bluuebunny/crossref_metadata_2025 huggingface dataset:
DMC: A Distributed Model Counter
Authors: Lagniez Jean-Marie , Marquis Pierre , Szczepanski Nicolas
Published: July 2018

We present and evaluate DMC, a distributed model counter for propositional CNF formulae based on the state-of-the-art sequential model counter D4. DMC can take advantage of a (possibly large) number of sequential model counters running on (possibly heterogeneous) computing units spread over a network of computers. For ensuring an efficient workload distribution, the model counting task is shared between the model counters following a policy close to work stealing. The number and the sizes of the messages which are exchanged by the jobs are kept small. The results obtained show DMC as a much more efficient counter than D4, the distribution of the computation yielding large improvements for some benchmarks. DMC appears also as a serious challenger to the parallel model counter CountAntom and to the distributed model counter dCountAntom.
Anytime Focal Search with Applications
Authors: Cohen Liron , Greco Matias , Ma Hang , Hernandez Carlos , Felner Ariel , Kumar T. K. Satish , Koenig Sven
Published: July 2018

Focal search (FS) is a bounded-suboptimal search (BSS) variant of A*. Like A*, it uses an open list whose states are sorted in increasing order of their f-values. Unlike A*, it also uses a focal list containing all states from the open list whose f-values are no larger than a suboptimality factor times the smallest f-value in the open list. In this paper, we develop an anytime version of FS, called anytime FS (AFS), that is useful when deliberation time is limited. AFS finds a "good" solution quickly and refines it to better and better solutions if time allows. It does this refinement efficiently by reusing previous search efforts. On the theoretical side, we show that AFS is bounded suboptimal and that anytime potential search (ATPS/ANA*), a state-of-the-art anytime bounded-cost search (BCS) variant of A*, is a special case of AFS. In doing so, we bridge the gap between anytime search algorithms based on BSS and BCS. We also identify different properties of priority functions, used to sort the focal list, that may allow for efficient reuse of previous search efforts. On the experimental side, we demonstrate the usefulness of AFS for solving hard combinatorial problems, such as the generalized covering traveling salesman problem and the multi-agent pathfinding problem.
Distributed Pareto Optimization for Subset Selection
Authors: Qian Chao , Li Guiying , Feng Chao , Tang Ke
Published: July 2018

The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.
A Fast Local Search Algorithm for Minimum Weight Dominating Set Problem on Massive Graphs
Authors: Wang Yiyuan , Cai Shaowei , Chen Jiejiang , Yin Minghao
Published: July 2018

The minimum weight dominating set (MWDS) problem is NP-hard and also important in many applications. Recent heuristic MWDS algorithms can hardly solve massive real world graphs effectively. In this paper, we design a fast local search algorithm called FastMWDS for the MWDS problem, which aims to obtain a good solution on massive graphs within a short time. In this novel local search framework, we propose two ideas to make it effective. Firstly, we design a new fast construction procedure with four reduction rules to cut down the size of massive graphs. Secondly, we propose the three-valued two-level configuration checking strategy to improve local search, which is interestingly a variant of configuration checking (CC) with two levels and multiple values. Experiment results on a broad range of massive real world graphs show that FastMWDS finds much better solutions than state of the art MWDS algorithms.
Algorithms for Fair Load Shedding in Developing Countries
Authors: Oluwasuji Olabambo I. , Malik Obaid , Zhang Jie , Ramchurn Sarvapali D.
Published: July 2018

Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. During load shedding, many homes are left without electricity, causing them inconvenience and discomfort. In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity. We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes.
Probabilistic bipolar abstract argumentation frameworks: complexity results
Authors: Fazzinga Bettina , Flesca Sergio , Furfaro Filippo
Published: July 2018

Probabilistic Bipolar Abstract Argumentation Frameworks (prBAFs), combining the possibility of specifying supports between arguments with a probabilistic modeling of the uncertainty, are considered, and the complexity of the fundamentalproblem of computing extensions' probabilities is addressed.The most popular semantics of supports and extensions are considered, as well as different paradigms for defining the probabilistic encoding of the uncertainty.Interestingly, the presence of supports, which does not alter the complexity of verifying extensions in the deterministic case, is shown to introduce a new source of complexity in some probabilistic settings, for which tractable cases are also identified.
Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method
Authors: Gu Shuqin , Hou Yuexian , Zhang Lipeng , Zhang Yazhou
Published: July 2018

Although Deep Neural Networks (DNNs) have achieved excellent performance in many tasks, improving the generalization capacity of DNNs still remains a challenge. In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs. EDM can be applied to hidden layers in fully connected neural networks or convolutional neural networks. We treat each hidden layer as an ensemble of several base learners through dividing all the hidden units into several non-overlap groups, and each group will be viewed as a base learner. EDM encourages DNNs to learn more diverse representations by minimizing the covariance between all base learners during the training step. Experimental results on MNIST and CIFAR datasets demonstrate that EDM can effectively reduce the overfitting and improve the generalization capacity of DNNs
Doubly Aligned Incomplete Multi-view Clustering
Authors: Hu Menglei , Chen Songcan
Published: July 2018

Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of  L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.
Structured Inference for Recurrent Hidden Semi-markov Model
Authors: Liu Hao , He Lirong , Bai Haoli , Dai Bo , Bai Kun , Xu Zenglin
Published: July 2018

Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into  Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.
Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
Authors: Lu Canyi , Feng Jiashi , Lin Zhouchen , Yan Shuicheng
Published: July 2018

The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] is an interesting convex penalty induced by the tensor SVD [Kilmer and Martin, 2011]. It plays a similar role as the matrix nuclear norm which is the convex surrogate of the matrix rank. Considering that the TNN based Tensor Robust PCA [Lu et al., 2018a] is an elegant extension of Robust PCA with a similar tight recovery bound, it is natural to solve other low rank tensor recovery problems extended from the matrix cases. However, the extensions and proofs are generally tedious. The general atomic norm provides a unified view of low-complexity structures induced norms, e.g., the l1-norm and nuclear norm. The sharp estimates of the required number of generic measurements for exact recovery based on the atomic norm are known in the literature. In this work, with a careful choice of the atomic set, we prove that TNN is a special atomic norm. Then by computing the Gaussian width of certain cone which is necessary for the sharp estimate, we achieve a simple bound for guaranteed low tubal rank tensor recovery from Gaussian measurements. Specifically, we show that by solving a TNN minimization problem, the underlying tensor of size n1×n2×n3 with tubal rank r can be exactly recovered when the given number of Gaussian measurements is O(r(n1+n2−r)n3). It is order optimal when comparing with the degrees of freedom r(n1+n2−r)n3. Beyond the Gaussian mapping, we also give the recovery guarantee of tensor completion based on the uniform random mapping by TNN minimization. Numerical experiments verify our theoretical results.
SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing
Authors: Luo Xin , Yin Xiao-Ya , Nie Liqiang , Song Xuemeng , Wang Yongxin , Xu Xin-Shun
Published: July 2018

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.
Cross-modal Bidirectional Translation via Reinforcement Learning
Authors: Qi Jinwei , Peng Yuxin
Published: July 2018

The inconsistent distribution and representation of image and text make it quite challenging to measure their similarity, and construct correlation between them. Inspired by neural machine translation to establish a corresponding relationship between two entirely different languages, we attempt to treat images as a special kind of language to provide visual descriptions, so that translation can be conduct between bilingual pair of image and text to effectively explore cross-modal correlation. Thus, we propose Cross-modal Bidirectional Translation (CBT) approach, and further explore the utilization of reinforcement learning to improve the translation process. First, a cross-modal translation mechanism is proposed, where image and text are treated as bilingual pairs, and cross-modal correlation can be effectively captured in both feature spaces of image and text by bidirectional translation training. Second, cross-modal reinforcement learning is proposed to perform a bidirectional game between image and text, which is played as a round to promote the bidirectional translation process. Besides, both inter-modality and intra-modality reward signals can be extracted to provide complementary clues for boosting cross-modal correlation learning. Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval, compared with 11 state-of-the-art methods on 3 datasets.
Cost-Effective Active Learning for Hierarchical Multi-Label Classification
Authors: Yan Yi-Fan , Huang Sheng-Jun
Published: July 2018

Active learning reduces the labeling cost by actively querying labels for the most valuable data. It is particularly important for multi-label learning, where the annotation cost is rather high because each instance may have multiple labels simultaneously. In many multi-label tasks, the labels are organized into hierarchies from coarse to fine. The labels at different levels of the hierarchy contribute differently to the model training, and also have diverse annotation costs. In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries. By incorporating the potential contribution of ancestor and descendant labels, a novel criterion is proposed to estimate the informativeness of each candidate query. Further, a subset selection method is introduced to perform active batch selection by balancing the informativeness and cost of each instance-label pair. Experimental results validate the effectiveness of both the proposed criterion and the selection method.
NeuCast: Seasonal Neural Forecast of Power Grid Time Series
Authors: Chen Pudi , Liu Shenghua , Shi Chuan , Hooi Bryan , Wang Bai , Cheng Xueqi
Published: July 2018

In the smart power grid, short-term load forecasting (STLF) is a crucial step in scheduling and planning for future load, so as to improve the reliability, cost, and emissions of the power grid. Different from traditional time series forecast, STLF is a more challenging task, because of the complex demand of active and reactive power from numerous categories of electrical loads and the effects of environment. Therefore, we propose NeuCast, a seasonal neural forecasting method, which dynamically models various loads as co-evolving time series in a hidden space, as well as extra weather conditions, in a neural network structure. NeuCast captures seasonality and patterns of the time series by integrating factor modeling and hidden state recognition. NeuCast can also detect anomalies and forecast under different temperature assumptions. Extensive experiments on 134 real-word datasets show the improvements of NeuCast over the stateof-the-art methods.
Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective
Authors: Xu Huang , Yu Zhiwen , Guo Bin , Teng Mingfei , Xiong Hui
Published: July 2018

A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed  for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Authors: Zhu Feng , Wang Yan , Chen Chaochao , Liu Guanfeng , Orgun Mehmet , Wu Jia
Published: July 2018

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.
Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
Authors: Zhuang Tao , Ou Wenwu , Wang Zhirong
Published: July 2018

In web search, mutual influences between documents have been studied from the perspective of

search result diversification. But the methods in web search is not directly applicable to e-commerce

search because of their differences. And little research has been done on the mutual influences between

items in e-commerce search. We propose a global optimization framework for mutual influence aware ranking

in e-commerce search. Our framework directly optimizes the Gross Merchandise Volume (GMV) for ranking,

and decomposes ranking into two tasks. The first task is mutual influence aware purchase probability estimation.

We propose a global feature extension method to incorporate mutual influences into the features of

an item. We also use Recurrent Neural Network (RNN) to capture influences related to ranking orders in purchase

probability estimation. The second task is to find the best ranking order based on the purchase probability estimations.

We treat the second task as a sequence generation problem and solved it using the beam search algorithm. We

performed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase

in GMV for the search engine over a strong baseline.
Representing Urban Functions through Zone Embedding with Human Mobility Patterns
Authors: Yao Zijun , Fu Yanjie , Liu Bin , Hu Wangsu , Xiong Hui
Published: July 2018

Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.
Medical Concept Embedding with Time-Aware Attention
Authors: Cai Xiangrui , Gao Jinyang , Ngiam Kee Yuan , Ooi Beng Chin , Zhang Ying , Yuan Xiaojie
Published: July 2018

Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal nature of EMR data. On the one hand, two consecutive medical concepts do not indicate they are temporally close, but the correlations between them can be revealed by the time gap. On the other hand, the temporal scopes of medical concepts often vary greatly (e.g., common cold and diabetes). In this paper, we propose to incorporate the temporal information to embed medical codes. Based on the Continuous Bag-of-Words model, we employ the attention mechanism to learn a ``soft'' time-aware context window for each medical concept. Experiments on public and proprietary datasets through clustering and nearest neighbour search tasks demonstrate the effectiveness of our model, showing that it outperforms five state-of-the-art baselines.
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Authors: Chen Muhao , Tian Yingtao , Chang Kai-Wei , Skiena Steven , Zaniolo Carlo
Published: July 2018

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning
Authors: Ilievski Vladimir , Musat Claudiu , Hossman Andreea , Baeriswyl Michael
Published: July 2018

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user's goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot's success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.
Show and Tell More: Topic-Oriented Multi-Sentence Image Captioning
Authors: Mao Yuzhao , Zhou Chang , Wang Xiaojie , Li Ruifan
Published: July 2018

Image captioning aims to generate textual descriptions for images. Most previous work generates a single-sentence description for each image. However, a picture is worth a thousand words. Single-sentence can hardly give a complete view of an image even by humans. In this paper, we propose a novel Topic-Oriented Multi-Sentence (\emph{TOMS}) captioning model, which can generate multiple topic-oriented sentences to describe an image. Different from object instances or attributes, topics mined by the latent Dirichlet allocation reflect hidden thematic structures in reference sentences of an image. In our model, each topic is integrated to a caption generator with a Fusion Gate Unit (FGU) to guide the generation of a sentence towards a certain topic perspective. With multiple sentences from different topics, our \emph{TOMS} provides a complete description of an image. Experimental results on both sentence and paragraph datasets demonstrate the effectiveness of our \emph{TOMS} in terms of topical consistency and descriptive completeness.
Teaching Machines to Ask Questions
Authors: Yao Kaichun , Zhang Libo , Luo Tiejian , Tao Lili , Wu Yanjun
Published: July 2018

We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.
Bayesian Active Edge Evaluation on Expensive Graphs
Authors: Choudhury Sanjiban , Srinivasa Siddhartha , Scherer Sebastian
Published: July 2018

We consider the problem of real-time motion planning that requires evaluating a minimal number of edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots sensing the world online like UAVs. Until now, this challenge has been addressed via laziness, i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters.
Double Auctions in Markets for Multiple Kinds of Goods
Authors: Segal-Halevi Erel , Hassidim Avinatan , Aumann Yonatan
Published: July 2018

Motivated by applications such as stock exchanges and spectrum auctions, there is a growing interest in mechanisms for arranging trade in two-sided markets. However, existing mechanisms are either not truthful, do not guarantee an asymptotically-optimal gain-from-trade, rely on a prior on the traders' valuations, or operate in limited settings such as a single type of good. We extend the random-sampling technique used in earlier works to multi-good markets where traders have gross-substitute valuations. We show a prior free, truthful and strongly-budget-balanced mechanism which guarantees near-optimal gain from trade when the market sizes of all goods grow to infinity at a similar rate.
A Symbolic Approach to Explaining Bayesian Network Classifiers
Authors: Shih Andy , Choi Arthur , Darwiche Adnan
Published: July 2018

We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We introduce two types of explanations for why a classifier may have classified an instance positively or negatively and suggest algorithms for computing these explanations. The first type of explanation identifies a minimal set of the currently active features that is responsible for the current classification, while the second type of explanation identifies a minimal set of features whose current state (active or not) is sufficient for the classification. We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature.
A Savage-style Utility Theory for Belief Functions
Authors: Zhou Chunlai , Qin Biao , Du Xiaoyong
Published: July 2018

In this paper, we provide an axiomatic justification for decision making with belief functions by studying the belief-function counterpart of Savage's Theorem where the state space is finite and the consequence set is a continuum [l, M] (l<M). We propose six axioms for a preference relation over acts, and then show that this axiomatization admits a definition of qualitative belief functions comparing preferences over events that guarantees the existence of a belief function on the state space. The key axioms are uniformity and an analogue of the independence axiom. The uniformity axiom is used to ensure that all acts with the same maximal and minimal consequences must be equivalent. And our independence axiom shows the existence of a utility function and implies the uniqueness of the belief function on the state space. Moreover, we prove without the independence axiom the neutrality theorem that two acts are indifferent whenever they generate the same belief functions over consequences. At the end of the paper, we compare our approach with other related decision theories for belief functions.
Medusa: Towards Simulating a Multi-Agent Hide-and-Seek Game
Authors: Tandon Akshat , Karlapalem Kamalakar
Published: July 2018

In the game of hide and seek, one or more agents (hiders) can hide behind objects to prevent other agents (seekers) from seeing them through their line of sight (visibility). These hider agents can be eventually found by seekers who explore the environment. The hide and seek model serves as an abstraction of many real world scenarios such as police agents trying to capture intruders hiding in a warehouse, security/patrolling agents on the lookout for any suspicious activity, spying drones trying to prevent themselves from being caught. Medusa allows modeling such scenarios by supporting the simulation, visualization and analysis of hider and seeker agent strategies under different environments.
Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks
Authors: Chen Yunpeng , Jin Xiaojie , Kang Bingyi , Feng Jiashi , Yan Shuicheng
Published: July 2018

The residual unit and its variations are wildly used in building very deep neural networks for alleviating optimization difficulty. In this work, we revisit the standard residual function as well as its several successful variants and propose a unified framework based on tensor Block Term Decomposition (BTD) to explain these apparently different residual functions from the tensor decomposition view. With the BTD framework, we further propose a novel basic network architecture, named the Collective Residual Unit (CRU). CRU further enhances parameter efficiency of deep residual neural networks by sharing core factors derived from collective tensor factorization over the involved residual units. It enables efficient knowledge sharing across multiple residual units, reduces the number of model parameters, lowers the risk of over-fitting, and provides better generalization ability. Extensive experimental results show that our proposed CRU network brings outstanding parameter efficiency -- it achieves comparable classification performance with ResNet-200 while using a model size as small as ResNet-50 on the ImageNet-1k and Places365-Standard benchmark datasets.
Anonymizing k Facial Attributes via Adversarial Perturbations
Authors: Chhabra Saheb , Singh Richa , Vatsa Mayank , Gupta Gaurav
Published: July 2018

A face image not only provides details about the identity of a subject but also reveals several attributes such as gender, race, sexual orientation, and age. Advancements in machine learning algorithms and popularity of sharing images on the World Wide Web, including social media websites, have increased the scope of data analytics and information profiling from photo collections. This poses a serious privacy threat for individuals who do not want to be profiled. This research presents a novel algorithm for anonymizing selective attributes which an individual does not want to share without affecting the visual quality of images. Using the proposed algorithm, a user can select single or multiple attributes to be surpassed while preserving identity information and visual content. The proposed adversarial perturbation based algorithm embeds imperceptible noise in an image such that attribute prediction algorithm for the selected attribute yields incorrect classification result, thereby preserving the information according to user's choice. Experiments on three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed algorithm not only anonymizes \textit{k}-attributes, but also preserves image quality and identity information.
Pengaruh Konsentrasi Asam Sulfat (H2SO4) dan Lama Fermentasi Terhadap Kadar Bioetanol Serasah Lamun
Authors: Ratnasari Devi , Hidayati Nasrul Rofiah , Dewi Nurul Kusuma
Published: July 2018

Energi alternatif dari bahan nabati merupakan salah satu upaya untuk mengurangi konsumsi masyarakat terhadap BBM. Bioetanol merupakan produk fermentasi yang dapat dibuat dari substrat yang mengandung karbohidrat. Indonesia merupakan negara kepulauan yang kaya akan sumber daya alam, salah satunya lamun. Serasah lamun dapat dimanfaatkan sebagai substrat bioetanol. Penelitian ini bertujuan untuk mengetahui pengaruh konsentrasi asam sulfat (H2SO4) dan lama fermentasi terhadap kadar bioetanol serasah lamun. Metode penelitian ekperimen dengan variabel terikat kadar bioetanol dan variabel bebas konsentrasi H2SO4 0,2 M, 0,3 M, 0,4 M dan 0,5 M serta lama fermentasi selama 2, 3, 4 dan 5 hari. Data diperoleh dari pengukuran kadar etanol dengan menggunakan piknometer. Analisis data dengan uji varian (ANOVA) dua jalur menggunakan 2 faktor dengan penggulangan 3 kali. Hasil penelitian menunjukkan konsentrasi H2SO4 0,5 M dan lama fermentasi 5 hari menghasilkan kadar etanol paling tinggi yaitu rata-rata 55%.
GIS 技术在水利工程中的应用展望
Authors: 陈 雪峰
Published: July 2018

GIS 技术在中国水利工程项目中应用开始于 90 年代初期， 其经历了了解、 初步应用、 深入研究三个环节。但是随着 GIS 技术在水利工程中应用范围不断的扩大， 它的层次也在不断的逐渐深入。再加上政府的大力支持， 给予政策上的引导， 未来将 GIS 技术在水利工程的应用推向更加层次的空间发展。基于此， 本文对 GIS 技术在水利工程中的应用展望进行分析， 旨在促进水利工程健康顺利的完成。
市政工程PPP投资项目风险管理浅谈
Authors: 陈 晓立
Published: July 2018

中国 PPP 投资项目工程建设已经进入了全国性攻坚阶段， 其中， 市政工程风险管理尤为重要。关系到百姓群众的生命财产安全与社会稳定发展。市政工程作为城市基础建设的重要组成部分， 风险管理的优劣直接影响各方利益， 进而造成较为严重的经济损失和社会影响， 应该时刻保持警钟长鸣。本文通过对新疆市政 PPP 投资项目风险管理的重要性与优化措施进行分析研究， 论述了 PPP 投资项目风险管理的决策和优化方式， 为新疆区域市政工程 PPP 投资项目的风险管理工作提供相应的参考意见。
城市轨道交通工程勘察工作中地下管线的保护分析
Authors: 李 磊
Published: July 2018

本文以地下管线的类型、 分布特征作为切入点， 重点分析地铁及轻轨岩土工程勘察工作中地下管线的保护相关内容， 包括当前的不足以及可行的建议等， 并给出具体措施， 旨在通过分析明确当前状况，完善对应理论， 为后续工作提供一定的支持和参考。
Effects of small-sided games and conventional aerobic interval training on various physiological characteristics and defensive and offensive skills used in soccer
Authors: Özcan İlker , Şahan Çağatay
Published: July 2018

The aim of this study was to investigate the effects of small-sided game training (SSGT) versus conventional aerobic interval training (CAIT) on soccer-specific endurance performance, lactate threshold levels (mmol·L-1), short-passing ability and defensive and offensive skills of a soccer match. Before and after a 6-week training intervention period, eighteen amateur soccer players (age 21.8±4.8 years) were tested. The tests included the anaerobic threshold (AnT) test, the Yo-Yo Intermittent Recovery Test Level 1 (Yo-Yo IR1), and the Loughborough Soccer Passing Test (LSPT). A manual notational match analysis system was utilized to evaluate the defensive and offensive skills of players during the matches. Both the SSGT and CAIT were performed two days a week and consisted of five sets of 6-minute periods of work at the individualized exercise intensity corresponding to the individual anaerobic threshold, with 3-minute of recovery periods between sets. Mann-Whitney U and Wilcoxon signed rank tests were used to examine the between- and within-group differences, respectively. Statistical analyses revealed that the SSGT group players exhibited significantly better in terms of the LSPT scores (p<.01) and the number of defensive and offensive skills (p<.05). However, no other significant differences in the other variables were observed (p>.05). The results of the study suggested that SSGT improved short-passing ability, various soccer skills and physiological parameters, while CAIT only improved physiological parameters. SSGT improves soccer-specific endurance and technical ability of players at the same time, meaning it is a time efficient way of training.
Energy expenditure and perceived exertion during active video games in relation to player mode and gender
Authors: Giancotti Giuseppe Francesco , Cortis Cristina
Published: July 2018

Active video games (AVG) increase energy expenditure (EE) with respect to sedentary video games. Although several AVG consoles allow playing in single-player (SP) or multiplayer (MP) mode, few studies investigated differences in relation to game modality in men and women. Therefore, the aim of this study was to evaluate gender-related psycho-physiological responses during Zumba Fitness Rush (ZFR) played in SP vs. MP mode. Twenty-four college students (women: 14, men: 10; age: 24.6±2.0 years; body weight: 60.2±12.5 kg; body height: 167.8±11.5 cm; BMI: 21.2±2.1 kg·m-2) participated in two experimental sessions: SP (a subject playing ZFR alone) and MP (two subjects playing ZFR simultaneously). Heart rate (HR), oxygen consumption (VO2) and EE (MET and kcal∙min-1) were continuously measured and averaged every 10 seconds, while Rating of Perceived Exertion (RPE) was recorded 30 minutes after the end of each session. A two (gender: women vs. men) by two (modality: SP vs. MP) ANOVA for repeated measures was applied. No differences emerged in relation to gender and game mode for %HRmax, VO2, MET, and RPE. Conversely, a main effect (p=.0007) for gender was found in EE, with significantly (p<.004) higher values in men in SP (women: 5.5±0.6 kcal·min-1; men: 8.3±1.4 kcal·min-1) and MP (women: 5.3±0.7 kcal·min-1; men: 7.6±1.9 kcal·min-1). Present findings suggest that ZFR could be classified as a moderate physical activity, proving to be an alternative form to traditional exercise. Although an effect of number of players could be expected, different player modes did not affect EE, probably because the rhythm of the music imposes a time constraint, independently of the presence of other players.
Are team sport games more motivating than individual exercise for middle-aged women?
Authors: Wikman Johan M. , Nielsen Glen
Published: July 2018

The aim of this study was to investigate the levels of motivation associated with participation in floorball (indoor hockey) and spinning, and how levels of motivation predicted continuation. A sample of 66 middleaged women participated in a 12-week intervention of either floorball or spinning. They filled out the Sport Motivation Scale in week 2 and week 11 of the intervention, and data on their continuation six and 12 months after the intervention was also collected. A repeated measures MANOVA showed that participants in the floorball group had higher levels of intrinsic and self-determined extrinsic motivation for the activity during the intervention period, suggesting that floorball is a more motivating activity. In addition, extrinsic motivation the introjected regulation increased in both groups during the intervention period. Intrinsic motivation, as well as extrinsic motivation introjected regulation, predicted participants’ continuation six and 12 months after the intervention, suggesting that motivation as viewed in a Self-Determination Theory perspective is important for continuation.
Exploring game performance in NBA playoffs
Authors: Mateus Nuno , Gonçalves Bruno
Published: July 2018

This study aimed to identify performance profiles in NBA playoffs and describe performances in the first and the last games of the series. Thirty games from the official box-scores and player-tracking data of the 2014/15 NBA playoffs were considered. A k-means cluster analysis was performed to group the players according to their game performance profiles and a discriminant analysis was conducted to identify the game-related statistics that best discriminated the groups. The first function correctly classified 64.2% of the cases and the second function classified 29.9% of the cases. The cluster analysis identified four different performance profiles and from the discriminant analysis emerged several offensive and defensive variables to classify the players in the clusters. The identified trends help to improve understanding of the game during different stages of playoffs. Coaches may use this information to better prepare their teams for different game scenarios.
Is there a need for intrinsic values in conservation biology?
Authors: Hyde Dominic
Published: July 2018

Conservation biology has amongst its aims the conservation of the biologically valuable. As a consequence, some underlying theory of value is invoked. Clear challenges to orthodox value theory have been on the table for some time now, with some arguing for recognition of intrinsic values in nature, and some conservation biologists subsequently drawing on such a view. However, this development of value theory has recently been criticised for lacking sufficient clarity and failing to serve the needs of decision-making in conservation biology. These criticisms serve to highlight confusion and difficulties in the development of a coherent and efficacious theory of intrinsic value for application in decision-making. They do not, however, count as sufficient grounds for the rejection of such a theory. Sylvan appears to offer a theory that avoids the criticisms made. The debate here is instructive in clearing away some of the misguided claims frequently encountered in this area and in seeing the value in Sylvan’s “deep green theory”.
Ontological Syncretistic Noneism
Authors: Voltolini Alberto
Published: July 2018

In this paper I want to claim, first, that despite close similarities, noneism (as developed in both Routley 1980 and Priest 20162) and Crane’s (2013) psychological reductionism are different ontological doctrines. For unlike the latter, the former is ontologically committed to objects that are nonentities. Once one splits ontological from existential commitment, this claim, I guess, is rather uncontroversial. Second, however, I want to claim something more controversial; namely, that this ontological interpretation of noneism naturally makes noneism be nonstandardly read as a form of allism, to be however appropriately distinguished from Quinean allism in terms of the different scope of the overall ontological domain on which the only particular/existential quantifier that there is ranges. This may orient a noneist towards a syncretistic view of existence, according to which, appearances notwithstanding, existence as a whole is captured both by means of second-order and by means of first-order related notions.