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"abstract": "Nowadays, there exist more than 300 million patients affected by about 7,000 rare disease all over the world, which comprises 3.5% to 5.9% of the global population. 40% of rare disease patients are diagnosed incorrectly before reaching a final diagnosis, of which 25% spend between 5 to 30 years on a chaotic journey through numerous referrals, investigations, and disease evolutions from early symptoms to a confirmatory diagnosis of their disease. Phenotypes are defined as observable characteristics and clinical traits of diseases and organisms. A significant lack of knowledge and insufficient characterization of the longitudinal phenotypic information of many rare diseases is a significant contributor to the continued existence of such diagnostic odyssey. In this study, to largely detect longitudinal phenotypic characterizations for rare disease, we formulated the problem of enriching rare disease phenotypic sets as a phenotype recommendation task and applied the graph convolutional network along with biomedical knowledge graph over Mayo Clinic electronic health records to achieve the goal.",
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"content": "Indirect visual displays (IVDs) play a significant role in providing full-spectrum views of the immediate environment in closed systems (e.g., tanks). However, currently little is known about how different fields of views (FoVs) in IVDs influence operator's decision-making in scenarios requiring search and shoot operations. The primary objective of this study was to determine the influence of varying degrees of FoVs on human decision-making in a terrainbased search-and-shoot scenario. A total of 25 participants performed in two FoV designs that were presented to them in a random order: A 180*2 FoV, where the computer screen was split into two sections (top and bottom) and each section provided a 180° FoV of the outside scene (front and back); and, a 90*4 FoV, where the computer screen was split into four sections, where each section provided a 90° FoV of the outside scene (front, back, left, and right). Results revealed that performance was better, frustration was less, and effort was less in the 180*2 FoV compared to the 90*4 FoV; however, the mental demand was more in the 180*2 FoV compared to the 90*4 FoV. We highlight the implication of our results for operator's decision-making in IVD tasks.",
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"abstract": "This paper presents a comparative study on the design of a monolithic 3DOF MEMS capacitive accelerometer using both analytical and numerical techniques. Monolithic accelerometer is a single structure having three individual single axis accelerometers on a single substrate and utilizes a surface micromachining technology using standard PolyMUMPs process. The designed accelerometer is 3mm×3.1mm in size, has 10.17µ g/√ Hz and 17.50µ g/√ Hz mechanical noise floor for in-plane and out-of-plane axes respectively. The total sense capacitance along x, y and z-axes is 68.5fF, 82.3fF and 6.19pF respectively. Sensitivity of 0.65fF/g, 0.78fF/g and 0.90pF/g is obtained for in-plane (x and y) and out-of-plane (z) axes respectively. Performing a detailed finite element analysis in ANSYS software, a displacement of 19.058µ m, 20.392µ m and 1.318µ m for x, y and z axes respectively are calculated approximately the same as calculated analytically under applied acceleration to the proof mass of the proposed accelerometer.",
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"content": "Parallel coordinates plots and RadViz are two visualization techniques that deal with multivariate data. They complement each other in identifying data patterns, clusters, and outliers. In this paper, we analyze multivariate software metrics linking the two approaches for detecting outliers, which could be the indicators for bad smells in software systems. Parallel coordinates plots provide an overview, whereas the RadViz representation allows for comparing a smaller subset of metrics in detail. We develop an interactive visual analytics system supporting automatic detection of bad smell patterns. In addition, we investigate the distinctive properties of outliers that are not considered harmful, but noteworthy for other reasons. We demonstrate our approach with open source Java systems and describe detected bad smells and other outlier patterns.",
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"abstract": "This work presents a neural networks approach for stock returns and uses mean-variance model for the selection of predicted portfolio thus formed. Four types of different neural network models have been used and their outputs have been compared at various regression orders. A new type of predictor called autoregressive moving reference neural network predictor has been used in all the four neural network models. In this predictor the differences between the values of the series of returns and a determined past value are the regression variables. To evaluate the performance of the predictor, various error measures have been used, taking the average of these error measures, the overall performance of the predictor has been tested. Experiments with real data from National stock exchange of India (NSE) were employed to examine the accuracy of this method.",
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"abstract": "With the rapid growth of the national economy, the electricity supply presents severe shortage, which directly affects the economic development and the normal life of people. Electricity supply has become a restricting factor on sustainable development of national economy. According to the recent situation of electricity demand, this paper uses time series analysis on its consumer to do scientific prediction, and put forward to prevent future electricity shortages, we should establish power early warning system, adopt time-of-use electricity price for a short term, and adjust the industrial structure, especially the internal structure of industrial products. Then we can promote economic growth mode to transform from extensive to intensive, which can improve the quality and efficiency of economic growth. Energy saving will be a long-term strategy policy of national economic development from the point of view of the long-term development of the electricity industry.",
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"content": "With the rapid growth of the national economy, the electricity supply presents severe shortage, which directly affects the economic development and the normal life of people. Electricity supply has become a restricting factor on sustainable development of national economy. According to the recent situation of electricity demand, this paper uses time series analysis on its consumer to do scientific prediction, and put forward to prevent future electricity shortages, we should establish power early warning system, adopt time-of-use electricity price for a short term, and adjust the industrial structure, especially the internal structure of industrial products. Then we can promote economic growth mode to transform from extensive to intensive, which can improve the quality and efficiency of economic growth. Energy saving will be a long-term strategy policy of national economic development from the point of view of the long-term development of the electricity industry.",
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"title": "Towards an Efficient Cyber-Physical System for First-Mile Taxi Transit in Urban Complex",
"normalizedTitle": "Towards an Efficient Cyber-Physical System for First-Mile Taxi Transit in Urban Complex",
"abstract": "As the urban size is constantly expanding, urban complex, which is an integrated area of business, catering and entertainment, has became an essential part of our daily life. However, urban complex leads to high passenger flow volume. One of the significant problems is customers spend more and more time on waiting for a taxi. To solve this problem, we present a novel mini-bus system, which transports customers from the exits of the complex to the optimal selected areas nearby to reduce the waiting time. Specifically, based on the harvested data of transportation and passenger flow volume in urban complexes, we establish this mini-bus system in Suzhou Center, one of the largest and most advanced urban complexes in China. The experimental results show that our system significantly reduces the waiting time for customer and thus improve the customer experience.",
"abstracts": [
{
"abstractType": "Regular",
"content": "As the urban size is constantly expanding, urban complex, which is an integrated area of business, catering and entertainment, has became an essential part of our daily life. However, urban complex leads to high passenger flow volume. One of the significant problems is customers spend more and more time on waiting for a taxi. To solve this problem, we present a novel mini-bus system, which transports customers from the exits of the complex to the optimal selected areas nearby to reduce the waiting time. Specifically, based on the harvested data of transportation and passenger flow volume in urban complexes, we establish this mini-bus system in Suzhou Center, one of the largest and most advanced urban complexes in China. The experimental results show that our system significantly reduces the waiting time for customer and thus improve the customer experience.",
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"keywords": [
"Cyber Physical Systems",
"Public Transport",
"Road Vehicles",
"Traffic Engineering Computing",
"First Mile Taxi Transit",
"Urban Complex",
"Urban Size",
"High Passenger Flow Volume",
"Transportation",
"Cyber Physical System",
"Mini Bus System",
"Public Transportation",
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"Data Analysis",
"Sensors",
"Optimization",
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"Taxi First Mile Transit Urban Complex"
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{
"affiliation": null,
"fullName": "Junjie Wang",
"givenName": "Junjie",
"surname": "Wang",
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{
"affiliation": null,
"fullName": "Peng Xu",
"givenName": "Peng",
"surname": "Xu",
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{
"affiliation": null,
"fullName": "Jinyang Li",
"givenName": "Jinyang",
"surname": "Li",
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},
{
"affiliation": null,
"fullName": "Xiaoshan Sun",
"givenName": "Xiaoshan",
"surname": "Sun",
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{
"affiliation": null,
"fullName": "Wenchong Tian",
"givenName": "Wenchong",
"surname": "Tian",
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{
"affiliation": null,
"fullName": "Jie Ling",
"givenName": "Jie",
"surname": "Ling",
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{
"affiliation": null,
"fullName": "Wei Zheng",
"givenName": "Wei",
"surname": "Zheng",
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{
"affiliation": null,
"fullName": "Hengchang Liu",
"givenName": "Hengchang",
"surname": "Liu",
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"proceeding": {
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"abstract": "Taxi ridesharing becomes promising and attractive because of the wide availability of taxis in a city and tremendous benefits of ridesharing, e.g., alleviating traffic congestion and reducing energy consumption. Existing taxi ridesharing schemes, however, are not efficient and practical, due to they simply match ride requests and taxis based on partial trip information and omit the offline passengers, who hail a taxi at roadside with no explicit requests to the system. In this paper, we consider the mobility-aware taxi ridesharing problem, and present mT- Share to address these limitations. mT-Share fully exploits the mobility information of ride requests and taxis to achieve efficient indexing of taxis/requests and better passenger-taxi matching, while still satisfying the constraints on passengers' deadlines and taxis' capacities. Specifically, mT-Share indexes taxis and ride requests with both geographical information and travel directions, and supports the shortest path based routing and probabilistic routing to serve both online and offline ride requests. Extensive experiments with a large real-world taxi dataset demonstrate the efficiency and effectiveness of mT-Share, which can response each ride request in milliseconds and with a moderate detour cost. Compared to state-of-the-art methods, mT-Share serves 42% and 62% more ride requests in peak and non-peak hours, respectively.",
"abstracts": [
{
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"content": "Taxi ridesharing becomes promising and attractive because of the wide availability of taxis in a city and tremendous benefits of ridesharing, e.g., alleviating traffic congestion and reducing energy consumption. Existing taxi ridesharing schemes, however, are not efficient and practical, due to they simply match ride requests and taxis based on partial trip information and omit the offline passengers, who hail a taxi at roadside with no explicit requests to the system. In this paper, we consider the mobility-aware taxi ridesharing problem, and present mT- Share to address these limitations. mT-Share fully exploits the mobility information of ride requests and taxis to achieve efficient indexing of taxis/requests and better passenger-taxi matching, while still satisfying the constraints on passengers' deadlines and taxis' capacities. Specifically, mT-Share indexes taxis and ride requests with both geographical information and travel directions, and supports the shortest path based routing and probabilistic routing to serve both online and offline ride requests. Extensive experiments with a large real-world taxi dataset demonstrate the efficiency and effectiveness of mT-Share, which can response each ride request in milliseconds and with a moderate detour cost. Compared to state-of-the-art methods, mT-Share serves 42% and 62% more ride requests in peak and non-peak hours, respectively.",
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"normalizedAbstract": "Taxi ridesharing becomes promising and attractive because of the wide availability of taxis in a city and tremendous benefits of ridesharing, e.g., alleviating traffic congestion and reducing energy consumption. Existing taxi ridesharing schemes, however, are not efficient and practical, due to they simply match ride requests and taxis based on partial trip information and omit the offline passengers, who hail a taxi at roadside with no explicit requests to the system. In this paper, we consider the mobility-aware taxi ridesharing problem, and present mT- Share to address these limitations. mT-Share fully exploits the mobility information of ride requests and taxis to achieve efficient indexing of taxis/requests and better passenger-taxi matching, while still satisfying the constraints on passengers' deadlines and taxis' capacities. Specifically, mT-Share indexes taxis and ride requests with both geographical information and travel directions, and supports the shortest path based routing and probabilistic routing to serve both online and offline ride requests. Extensive experiments with a large real-world taxi dataset demonstrate the efficiency and effectiveness of mT-Share, which can response each ride request in milliseconds and with a moderate detour cost. Compared to state-of-the-art methods, mT-Share serves 42% and 62% more ride requests in peak and non-peak hours, respectively.",
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"affiliation": "Shenzhen University,Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ),P. R. China",
"fullName": "Zhidan Liu",
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"affiliation": "Shenzhen University,College of Computer Science and Software Engineering,P R. China",
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"affiliation": "Shenzhen University,College of Computer Science and Software Engineering,P R. China",
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"title": "Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest",
"normalizedTitle": "Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest",
"abstract": "Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).",
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"content": "Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).",
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"normalizedAbstract": "Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).",
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"Traffic Engineering Computing",
"Taxi Demand Prediction",
"LSTM Based Deep Sequence Model",
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"abstract": "Hiroshima City is surrounded by mountains on its three sides, the east, north, and west, except the south facing the sea, and besides, its urban area places delta region. For this reason, the population of Hiroshima City is concentrated in the slope residential estates at the surrounding mountains of the city. However, the living environment of the suburban slope residential estates doesn't seem so suitable for vulnerable road users, mainly elderly people. Thus in this paper, based on the traffic attitude survey results for residents in the slope residential estates at Itsukaichi district, Hiroshima City, previously conducted by our previous works, we examine an effective way to support vulnerable road users and to enrich their life. Especially, we develop a prototype system to support the transportation for residents living in a place where public transportation is not sufficiently developed.",
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"abstract": "Recently, the transportation means of the elderly have been insufficient due to aging and depopulation, therefore, the area volunteers have provided the demand-responsive transport (DRT) for seniors such as senior transportation service and demand bus. However, the demands collection via telephone or mobile applications has problems: the telephone call imposes a heavy burden on volunteers, and the mobile applications hinder the elderly who are not using or familiar with smartphone from using the service. In this paper, we propose a pick-up demand collection system that easily collects the demands from the elderly and makes them visible to the transportation manager. To collect demands, we designed the demand transmission device having LPWA (Low Power Wide Area) and a user interface for the elderly. As a result of asking the elderly to operate our prototype demand transmission device, it was found that the operability was better for the elderly than the existing input interface with the smartphone. In addition, we confirmed that the transmission device can transmit the demands through repeater devices with multihop communication by LPWA even if the demand request is not directly reachable to gateway due to the influence of radio shielding objects.",
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"content": "Recently, the transportation means of the elderly have been insufficient due to aging and depopulation, therefore, the area volunteers have provided the demand-responsive transport (DRT) for seniors such as senior transportation service and demand bus. However, the demands collection via telephone or mobile applications has problems: the telephone call imposes a heavy burden on volunteers, and the mobile applications hinder the elderly who are not using or familiar with smartphone from using the service. In this paper, we propose a pick-up demand collection system that easily collects the demands from the elderly and makes them visible to the transportation manager. To collect demands, we designed the demand transmission device having LPWA (Low Power Wide Area) and a user interface for the elderly. As a result of asking the elderly to operate our prototype demand transmission device, it was found that the operability was better for the elderly than the existing input interface with the smartphone. In addition, we confirmed that the transmission device can transmit the demands through repeater devices with multihop communication by LPWA even if the demand request is not directly reachable to gateway due to the influence of radio shielding objects.",
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"abstract": "A curated literature collection on a specific topic helps researchers to find relevant articles quickly. Assigning multiple keywords to each article is one of the techniques to structure such a collection. But it is challenging to assign all the keywords consistently without any gaps or ambiguities. We propose to support the user with a machine learning technique that suggests keywords for articles in a literature collection browser. We provide visual explanations to make the keyword suggestions transparent. The suggestions are based on previous keyword assignments. The machine learning technique learns on the fly from the interactive assignments of the user. We seamlessly integrate the proposed technique in an existing literature collection browser and investigate various usage scenarios through an early prototype.",
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"content": "A curated literature collection on a specific topic helps researchers to find relevant articles quickly. Assigning multiple keywords to each article is one of the techniques to structure such a collection. But it is challenging to assign all the keywords consistently without any gaps or ambiguities. We propose to support the user with a machine learning technique that suggests keywords for articles in a literature collection browser. We provide visual explanations to make the keyword suggestions transparent. The suggestions are based on previous keyword assignments. The machine learning technique learns on the fly from the interactive assignments of the user. We seamlessly integrate the proposed technique in an existing literature collection browser and investigate various usage scenarios through an early prototype.",
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"fullName": "Shivam Agarwal",
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"affiliation": "University of Duisburg-Essen,Germany",
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"abstract": "Computer musicians are a community of end-user programmers who often use visual programming languages such as Max/MSP or Pure Data to realize their musical compositions. This research study conducts a multifaceted analysis of the software development practices of computer musicians when programming in these visual music-oriented languages. A statistical analysis of project metadata harvested from software repositories hosted on GitHub reveals that in comparison to the general population of software developers, computer musicians' repositories have less commits, less frequent commits, more commits on weekends, yet similar numbers of bug reports and similar numbers of contributing authors. Analysis of source code in these repositories reveals that the vast majority of code can be reconstructed from duplicate fragments. Finally, these results are corroborated by a survey of computer musicians and interviews with individuals in this end-user community. Based on this analysis and feedback from computer musicians we find that there are many avenues where software engineering can be applied to help aid this community of end-user programmers.",
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"abstract": "Plagiarism is a debated and controversial topic in different fields. For example, in Law, where the subjectivity of the judges that have to pronounce a suspicious case usually lead to long and often unsolved cases, and in Music, where huge amounts of money are invested every year to face and try to solve suspicious cases. In this scenario, the automatic detection of music plagiarism is fundamental by representing useful support for judges during their pronouncements and an important result to avoid musicians spending more time in court than on composing music. This paper shows how the combination of visual analytics and the employment of adaptive meta-heuristics can assist domain experts in judging suspicious cases. Solutions will be presented as part of PlagiarismDetection, a cross-platform tool that leverages text-similarity algorithms, computational intelligence, optimization methods, and visualization techniques to enable new critical approaches to music plagiarism analysis.",
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"abstract": "This paper presents the Media Watch on Climate Change, a public Web portal that captures and aggregates large archives of digital content from multiple stakeholder groups. Each week it assesses the domain-specific relevance of millions of documents and user comments from news media, blogs, Web 2.0 platforms such as Facebook, Twitter and YouTube, the Web sites of companies and NGOs, and a range of other sources. An interactive dashboard with trend charts and complex map projections not only shows how often and where environmental information is published, but also provides a real-time account of concepts that stakeholders associate with climate change. Positive or negative sentiment is computed automatically, which not only sheds light on the impact of education and public outreach campaigns that target environmental literacy, but also help to gain a better understanding of how others perceive climate-related issues.",
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"abstract": "In our previous research we investigated the question of whether social media commentary merely confirmed previously held convictions or whether it changed them. We found that the topic, purpose, and certain individual characteristics factored into this outcome. In this paper, we further identified from that data collection characteristics of social media discourse that led to taking-action. Affect intensity combined with one's previous sentiment conviction were determinants in whether one simply asserted an opinion compared with \"doing something about it.\"",
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"abstract": "Smart agriculture involves the use of technology such as drones, GPS, robotics, IoT, AI, big data, and solar energy to improve farming practices. As with any disruptive innovation, however, stakeholder expectations can be misaligned from what the innovation can actually deliver. There can also be varying perspectives on what the innovation entails, related topics of interest, and impediments to large scale adoption. This study examines public perception of smart agriculture and its perceived drivers and challenges as present in social media discourse. We collected online posts from Twitter, Reddit, forums, online news and blogs between January 2010 and December 2018 for analysis. Results show that 38% of social media posts contained emotion with 52% joy, 21% anger and 12% sadness. Through topic analysis, we discovered seven key drivers and challenges for smart agriculture which included: enabling technologies, data ownership and privacy, accountability and trust, energy and infrastructure, investment, job security, and climate change.",
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"content": "Smart agriculture involves the use of technology such as drones, GPS, robotics, IoT, AI, big data, and solar energy to improve farming practices. As with any disruptive innovation, however, stakeholder expectations can be misaligned from what the innovation can actually deliver. There can also be varying perspectives on what the innovation entails, related topics of interest, and impediments to large scale adoption. This study examines public perception of smart agriculture and its perceived drivers and challenges as present in social media discourse. We collected online posts from Twitter, Reddit, forums, online news and blogs between January 2010 and December 2018 for analysis. Results show that 38% of social media posts contained emotion with 52% joy, 21% anger and 12% sadness. Through topic analysis, we discovered seven key drivers and challenges for smart agriculture which included: enabling technologies, data ownership and privacy, accountability and trust, energy and infrastructure, investment, job security, and climate change.",
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"affiliation": "Dakota State University,College of Business and Information Systems,Madison,USA",
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"affiliation": "Dakota State University,College of Business and Information Systems,Madison,USA",
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"abstract": "Microblog ranking is a hot research topic in recent years. Most of the related works apply TF-IDF metric for calculating content similarity while neglecting their semantic similarity. And most existing search engines which retrieve the microblog list by string matching the search keywords is not competent to provide a reliable list for users when dealing with polysemy and synonym. Besides, treating all the users with same authority for all topics is intuitively not ideal. In this paper, a comprehensive strategy for microblog ranking is proposed. First, we extend the conventional TF-IDF based content similarity with exploiting knowledge from WordNet. Then, we further incorporate a new feature for microblog ranking that is the topical relation between search keyword and its retrieval. Author topical authority is also incorporated into the ranking framework as an important feature for microblog ranking. Gradient Boosting Decision Tree(GBDT), then is employed to train the ranking model with multiple features involved. We conduct thorough experiments on a large-scale real-world Twitter dataset and demonstrate that our proposed approach outperform a number of existing approaches in discovering higher quality and more related microblogs.",
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"content": "Microblog ranking is a hot research topic in recent years. Most of the related works apply TF-IDF metric for calculating content similarity while neglecting their semantic similarity. And most existing search engines which retrieve the microblog list by string matching the search keywords is not competent to provide a reliable list for users when dealing with polysemy and synonym. Besides, treating all the users with same authority for all topics is intuitively not ideal. In this paper, a comprehensive strategy for microblog ranking is proposed. First, we extend the conventional TF-IDF based content similarity with exploiting knowledge from WordNet. Then, we further incorporate a new feature for microblog ranking that is the topical relation between search keyword and its retrieval. Author topical authority is also incorporated into the ranking framework as an important feature for microblog ranking. Gradient Boosting Decision Tree(GBDT), then is employed to train the ranking model with multiple features involved. We conduct thorough experiments on a large-scale real-world Twitter dataset and demonstrate that our proposed approach outperform a number of existing approaches in discovering higher quality and more related microblogs.",
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"abstract": "Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic ego-networks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we develop Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.",
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"abstract": "Social media is increasingly important for political and social activism in Mexico. In particular, Twitter has played a significant role in influencing government decision making and shaping the relationships between governments, citizens, politicians, and other stakeholders. Within the last few months, some commentators even argue that Mexican politics has a new influential actor: \"I'm Number 132\" (a student-based social movement using Twitter and YouTube). After the Arab Spring and the uprisings that have led to significant political changes in Egypt, Tunisia, and Iran, the Mexican case could provide new insights to understand these social movements. Understanding the students' political mobilization \"I'm Number 132\" in the context of the 2012 presidential election in Mexico, and how they have been using social media tools to communicate their concerns and organize protests across the country, could help us to explain why and how these social media-enabled political movements emerge and evolve.",
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"abstract": "The extensive use of digital social media by social movement actors is an emerging trend that restructures the communication dynamics of social protest, and it is widely credited with contributing to the successful mobilizations of recent movements (e.g., Arab Spring, Occupy Wall Street). Yet, our understanding of both the roles played by social movement's use of social media and the extent of its impact is largely derived from anecdotal evidence, news reports, and a thin body of scholarly research on Web-based technologies. In this research we explore several computational methods for measuring the impact of social media on a social movement. Inspired by methodologies originally developed for analyzing computer networks and other dynamic systems, these methods measure various static and dynamic aspects of social networks, and their relations to an underlying social movement. We demonstrated the feasibility and benefits of these measurement methods in the context of Twitter and the Occupying Wall Street movement (OWS). By analyzing tweets related to OWS, we demonstrated the link between the vitality of the movement and the volume of the related tweets over time. We show that there is a positive correlation between the dynamic of tweets and the short-term trend of OWS. The correlation makes it possible to forecast the short-term trend of a social movement using social media data. By ranking users based on the number of their OWS-related tweets and the durations of their tweeting, we are able to identify “buzz makers”. Using a strategy similar to the page-rank algorithm, we define the influence of a user by the number of re-tweets that his/her original tweets incite. By tracing where OWS-related tweets are generated, we measure the geographic diffusion of OWS. By analyzing the percentage of OWS tweets generated from different sources, we show that smart phones and applications such as tweet deck had been used extensively for tweeting in the OWS movement. This indicates the involvement of a younger and more technology-inclined generation in OWS.",
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"content": "The extensive use of digital social media by social movement actors is an emerging trend that restructures the communication dynamics of social protest, and it is widely credited with contributing to the successful mobilizations of recent movements (e.g., Arab Spring, Occupy Wall Street). Yet, our understanding of both the roles played by social movement's use of social media and the extent of its impact is largely derived from anecdotal evidence, news reports, and a thin body of scholarly research on Web-based technologies. In this research we explore several computational methods for measuring the impact of social media on a social movement. Inspired by methodologies originally developed for analyzing computer networks and other dynamic systems, these methods measure various static and dynamic aspects of social networks, and their relations to an underlying social movement. We demonstrated the feasibility and benefits of these measurement methods in the context of Twitter and the Occupying Wall Street movement (OWS). By analyzing tweets related to OWS, we demonstrated the link between the vitality of the movement and the volume of the related tweets over time. We show that there is a positive correlation between the dynamic of tweets and the short-term trend of OWS. The correlation makes it possible to forecast the short-term trend of a social movement using social media data. By ranking users based on the number of their OWS-related tweets and the durations of their tweeting, we are able to identify “buzz makers”. Using a strategy similar to the page-rank algorithm, we define the influence of a user by the number of re-tweets that his/her original tweets incite. By tracing where OWS-related tweets are generated, we measure the geographic diffusion of OWS. By analyzing the percentage of OWS tweets generated from different sources, we show that smart phones and applications such as tweet deck had been used extensively for tweeting in the OWS movement. This indicates the involvement of a younger and more technology-inclined generation in OWS.",
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"abstract": "This paper proposes a method for estimating microblogging user location to determine local topics of importance based on area-specific term cooccurrence. Geotagged information on social media has not previously been sufficient to determine local topics, however, the amount of information available on social media has continued to expand due to the widespread use of smartphones. Notably, the amount of information generated from regional cities is significantly smaller than that from metropolitan cities. Hence, we must estimate the location of each user in a regional city to obtain adequate local information for determining local topics. To extract this information, we define area-specific scores of terms and co occurrences that are calculated using term frequency, as well as average and standard deviation of the longitude and latitude of raw geotagged information.",
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