nils-herrmann/clean_scirepeval huggingface dataset:
Classification of EEG signals using fractional calculus and wavelet support vector machine

The behavior of many physical and biological processes and systems can be described satisfactorily by fractional order models. A new method, termed fractional linear prediction (FLP) based on fractional calculus, is used to model ictal and seizure-free EEG signals. Through numerical simulations it is demonstrated that, the EEG signal can be modeled accurately, by using a few integrals of fractional orders as basis functions. The parameters obtained from modeling are used for analysis and classification using support vector machines (SVM). It is found that improvements in classification accuracy is possible by using wavelet support vector machines using wavelet kernel functions such as Mexican hat wavelet and Morlet wavelet.
Kernelized structural SVM learning for supervised object segmentation

Object segmentation needs to be driven by top-down knowledge to produce semantically meaningful results. In this paper, we propose a supervised segmentation approach that tightly integrates object-level top down information with low-level image cues. The information from the two levels is fused under a kernelized structural SVM learning framework. We defined a novel nonlinear kernel for comparing two image-segmentation masks. This kernel combines four different kernels: the object similarity kernel, the object shape kernel, the per-image color distribution kernel, and the global color distribution kernel. Our experiments show that the structured SVM algorithm finds bad segmentations of the training examples given the current scoring function and punishes these bad segmentations to lower scores than the example (good) segmentations. The result is a segmentation algorithm that not only knows what good segmentations are, but also learns potential segmentation mistakes and tries to avoid them. Our proposed approach can obtain comparable performance to other state-of-the-art top-down driven segmentation approaches yet is flexible enough to be applied to widely different domains.
A Discontinuous Galerkin Time-Domain Method With Dynamically Adaptive Cartesian Mesh for Computational Electromagnetics

A discontinuous Galerkin time-domain (DGTD) method based on dynamically adaptive Cartesian mesh (ACM) is developed for a full-wave analysis of electromagnetic (EM) fields. The benefits of hierarchical Cartesian grids and adaptive mesh refinement are demonstrated for linear EM propagation problems. The developed DGTD-ACM achieves a desired accuracy by refining nonconformal meshes near material interfaces to reduce stair-casing errors without sacrificing the high efficiency afforded with uniform Cartesian meshes. More importantly, DGTD-ACM can dynamically refine the mesh to resolve the local variation of the fields during propagation of EM pulses. A local time-stepping scheme is adopted to alleviate the constraint on the time-step size due to the stability condition of the explicit time integration. It is shown by numerical examples that the proposed method can achieve a good numerical accuracy and reduce the computational time effectively for linear problems of EM propagation in dispersive media. With further development, the method is expected to provide a powerful tool for solving nonlinear EM problems in plasma physics and electronics.
Mindfulness and emotion regulation in depression and anxiety: common and distinct mechanisms of action.

The current study seeks to investigate the mechanisms through which mindfulness is related to mental health in a clinical sample of adults by examining (1) whether specific cognitive emotion regulation strategies (rumination, reappraisal, worry, and nonacceptance) mediate associations between mindfulness and depression and anxiety, respectively, and (2) whether these emotion regulation strategies operate uniquely or transdiagnostically in relation to depression and anxiety.



Participants were 187 adults seeking treatment at a mood and anxiety disorders clinic in Connecticut. Participants completed a battery of self-report measures that included assessments of depression and anxiety (Mood and Anxiety Symptom Questionnaire), and emotion regulation (Ruminative Response Scale, Penn State Worry Questionnaire, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Scale).



Simple mediation analyses indicated that rumination and worry significantly mediated associations between mindfulness and anxiety symptoms, whereas rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. Multiple mediation analyses showed that worry significantly mediated associations between mindfulness and anxiety symptoms and rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms.



Findings suggest that mindfulness operates through distinct and common mechanisms depending on clinical context.
A review on plant disease detection using image processing

India is the agriculture based country, since it contributes 7.68 percent of total global agricultural output. In India, agricultural sector contributes about seventeen percentage of total Indian gross domestic product (GDP). Effective growth and improved yield of plants are necessary for increment of farmer's profit and economy of India. For this purpose farmers need domain experts for manual monitoring of plants. But manual monitoring will not give satisfactory result all the time. Moreover, domain experts are not available at all regions and are expensive as farmers have to pay fees including travelling charges. Hence, it requires developing an efficient smart farming technique which will help for better yield and growth with less human efforts. In this paper, we provide a review on methods developed by various researchers for detection of diseases in plants, in the field of image processing. It includes research in disease detection of plants such as apple, grapes, pepper, pomegranate, tomato etc.
Dual-Band Loop-Dipole Composite Unidirectional Antenna for Broadband Wireless Communications

The design of a novel dual-band loop-dipole composite unidirectional antenna for broadband wireless applications is proposed. First, design guidelines of the antenna are presented, and then, a step-by-step derivation of the antenna geometry is drawn. The antenna's operation principle is analyzed by carrying out parametric studies of surface current distribution, impedance and radiation patterns. The reflection coefficient, radiation patterns and gain of the antenna are numerically and experimentally studied to verify the operation principle and the design approach. It is demonstrated that two pass-bands, from 1.54 to 3.24 and from 4.88 to 6.80 GHz, of reflection coefficient smaller than -10 dB are obtained. The antenna has stable and unidirectional radiation pattern with low cross polarization within its pass-bands. The average gain of the antenna is 6.7 dBi at low frequency band and 6.5 dBi at high frequency band.
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks

Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non–linearly encoded in the embedding space, which questions the cosine–based, unsupervised, evaluation methods. All results and analysis scripts are available online.
Optimal Configuration for Urban VANETs Routing using Particle Swarm Optimization

is initially designed for ad hoc networks (MANETs). This protocol collects information to build and maintain the route between the source and the destination with minimum cost by periodically exchange Hello and control messages. The aggregate function of communication cost for all nodes computed based on the required performance metrics. The standard configuration of OLSR is insufficient with vehicular ad hoc networks (VANETs) due to its limited Wi-Fi coverage and high mobility that cause rapid changes in its topology. The limitation of the ordinary OLSR could produce the greatest amount of the generated routing overhead which in turn leads to a high degradation in the overall VANETs performance. In this paper, Particle Swarm Optimization is used to find optimal parameters to tune OLSR by aggregating the response performance metrics generated in a realistic VANETs simulation. The simulation results show that OLSR with optimum parameters achieved better performance in urban environment as compared to the ordinary OLSR.
Relative clustering validity criteria: A comparative overview

Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves.  2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3:
RoboRay hand: A highly backdrivable robotic hand with sensorless contact force measurements

This paper presents a 14-DOF robotic hand including 5 fingers and a wrist. The hand has a new tendon-driven mechanism which minimizes frictional loss and maximizes efficiency and backdrivability. In order to accomplish high efficiency and backdrivability as well as human-like payload and dexterity in a compact size, two novel mechanical concepts are proposed. Firstly, the actuators are placed according to the functions of fingers - high power grasping and precise manipulation - instead of positioning at each joint. For the high power grasping, 7 high payload motors are positioned in the forearm, and 5 small size motors are positioned in the palm for the precise manipulation. Secondly, a new tension decoupling mechanism is proposed to the 2-DOF wrist joint, which delivers wire motions of the forearm motors to the fingers without frictional loss or coupling with wrist motion. A total weight including the forearm is 1.59kg which is similar to human. The fingertip force is 15N which is sufficient for most of household work. High backdrivability enables the contact force sensing by measuring a motor current without additional sensors. A detectable minimum contact force was 0.735N. In order to enhance the contact force sensing capability, a friction compensation algorithm was applied, which resulted to the minimum contact force as 0.196N. Theoretical and experimental analyses are also performed.
Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.
Natural Image Colorization

In this paper, we present an interactive system for users to easily colorize the natural images of complex scenes. In our system, colorization procedure is explicitly separated into two stages: Color labeling and Color mapping. Pixels that should roughly share similar colors are grouped into coherent regions in the color labeling stage, and the color mapping stage is then introduced to further fine-tune the colors in each coherent region. To handle textures commonly seen in natural images, we propose a new color labeling scheme that groups not only neighboring pixels with similar intensity but also remote pixels with similar texture. Motivated by the insight into the complementary nature possessed by the highly contrastive locations and the smooth locations, we employ a smoothness map to guide the incorporation of intensity-continuity and texture-similarity constraints in the design of our labeling algorithm. Within each coherent region obtained from the color labeling stage, the color mapping is applied to generate vivid colorization effect by assigning colors to a few pixels in the region. A set of intuitive interface tools is designed for labeling, coloring and modifying the result. We demonstrate compelling results of colorizing natural images using our system, with only a modest amount of user input.
Personalization Research in E-Commerce - a State of the Art Review (2000-2008)

E-commerce product personalization and website personalization have been a topic of interest to a lot of researchers during the last years. As such, academic research in the area of personalization in e-commerce should rely on a rigorous literature review first in order to identify existing research and acknowledge the state of the art in research. This article provides a vast overview on personalization literature with a special focus on e-commerce (e.g. recommender systems). This literature, respectively our approach, can be used by fellow researchers to identify the basic literature for their own work. It serves as a rigorous way of retrieving literature for other fields in their scientific research. A complete reference list is presented as well as additional figures and tables analyzing the personalization literature in general and according to an emerging classification. The three major categories of personalization research in e-commerce are: “implementation”, “theoretical foundations”, and “user centric aspects” under which there are several subcategories into which the papers can be classified. The reviewed articles (42 in total out of 15,116) were taken from all the relevant, high ranked (A to C) journals according to the journal ranking list:
Commonsense for Generative Multi-Hop Question Answering Tasks

Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multihop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectivelygated attention mechanism. This boosts the model’s performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.
A Powered Leg Orthosis for Gait Rehabilitation of Motor-Impaired Patients

This paper describes a powered leg orthosis for gait rehabilitation of patients with walking disabilities. The paper proposes controllers which can apply suitable forces on the leg so that it moves on a desired trajectory. The description of the controllers, simulations and experimental results with the powered orthosis are presented in the paper. Currently, experiments have been performed with a dummy leg in the orthosis. In the coming months, this powered orthosis will be used on healthy subjects and stroke patients.
A flexible 3D object localization system for industrial part handling

We present a flexible system that can scan and localize workpieces in 3D for assembly and pick and place operations. The system contains a vision robot that acquires 3D point clouds by performing sweeps with a laser triangulation sensor. Because the sensor is mounted on a robot, we can choose a viewpoint and a scanner trajectory that is optimal for a given task. For example, we can sweep along a semicircular trajectory in order to recognize and grasp parts from a pallet. With the same vision robot, we can perform a sweep of the packaging container in order to recognize and manipulate elements of cardboard. The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose verification and pose refinement that significantly increase the accuracy and robustness of the system. As an application of the system, an industrial prototype workcell is presented. The task is to recognize, grasp and transfer parts of office chairs, such as seats, seat backs and armrests, from a pallet to a cardboard container. It demonstrates how the vision system is easily set up to recognize three different components by using CAD models. The prototype workcell further demonstrates how the pose estimation results can be fed directly to a separate handling robot in order to grasp a chair seat. A series of ten experiments was performed where the chair seat was placed in arbitrary poses in the pallet. Pose estimations were performed in just over one second per experiment, and the obtained accuracy was well within the tolerances for the grasp operation in all ten cases.
A Self-tuning Failure Detection Scheme for Cloud Computing Service

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.