Miracle-dz/UniFAR-FTUs huggingface dataset:
DistrustRank: Spotting False News Domains

In this paper we propose a semi-supervised learning strategy to automatically separate fake News from reliable News sources: DistrustRank. We first select a small set of unreliable News, manually evaluated and classified by experts on fact checking portals. Once this set is created, DistrustRank constructs a weighted graph where nodes represent websites, connected by edges based on a minimum similarity between a pair of websites. Next it computes the centrality using a biased PageRank, where a bias is applied to the selected set of seeds. As an output of the proposed model we obtain a trust (or distrust) rank that can be used in two ways: a) as a counter-bias to be applied when News about a specific subject is ranked, in order to discount possible boosts achieved by false claims; and b) to assist humans to identify sources that are likely to be source of fake News (or that are likely to be reputable), suggesting websites that should be examined more closely or to be avoided. In our experiments, DistrustRank outperforms the supervised approaches in either ranking and classification task.
Combined Regression and Tripletwise Learning for Conversion Rate Prediction in Real-Time Bidding Advertising

In real-time bidding advertising (RTB), the buyers bid for individual advertisement impressions provided by publishers in real time. The final goal of the buyers is to maximize the return on their investment. To gain higher returns, buyers prefer to first purchase more conversion impressions than click-only ones and then purchase more click-only impressions prior to non-click ones. Simultaneously, to reduce the expense, they need to accurately estimate a reasonable bid price, the predicted precision of which depends on the precision of the predicted conversion rate (CVR) or predicted click-through rate (CTR). Therefore, the predicted CVR or predicted CTR must provide not only good ranking values but also correct regression estimations. This paper is focused on the CVR estimation problem for buy-sides in RTB and a combined regression and tripletwise ranking method (CRT) is proposed that jointly considers regression loss and tripletwise ranking loss to estimate the CVR. This method attempts to rank conversion impressions above click-only ones and simultaneously rank click-only impressions above non-click ones. Meanwhile, through simultaneously utilizing the historical conversion and click information to alleviate sparsity, the CRT method is also aimed to achieve a good two category-ranking performance, as well as a good regression performance for predicting the CVR.
Identifying and Modeling Information Resumption Behaviors in Cross-Device Search

Enlightened by task resumption behaviors in cross-session search, we explore information resumption behaviors in cross-device search. In order to find important features of information resumption behaviors, we conducted a user experiment and modeled information resumption behaviors using machine learning. The model of C5.0 Decision Tree outperformed and showed that features of FamiliarityScores, AveEditDistance, AveQueryEffectiveRate and ValidClickRate are of importance.
From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems

We revisit the Probability Ranking Principle in the context of recommender systems. We find a key difference in the retrieval protocol with respect to query-based search, that leads to the identification of different optimal ranking principles for discovery-oriented recommendation. Based on this finding, we revise the effectiveness of common non-personalized ranking functions in respect to the new principles. We run an experiment confirming and illustrating our theoretical analysis, and providing further observations and hints for reflection and future research.
Execution Phase Prediction Based on Phase Precursors and Locality

This paper focuses on different methods developed to detect the upcoming execution phase of a workload with regards to power demands. By controlling the state of the processor in power demanding phases, the operating system can maintain a relatively steady power pattern in the workload, leading to higher power headroom in the system. We compared two main approaches in phase prediction. Firstly, we show that by detecting the precursors leading to an upcoming phase, the system can speculate the next phase with high accuracy. Additionally, we compared this method with another approach which relies on the assumption of phase locality, expecting the current dominant phase to continue in the near future. Our results show that by detecting the precursors we can detect 81% of the upcoming phases with lower processor frequency switching overhead compared to most of the proposed locality-based methods.
The City Brain: Towards Real-Time Search for the Real-World

A city is an aggregate of a huge amount of heterogeneous data. However, extracting meaningful values from that data remains challenging. City Brain is an end-to-end system whose goal is to glean irreplaceable values from big city data, specifically from videos, with the assistance of rapidly evolving AI technologies and fast-growing computing capacity. From cognition to optimization, to decision-making, from search to prediction and ultimately, to intervention, City Brain improves the way we manage the city, as well as the way we live in it. In this talk, firstly we will introduce current practices of the City Brain platform in a few cities in China, including what we can do to achieve the goal and make it a reality. Then we will focus on visual search technologies and applications that we can apply on the city data. Last, a few video demos will be shown, followed by highlighting a few future directions of city computing.
Experiences in Introducing Blended Learning in an Introductory Programming Course

As students shall be able to apply their gained theoretical knowledge practically, usually exercises are offered. But assessment of the exercises is a very time-consuming task and due to the increasing number of students often even infeasible. Though programming exercises can be efficiently tested against expected output, the assessment systems often only deliver feedback regarding the correctness and sometimes additionally which test cases fail. But so far, these systems are not able to identify the reason for the error. Therefore, there should also be human tutors available with which students can discuss their solution and which can help them in identifying their misconceptions. In this paper, we describe the experiences we made when we introduced blended learning to our introductory programming course, which problems occurred, and how we plan to deal with the problems.
Verification of the Extended Roofline Model for Asynchronous Many Task Runtimes

Asynchronous Many Task (AMT) runtimes promise application designers the ability to better utilize novel hardware resources and to take advantages of the idle times that might arise from the discrepancies due to mismatches between software and hardware components. To foresee possible problems between hardware and software components (described as mismatches), designers usually use models to predict and analyze application behaviors. However, current models are ill suited for the AMT crowd because of its dynamic behavior and agility. To this effect, we developed an extended roofline model that aims to provide upper bounds on execution for AMT frameworks. This work focuses on the validation and error characterization of this model using different statistical techniques and a large set of experiments to evaluate and characterize its error and its sources. We found out that in the worst case, the error can grow to an order of magnitude, however there are several techniques to increase the model accuracy given a machine configuration.
Assessing the process maturity utilized in software engineering team project courses

As part of their curriculum, many computer science departments offer an introductory software engineering course. This course normally provides an introduction to software engineering topics in conjunction with a semester long team project. The typical goals of this project are to provide students with a team based realistic software development project experience. To ensure students acquire the correct lessons from this project experience, it is essential that the teams utilize well-defined software development processes similar to those practised by leading software development organizations. Since its inception, the Software Engineering Institute Capability Maturity Model (CMM) has served as a guide for organizations seeking to improve their development practices. The CMM identifies five levels of maturity, each of which exemplifies the utilization of key software engineering practices. Organizations can utilize the CMM to assess the maturity of their development processes via a self-assessment questionnaire, Although most organizations would like to believe their processes are mature, most are at the first two levels of maturity. In an effort to assess the maturity of development practices utilized in software engineering courses, an "academic" version of the CMM questionnaire was developed. This questionnaire was distributed to a sample of software engineering instructors in an effort to assess the maturity of academic software engineering course projects. The questionnaire and the survey results are presented and discussed. The goal of this effort is to provide an assessment of current software engineering course project education. As in the commercial world, this assessment will help pinpoint areas of improvement. It will also provide a challenge to software engineering instructors to "practice what they preach " and strive for higher levels of process maturity.
Related or Duplicate: Distinguishing Similar CQA Questions via Convolutional Neural Networks

Plenty of research attempts target the automatic duplicate detection in Community Question Answering (CQA) systems and frame the task as a supervised learning problem on the question pairs. However, these methods rely on handcrafted features, leading to the difficulty of distinguishing related and duplicate questions as they are often textually similar. To tackle this issue, we propose to leverage neural network architecture to extract "deep" features to identify whether a question pair is duplicate or related. In particular, we construct question correlation matrices, which capture the word-wise similarities between questions. The constructed matrices are input to our proposed convolutional neural network (CNN), in which the convolutional operation moves through the two dimensions of the matrices. Empirical studies on a range of real-world CQA datasets confirm the effectiveness of our proposed correlation matrices and the CNN. Our method outperforms the state-of-the-art methods and achieves better classification performance.
SIGIR 2018 Workshop on eCommerce (ECOM18)

eCommerce Information Retrieval has received little attention in the academic literature, yet it is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, and others). SIGIR has for several years seen sponsorship from these kinds of organisations, who clearly value the importance of research into Information Retrieval. The purpose of this workshop is to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, and to examine how to build datasets for research into this fascinating topic. eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities (the kinds of search seen in when buying are different from those of web-page search (i.e. it is not clear how shopping and buying relate to the standard web-search interaction models)) through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings). This workshop is a follow up to the "SIGIR 2017 workshop on eCommerce (ECOM17)", which was organized at SIGIR 2017, Tokyo. In the 2018 workshop, in addition to a data challenge, we will be following up on multiple aspects that were discussed in the 2017 workshop.
Empowering children to author digital media effects for reader's theatre

The Reader's Theatre (RT) is a pedagogical activity, in which the children read aloud to an audience. Prior research established that the RT can be used to foster reading fluency and ultimately to increase reading skills [11]. However, this activity still has the margin to grow its potential. One way to support and empower children in this pedagogical activity is by allowing them to collaboratively define digital media effects that can be triggered when the reading activity takes place. In this paper, we describe STREEN (Story Reading Environmental Enrichment), an immersive multimodal learning environment, which was inspired in the classical Reader's Theatre. STREEN is being directed towards primary school children and follows a Design-Based Research approach. Following the description of the system, we share findings gathered in the co-design sessions with children and discuss the obtained design insights.
T. Alan Hall (1997). The phonology of coronals. Amsterdam: John Benjamins. Pp. 176.

This book provides a comprehensive view of coronal consonants, that is those consonants produced with the blade or tip of the tongue. Hall's main objective is to argue for a set of features by which the complete set of coronal consonants is distinguished. The analyses correlate phonetic and phonological evidence, drawing on data from over 120 languages and dialects (there is a good language index). The book takes as its point of departure the fact that coronals are unique in having a greater number of subplaces of articulation than either labials or dorsals. The bulk of the evidence comes from cross-linguistic contrasts and gaps in inventories. In other words, the evidence largely consists in showing that certain contrasts do/do not exist in various languages, or in refuting, often on phonetic grounds, contrasts that have been claimed to exist or not for given languages. The breadth of the study, and its focus on the distribution of coronals in inventories, makes this book an excellent complement to other work on coronals, such as Paradis & Prunet (1991), Hume (1992), etc. The book is made up of four chapters, a brief conclusion and two short appendices containing an IPA symbol chart and definitions of features. The first chapter defines what it means to be coronal, and shows that the set of coronal sounds constitutes a natural class. It further sets out to separate the real coronals from the palatal pretenders. To begin with, the palatal fricatives are argued to be dorsal rather than coronal. The stops, nasals and the glide [j], which are often called palatal, are shown to be ALVEOLOPALATAL. Thus sounds like [c], [n] and [j] are coronal while [9] is not. In the course of the argument, Hall concurs that coronal is privative, but claims that coronal is not underspecified, which contrasts with the position taken, for instance, in Paradis & Prunet
Possessive Pronominal Anaphor Resolution in Portuguese Written Texts

This paper describes a proposal for Portuguese possessive pronominal anaphor (PPA) resolution, a problem little considered so far. Particularly, we address the problem of Portuguese 3rd person intrasentential PPAs seu/sua/seus/suas (his/her/their/its, for human and non-human subjects in English), which constitute 30% of pronominal occurrences in our corpus (Brazilian laws about environment protection). Considering some differences between PPAs and other kinds of anaphors, such as personal or demonstrative pronouns, we define three knowledge sources (KSs) for PPA resolution: surface patterns (taking in account factors such as syntactic parallelism), possessive relationship rules and sentence centering. These knowledge sources are organized in a blackboard architecture for PPA resolution, which provides both knowledge and procedure distribution among autonomous entities (reflexive agents), each of them specialized in a particular aspect of the problem solving. The proposal has been implemented and its results are discussed at the end of the work. 1. Introduction Most work on anaphor resolution apply syntactic constraints (c-command, gender and number agreement, etc) to select for human English), pronominal (Brazilian protection). the appropriate anaphoric referent. However, these constraints are not suitable for possessive pronominal anaphor (PPA) resolution in Portuguese, which requires a more specific approach. This paper describes a resolution strategy for a problem little considered so far, PPAs "seu/sua/seus/suas" (his/her/their/its, and non-human subjects in which constitute 3 0% of occurrences in our corpus laws about environment The paper is structured as follows. We present some characteristics of Portuguese PPAs (section 2). We then describe some factors in PPA resolution and the way these factors can determine PPAs antecedents (section 3). Factors are implemented as knowledge sources in a blackboard architecture (section 4), and finally we present the results of our implementation (section 5). From the interpretation point of view, PPAs are widely different from other kinds of anaphors, such as personal or demonstrative pronouns. In this section we present some specific characteristics of Portuguese PPAs "seu/sua/seus/suas", by means of generic examples in natural language. Some of these examples, however, may be inappropriate in 1010
Large Scale Search Engine Marketing (SEM) at Airbnb

Airbnb is an online marketplace which connects hosts and guests all over the world. Our inventory includes over 4.5 million listings, which enable the travel of over 300 million guests. The growth team at Airbnb is responsible for helping travelers find Airbnb, in part by participating in ad auctions on major search platforms such as Google and Bing. In this talk, we will describe how ad- vertising efficiently on these platforms requires solving several information retrieval and machine learning problems, including query understanding, click value estimation, and realtime pacing of our expenditure.
Item Retrieval as Utility Estimation

Retrieval systems have greatly improved over the last half century, estimating relevance to a latent user need in a wide variety of areas. One common task in e-commerce and science that has not enjoyed such advancements is searching through a catalog of items. Finding a desirable item in such a catalog requires that the user specify desirable item properties, specifically desirable attribute values. Existing item retrieval systems assume the user can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database, but this is often challenging, particularly given multiple numeric attributes. Such systems avoid inferring query intent, instead requiring the user to precisely specify what matches the query. A contrasting approach would be to estimate how well items match the user's latent desires and return items ranked by this estimation. Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes. In two user studies (choosing airline tickets and meal plans) using Amazon Mechanical Turk, we evaluate our novel approach against the de facto standard of Boolean retrieval and several models proposed in the literature. We use a novel competitive game to motivate test subjects and compare methods based on the results of the subjects' initial query and their success in the game. In our experiments, our new method significantly outperformed the others, whereas the Boolean approaches had the worst performance.
Subarashii: Encounters in Japanese Spoken Language Education

Automatic speech recognition is used in a system to offer first-level, computer-based exercises in the Japanese language for beginning high school students. Implementing a preprototype version of the Subarashii system identified strengths and limitations of continuous speech recognition technology in supporting open dialogue practice, in which utterance choices are not written on the screen. Lessons learned from this implementation have led to novel methods in the development of materials for computer-based interactive spoken language education.
The storytellers project

The Storytellers Project is a library service connecting a community of senior readers to children and their families for remote reading aloud sessions. Children use the Storybell robot at home to connect with seniors willing to read, the Storybell robot transmits their reading and projects on the wall pictures of the story being read. The paper describes the people centred research process as the project is still a concept in its research phase.
POSTER: Zero-Day Evasion Attack Analysis on Race between Attack and Defense

Deep neural networks (DNNs) exhibit excellent performance in machine learning tasks such as image recognition, pattern recognition, speech recognition, and intrusion detection. However, the usage of adversarial examples, which are intentionally corrupted by noise, can lead to misclassification. As adversarial examples are serious threats to DNNs, both adversarial attacks and methods of defending against adversarial examples have been continuously studied. Zero-day adversarial examples are created with new test data and are unknown to the classifier; hence, they represent a more significant threat to DNNs. To the best of our knowledge, there are no analytical studies in the literature of zero-day adversarial examples with a focus on attack and defense methods through experiments using several scenarios. Therefore, in this study, zero-day adversarial examples are practically analyzed with an emphasis on attack and defense methods through experiments using various scenarios composed of a fixed target model and an adaptive target model. The Carlini method was used for a state-of-the-art attack, while an adversarial training method was used as a typical defense method. We used the MNIST dataset and analyzed success rates of zero-day adversarial examples, average distortions, and recognition of original samples through several scenarios of fixed and adaptive target models. Experimental results demonstrate that changing the parameters of the target model in real time leads to resistance to adversarial examples in both the fixed and adaptive target models.
Green Named Data Networking Using Renewable Energy

While communication technologies are evolving rapidly, there is still the nontrivial matter of our communication system being green. In this paper, we propose a new green approach for Named Data Networking (NDN) where content requests and caching perform towards greener content delivery. We design a forwarding and caching strategy, where we first define the greenness of nodes and identify corresponding green paths, then encourage traffic to aggregate on green paths powered by more eco-friendly renewable energy. We validate our solution with a variety of simulations using real network topologies and renewable energy statistics from the US, and the results show that applying the proposed green NDN strategy achieves significant gains.