bluuebunny/crossref_metadata_2025 huggingface dataset:
African Philosophy in the Contemporary World
Authors: Chukwujekwu Ejike Sam-Festus
Published: September 2019

This author focuses on African philosophy issues, ideas and the major trends of its development in the contemporary world. The researcher emphasizes that one of the main trends in the development of African philosophy in the contemporary world is the search for African identity, ideas of African renaissance. The development of African philosophy in the early and mid 20th century had the immense influence of the anticolonial movement, which resulted in rapid development of socio-political movements. The article discusses the most influential of them, such as negritude, Pan-Africanism, African socialism, and others. The paper is of particular interest due to the fact that the countries of this continent have undergone a different path of development. From the very moment of the discovery of the continent, the states located in it were viewed and existed as colonies of European countries, i.e. they were ruled by the metropolis, which established its political and economic power. In addition to the negative impact of the metropolis it had a positive one. With the arrival of the Europeans European culture and values came, that ultimately led to the fight for independence. The article also discusses the major stages of European colonization of the 15th-19th centuries, also the difficulties and challenges in obtaining freedom from the metropolis, which became a new impetus for the development of philosophical views. Taking into account African identity and European culture and values, the significant role of the African Renaissance and socio-political teachings are considered as a way of developing African philosophy in the modern world. The author pays special attention to the African historiographic survey, to four periodization stages of African philosophy: (early period, second period, late period, and a new era). The researcher also presents traditional thought and suggestions of a typology of tendencies by African philosophers in modern African philosophy.
Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction
Authors: Li Youru , Zhu Zhenfeng , Kong Deqiang , Xu Meixiang , Zhao Yao
Published: September 2019

Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
Authors: Li Zhongnian , Zhang Tao , Wan Peng , Zhang Daoqiang
Published: September 2019

Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of structure information of MRI images that is crucial for clinical diagnosis. To tackle this problem, we propose the Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at both local and global scale. SEGAN defines a new structure regularization called Patch Correlation Regularization (PCR) which allows for efficient extraction of structure information. In addition, to further enhance the ability to uncover structure information, we propose a novel generator SU-Net by incorporating multiple-scale convolution filters into each layer. Besides, we theoretically analyze the convergence of stochastic factors contained in training process. Experimental results show that SEGAN is able to learn target structure information and achieves state-of-theart performance for CS-MRI reconstruction.
AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction
Authors: Ma Tianle , Zhang Aidong
Published: September 2019

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the “big p, small N” problem (i.e., a relatively small number of samples with highdimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data.
Cooperative Multimodal Approach to Depression Detection in Twitter
Authors: Gui Tao , Zhu Liang , Zhang Qi , Peng Minlong , Zhou Xu , Ding Keyu , Chen Zhigang
Published: September 2019

The advent of social media has presented a promising new opportunity for the early detection of depression. To do so effectively, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about depression. The second challenge is that due to the variety of content types posted by users, it is difficult to extract many of the relevant indicator texts and images. In this work, we propose the use of a novel cooperative multi-agent model to address these challenges. From the historical posts of users, the proposed method can automatically select related indicator texts and images. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin (over 30% error reduction). In several experiments and examples, we also verify that the selected posts can successfully indicate user depression, and our model can obtained a robust performance in realistic scenarios.
Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis
Authors: Shimosaka Masamichi , Hayakawa Yuta , Tsubouchi Kota
Published: September 2019

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.
Exploiting the Contagious Effect for Employee Turnover Prediction
Authors: Teng Mingfei , Zhu Hengshu , Liu Chuanren , Zhu Chen , Xiong Hui
Published: September 2019

Talent turnover often costs a large amount of business time, money and performance. Therefore, employee turnover prediction is critical for proactive talent management. Existing approaches on turnover prediction are mainly based on profiling of employees and their working environments, while the important contagious effect of employee turnovers has been largely ignored. To this end, in this paper, we propose a contagious effect heterogeneous neural network (CEHNN) for turnover prediction by integrating the employee profiles, the environmental factors, and more importantly, the influence of turnover behaviors of co-workers. Moreover, a global attention mechanism is designed to evaluate the heterogeneous impact on potential turnover behaviors. This attention mechanism can improve the interpretability of turnover prediction and provide actionable insights for talent retention. Finally, we conduct extensive experiments and case studies on a realworld dataset from a large company to validate the effectiveness of the contagious effect for turnover prediction.
Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering
Authors: Guo Jun , Ye Jiahui
Published: September 2019

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.
TET-GAN: Text Effects Transfer via Stylization and Destylization
Authors: Yang Shuai , Liu Jiaying , Wang Wenjing , Guo Zongming
Published: September 2019

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via oneshot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.
Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning
Authors: Zhao Ji , Peng Dan , Wu Chuhan , Chen Huan , Yu Meiyu , Zheng Wanji , Ma Li , Chai Hua , Ye Jieping , Qie Xiaohu
Published: September 2019

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Authors: Zheng Panpan , Yuan Shuhan , Wu Xintao
Published: September 2019

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.
DeepDPM: Dynamic Population Mapping via Deep Neural Network
Authors: Zong Zefang , Feng Jie , Liu Kechun , Shi Hongzhi , Li Yong
Published: September 2019

Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a timeembedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.
Human-Like Sketch Object Recognition via Analogical Learning
Authors: Chen Kezhen , Rabkina Irina , McLure Matthew D. , Forbus Kenneth D.
Published: September 2019

Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.
Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks
Authors: He Shizhu , Liu Kang , An Weiting
Published: September 2019

Customers ask questions, and customer service staffs answer those questions. It is the basic service manner of customer service (CS). The progress of CS is a typical multi-round conversation. However, there are no explicit corresponding relations among conversational utterances. This paper focuses on obtaining explicit alignments of question and answer utterances in CS. It not only is an important task of dialogue analysis, but also able to obtain lots of valuable train data for learning dialogue systems. In this work, we propose end-to-end models for aligning question (Q) and answer (A) utterances in CS conversation with recurrent pointer networks (RPN). On the one hand, RPN-based alignment models are able to model the conversational contexts and the mutual influence of different Q-A alignments. On the other hand, they are able to address the issue of empty and multiple alignments for some utterances in a unified manner. We construct a dataset from an in-house online CS. The experimental results demonstrate that the proposed models are effective to learn the alignments of question and answer utterances.
Attentive Tensor Product Learning
Authors: Huang Qiuyuan , Deng Li , Wu Dapeng , Liu Chang , He Xiaodong
Published: September 2019

This paper proposes a novel neural architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures of natural language in deep learning models. ATPL exploits Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, to integrate deep learning with explicit natural language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via the TPR-based deep neural network; 2) the use of attention modules to compute TPR; and 3) the integration of TPR with typical deep learning architectures including long short-term memory and feedforward neural networks. The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. Our ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a natural language sentence. The experimental results demonstrate the effectiveness of the proposed approach in all these three natural language processing tasks.
Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases
Authors: Sharma Abhishek , Goolsbey Keith M.
Published: September 2019

Cognitive systems must reason with large bodies of general knowledge to perform complex tasks in the real world. However, due to the intractability of reasoning in large, expressive knowledge bases (KBs), many AI systems have limited reasoning capabilities. Successful cognitive systems have used a variety of machine learning and axiom selection methods to improve inference. In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps. We test the efficacy of this approach on a very large and expressive KB, Cyc. Experimental results on hundreds of queries show that this method is highly effective in reducing inference time and improving question-answering (Q/A) performance.
Deep Bayesian Optimization on Attributed Graphs
Authors: Cui Jiaxu , Yang Bo , Hu Xia
Published: September 2019

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs; existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art.
Clairvoyant Restarts in Branch-and-Bound Search Using Online Tree-Size Estimation
Authors: Anderson Daniel , Hendel Gregor , Le Bodic Pierre , Viernickel Merlin
Published: September 2019

We propose a simple and general online method to measure the search progress within the Branch-and-Bound algorithm, from which we estimate the size of the remaining search tree. We then show how this information can help solvers algorithmically at runtime by designing a restart strategy for MixedInteger Programming (MIP) solvers that decides whether to restart the search based on the current estimate of the number of remaining nodes in the tree. We refer to this type of algorithm as clairvoyant. Our clairvoyant restart strategy outperforms a state-of-the-art solver on a large set of publicly available MIP benchmark instances. It is implemented in the MIP solver SCIP and will be available in future releases.
Constraint-Based Sequential Pattern Mining with Decision Diagrams
Authors: Hosseininasab Amin , Hoeve Willem-Jan van , Cire Andre A.
Published: September 2019

Constraint-based sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram (MDD) representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.
Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization
Authors: Liu Jingchang , Xu Linli , Guo Junliang , Sheng Xin
Published: September 2019

We focus on empirical risk minimization with a composite regulariser, which has been widely applied in various machine learning tasks to introduce important structural information regarding the problem or data. In general, it is challenging to calculate the proximal operator with the composite regulariser. Recently, proximal average (PA) which involves a feasible proximal operator calculation is proposed to approximate composite regularisers. Augmented with the prevailing variance reducing (VR) stochastic methods (e.g. SVRG, SAGA), PA based algorithms would achieve a better performance. However, existing works require a fixed stepsize, which needs to be rather small to ensure that the PA approximation is sufficiently accurate. In the meantime, the smaller stepsize would incur many more iterations for convergence. In this paper, we propose two fast PA based VR stochastic methods – APA-SVRG and APA-SAGA. By initializing the stepsize with a much larger value and adaptively decreasing it, both of the proposed methods are proved to enjoy the (ô n log 1/ε + mo 1/ε) iteration complexity to achieve the accurate solutions, where m0 is the initial number of inner iterations and n is the number of samples. Moreover, experimental results demonstrate the superiority of the proposed algorithms.
A PSPACE Subclass of Dependency Quantified Boolean Formulas and Its Effective Solving
Authors: Scholl Christoph , Roland Jiang Jie-Hong , Wimmer Ralf , Ge-Ernst Aile
Published: September 2019

Dependency quantified Boolean formulas (DQBFs) are a powerful formalism, which subsumes quantified Boolean formulas (QBFs) and allows an explicit specification of dependencies of existential variables on universal variables. This enables a succinct encoding of decision problems in the NEXPTIME complexity class. As solving general DQBFs is NEXPTIME complete, in contrast to the PSPACE completeness of QBF solving, characterizing DQBF subclasses of lower computational complexity allows their effective solving and is of practical importance.Recently a DQBF proof calculus based on a notion of fork extension, in addition to resolution and universal reduction, was proposed by Rabe in 2017. We show that this calculus is in fact incomplete for general DQBFs, but complete for a subclass of DQBFs, where any two existential variables have either identical or disjoint dependency sets over the universal variables. We further characterize this DQBF subclass to be ΣP3 complete in the polynomial time hierarchy. Essentially using fork extension, a DQBF in this subclass can be converted to an equisatisfiable 3QBF with only a linear increase in formula size. We exploit this conversion for effective solving of this DQBF subclass and point out its potential as a general strategy for DQBF quantifier localization. Experimental results show that the method outperforms state-of-the-art DQBF solvers on a number of benchmarks, including the 2018 DQBF evaluation benchmarks.
Algorithms for Average Regret Minimization
Authors: Storandt Sabine , Funke Stefan
Published: September 2019

In this paper, we study a problem from the realm of multicriteria decision making in which the goal is to select from a given set S of d-dimensional objects a minimum sized subset S0 with bounded regret. Thereby, regret measures the unhappiness of users which would like to select their favorite object from set S but now can only select their favorite object from the subset S0. Previous work focused on bounding the maximum regret which is determined by the most unhappy user. We propose to consider the average regret instead which is determined by the sum of (un)happiness of all possible users. We show that this regret measure comes with desirable properties as supermodularity which allows to construct approximation algorithms. Furthermore, we introduce the regret minimizing permutation problem and discuss extensions of our algorithms to the recently proposed k-regret measure. Our theoretical results are accompanied with experiments on a variety of inputs with d up to 7.
Low-Rank Semidefinite Programming for the MAX2SAT Problem
Authors: Wang Po-Wei , Kolter J. Zico
Published: September 2019

This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches. Semidefinite programming techniques are well-known as a theoretical tool for approximating maximum satisfiability problems, but their application has traditionally been very limited by their speed and randomized nature. Our approach overcomes this difficult by using a recent approach to low-rank semidefinite programming, specialized to work in an incremental fashion suitable for use in an exact search algorithm. The method can be used both within complete or incomplete solver, and we demonstrate on a variety of problems from recent competitions. Our experiments show that the approach is faster (sometimes by orders of magnitude) than existing state-of-the-art complete and incomplete solvers, representing a substantial advance in search methods specialized for MAX2SAT problems.
Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization
Authors: Wang Yiyang , Liu Risheng , Ma Long , Song Xiaoliang
Published: September 2019

We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems. Although the task embedded strategies bring inaccuracies in sub-problem optimizations, we provide a realizable criterion to control the errors, meanwhile, to ensure robust performances with rigid theoretical analyses. By respectively embedding ADMM and a residual-type CNN in our algorithm framework, the experimental results verify both efficiency and effectiveness of embedding task-oriented strategies in coordinate descent for solving practical problems.
Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface
Authors: Xiang Qiao , Yu Haitao , Aspnes James , Le Franck , Kong Linghe , Yang Y. Richard
Published: September 2019

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds.
Pareto Optimal Allocation under Compact Uncertain Preferences
Authors: Aziz Haris , Biro Peter , De Haan Ronald , Rastegari Baharak
Published: September 2019

The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents’ preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
Authors: Li Chao , Deng Cheng , Wang Lei , Xie De , Liu Xianglong
Published: September 2019

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.
From Recommendation Systems to Facility Location Games
Authors: Ben-Porat Omer , Goren Gregory , Rosenberg Itay , Tennenholtz Moshe
Published: September 2019

Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a mediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of intervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.
Comparative Document Summarisation via Classification
Authors: Bista Umanga , Mathews Alexander , Shin Minjeong , Menon Aditya Krishna , Xie Lexing
Published: September 2019

Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.
Entity Alignment between Knowledge Graphs Using Attribute Embeddings
Authors: Trisedya Bayu Distiawan , Qi Jianzhong , Zhang Rui
Published: September 2019

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.
Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer
Authors: Chen Jin , Lian Defu , Zheng Kai
Published: September 2019

One-class collaborative filtering (OCCF) problems are vital in many applications of recommender systems, such as news and music recommendation, but suffers from sparsity issues and lacks negative examples. To address this problem, the state-of-the-arts assigned smaller weights to unobserved samples and performed low-rank approximation. However, the ground-truth ratings of unobserved samples are usually set to zero but ill-defined. In this paper, we propose a ranking-based implicit regularizer and provide a new general framework for OCCF, to avert the ground-truth ratings of unobserved samples. We then exploit it to regularize a ranking-based loss function and design efficient optimization algorithms to learn model parameters. Finally, we evaluate them on three realworld datasets. The results show that the proposed regularizer significantly improves ranking-based algorithms and that the proposed framework outperforms the state-of-the-art OCCF algorithms.
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Authors: Yang Xun , Ma Yunshan , Liao Lizi , Wang Meng , Chua Tat-Seng
Published: September 2019

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multirelational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items in a unified embedding space where a category-specific relation (category-comp-category) is modeled as a vector translation operating on the embeddings of compatible items from the corresponding categories. By this way, we not only capture the specific notion of compatibility conditioned on a specific pair of complementary categories, but also preserve the global notion of compatibility. We also design a deep fashion item encoder which exploits the complementary characteristic of visual and textual features to represent the fashion products. To the best of our knowledge, this is the first work that uses category-specific complementary relations to model the category-aware compatibility between items in a translation-based embedding space. Extensive experiments demonstrate the effectiveness of TransNFCM over the state-of-the-arts on two real-world datasets.
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
Authors: Bjorck Johan , Rappazzo Brendan H. , Chen Di , Bernstein Richard , Wrege Peter H. , Gomes Carla P.
Published: September 2019

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth’s biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a costeffective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies.
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Authors: Chen Di , Gomes Carla P.
Published: September 2019

Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased — in particular, aligned more with the citizens’ preferences than with scientific objectives. We propose the Shift Compensation Network (SCN), an end-to-end learning scheme which learns the shift from the scientific objectives to the biased data while compensating for the shift by re-weighting the training data. Applied to bird observational data from the citizen science project eBird, we demonstrate how SCN quantifies the data distribution shift and outperforms supervised learning models that do not address the data bias. Compared with competing models in the context of covariate shift, we further demonstrate the advantage of SCN in both its effectiveness and its capability of handling massive high-dimensional data.
Understanding Dropouts in MOOCs
Authors: Feng Wenzheng , Tang Jie , Liu Tracy Xiao
Published: September 2019

Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).What are the major factors that cause the users to drop out?What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high correlation between dropouts of different courses and strong influence between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.
PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification
Authors: Golany Tomer , Radinsky Kira
Published: September 2019

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting.