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Due to the increasing popularity of cooking-recipe sharing sites and the success of complex network science, attention has recently been devoted to developing an effective networkbased method of analyzing the characteristics of ingredient combinations used in recipes. Unlike previous approaches dealing with static properties, we aim at analyzing the dynamical changes in ingredient pairs jointly used...
Anecdotal evidence suggests that the variety of Big data is one of the most challenging problems in Computer Science research today [Stonebraker, 2012], [Ou et al., 2017], [Guo et al., 2016], [Bai et al., 2016]. First, Big data comes at us from a myriad of data sources, hence its shape and flavor differ. Second, hundreds of data management systems which work with Big data support different APIs and...
With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving artificial intelligence problems. Thus, a multi-algorithm...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Action Rules are vital data mining method for gaining actionable knowledge from the datasets. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions...
We present a fast range search algorithm, which greatly reduces unnecessary distance computations, based on a technique to prune redundant distance computations. Theoretical and experimental analysis have shown that the proposed algorithm significantly improves the original k-D tree based algorithm, which runs in O(log(n)) time either in low dimension or the searching range is small. In the case where...
Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose...
The use of RPE as a measure of Internal load has become a common methodology used in team sports owing to its low cost. The aim of this study was to build a machine learning process able to describe the players' RPE by the external load extracted from the GPS. In this paper, we propose a multidimensional approach to assess the RPE in professional soccer which is based on GPS measurements and machine...
Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players with...
In this paper, we propose a very simple method for learning relationships between events by accounting for the spatial or temporal sequence of occurrence of the events. The underlying idea behind our proposed method is that for certain data processing application, such as data collected from retail shoppers, relational access to data is more useful and immediately informative than sequential access...
Identifying meaningful signal buried in noise is a problem of interest arising in diverse scenarios of data-driven modeling. We present here a theoretical framework for exploiting intrinsic geometry in data that resists noise corruption, and might be identifiable under severe obfuscation. Our approach is based on uncovering a valid complete inner product on the space of ergodic stationary finite valued...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on...
The special characteristics of time series data, such as their high dimensionality and complex dependencies between variables make the problem of detecting anomalies in time series very challenging. Anomalies and more precisely dependency anomalies ensue from the temporal causal depen-dencies. Furthermore the graphical Granger causal models provide an appropriate environment to capture all the temporal...
Lung cancer is one of the most common types of cancer originated from malignant lung nodules. Early detection of lung nodule is key in prevention of lung cancer. In this paper, we developed an online content-based image retrieval (CBIR) system to assist novice radiologists in identifying lung nodules. The system takes advantages of cloud computing and deep learning to retrieve similar lung nodules...
Automatic creation of polarity dictionaries is an important issue, as explanations of prediction models are often required in the financial industry. This paper proposes a novel method of developing an interpretable and predictable neural network model. The neural network model we built can extract polarity scores of concepts from documents. Furthermore, we can detect pairwise interactions between...
As the use of the Internet grows every year, e-commerce's usage does as well. There is a tough competition between companies to be able to attract customers to use their services. The design of a website is crucial to retain a customer, and a retained client is more valuable over time, so understanding what attracts the attention of a potential client on a website is really important. This work proposes...
Criminal activity in the Internet is becoming more sophisticated. Traditional information security techniques hardly cope with recent trends. Honeypots proved to be a valuable source of threat intelligence. In this work several Honeypots are combined into a Honeynet and observed exploitation attempts. The Honeynet consists of six Honeypots and was operated for 222 days. 12 million exploitation attempts...
This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.
Following the trend of big data, the business value of data is becoming a hot research field in recent years. The novel concept of Data Jacket introduced by Ohsawa et al. solved the difficult problem of data transactions due to the particular characteristic of data, i.e. the safeguarding privacy. In order to make sure the mechanism of the market of data, there are some researchers proposed a gamified...
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