Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints. To address these issues, we propose Mordred, a developed descriptor-calculation software application that can calculate more than 1800 two- and three-dimensional descriptors. It is freely available via GitHub. Mordred can be easily installed and used in the command line interface, as a web application, or as a high-flexibility Python package on all major platforms (Windows, Linux, and macOS). Performance benchmark results show that Mordred is at least twice as fast as the well-known PaDEL-Descriptor and it can calculate descriptors for large molecules, which cannot be accomplished by other software. Owing to its good performance, convenience, number of descriptors, and a lax licensing constraint, Mordred is a promising choice of molecular descriptor calculation software that can be utilized for cheminformatics studies, such as those on quantitative structure–property relationships.

/pdf/mordred-a-molecular-descriptor-calculator-4jqsz8r4bi.pdf

Mordred: a molecular descriptor calculator

Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions. These data can be very useful to transit planners, from the day-to-day operation of the transit system to the strategic long-term planning of the network. This review covers several aspects of smart card data use in the public transit context. First, the technologies are presented: the hardware and information systems required to operate these tools; and privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are addressed. Then, the various uses of the data at three levels of management are described: strategic (long-term planning), tactical (service adjustments and network development), and operational (ridership statistics and performance indicators). Also reported are smart card commercialization experiments conducted all over the world. Finally, the most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.

Smart card data use in public transit: A literature review

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or this https URL.

/pdf/pyod-a-python-toolbox-for-scalable-outlier-detection-228qajm3b5.pdf

PyOD: A Python Toolbox for Scalable Outlier Detection

The frontoparietal control network (FPCN) plays a central role in executive control. It has been predominantly viewed as a unitary domain general system. Here, we examined patterns of FPCN functional connectivity (FC) across multiple conditions of varying cognitive demands, to test for FPCN heterogeneity. We identified two distinct subsystems within the FPCN based on hierarchical clustering and machine learning classification analyses of within-FPCN FC patterns. These two FPCN subsystems exhibited distinct patterns of FC with the default network (DN) and the dorsal attention network (DAN). FPCNA exhibited stronger connectivity with the DN than the DAN, whereas FPCNB exhibited the opposite pattern. This twofold FPCN differentiation was observed across four independent datasets, across nine different conditions (rest and eight tasks), at the level of individual-participant data, as well as in meta-analytic coactivation patterns. Notably, the extent of FPCN differentiation varied across conditions, suggesting flexible adaptation to task demands. Finally, we used meta-analytic tools to identify several functional domains associated with the DN and DAN that differentially predict activation in the FPCN subsystems. These findings reveal a flexible and heterogeneous FPCN organization that may in part emerge from separable DN and DAN processing streams. We propose that FPCNA may be preferentially involved in the regulation of introspective processes, whereas FPCNB may be preferentially involved in the regulation of visuospatial perceptual attention.

https://www.pnas.org/content/pnas/115/7/E1598.full.pdf

Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks.

Ensembles for feature selection: A review and future trends

Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in todays world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

/pdf/rapidminer-data-mining-use-cases-and-business-analytics-4jnrrxg0j0.pdf

RapidMiner: Data Mining Use Cases and Business Analytics Applications

Increases in congestion levels caused by adverse weather conditions are difficult to predict and therefore urban bus operators cannot incorporate appropriate changes into their planning, scheduling, and management decisions. Adverse weather conditions have an impact on the level of service an operator provides. They also result in higher levels of congestion due to an increase of personal car usage. The aim of the research paper is to investigate the impact of adverse weather conditions on urban bus performance measures. The Irish city which is used for this study given its geographical location experiences a maritime climate, dominated by low pressure from the Atlantic bringing cold wet weather with the trade winds. The study includes various types of performance measures such as ridership, frequency, headway regularity and travel time, which are analysed both in the presence and absence of adverse weather conditions. The performance measures include changing variables such as stage and destination, peak and off-peak, inbound and outbound in order to provide a comprehensive analysis. The data used for this research originate from an electronic fare collection system. 46 million individual boarding records are stored in the database. The results of the research paper include the calculation and presentation of various analysed performance measures followed by an extensive interpretation of how this information can support decision-making. The quantitative analysis method aims to improve and adjust planning, scheduling, and management decisions of urban bus operators and thereby alter and improve operations and level of service provided.

/pdf/the-impact-of-adverse-weather-conditions-on-urban-bus-3uja4mqlth.pdf

The impact of adverse weather conditions on urban bus performance measures

Understanding the behaviour of public transport passengers is key to providing a system from which passengers will derive the maximum benefit. One method of analysing this behaviour is with the use of passenger boarding data, stored in a database. Such a database may be improved by enriching the already existing dataset by applying specific algorithms. This paper describes an iterative classification algorithm that classifies passenger boardings into two categories; transfer journeys and single journeys. The dataset used was from an urban public transport operator with a large fleet (over 1000 buses) and data of 48 million magnetic strip card boardings from 1998 and 1999. This paper details the process involved in the initial development of the iterative classification algorithm, the analysis of transfer node identification matrices, waiting time information charts and spatial first/second boarding matrices. When the algorithm is applied to the dataset it provides transport planners with valuable information with regard to passenger boardings, transfers and waiting times which can assist them in transport planning and policymaking. The purpose of this paper is to describe the automatic generation of a new data attribute that cannot be derived directly and therefore increases the future utilization of the dataset. The paper presents various analyses based on the extended and enriched database to illustrate this point.

/pdf/transfer-journey-identification-and-analyses-from-electronic-3j3qxtqyph.pdf

Transfer journey identification and analyses from electronic fare collection data

Android is most popular operating system for smartphones and small devices with 86.6% market share (Chau 2016). Its open source nature makes it more prone to attacks creating a need for malware analysis. Main approaches for detecting malware intents of mobile applications are based on either static analysis or dynamic analysis. In static analysis, apps are inspected for suspicious patterns of code to identify malicious segments. However, several obfuscation techniques are available to provide a guard against such analysis. The dynamic analysis on the other hand is a behavior-based detection method that involves investigating the run-time behavior of the suspicious app to uncover malware. The present study extracts the system call behavior of 216 malicious apps and 278 normal apps to construct a feature vector for training a classifier. Seven data classification techniques including decision tree, random forest, gradient boosting trees, k-NN, Artificial Neural Network, Support Vector Machine and deep learning were applied on this dataset. Three feature ranking techniques were usedto select appropriate features from the set of 337 attributes (system calls). These techniques of feature ranking included information gain, Chi-square statistic and correlation analysis by determining weights of the features. After discarding select features with low ranks the performances of the classifiers were measured using accuracy and recall. Experiments show that Support Vector Machines (SVM) after selecting features through correlation analysis outperformed other techniques where an accuracy of 97.16% is achieved with recall 99.54% (for malicious apps). The study also contributes by identifying the set of systems calls that are crucial in identifying malicious intent of android apps.

https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1395&context=scschcomcon

Dynamic behavior analysis of android applications for malware detection

Understanding the behavior of public transport passengers is key to providing a system from which passengers will derive the maximum benefit. One method of analyzing this behavior is with the use of passenger boarding data, stored in a database. Such a database may be improved by enriching the already existing dataset by applying specific algorithms. This paper describes an iterative classification algorithm that classifies passenger boardings at trip level into two categories; transfer journeys (linked trips) and single journeys. The dataset used was from an urban public transport operator with a large fleet (over 1000 buses) and data of 48 million magnetic strip card boardings from 1998 and 1999. This paper details the proposed iterative classification algorithm as well as various validation methods such as survey based verification, simple random sampling and Monte Carlo simulations. The outcome of the algorithm is positive and serves the successful identification of transfer journeys at trip level. The algorithm is fast, robust and produces results with an acceptable error rate even when larger amounts of noise is introduced to the EFC data set. This newly created attribute can then be re-used for other automated processes based on Electronic Fare Collection (EFC) data such as Origin/Destination identification at trip level. Furthermore detailed analyses on transfer node identification and volume, waiting time at transfer nodes and passenger behavioral studies can be created as linked trip information at trip level exists.

Automated Identification of Linked Trips at Trip Level Using Electronic Fare Collection Data

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