A data warehouse is a special database used for storing business oriented information for future analysis and decision-making. In business scenarios, where some of the data or the business attributes are fuzzy, it may be useful to construct a warehouse that can support the analysis of fuzzy data. Here, we outline how Kimballpsilas methodology for the design of a data warehouse can be extended to the construction of a fuzzy data warehouse. A case study demonstrates the viability of the methodology.
No abstract is provided for this article.
No abstract is provided for this article.
Predictive regression models aim to find the most accurate solution to a given problem, often without any constraints related to the model’s predicted values. Such constraints have been used in prior research where they have been applied to a subpopulation within the training dataset which is of greater interest and importance. In this research we introduce a new setting of regression problems, in which each instance can be assigned a different constraint, defined based on the value of the target (predicted) attribute. The new use of constraints is taken into account and incorporated into the learning process, and is also considered when evaluating the induced model. We propose two algorithms which are modifications to the regression boosting method. There are two advantages of the proposed algorithms: they are not dependent on the base learner used during the learning process, and they can be adopted by any boosting technique. We implemented the algorithms by modifying the gradient boosting trees (GBT) model, and we also introduced two measures for evaluating the models that were trained to solve the constraint problems. We compared the proposed algorithms to three baseline algorithms using four real-life datasets. Due to the algorithms’ focus on satisfying the constraints, in most cases the results showed significant improvement in the constraint-related measures, with just a minimal effect on the general prediction error. The main impact of the proposed approach is in its ability to derive a model with a higher level of assurance for specific cases of interest (i.e., the constrained cases). This is extremely important and has great significance in various use cases and expert and intelligent systems, particularly critical systems, such as critical healthcare systems (e.g., when predicting blood pressure or blood sugar level), safety systems (e.g., when aiming to estimate the distance of cars or airplanes from other objects), or critical industrial systems (e.g., require to estimate their usability along time). In each of these cases, there is a subpopulation of all instances that is of greater interest to the expert or system, and the sensitivity of the model’s error changes according to the real value of the predicted feature. For example, for a subpopulation of patients (e.g., patients under the age of eight, or patients known to be at risk), physicians often require a sensitive model that accurately predicts blood pressure values.
It is our great pleasure to welcome you to the 2013 ACM International Workshop on Computational Scientometrics: Theory and Applications -- CSTA'13. This is the first time when the CSTA workshop has been organized in the ACM International Conference on Information and Knowledge Management. The primary goals and objectives of the workshop are to promote both theoretical results and practical applications within digital libraries to better answer questions such as how do research ideas emerge, evolve, or disappear as a topic, what is a good measure of quality of published works, what are the most promising areas of research, how authors connect and influence each other, who are the experts in a field, what works are similar, and who funds a particular research topic. The workshop aims at bringing together researchers with diverse interdisciplinary backgrounds interested in mining the web, large digital libraries and other relevant databases for research related publications and data. The combination of classical bibliometrics and novel text mining provides a synergy unavailable within each approach taken independently. In this proposed workshop, we also focus on bibliometrics analysis by using sophisticated text mining, or natural language processing methods, which will enable researchers to generate innovative research topics, e.g. full-text citation analysis. We expect people interested in the practical applications within digital libraries such as citation analysis and recommendation, scientific and research trends, expert finding, and collaborator recommendation, to attend the CSTA'13 workshop. The call for papers attracted submissions from Europe, Australia, and the United States. The program committee accepted 6 papers that cover topics, including program committees recommendation for academic conferences, collaborators recommendation, and citations classification and labeling. In addition, the program includes a keynote speech by Xiaozhong Liu on Full-text Citation Analysis and its Application.
Next generation sequencing (NGS) is becoming the new gold standard in public health microbiology. Like any disruptive technology, its growing popularity inevitably attracts cyber security actors, for whom the health sector is attractive because it combines mission-critical infrastructure and high-value data with cybersecurity vulnerabilities. In this Perspective, we explore cyber security aspects of microbial NGS. We discuss the motivations and objectives for such attack, its feasibility and implications, and highlight policy considerations aimed at threat mitigation. Particular focus is placed on the attack vectors, where the entire process of NGS, from sample to result, could be vulnerable, and a risk assessment based on probability and impact for representative attack vectors is presented. Cyber attacks on microbial NGS could result in loss of confidentiality (leakage of personal or institutional data), integrity (misdetection of pathogens) and availability (denial of sequencing services). NGS platforms are also at risk of being used as propagation vectors, compromising an entire system or network. Owing to the rapid evolution of microbial NGS and its applications, and in light of the dynamics of the cyber security domain, frequent risk assessments should be carried out in order to identify new threats and underpin constantly updated public health policies.
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. We also implement GADGET, a software framework to convert any malware binary to a binary undetected by malware classifiers, using the proposed attack, without access to the malware source code.
Time series classification is a field which has drawn much attention over the past decade. A new approach for classification of time series uses classification trees based on shapelets. A shapelet is a subsequence extracted from one of the time series in the dataset. A disadvantage of this approach is the time required for building the shapelet-based classification tree. The search for the best shapelet requires examining all subsequences of all lengths from all time series in the training set. A key goal of this work was to find an evaluation order of the shapelets space which enables fast convergence to an accurate model. The comparative analysis we conducted clearly indicates that a random evaluation order yields the best results. Our empirical analysis of the distribution of high-quality shapelets within the shapelets space provides insights into why randomized shapelets sampling is superior to alternative evaluation orders. We present an algorithm for randomized model generation for shapelet-based classification that converges extremely quickly to a model with surprisingly high accuracy after evaluating only an exceedingly small fraction of the shapelets space.
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. We also implement GADGET, a software framework to convert any malware binary to a binary undetected by malware classifiers, using the proposed attack, without access to the malware source code.
No abstract is provided for this article.
In this paper, we present a method for boosting collaborative filtering by integrating spatial information about geo-referenced items (e.g., photos). In particular, we developed a method to estimate missing ratings by propagating an item's neighbor's ratings based on the similarity of geospatial information. An empirical evaluation shows that geospatial information significantly improves recommendation results, and its contribution grows with the ratings data's level of sparseness. We illustrate the usefulness of the method for a photo recommendation task using data obtained from two popular photo-sharing websites: Flickr and Panoramio. A comparison with state-of-the-art methods indicates the superiority of the proposed method, implying that geospatial information should be considered, when available.
No abstract is provided for this article.
