nils-herrmann/clean_scirepeval huggingface dataset:
Loyal to your city? A data mining analysis of a public service loyalty program

a r t i c l e i n f o Keywords: Knowledge discovery Data mining CRM Behavioral data Loyalty card Customer loyalty programs are largely present in the private sector and have been elaborately studied. Applications from the private sector have found resonance in a public setting, however, simply extrapolating research results is not acceptable, as their rationale inherently differs. This study focuses on data from a loyalty program issued by the city of Antwerp (Belgium). The aim of the loyalty card entails large citizen participation, however, an active user base of only 20 % is reached. Predictive techniques are employed to increase this number. Using spatial behavioral user information, a Naive Bayes classifier and a Support Vector Machine are used which result in models capable of predicting whether a user will actively use its card, whether a user will defect in the near future and which locations a user will visit. Also, a projection of spatial behavioral data onto even more fine-grained spatio-temporal data is performed. The results are promising: the best model achieves an AUC value of 92.5 %, 85.5 % and 88.12 % (averaged over five locations) for the predictions, respectively. Moreover, as behavior is modeled in more detail, better predictions are made. Two main contributions are made in this study. First, as a theoretical contribution, fine-grained behavioral data contributes to a more sound decision-making process. Second, as a practical contribution, the city of Antwerp can now make tailored strategic decisions to increase its active user base. Since the evolution from a product-centric to a customer-centric approach, managing customer relationships has become a necessity for organizations . Customers need to be understood on a per-customer basis in terms of their needs, preferences and potential [2,3]. Maintaining close ties to customers may lead to a competitive advantage and to customer loyalty . In order for organizations to initiate and maintain connections to their customers, Customer Relationship Management (CRM) systems are used. Data mining forms an integral part of these systems and provides insight into raw customer data . Consequently, organizations can closely monitor their customers and anticipate accordingly through proactive marketing such as offering personalized incentives . An important source for CRM data constitutes the output of loyalty programs and more specifically from loyalty cards . Loyalty card data is typically characterized by a large sample of transactional data connected to the customers, for which also socio-demographic …
Shedding light on thirteen years of darkness: content analysis of articles pertaining to transgender issues in marriage/couple and family therapy journals.

What is the extent to which marriage/couple and family therapy (M/CFT) journals address transgender issues and how many of them say they are inclusive of transgender persons when they are not? To answer these queries, a content analysis was conducted on articles published in M/CFT literature from 1997 through 2009. Of the 10,739 articles examined in 17 journals, only nine (0.0008%) focused on transgender issues or used gender variance as a variable. Findings support the assertion that transgender issues are ignored and marginalized by M/CFT scholars and researchers alike.
Fuzzy Based Control Using Lab view For Temperature Process

This project aims at designing and implementing a fuzzy controller for Multiple Input Single Output temperature process. Temperature control of water in the tank is achieved by varying current to the heating rod and inlet flow rate by a fuzzy controller. The system consists of a tank, reservoir, variable speed pump, temperature sensor placed inside a heating tank containing the heating rod, voltage controlled current source and computer. Water is pumped into the tank from reservoir and RTD measures the current temperature. The signal from the temperature sensor is sent to the DAQ interfaced to the computer. LabVIEW software is used to acquire the input signal and send the output signal that is determined by the control algorithm. Fuzzy logic controller is designed in LabVIEW. Based on the set point temperature, the controller sets the appropriate current to the heating rod. If the required temperature is less than that sensed by the temperature sensor, the flow rate of water into the tank is controlled by a variable speed pump. While conventional controllers are analytically described by a set of equations, the FLC is described by a knowledge-based algorithm. Thus this system is highly efficient in both heating and reducing the temperature of the tank. A fuzzy logic controller gives faster response, is more reliable and recovers quickly from system upsets. It also works well to uncertainties in the process variables and it does not require mathematical modelling.
Framework for Governance in Open Source Communities

In recent years, the development of software in open source communities has attracted immense attention from research and practice. The idea of commercial quality, free software, and open source code accelerated the development of well-designed open source software such as Linux, Apache tools, or Perl. Intrinsic motivation, group identification processes, learning, and career concerns are the key drivers for a successful cooperation among the participants. These factors and most mechanisms of control, coordination, and monitoring forms of open source communities can hardly be explained by traditional organizational theories. In particular, the micro and macro structures of open source communities and their mode of operation are hardly compatible with the central assumption of the New Institutional Theory, like opportunistic behavior. The aim of this contribution is to identify factors that sustain the motivation of the community members over the entire life cycle of an open source project. Adequate coordination and controlling mechanisms for the governance in open source communities may be extracted.
An ensemble-based approach to fast classification of multi-label data streams

Network operators are continuously confronted with online events, such as online messages, blog updates, etc. Due to the huge volume of these events and the fast changes of the topics, it is critical to manage them promptly and effectively. There have been many softwares and algorithms developed to conduct automatic classification over these stream data. Conventional approaches focus on single-label scenarios, where each event can only be tagged with one label. However, in many stream data, each event can be tagged with more than one labels. Effective stream classification systems should be able to consider the unique properties of multi-label stream data, such as large data volumes, label correlations and concept drifts. To address these challenges, in this paper, we propose an efficient and effective method for multi-label stream classification based on an ensemble of fading random trees. The proposed model can efficiently process high-speed multi-label stream data with concept drifts. Empirical studies on real-world tasks demonstrate that our method can maintain a high accuracy in multi-label stream classification, while providing a very efficient solution to the task.
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Recurrent neural network (RNN) models are widely used for processing sequential data governed by a latent tree structure. Previous work shows that RNN models (especially Long Short-Term Memory (LSTM) based models) could learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree-based models. This work proposes a new inductive bias Ordered Neurons, which enforces an order of updating frequencies between hidden state neurons. We show that the ordered neurons could explicitly integrate the latent tree structure into recurrent models. To this end, we propose a new RNN unit: ON-LSTM, which achieve good performances on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference1.
Adaptive content generation for games

From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.
RGB-Depth SLAM Review

Simultaneous Localization and Mapping (SLAM) have made the real time dense reconstruction possible increasing the prospects of navigation, tracking, and augmented reality problems. Some breakthroughs have been achieved in this regard during past few decades and more remarkable works are still going on. This paper presents an overview of SLAM approaches that have been developed till now. Kinect Fusion algorithm, its variants and further developed approaches are discussed in detailed. The algorithms and approaches are compared for their effectiveness in tracking and mapping based on Root Mean Square error over online available
Every Move You Make: Exploring Practical Issues in Smartphone Motion Sensor Fingerprinting and Countermeasures

The ability to track users’ activities across different websites and visits is a key tool in advertising and surveillance. The HTML5 DeviceMotion interface creates a new opportunity for such tracking via fingerprinting of smartphone motion sensors. We study the feasibility of carrying out such fingerprinting under real-world constraints and on a large scale. In particular, we collect measurements from several hundred users under realistic scenarios and show that the state-of-the-art techniques provide very low accuracy in these settings. We then improve fingerprinting accuracy by changing the classifier as well as incorporating auxiliary information. We also show how to perform fingerprinting in an open-world scenario where one must distinguish between known and previously unseen users. We next consider the problem of developing fingerprinting countermeasures; we evaluate the usability of a previously proposed obfuscation technique and a newly developed quantization technique via a large-scale user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.
Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN

In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless communication system, in which DNNs are employed for all signal-related functionalities, including encoding, decoding, modulation, and equalization. However, accurate instantaneous channel transfer function, i.e., the channel state information (CSI), is necessary to compute the gradient of the DNN representing. In many communication systems, the channel transfer function is hard to obtain in advance and varies with time and location. In this article, this constraint is released by developing a channel agnostic end-to-end system that does not rely on any prior information about the channel. We use a conditional generative adversarial net (GAN) to represent the channel effects, where the encoded signal of the transmitter will serve as the conditioning information. In addition, in order to obtain accurate channel state information for signal detection at the receiver, the received signal corresponding to the pilot data is added as a part of the conditioning information. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) and Rayleigh fading channels, which opens a new door for building data-driven communication systems.
1 Mobile Landscapes : using location data from cellphones for urban analysis

The technology for determining the geographic location of cell phones and other hand-held devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as Location Based Services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. This paper aims to review and introduce the potential of this technology to the urban planning community. In addition, it presents the ‘Mobile Landscapes’ project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.
Throughput Estimation for Short Lived TCP Cubic Flows

Mobile devices are increasingly becoming the dominant device for Internet access. The network throughput achieved by a mobile device directly affects the performance and user experience. Throughput measurement techniques thus play an important role in predicting expected performance. Measurement techniques that require the transfer of large amounts of data can provide higher accuracy but incur large overhead. Further, since most mobile cellular plans impose usage quota, the overhead of such measurements over cellular networks can become quite high. Smaller data transfers have also been used to measure the throughput. Due to the conservative TCP slow start behaviour, however, these measurements often underestimate the achievable throughput. Considering these weaknesses in existing throughput measurement techniques, we propose a throughput estimation technique for TCP Cubic that uses 1 MB of data transfer to predict the throughput for prevalent large transfer sizes in mobile traffic such as 5 MB, 10 MB and 20 MB. Our evaluation shows that our approach can achieve high accuracy with low overhead, in predicting the achievable throughput.
Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification

The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with LogEuclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the matrix logarithms from the original tangent space to a new tangent space of more discriminability. Under the tangent map framework, the novel metric learning can then be formulated as an optimization problem of seeking a Mahalanobis-like matrix, which can take the advantage of traditional metric learning techniques. Extensive evaluations on several image set classification tasks demonstrate the effectiveness of our proposed metric learning method.
Age of language acquisition and cortical language organization in multilingual patients undergoing awake brain mapping.

Most knowledge regarding the anatomical organization of multilingualism is based on aphasiology and functional imaging studies. However, the results have still to be validated by the gold standard approach, namely electrical stimulation mapping (ESM) during awake neurosurgical procedures. In this ESM study the authors describe language representation in a highly specific group of 13 multilingual individuals, focusing on how age of acquisition may influence the cortical organization of language.  Thirteen patients who had a high degree of proficiency in multiple languages and were harboring lesions within the dominant, left hemisphere underwent ESM while being operated on under awake conditions. Demographic and language data were recorded in relation to age of language acquisition (for native languages and early- and late-acquired languages), neuropsychological pre- and postoperative language testing, the number and location of language sites, and overlapping distribution in terms of language acquisition time. Lesion growth patterns and histopathological characteristics, location, and size were also recorded. The distribution of language sites was analyzed with respect to age of acquisition and overlap.  The functional language-related sites were distributed in the frontal (55%), temporal (29%), and parietal lobes (16%). The total number of native language sites was 47. Early-acquired languages (including native languages) were represented in 97 sites (55 overlapped) and late-acquired languages in 70 sites (45 overlapped). The overlapping distribution was 20% for early-early, 71% for early-late, and 9% for late-late. The average lesion size (maximum diameter) was 3.3 cm. There were 5 fast-growing and 7 slow-growing lesions.  Cortical language distribution in multilingual patients is not homogeneous, and it is influenced by age of acquisition. Early-acquired languages have a greater cortical representation than languages acquired later. The prevalent native and early-acquired languages are largely represented within the perisylvian left hemisphere frontoparietotemporal areas, and the less prevalent late-acquired languages are mostly overlapped with them.
Multimedia Forensics Is Not Computer Forensics

The recent popularity of research on topics of multimedia forensics justifies reflections on the definition of the field. This paper devises an ontology that structures forensic disciplines by their primary domain of evidence. In this sense, both multimedia forensics and computer forensics belong to the class of digital forensics, but they differ notably in the underlying observer model that defines the forensic investigator’s view on (parts of) reality, which itself is not fully cognizable. Important consequences on the reliability of probative facts emerge with regard to available counter-forensic techniques: while perfect concealment of traces is possible for computer forensics, this level of certainty cannot be expected for manipulations of sensor data. We cite concrete examples and refer to established techniques to support our arguments.
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation

A review text is normally represented as a bag-of-words (BOW) in sentiment classification. Such a simplified BOW model has fundamental deficiencies in modeling some complex linguistic phenomena such as negation. In this work, we propose a dual-view co-training algorithm based on dual-view BOW representation for semisupervised sentiment classification. In dual-view BOW, we automatically construct antonymous reviews and model a review text by a pair of bags-of-words with opposite views. We make use of the original and antonymous views in pairs, in the training, bootstrapping and testing process, all based on a joint observation of two views. The experimental results demonstrate the advantages of our approach, in meeting the two co-training requirements, addressing the negation problem, and enhancing the semi-supervised sentiment classification efficiency.