nils-herrmann/clean_scirepeval huggingface dataset:
Learned lessons in credit card fraud detection from a practitioner perspective

Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non stationary distribution of the data, highly imbalanced classes distributions and continuous streams of transactions. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about which is the best strategy to deal with them. In this paper we provide some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment. The analysis is made possible by a real credit card dataset provided by our industrial partner.
Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six degreeof-freedom (DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the doctors correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, our idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases vignetting, reflection distortions, noise, sudden camera movements and lack of distinguishable features.
Personality development: stability and change.

In this review, we evaluate four topics in the study of personality development where discernible progress has been made since 1995 (the last time the area of personality development was reviewed in this series). We (a) evaluate research about the structure of personality in childhood and in adulthood, with special attention to possible developmental changes in the lower-order components of broad traits; (b) summarize new directions in behavioral genetic studies of personality; (c) synthesize evidence from longitudinal studies to pinpoint where and when in the life course personality change is most likely to occur; and (d) document which personality traits influence social relationships, status attainment, and health, and the mechanisms by which these personality effects come about. In each of these four areas, we note gaps and identify priorities for further research.
Spatio-Temporal Modeling of Check-ins in Location-Based Social Networks

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users’ movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters by using an efficient EM algorithm, which distributes over the users. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives.
Streaming Anomaly Detection Using Randomized Matrix Sketching

Timely and accurate detection of anomalies in massive data streams have important applications in preventing machine failures, intrusion detection, and dynamic load balancing, etc. In this paper, we introduce a new anomaly detection algorithm, which can detect anomalies in a streaming fashion by making only one pass over the data while utilizing limited storage. The algorithm uses ideas from matrix sketching and randomized low-rank matrix approximations to maintain an approximate low-rank orthogonal basis of the data in a streaming model. Using this constructed orthogonal basis, anomalies in new incoming data are detected based on a simple reconstruction error test. We theoretically prove that our algorithm compares favorably with an offline approach based on global singular value decomposition updates. The experimental results show the effectiveness and efficiency of our approach over other popular fast anomaly detection methods.
Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm

In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side information and the observed data. Our method is based on a nonparametric Bayesian hierarchical model that combines the probabilistic model for the data instance and the one for the side-information. An efficient Gibbs sampling algorithm is proposed for posterior inference. Using the small-variance asymptotics of our probabilistic model, we then derive a new deterministic clustering algorithm (RDP-means). It can be viewed as an extension of K-means that allows for the inclusion of side information and has the additional property that the number of clusters does not need to be specified a priori. Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values. The results of our experiments show strong results for our probabilistic and deterministic approaches under these conditions when compared to other algorithms in the literature.
Discrete Exterior Calculus

We present a theory and applications of discrete exterior calculus on simplicial complexes of arbitrary finite dimension. This can be thought of as calculus on a discrete space. Our theory includes not only discrete differential forms but also discrete vector fields and the operators acting on these objects. This allows us to address the various interactions between forms and vector fields (such as Lie derivatives) which are important in applications. Previous attempts at discrete exterior calculus have addressed only differential forms. We also introduce the notion of a circumcentric dual of a simplicial complex. The importance of dual complexes in this field has been well understood, but previous researchers have used barycentric subdivision or barycentric duals. We show that the use of circumcentric duals is crucial in arriving at a theory of discrete exterior calculus that admits both vector fields and forms.
Supervised topic models for multi-label classification

Recently, some publications indicated that the generative modeling approaches, i.e., topic models, achieved appreciated performance on multi-label classification, especially for skewed data sets. In this paper, we develop two supervised topic models for multi-label classification problems. The two models, i.e., Frequency-LDA (FLDA) and Dependency-Frequency-LDA (DFLDA), extend Latent Dirichlet Allocation (LDA) via two observations, i.e., the frequencies of the labels and the dependencies among different labels. We train the models by the Gibbs sampler algorithm. The experiment results on well known collections demonstrate that our two models outperform the state-of-the-art approaches. & 2014 Elsevier B.V. All rights reserved.
OpenKN: An open knowledge computational engine for network big data

With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open knowledge network (OpenKN), which is a self-adaptive and evolutionable knowledge computational engine for network big data. To the best of our knowledge, this is the first work of designing the end-to-end and holistic knowledge processing pipeline in regard with the network big data. Moreover, to capture the evolutionable computing capability of OpenKN, we present the evolutionable knowledge network for knowledge representation. A case study demonstrates the effectiveness of the evolutionable computing of OpenKN.
A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems

This paper describes a new maximum-power-point-tracking method for a photovoltaic system based on the Lagrange Interpolation Formula and proposes the particle swarm optimization method. The proposed control scheme eliminates the problems of conventional methods by using only a simple numerical calculation to initialize the particles around the global maximum power point. Hence, the suggested control scheme will utilize less iterations to reach the maximum power point. Simulation study is carried out using MATLAB/SIMULINK and compared with the Perturb and Observe method, the Incremental Conductance method, and the conventional Particle Swarm Optimization algorithm. The proposed algorithm is verified with the OPAL-RT real-time simulator. The simulation results confirm that the proposed algorithm can effectively enhance the stability and the fast tracking capability under abnormal insolation conditions.
Classifying Emotions in Customer Support Dialogues in Social Media

Providing customer support through social media channels is gaining increasing popularity. In such a context, automatic detection and analysis of the emotions expressed by customers is important, as is identification of the emotional techniques (e.g., apology, empathy, etc.) in the responses of customer service agents. Result of such an analysis can help assess the quality of such a service, help and inform agents about desirable responses, and help develop automated service agents for social media interactions. In this paper, we show that, in addition to text based turn features, dialogue features can significantly improve detection of emotions in social media customer service dialogues and help predict emotional techniques used by customer service agents.
Pragmatism vs interpretivism in qualitative information systems research

Qualitative research is often associated with interpretivism, but alternatives do exist. Besides critical research and sometimes positivism, qualitative research in information systems can be performed following a paradigm of pragmatism. This paradigm is associated with action, intervention and constructive knowledge. This paper has picked out interpretivism and pragmatism as two possible and important research paradigms for qualitative research in information systems. It clarifies each paradigm in an ideal-typical fashion and then conducts a comparison revealing commonalities and differences. The possibilities of combining pragmatism and interpretivism in qualitative research in information systems are analysed. A research case that combines interpretivism and pragmatism is used for illustration. The paper thus contributes to a discussion about different paradigms and methods for qualitative research in information systems.
Are we experiencing a big data bubble?

Fatma Özcan IBM Almaden Research Center San Jose, CA fozcan@us.ibm.com Nesime Tatbul Intel Labs and MIT Cambridge, MA tatbul@csail.mit.edu Daniel J. Abadi Yale University New Haven, CT dna@cs.yale.edu Marcel Kornacker Cloudera San Francisco, CA marcel@cloudera.com C Mohan IBM Almaden Research Center San Jose, CA cmohan@us.ibm.com Karthik Ramasamy Twitter, Inc. San Francisco, CA karthik@twitter.com Janet Wiener Facebook, Inc. Menlo Park, CA jlw@fb.com
A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm

In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases - The graph cuts and the random walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an l1 or an l2 norm. Here, we explore the segmentation algorithm defined by an linfin norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the graph cuts or random walker methods.
Steerable miniature jumping robot

Jumping is used in nature by many small animals to locomote in cluttered environments or in rough terrain. It offers small systems the benefit of overcoming relatively large obstacles at a low energetic cost. In order to be able to perform repetitive jumps in a given direction, it is important to be able to upright after landing, steer and jump again. In this article, we review and evaluate the uprighting and steering principles of existing jumping robots and present a novel spherical robot with a mass of 14 g and a size of 18 cm that can jump up to 62 cm at a take-off angle of 75°, recover passively after landing, orient itself, and jump again. We describe its design details and fabrication methods, characterize its jumping performance, and demonstrate the remote controlled prototype repetitively moving over an obstacle course where it has to climb stairs and go through a window.
Uptake and retention of microplastics by the shore crab Carcinus maenas.

Microplastics, plastics particles <5 mm in length, are a widespread pollutant of the marine environment. Oral ingestion of microplastics has been reported for a wide range of marine biota, but uptake into the body by other routes has received less attention. Here, we test the hypothesis that the shore crab (Carcinus maenas) can take up microplastics through inspiration across the gills as well as ingestion of pre-exposed food (common mussel Mytilus edulis). We used fluorescently labeled polystyrene microspheres (8-10 μm) to show that ingested microspheres were retained within the body tissues of the crabs for up to 14 days following ingestion and up to 21 days following inspiration across the gill, with uptake significantly higher into the posterior versus anterior gills. Multiphoton imaging suggested that most microspheres were retained in the foregut after dietary exposure due to adherence to the hairlike setae and were found on the external surface of gills following aqueous exposure. Results were used to construct a simple conceptual model of particle flow for the gills and the gut. These results identify ventilation as a route of uptake of microplastics into a common marine nonfilter feeding species.
Human posture recognition using human skeleton provided by Kinect

Human posture recognition is an attractive and challenging topic in computer vision because of its wide range of application. The coming of low cost device Kinect with its SDK gives us a possibility to resolve with ease some difficult problems encountered when working with conventional cameras. In this paper, we explore the capacity of using skeleton information provided by Kinect for human posture recognition in a context of a health monitoring framework. We conduct 7 different experiments with 4 types of features extracted from human skeleton. The obtained results show that this device can detect with high accuracy four interested postures (lying, sitting, standing, bending).
Remote fish aquaculture monitoring system based on wireless transmission technology

This article establishes an automated monitoring system for the fish farm aquaculture environment. The fish farm setting is usually in convenient places without common place traffic. The proposed system is network surveillance combined with mobile devices and a remote platform to collect real-time farm environmental information. This system permits real-time observation and control of fish farms with dissolved oxygen sensors, temperature sensing elements using A/D and 8051 module signal conversion. The real-time data is captured and displayed via ZigBee wireless transmission signal transmitter to remote computer terminals. Visual Basic 2010 software is used to design the interface functions and control sensing module. This system is low-cost, low power, easy operation with wireless transmission capability. A continuous, stable power supply is very important for the aquaculture industry. The proposed system will use municipal electricity coupled with a battery power source to provide power with battery intervention if municipal power is interrupted. The battery system is designed to avoid the self-discharge phenomenon which reduces the battery lifetime. Solar power is used to provide charging at any time.
Battery Thermal Management in EVs and HEVs : Issues and Solutions

Thermal management of batteries in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is essential for effective operation in all climates. This has been recognized in the design of battery modules and packs for pre-production prototype or production EVs and HEVs. Designs are evolving and various issues are being addressed. There are trade-offs between performance, functionality, volume, mass, cost, maintenance, and safety. In this paper, we will review some of the issues and associated solutions for battery thermal management and what information is needed for proper design of battery management systems. We will discuss such topics as active cooling versus passive cooling, liquid cooling versus air cooling, cooling and heating versus cooling only systems, and relative needs of thermal management for VRLA, NiMH, and Li-Ion batteries.
An Algorithm for Nudity Detection

This paper presents an algorithm for detecting nudity in color images. A skin color distribution model based on the RGB, Normalized RGB, and HSV color spaces is constructed using correlation and linear regression. The skin color model is used to identify and locate skin regions in an image. These regions are analyzed for clues indicating nudity or nonnudity such as their sizes and relative distances from each other. Based on these clues and the percentage of skin in the image, an image is classified nude or non-nude. The skin color distribution model performs with 96.29% recall and 6.76% false positive rate on a test set consisting of 2,303,824 manually labeled skin pixels and 24,285,952 manually labeled non-skin pixels. The Nudity Detection Algorithm is able to detect nudity with a 94.77% recall and a false positive rate of 5.04% on a set of images consisting of 421 nude images and 635 non-nude images.
Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system

To develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next ten years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system.  Linguistic fuzzy rule-based classification systems are used, since they provide a good classification rate and a highly interpretable model. More specifically, a new methodology to combine fuzzy rule-based classification systems with interval-valued fuzzy sets is proposed, which is composed of three steps: 1) the modelling of the linguistic labels of the classifier using interval-valued fuzzy sets; 2) the use of theKα operator in the inference process and 3) the application of a genetic tuning to find the best ignorance degree that each interval-valued fuzzy set represents as well as the best value for the parameter α of theKα operator in each rule.  Correspondingauthor. Tel:+34-948166048. Fax:+34-948168924 Email addresses: joseantonio.sanz@unavarra.es (Jośe Antonio Sanz ), mikel.galar@unavarra.es (Mikel Galar),aranzazu.jurio@unavarra.es (Aranzazu Jurio), antonio.brugos@unavarra.es (Antonio Brugos), miguel.pagola@unavarra.es (Miguel Pagola),bustince@unavarra.es (Humberto Bustince)  2013
0 Fractional Derivatives , Fractional Integrals , and Fractional Differential Equations in Matlab

The term fractional calculus is more than 300 years old. It is a generalization of the ordinary differentiation and integration to non-integer (arbitrary) order. The subject is as old as the calculus of differentiation and goes back to times when Leibniz, Gauss, and Newton invented this kind of calculation. In a letter to L’Hospital in 1695 Leibniz raised the following question (Miller and Ross, 1993): “Can the meaning of derivatives with integer order be generalized to derivatives with non-integer orders?" The story goes that L’Hospital was somewhat curious about that question and replied by another question to Leibniz. “What if the order will be 1/2?" Leibniz in a letter dated September 30, 1695 replied: “It will lead to a paradox, from which one day useful consequences will be drawn." The question raised by Leibniz for a fractional derivative was an ongoing topic in the last 300 years. Several mathematicians contributed to this subject over the years. People like Liouville, Riemann, and Weyl made major contributions to the theory of fractional calculus. The story of the fractional calculus continued with contributions from Fourier, Abel, Leibniz, Grünwald, and Letnikov. Nowadays, the fractional calculus attracts many scientists and engineers. There are several applications of this mathematical phenomenon in mechanics, physics, chemistry, control theory and so on (Caponetto et al., 2010;  2006; Monje et al., 2010; Oldham and Spanier, 1974; Oustaloup, 1995; Podlubny, 1999). It is natural that many authors tried to solve the fractional derivatives, fractional integrals and fractional differential equations in Matlab. A few very good and interesting Matlab functions were already submitted to the MathWorks, Inc. Matlab Central File Exchange, where they are freely downloadable for sharing among the users. In this chapter we will use some of them. It is worth mentioning some addition to Matlab toolboxes, which are appropriate for the solution of fractional calculus problems. One of them is a toolbox created by CRONE team (CRONE, 2010) and another one is the Fractional State–Space Toolkit developed by Dominik Sierociuk (Sierociuk, 2005). Last but not least we should also mention a Matlab toolbox created by Dingyü Xue (Xue, 2010), which is based on Matlab object for fractional-order transfer function and some manipulation with this class of the transfer function. Despite that the mentioned toolboxes are mainly for control systems, they can be “abused" for solutions of general problems related to fractional calculus as well.