nils-herrmann/clean_scirepeval huggingface dataset:
Spatial representation of magnitude in humans (Homo sapiens), Western lowland gorillas (Gorilla gorilla gorilla), and American black bears (Ursus americanus)

The spatial-numerical association of response codes (SNARC) effect is the tendency for humans to respond faster to relatively larger numbers on the left or right (or with the left or right hand) and faster to relatively smaller numbers on the other side. This effect seems to occur due to a spatial representation of magnitude either in occurrence with a number line (wherein participants respond to relatively larger numbers faster on the right), other representations such as clock faces (responses are reversed from number lines), or culturally specific reading directions, begging the question as to whether the effect may be limited to humans. Given that a SNARC effect has emerged via a quantity judgement task in Western lowland gorillas and orangutans (Gazes et al.,  2017), we examined patterns of response on a quantity discrimination task in American black bears, Western lowland gorillas, and humans for evidence of a SNARC effect. We found limited evidence for SNARC effect in American black bears and Western lowland gorillas. Furthermore, humans were inconsistent in direction and strength of effects, emphasizing the importance of standardizing methodology and analyses when comparing SNARC effects between species. These data reveal the importance of collecting data with humans in analogous procedures when testing nonhumans for effects assumed to bepresent in humans.
Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals

The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.
The LANAP Protocol (laser-assisted new attachment procedure) - A Minimally Invasive Bladeless Procedure.

Khadtare Y, Chaudhari A, Waghmare P, Prashant S. (laser-assisted new attachment procedure) The LANAP Protocol A Minimally Invasive Bladeless Procedure.  1 2 2 3 Dr. Yogesh Khadtare , Dr. Amit Chaudhari , Dr. Pramod Waghmare , Dr. Shekhar Prashant Review Article Journal of Periodontal Medicine & Clinical Practice JPMCP Journal of Periodontal Medicine & Clinical Practice
Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing

Designers face many system optimization problems when building distributed systems. Traditionally, designers have relied on optimization techniques that require either prior knowledge or centrally managed runtime knowledge of the system's environment, but such techniques are not viable in dynamic networks where topology, resource, and node availability are subject to frequent and unpredictable change. To address this problem, we propose collaborative reinforcement learning (CRL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized networks. We evaluate an implementation of CRL in a routing protocol for mobile ad hoc networks, called SAMPLE. Simulation results show how feedback in the selection of links by routing agents enables SAMPLE to adapt and optimize its routing behavior to varying network conditions and properties, resulting in optimization of network throughput. In the experiments, SAMPLE displays emergent properties such as traffic flows that exploit stable routes and reroute around areas of wireless interference or congestion. SAMPLE is an example of a complex adaptive distributed system.
A new approach to wafer sawing: stealth laser dicing technology

Stealth Dicing (SD) technology has high potential to replace the conventional blade sawing and laser grooving. The dicing method has been widely researched since 2005  especially for thin wafer (⇐ 12 mils). SD cutting has good quality because it has dry process during laser cutting, extremely narrow scribe line and multi-die sawing capability. However, along with complicated package technology, the chip quality demands fine and accurate pitch which conventional blade saw is impossible to achieve. This paper is intended as an investigation in high performance SD sawing, including multi-pattern wafer and DAF dicing tape capability. With the improvement of low-K substrate technology and min chip scale size, SD cutting is more important than other methods used before. Such sawing quality also occurs in wafer level chip scale package. With low-K substrate and small package, the SD cutting method can cut the narrow scribe line easily (15 um), which can lead the WLCSP to achieve more complicated packing method successfully.
Universal Tuning System for Series-Resonant Induction Heating Applications

This paper presents a universal tuning system for harmonic operation of series-resonant inverters (SRI), based on a self-oscillating switching method. In the new tuning system, SRI can instantly operate in one of the switching frequency harmonics, e.g., the first, third, or fifth harmonic. Moreover, the new system can utilize pulse density modulation (PDM), phase shift (PS), and power–frequency control methods for each harmonic. Simultaneous combination of PDM and PS control method is also proposed for smoother power regulation. In addition, this paper investigates performance of selected harmonic operation based on phase-locked loop (PLL) circuits. In comparison with the fundamental harmonic operation, PLL circuits suffer from stability problem for the other harmonic operations. The proposed method has been verified using laboratory prototypes with resonant frequencies of 20 up to 75 kHz and output power of about 200 W.
Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding

Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity. Using dynamics of facial and head movement and vocalization, we trained classifiers to detect three levels of depression severity. Participants were a community sample diagnosed with major depressive disorder. They were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at seven-week intervals over a period of 21 weeks. At each interview, they were scored by the HRSD as moderately to severely depressed, mildly depressed, or remitted. Logistic regression classifiers using leave-one-participant-out validation were compared for facial movement, head movement, and vocal prosody individually and in combination. Accuracy of depression severity measurement from facial movement dynamics was higher than that for head movement dynamics, and each was substantially higher than that for vocal prosody. Accuracy using all three modalities combined only marginally exceeded that of face and head combined. These findings suggest that automatic detection of depression severity from behavioral indicators in patients is feasible and that multimodal measures afford the most powerful detection.
Ensemble Learning Multiple LSSVR With Improved Harmony Search Algorithm for Short-Term Traffic Flow Forecasting

Short-term traffic flow forecasting plays an important role in current intelligent transportation system. For most models, the selection of time lag is a crucial factor affecting the forecasting performance. Instead of choosing a single time lag when constructing model, in this paper, we propose a novel approach attempting to construct multiple base forecasting models, each with different time lag and performance. Least squares support vector regression (LSSVR) with the Gaussian kernel function is adopted as the base model because of its nonlinear modeling capability, as well as empirical performance. Then, the outputs of these base models are integrated to produce final prediction through another LSSVR with the linear kernel function. This ensemble forecasting framework consists of many parameters that need to be adjusted. To address this issue, an improved harmony search algorithm tailored for our forecasting system is further developed for seeking the optimal parameters. The real-world traffic flow data are collected from several observation sites located around the intersection of Interstate 205 and Interstate 84 freeways in Portland, OR, USA. Experimental results verify that the proposed approach is able to provide better forecasting performance in comparison with other competing methods.
The experience of ethics: Evaluation of self harm risks in automated vehicles

Automated vehicles, but also safety or driver assistance systems for manually driven cars, will soon face situations where they have to choose between several options with negative or even lethal outcome for the one or the other party. Experimental ethics is an approach to evaluate expectations humans put into the morality of digital agents. With this work we present a personalized abstraction of the “Trolley Problem” evaluated in a driving simulator. The aim of the study was to assess drivers' individual attitudes in ethical decisions and derive common knowledge about how to solve such situations. In contrast to previous work, the study at hand looks at the problem from a holistic point of view, including uncertainty and accident risk (presented to subjects as their probability to survive). Furthermore, subjective scales and semi-structured interviews to determine subjects' justifications for ethical decisions complemented the setting. Our results (n=40) suggest that most drivers want their vehicles to act in an utilitarian way and opt for the more severe collision — even when their own probability to survive is substantially low. In addition, age and size of the injured party have a significant effect on the results. Qualitative data (interviews) indicate that the justification, in particular for decisions with the same outcome, strongly differs as most people have embodied their own moral concepts.
Local Search Methods for k-Means with Outliers

We study the problem of k-means clustering in the presence of outliers. The goal is to cluster a set of data points to minimize the variance of the points assigned to the same cluster, with the freedom of ignoring a small set of data points that can be labeled as outliers. Clustering with outliers has received a lot of attention in the data processing community, but practical, efficient, and provably good algorithms remain unknown for the most popular k-means objective. Our work proposes a simple local search-based algorithm for k-means clustering with outliers. We prove that this algorithm achieves constant-factor approximate solutions and can be combined with known sketching techniques to scale to large data sets. Using empirical evaluation on both synthetic and large-scale real-world data, we demonstrate that the algorithm dominates recently proposed heuristic approaches for the problem.
Meshless quadrangulation by global parameterization

Point cloud is a basic description of discrete shape information. Parameterization of unorganized points is important for shape analysis and shape reconstruction of natural objects. In this paper we present a new algorithm for global parameterization of an unorganized point cloud and its application to the meshing of the cloud. Our method is guided by principal directions so as to preserve the intrinsic geometric properties. After initial estimation of principal directions, we develop a kNN(k-nearest neighbor) graph-based method to get a smooth direction field. Then the point cloud is cut to be topologically equivalent to a disk. The global parameterization is computed and its gradients align well with the guided direction field. A mixed integer solver is used to guarantee a seamless parameterization across the cut lines. The resultant parameterization can be used to triangulate and quadrangulate the point cloud simultaneously in a fully automatic manner, where the shape of the data is of any genus. & 2011 Elsevier Ltd. All rights reserved.
A machine learning methods: Outlier detection in WSN

Wireless sensor networks are gaining more and more attention these days. They gave us the chance of collecting data from noisy environment. So it becomes possible to obtain precise and continuous monitoring of different phenomenons. However wireless Sensor Network (WSN) is affected by many anomalies that occur due to software or hardware problems. So various protocols are developed in order to detect and localize faults then distinguish the faulty node from the right one. In this paper we are concentrated on a specific type of faults in WSN which is the outlier. We are focus on the classification of data (outlier and normal) using three different methods of machine learning then we compare between them. These methods are validated using real data obtained from motes deployed in an actual living lab.
Anti-Plaque Efficacy of Herbal and 0.2% Chlorhexidine Gluconate Mouthwash: A Comparative Study

Mouthwashes are an adjunct to, not a substitute for, regular brushing and flossing. Chlorohexidine is cationic bis-biguanide broad spectrum antiseptic with both anti-plaque and antibacterial properties. It has side-effects like brownish discoloration of teeth and dorsum of the tongue, taste perturbation, oral mucosal ulceration, etc. To compare the antiplaque efficacy of herbal and chlorohexidine gluconate mouthwash.



A double-blinded parallel, randomized controlled clinical trial was conducted in the Department of Periodontics, MNR Dental College. Totally 100 preclinical dental students were randomized into three groups (0.2% chlorohexidine, Saline and herbal mouthwash). All the groups were made to refrain from their regular mechanical oral hygiene measures and were asked to rinse with given respective mouthwashes for 4 days. The gingival and plaque scores are evaluated on 1(st) day, and 5(th) day, and differences were compared statistically.



There was no significant difference in the gingival index (GI) and plaque index (PI) scores of the pre-rinsing scores of three groups and mean age of subjects in the three age groups, suggesting selected population for the three groups was homogenous. Mean GI and PI scores at the post rinsing stage were least for the Group A, followed by B and C. The difference of post rinsing PI and GI scores between Group A and Group B were statistically non-significant, which means anti-gingivitis and plaque inhibiting properties are similar for both.



Within the limitations of this study chlorhexidine gluconate and herbal mouthwash (Hiora) showed similar anti plaque activity with latter showing no side effects.
Author ' s personal copy Modeling viscoelastic dielectrics

Dielectric elastomers, as an important category of electroactive polymers, are known to have viscoelastic properties that strongly affect their dynamic performance and limit their applications. Very few models accounting for the effects of both electrostatics and viscoelasticity exist in the literature, and even fewer are capable of making reliable predictions under general loads and constraints. Based on the principles of nonequilibrium thermodynamics, this paper develops a field theory that fully couples the large inelastic deformations and electric fields in deformable dielectrics. Our theory recovers existing models of elastic dielectrics in the equilibrium limit. The mechanism of instantaneous instability, which corresponds to the pull-in instability often observed on dielectric elastomers, is studied in a general non-equilibrium state. The current theoretical framework is able to adopt most finite-deformation constitutive relations and evolution laws of viscoelastic solids. As an example, a specific material model is selected and applied to the uniform deformation of a dielectric elastomer. This model predicts the stability criteria of viscoelastic dielectrics and its dependence on loading rate, pre-stress, and relaxation. The dynamic response, as well as the hysteresis behavior of a viscoelastic dielectric elastomer under cyclic electric fields, is also studied. & 2010 Elsevier Ltd. All rights reserved.
Towards Integrated Tool Support for the User Requirements Notation

The User Requirements Notation (URN) combines the Goaloriented Requirement Language (GRL) with the Use Case Map (UCM) scenario notation. Although tools exist in isolation for both views, they are currently not meant to work together, hence preventing one to exploit URN to its fullest extent. This paper presents jUCMNav, a new Eclipse-based tool that supports both UCM and GRL in an integrated way. jUCMNav supports links between the two languages that can be exploited during analysis. An overview of the current editing and analysis capabilities is given, with a particular emphasis on the new concept of GRL strategies, which simplify the evaluation of GRL models. The extensibility of the tool is also discussed.
Swimming Stroke Kinematic Analysis with BSN

The recent maturity of body sensor networks has enabled a wide range of applications in sports, well-being and healthcare. In this paper, we hypothesise that a single unobtrusive head-worn inertial sensor can be used to infer certain biomotion details of specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmer's goggles, limiting the disturbance to a minimum. Features extracted from the recorded acceleration such as the pitch and roll angles allow to recognise the type of stroke, as well as basic biomotion indices. The system proposed represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring.
Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology

Conservation and maintenance of historic buildings have exceptional requirements and need a detailed diagnosis and an accurate as-is documentation. This paper reports the use of Unmanned Aerial Vehicle (UAV) imagery to create an Intelligent Digital Built Heritage Model (IDBHM) based on Building Information Modeling (BIM) technology. Our work outlines a model-driven approach based on UAV data acquisition, photogrammetry, post-processing and segmentation of point clouds to promote partial automation of BIM modeling process. The methodology proposed was applied to a historical building facade located in Brazil. A qualitative and quantitative assessment of the proposed segmentation method was undertaken through the comparison between segmented clusters and as-designed documents, also as between point clouds and ground control points. An accurate and detailed parametric IDBHM was created from high-resolution Dense Surface Model (DSM). This Model can improve conservation and rehabilitation works. The results demonstrate that the proposed approach yields good results in terms of effectiveness in the clusters segmentation, compared to the as-designed
Construction of BPMN-based Business Process Model Base

On the purpose of managing process models to make them more practical and effective in enterprises, a construction of BPMN-based Business Process Model Base is proposed. Considering Business Process Modeling Notation (BPMN) is used as a standard of process modeling, based on BPMN, the process model transformation is given, and business blueprint modularization management methodology is used for process management. Therefore, BPMN-based Business Process Model Base provides a solution of business process modeling standardization, management and execution so as to enhance the business process reuse.
Animal cell cultures: recent achievements and perspectives in the production of biopharmaceuticals

There has been a rapid increase in the number and demand for approved biopharmaceuticals produced from animal cell culture processes over the last few years. In part, this has been due to the efficacy of several humanized monoclonal antibodies that are required at large doses for therapeutic use. There have also been several identifiable advances in animal cell technology that has enabled efficient biomanufacture of these products. Gene vector systems allow high specific protein expression and some minimize the undesirable process of gene silencing that may occur in prolonged culture. Characterization of cellular metabolism and physiology has enabled the design of fed-batch and perfusion bioreactor processes that has allowed a significant improvement in product yield, some of which are now approaching 5 g/L. Many of these processes are now being designed in serum-free and animal-component-free media to ensure that products are not contaminated with the adventitious agents found in bovine serum. There are several areas that can be identified that could lead to further improvement in cell culture systems. This includes the down-regulation of apoptosis to enable prolonged cell survival under potentially adverse conditions. The characterization of the critical parameters of glycosylation should enable process control to reduce the heterogeneity of glycoforms so that production processes are consistent. Further improvement may also be made by the identification of glycoforms with enhanced biological activity to enhance clinical efficacy. The ability to produce the ever-increasing number of biopharmaceuticals by animal cell culture is dependent on sufficient bioreactor capacity in the industry. A recent shortfall in available worldwide culture capacity has encouraged commercial activity in contract manufacturing operations. However, some analysts indicate that this still may not be enough and that future manufacturing demand may exceed production capacity as the number of approved biotherapeutics increases.