nils-herrmann/clean_scirepeval huggingface dataset:
Robust Image Watermarking Based on Multiscale Gradient Direction Quantization

We propose a robust quantization-based image watermarking scheme, called the gradient direction watermarking (GDWM), based on the uniform quantization of the direction of gradient vectors. In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. The proposed scheme has the following advantages: 1) increased invisibility of the embedded watermark because the watermark is embedded in significant gradient vectors, 2) robustness to amplitude scaling attacks because the watermark is embedded in the angles of the gradient vectors, and 3) increased watermarking capacity as the scheme uses multiple-scale embedding. The gradient vector at a pixel is expressed in terms of the discrete wavelet transform (DWT) coefficients. To quantize the gradient direction, the DWT coefficients are modified based on the derived relationship between the changes in the coefficients and the change in the gradient direction. Experimental results show that the proposed GDWM outperforms other watermarking methods and is robust to a wide range of attacks, e.g., Gaussian filtering, amplitude scaling, median filtering, sharpening, JPEG compression, Gaussian noise, salt & pepper noise, and scaling.
DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes

We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid. Existing solutions to this problem often rely strongly on hand-engineered features and vanishing point detection, which are prone to failure in the presence of clutter. In this paper, we present a method that uses a fully convolutional neural network (FCNN) in conjunction with a novel optimization framework for generating layout estimates. We demonstrate that our method is robust in the presence of clutter and handles a wide range of highly challenging scenes. We evaluate our method on two standard benchmarks and show that it achieves state of the art results, outperforming previous methods by a wide margin.
Animating Hair with Loosely Connected Particles

This paper presents a practical approach to the animation of hair at an interactive frame rate. In our approach, we model the hair as a set of particles that serve as sampling points for the volume of the hair, which covers the whole region where hair is present. The dynamics of the hair, including hair-hair interactions, is simulated using the interacting particles. The novelty of this approach is that, as opposed to the traditional way of modeling hair, we release the particles from tight structures that are usually used to represent hair strands or clusters. Therefore, by making the connections between the particles loose while maintaining their overall stiffness, the hair can be dynamically split and merged during lateral motion without losing its lengthwise coherence.
Multi-class weather classification on single images

Multi-class weather classification from single images is a fundamental operation in many outdoor computer vision applications. However, it remains difficult and the limited work is carried out for addressing the difficulty. Moreover, existing method is based on the fixed scene. In this paper we present a method for any scenario multi-class weather classification based on multiple weather features and multiple kernel learning. Our approach extracts multiple weather features and takes properly processing. By combining these features into high dimensional vectors, we utilize multiple kernel learning to learn an adaptive classifier. We collect an outdoor image set that contains 20K images called MWI (Multi-class Weather Image) set. Experimental results show that the proposed method can efficiently recognize weather on MWI dataset.
Just in time , total quality management , and supply chain management : understanding their linkages and impact on business performance

In recent years, numerous approaches have been proposed to improve operations performance. Three in particular, just in time, supply chain management, and quality management, have received considerable attention.While the three are sometimes viewed and implemented as if they were independent and distinct, they can also be used as three prongs of an integrated operations strategy. This study empirically examines the extent to which just in time, supply chain management, and quality management are correlated, and how they impact business performance. Results demonstrate that at both strategic and operational levels, linkages exist between how just in time, total quality management, and supply chain management are viewed by organizations as part of their operations strategy. Results also indicate that a commitment to quality and an understanding of supply chain dynamics have the greatest e5ect on performance. ? 2004
Topology comparison for 6.6kW On board charger: Performance, efficiency, and selection guideline

This paper compares various topologies for 6.6kW on-board charger (OBC) to find out suitable topology. In general, OBC consists of 2-stage; power factor correction (PFC) stage and DC-DC converter stage. Conventional boost PFC, interleaved boost PFC, and semi bridgeless PFC are considered as PFC circuit, and full-bridge converter, phase shift full-bridge converter, and series resonant converter are taken into account for DC-DC converter circuit. The design process of each topology is presented. Then, loss analysis is implemented in order to calculate the efficiency of each topology for PFC circuit and DC-DC converter circuit. In addition, the volume of magnetic components and number of semi-conductor elements are considered. Based on these results, topology selection guideline according to the system specification of 6.6kW OBC is proposed.
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation

While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior. We present an interactive interface for visualizing and intervening behavior of NMT, specifically concentrating on the behavior of beam search mechanism and attention component. The tool (1) visualizes search tree and attention and (2) provides interface to adjust search tree and attention weight (manually or automatically) at real-time. We show the tool help users understand NMT in various ways.
Obstacle Detection for Visually Impaired Using Raspberry Pi and Ultrasonic Sensors

This paper proposes to develop an electronic device for obstacle detection in the path of visually impaired people. This device assists a user to walk without colliding with any obstacles in their path. It is a wearable device in the form of a waist belt that has ultrasonic sensors and raspberry pi installed on it. This device detects obstacles around the user up to 500cm in three directions i.e. front, left and right using a network of ultrasonic sensors. These ultrasonic sensors are connected to raspberry pi that receives data signals from these sensors for further data processing. The algorithm running in raspberry pi computes the distance from the obstacle and converts it into text message, which is then converted into speech and conveyed to the user through earphones/speakers. This design is benefitial in terms of it’s portability, low-cost, low power consumption and the fact that neither the user nor the device requires initial training. embedded systems; raspberry pi; speech feedback; ultrasonic sensor; visually impaired;
A Survey of Uncertain Data Algorithms and Applications

In recent years, a number of indirect data collection methodologies have lead to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, since the corresponding deterministic value is not known. This increases the challenge of mining and managing uncertain data, since the precise behavior of the underlying data is no longer known. In this paper, we provide a survey of uncertain data mining and management applications. In the field of uncertain data management, we will examine traditional methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as classification and clustering. We will also examine a general transform based technique for mining uncertain data. We discuss the models for uncertain data, and how they can be leveraged in a variety of applications. We discuss different methodologies to process and mine uncertain data in a variety of forms.
Psychiatric and psychological aspects in the Ehlers-Danlos syndromes.

There is increasing amount of evidence pointing toward a high prevalence of psychiatric conditions among individuals with hypermobile type of Ehlers-Danlos syndrome (JHS/hEDS). A literature review confirms a strong association between anxiety disorders and JHSh/hEDS, and there is also limited but growing evidence that JHSh/hEDS is also associated with depression, eating, and neuro-developmental disorders as well as alcohol and tobacco misuse. The underlying mechanisms behind this association include genetic risks, autonomic nervous system dysfunction, increased exteroceptive and interoceptive mechanisms and decreased proprioception. Recent neuroimaging studies have also shown an increase response in emotion processing brain areas which could explain the high affective reactivity seen in JHS/hEDS. Management of these patients should include psychiatric and psychological approaches, not only to relieve the clinical conditions but also to improve abilities to cope through proper drug treatment, psychotherapy, and psychological rehabilitation adequately coupled with modern physiotherapy. A multidimensional approach to this "neuroconnective phenotype" should be implemented to ensure proper assessment and to guide for more specific treatments. Future lines of research should further explore the full dimension of the psychopathology associated with JHS/hEDS to define the nature of the relationship.
A Fast Self-Tuning Background Subtraction Algorithm

In this paper, a fast pixel-level adapting background detection algorithm is presented. The proposed background model records not only each pixel's historical background values, but also estimates the efficacies of these values, based on the occurrence statistics. It is therefore capable of removing the least useful background values from the background model, selectively adapting to background changes with different timescales, and restraining the generation of ghosts. A further control process adjusts the individual decision threshold for each pixel, and reduces high frequency temporal noise, based on a measure of classification uncertainty in each pixel. Evaluation results based on the ChangeDetection.net database are presented in this paper. The results indicate that the proposed algorithm outperforms the majority of earlier state-of-the-art algorithms not only in terms of accuracy, but also in terms of processing speed.
Human-Data Interaction: The Human Face of the Data-Driven Society

The increasing generation and collection of personal data h as created a complex ecosystem, often collaborative but som etimes combative, around companies and individuals engaging in th e use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Inter action (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer s cience, statistics, sociology, psychology and behavioura l economics. We expose the challenges that HDI raises, organised into thr ee core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interest ed parties in the personal and big data ecosystems.
Non-Rigid Registration Under Isometric Deformations

We present a robust and efficient algorithm for the pairwise non-rigid registration of partially overlapped 3D surfaces. Our approach treats non-rigid registration as an optimization problem and solves it by alternating between correspondence and deformation optimization. Assuming approximately isometric deformations, robust correspondences are generated using a pruning mechanism based on geodesic consistency. We iteratively learn an appropriate deformation discretization from the current set of correspondences and use it to update the correspondences in the next iteration. Our algorithm is able to register partially similar point clouds that undergo large deformations, in just a few seconds. We demonstrate the potential of our algorithm in various applications such as example based articulated segmentation, and shape interpolation.
A review of single-phase improved power quality AC-DC converters

Solid-state switch-mode rectification converters have reached a matured level for improving power quality in terms of power-factor correction (PFC), reduced total harmonic distortion at input ac mains and precisely regulated dc output in buck, boost, buck–boost and multilevel modes with unidirectional and bidirectional power flow. This paper deals with a comprehensive review of improved power quality converters (IPQCs) configurations, control approaches, design features, selection of components, other related considerations, and their suitability and selection for specific applications. It is targeted to provide a wide spectrum on the status of IPQC technology to researchers, designers and application engineers working on switched-mode ac–dc converters. A classified list of more than 450 research publications on the state of art of IPQC is also given for a quick reference.
A neural network approach to complete coverage path planning

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.
An Earth-system perspective of the global nitrogen cycle

The massive acceleration of the nitrogen cycle as a result of the production and industrial use of artificial nitrogen fertilizers worldwide has enabled humankind to greatly increase food production, but it has also led to a host of environmental problems, ranging from eutrophication of terrestrial and aquatic systems to global acidification. The findings of many national and international research programmes investigating the manifold consequences of human alteration of the nitrogen cycle have led to a much improved understanding of the scope of the anthropogenic nitrogen problem and possible strategies for managing it. Considerably less emphasis has been placed on the study of the interactions of nitrogen with the other major biogeochemical cycles, particularly that of carbon, and how these cycles interact with the climate system in the presence of the ever-increasing human intervention in the Earth system. With the release of carbon dioxide (CO2) from the burning of fossil fuels pushing the climate system into uncharted territory, which has major consequences for the functioning of the global carbon cycle, and with nitrogen having a crucial role in controlling key aspects of this cycle, questions about the nature and importance of nitrogen–carbon–climate interactions are becoming increasingly pressing. The central question is how the availability of nitrogen will affect the capacity of Earth’s biosphere to continue absorbing carbon from the atmosphere (see page 289), and hence continue to help in mitigating climate change. Addressing this and other open issues with regard to nitrogen–carbon–climate interactions requires an Earth-system perspective that investigates the dynamics of the nitrogen cycle in the context of a changing carbon cycle, a changing climate and changes in human actions.
A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

In this paper, we present a scalable and exact solution for probabilistic linear discriminant analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the-art performance for both face and speaker recognition. However, it has one major drawback: At training time estimating the latent variables requires the inversion and storage of a matrix whose size grows quadratically with the number of samples for the identity (class). To date, two approaches have been taken to deal with this problem, to 1) use an exact solution that calculates this large matrix and is obviously not scalable with the number of samples or 2) derive a variational approximation to the problem. We present a scalable derivation which is theoretically equivalent to the previous nonscalable solution and thus obviates the need for a variational approximation. Experimentally, we demonstrate the efficacy of our approach in two ways. First, on labeled faces in the wild, we illustrate the equivalence of our scalable implementation with previously published work. Second, on the large Multi-PIE database, we illustrate the gain in performance when using more training samples per identity (class), which is made possible by the proposed scalable formulation of PLDA.
Seeing, thinking, and feeling: emotion-regulating effects of gaze-directed cognitive reappraisal.

Cognitive reappraisal (CR) is an emotion-regulatory (ER) process that is theorized to operate via changes in appraisals. CR is distinct from attentional deployment (AD), an ER process that is theorized to operate via changes in attention. However, a recent neuroimaging study has suggested that the ER effects of CR might largely be explained by AD. In this study, I manipulated CR while holding visual AD constant across CR conditions. In a randomized within-subjects design, 54 participants used CR to increase and decrease emotion in response to unpleasant pictures. This was compared with simply viewing the pictures. On all trials, gaze was directed to a circumscribed area of the pictures. Even with gaze held constant across conditions, increase reappraisals led to higher ratings of emotional intensity, greater corrugator activity, and greater autonomic arousal. In addition, decrease reappraisals led to lower ratings of emotional intensity and lower corrugator activity, the latter of which held only when gaze was directed to arousing information. Overall, the results suggest that changes in appraisal are the likely mechanism for the ER effects of CR.
Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study.

The relationship of health risk behavior and disease in adulthood to the breadth of exposure to childhood emotional, physical, or sexual abuse, and household dysfunction during childhood has not previously been described.



A questionnaire about adverse childhood experiences was mailed to 13,494 adults who had completed a standardized medical evaluation at a large HMO; 9,508 (70.5%) responded. Seven categories of adverse childhood experiences were studied: psychological, physical, or sexual abuse; violence against mother; or living with household members who were substance abusers, mentally ill or suicidal, or ever imprisoned. The number of categories of these adverse childhood experiences was then compared to measures of adult risk behavior, health status, and disease. Logistic regression was used to adjust for effects of demographic factors on the association between the cumulative number of categories of childhood exposures (range: 0-7) and risk factors for the leading causes of death in adult life.



More than half of respondents reported at least one, and one-fourth reported > or = 2 categories of childhood exposures. We found a graded relationship between the number of categories of childhood exposure and each of the adult health risk behaviors and diseases that were studied (P < .001). Persons who had experienced four or more categories of childhood exposure, compared to those who had experienced none, had 4- to 12-fold increased health risks for alcoholism, drug abuse, depression, and suicide attempt; a 2- to 4-fold increase in smoking, poor self-rated health, > or = 50 sexual intercourse partners, and sexually transmitted disease; and 1.4- to 1.6-fold increase in physical inactivity and severe obesity. The number of categories of adverse childhood exposures showed a graded relationship to the presence of adult diseases including ischemic heart disease, cancer, chronic lung disease, skeletal fractures, and liver disease. The seven categories of adverse childhood experiences were strongly interrelated and persons with multiple categories of childhood exposure were likely to have multiple health risk factors later in life.



We found a strong graded relationship between the breadth of exposure to abuse or household dysfunction during childhood and multiple risk factors for several of the leading causes of death in adults.
Multimodal Approaches for Emotion Recognition: A Survey

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing–emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user’s emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and pshysiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.