nils-herrmann/clean_scirepeval huggingface dataset:
GRADE: Automatic Graph Enhanced Coherence Metric for Evaluating Open Domain Dialogue Systems
Venue: EMNLP
Year: 2020

Automatically evaluating dialogue coherence is a challenging but high demand ability for developing high quality open domain dialogue systems. However, current evaluation metrics consider only surface features or utterance level semantics, without explicitly considering the fine grained topic transition dynamics of dialogue flows. Here, we first consider that the graph structure constituted with topics in a dialogue can accurately depict the underlying communication logic, which is a more natural way to produce persuasive metrics. Capitalized on the topic level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph enhanced Representations for Automatic Dialogue Evaluation. Specifically, GRADE incorporates both coarse grained utterance level contextualized representations and fine grained topic level graph representations to evaluate dialogue coherence. The graph representations are obtained by reasoning over topic level dialogue graphs enhanced with the evidence from a commonsense graph, including k hop neighboring representations and hop attention weights. Experimental results show that our GRADE significantly outperforms other state of the art metrics on measuring diverse dialogue models in terms of the Pearson and Spearman correlations with human judgements. Besides, we release a new large scale human evaluation benchmark to facilitate future research on automatic metrics.
High efficiency rectifier for RF energy harvesting in the GSM band
Venue: 2017 IEEE International Symposium on Antennas and Propagation USNC/URSI National Radio Science Meeting
Year: 2017

In this paper, a miniature RECtifying antENNA (rectenna) operating in the GSM band is designed and realized. The RF micro generator consists of a simple rectifier circuit combined with a dipole antenna. The miniaturization of the circuit lies in avoiding the adaptation input filter and in optimizing the transmission lines by the gradient method search. The designed circuit is based on a Schenkel voltage doubler rectifier and Schottky diodes HSMS 2850 of AVAGO and is optimized through electromagnetic simulation software, Agilent ADS 2014 environment. Experimental results show an RF DC conversion efficiency up to 40% with an output DC voltage of 1.8 V at 10 dBm of input power. A Wireless Power Transmission (WPT) experience demonstrates an average voltage of 22 mV over a distance of 3 m.
Integrated Reconstruction Methods of Time series NDVI Dataset
Year: 2010

Time series of Normalized Difference Vegetation Index(NDVI) datasets have been used in detecting the long term vegetation cover changes in regional,continental or global scales.They are also successfully applied to extract the biophysical parameters of vegetation cover.Normally,there are quite frequently fluctuations because of atmospheric condition and sensor effect in the NDVI dataset.According to the comparative analysis of five widely used NDVI reconstruction algorithms,two integrated approaches were developed based on standard deviation weight and characteristics of noise points respectively.The reconstructed results were validated and assessed by using some in suit NDVI measurements carried out during late May to early August,2009.The result shows that these two integrated methods are better than the five separate methods above.They do not only retain most of the original data,but also modify the noise to the utmost extent.NDVI time series datasets produced by these two approaches can be better applied in the researches on global and regional environmental change,vegetation dynamic,and so on.
Customer feedback gathering and management tools for product service system design
Year: 2018

Modern manufacturing industries are being shifted towards a new business model where low cost and high customer value solutions should be designed and provided. The use of advanced technology and mobile applications will support companies to approach customers, and extract and analyze their opinion. Towards that, this research work presents tools for customers' feedback gathering and management to support efficient and accurate product and service designs. Mobile applications for customer feedback gathering on products and services and an importance satisfaction analysis based on modular design approach are proposed. The tools are validated in a real case study from the robotics industry.
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Venue: 2014 IEEE Conference on Computer Vision and Pattern Recognition
Year: 2014

Recently introduced cost effective depth sensors coupled with the real time skeleton estimation algorithm of Shotton et al.  have generated a renewed interest in skeleton based human action recognition. Most of the existing skeleton based approaches use either the joint locations or the joint angles to represent a human skeleton. In this paper, we propose a new skeletal representation that explicitly models the 3D geometric relationships between various body parts using rotations and translations in 3D space. Since 3D rigid body motions are members of the special Euclidean group SE(3) the proposed skeletal representation lies in the Lie group SE(3)x. .xSE(3) which is a curved manifold. Using the proposed representation, human actions can be modeled as curves in this Lie group. Since classification of curves in this Lie group is not an easy task, we map the action curves from the Lie group to its Lie algebra, which is a vector space. We then perform classification using a combination of dynamic time warping, Fourier temporal pyramid representation and linear SVM. Experimental results on three action datasets show that the proposed representation performs better than many existing skeletal representations. The proposed approach also outperforms various state of the art skeleton based human action recognition approaches.
UPAYA PENINGKATAN HASIL BELAJAR PAI MATERI PRAKTEK WUDHUMELALUI METODE PICTURE AND PICTURE SISWA KELAS II SD NEGERI 1 MANDURAGA KECAMATAN KALIMANAH KABUPATEN PURBALINGGA TAHUN PELAJARAN 2018/2019
Year: 2019

Berbagai media yang diterapkan dalam proses pendidikan dapat menyentuh pemahaman anak didik dalam menerima pelajaran yang disampaikan oleh guru, baik itu media visual ataupun non visual. Seperti usaha guru untuk meningkatkan hasil belajar PAI mareti wudhu melalui metode picture and picture siswa kelas II di SD Negeri 1 Manduraga Kecamatan Kalimanah Kabupaten Purbalingga Tahun Pelajaran 2018/2019. Jenis penelitian ini adalah penelitian tindakan kelas artinya penelitian yang bertujuan untuk meningkatkan hasil belajar siswa dalam mata pelajaran Pendidikan Agama Islam materi wudhu. Dari hasil penelitian dan analisis data diperoleh bahwa penggunaan media picture and picture dapat meningkatkan hasil belajar siswa materi wudhu. Hal tersebut dapat dibuktikan Pada prasiklus ketrampilan berwudhu siswa memperoleh rata rata nilai 63,77 dengan prosentasse 27,77% hanya ada 5 anak yang tuntas mencapai KKM, Pada siklus pertama nilai rata rata siswa naik menjadi 70,50 dengan prosentase ketuntasan 50% adanya peningkatan menjadi 9 anak yang mencapai ketuntasan. Pada siklus kedua peningkatan secara signifikan terjadi dengan nilai rata rata 82,88 dengan prosentasi ketuntasan siswa adalah 88,88% siswa yang berjumlah 16 anak telah mencapai nilai KKM. Hanya ada 2 anak yang belum mencapai ketuntasan belajar. Kesimpulan dari penelitian ini, dengan melalui metode picture and picture dapat meningkatkan hasil belajar siswa kelas II mata pelajaran PAI materi wudhu SD Negeri 1 Manduraga Kecamatan Kalimanah Kabupaten Purbalingga Tahun Pelajaran 2018/2019 dapat digunakan sebagai salah satu metode alternatif pembelajaran PAI.
Social anxiety and the interpretation of morphed facial expressions following exclusion and inclusion.
Venue: Journal of behavior therapy and experimental psychiatry
Year: 2019

Negative interpretation biases are postulated to play etiological and maintaining roles in social anxiety (SA) However, empirical support for interpretation biases of facial expression in SA is inconsistent. Given the importance of signals of (dis)approval in SA, our objective was to examine whether SA is associated with enhanced sensitivity to such signals especially following exclusion.  In Study 1, participants (N 139) underwent an exclusion/inclusion manipulation and were then presented with video clips of smiles gradually changing into disgust expressions (smile to disgust) In Study 2 (N 203) participants saw smile to disgust as well as disgust to smile clips following an exclusion/inclusion manipulation. Participants' task in both studies was to detect the offset of the initial expression.  Results of Study 1 show that detection latency of smiles' disappearance is negatively associated with SA severity. The results of Study 2 suggest that this association is stronger following exclusion, and specific to the smile to disgust as opposed to the disgust to smile, transitions.  Our studies did not examine whether the observed interpretation bias was specific to SA.  Our findings support and refine cognitive theories of SA, suggesting that interpretation biases for facial information in SA may be especially pronounced following exclusion.
THE JOURNALIST IS MARKETING THE NEWS
Year: 2016

This study, based on case studies of three online newsrooms, seeks to understand the patterns of how journalists use social media in their news work. Through 150 hours of observations and interviews with 31 journalists, the study found that journalists are normalizing social media while also reworking some of their norms and routines around it, a process of journalistic negotiation. They are balancing editorial autonomy and the other norms that have institutionalized journalism, on one hand, and the increasing influence exerted by the audience perceived to be the key for journalism's survival on the other. In doing so, journalists are also seeing a reworking of their traditional gatekeeping role, finding themselves having to also market the news.
Two ways to the top: evidence that dominance and prestige are distinct yet viable avenues to social rank and influence.
Venue: Journal of personality and social psychology
Year: 2013

The pursuit of social rank is a recurrent and pervasive challenge faced by individuals in all human societies. Yet, the precise means through which individuals compete for social standing remains unclear. In 2 studies, we investigated the impact of 2 fundamental strategies Dominance (the use of force and intimidation to induce fear) and Prestige (the sharing of expertise or know how to gain respect) on the attainment of social rank, which we conceptualized as the acquisition of (a) perceived influence over others (Study 1) (b) actual influence over others' behaviors (Study 1) and (c) others' visual attention (Study 2) Study 1 examined the process of hierarchy formation among a group of previously unacquainted individuals, who provided round robin judgments of each other after completing a group task. Results indicated that the adoption of either a Dominance or Prestige strategy promoted perceptions of greater influence, by both group members and outside observers, and higher levels of actual influence, based on a behavioral measure. These effects were not driven by popularity; in fact, those who adopted a Prestige strategy were viewed as likable, whereas those who adopted a Dominance strategy were not well liked. In Study 2, participants viewed brief video clips of group interactions from Study 1 while their gaze was monitored with an eye tracker. Dominant and Prestigious targets each received greater visual attention than targets low on either dimension. Together, these findings demonstrate that Dominance and Prestige are distinct yet viable strategies for ascending the social hierarchy, consistent with evolutionary theory.
A Study on Information seeking Benefits of Fashion Applications of Users and their Effects on loyalty and Satisfaction Focused on iPhone Fashion Applications
Year: 2012

Considering the rapid growth of mobile industry and the emerging application marketing, this study focused on fashion applications, which had not drawn much academic attention before, and analysed relevant content composition, information seeking benefits based on users` lifestyles and resulting differences in satisfaction and loyalty. In short, fashion applications were divided into fashion and non fashion brand apps. Brand apps were subdivided into luxury, women`s wear, men`s wear, casual wear, sportswear, accessories and the like. Non brand apps included commerce, magazine, information, style tip, SNS and wardrobe manager types. As for users` lifestyle based information search, the higher the tendency to search information on the internet and to favour reasonable consumption, the more basic information on products and brands they sought, whereas the higher the tendency to look for high profile brands and to favour global culture, the more additional information like images or entertainments they sought. To sum up, given that studies on fashion specific applications were rare, the present study reviewed and categorized current fashion applications, and suggested theoretical and practical groundwork for developing content based on target consumers` lifestyle and for planning fashion applications in practice.
Resilience: resistance factor for depressive symptom.
Venue: Journal of psychiatric and mental health nursing
Year: 2009

The purpose was to explore influence of resilience on the willingness of African Americans aged 65 and over to seek mental health care for depressive symptoms. Specifically, the study examined relationships between personal resilience and willingness of undiagnosed, community dwelling older adults to seek mental health care for depressive symptoms. A cross sectional, correlational, causal modelling design was used to study older African Americans (N= 158; 121 women and 37 men) recruited from churches, retirement organizations and senior nutrition centres. Participants completed study instruments to measure depressive symptoms, resilience, willingness to seek mental health care, and general demographics information. Descriptive statistics and multiple regression analyses were preformed. Depressive symptoms and resilience accounted for 15.4% of the willingness to seek mental health care variance; extraction of resilience lowered variance to 0.9% A direct, predictive relationship between resilience and willingness to seek mental health care was documented. Understanding resilience and willingness to seek mental health care supports future research for interventions that bolster resilience in older adults. Identifying the influence of resilience on such willingness may provide direction for developing interventions for older African Americans and may be applicable to vulnerable, marginalized and minority older adults worldwide.
Learning Efficient Stereo Matching Network With Depth Discontinuity Aware Super Resolution
Venue: IEEE Access
Year: 2019

Deep convolutional neural networks (CNNs) have shown great potential to provide accurate depth estimation based on stereo images. Previous work has focused on developing robust stereo matching architectures, while little attention has been paid on improving the network efficiency. In this paper, we propose an efficient Siamese CNN architecture that combines the low resolution disparity estimation and the depth discontinuity aware super resolution. Specifically, we propose to construct, filter and perform regression on a low resolution cost volume through the designed stereo matching backbone network. A fast depth discontinuity aware super resolution subnetwork is proposed for upsampling the low resolution disparity map to the desired resolution. Under the guidance of the intensity edge features extracted from the left color image, depth edge residuals are hierarchically learned to refine the upsampled depth map. A delayed upsampling structure is designed to ensure that the computational complexity is proportional to the spatial size of the input disparity map. We also propose to supervise the first derivative loss of the predicted disparity map that makes the network adaptively aware of the depth discontinuity edges. Experiments show that the proposed stereo matching network achieves a comparable prediction accuracy and much faster running speed compared with state of the art methods.
How to win the consumer's heart? Exploring appraisal determinants of consumer pre consumption emotions
Year: 2020

Emotions are key drivers of consumption in the tourism and hospitality sector. Therefore, advancing our understanding of the determinants of consumer emotions is critical for tourism and hospitality marketers to effectively influence consumer affect driven consumption behavior. However, research on the determinants of pre consumption emotions remains under explored. This study aims to fill the research gap and proposes a theoretical framework to explain affect driven behavior. Grounded on the cognitive appraisal theory of emotions, we hypothesize that marketing stimuli are personally appraised based on several dimensions such as novelty and goal relevance. These appraisal dimensions determine the elicitation of positive emotions, leading to behavioral intentions. A total of 655 participants were recruited for an empirical study to test this framework in the context of a tourism event experience. We use an experimental research design to examine consumer appraisals, emotions and behavioral intentions in four conditions: two marketing visual formats (poster vs. video) over two delivery channels (advertising vs. e word of mouth) Research outcomes support the theoretical framework of consumer affect driven behavior, suggesting that the elicitation of positive emotions beneficially guides consumer behavior in goal striving and novelty seeking. Based on this, emotion focused marketing implications are recommended, including goal based segmentation, goal relevant content development and the use of eWOM as an emotion enhancing message delivery channel.
3D Point Cloud Geometry Compression on Deep Learning
Venue: ACM Multimedia
Year: 2019

3D point cloud presentation has been widely used in computer vision, automatic driving, augmented reality, smart cities and virtual reality. 3D point cloud compression method with higher compression ratio and tiny loss is the key to improve data transportation efficiency. In this paper, we propose a new 3D point cloud geometry compression method based on deep learning, also an auto encoder performing better than other networks in detail reconstruction. It can reach much higher compression ratio than the state of art while keeping tolerable loss. It also supports parallel compressing multiple models by GPU, which can improve processing efficiency greatly. The compression process is composed of two parts. Firstly, Raw data is compressed into codeword by extracting feature of raw model with encoder. Then, the codeword is further compressed with sparse coding. Decompression process is implemented in reverse order. Codeword is recovered and fed into decoder to reconstruct point cloud. Detail reconstruction ability is improved by a hierarchical structure in our decoder. Latter outputs are grown from former fuzzier outputs. In this way, details are added to former output by latter layers step by step to make a more precise prediction. We compare our method with PCL compression and Draco compression on ShapeNet40 part dataset. Our method may be the first deep learning based point cloud compression algorithm. The experiments demonstrate it is superior to former common compression algorithms with large compression ratio, which can also reserve original shapes with tiny loss.
FABRICATION AND CHARACTERIZATION OF SUPERCAPACITOR ELECTRODE BY 3D PRINTING
Year: 2018

Herein, we presents a study on the fabrication and characterization of supercapacitor electrode by 3D printing. A colloidal suspension containing carbon nanotubes (CNTs) and cobalt ferrite nanoparticles (CoFe2O4 NPs) was used as ink. The ink was successfully printed on aluminium substrate using a modified 3D printer followed by solvent evaporation to form a porous CNTs/CoFe2O4 aerogel film. The characterization results showed that the film has porous surface, high electrical conductivity and good electrochemical properties, indicating its promising application as supercapacitor electrode for energy conversion and storage.
Why do These Match? Explaining the Behavior of Image Similarity Models
Venue: ECCV
Year: 2020

Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering. In this paper, we introduce Salient Attributes for Network Explanation (SANE) to explain image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification score. In this task, an explanation depends on both of the input images, so standard methods do not apply. Our SANE explanations pairs a saliency map identifying important image regions with an attribute that best explains the match. We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition. Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2. Code available at: this https URL
Effect of Polyherbal Formulation in Treatment of Poly Cystic Ovarian Syndrome (PCOS)
Year: 2013

Poly cystic ovarian syndrome (PCOS) is a complex syndrome affecting 5 to 15% of the females of reproductive as well as premenopausal age. It involves various symptoms related to metabolism and reproductive system. According to Ayurveda it is a disorder of Artav vah strotus caused by vitiation of Kapha and Vata. In this study a Polyherbal formulation prepared by Unijules life Sciences ltd. is evaluated on 36 patients suffering from various symptoms of PCOS for three months treatment duration. A highly significant result was observed in pain during menstruation, interval between two menses, follicular size and in decreasing the BMI i.e. in obesity also. Overall this study established a good product useful for PCOS without causing any unwanted effects.
Academic Writing at the Graduate Level: Improving the Curriculum through Faculty Collaboration.
Year: 2013

This article describes a collaborative self study undertaken to identify the source of academic writing difficulties among graduate students and find ways to address them. Ten faculty members in a college of education came together to define the problem and to analyze data gleaned from faculty and student surveys, course documents, course assignments, and course assessments. We found discrepancies between faculty and student perceptions about graduate preparation for academic writing and between the espoused and enacted curriculum. Both faculty and students identified problems associated with synthesizing theory and research. We discuss the need for teacher scholars in today's educational environment, the challenges facing curriculum improvement, and several program specific measures being undertaken to address identified gaps in academic writing and critical thinking.