nils-herrmann/clean_scirepeval huggingface dataset:
Optimization of Invertase from Aspergillus niger Grown on Low Cost Agricultural Wastes by Response Surface Methodology (RSM)
Year: 2018

5  Response surface methodology (RSM) is an optimization technique that designs the most suitable 6 combination of media components and cultural conditions using statistical and mathematical principles for 7 enhanced production of bioproducts. This study explores the use of RSM in optimizing media components 8 and cultural conditions for the production of invertase by Aspergillus niger grown on potato peels (P1) and 9 pineapple peels (P2) under solid state fermentation.  Chemical analyses of P1 and P2 were carried out using standard methods. Invertase 11 production was screened using Fehling solution test coupled with invertase activity assay. Reducing sugar 12 was estimated using dinitrosalicylic acid (DNSA) procedure. The RSM optimization design involved four 13 (4) independent variables (pH, temperature, pineapple peels concentrations, and potatoes peels 14 concentrations) at five (5) levels, screened through thirty (30) different experimental runs using central 15 composite design (CCD) 16  Optimal fermentation conditions that yielded maximum invertase (321.4 U/mL) and biomass 17 (11.34 mg/mL) by A. niger was achieved with the combination of pH 9.0, temperature 35 C, pineapple 18 peels concentration 10% and potato peels concentration 50% The model used gave a predicted R Squared 19 of 0.2258. Negative "Predicted R squared" implies that the overall mean may be a better predictor of the 20 response than the current model. "Adequate precision" of 6.190 (check spacing) was obtained, showing that 21 the model can be used to navigate the design space. 22  This result has demonstrated the efficiency of RSM technique to optimize invertase 23 production from A. niger using potato and pineapple peels as substrates. The use of local substrates can 24 make invertase production economically attractive. 25 26
Ambient backscatter communication based smart 5G IoT network
Venue: EURASIP J. Wirel. Commun. Netw.
Year: 2021

In this paper, we propose an ambient backscatter communication based smart 5G IoT network. The network consists of two parts, namely a real time data transmission system based on ambient backscatter communication and a real time big data analysis system based on the combination of shallow neural networks and deep neural networks. The real time data transmission system based on ambient backscatter communication can extend the standby time of data collection equipment, reduce the size of the equipment, and increase the comfort of wearing. The real time big data analysis system combining the shallow neural network and the deep neural network can greatly reduce the pressure caused by the frequent deep neural network calculations of the MEC and greatly reduce the energy consumed by the MEC for remote real time monitoring.
SVM based multi label learning with missing labels for image annotation
Venue: Pattern Recognit.
Year: 2018

Recently, multi label learning has received much attention in the applications of image annotation and classification. However, most existing multi label learning methods do not consider the consistency of labels, which is important in image annotation, and assume that the complete label assignment for each training image is available. In this paper, we focus on the issue of multi label learning with missing labels, where only partial labels are available, and propose a new approach, namely SVMMN for image annotation. SVMMN integrates both example smoothness and class smoothness into the criterion function. It not only guarantees the large margin but also minimizes the number of samples that live in the large margin area. To solve SVMMN, we present an effective and efficient approximated iterative algorithm, which has good convergence. Extensive experiments on three widely used benchmark databases in image annotations illustrate that our proposed method achieves better performance than some state of the art multi label learning methods.
Using Transcranial Direct Current Stimulation to Treat Stroke Patients With Aphasia
Venue: Stroke
Year: 2010

Recent research suggests that increased left hemisphere cortical activity, primarily of the left frontal cortex, is associated with improved naming performance in stroke patients with aphasia. Our aim was to determine whether anodal transcranial direct current stimulation (tDCS) a method thought to increase cortical excitability, would improve naming accuracy in stroke patients with aphasia when applied to the scalp overlying the left frontal cortex.  Ten patients with chronic stroke induced aphasia received 5 days of anodal tDCS (1 mA for 20 minutes) and 5 days of sham tDCS (for 20 minutes, order randomized) while performing a computerized anomia treatment. tDCS positioning was guided by a priori functional magnetic resonance imaging results for each individual during an overt naming task to ensure that the active electrode was placed over structurally intact cortex.  Results revealed significantly improved naming accuracy of treated items (F[1,9]=5.72, P<0.040) after anodal tDCS compared with sham tDCS. Patients who demonstrated the most improvement were those with perilesional areas closest to the stimulation site. Crucially, this treatment effect persisted at least 1 week after treatment.  Our findings suggest that anodal tDCS over the left frontal cortex can lead to enhanced naming accuracy in stroke patients with aphasia and, if proved to be effective in larger studies, may provide a supplementary treatment approach for anomia.
A Study of Wuthering Heights from the Perspective of Eco Criticism
Year: 2020

This research paper attempts to explore the novel, Wuthering Heights, through the lens of Eco criticism and it explores the relationship between human and nature in the novel. Literature can be perceived as an aesthetically and culturally constructed part of the environment, since it directly addresses the questions of human constructions, such as meaning, value, language, and imagination, which can, then, be linked to the problem of ecological consciousness that humans need to attain.Consciousness raising in environmental thinking, and the ethical and aesthetic dilemmas posed by the global ecological crisis. The task of ecocriticism, is to express a conceptual foundation for the study of interconnections between literature and the environment. Through An Eco criticism viewpoint the researcher would like to discuss about the emotional and physical of characters Get along nature as essential for building development based novel of ecological self.
PolSAR image classification based on multi scale stacked sparse autoencoder
Venue: Neurocomputing
Year: 2019

Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi scale feature extraction method based on stacked sparse autoencoder (SSAE) named the multi scale SSAE (MS SSAE) to improve the classification performance. This method extracts the deep multi scale features by a two stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi scale features. Then, a 1 D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS SSAE can capture discriminative multi scale features. The experimental results certify that the MS SSAE can not only improve the classification accuracy, but also remain the details in the image.
Dissocial Personality Traits and Past Experiences Matter in How People Perceive the Police
Venue: Journal of police and criminal psychology
Year: 2020

The study investigated the contributions of dissocial traits and experience with the police to police perception. Participants (261: males 115, females 146, mean age 25.87) completed psychometric measures deviant personality traits, police perception, and experience with the police. Regression analysis was used to examine relationship patterns. Results indicated that people detested the police as they got older (b 0.19, t 2.06, p 0.04) Also, having an encounter was associated with more negative perception of the (b 0.16, t 1.97, p 0.04) Among the dark triad traits, only narcissism predicted police perception such that higher narcissism traits was associated with higher negative perception of the police (b 0.46, t 2.21, p 0.03) Findings seem to suggest that the police, especially in Nigeria, needs to do more to improve their connections with citizens; albeit, individual personality disposition matters, and should be a potential issue to consider when the police deals with citizens.
Gateway to Language: The Perception of Prosody at Birth
Year: 2018

The surprising ease and efficiency with which human infants, but not kittens or young chimps, acquire language has puzzled scholar for decades. We now have a relatively good empirical and theoretical description of the later stages of language acquisition. However, despite considerable research efforts, the initial stage, i.e. the division of labor between the biologically endowed abilities the infant brings to the task of language acquisition and the learning that takes place on the basis of experience, remains poorly understood. Here, I put forth the hypothesis that prosody might be infants' first gateway to language, ensuring the link between prenatal and postnatal language experience. I will review evidence suggesting that the prosody of the native language(s) experienced prenatally already shapes infants' speech perception abilities and their neural correlates. I will also show that prosody plays an important bootstrapping role later during the acquisition of syntax. I propose that these two facts are strongly related, and provide insight about the key role that prosody plays during the early stages of language acquisition.
Orthologue chemical space and its influence on target prediction
Venue: Bioinform.
Year: 2018

In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice.  Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance. Availability and implementation Orthologue based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2. Supplementary information Supplementary data are available at Bioinformatics online.
Classification of time series images using deep convolutional neural networks
Venue: International Conference on Machine Vision
Year: 2018

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state of the art TSC algorithms.
Score level fusion scheme based on adaptive local Gabor features for face iris fingerprint multimodal biometric
Venue: J. Electronic Imaging
Year: 2014

A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face iris fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single scalar score via a trained, supported, vector regression model for a final decision. A large scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database fafb and CASIA V3 Interval together with FVC2004 DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state of the art individual methods and other fusion approaches for face iris fingerprint multimodal biometric systems.
A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques
Venue: PeerJ
Year: 2020

Members of a society interact using a variety of social behaviors, giving rise to a multi faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals' health and fitness. Multilayer network approaches, where each interaction type represents a different layer of the social network, have the potential to better capture this complexity than single layer approaches. Calculating individuals' centrality within a multilayer social network can reveal keystone individuals and more fully characterize social roles. However, existing measures of multilayer centrality do not account for differences in the dynamics and functionality across interaction layers. Here we validate a new method for quantifying multiplex centrality called consensus ranking by applying this method to multiple social groups of a well studied nonhuman primate, the rhesus macaque. Consensus ranking can suitably handle the complexities of animal social life, such as networks with different properties (sparse vs. dense) and biological meanings (competitive vs. affiliative interactions) We examined whether individuals' attributes or socio demographic factors (sex, age, dominance rank and certainty, matriline size, rearing history) were associated with multiplex centrality. Social networks were constructed for five interaction layers (i.e. aggression, status signaling, conflict policing, grooming and huddling) for seven social groups. Consensus ranks were calculated across these five layers and analyzed with respect to individual attributes and socio demographic factors. Generalized linear mixed models showed that consensus ranking detected known social patterns in rhesus macaques, showing that multiplex centrality was greater in high ranking males with high certainty of rank and females from the largest families. In addition, consensus ranks also showed that females from very small families and mother reared (compared to nursery reared) individuals were more central, showing that consideration of multiple social domains revealed individuals whose social centrality and importance might otherwise have been missed.
Effects of managerial coaching on critical reflection: mediating role of learning goal orientation
Year: 2020

The purpose of this paper is to investigate the direct and indirect effects of managerial coaching on critical reflection mediated through learning goal orientation.,The authors conducted a questionnaire survey of 169 employees in 53 teams at an IT firm in Japan. The data were examined using multi level analyses.,Managerial coaching has a direct positive effect on critical reflection, and the relationship is mediated by learning goal orientation.,The characteristics of the sample may limit the generalization of the findings. Future research should verify the model in other industries and countries.,The organizations should note that managerial coaching is useful not only for facilitating employees' problem solving but also for enhancing their critical reflection. It should also be noted that employees can reflect critically on their beliefs and work routines when they have learning goals.,Although few quantitative studies have investigated the determinants of critical reflection, the present research reveals the overlooked functions of managerial coaching in promoting employees' learning.
Liang Xing Chi Zi Zai Ju Pian Fu Yi Xi (PVDF)Mo Biao Mian Jie Zhi Gai Xing De Yan Jiu
Year: 2013

Wei Zeng Qiang Mo De Qin Shui Xing ,Ti Gao Kang Wu Ran Neng Li ,Cai Yong Yuan Zi Zhuan Yi Zi You Ji Fan Ying (ATRP),Jiang Jia Ji Bing Xi Suan Qian Yi Zhi (HEMA)Jie Zhi Dao Ju Pian Fu Yi Xi (PVDF)Mo Biao Mian ;Zai Yi Xiao Suan Shi An (CAN)Wei Yin Fa Ji ,N,N' Ya Jia Ji Shuang Bing Xi Xian E (MBAA)Wei Jiao Lian Ji ,Tong Guo Zi You Ji Ju He Fan Ying Jiang Liang Xing Chi Zi Dan Ti 3 (Jia Ji Bing Xi Xian E Ji )Bing Ji Er Jia Ji (3 Liu Dai Bing Ji )Qing Yang Hua An Nei Yan (MPDSAH)Cheng Gong Jie Zhi Dao Mo Biao Mian .Cai Yong Quan Fan She Hong Wai Guang Pu Yi (ATR FTIR) XShe Xian Guang Dian Zi Neng Pu Yi (XPS) Sao Miao Dian Zi Xian Wei Jing (SEM)Yi Ji Jie Hong Jiao Ce Ding Yi Fen Xi Liao Gai Xing Qian Hou Mo Biao Mian Xing Zhi Ji Jie Gou Xing Tai Bian Hua .Sui Jie Zhi Shi Jian De Zeng Jia ,Jie Zhi Mi Du (GD)Zhu Jian Sheng Gao ,Mo Kong Jing Bian Xiao ,Kong Xi Lu Jiang Di ,Dan Tong Shi Mo Biao Mian Qin Shui Xing Ming Xian Zeng Qiang .Tong Guo Niu Xie
Continual Reinforcement Learning in 3D Non stationary Environments
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020

High dimensional always changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end to end model free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.
A 2nd Order Current Mode Filter with 14dB Variable Gain and 650MHz to 1GHz Tuning Range in 28nm CMOS
Venue: 2021 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2021

An open loop baseband filter with bandwidth of 1GHz suitable for a receiver operated in the 5G high frequency band (above 24GHz) is presented. The filter is based on a gain boosted Common Gate topology that implements a 2nd order low pass filter in the current domain. A frequency dependent active negative capacitance circuit is used to boost the quality factor of the filter. This not only improves in band flatness but also increases out of band selectivity. A test chip has been realized in 28nm CMOS technology. The filter bandwidth can be programmed from 650 MHz to 1 GHz and the current gain changed by 14 dB, with the power dissipation scaling from 11 mW to 5 mW. In band IIP3 is +2 dBm and the input noise integrated from 10 MHz to 1 GHz is lower than 170 mVrms.
Social Cognition, Social Identity, and Intergroup Relations A Festschrift in Honor of Marilynn B. Brewer
Year: 2011

R.W. Livingston, G.J. Leonardelli, R.M. Kramer, Rigor with Relevance: The Many Legacies of Marilynn Brewer. Part 1. Social Cognition. M. Karasawa, Categorization Based Versus Person Based Explanations of Behaviors: Implications from the Dual Process Model. D.L. Hamilton, J.M. Chen, N. Way, Dynamic Aspects of Entitativity: From Group Perceptions to Social Interaction. L.R. Caporael, G.D. Reeder, New Evolutionary Perspectives on Theory of Mind. Part 2. Social Identity and Intergroup Relations. K. Schmid, M. Hewstone, Social Identity Complexity: Theoretical Implications for the Social Psychology of Intergroup Relations. G.J. Leonardelli, C.L. Pickett, J.E. Joseph, Y.D. Hess, Optimal Distinctiveness in Nested Categorization Contexts: Moving from Dueling Identities to a Dual Identity. C.L. Pickett, P. Smaldino,  Based Modeling as a Tool for Studying Social Identity Processes: The Case of Optimal Distinctiveness Theory. A.C. Rumble, Religion as Collective Identity. K. Gonsalkorale, W. von Hippel, Intergroup Relations in the 21st Century: Ingroup Positivity and Outgroup Negativity Among Members of an Internet Hate Group. M.M. McDonald, C.D. Navarrete, J. Sidanius, Developing a Theory of Gendered Prejudice: An Evolutionary and Social Dominance Perspective. W.D. Crano, V. Hemovich, Intergroup Relations and Majority or Minority Group Influence. M. Yuki, Intragroup Relationships and Intergroup Comparisons as Two Sources of Collectivism. Part 3. Applications and Implications. S. Schneider, W.M. George, S. Carroll, E.D. Middleton, How Leaders Transform Followers: Organizational Identity as a Mediator of Follower Attitudes in Two Samples. R.M. Kramer, Cooperation and the Commons: Lab and Field Explorations of a Persistent Dilemma. E. Castano, Moral Disengagement and Morality Shifting in the Context of Collective Violence. W.W. Maddux, A Movable Feast: How Transformational Cross Cultural Experiences Facilitate Creativity. Y. R. Chen, G. Zhao, J. Lee, Trust in the Manager as a Supervisor or a Group Leader? Toward a Relational Versus Collective Distinction in Procedural Justice. Part 4.  Brewer, In Retrospect.
Firm's Value Prediction Based on Profitability Ratios and Dividend Policy
Year: 2020

This study aims to empirically prove the research framework regarding the Firm's Value based on Profitability Ratios associated with Dividend Policy. The firm's Value is measured using a Price to Book Value (PBV) Approach.  This study included a sample of 11 firms under the automotive and components sib sector listed in the Indonesia Stock Exchange. It included data for the period of 2014 2018. This study applied path analysis using the Sobel test of the direct and indirect effects using IBM SPSS 23.0.  The study finds that Profitability Ratios has no significant effect on the Dividend Policy, while the Dividend Policy has no significant effect on Firm's Value too. However, the Dividend Policy does not mediate the effect of Profitability Ratios on the Firm's Value.  This study could be extended further by considering all listed firms of IDX which may provide us more insights into the measurement of the financial ratios in the context of Indonesia.