nils-herrmann/clean_scirepeval huggingface dataset:
Churn Prediction System for Telecom using Filter-Wrapper and Ensemble Classification

Churn prediction in telecom is a challenging data mining task for retaining customers, especially, when we have imbalanced class distribution, high dimensionality and large number of samples in training set. To cope with this challenging task of churn prediction, we propose a new intelligent churn prediction system for telecom, named FW-ECP. The novelty of the FW-ECP lies in its ability to combine both filterand wrapper-based feature selection as well as exploit the learning capability of an ensemble classifier built using diverse base classifiers. In the filter phase, Particle Swarm Optimization-based undersampling and mRMR feature selection are employed to reduce the effect of imbalanced class distribution and large dimensionality. In Wrapper phase, we employ Genetic Algorithm that further discards irrelevant and redundant features. Random Forest, Rotation Forest, RotBoost and SVMs are then employed to exploit the new feature space. Finally, the ensemble classifier is constructed using both majority voting and stacking. We have tested and compared the performance of proposed FW-ECP system on two publicly available standard telecom datasets: Orange and Cell2Cell. FW-ECP takes into account both the imbalanced nature and large dimensionality of the training sets and yields better prediction performances compared with existing state-of-the-art approaches. The feature spaces for the Orange and Cell2Cell datasets are reduced to 24D and 18D, from 260D and 76D, respectively. The AUCs obtained by FW-ECP are 0.85 and 0.82 for Orange and Cell2Cell datasets, respectively.
A printed leaky-wave antenna with a sinusoidally modulated surface reactance

In this paper, a printed leaky-wave antenna with a sinusoidally modulated surface reactance (SMRS) is reported. The TM polarized antenna consists of metallic strips with periodically varying gaps printed on a grounded dielectric substrate. The designed antenna radiates at thirty degrees from broadside at 10 GHz, and exhibits a gain of 14.7 dB. Theoretical, simulation, and experimental results are shown. A design procedure allowing for an arbitrary radiation angle is outlined.
Use alone or in Combination of Red and Infrared Laser in Skin Wounds.

A systematic review was conducted covering the action of red laser, infrared and combination of both, with emphasis on cutaneous wound therapy, showing the different settings on parameters such as fluency, power, energy density, time of application, frequency mode and even the type of low-power lasers and their wavelengths. It was observed that in general, the lasers brings good clinical and histological results mainly, but there is not a protocol that defines a dosage of use that has predictability of therapeutic success in repairing these wounds.
Cross versus Within-Company Cost Estimation Studies: A Systematic Review

The objective of this paper is to determine under what circumstances individual organizations would be able to rely on cross-company-based estimation models. We performed a systematic review of studies that compared predictions from cross-company models with predictions from within-company models based on analysis of project data. Ten papers compared cross-company and within-company estimation models; however, only seven presented independent results. Of those seven, three found that cross-company models were not significantly different from within-company models, and four found that cross-company models were significantly worse than within-company models. Experimental procedures used by the studies differed making it impossible to undertake formal meta-analysis of the results. The main trend distinguishing study results was that studies with small within-company data sets (i.e., $20 projects) that used leave-one-out cross validation all found that the within-company model was significantly different (better) from the cross-company model. The results of this review are inconclusive. It is clear that some organizations would be ill-served by cross-company models whereas others would benefit. Further studies are needed, but they must be independent (i.e., based on different data bases or at least different single company data sets) and should address specific hypotheses concerning the conditions that would favor cross-company or within-company models. In addition, experimenters need to standardize their experimental procedures to enable formal meta-analysis, and recommendations are made in Section 3.
SCIM – spinal cord independence measure: a new disability scale for patients with spinal cord lesions

The Spinal Cord Independence Measure (SCIM) is a new disability scale developed specifically for patients with spinal cord lesions in order to make the functional assessments of patients with paraplegia or tetraplegia more sensitive to changes. The SCIM includes the following areas of function: self-care (subscore 0 – 20), respiration and sphincter management (0 – 40) and mobility (0 – 40). Each area is scored according to its proportional weight in these patients' general activity. The final score ranges from 0 to 100. This study was performed to evaluate the reliability of the SCIM and its sensitivity to functional changes in spinal cord lesion patients compared with the Functional Independence Measure (FIM). Thirty patients were included. Scores were recorded one week after admission and thereafter every month during hospitalization. Each area of function was assessed by a pair of staff members from the relevant discipline. The comparison of scores between each pair of raters revealed a remarkable consistency (r=0.91 – 0.99; P<0.0001; slope≈1; constant≈0). The total SCIM score (mean=51, SD=21) was lower than the total FIM score (mean=87, SD=23) owing to the difference in scale range structure and the relatively high cognitive scores of our patients; however, a relationship was noted between the scores of both scales (r=0.85, P<0.01). The SCIM was more sensitive than the FIM to changes in function of spinal cord lesion patients: the SCIM detected all the functional changes detected by the FIM total scoring, but the FIM missed 26% of the changes detected by the SCIM total scoring. The mean difference between consecutive scores was higher for the SCIM (P<0.01). We conclude that the SCIM is a reliable disability scale and is more sensitive to changes in function in spinal cord lesion patients than the FIM. The SCIM when administered by a multidisciplinary team, may be a useful instrument for assessing changes in everyday performance in patients with spinal cord lesion.
Field-oriented developments for LineScout Technology and its deployment on large water crossing transmission lines

Condition assessments for power transmission line infrastructure and conductors are essential to maintain a reliable and efficient system. LineScout is a high-performance robot designed to undertake detailed, comprehensive inspections of transmission lines, which contributes to maintaining the integrity of equipment and the safety of inspection crews. This paper outlines Hydro-Québec’s LineScout Technology, describing briefly the main systems and key design issues and discussing the technology’s impact and benefits on power transmission line inspection and maintenance practices. The paper then focuses on field results gathered over the past 2 years with LineScout Technology. Very important lessons were learned from more than 20 field deployments of the robot on live transmission lines. An appreciable amount of significant feedback came from these field visits and was decisive in improving the technology. Specifically, transportation and installation methods and onboard energy management strategy are presented. Also discussed, for the first time, is a simplified wheel contact radius estimate, useful for improving the wheel odometer readings. Finally, one field deployment on a large water crossing transmission line is presented in detail. The paper discusses the importance of the work done and of newly implemented features in terms of the quality and efficiency of inspections, the reliability of systems, and the safe operation of the technology.
Aging: a theory based on free radical and radiation chemistry.

The phenomenon of growth, decline and death—aging—has been the source of considerable speculation (1, 8, 10). This cycle seems to be a more or less direct function of the metabolic rate and this in turn depends on the species (animal or plant) on which are superimposed the factors of heredity and the effects of the stresses and strains of life—which alter the metabolic activity. The universality of this phenomenon suggests that the reactions which cause it are basically the same in all living things. Viewing this process in the light of present day free radical and radiation chemistry and of radiobiology, it seems possible that one factor in aging may be related to deleterious side attacks of free radicals (which are normally produced in the course of cellular metabolism) on cell constituents. Irradiation of living things induces mutation, cancer, and aging (9). Inasmuch as these also arise spontaneously in nature, it is natural to inquire if the processes might not be similar. It is believed that one mechanism of irradiation effect is through liberation of OH and HO2 radicals (12). There is evidence, although indirect, that these two highly active free radicals are produced normally in living systems. In the first place, free radicals are present in living cells; this was recently demonstrated in vivo by a paramagnetic resonance absorption method (3). Further, it was shown that the concentration of free radicals increased with increasing metabolic activity in conformity with the postulates set forth some years ago that free radicals were involved in biologic oxidation-reduction reactions (11, 13). Are some of these free radicals OH and/or HO2, or radicals of a similar high order of reactivity, and where might they arise in the cell?
A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.
IMAGE processing based intelligent traffic controlling and monitoring system using Arduino

The tremendous change in vehicle fleet size and consequently in the volume of traffic is not followed by increase in the space of the road in all the time. This causes traffic congestion mostly in all the urban areas. In order to avoid traffic jams, we need to find a solution. In previous decades many technologies were developed and designed solutions to make road ways safer. Some among these techniques were classic where as other are integrated. New designed systems are capable of informing drivers about the traffic conditions and possible hazards of the road way with the help of intelligent transportation system. This project proposes Internet concepts of Things-based approach that solve problems raised by traffic congestion this gives solution to raising traffic related pollution. This architecture is composed of two modules i.e., hardware module and software module. The system utilizes new technologies for real-time collection, organization and transmission which provide the information to estimate the accurate traffic density exploited by traffic-aware applications.
ECG Monitoring Garment Using Conductive Carbon Paste for Reduced Motion Artifacts

The heart is a fundamental organ of the human circulatory system and the continuous measurement of electrocardiogram (ECG) signals is of great importance for pre-detection of heart diseases. Dry electrodes that do not require electrolyte gel have been developed for wearable ECG monitoring applications. However, this kind of electrode often introduces motion artifacts because of the high contact impedance between the electrode and skin. We propose a wearable ECG monitoring garment that employs electrodes made of conductive carbon-based paste. This paste is directly applied to the skin and after drying for 5 min, it forms a patch electrode that is detachable and flexible. The contact impedance between the patch electrode and the skin is very low because the paste covers the skin in a conformal manner. The experimental results show that the contact area of the carbon-based paste on the skin replica is almost 100%. At frequencies under 10 Hz, the contact impedance of the patch electrode is of 70.0 kΩ, much lower than the typical 118.7 kΩ impedance of a Ag/AgCl electrode. We also demonstrate that the ECG signals measured using the custom-designed garment and the patch electrodes are very stable even during actions such as walking and running.
Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis

The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.
3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences

In this paper, we first review the approaches to recover 3D shape and related movements of a human and then we present an easy and reliable approach to recover a 3D model using just one image or monocular video sequence. A simplification of the perspective camera model is required, due to the absence of stereo view. The human figure is reconstructed in a skeleton form and to improve the visual quality, a pre-defined human model is also fitted to the recovered 3D data.
Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a genuine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently proposed method which aims to improve KB coverage by performing inference directly over the KB graph. For the first time, we demonstrate that addition of edges labeled with latent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRAbased approaches on the KB inference task. We present extensive experimental results validating this finding. The resources presented in this paper are publicly available.
An adaptive routing algorithm: enhanced confidence-based Q routing algorithm in network traffic

Confidence-based Q (CQ) Routing Algorithm is an adaptive network routing algorithm. CQ Routing Algorithm evaluates how confidence value (C value) can be used to improve the quality of exploration in Q Routing Algorithm for adaptive packet routing in communication networks. However, the C value incompletely evaluates how closely the Q value represents the current condition of the network in a given length of time, which is measured in term of estimated delivery time for a packet to arrive at its destination. In this paper, an Enhanced Confidence-based Q (ECQ) Routing Algorithm is proposed. The Variable of Decay Constant and Update All Q value approaches are introduced for updating the C values of non-selected Q values. Using these C values would make those non-selected Q values more competitive in order to achieve updated and more reliable values. The quality of exploration in CQ Routing Algorithm would be improved. The performance of ECQ and CQ Routing Algorithms are compared to prove this improvement. ECQ and CQ Routing Algorithms are tested on an irregular 6 x 6 nodes network grid.  Adaptive routing algorithm; Confidence value; Confidence-based Q routing
Brain state decoding for rapid image retrieval

Human visual perception is able to recognize a wide range of targets under challenging conditions, but has limited throughput. Machine vision and automatic content analytics can process images at a high speed, but suffers from inadequate recognition accuracy for general target classes. In this paper, we propose a new paradigm to explore and combine the strengths of both systems. A single trial EEG-based brain machine interface (BCI) subsystem is used to detect objects of interest of arbitrary classes from an initial subset of images. The EEG detection outcomes are used as input to a graph-based pattern mining subsystem to identify, refine, and propagate the labels to retrieve relevant images from a much larger pool. The combined strategy is unique in its generality, robustness, and high throughput. It has great potential for advancing the state of the art in media retrieval applications. We have evaluated and demonstrated significant performance gains of the proposed system with multiple and diverse image classes over several data sets, including those from Internet (Caltech 101) and remote sensing images. In this paper, we will also present insights learned from the experiments and discuss future research directions.
Traffic sign recognition with multi-scale Convolutional Networks

We apply Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the GTSRB competition. ConvNets are biologically-inspired multi-stage architectures that automatically learn hierarchies of invariant features. While many popular vision approaches use hand-crafted features such as HOG or SIFT, ConvNets learn features at every level from data that are tuned to the task at hand. The traditional ConvNet architecture was modified by feeding 1st stage features in addition to 2nd stage features to the classifier. The system yielded the 2nd-best accuracy of 98.97% during phase I of the competition (the best entry obtained 98.98%), above the human performance of 98.81%, using 32×32 color input images. Experiments conducted after phase 1 produced a new record of 99.17% by increasing the network capacity, and by using greyscale images instead of color. Interestingly, random features still yielded competitive results (97.33%).
Developmental kinesiology: three levels of motor control in the assessment and treatment of the motor system.

Three levels of sensorimotor control within the central nervous system (CNS) can be distinguished. During the neonatal stage, general movements and primitive reflexes are controlled at the spinal and brain stem levels. Analysis of the newborn's spontaneous general movements and the assessment of primitive reflexes is crucial in the screening and early recognition of a risk for abnormal development. Following the newborn period, the subcortical level of the CNS motor control emerges and matures mainly during the first year of life. This allows for basic trunk stabilization, a prerequisite for any phasic movement and for the locomotor function of the extremities. At the subcortical level, orofacial muscles and afferent information are automatically integrated within postural-locomotor patterns. Finally, the cortical (the highest) level of motor control increasingly becomes activated. Cortical control is important for the individual qualities and characteristics of movement. It also allows for isolated segmental movement and relaxation. A child with impaired cortical motor control may be diagnosed with developmental dyspraxia or developmental coordination disorder. Human ontogenetic models, i.e., developmental motor patterns, can be used in both the diagnosis and treatment of locomotor system dysfunction.
American Sign Language Translation through Sensory Glove ; SignSpeak

To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.
Learning in and about complex systems

Change is accelerating, and the complexity of the systems in which we live is growing. Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle. Many scholars call for the development of 'systems thinking' to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves, inadequate and ambiguous outcome feedback, systematic 'misperceptions of feedback' where our cognitive maps omit important feedback processes, delays, stocks and flows, and nonlinearities that characterize complex systems, inability to simulate mentally the dynamics of our cognitive maps, poor interpersonal and organizational inquiry skills, and poor scientific reasoning skills. To be successful methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams.