nils-herrmann/clean_scirepeval huggingface dataset:
Measuring retail company performance using credit scoring techniques

This paper proposes a theoretical framework for predicting financial distress based on Hunt’s (2000) ‘Resource-Advantage (R-A) Theory of Competition’. The study focuses on the US retail market. Five credit scoring methodologies –Naïve Bayes, Logistic Regression, Recursive Partition, Artificial Neural Network, and Sequential Minimal Optimization— are used on a sample of 195 healthy companies and 51 distressed firms over different time periods from 1994 to 2002. Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability. In the time period of one year before financial distress, logistic regression model shows identical performance with neural network model based on the accuracy rate and shows the best performance in terms of AUROC value. Other models are slightly worse for predicting financial distress, but still present high accuracy rate and AUROC value. Moreover, the methodologies remain sound even five years prior to financial distress with classification accuracy rates above 85% and AUROC values above 0.85 for all five methodologies. This paper also shows external environment influences exist based on the naïve bayes, logistic, recursive partition and SMO models, but these influences are weak. With regards to the model applicability, a subset of the different models is compared with Moody’s rankings. It is found that both SMO and Logistic models are better than the Neural Network model in terms of similarity with Moody’s ranking, with SMO being slightly better than the Logistic Model.
Rectovestibular fistula with vaginal atresia: our experience and a proposed course of management

Rectovestibular fistula with coexisting vaginal atresia poses a surgical dilemma with regard to the timing and type of reconstruction. We present our experience and suggest an appropriate course of management. Seven patients with rectovestibular fistula and coexisting vestibular atresia were operated in our hospital during January 2004 through December 2013. The details of their bowel, menstrual and sexual functions were recorded. Five of the seven patients who underwent anoplasty in childhood presented to us in their teens with primary amenorrhea and cyclical abdominal pain. All five had sigmoid colon neovaginoplasty. Four of these had the uterus or its remnants anastomosed to the neovagina. All four have regular menstrual cycles. One patient is sexually active and has satisfactory sexual function. The bowel function in all the five patients is good. The remaining two patients presented in their infancy and had the anorectovestibular fistula left as the neovagina. The recto-sigmoid was pulled down to form the neoanus. Both these patients have bowel incontinence. We recommend the rectovestibular fistula be used as the neoanus and not as the neovagina. Delayed bowel vaginal replacement has excellent results and allows for optimal assessment of functioning uterine body or remnants.
Developing a bioinspired pole climbing robot

This paper proposes a pole climbing robot that has an ability to climb pipes. The novelty of this design is that it uses no motors to climb on the pole. Till now many robots have been fabricated with the ability to climb pipes but most of them solely depend on DC motors. The use of DC motor induces the risk of loosening the grip of the robot in case of power failure that may lead to disastrous situations if robot is working on high altitudes. This could be easily avoidable by the use of electro-pneumatics and by using self-locking circuits. We used two pairs of pneumatic cylinders as linear actuators for gripping the pole i.e. one pair for one claw. Besides this two more cylinders are used for climbing purpose. Unlike other mechanisms, installation is extremely easy. It is like controlling a radio controlled car. This mechanism can be easily modified for different payload capacities by simply using the appropriate diameter of the pneumatic cylinders. The robot can be controlled in tele-operated mode as well as autonomous mode which is a closed loop system. It has a high accuracy with 4mm error per cycle that has a cycle time period of 5 seconds. This robot can be very useful in oil industries for inspection of pipes and power poles in electric industries. It can serve of great help in cases of emergency situation caused during fire accidents.
Magnetic field effects on plant growth, development, and evolution

The geomagnetic field (GMF) is a natural component of our environment. Plants, which are known to sense different wavelengths of light, respond to gravity, react to touch and electrical signaling, cannot escape the effect of GMF. While phototropism, gravitropism, and tigmotropism have been thoroughly studied, the impact of GMF on plant growth and development is not well-understood. This review describes the effects of altering magnetic field (MF) conditions on plants by considering plant responses to MF values either lower or higher than those of the GMF. The possible role of GMF on plant evolution and the nature of the magnetoreceptor is also discussed.
Image Deblurring via Enhanced Low-Rank Prior

Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.
Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images

Atlas selection and label fusion are two major challenges in multi-atlas segmentation. In this paper, we propose a novel deep fusion net for better solving these challenges. Deep fusion net is a deep architecture by concatenating a feature extraction subnet and a non-local patchbased label fusion (NL-PLF) subnet in a single network. This network is trained end-to-end for automatically learning deep features achieving optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. Experimental results on Cardiac MR images for left ventricular segmentation demonstrate that our approach is effective both in atlas selection and multi-atlas label fusion, and achieves state of the art in performance.
Evaluating Marketing Campaigns of Banking Using Neural Networks

—Marketing campaigns of banking institutions is vital in all banks. The marketing campaigns were based on phone calls. Phone calls have an important influence in the behavior of customers. This paper proposed neural network to evaluate the bank marketing. This assessment will highlight the importance of marketing in the banks and thus the importance of phone calls. A feed-forward back propagation neural network with tan-sigmoid transfer functions is used in this paper to predict if the customer subscribes the deposit. The data set is obtained from UCI machine learning repository. The results of applying the proposed neural network methodology to predict subscribe based upon selected phone calls parameters show abilities of the network to learn the patterns corresponding to customer subscribes the deposit. The percent correctly classified in the simulation sample by the proposed neural network is 90 percent.
A study of free air ball formation in palladium-coated copper and bare copper bonding wire

Due to the continuous rise in gold prices, copper (Cu) wire has become an alternative to gold (Au) wire in the field of LSI interconnection. The characteristics of Free Air Ball (FAB), which is used for the ball bonding, give a large influence on the bond reliability. Understanding the mechanism of the FAB formation is an important step to further the application of Cu wire, including the next generation high density packaging and in-automotive IC applications. In this study, the FAB formation process was investigated in detail employing the high speed camera as a means of visualization. The studies verified that the electronic flame-off (EFO) conditions have significant effects on plasma characteristics, initiation of wire tip melting, and rate of ball rising. Further study was performed on the formation process of the irregular FABs, such as off-centered FABs and pointed FABs, which have been a matter of concern in the ball bonding; the camera analyses showed that the ball-tilting occurs from the initial stage of the wire tip melting while the transition into the pointed ball takes place during the solidification process. The formation processes of the FABs elucidated from the experiments are useful for the consideration of the stable FAB formation.
Representing Concepts in Time

People make use of concepts in all aspects of their lives. Concepts are mental entities, which structure our experiences and support reasoning in the world. They are usually regarded as static, although there is ample evidence that they change over time with respect to structure, content, and relation to real-world objects and processes. Recent research considers concepts as dynamical systems, emphasizing this potential for change. In order to analyze the alteration of concepts in time, a formal representation of this process is necessary. This paper proposes an algebraic model for representing dynamic conceptual structures, which integrates two theories from geography and cognitive science, i.e., time geography and conceptual spaces. Such representation allows for investigating the development of a conceptual structure along space-time paths and serves as a foundation for querying the structure of concepts at a specific point in time or for a time interval. The geospatial concept of ‘landmark’ is used to demonstrate the formal specifications.
Kalman Particle Filter for lane recognition on rural roads

Despite the availability of lane departure and lane keeping systems for highway assistance, unmarked and winding rural roads still pose challenges to lane recognition systems. To detect an upcoming curve as soon as possible, the viewing range of image-based lane recognition systems has to be extended. This is done by evaluating 3D information obtained from stereo vision or imaging radar in this paper. Both sensors deliver evidence grids as the basis for road course estimation. Besides known Kalman Filter approaches, Particle Filters have recently gained interest since they offer the possibility to employ cues of a road, which can not be described as measurements needed for a Kalman Filter approach. We propose to combine both principles and their benefits in a Kalman Particle Filter. The comparison between the results gained from this recently published filter scheme and the classical approaches using real world data proves the advantages of the Kalman Particle Filter.
Multi-level bootstrap analysis of stable clusters in resting-state fMRI

A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.
Decision-Making in a Robotic Architecture for Autonomy

This paper presents an overview of the intelligent decision-making capabilities of the CLARAty robotic architecture for autonomy. CLARAty is a two layered architecture where the top Decision Layer contains techniques for autonomously creating a plan of robot commands and the bottom Functional Layer provides standard robot capabilities that interface to system hardware. This paper focuses on the Decision Layer organization and capabilities. Specifically, the Decision Layer provides a framework for utilizing AI planning and executive techniques, which provide onboard, autonomous command generation and replanning for planetary rovers. The Decision Layer also provides a flexible interface to the Functional Layer, which can be tailored based on user preferences and domain features. This architecture is currently being tested on several JPL rovers.
DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning

Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.
Review of axial flux induction motor for automotive applications

Hybrid and electric vehicles have been the focus of many academic and industrial studies to reduce transport pollution; they are now established products. In hybrid and electric vehicles, the drive motor should have high torque density, high power density, high efficiency, strong physical structure and variable speed range. An axial flux induction motor is an interesting solution, where the motor is a double sided axial flux machine. This can significantly increase torque density. In this paper a review of the axial flux motor for automotive applications, and the different possible topologies for the axial field motor, are presented.
Example-based hinting of true type fonts

Hinting in TrueType is a time-consuming manual process in which a typographer creates a sequence of instructions for better fitting the characters of a font to a grid of pixels. In this paper, we propose a new method for automatically hinting TrueType fonts by transferring hints of one font to another. Given a hinted source font and a target font without hints, our method matches the outlines of corresponding glyphs in each font, and then translates all of the individual hints for each glyph from the source to the target font. It also translates the control value table (CVT) entries, which are used to unify feature sizes across a font. The resulting hinted font already provides a great improvement over the unhinted version. More importantly, the translated hints, which preserve the sound, hand-designed hinting structure of the original font, provide a very good starting point for a professional typographer to complete and fine-tune, saving time and increasing productivity. We demonstrate our approach with examples of automatically hinted fonts at typical display sizes and screen resolutions. We also provide estimates of the time saved by a professional typographer in hinting new fonts using this semi-automatic approach.
The impact of preprocessing on text classification

Preprocessing is one of the key components in a typical text classification framework. This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text domain, text language, and dimension reduction. For this purpose, all possible combinations of widely used preprocessing tasks are comparatively evaluated on two different domains, namely e-mail and news, and in two different languages, namely Turkish and English. In this way, contribution of the preprocessing tasks to classification success at various feature dimensions, possible interactions among these tasks, and also dependency of these tasks to the respective languages and domains are comprehensively assessed. Experimental analysis on benchmark datasets reveals that choosing appropriate combinations of preprocessing tasks, rather than enabling or disabling them all, may provide significant improvement on classification accuracy depending on the domain and language studied on.
Detecting Android Malware By Using A Machine Learning Ensemble Method

Android has become the most popular mobile operating system in recent years. As its popularity has increased, so have the number of attacks to the platform. Samples of malware have been found in different popular Android apps markets, including the Google Play store. Most anti-virus software uses a signature-based approach to detect malware, however, it fails to detect unknown malware. Different techiniques have been proposed to detect unknown malware, one of which is using machine learning. This research focuses on using a combination of existing feature extraction methods and the use of an ensemble classifier to improve the accuracy of malware detection in Android. A proposal is given on how to do the feature extraction, which ensemble classifer should be used, how to train the classifier, and how to evaluate the results.
Core stability exercise principles.

Core stability is essential for proper load balance within the spine, pelvis, and kinetic chain. The so-called core is the group of trunk muscles that surround the spine and abdominal viscera. Abdominal, gluteal, hip girdle, paraspinal, and other muscles work in concert to provide spinal stability. Core stability and its motor control have been shown to be imperative for initiation of functional limb movements, as needed in athletics. Sports medicine practitioners use core strengthening techniques to improve performance and prevent injury. Core strengthening, often called lumbar stabilization, also has been used as a therapeutic exercise treatment regimen for low back pain conditions. This article summarizes the anatomy of the core, the progression of core strengthening, the available evidence for its theoretical construct, and its efficacy in musculoskeletal conditions.
Design of an Ultra-Wideband Pulse Generator Based on Avalanche Transistor

Based on the avalanche effect of avalanche transistor, a kind of ultra-wideband nanosecond pulse circuit has been designed, whose frequency, pulse width and amplitude are tunable. In this paper, the principle, structure and selection of components' parameters in the circuit are analyzed in detail. The circuit generates periodic negative pulse, whose pulse full width is 890 ps and pulse amplitude is -11.2 V in simulation mode. By setting up circuit for experiment and changing parameters properly, a kind of ultra-wideband pulse with pulse width of 2.131 ns and pulse amplitude of -9.23 V is achieved. With the features such as simple structure, stable and reliable performance and low cost, this pulse generator is applicable to ultra-wideband wireless communication system.
Solving the data overload: Device-to-device bearer control architecture for cellular data offloading

The explosive growth of the mobile user population and multimedia services are causing a severe traffic overload problem in the cellular network. The Third-Generation Partnership Project (3GPP) has defined data offloading as a critical area to cope with this problem. Local IP access (LIPA) and selected IP traffic offload (SIPTO) are considered to be the typical technologies to implement cellular data offloading. Currently, device-to-device (D2D) communication has been proposed as a new dataoffloading solution. The D2D communication underlying a cellular infrastructure reduces the radio access network load as well as the core network load. The cellular network needs to establish a new type of radio bearer for D2D communications, and this bearer can provide the direct traffic path between devices. In this article, we propose a D2D bearer control architecture for D2D communications supported by a longterm evolution-advanced (LTE-A) infrastructure. This could be a feasible and efficient solution for offloading data from the cellular network.
Zexe: Enabling Decentralized Private Computation

Ledger-based systems that enable rich applications often suffer from two limitations. First, validating a transaction requires re-executing the state transition that it attests to. Second, transactions not only reveal which application had a state transition but also reveal the application’s internal state. Unfortunately, expensive re-execution and lack of privacy rule out many use cases. We design, implement, and evaluate Zexe, a ledger-based system where users can execute offline computations and subsequently produce transactions, attesting to the correctness of these computations, that satisfy two main properties. First, transactions hide all information about the offline computations. Second, transactions can be validated by anyone in constant time, regardless of the offline computation. The core ofZexe is a protocol for a new cryptographic primitive that we introduce, decentralized private computation (DPC). The security guarantees of DPC are concisely expressed via an ideal functionality, which our protocol provably achieves. In order to achieve an efficient implementation of our protocol, we leverage tools in the area of cryptographic proofs, including succinct zero knowledge proofs and recursive proof composition. Overall, transactions in Zexe are 968 bytes regardless of the offline computation, and generating them takes less than 2 minutes plus a time that grows with the offline computation. To facilitate real-world deployments, Zexe also provides support for delegating the process of producing a transaction to an untrusted worker, and support for threshold transactions and blind transactions.