Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Camouflaged Needle in a Haystack?: Pornographic Products Detection via Berrypicking Tree Model

It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.
Explanatory and Actionable Debugging for Machine Learning: A TableQA Demonstration

Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it "actionable" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.
The homomorphism property in query containment and data integration

We often add arithmetic to extend the expressiveness of query languages, tuple generating dependencies and data exchange mappings, and study the complexity of problems such as testing query containment and finding certain answers. When adding arithmetic comparisons, the complexity of such problems is higher than the complexity of their counterparts without them. It has been observed that we can achieve lower complexity if we restrict some of the comparisons to be closed or open semi-interval comparisons. Here, focusing on the problem of containment for conjunctive queries with arithmetic comparisons (CQAC queries, for short), we prove upper bounds on the computational complexity. Our main methodology uses a general property of CQACs and tuple generating dependencies with arithmetic comparisons which is called the homomorphism property. When the homomorphism property holds, then the complexity of the above problems can be improved. We syntactically characterize subclasses of CQACs queries that have the homomorphism property, and we give a detailed proof that contains components that can be used to prove more results of the same kind. Similar methodology can be applied to achieve better upper bounds on the complexity of testing query containment under dependencies, finding query rewritings, and finding certain answers in data exchange. This is done by improving the complexity of the chase algorithm for tuple generating dependencies with arithmetic comparisons.
HSSN: an ontology for hybrid semantic sensor networks

Semantic web techniques (e.g., ontologies) have been recently adopted for sensor network modeling. However, existing works do not fully address these challenges: (i) representing different sensor types (e.g., mobile/static sensors) to enrich the network with different data and ensure better coverage; (ii) representing a variety of platforms (e.g., environments, devices) for sensor deployment, thus, integrating new components (e.g., mobile phones); (iii) representing the diverse data (scalar/multimedia) needed for various applications (e.g., event detection); and (iv) proposing a generic model to allow re-usability in various application domains. In this paper, we propose HSSN, an ontology that extends the Semantic Sensor Network (SSN) ontology which is already re-usable and considers various platforms. We extend the representation of sensors, sensed data, and deployment environments to cope with these challenges. We evaluate the consistency, accuracy, clarity, and performance of HSSN.
Hot Topic-Aware Retweet Prediction with Masked Self-attentive Model

Social media users create millions of microblog entries on various topics each day. Retweet behaviour play a crucial role in spreading topics on social media. Retweet prediction task has received considerable attention in recent years. The majority of existing retweet prediction methods are focus on modeling user preference by utilizing various information, such as user profiles, user post history, user following relationships, etc. Yet, the users exposures towards real-time posting from their followees contribute significantly to making retweet predictions, considering that the users may participate into the hot topics discussed by their followees rather than be limited to their previous interests. To make efficient use of hot topics, we propose a novel masked self-attentive model to perform the retweet prediction task by perceiving the hot topics discussed by the users' followees. We incorporate the posting histories of users with external memory and utilize a hierarchical attention mechanism to construct the users' interests. Hence, our model can be jointly hot-topic aware and user interests aware to make a final prediction. Experimental results on a dataset collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods.
From Query Variations To Learned Relevance Modeling

Thinking in terms of an information need instead of simply queries provides a rich set of new opportunities in improving the effectiveness of search [6]. User queries may vary a lot for a single information need [3, 9], as a query is often under-specified. Many techniques have been proposed to enrich a single query, for example relevance modeling [8]. These techniques focus on improving overall system performance but may fail in some occasions. Instead of optimizing for a single query, another direction is to use multiple query variations to represent an information need. With fusion techniques, query variations can improve system performance while failing fewer queries [2, 6]. Using query variations is a simple and appealing idea, but collecting them is a manually intensive task. Existing high quality query variations are collected from crowd-sourcing workers and domain experts [1]. Therefore the process cannot be automated and deployed in a production system. Researchers have investigated random walks on click graphs [5] and relevance models [4] to automatically generate query variations, but experiments show that there is still a big gap between automatic variations and human generated ones. In this PhD project,we focus on automatic query variation generation and try to reduce the gap to human generated variations.We plan to leverage advances of transfer learning and classic relevance modeling to generate high quality query variations to improve system performance.
TrecTools: an Open-source Python Library for Information Retrieval Practitioners Involved in TREC-like Campaigns

This paper introduces TrecTools, a Python library for assisting Information Retrieval (IR) practitioners with TREC-like campaigns. IR practitioners tasked with activities like building test collections, evaluating systems, or analysing results from empirical experiments commonly have to resort to use a number of different software tools and scripts that each perform an individual functionality - and at times they even have to implement ad-hoc scripts of their own. TrecTools aims to provide a unified environment for performing these common activities. Written in the most popular programming language for Data Science, Python, TrecTools offers an object-oriented, easily extensible library. Existing systems, e.g., trec_eval, have considerable barrier to entry when it comes to modify or extend them. Furthermore, many existing IR measures and tools are implemented independently of each other, in different programming languages. TrecTools seeks to lower the barrier to entry and to unify existing tools, frameworks and activities into one common umbrella. Widespread adoption of a centralised solution for developing, evaluating, and analysing TREC-like campaigns will ease the burden on organisers and provide participants and users with a standard environment for common IR experimental activities. TrecTools is distributed as an open source library under the MIT license at https://github.com/joaopalotti/trectools
Parrot: A Python-based Interactive Platform for Information Retrieval Research

Open source softwares play an important role in information retrieval research. Most of the existing open source information retrieval systems are implemented in Java or C++ programming language. In this paper, we propose Parrot1, a Python-based interactive platform for information retrieval research. The proposed platform has mainly three advantages in comparison with the existing retrieval systems: (1) It is integrated with Jupyter Notebook, an interactive programming platform which has proved to be effective for data scientists to tackle big data and AI problems. As a result, users can interactively visualize and diagnose a retrieval model; (2) As an application written in Python, it can be easily used in combination with the popular deep learning frameworks such as Tersorflow and Pytorch; (3) It is designed especially for researchers. Less code is needed to create a new retrieval model or to modify an existing one. Our efforts have focused on three functionalists: good usability, interactive programming, and good interoperability with the popular deep learning frameworks. To confirm the performance of the proposed system, we conduct comparative experiments on a number of standard test collections. The experimental results show that the proposed system is both efficient and effective, providing a practical framework for researchers in information retrieval.
PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data

Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.
Searching for Communities: a Facebook Way

Giving people the power to build community is central to Facebook's mission. Technically, searching for communities poses very different challenges compared to the standard IR problems. First, there is a vocabulary mismatch problem since most of the content of the communities is private. Second, the common labeling strategies based on human ratings and clicks do not work well due to limited public content available to third-party raters and users at search time. Finally, community search has a dual objective of satisfying searchers and growing the number of active communities. While A/B testing is a well known approach for assessing the former, it is an open question on how to measure progress on the latter. This talk discusses these challenges in depth and describes our solution.
Human articular movement algorithm to simulate muscle contraction and embedded tissue deformation

The visualization of human articular movements associated with internal deformation is critical for many fields including biomechanics. In this work, we present a novel algorithm to describe realistic articular movement in a human model, which effectivelly combines free-form deformation and simple constrained deformation. The algorithm provides the articular movement with contractions/extensions in muscles followed by the deformations of embedded tissues, such as blood vessels, lymphatics, and nerves, treating the bones as a rigid body. An arm bending simulation of a human model using the algorithm was performed. The proposed algorithm has the potential for development as a hybrid method that combines multi-physical simulations and geometric modeling.
Beyond Keyword Targeting: An End-to-End Ad Retrieval Framework for Sponsored Search

As the main revenue source for search engines, sponsored search system retrieves and allocates ads to display on the search result pages. Keyword targeting is widely adopted by most sponsored search systems as the basic model for expressing the advertiser's business and retrieving related ads. In this targeting model, the advertiser should cautiously select lots of keywords relevant to their business to optimize their campaigns, and the sponsored search system retrieves ads based on the relevance between queries and keywords. However, since there is a huge inventory of possible queries and the new queries grow dramatically, it is a great challenge for advertisers to identify and collect lots of relevant bid keywords for their ads, and it also takes great effort to select and maintain high-quality keywords and set corresponding match types for them. In the meantime, the keyword targeting leads to a multi-stage retrieval architecture as it contains the matching between query and keywords and the matching between keywords and ads. The retrieval funnel based on keyword targeting cannot achieve straightforward and optimal matching between search queries and ads. Consequently, traditional keyword targeting method gradually becomes the bottleneck of optimizing advertisers' campaigns and improving the monetization of search ads. In this paper, we present an end-to-end ad retrieval framework for sponsored search. This framework can break the limits of keyword targeting and can achieve direct matching from query to related ads. The framework has been deployed at Baidu's sponsored search system and the experimental result validates its effectiveness. We will describe the design and architecture of this framework, and hope that this framework can give some inspiration for the sponsored search industry.
Which Diversity Evaluation Measures Are "Good"?

This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users' preferences. The gold preferences were contructed by hiring 15 assessors, who independently examined 1,127 SERP pairs and made preference assessments. Two sets of preference assessments were obtained: one based on a relevance question "Which SERP is more relevant? '' and the other based on a diversity question "Which SERP is likely to satisfy a higher number of users? '' To our knowledge, our study is the first to have collected diversity preference assessments in this way and evaluated diversity measures successfully. Our main results are that (a) Popular adhoc IR measures such as nDCG actually align quite well with the gold relevance preferences; and that (b) While the ♯-measures align well with the gold diversity preferences, intent-aware measures perform relatively poorly. Moreover, as by-products of our analysis of existing evaluation measures, we define new adhoc measures called iRBU (intentwise Rank-Biased Utility) and EBR (Expected Blended Ratio); we demonstrate that an instance of iRBU performs as well as nDCG when compared to the gold relevance preferences. On the other hand, the original RBU, a recently-proposed diversity measure, underperforms the best ♯-measures when compared to the gold diversity preferences.
Small steps in physics simulation

In this paper we re-examine the idea that implicit integrators with large time steps offer the best stability/performance trade-off for stiff systems. We make the surprising observation that performing a single large time step with n constraint solver iterations is less effective than computing n smaller time steps, each with a single constraint solver iteration. Based on this observation, our approach is to split every visual time step into n substeps of length Δt/n and to perform a single iteration of extended position-based dynamics (XPBD) in each such substep. When compared to a traditional implicit integrator with large time steps we find constraint error and damping are significantly reduced. When compared to an explicit integrator we find that our method is more stable and robust for a wider range of stiffness parameters. This result holds even when compared against more sophisticated implicit solvers based on Krylov methods. Our method is straightforward to implement, and is not sensitive to matrix conditioning nor is it to overconstrained problems.
Interpretable Fashion Matching with Rich Attributes

Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored. This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a corpus of matched pairs of items, we not only can predict the compatibility score of unseen pairs, but also learn the interpretable patterns that lead to a good match, e.g., white T-shirt matches with black trouser. We propose a new solution named A ttribute-based I nterpretable C ompatibility (AIC) method, which consists of three modules: 1) a tree-based module that extracts decision rules on matching prediction; 2) an embedding module that learns vector representation for a rule by accounting for the attribute semantics; and 3) a joint modeling module that unifies the visual embedding and rule embedding to predict the matching score. To justify our proposal, we contribute a new Lookastic dataset with fashion attributes available. Extensive experiments show that AIC not only outperforms several state-of-the-art methods, but also provides good interpretability on matching decisions.
An Neo4j implementation for designing fuzzy graph databases

Imperfect data express their meanings incompletely and the Theory of Fuzzy Sets arises as mathematically support for the interpretation of those data. The union of these concepts describes a new data type, called fuzzy data. We discuss the use of fuzzy data in Graph Databases. Previous works define fuzzy queries on Graph Databases but the data stored is a regular and perfect data. In that works we extent a Graph Database and lets the users store information in the fuzzy and imperfect data. The databases management system Neo4j is proposed to developed the application and the Cypher database languages to describes the imperfect data definitions. We uses a social network use case to illustrated the works,
EigenFit for consistent elastodynamic simulation across mesh resolution

Elastodynamic system simulation is a key procedure in computer graphics and robotics applications. To enable these simulations, the governing differential system is discretized in space (employing FEM) and then in time. For many simulation-based applications keeping the spatial resolution of the computational mesh effectively coarse is crucial for securing acceptable computational efficiency. However, this can introduce numerical stiffening effects that impede visual accuracy. We propose and demonstrate, for both linear and nonlinear force models, a new method called EigenFit that improves the consistency and accuracy of the lower energy, primary deformation modes, as the spatial mesh resolution is coarsened. EigenFit applies a partial spectral decomposition, solving a generalized eigenvalue problem in the leading mode subspace and then replacing the first several eigenvalues of the coarse mesh by those of the fine one at rest. EigenFit's performance relies on a novel subspace model reduction technique which restricts the spectral decomposition to finding just a few of the leading eigenmodes. We demonstrate its efficacy on a number of objects with both homogenous and heterogenous material distributions.
CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation

In e-commerce, users' demands are not only conditioned by their profile and preferences, but also by their recent purchases that may generate new demands, as well as periodical demands that depend on purchases made some time ago. We call them respectively short-term demands and long-term demands. In this paper, we propose a novel self-attentive Continuous-Time Recommendation model (CTRec) for capturing the evolving demands of users over time. For modeling such time-sensitive demands, a Demand-aware Hawkes Process (DHP) framework is designed in CTRec to learn from the discrete purchase records of users. More specifically, a convolutional neural network is utilized to capture the short-term demands; and a self-attention mechanism is employed to capture the periodical purchase cycles of long-term demands. All types of demands are fused in DHP to make final continuous-time recommendations. We conduct extensive experiments on four real-world commercial datasets to demonstrate that CTRec is effective for general sequential recommendation problems, including next-item and next-session/basket recommendations. We observe in particular that CTRec is capable of learning the purchase cycles of products and estimating the purchase time of a product given a user.