bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback
Authors: Chen Kun , Cai Kechao , Huang Longbo , Lui John C.S. 
Published: July 2018

The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the attractiveness of the link itself, revenue can only be obtained from user actions after clicks, e.g., purchasing after being directed to the product pages by recommendation links. Thus, web links have an intrinsic multi-level feedback structure. With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold. The key challenge of the problem is that each link's multi-level feedbacks are stochastic, and unobservable unless the link is selected. We model this problem with a constrained stochastic multi-armed bandit formulation, and design an efficient link selection algorithm, called Constrained Upper Confidence Bound algorithm (Con-UCB). We prove O(sqrt(T ln(T))) bounds on both regret and violation of the attractiveness constraint. We also conduct extensive experiments on three real-world datasets, and show that Con-UCB outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.
Interpretable Drug Target Prediction Using Deep Neural Representation
Authors: Gao Kyle Yingkai , Fokoue Achille , Luo Heng , Iyengar Arun , Dey Sanjoy , Zhang Ping 
Published: July 2018

The identification of drug-target interactions (DTIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins. Traditional DTI prediction methods are either time consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). In this work, we propose an end-to-end neural network model that predicts DTIs directly from low level representations. In addition to making predictions, our model provides biological interpretation using two-way attention mechanism. Instead of using simplified settings where a dataset is evaluated as a whole, we designed an evaluation dataset from BindingDB following more realistic settings where predictions of unobserved examples (proteins and drugs) have to be made. We experimentally compared our model with matrix factorization, similarity-based methods, and a previous deep learning approach. Overall, the results show that our model outperforms other approaches without requiring domain knowledge and feature engineering. In a case study, we illustrated the ability of our approach to provide biological insights to interpret the predictions.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Authors: Ma Tengfei , Xiao Cao , Zhou Jiayu , Wang Fei 
Published: July 2018

Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.
Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information
Authors: Wang Yang , Xiao Yiwei , Xie Xike , Chen Ruoyu , Liu Hengchang 
Published: July 2018

Recent advances in video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.
Tractable (Simple) Contests
Authors: Levy Priel , Sarne David , Aumann Yonatan 
Published: July 2018

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.
3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces
Authors: Yang Liang , Guo Yuanfang , Jin Di , Fu Huazhu , Cao Xiaochun 
Published: July 2018

Combinational network embedding, which learns the node representation by exploring both topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other. Most of the existing methods either consider the topological and non-topological information being aligned or possess predetermined preferences during the embedding process.Unfortunately, previous methods fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts. To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework. The proposed model automatically leans to the information which is more discriminative.The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.
DyNMF: Role Analytics in Dynamic Social Networks
Authors: Pei Yulong , Zhang Jianpeng , Fletcher George , Pechenizkiy Mykola 
Published: July 2018

Roles of nodes in a social network (SN) represent their functions, responsibilities or behaviors within the SN. Roles typically evolve over time, making role analytics a challenging problem. Previous studies either neglect role transition analysis or perform role discovery and role transition learning separately, leading to inefficiencies and limited transition analysis. We propose a novel dynamic non-negative matrix factorization (DyNMF) approach to simultaneously discover roles and learn role transitions. DyNMF explicitly models temporal information by introducing a role transition matrix and clusters nodes in SNs from two views: the current view and the historical view. The current view captures structural information from the current SN snapshot and the historical view captures role transitions by looking at roles in past SN snapshots. DyNMF efficiently provides more effective analytics capabilities, regularizing roles by temporal smoothness of role transitions and reducing uncertainties and inconsistencies between snapshots. Experiments on both synthetic and real-world SNs demonstrate the advantages of DyNMF in discovering and predicting roles and role transitions.
Axiomatization of the PageRank Centrality
Authors: Wąs Tomasz , Skibski Oskar 
Published: July 2018

We propose an axiomatization of PageRank. Specifically, we introduce five simple axioms—Foreseeability, Outgoing Homogeneity, Monotonicity, Merging, and Dummy Node—and show that PageRank is the only centrality measure that satisfies all of them. Our axioms give a new conceptual and theoretical underpinnings of PageRank and show how it differs from other centralities.
What Game Are We Playing? End-to-end Learning in Normal and Extensive Form Games
Authors: Ling Chun Kai , Fang Fei , Kolter J. Zico 
Published: July 2018

Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents. This paper deals with the relatively under-explored but equally important "inverse" setting, where the parameters of the underlying game are not known to all agents, but must be learned through observations. We propose a differentiable, end-to-end learning framework for addressing this task. In particular, we consider a regularized version of the game, equivalent to a particular form of quantal response equilibrium, and develop 1) a primal-dual Newton method for finding such equilibrium points in both normal and extensive form games; and 2) a backpropagation method that lets us analytically compute gradients of all relevant game parameters through the solution itself. This ultimately lets us learn the game by training in an end-to-end fashion, effectively by integrating a "differentiable game solver" into the loop of larger deep network architectures. We demonstrate the effectiveness of the learning method in several settings including poker and security game tasks.
Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer
Authors: Feng Xiaocheng , Feng Xiachong , Qin Bing , Feng Zhangyin , Liu Ting 
Published: July 2018

Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources and lack of annotated data, taggers tend to have lower performances.To narrow this gap, we propose three novel strategies to enrich the semantic representations of low resource languages: we first develop neural networks to improve low resource word representations by knowledge transfer from high resource language using bilingual lexicons. Further, a lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections.Thirdly, we regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into our neural architecture. Experiments on two low resource languages (including Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8\% improvement). Moreover, on Chinese OntoNotes 4.0 dataset, our approach achieved an F-score of 83.07\% with 2.91\% absolute gain compared to the state-of-the-art results.
Automata-Theoretic Foundations of FOND Planning for LTLf and LDLf Goals
Authors: De Giacomo Giuseppe , Rubin Sasha 
Published: July 2018

We study planning for LTLf and LDLf temporally extended goals in nondeterministic fully observable domains (FOND). We consider both strong and strong cyclic plans, and develop foundational automata-based techniques to deal with both cases. Using these techniques we provide the computational characterization of both problems, separating the complexity in the size of the domain specification from that in the size of the formula. Specifically we establish them to be EXPTIME-complete and 2EXPTIME-complete, respectively, for both problems. In doing so, we also show 2EXPTIME-hardness for strong cyclic plans, which was open.
Active Object Reconstruction Using a Guided View Planner
Authors: Yang Xin , Wang Yuanbo , Wang Yaru , Yin Baocai , Zhang Qiang , Wei Xiaopeng , Fu Hongbo 
Published: July 2018

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.
Specific water consumption for households in 2014
Authors: Meyer Chloé 
Published: July 2018

This water consumption is expressed in liters/capita/day. The consumption of potable water is widely variable, with a large gap between cities. Household consumption per capita varies from 28 to 631 liters per day, a factor of 20.
The full data are available online. For more information, visit: www.waterstatistics.org

Drinking water
Supply
Presence of water in 397 water points in Mexico (2013-2016)
Authors: Meyer Chloé 
Published: July 2018

This layer identifies if any water was available at the water point on the day of the visit (recognizing that it may be a limited flow) in Mexico. Data was gathered from the Water Point Data Exchange (WPDx) platform, a tool established for sharing water point data throughout the global water sector.
The goal of the Water Point Data Exchange is to simplify the way water point data is shared so all stakeholders can work more efficiently.
For more information, visit the WPDx website at https://www.waterpointdata.org/

Accessibility
Drinking water
Supply
Presence of water in 39 water points in Colombia (2013)
Authors: Meyer Chloé 
Published: July 2018

This layer identifies if any water was available at the water point on the day of the visit (recognizing that it may be a limited flow) in Honduras. Data was gathered from the Water Point Data Exchange (WPDx) platform, a tool established for sharing water point data throughout the global water sector.
The goal of the Water Point Data Exchange is to simplify the way water point data is shared so all stakeholders can work more efficiently.
For more information, visit the WPDx website at https://www.waterpointdata.org/

Drinking water
Storage
Supply
Estimation of Dispersion Phenomena for Selected Sites in Babylon Governorate
Authors: Abed Abdulkareem H. 
Published: July 2018

This investigation is conducted to estimate the dispersion phenomena for selected sites in Babylon governorate (ALkheder – Almossiab, ALsadda, ALbunafaa, ALhashmia,).
The main aim of this investigation is to study dispersion phenomena, and identification and classification of soils to (high, intermediate, and non-dispersion) depending on special test methods in dispersion phenomena. The experimental tests that were done; indexes, physical, and chemical tests to natural soils and special tests were done to the identification and classification of dispersion soils.
The results of crumb test showed weak dispersion (Grade 2) for AL sadda soil, but the soils of ((ALkhezer – Almossaab, AL.bunafaa, AL.hashmia) showed intermediate dispersion (Grade 3).
The results of the double hydrometer test showed that ALkheder – Al.mossaab, and ALbunafaa were of intermediate dispersion soils, and for AL.hashmia, and AL sadda soils were non – dispersive.
The results of slacking test indicated that (ALkheder - Almossaab, ALbunafeej) were dispersive soils and (AL.hashmia, ALsadda soils) were non– dispersive.
While the results of soluble salts in pore water test indicated that all soils were non–dispersive but with different ratios according to its position in zone B of the classification chart.
High Sensitive MEMS Intraocular Capacitive Pressure Sensor (Glaucoma)
Authors: Abbas Tania Mohammed , Al-Gayem Qais Kareem 
Published: July 2018

Micro Electro Mechanical Systems (MEMS) are a small-scale technology that was largely adopted by the IC industry and applied to miniaturize of all systems (electrical systems, mechanical, optical, fluidic, magnetic, etc.). Minimization has been accomplished with small manufacturing processes. A Capacitive pressure sensor is simply a diaphragm-type device in which the diaphragm displacement is determined by measuring the capacitance change between the diaphragm and a metal plate that is close to it.
For this purpose, intraocular pressure sensors are important in detection and treatment of an incurable disease called glaucoma. To improve the sensitivity of the capacitive pressure sensor, low stress doped polysilicon material is used as a biocompatible material. Glaucoma is a group of eye diseases that occurs by high intraocular pressure (IOP). IOP is the pressure exerted by the ocular fluid called aqueous humor (the clear fluid inside the eye) that fills the anterior chamber of the eye
The results Shows the simulated relation between capacitance and pressure for clamped ++silicon and polysilicon clamped. It can be seen from figure that the initial capacitance for clamped p++ silicon is about 1.81 pF the capacitance varies from 1.81 to 2.162 pF for clamped p++silicon and clamped polysilicon diaphragm, respectively, so the total variation of the capacitance. This result shows the use of poly silicon material in diaphragm is high sensitivity than the p++ silicon.
MAKROALGA DI DAERAH INTERTIDAL PULAU LOMBOK BAGIAN SELATAN
Authors: Lalu Japa, Ahmad Raksun Karnan, Didik Santoso, 
Published: July 2018

AbstrakMacroalga adalah sumber daya penting tidak hanya untuk komunitas intertidal, tetapi juga untuk manusia, seperti makanan, kosmetik, dan farmasi. Hal ini lebih mungkin bahwa sebagai bagian dari daerah tropis, zona intertidal Lombok memiliki keragaman makroalga yang tinggi. Sayangnya, keberadaan biota ini, terutama di daerah ini, sedikit diketahui. Makalah ini menggambarkan keanekaragaman hayati dan keadaan makroalga di Zona Intertidal Selatan Pulau Lombok. Informasi tentang keberadaan biota ini sangat membantu pemerintah dan masyarakat untuk mendapatkan sumber senyawa baru yang berharga yang diperlukan untuk kehidupan kita Kata kunci: zona intertidal, Lombok, makroalga.ÃÂ ÃÂ ÃÂ ÃÂ AbstractÃÂ Macroalga is an important resources not only for intertidal communities, but also for human, such as food, cosmetic, and pharmacy. It is more likely that as a part of tropical area, intertidal zone of Lombok has a high diversity of macroalga. Unfortunately, the existency of this biota, mainly in this area, are litle known. This paper describe the biodiversity and state of macroalga in the Southern Intertidal Zone of Lombok Island. Information ÃÂ on ÃÂ the ÃÂ existency ÃÂ of ÃÂ this ÃÂ biota ÃÂ is ÃÂ veryÃÂ helpful ÃÂ for ÃÂ government ÃÂ and community to obtain new other valuable compound sources required for our life.ÃÂ ÃÂ Key words: intertidal zone, Lombok, macroalga.ÃÂ
Trade Liberalization-Trade Revenue Nexus: An Empirical Enquiry of Trade Laffer Curve of Pakistan
Authors: Ahmad Khalil 
Published: July 2018

This study tries to explore the trade revenue implication of trade liberalization for Pakistan. Laffer curve approach is used to see the non-linear relationship between trade openness and trade tax revenue. Empirical evidence shows that trade openness has constructive impact on trade tax revenue in linear term. The non-linear relationship has also been explored in the presence of other macroeconomic variables. The marginal impact of non-liner squire trade liberalization has adverse effect on trade tax revenue. In the long run, the empirical investigation proves the existence of inverted U-shaped behavior between trade liberalization and trade tax revenue in Pakistan but this behavior does not exist in short run.
Manfaat Media Flipchart dan Spanduk dalam Perilaku Kesehatan 1000 HPK di Sulawesi Tengah
Authors: Nasrul Nasrul , Zainul Zainul , Hafid Fahmi , Taqwin Taqwin 
Published: July 2018

Flipchart dan spanduk merupakan media komunikasi perubahan perilaku yang dapat meningkatkan efektifitas dari upaya program spesifik 1000 Hari Pertama Kehidupan (HPK). Penelitian bertujuan membuktikan manfaat media flipchart dan spanduk dalam perilaku kesehatan 1000 HPK di Sulawesi Tengah. Desain quasi eksperimen pretest and post test with control design yang terdiri tiga kelompok flipchart (n=32), spanduk (n=32) dan kontrol (n=31). Intervensi selama 6 bulan di Kabupaten Sigi, Touna dan Bangkep mulai 15 April hingga 28 Oktober 2016. Populasi penelitian adalah ibu yang memiliki anak umur 6-23 bulan. Sampel sebanyak 95 orang. Data dianalisis dengan uji chi-square, paired t test dan anova test. Hasil penelitian menunjukkan setelah intervensi 6 bulan terdapat peningkatan rerata perilaku 0,53±0,8 pada kelompok I dengan intervensi flipchart dan peningkatan rerata perilaku 0.71±0,7 pada kelompok II dengan intervensi spanduk. Peningkatan rerata perilaku antar kelompok I dengan intervensi flipchart, kelompok II dengan intervensi spanduk dan kontrol berbeda bermakna secara statistik (p=0,002). Perilaku yang meningkat secara signifikan adalah perilaku menimbang anak secara teratur. Flipchart dan spanduk bermanfaat meningkatkan perilaku kesehatan 1000 HPK di Sulawesi Tengah.