OpenRaiser/Intern-Atlas huggingface dataset:
Intelligent Traffic Congestion Classification System using Artificial Neural Network
Authors: Md Ashifuddin Mondal, Zeenat Rehena
Venue: WWW
Year: 2019

Managing the ever increasing road traffic congestion due to enormous vehicular growth is a big concern all over the world. Tremendous air pollution, loss of valuable time and money are the common consequences of traffic congestion in urban areas. IoT based Intelligent Transportation System (ITS) can help in managing the road traffic congestion in an efficient way. Estimation and classification of the traffic congestion state of different road segments is one of the important aspects of intelligent traffic management. Traffic congestion state recognition of different road segments helps the traffic management authority to optimize the traffic regulation of a transportation system. The commuters can also decide their best possible route to the destination based on traffic congestion state of different road segments. This paper aims to estimate and classify the traffic congestion state of different road segments within a city by analyzing the road traffic data captured by in-road stationary sensors. The Artificial Neural Network (ANN) based system is used to classify traffic congestion states. Based on traffic congestion status, ITS will automatically update the traffic regulations like, changing the queue length in traffic signal, suggesting alternate routes. It also helps the government to device policies regarding construction of flyover/alternate route for better traffic management.
Leveraging AI and Sensor Fabrics to Evolve Smart City Solution Designs
Authors: William H. Money, Stephen J. Cohen
Venue: WWW
Year: 2019

Cities have entered the age of the sensor and located sensors everywhere over and under cities. The sensors monitor a host of factors that assess City operations and life such as air quality, noise, city services and traffic. Further, the sensors have “gone mobile” with announcements of situation aware mobile sensor platforms designed for city-level security and public safety. These wearable sensor platforms combine video, audio, and location data with Internet of Things (IoT) capabilities. However, the many sensors and functional platforms have not yet made the cities employing these many diverse sensors truly Smart. We are analyzing why the success toward the Smart city is limited, or late in coming. The explanations for the constrained effectiveness are assigned to many factors, but one of significance can be teased from a long-accepted explanation that associates data, information, and knowledge. Smart Cities need to effectively use the sensor data and the information assembled from these interpreted and organized data to create knowledge that serves the city and its people by answering and resolving key problems and questions. But the systems and analytic models needed to associate these data from many sensors have yet to be designed, constructed, and proven in the complex cities of today. Thus, the data (and information from the diverse sensors) lacks crucial integration and coordination for decisions and sense-making. While these sensor-based systems were, and in many cases are meeting some intended functionally discrete goals, they appear to be better described as data collection tools feeding centralized analytical engines. They are point solutions with specialized or targeted sensors feeding specialized solutions. This is a significant limiting factor in a city's drive to improve the quality of life and the efficiency of the services a city provides to its stakeholders. In this paper we present current trends in Smart City development, emerging issues with data and complexity growth, and proposes a mean to leverage the advancing technologies to address the integration problem.
Fine-grained Type Inference in Knowledge Graphs via Probabilistic and Tensor Factorization Methods
Authors: A. B. M. Moniruzzaman, Richi Nayak, Maolin Tang, Balasubramaniam Thirunavukarasu
Venue: WWW
Year: 2019

Knowledge Graphs (KGs) have been proven to be incredibly useful for enriching semantic Web search results and allowing queries with a well-defined result set. In recent years much attention has been given to the task of inferring missing facts based on existing facts in a KG. Approaches have also been proposed for inferring types of entities, however these are successful in common types such as 'Person', 'Movie', or 'Actor'. There is still a large gap, however, in the inference of fine-grained types which are highly important for exploring specific lists and collections within web search. Generally there are also relatively fewer observed instances of fine-grained types present to train in KGs, and this poses challenges for the development of effective approaches. In order to address the issue, this paper proposes a new approach to the fine-grained type inference problem. This new approach is explicitly modeled for leveraging domain knowledge and utilizing additional data outside KG, that improves performance in fine-grained type inference. Further improvements in efficiency are achieved by extending the model to probabilistic inference based on entity similarity and typed class classification. We conduct extensive experiments on type triple classification and entity prediction tasks on Freebase FB15K benchmark dataset. The experiment results show that the proposed model outperforms the state-of-the-art approaches for type inference in KG, and achieves high performance results in many-to-one relation in predicting tail for KG completion task.
TOMI: A Framework for Smart Tourism on the Move Innovation
Authors: Vaia Moustaka, Athena Vakali, Nikos Zikos, Theoklis Tsirakidis, Leonidas G. Anthopoulos
Venue: WWW
Year: 2019

Smart technologies advancements, emerging markets competition and sustainability needs have radically changed tourism and transport sectors. The key features of this change are the exploitation of evolving Big Data in the business intelligence context, the development of customized services tailored to the needs of consumers with the purpose of improving their experience, and the development of new business models based on the interaction between business and consumers. This is due to the capacity of smart transport technologies to integrate customers sensing and in this way a novel framework aimed at: i) developing personalized transport services in the tourism sector and ii) creating and delivering patterns of tourist consumer behavior according to specific target groups and market segments at tourist destination or country level is designed and outlined. The proposed “TΟMI” framework, exploits tour data analytics, in order to enable the deployment of personalized tour services that will be beneficial for tour operators, travellers and any other interested parties (local stakeholders, tourism entrepreneurs, etc.). The exploitation of the “TOMI” framework for the purpose of organizing tours in a city is also addressed through a case study on the city of Thessaloniki.
Estimating Walk-Based Similarities Using Random Walk
Authors: Shogo Murai, Yuichi Yoshida
Venue: WWW
Year: 2019

Measuring similarities between vertices is an important task in network analysis, which has numerous applications. One major approach to define a similarity between vertices is by accumulating weights of walks between them that encompasses personalized PageRank (PPR) and Katz similarity. Although many effective methods for PPR based on efficient simulation of random walks have been proposed, these techniques cannot be applied to other walk-based similarity notions because the random walk interpretation is only valid for PPR. In this paper, we propose a random-walk reduction method that reduces the computation of any walk-based similarity to the computation of a stationary distribution of a random walk. With this reduction, we can exploit techniques for PPR to compute other walk-based similarities. As a concrete application, we design an indexing method for walk-based similarities with which we can quickly estimate the similarity value of queried pairs of vertices, and theoretically analyze its approximation error. Our experimental results demonstrate that the instantiation of our method for Katz similarity is two orders of magnitude faster than existing methods on large real networks, without any deterioration in solution quality.
Learning to Bid in Revenue Maximizing Auction
Authors: Thomas Nedelec, Noureddine El Karoui, Vianney Perchet
Venue: WWW
Year: 2019

We consider the problem of the optimization of bidding strategies in prior-dependent revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid distributions. Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation of the objective functional in order to use gradient descent methods. Our approach is simple, general and can be applied to various value distributions and revenue-maximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy.
Think Outside the Dataset: Finding Fraudulent Reviews using Cross-Dataset Analysis
Authors: Shirin Nilizadeh, Hojjat Aghakhani, Eric Gustafson, Christopher Kruegel, Giovanni Vigna
Venue: WWW
Year: 2019

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews. We evaluated OneReview on about 805K and 462K reviews from Yelp and TripAdvisor, respectively to identify fraud on Yelp. Supervised machine learning yields excellent results, with 97% accuracy. We applied the created model on suspicious reviews and detected about 62K fraudulent reviews (about 8% of all the Yelp reviews). We further analyzed the detected fraudulent reviews and their authors, and located several spam campaigns in the wild, including campaigns against specific businesses, as well as campaigns consisting of several hundreds of socially-networked untrustworthy accounts.
Generating Product Descriptions from User Reviews
Authors: Slava Novgorodov, Guy Elad, Ido Guy, Kira Radinsky
Venue: WWW
Year: 2019

Product descriptions play an important role in the e-commerce ecosystem, conveying to buyers information about a merchandise they may purchase. Yet, on leading e-commerce websites, with high volumes of new items offered for sale every day, product descriptions are often lacking or missing altogether. Moreover, many descriptions include information that holds little value and sometimes even disrupts buyers, in an attempt to draw attention and purchases. In this work, we suggest to mitigate these issues by generating short crowd-based product descriptions from user reviews . We apply an extractive approach, where review sentences are used in their original form to compose the product description. At the core of our method is a supervised approach to identify candidate review sentences suitable to be used as part of a description. Our analysis, based on data from both the Fashion and Motors domains, reveals the top reasons for review sentences being unsuitable for the product's description and these are used, in turn, as part of a deep multi-task learning architecture. We then diversify the set of candidates by removing redundancies and, at the final step, select the top candidates to be included in the description. We compare different methods for each step and also conduct an end-to-end evaluation, based on rating from professional annotators, showing the generated descriptions are of high quality.
Reconciliation k-median: Clustering with Non-polarized Representatives
Authors: Bruno Ordozgoiti, Aristides Gionis
Venue: WWW
Year: 2019

We propose a new variant of the k-median problem, where the objective function models not only the cost of assigning data points to cluster representatives, but also a penalty term for disagreement among the representatives. We motivate this novel problem by applications where we are interested in clustering data while avoiding selecting representatives that are too far from each other. For example, we may want to summarize a set of news sources, but avoid selecting ideologically-extreme articles in order to reduce polarization. To solve the proposed k-median formulation we adopt the local-search algorithm of Arya et al. [2], We show that the algorithm provides a provable approximation guarantee, which becomes constant under a mild assumption on the minimum number of points for each cluster. We experimentally evaluate our problem formulation and proposed algorithm on datasets inspired by the motivating applications. In particular, we experiment with data extracted from Twitter, the US Congress voting records, and popular news sources. The results show that our objective can lead to choosing less polarized groups of representatives without significant loss in representation fidelity.
Mining the Relationship BetweenCar Theft and Places of Social Interest in Santiago Chile
Authors: Karen Y. Oróstica, Barbara Poblete
Venue: WWW
Year: 2019

Recent work suggests that certain places can be more attractive for car theft based how many people regularly visit them, as well as other factors. In this sense, we must also consider the city or district itself where vehicles are stolen. All cities have different cultural and socioeconomic characteristics that influence car theft patterns. In particular, the distribution of public services and places attract a large crowd could play a key role in the occurrence of car theft. Santiago, a city that displays drastic socioeconomic differences among its districts, presents increasingly-high car theft rates. This represents a serious issue for the city, as for any other major city, which –at least for Santiago– has not been analyzed in depth using quantitative approaches. In this work, we present a preliminary study of how places that create social interest, such as restaurants, bars, schools, and shopping malls, increase car theft frequency in Santiago. We also study if some types of places are more attractive than others for this type of crime. To evaluate this, we propose to analyze car theft points (CTP) from insurance companies and their relationship with places of social interest (PSI) extracted from Google Maps, using a proximity based approach. Our findings show a high correlation between CTP and PSI for all of the social interest categories that we studied in the different districts of the Santiago. In particular our work contributes to the understanding of the social factors that are associated to car thefts.
ActiveLink: Deep Active Learning for Link Prediction in Knowledge Graphs
Authors: Natalia Ostapuk, Jie Yang, Philippe Cudré-Mauroux
Venue: WWW
Year: 2019

Neural networks have recently been shown to be highly effective at predicting links for constructing knowledge graphs. Existing research has mainly focused on designing 1) deep neural network models that are expressive in capturing fine-grained semantics, e.g., NTN and ConvE, but that are however less scalable; or 2) shallow models that are scalable, e.g., TransE and DistMult, yet limited in capturing expressive semantic features. In this work, we demonstrate that we can get the best of both worlds while drastically reducing the amount of data needed to train a deep network by leveraging active learning. We present a novel deep active learning framework, ActiveLink, which can be applied to actively train any neural link predictor. Inspired by recent advances in Bayesian deep learning, ActiveLink takes a Bayesian view on neural link predictors, thereby enabling uncertainty sampling for deep active learning. ActiveLink extends uncertainty sampling by exploiting the underlying structure of the knowledge graph, i.e., links between entities, to improve sampling effectiveness. To accelerate model training, ActiveLink further adopts an incremental training method that allows deep neural networks to be incrementally trained while optimizing their generalizability at each iteration. Extensive validation on real-world datasets shows that ActiveLink is able to match state-of-the-art approaches while requiring only 20% of the original training data.
InfraNodus: Generating Insight Using Text Network Analysis
Authors: Dmitry Paranyushkin
Venue: WWW
Year: 2019

In this paper we present a web-based open source tool and a method for generating insight from any text or discourse using text network analysis. The tool (InfraNodus) can be used by researchers and writers to organize and to better understand their notes, to measure the level of bias in discourse, and to identify the parts of the discourse where there is a potential for insight and new ideas. The method is based on text network analysis algorithm, which represents any text as a network and identifies the most influential words in a discourse based on the terms' co-occurrence. Graph community detection algorithm is then applied in order to identify the different topical clusters, which represent the main topics in the text as well as the relations between them. The community structure is used in conjunction with other measures to identify the level of bias or cognitive diversity of the discourse. Finally, the structural gaps in the graph can indicate the parts of the discourse where the connections are lacking, therefore highlighting the areas where there's a potential for new ideas. The tool can be used as stand-alone software by end users as well as implemented via an API into other tools. Another interesting application is in the field of recommendation systems: structural gaps could indicate potentially interesting non-trivial connections to any connected datasets.
Nameles: An intelligent system for Real-Time Filtering of Invalid Ad Traffic
Authors: Antonio Pastor, Matti Antero Parssinen, Patricia Callejo, Pelayo Vallina, Rubén Cuevas, Ángel Cuevas, Mikko Kotila, Arturo Azcorra
Venue: WWW
Year: 2019

Invalid ad traffic is an inherent problem of programmatic advertising that has not been properly addressed so far. Traditionally, it has been considered that invalid ad traffic only harms the interests of advertisers, which pay for the cost of invalid ad impressions while other industry stakeholders earn revenue through commissions regardless of the quality of the impression. Our first contribution consists of providing evidence that shows how the Demand Side Platforms (DSPs), one of the most important intermediaries in the programmatic advertising supply chain, may be suffering from economic losses due to invalid ad traffic. Addressing the problem of invalid traffic at DSPs requires a highly scalable solution that can identify invalid traffic in real time at the individual bid request level. The second and main contribution is the design and implementation of a solution for the invalid traffic problem, a system that can be seamlessly integrated into the current programmatic ecosystem by the DSPs. Our system has been released under an open source license, becoming the first auditable solution for invalid ad traffic detection. The intrinsic transparency of our solution along with the good results obtained in industrial trials have led the World Federation of Advertisers to endorse it.
Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference
Authors: Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang
Venue: WWW
Year: 2019

Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions: 1) labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used; and 2) knowledge graph embedding is affected by entity's degree difference, which brings challenges to align high frequent and low frequent entities. We propose a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment. Furthermore, we improve the knowledge graph embedding with awareness of the degree difference by performing the adversarial training. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy.
Integrating Bayesian and Neural Networks for Discourse Coherence
Authors: Jinhua Peng, Zongyang Ma, Di Jiang, Hua Wu
Venue: WWW
Year: 2019

In dialogue systems, discourse coherence is an important concept that measures semantic relevance between an utterance and its context. It plays a critical role in determining the inappropriate reply of dialogue systems with regard to a given dialogue context. In this paper, we present a novel framework for evaluating discourse coherence by seamlessly integrating Bayesian and neural networks. The Bayesian network corresponds to Coherence-Pivoted Latent Dirichlet Allocation (cpLDA). cpLDA concentrates on generating the fine-grained topics from dialogue data and takes both local and global semantics into account. The neural network corresponds to Multi-Hierarchical Coherence Network (MHCN). Coupled with cpLDA, MHCN quantifies discourse coherence between an utterance and its context by comprehensively utilizing original texts, topic distribution and topic embedding. Extensive experiments show that the proposed framework yields superior performance comparing with the state-of-the-art methods.
Hi Doppelgänger : Towards Detecting Manipulation in News Comments
Authors: Jan Pennekamp, Martin Henze, Oliver Hohlfeld, Andriy Panchenko
Venue: WWW
Year: 2019

Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.