nils-herrmann/clean_scirepeval huggingface dataset:
What causes hidradenitis suppurativa?

Hidradenitis suppurativa (HS)--a rather common, very chronic and debilitating inflammatory skin appendage disorder with a notoriously underestimated burden of disease--has long been a playground for the high priests of nomenclature: Ask a bunch of eminent dermatologists and skin pathologists to publicly share their thoughts on what causes HS, and they will soon get entrenched in a heated debate on whether this historical term is a despicable misnomer. Fortunately, the recently founded Hidradenitis Suppurativa Foundation (HSF; http://www.hs-foundation.org), to which EXP DERMATOL serves as home journal, has broken with this unproductive tradition and has encouraged publication of the current CONTROVERSIES feature. This is exclusively devoted to discussing the pathobiology of this chronic neutrophilic folliculitis of unknown origin. Although traces of terminological bickering remain visible, it does the HS experts in our virtual debate room credit that they engage in a constructive and comprehensive dissection of potential pathogenesis pathways that may culminate in the clinical picture we know under the competing terms HS or acne inversa. These experts sketch more often complementary than mutually exclusive pathogenesis scenarios, and the outlines of a conceivable consensus on the many open pathobiology questions begin to emerge in these CONTROVERSIES. Hopefully, this heralds a welcome new tradition: to get to the molecular heart of HS pathogenesis, which can only be achieved by a renaissance of solid basic HS research, as the key to developing more effective HS therapy.
Narcissism assessment in social – personality research : Does the association between narcissism and psychological health result from a confound with self-esteem ?

Influential social and personality psychology research indicates that narcissism is related to psychological health. Such inferences are open to question, however, because they nearly all rely on the same selfreport instrument—the Narcissistic Personality Inventory (NPI; Raskin & Hall, 1979, 1981)—to operationalize and measure narcissism. This is problematic because numerous NPI items do not appear to correspond to common definitions or manifestations of narcissism, and may instead be indicative of selfesteem. Two studies demonstrate that the NPI’s confound with self-esteem accounts for the purported relationship between narcissism and psychological health. This suggests that inferences about narcissism and psychological health may need to be reinterpreted. Results also highlight the need for measures that correspond more directly to core components of narcissism.
Triple Band Annular Ring Loaded Stacked Circular Patch Microstrip Antenna

In this paper a novel structure of annular ring loaded stacked circular patch microstrip antenna is theoretically analysed to observe various parameters such as return loss, input impedance, gain, directivity and radiation pattern. It is found that antenna possess three band of operation which signify the compactness and multiband operation of antenna. The antenna is resonating at three operating frequencies 1.720, 2.950, 3.060 GHz. The proposed theory is verified by simulation using Ansoft’s HFSS and theoretical results are in good agreement with simulated results. The antenna is useful for multi-services operations such as WLAN, GSM, UMTS, and WiMAX services.
Applying Data Mining to Customer Churn Prediction in an Internet Service Provider

A business incurs much higher charges when attempting to win new customers than to retain existing ones. As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning. However, customer retention efforts have also been costing organizations large amounts of resources. Same is the situation in ISP industry in I.R.Iran. Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other. Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe (FW) algorithms regained popularity in recent years due to their simplicity, effectiveness and theoretical guarantees. MP and FW address optimization over the linear span and the convex hull of a set of atoms, respectively. In this paper, we consider the intermediate case of optimization over the convex cone, parametrized as the conic hull of a generic atom set, leading to the first principled definitions of non-negative MP algorithms for which we give explicit convergence rates and demonstrate excellent empirical performance. In particular, we derive sublinear (O(1/t)) convergence on general smooth and convex objectives, and linear convergence (O(e−t)) on strongly convex objectives, in both cases for general sets of atoms. Furthermore, we establish a clear correspondence of our algorithms to known algorithms from the MP and FW literature. Our novel algorithms and analyses target general atom sets and general objective functions, and hence are directly applicable to a large variety of learning settings.
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of IL. The main challenge for an IL algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of IL algorithms. We present RWalk, a generalization of EWC++ (our efficient version of EWC [7]) and Path Integral [26] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various IL algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off between forgetting and intransigence.
Protein folding and misfolding

The manner in which a newly synthesized chain of amino acids transforms itself into a perfectly folded protein depends both on the intrinsic properties of the amino-acid sequence and on multiple contributing influences from the crowded cellular milieu. Folding and unfolding are crucial ways of regulating biological activity and targeting proteins to different cellular locations. Aggregation of misfolded proteins that escape the cellular quality-control mechanisms is a common feature of a wide range of highly debilitating and increasingly prevalent diseases.
Development of Inchworm In-Pipe Robot Based on Self-Locking Mechanism

In-pipe robots are used to carry sensors and some other repairing instruments to perform inspection and maintenance jobs inside pipelines. In this paper, a self-locking mechanism is presented to improve the traction ability of in-pipe robots and avoid their traditional limitations. The structure of this type of in-pipe robot is presented and some critical design issues on the principle of self-locking mechanism are discussed. Prototypes of 19 mm diameter were produced, and related experiments were performed on specially designed test platform. The traction ability of the proposed in-pipe robot was measured experimentally to be 15.2 N, far beyond its maximum static friction of 0.35 N with the inner surface of pipeline. It means that this development has broken the traditional limitation of in-pipe robot whose traction ability was smaller than its maximum static friction with the pipeline.
An Integrated Success Model for Evaluating Information System in Public Sectors

This study modified the dimensions of the TAM and D&M IS Success Models and added additional two success dimensions (Management support and Training). An integrated model for evaluating IS success was generated; the proposed model has been validated by an empirical study based on a questionnaire. The results indicated that information quality has a strong significant influence on IS success (81.9%) followed by Behavioral intention (80.2%); Perceived usefulness (78.8%); whereas the least one is user involvement (70%). The overall results show that the proposed model can be beneficial for decision makers in organizations on evaluating the implementation of information systems.  DeLone & McLean IS success model, Technology Acceptance Model, Integrated IS Success Model
Stochastic blockmodels: First steps

A stochastic model is proposed for social networks in which the actors in a network are partItIoned mto subgroups called blocks. The model provides a stochastrc generalization of the blockmodel. Estimation techniques are developed for the special case of a single relation social network, with blocks specified D prrorr. An extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition. The extended model prowdes a one degree-of-freedom test of the model. A numerical example from the social network hterature 1s used to illustrate the methods.
Reconstructing the giant: On the importance of rigour in documenting the literature search process

Science is a cumulative endeavour as new knowledge is often created in the process of interpreting and combining existing knowledge. This is why literature reviews have long played a decisive role in scholarship. The quality of literature reviews is particularly determined by the literature search process. As Sir Isaac Newton eminently put it: “If I can see further, it is because I am standing on the shoulders of giants.” Drawing on this metaphor, the goal of writing a literature review is to reconstruct the giant of accumulated knowledge in a specific domain. And in doing so, a literature search represents the fundamental first step that makes up the giant’s skeleton and largely determines its reconstruction in the subsequent literature analysis. In this paper, we argue that the process of searching the literature must be comprehensibly described. Only then can readers assess the exhaustiveness of the review and other scholars in the field can more confidently (re)use the results in their own research. We set out to explore the methodological rigour of literature review articles published in ten major information systems (IS) journals and show that many of these reviews do not thoroughly document the process of literature search. The results drawn from our analysis lead us to call for more rigour in documenting the literature search process and to present guidelines for crafting a literature review and search in the IS domain.
Design of novel capacitive type torque sensor for robotic applications

This paper introduces a novel capacitive type torque sensor. The developed torque sensor measures single axis torque value by using two capacitance transducing cells. To make a linear relation between the capacitance and the deformation, the shape of flexure hinge is considered to be deformed constantly. Also, the nonlinear curve fitting method is used to fit the capacitance linearly. Finally, the performance of the sensor is verified in reference to a commercial torque sensor.
A Review: Driver Drowsiness Detection System

Driver drowsiness is one of the major causes of traffic accidents. It is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some of these crashes could be prevented. In order to reliably detect the drowsiness, it depends on the presentation of timely warnings of drowsiness. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. Based on the type of data used, drowsiness detection can be conveniently separated into th e two categories of intrusive and non-intrusive methods. During the survey, non-intrusive methods detect drowsiness by measuring driving behavior and sometimes eye features, through which camera based detection system is the best method and so are useful for real world driving situations. This paper presents the review of existed drowsiness detection techniques that will be used in this system like Circular Hough Transform, FCM, Lab Color Space etc.
Automatic License Plate Recognition Using Local Binary Pattern and Histogram Matching

This paper proposes new real time license plate recognition (LPR) system that is capable of motion tracking and recognition of license plate. The best frame taken from the video has been chosen which is found to be about 4 m apart from camera position. For further processing, lower half section of vehicle image has been cropped of sized (450 × 140) while tracking. Local Binary Pattern (LBP) and histogram matching technique are used to detect license plate. Due to the robustness of LBP features, this method can adaptively deal with various changes such as rotation, scaling, and illumination in the license plate. Segmen‐ tation of the plate region into disjoint characters has been done with bounding box technique with some modifications. Recognition has been done by calculating histogram features. Minimum distance classifier has been used for features matching. The system is tested on more than 300 images and it gives 96.14% detection and 89.35% of recognition accuracy. This system is designed to recog‐ nize license plate of small, medium as well as large vehicles. It is also capable to detect single line and two line license plates format.
Convolutional Sequence to Sequence Model with Non-Sequential Greedy Decoding for Grapheme to Phoneme Conversion

The greedy decoding method used in the conventional sequence-to-sequence models is prone to producing a model with a compounding of errors, mainly because it makes inferences in a fixed order, regardless of whether or not the model's previous guesses are correct. We propose a non-sequential greedy decoding method that generalizes the greedy decoding schemes proposed in the past. The proposed method determines not only which token to consider, but also which position in the output sequence to infer at each inference step. Specifically, it allows the model to consider easy parts first, helping the model infer hard parts more easily later by providing more information. We study a grapheme-to-phoneme conversion task with a fully convolutional encoder-decoder model that embeds the proposed decoding method. Experiment results show that our model shows better performance than that of the state-of-the-art model in terms of both phoneme error rate and word error rate.
A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy

Nowadays, a prosperity of electronic commerce (E-commerce) not only gives more convenience to consumers but brings more new opportunities in online advertising and marketing. Online advertisers can understand more about consumer preferences based on their daily online shopping and browsing behaviors. The development of big data and cloud computing techniques further empower advertisers and marketers to have a data-driven and consumer-specific preference recommendation based on the online browsing histories. In this research, a decision support system is proposed to predict a consumer purchase intention during browsing sessions. The proposed decision support system categorizes online browsing activities into purchase-oriented and general sessions using extreme boosting machines. With the browsing content entropy features, the proposed method achieves 41.81% recall and 34.35% F score. It further shows its strong predictive capability compared to other benchmark algorithms including logistic regression and traditional ensemble models. The proposed method can be implemented in real-time bidding algorithms for online advertising strategies. Ad deliveries on browsing session with potential purchase intention not only improve the effectiveness of advertisements but significantly increase last-touch attributions for campaign performance.
Visual exploration and evaluation of climate-related simulation data

Large, heterogeneous volumes of simulation data are calculated and stored in many disciplines, e.g. in climate and climate impact research. To gain insight, current climate analysis applies statistical methods and model sensitivity analyzes in combination with standard visualization techniques. However, there are some obstacles for researchers in applying the full functionality of sophisticated visualization, exploiting the available interaction and visualization functionality in order to go beyond data presentation tasks. In particular, there is a gap between available and actually applied multi-variate visualization techniques. Furthermore, visual data comparison of simulation (and measured) data is still a challenging task. Consequently, this paper introduces a library of visualization techniques, tailored to support exploration and evaluation of climate simulation data. These techniques are integrated into the easy-to-use visualization framework SimEnvVis - designed as a front-end user interface to a simulation environment - which provides a high level of user support generating visual representations.
Mining Efficient Taxi Operation Strategies From Large Scale Geo-Location Data

Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton’s polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.
CloudTracker: Using Execution Provenance to Optimize the Cost of Cloud Use

In this work, we investigate tools that enable dollar cost optimization of scientific simulations using commercial clouds. We present a framework, called CLOUDTRACKER, that transparently records information from a simulation that is executed in a commercial cloud so that it may be “replayed” exactly to reproduce its results. Using the automated CLOUDTRACKER provenance and replay facilities, scientists can choose either to store the results of a simulation or to reproduce it on-demand – whichever is more cost efficient in terms of the dollar cost charged for storage and computing by the commercial cloud provider. We present a prototype implementation of CLOUDTRACKER for the Amazon AWS commercial cloud and the StochSS stochastic simulation system. Using this prototype, we analyze the storage-versus-compute cost tradeoffs for different classes of StochSS simulations when deployed and executed in AWS.
Radiomics: extracting more information from medical images using advanced feature analysis.

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
Chapter 8 Location-Based Social Networks : Users

In this chapter, we introduce and define the meaning of location-based social network (LBSN) and discuss the research philosophy behind LBSNs from the perspective of users and locations. Under the circumstances of trajectory-centric LBSN, we then explore two fundamental research points concerned with understanding users in terms of their locations. One is modeling the location history of an individual using the individual’s trajectory data. The other is estimating the similarity between two different people according to their location histories. The inferred similarity represents the strength of connection between two users in a locationbased social network, and can enable friend recommendations and community discovery. The general approaches for evaluating these applications are also presented.
Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder

The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.
Iris Recognition Based on Combined Feature of GLCM and Wavelet Transform

Iris biometric is considered to be one of the most efficient and trusted biometric method for authenticating users. This paper presents a method for iris recognition based on hybrid feature set of wavelet and GLCM features, a non-filter based technique, combined with Haar wavelet transform to increase the efficiency of the system. Here we combine frequency domain feature with spatial domain feature to increase overall efficiency of system. Probability neural Network is used to classify the features. Results show that the overall system efficiency is 94% with false rejection rate higher than false acceptance rate. The technique is tested on CASIA Iris database.