation of these approaches on social media, big data and the web. We received 11 papers from all around the world covering a broad range of topics, and we accepted 8 papers resulting in a 72% acceptance rate. We evaluated them regarding relevance, quality, and novelty, selecting 4 full papers and 4 short papers. Each paper was reviewed by 3 reviewers and then decisions were made from the reviews and the workshop chairs.

Relevance

Social media analytics

10.1145/3184558.3192309

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Which Is the Greenest Way Home? A Lightweight Eco-Route Recommendation Framework Based on Personal Driving Habits

Huihui Chen Bin Guo Zhiwen Yu Alvin Chin Jilei Tian

A vehicle's fuel consumption is strongly related to both its loading and the driver's driving behavior, such as aggressive/tender acceleration, improper/proper gear change or running/stopping the engine while waiting. This paper introduces EasyRoute, an economical route recommendation system for modern vehicles, implemented in smartphones, to improve fuel efficiency. EasyRoute senses the vehicle's fuel consumption through the On-Board Diagnostics (OBD) adapter and then models the driver's personal fuel consumption according to OBD data from two aspects: i) when the vehicle is moving and ii) when the vehicle is idling or waiting. Based on the crowdsourced traffic information, EasyRoute can near-correctly predict total fuel consumptions of different routes and recommend drivers with the greenest route. We describe the EasyRoute framework and evaluate it by collecting OBD and GPS data from 559 taxis in Beijing. Comparing with some commonly used baselines with error metrics, the experimental results show that using a small 10-minute dataset for training, the total fuel consumption estimated by EasyRoute has a relative error of at least 30% less than the baselines.

Taxis

Consumption

10.1109/msn.2016.038

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Citations (3)

Distance-Based Neural Combinatorial Optimization for Context-based Route Planning

Sascha Hamzehi Klaus Bogenberger Bernd Kaltenhäuser Jilei Tian Alvin Chin

Platform-based large-scale journey planning of autonomous vehicles and context-sensitive route planning applications require new scalable approaches in order to work within an on-demand mobility service. In this work we present and test a machine learning-based approach for distance-based roundtrip planning in a Traveling Salesman Problem (TSP) setting. We introduce our applied Distance-Based Pointer Network (DBPN) algorithm which solves mini-batches of multiple symmetric and asymmetric 2D Euclidean TSPs. We provide our algorithm and test results for symmetric and asymmetric TSP distances, as present in real road and traffic networks. Subsequently, we compare our results with an industry standard routing solver OR-Tools. Here, we focus on solving comparably small TSP instances which commonly occur on our platform-based service. Our results show that compared to the State-of-the-Art methods such as the Coordinate-Based Pointer Network (CBPN) and OR-Tools, our approach solves asymmetric TSPs which cannot be solved by the CBPN approach. The results furthermore show that our approach achieves near-optimal results by a 5.9% mean absolute percentage error, compared to the OR-Tools solution. By solving 1000 TSPs, we show that our DBPN approach is approximately 27 times faster than the OR-Tools solver.

Solver

Vehicle Routing Problem

10.1109/vtc2021-spring51267.2021.9448741

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Mining, Modeling, and Recommending 'Things' in Social Media: 4th International Workshops, MUSE 2013, Prague, Czech Republic, September 23, 2013

Springer eBooks (2014)

Martin Atzmueller Alvin Chin Christoph Scholz Christoph Trattner

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