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Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-12 Mario Barbareschi, Salvatore Barone, Nicola Mazzocca
So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design
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Discovering cluster evolution patterns with the Cluster Association-aware matrix factorization Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-09 Wathsala Anupama Mohotti, Richi Nayak
Tracking of document collections over time (or across domains) is helpful in several applications such as finding dynamics of terminologies, identifying emerging and evolving trends, and concept drift detection. We propose a novel ‘Cluster Association-aware’ Non-negative Matrix Factorization (NMF)-based method with graph-based visualization to identify the changing dynamics of text clusters over time/domains
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Efficient unsupervised drift detector for fast and high-dimensional data streams Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-09 Vinicius M. A. Souza, Antonio R. S. Parmezan, Farhan A. Chowdhury, Abdullah Mueen
Stream mining considers the online arrival of examples at high speed and the possibility of changes in its descriptive features or class definitions compared with past knowledge (i.e., concept drifts). The fast detection of drifts is essential to keep the predictive model updated and stable in changing environments. For many applications, such as those related to smart sensors, the high number of features
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Progressive approaches to flexible group skyline queries Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-09 Zhibang Yang, Xu Zhou, Kenli Li, Yunjun Gao, Keqin Li
The G-Skyline (GSky) query is formulated to report optimal groups that are not dominated by any other group of the same size. Particularly, a given group \(G_1\) dominates another group \(G_2\) if for any point \(p\in G_1\), p dominates or equals to points \(p{'}\in G_2\); at the same time, there is at least one point p dominating \(p{'}\). Most existing group skyline queries need to calculate an aggregate
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Comparing ontologies and databases: a critical review of lifecycle engineering models in manufacturing Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-03 Borja Ramis Ferrer, Wael M. Mohammed, Mussawar Ahmad, Sergii Iarovyi, Jiayi Zhang, Robert Harrison, Jose Luis Martinez Lastra
The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management (PLM) and product, process, resource (PPR) modeling. These paradigms are complementary, and the latter could be considered a more neutral version of the former. There are two main technologies associated with these paradigms:
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Deep graph transformation for attributed, directed, and signed networks Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-03 Xiaojie Guo, Liang Zhao, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Generalized from image and language translation, the goal of graph translation or transformation is to generate a graph of the target domain on the condition of an input graph of the source domain. Existing works are limited to either merely generating the node attributes of graphs with fixed topology or only generating the graph topology without allowing the node attributes to change. They are prevented
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The impact of data difficulty factors on classification of imbalanced and concept drifting data streams Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-04-01 Dariusz Brzezinski, Leandro L. Minku, Tomasz Pewinski, Jerzy Stefanowski, Artur Szumaczuk
Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition
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Efficient discovery of co-location patterns from massive spatial datasets with or without rare features Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-27 Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Lihua Zhou
A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we
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Incremental communication patterns in online social groups Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-26 Andrea Michienzi, Barbara Guidi, Laura Ricci, Andrea De Salve
In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status
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Kernel-based regression via a novel robust loss function and iteratively reweighted least squares Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-20 Hongwei Dong, Liming Yang
Least squares kernel-based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called \(\ell _s\)-loss,
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Learning diffusion model-free and efficient influence function for influence maximization from information cascades Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-19 Qi Cao, Huawei Shen, Jinhua Gao, Xueqi Cheng
When considering the problem of influence maximization from information cascades, one essential component is influence estimation. Traditional approaches for influence estimation generally follow a two-stage framework, i.e., learn a hypothetical diffusion model from information cascades and then calculate the influence spread according to the learned diffusion model via Monte Carlo simulation or heuristic
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Unifying community detection and network embedding in attributed networks Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-17 Yu Ding, Hao Wei, Guyu Hu, Zhisong Pan, Shuaihui Wang
Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others. Most of the existing approaches do community detection and network embedding in a separate manner, and ignore node attributes
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Cost-sensitive selection of variables by ensemble of model sequences Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-11 Donghui Yan, Zhiwei Qin, Songxiang Gu, Haiping Xu, Ming Shao
Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been
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Expert-driven trace clustering with instance-level constraints Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-03-01 Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, however, is that their solutions are usually hard to
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Towards metrics-driven ontology engineering Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-23 Alba Fernández-Izquierdo, María Poveda-Villalón, Asunción Gómez-Pérez, Raúl García-Castro
The software engineering field is continuously making an effort to improve the effectiveness of the software development process. This improvement is performed by developing quantitative measures that can be used to enhance the quality of software products and to more accurately describe, better understand and manage the software development life cycle. Even if the ontology engineering field is constantly
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Mining discriminative itemsets in data streams using the tilted-time window model Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-15 Majid Seyfi, Richi Nayak, Yue Xu, Shlomo Geva
A discriminative itemset is a frequent itemset in the target data stream with much higher frequency than that of the same itemset in the rest of the data streams in the dataset. The discriminative itemsets describe the distinguishing features between data streams. Mining discriminative itemsets in data streams is very important, where continuously arriving transactions can be inserted in fast speed
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PPNW: personalized pairwise novelty loss weighting for novel recommendation Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-15 Kachun Lo, Tsukasa Ishigaki
Most works of recommender systems focus on providing users with highly accurate item predictions based on the assumption that accurate suggestions can best satisfy users. However, accuracy-focused models also create great system bias towards popular items and, as a result, unpopular items rarely get recommended and will stay as “cold items” forever. Both users and item providers will suffer in such
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Transfer learning for fine-grained entity typing Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-13 Feng Hou, Ruili Wang, Yi Zhou
Fine-grained entity typing (FGET) is to classify the mentions of entities into hierarchical fine-grained semantic types. There are two main issues with existing FGET approaches. Firstly, the process of training corpora for FGET is normally to label the data automatically, which inevitably induces noises. Existing approaches either directly tweak noisy labels in corpora by heuristics or algorithmically
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AQUA+: Query Optimization for Hybrid Database-MapReduce System Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-05 Zhifei Pang, Sai Wu, Haichao Huang, Zhouzhenyan Hong, Yuqing Xie
MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. However, there are many existing applications maintaining their data in a distributed database. It is costly to export those data into the storage system of MapReduce (normally
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A scalable framework for large time series prediction Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-05 Youssef Hmamouche, Lotfi Lakhal, Alain Casali
Knowledge discovery systems are nowadays supposed to store and process very large data. When working with big time series, multivariate prediction becomes more and more complicated because the use of all the variables does not allow to have the most accurate predictions and poses certain problems for classical prediction models. In this article, we present a scalable prediction process for large time
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Selectivity estimation with density-model-based multidimensional histogram Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-02-02 Meifan Zhang, Hongzhi Wang
Histograms are widely used in selectivity estimation for one-dimensional data. Using the one-dimensional histograms to estimate the selectivity of the multidimensional queries will result in a high estimation error, unless the assumption of attribute independence is true. Constructing a multidimensional histogram also brings great challenges. The storage of a multidimensional histogram exponentially
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PE-MSC: partial entailment-based minimum set cover for text summarization Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-28 Anand Gupta, Manpreet Kaur, Sonaali Mittal, Swati Garg
The notion of Textual Entailment (TE) is an established indicator of text connectedness. It captures semantic relationships between texts. Recently, it has been used successfully for determining sentence salience in many text summarization methods. However, it has been reported in previous works that the standard textual entailment is not ideal for measuring sentence salience. This is because textual
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Multi-layer linear embedding with feature subset selection Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-18 F. Dornaika
Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: manifold learning and feature selection. Manifold learning (or data embedding) attempts to compute a subspace from original data by feature recombination/transformation. Feature selection aims to select the most relevant features in the original space. In this
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Tracking triadic cardinality distributions for burst detection in high-speed graph streams Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-18 Junzhou Zhao, Pinghui Wang, Zhouguo Chen, Jianwei Ding, John C. S. Lui, Don Towsley, Xiaohong Guan
In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people’s sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these
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Auto-labelling entities in low-resource text: a geological case study Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-15 Majigsuren Enkhsaikhan, Wei Liu, Eun-Jung Holden, Paul Duuring
Studies on named entity recognition (NER) often require a substantial amount of human-annotated training data. This makes technical domain-specific NER from industry data especially challenging as labelled data are scarce. Despite English as the surface language, technical jargon and writing conventions used in technical documents render the low-resource language challenges where techniques such as
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Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-15 Xin Hu, Jiangli Duan, Depeng Dang
Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of
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Distributed processing of regular path queries in RDF graphs Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Xintong Guo, Hong Gao, Zhaonian Zou
SPARQL 1.1 offers a type of navigational query for RDF systems, called regular path query (RPQ). A regular path query allows for retrieving node pairs with the paths between them satisfying regular expressions. Regular path queries are always difficult to be evaluated efficiently because of the possible large search space. Thus there has been no scalable and practical solution so far. In this paper
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Saliency-based YOLO for single target detection Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Jun-ying Hu, C.-J. Richard Shi, Jiang-she Zhang
At present, You only look once (YOLO) is the fastest real-time object detection system based on a unified deep neural network. During training, YOLO divides the input image to \(S \times S \) gird cells and the only one grid cell that contains the center of an object, takes charge of detecting that object. It is not sure that the cell corresponding to the center of the object is the best choice to
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L -ideals and rough sets based on L -ideals Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Ali Akbar Estaji, Toktam Haghdadi, Javad Farokhi Ostad
Let D be a distributive lattice, and let L be a frame. In this article, we introduce the notion of L-ideals of D. We show that the set of all L-ideals of D is a distributive lattice, and some essential properties of this lattice are studied. Also, we discuss some special elements of this lattice. Moreover, we define a novel congruence relation for the concept of L-ideal of D. Finally, we study some
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Efficient computation of deletion-robust k -coverage queries Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Jiping Zheng, Xingnan Huang, Yuan Ma
Extracting a controllable subset from a large-scale dataset so that users can fully understand the entire dataset is a significant topic for multicriteria decision making. In recent years, this problem has been widely studied, and various query models have been proposed, such as top-k, skyline, k-regret and k-coverage queries. Among these models, the k-coverage query is an ideal query method; this
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Deep dynamic neural networks for temporal language modeling in author communities Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Edouard Delasalles, Sylvain Lamprier, Ludovic Denoyer
Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models are learned from word sequences only, and authors’ identities, as well as publication
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RELINE: point-of-interest recommendations using multiple network embeddings Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-13 Giannis Christoforidis, Pavlos Kefalas, Apostolos N. Papadopoulos, Yannis Manolopoulos
The rapid growth of users’ involvement in Location-Based Social Networks has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users’ preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of points-of-interest (POIs) recommendation by existing models is inadequate
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Closed form word embedding alignment Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-09 Sunipa Dev, Safia Hassan, Jeff M. Phillips
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional
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Warped softmax regression for time series classification Knowl. Inf. Syst. (IF 2.936) Pub Date : 2021-01-02 Brijnesh Jain
Linear models are a mainstay in statistical pattern recognition but do not play a role in time series classification, because they fail to account for temporal variations. To overcome this limitation, we combine linear models with dynamic time warping (dtw). We analyze the resulting warped-linear models theoretically and empirically. The three main theoretical results are (i) the Representation Theorem
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A hybrid neural network approach to combine textual information and rating information for item recommendation Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-23 Donghua Liu, Jing Li, Bo Du, Jun Chang, Rong Gao, Yujia Wu
Collaborative filtering (CF) is a common method used by many recommender systems. Traditional CF algorithms exploit users’ ratings as the sole information source to learn user preferences. However, ratings usually sparse cause a serious impact on the recommendation results. Most existing CF algorithms use ratings and textual information to alleviate the sparsity of data and then utilize matrix factorization
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Partial multi-label learning with noisy side information Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-23 Lijuan Sun, Songhe Feng, Gengyu Lyu, Hua Zhang, Guojun Dai
Partial multi-label learning (PML) aims to learn from the training data where each training example is annotated with a candidate label set, among which only a subset is relevant. Despite the success of existing PML approaches, a major drawback of them lies in lacking of robustness to noisy side information. To tackle this problem, we introduce a novel partial multi-label learning with noisy side information
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Improving spectral clustering with deep embedding, cluster estimation and metric learning Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-22 Liang Duan, Shuai Ma, Charu Aggarwal, Saket Sathe
Spectral clustering is one of the most popular modern clustering algorithms. It is easy to implement, can be solved efficiently, and very often outperforms other traditional clustering algorithms such as k-means. However, spectral clustering could be insufficient when dealing with most datasets having complex statistical properties, and it requires users to specify the number k of clusters and a good
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Dealing with heterogeneity in the context of distributed feature selection for classification Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-21 José Luis Morillo-Salas, Verónica Bolón-Canedo, Amparo Alonso-Betanzos
Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be handled in a centralized manner. A possible solution to this problem is to distribute the data over several processors and combine the different results. We propose a methodology to distribute
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BestNeighbor: efficient evaluation of kNN queries on large time series databases Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-16 Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas, Dennis Shasha, Patrick Valduriez
This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating k nearest neighbor queries on large databases of time series, compares them based on various measures of quality and time performance, and offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set
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A repairing missing activities approach with succession relation for event logs Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-11 Jie Liu, Jiuyun Xu, Ruru Zhang, Stephan Reiff-Marganiec
In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based
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Anytime mining of sequential discriminative patterns in labeled sequences Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-10 Romain Mathonat, Diana Nurbakova, Jean-François Boulicaut, Mehdi Kaytoue
It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of patterns covering sets of objects having interesting properties, e.g., they characterize or discriminate
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A graph grammar and $$K_{4}$$ K 4 -type tournament-based approach to detect conflicts of interest in a social network Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-07 Saadia Albane, Hachem Slimani, Hamamache Kheddouci
In this paper, we introduce a new approach based on properties of graph grammars to detect conflicts of interest (COIs) in a field represented in the form of a social network. The approach consists of specializing the adaptive star graph grammar (ASGG) of Drewes et al. (Theor Comput Sci 411:3090–3109, 2010) to express kind of subgraphs that we call \(K_4\)-type tournament graphs, corresponding to COIs
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Statistical model for reproducibility in ranking-based feature selection Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-05 Ari Urkullu, Aritz Pérez, Borja Calvo
The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the reproducibility of ranking-based feature subset selection algorithms. When applied to data, this family of algorithms builds an ordering of variables in terms of a measure of relevance
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e-Recruitment recommender systems: a systematic review Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-05 Mauricio Noris Freire, Leandro Nunes de Castro
Recommender Systems (RS) are a subclass of information filtering systems that seek to predict the rating or preference a user would give to an item. e-Recruitment is one of the domains in which RS can contribute due to presenting a list of interesting jobs to a candidate or a list of candidates to a recruiter. This study presents an up-to-date systematic review of recommender systems applied to e-Recruitment
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Specifying and computing causes for query answers in databases via database repairs and repair-programs Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-11-03 Leopoldo Bertossi
There is a recently established correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints. In this work, answer-set programs that specify database repairs are used as a basis for solving computational and reasoning problems around causality in databases, including causal responsibility. Furthermore
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CANE: community-aware network embedding via adversarial training Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-29 Jia Wang, Jiannong Cao, Wei Li, Senzhang Wang
Network embedding aims to learn a low-dimensional representation vector for each node while preserving the inherent structural properties of the network, which could benefit various downstream mining tasks such as link prediction and node classification. Most existing works can be considered as generative models that approximate the underlying node connectivity distribution in the network, or as discriminate
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A differentially private algorithm for range queries on trajectories Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-28 Soheila Ghane, Lars Kulik, Kotagiri Ramamoharao
We propose a novel algorithm to ensure \(\epsilon \)-differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state of the art, our method achieves significantly lower error as
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Learning credible DNNs via incorporating prior knowledge and model local explanation Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-21 Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu
Recent studies have shown that state-of-the-art DNNs are not always credible, despite their impressive performance on the hold-out test set of a variety of tasks. These models tend to exploit dataset shortcuts to make predictions, rather than learn the underlying task. The non-credibility could lead to low generalization, adversarial vulnerability, as well as algorithmic discrimination of the DNN models
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Hashtag recommendation for short social media texts using word-embeddings and external knowledge Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-14 Nagendra Kumar, Eshwanth Baskaran, Anand Konjengbam, Manish Singh
With the rapid growth of Twitter in recent years, there has been a tremendous increase in the number of tweets generated by users. Twitter allows users to make use of hashtags to facilitate effective categorization and retrieval of tweets. Despite the usefulness of hashtags, a major fraction of tweets do not contain hashtags. Several methods have been proposed to recommend hashtags based on lexical
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Adversarially regularized medication recommendation model with multi-hop memory network Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-10 Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue
Medication recommendation is attracting enormous attention due to its promise in effectively prescribing medicines and improving the survival rate of patients. Among all challenges, drug–drug interactions (DDI) related to undesired duplication, antagonism, or alternation between drugs could lead to fatal side effects. Previous researches usually provide models with DDI knowledge to achieve DDI reduction
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An experimental study of graph-based semi-supervised classification with additional node information Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-10-09 Bertrand Lebichot, Marco Saerens
The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As this information can take different forms, it is important to use all the available data representations for prediction; this is often referred to multi-view learning. In this paper, we consider semi-supervised classification
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Learning sequence-to-sequence affinity metric for near-online multi-object tracking Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-07-30 Weijiang Feng, Long Lan, Xiang Zhang, Zhigang Luo
In this paper, we propose a sequence-to-sequence affinity metric for the data association of near-online multi-object tracking. The proposed metric learns the affinity between track sequence consisting of the already associated detections and hypothesis sequence consisting of detections in the near future. With the potential hypothesis sequences, we leverage the idea that if a track sequence has a
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A relative position attention network for aspect-based sentiment analysis Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-24 Chao Wu, Qingyu Xiong, Min Gao, Qiude Li, Yang Yu, Kaige Wang
Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text
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Transferring trading strategy knowledge to deep learning models Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-24 Avraam Tsantekidis, Anastasios Tefas
Trading strategies are constantly being employed in the financial markets in order to increase consistency, reduce human errors of judgment and boost the probability of taking profitable market positions. In this work, we attempt to transfer the knowledge of several different types of trading strategies to deep learning models. The trading strategies are applied on price data of foreign exchange trading
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Incremental one-class collaborative filtering with co-evolving side networks Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-17 Chen Chen, Yinglong Xia, Hui Zang, Jundong Li, Huan Liu, Hanghang Tong
One-class collaborative filtering (OCCF) is a fundamental research problem in a myriad of applications where the preferences of users can only be implicitly inferred from their one-class feedback (e.g., click an ad or purchase a product). The main challenges of OCCF lie in the sparsity of user feedback and the ambiguity of unobserved preferences. To effectively address the above two challenges, side
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Cross-domain recommender system using generalized canonical correlation analysis Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-14 Seyed Mohammad Hashemi, Mohammad Rahmati
Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until
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A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-12 Mehri Davtalab, Ali Asghar Alesheikh
In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work
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Geometric consistency of triangular fuzzy multiplicative preference relation and its application to group decision making Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-10 Feng Wang
The triangular fuzzy multiplicative preference relation (TFMPR) has attracted the attention of many scholars. This paper investigates the geometric consistency of TFMPR and applies it to group decision making (GDM). Firstly, by introducing two parameters, a triangular fuzzy number is transformed into an interval. According to the geometric consistency of interval multiplicative preference relation
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Learning cell embeddings for understanding table layouts Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-09-07 Majid Ghasemi-Gol, Jay Pujara, Pedro Szekely
There is a large amount of data on the web in tabular form, such as Excel sheets, CSV files, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (such as font size, border type, and background color) to classify tabular cells by their role in the
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Exploring the collective human behavior in cascading systems: a comprehensive framework Knowl. Inf. Syst. (IF 2.936) Pub Date : 2020-08-27 Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu
The collective behavior describing spontaneously emerging social processes and events is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots, and so on. However, detecting, quantifying, and modeling the collective behavior in cascading systems at large scale are seldom explored. In
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