nils-herrmann/clean_scirepeval huggingface dataset:
Towards a Job Title Classification System

Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine learning-based semi-supervised job title classification system for the online job recruitment domain currently in production at CareerBuilder.com and propose enhancements to it. The system leverages a varied collection of classification as well clustering algorithms. These algorithms are encompassed in an architecture that facilitates leveraging existing off-the-shelf machine learning tools and techniques while keeping into consideration the challenges of constructing a scalable classification system for a large taxonomy of categories. As a continuously evolving system that is still under development we first discuss the existing semi-supervised classification system which is composed of both clustering and classification components in a proximity-based classifier setup and results of which are already used across numerous products at CareerBuilder. We then elucidate our long-term goals for job title classification and propose enhancements to the existing system in the form of a two-stage coarse and fine level classifier augmentation to construct a cascade of hierarchical vertical classifiers. Preliminary results are presented using experimental evaluation on real world industrial data.
Vector controlled brushless doubly fed twin stator cascaded induction generator for variable speed wind generation connected to weak grids

Due to continuous concerns about environmental pollution and a possible energy shortage, renewable energy systems, based mainly on wind power, solar energy, small hydro-electric power, etc have been implemented. Wind energy seems certain to play a major part in the world's energy future. In spite of sudden wind speed variations, wind farm generators should always be capable of extracting the maximum possible mechanical power from the wind and turning it into electrical power. Nowadays, most of the installed wind turbines are based on doubly-fed induction generators (DFIGs), wound rotor synchronous generators (WRSG) and permanent magnet synchronous generators (PMSGs). The DFIG equipped wind turbine has several advantages over others. One of which, the power converter in such wind turbines only deals with rotor power, hence the converter rating can run at reduced power rating. However DFIG has the famous disadvantage of the presence of slip rings which leads to increased maintenance costs and reduced life-time. Hence, brushless doubly fed induction machines (BDFIMs) can be considered as a viable alternative. In this paper, the brushless doubly fed twin stator induction generator (BDFTSIG) is modeled in details. A wind energy conversion system (WECS) utilizing a proposed indirect vector controlled BDFTSIG is presented. The proposed controller performance is investigated under various loading conditions showing enhanced transient and minimal steady state oscillations in addition to complete active/reactive power decoupling.
The Lomax generator of distributions: Properties, minification process and regression model

We propose a new class of distributions called the Lomax generator with two extra positive parameters to generalize any continuous baseline distribution. Some special models such as the Lomax-normal, Lomax–Weibull, Lomax-log-logistic and Lomax–Pareto distributions are discussed. Some mathematical properties of the new generator including ordinary and incomplete moments, quantile and generating functions, mean and median deviations, distribution of the order statistics and some entropy measures are presented. We discuss the estimation of the model parameters by maximum likelihood. We propose a minification process based on the marginal Lomax-exponential distribution. We define a logLomax–Weibull regression model for censored data. The importance of the new generator is illustrated by means of three real data sets.
A Review of Contemporary E-voting : Requirements , Technology , Systems and Usability

E-voting technology has been developed for more than 30 years. However it is still distance away from serious application. The major challenges are to provide a secure solution and to gain trust from the voters in using it. In this paper we try to present a comprehensive review to e-voting by looking at these challenges. We summarized the vast amount of security requirements named in the literature that allows researcher to design a secure system. We reviewed some of the e-voting systems found in the real world and the literature. We also studied how a e-voting system can be usable by looking at different usability research conducted on e-voting. Summarizes on different cryptographic tools in constructing e-voting systems are also presented in the paper. We hope this paper can served as a good introduction for e-voting researches.
The importance of the label hierarchy in hierarchical multi-label classification

We address the task of hierarchical multi-label classification (HMC). HMC is a task of structured output prediction where the classes are organized into a hierarchy and an instance may belong to multiple classes. In many problems, such as gene function prediction or prediction of ecological community structure, classes inherently follow these constraints. The potential for application of HMC was recognized by many researchers and several such methods were proposed and demonstrated to achieve good predictive performances in the past. However, there is no clear understanding when is favorable to consider such relationships (hierarchical and multi-label) among classes, and when this presents unnecessary burden for classification methods. To this end, we perform a detailed comparative study over 8 datasets that have HMC properties. We investigate two important influences in HMC: the multiple labels per example and the information about the hierarchy. More specifically, we consider four machine learning tasks: multi-label classification, hierarchical multi-label classification, single-label classification and hierarchical single-label classification. To construct the predictive models, we use predictive clustering trees (a generalized form of decision trees), which are able to tackle each of the modelling tasks listed. Moreover, we investigate whether the influence of the hierarchy and the multiple labels carries over for ensemble models. For each of the tasks, we construct a single tree and two ensembles (random forest and bagging). The results reveal that the hierarchy and the multiple labels do help to obtain a better single tree model, while this is not preserved for the ensemble models.
Simple Fusion: Return of the Language Model

Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation. We investigate an alternative simple method to use monolingual data for NMT training: We combine the scores of a pre-trained and fixed language model (LM) with the scores of a translation model (TM) while the TM is trained from scratch. To achieve that, we train the translation model to predict the residual probability of the training data added to the prediction of the LM. This enables the TM to focus its capacity on modeling the source sentence since it can rely on the LM for fluency. We show that our method outperforms previous approaches to integrate LMs into NMT while the architecture is simpler as it does not require gating networks to balance TM and LM. We observe gains of between +0.24 and +2.36 BLEU on all four test sets (English-Turkish, TurkishEnglish, Estonian-English, Xhosa-English) on top of ensembles without LM. We compare our method with alternative ways to utilize monolingual data such as backtranslation, shallow fusion, and cold fusion.
Additional Multi-Touch Attribution for Online Advertising

Multi-Touch Attribution studies the effects of various types of online advertisements on purchase conversions. It is a very important problem in computational advertising, as it allows marketers to assign credits for conversions to different advertising channels and optimize advertising campaigns. In this paper, we propose an additional multi-touch attribution model (AMTA) based on two obvious assumptions: (1) the effect of an ad exposure is fading with time and (2) the effects of ad exposures on the browsing path of a user are additive. AMTA borrows the techniques from survival analysis and uses the hazard rate to measure the influence of an ad exposure. In addition, we both take the conversion time and the intrinsic conversion rate of users into consideration to generate the probability of a conversion. Experimental results on a large real-world advertising dataset illustrate that the our proposed method is superior to state-of-the-art techniques in conversion rate prediction and the credit allocation based on AMTA is reasonable.
Beyond Sentiments and Opinions: Exploring Social Media with Appraisal Categories

The digital era arrives with a whole set of disruptive technologies that creates both risk and opportunity for open sources analysis. Although the sheer quantity of online conversations makes social media a huge source of information, their analysis is still a challenging task and many of traditional methods and research methodologies for data mining are not fit for purpose. Social data mining revolves around subjective content analysis, which deals with the computational processing of texts conveying people's evaluations, beliefs, attitudes and emotions. Opinion mining and sentiment analysis are the main paradigm of social media exploration and both concepts are often interchangeable. This paper investigates the use of appraisal categories to explore data gleaned for social media, going beyond the limitations of traditional sentiment and opinion-oriented approaches. Categories of appraisal are grounded on cognitive foundations of the appraisal theory, according to which people's emotional response are based on their own evaluative judgments or appraisals of situations, events or objects. A formal model is developed to describe and explain the way language is used in the cyberspace to evaluate, express mood and subjective states, construct personal standpoints and manage interpersonal interactions and relationships. A general processing framework is implemented to illustrate how the model is used to analyze a collection of tweets related to extremist attitudes.
The DLR Robot Motion Simulator Part II: Optimization based path-planning

In Part I of this paper, a novel motion simulator platform is presented, the DLR Robot Motion Simulator with 7 degrees of freedom (DOF). In this Part II, a path-planning algorithm for mentioned platform will be discussed. By replacing the widely used hexapod kinematics by an antropomorhic, industrial robot arm mounted on a standard linear axis, a comparably larger workspace at lower hardware costs can be achieved. But the serial, redundant kinematics of the industrial robot system also introduces challenges for the path-planning as singularities in the workspace, varying movability of the system and the handling of robot system's kinematical redundancy. By solving an optimization problem with constraints in every sampling step, a feasible trajectory can be generated, fulfilling the task of motion cueing, while respecting the robot's dynamic constraints.
Software defect prediction using Bayesian networks

There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.
Effect of Hurdle Technology in Food Preservation: A Review.

Hurdle technology is used in industrialized as well as in developing countries for the gentle but effective preservation of foods. Hurdle technology was developed several years ago as a new concept for the production of safe, stable, nutritious, tasty, and economical foods. Previously hurdle technology, i.e., a combination of preservation methods, was used empirically without much knowledge of the governing principles. The intelligent application of hurdle technology has become more prevalent now, because the principles of major preservative factors for foods (e.g., temperature, pH, aw, Eh, competitive flora), and their interactions, became better known. Recently, the influence of food preservation methods on the physiology and behavior of microorganisms in foods, i.e. their homeostasis, metabolic exhaustion, stress reactions, are taken into account, and the novel concept of multi-target food preservation emerged. The present contribution reviews the concept of the potential hurdles for foods, the hurdle effect, and the hurdle technology for the prospects of the future goal of a multi-target preservation of foods.
Monaural Audio Speaker Separation with Source Contrastive Estimation

We propose an algorithm to separate simultaneously speaking persons from each other, the “cocktail party problem”, using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by correlating and decorrelating a given input vector with output weights. Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference. Doing so will ensure that source separation is explicitly speaker-independent. Our approach is similar to recent deep neural network clustering and permutation-invariant training research; we use weighted spectral features and masks to augment individual speaker frequencies while filtering out other speakers. We avoid, however, the severe computational burden of other approaches with our technique. Furthermore, by training a vector space rather than combinations of different speakers or differences thereof, we avoid the so-called permutation problem during training. Our algorithm offers an intuitive, computationally efficient response to the cocktail party problem, and most importantly boasts better empirical performance than other current techniques.
High-intensity focused ultrasound treatment for skin: ex vivo evaluation.

High-intensity focused ultrasound (HIFU) has been used for skin tightening. However, there is a rising concern of irreversible adverse effects. Our aim was to evaluate the depth of thermal injury zone after HIFU energy passes through different condition.



To analyze the consistency of the HIFU device, phantom tests were performed. Simulations were performed on ex vivo porcine tissues to estimate the area of the thermal coagulation point (TCP) according to the applied energy and skin condition. The experiment was designed in three orientations: normal direction (from epidermis to fascia), reverse direction (from fascia to epidermis), and normal direction without epidermis.



The TCP was larger and wider depending on the applied fluence and handpieces (HPs). When we measured TCP in different directions, the measured area in the normal direction was more superficially located than that in the reverse direction. The depth of the TCP in the porcine skin without epidermis was detected at 130% deeper than in skin with an intact epidermis.



The affected area by HIFU is dependent on the skin condition and the characteristics of the HP and applied fluence. Considerations of these factors may be the key to minimize the unwanted adverse effects.
Modified Hausdorff Fractal Dimension (MHFD)

The Hausdorff fractal dimension has been a fast-to-calculate method to estimate complexity of fractal shapes. In this work, a modified version of this fractal dimension is presented in order to make it more robust when applied in estimating complexity of non-fractal images. The modified Hausdorff fractal dimension stands on two features that weaken the requirement of presence of a shape and also reduce the impact of the noise possibly presented in the input image. The new algorithm has been evaluated on a set of images of different character with promising performance.
Data Mining And Strategic Marketing In The Airline Industry

In the airline industry, data analysis and data mining are a prerequisite to push customer relationship management (CRM) ahead. Knowledge about data mining methods, marketing strategies and airline business processes has to be combined to successfully implement CRM. This paper is a case study and gives an overview about distinct issues, which have to be taken into account in order to provide a first solution to run CRM processes. We do not focus on each individual task of the project; rather we give a sketch about important steps like data preparation, customer valuation and segmentation and also explain the limitation of the solutions.
Signature Segmentation from Machine Printed Documents Using Conditional Random Field

Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset1 and we have obtained encouraging results.
Insight and reduction of MapReduce stragglers in heterogeneous environment

Speculative and clone execution are existing techniques to overcome the problems of task stragglers and performance degradation in heterogeneous clusters for big data processing. In this paper, we propose an alternative approach to solving the problems based on analysis results of profiling and the relations of the system parameters. Our approach adjusts the amount of task slots of nodes dynamically to match the processing power of the nodes, according to current task progress rate and resource utilization. It contrasts with the existing techniques by attempting to prevent task stragglers from occurring in the first place through maintaining a balance between resource supply and demand. We have implemented this method in the Hadoop MapReduce platform, and the TPC-H benchmark results show that it achieves 20-30% performance improvement and 35-88% less stragglers than existing techniques.
Psychological perspectives of inflammatory bowel disease patients undergoing surgery: rightful concerns and preconceptions.

Surgery has been associated with variable effect on the quality of life of inflammatory bowel disease (IBD) patients, depending on clinical patterns and baseline disease characteristics. However, surgical treatment is often conceived by these patients with distress and considered as the failure of their therapies. Lack of control, risk of complications, defacement of the body image, need of ostomy and hospitalization may be triggering concerns leading to anguish and anxiety. Even though the quality of life in most cases generally improves after surgery, some particular aspects such as sexual activity, bowel movements and the ability to deal with a possible stoma may present a slower amelioration trend. These problems represent common causes of distress and may lead to an heightened risk of depression and anxiety with respect to background population. The psychological impact and apprehension surrounding surgery will be discussed in this review. Pros and cons of the surgical treatment in various IBD populations and its long-term sequelae in terms of quality of life and psychological well-being will be highlighted. Furthermore the tools to encompass these issues such as complete patient information, gastroenterologist/surgeon synergy and psychological counseling will be explored analyzing their respective roles.
Fisheye lens camera system calibration and localization error analysis

The paper presents a novel calibration method for fisheye lens. Five parameters, which fully reflect characters of fisheye lens, are proposed. Linear displacement platform is used to acquire precise sliding displacement between the target image and fisheye lens. Laser calibration method is designed to obtain the precise value of optical center. A convenient method, which is used to calculate the virtual focus of the fisheye lens, is proposed. To verify the result, indoor environment is built up to measure the localization error of omni-directional robot. Image including landmarks is acquired by fisheye lens and delivered to DSP (Digital Signal Processor) to futher process. Error analysis to localization of omni-directional robot is showed in the conclusion.