nils-herrmann/clean_scirepeval huggingface dataset:
Analytic approach to study a hybrid plasmonic waveguide fed and numerically design a nano antenna based on the new director.
Venue: Optics express
Year: 2020

In this paper, a wideband InP based hybrid plasmonic nano antenna (HPNA) operating at telecommunication wavelengths has been proposed. Monolithically integrating InP based lasers with hybrid plasmonic waveguide (HPW) as a feed line of the proposed HPNA on the same InGaAsP/InP wafer can increase the antenna efficiency. A new vertical director has been employed to have a highly directive horizontal radiation pattern. This enhancement is attributed to the efficient coupling between the radiation patterns of arm elements as well as reduced side lobes and back lobes levels due to the achieved impedance matching. As a result, the directivity has been increased considerably, 3.6 dBi at 193.5 THz (1550 nm) and 1.1 dBi at 229 THz (1310 nm) The HPNA shows the high directivity, total efficiency and quality factor of 11.8, 97.49% and 94.57, respectively. Further, to verify the validity of confining the fundamental TM mode to a thin layer with the lower refractive index, both theoretical and numerical methods have been employed. Therefore, we have derived an analytical formula to investigate the HPW dispersion relation based on the transfer matrix theory and genetic algorithm. Moreover, due to the HPNA ability to receive an optical signal from free space and transmit it to the waveguide based on the reciprocity theorem, the HPNA performance as an optical wireless on chip nano link has been investigated analytically and numerically. Additionally, to obtain a high optical power signal and steering the beam angle, the antenna gain and directivity have been calculated with two different types of array structure by controlling the relative phase shift between the array elements and elements number. To validate the array design performance, a three dimensional full wave numerical simulation and array factor theory have been exploited. The HPNA fabrication is compatible with generic foundry technology.
TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter related Microblogs on Twitter
Venue: ArXiv
Year: 2021

In the past few years, there has been a significant rise in toxic and hateful content on various social media platforms. Recently Black Lives Matter movement came into the picture, causing an avalanche of user generated responses on the internet. In this paper, we have proposed a Black Lives Matter related tweet hate speech datasetTweetBLM. Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement. We annotated the tweets into two classes, i.e. "HATE" and "NON HATE" based on their content related to racism erupted from the movement for black community. In this work, we also generated useful statistical insights on our dataset and performed a systematic analysis of various machine learning models such as Random Forest, CNN, LSTM, Bi LSTM, Fasttext, BERTbase and BERTlarge for the classification task on our dataset. Through our work, we aim at contributing to the substantial efforts of the research community for the identification and mitigation of hate speech on the internet. The dataset is publicly available.
Do Psychological Shocks Affect Financial Risk Taking Behavior? A Study of U.S. Veterans
Year: 2012

Traditional economic theories assume that individuals are endowed with certain risk preferences that are unaltered by experiences. However, recent evidence indicates that macroeconomic shocks do have an effect on an individual's willingness to take financial risks. In the context of investment decisions, we examine empirically whether individual's risk preferences are affected by other types of traumatic life experiences. Using a unique proprietary data set, we investigate whether personal traumatic experiences such as the combat experiences of veterans have long term effects on financial risk taking behavior. We find that having experienced combat decreases the probability of investing in risky assets. Key policy implications are noted.
Dependence of economic impacts of climate change on anthropogenically directed pathways
Venue: Nature Climate Change
Year: 2019

There are great uncertainties in the projected economic impacts of climate change1, arising from uncertainties in the climate response2, the climate change mitigation pathway3 and the socioeconomic development pathway4. Although the relative contributions of these factors are important for climate change related decision making, they are poorly understood. Here, we show to what extent the projected economic impacts of climate change can be attributed to these three factors. Our modelling framework consisting of global, multisectoral impact models coupled with an integrated assessment model enables us to estimate the global total economic impacts of climate change while incorporating these uncertainty sources. Whereas the most pessimistic pathway without mitigation would result in a net economic impact equivalent to 6.6% (3.9 8.6% of global gross domestic product at the end of this century, the pathways with stringent mitigation would limit the impact to around or less than 1% Although the uncertainties are great, the climate change mitigation pathway is the dominant factor and socioeconomic developments can also contribute to alleviate the impacts of climate change. These results suggest that decisions on mitigation and development have a great influence in determining the economic impacts of climate change, regardless of the uncertainties in the climate response. Understanding which factors influence future economic impacts from climate change is important for informing mitigation and adaptation strategies. This study demonstrates that projected economic impacts are primarily attributed to variation in socioeconomic development and future emissions trajectories, rather than uncertainties in the climate response.
Towards Harnessing Natural Language Generation to Explain Black box Models
Venue: NL4XAI
Year: 2020

The opaque nature of many machine learning techniques prevents the wide adoption of powerful information processing tools for high stakes scenarios. The emerging field eXplainable Artificial Intelligence (XAI) aims at providing justifications for automatic decision making systems in order to ensure reliability and trustworthiness in the users. For achieving this vision, we emphasize the importance of a natural language textual modality as a key component for a future intelligent interactive agent. We outline the challenges of XAI and review a set of publications that work in this direction.
Penggunaan Ict Dalam Kalangan Guru Pelatih Matematik Fakulti Pendidikan, Utm.
Year: 2010

Tujuan kajian ini adalah untuk melihat masalah dalam penggunaan ICT dalam kalangan guru guru pelatih Matematik Fakulti Pendidikan, Universiti Teknologi Malaysia Skudai Johor. Kajian ini melibatkan seramai 50 orang guru pelatih Sarjana Muda Sains serta Pendidikan Matematik (SPM) dan Sarjana Muda Sains dan Komputer serta Pendidikan Matematik (SPT) yang telah menjalani latihan mengajar. Instrumen kajian yang digunakan ialah set soalan soal selidik yang mengandungi 32 soalan. Data yang diperolehi telah diproses dengan menggunakan perisian Statitiscal Package for Sosial Sciences (SPSS) Version 13.0 untuk menilai peratusan dan min. Dapatan kajian menunjukkan bahawa terdapat beberapa masalah yang wujud dalam aspek pengurusan dan pentadbiran sekolah yang berkaitan dengan ICT (min 3.43) aspek peralatan dan perkakasan ICT (min 3.23) aspek pengendalian peralatan ICT (min 3.54) dan aspek penggunaan perisian PBK (min 3.67) Ini bermakna hasil kajian menunjukkan kesemua aspek yang dikaji berada pada tahap bermasalah. Berdasarkan dapatan kajian, beberapa cadangan telah diberikan untuk meningkatkan lagi penggunaan ICT di kalangan guru pelatih di sekolah.
User Centered Design and Interactive Health Technologies for Patients
Venue: Computers, informatics, nursing CIN
Year: 2009

Despite recommendations that patients be involved in the design and testing of health technologies, few reports describe how to involve patients in systematic and meaningful ways to ensure that applications are customized to meet their needs. User centered design is an approach that involves end users throughout the development process so that technologies support tasks, are easy to operate, and are of value to users. In this article, we provide an overview of user centered design and use the development of Pocket Personal Assistant for Tracking Health (Pocket PATH) to illustrate how these principles and techniques were applied to involve patients in the development of this interactive health technology. Involving patient users in the design and testing ensured functionality and usability, therefore increasing the likelihood of promoting the intended health outcomes.
Neural network with bee colony optimization for MRI brain cancer image classification
Venue: Int. Arab J. Inf. Technol.
Year: 2016

B rain tumor is one of the foremost causes for the increase in mortality among children and adults. Computer visions are being used by doctors to analysis and diagnose the medical problems. Magnetic Resonance Imaging (MRI) is a medical imaging technique, which is used to visualize internal structures of MRI brain images for analyzing normal and abnormal prototypes of brain while diagnosing. It is a non"invasive method to take picture of brain and the surrounding images. Image processing techniques are used to extract meaningful information from medical images for the purpose of diagnosis and prognosis. Raw MRI brain images are not suitable for processing and analysis since noise and low contrast affect the quality of the MRI images. The classification of MRI brain images is emphasized in this paper for cancer diagnosis. It can consist of four steps: Pre"processing, identification of region of interest, feature extraction and classification. For improving quality of the image, partial differential equations method is proposed and its result is compared with other methods such as block analysis method, opening by reconstruction method and histogram equalization method using statistical parameters such as carrier signal to ratio, peak signal"to"ratio, structural similarity index measure, figure of merit, mean square error. The enhanced image is converted into bi"level image, which is utilized for sharpening the regions and filling the gaps in the binarized image using morphological operators. Region of Interest (ROI) is identified by applying region growing method for extorting the five features. The classification is performed based on the extracted image feature to determine whether the brain image is normal or abnormal and it is also, introduced hybridization of Neural Network (NN) with bee colony optimization for the classification and estimation of cancer affect on given MRI image. The performance of the proposed classifier is compared with traditional NN classifier using statistical measures such as sensitivity, specificity and accuracy. The experiment is conducted over 100 MRI brain images.
Image Quality Assessment A Multiscale Geometric Analysis Based Framework and Examples
Venue: Handbook of Natural Computing
Year: 2012

This chapter is about objective image quality assessment (IQA) which has been recognized as an effective and efficient way to predict the visual quality of distorted images. Basically, IQA has three different dependent degrees on original images, namely, full reference (FR) noreference (NR) and reduced reference (RR) To begin with, the fundamentals of IQA are introduced and a broad treatment of the current state of the art IQA are also given. A novel framework for IQA is focused upon to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA) contrast sensitivity function (CSF) and Weber's law of just noticeable difference (JND) Thorough empirical studies were carried out upon the laboratory for image and video engineering (LIVE) database against subjectivemean opinion score (MOS) and demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results well reflect the visual quality of images.
Image denoising via correlation based sparse representation
Venue: Signal Image Video Process.
Year: 2017

Error based Orthogonal Matching Pursuit employed in many image denoising algorithms (e.g. K means singular value decomposition (K SVD) algorithm) tries to reconstruct the clean image patch by projecting the observed noisy patch onto a dictionary and picking the atom with maximum orthogonal projection. This approach does indeed minimize the power in the residual. However, minimizing the power in the residual does not guarantee that selected atoms will match the clean image patch. This leaves behind a residual that contains structures from the clean image patch. This problem becomes more pronounced at high noise levels. We propose a simple correlation based sparse coding algorithm that is better able to pick the atom that matches the clean patch. This is achieved by picking atoms that force the residual patch to have autocorrelation similar to the autocorrelation of contaminating noise. Autocorrelation based sparse coding and dictionary update stages are iterated, and dictionaries are learned from noisy image patches. The proposed algorithm is compared with the K SVD denoising algorithm, BM3D and EPLL algorithms. Our results indicate that the proposed algorithm is significantly better than K SVD and EPLL denoising. At the noise power 100, the improvement over K SVD denoising algorithm for Barbara and fingerprint images is 1.14 and 2.64 dB, respectively. The proposed algorithm gives results that are visually comparable or better than BM3D algorithm.
Pretreated animal and human waste as a substantial nutrient source for cultivation of microalgae for biodiesel production
Venue: Environmental Science and Pollution Research
Year: 2018

The use of human and animal wastes for fertilization of aquaculture ponds has been practiced for thousands of years. In the present work, we have used the excreta (human urine, poultry waste, cow dung, and urine) as a nutrient source for the cultivation of Chlorella singularis, Micractinium pusillum, and Chlorella sorokiniana strains of microalgae. Different solid wastes were treated with 60 mM H2SO4 for the extraction of nutrients. After treatment, the supernatant of different solid wastes and liquid waste were diluted 5, 10, 15, and 20% to be used as a media for the cultivation of microalgae. Chlorella sorokiniana was able to grow in all concentration of excreta media. The maximum growth rate 140 3.1 mg/L/day and lipid production (45.5 2.3 mg/L/day) was obtained in 20% poultry. Among the different excreta media used for cultivation of microalgae, poultry media displayed the best results and thus, should be used for large scale cultivation of microalgae.
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
Venue: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017

We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN performs a mapping from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model, (2) the student DNN outperforms the original DNN, and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.
Combining resistance spot welding and friction element welding for dissimilar joining of aluminum to high strength steels
Venue: Journal of Materials Processing Technology
Year: 2019

A multi process joining technique that combined resistance spot welding and friction element welding was used to produce a three sheet multi stack of advanced high strength steel and precipitation hardened aluminum. Each weld process was tested independently and as a combination to better understand how multi step welding affects joint performance. Weld interfaces and surrounding heat affected zones were investigated using microstructural and mechanical techniques, including scanning electron microscopy and nanoindentation. All tests indicated that excellent strength was maintained above the maximum breaking force threshold regardless of offset distance between the friction element weld and the center of the resistance spot weld nugget. A change in fracture mode was observed with interfacial fracture occurring at offset distances below 7 mm and nugget pull out or partial thickness failure occurred at offset distances above 7 mm. The present study indicates that hybrid joining techniques have minimal effect on both the mechanical and microstructural weld properties and allow for new dissimilar metal weld designs to be implemented in the automotive industry.
Group 2 Innate Lymphoid Cells in Airway Diseases.
Venue: Chest
Year: 2019

Group 2 innate lymphoid cells (ILC2s) are increasingly recognized as a key controller of type 2 inflammation, and are well known to be highly elevated in human airway type 2 inflammatory diseases including allergic rhinitis, chronic rhinosinusitis with nasal polyps, and asthma. ILC2 mediated production of type 2 cytokines initiates and amplifies airway inflammation via activation of eosinophils, B cells, mast cells, macrophages, fibroblasts, and epithelial cells in these diseases. ILC2s require at least three major signals to fully activate and robustly produce type 2 cytokines. IL 1 family cytokines (IL 1b, IL 18, IL 33) IL 25, and TNF superfamilies (TNF, TL1A, GITR L, RANK L) activate the NF kB and AP 1 pathways that initiate production of IL 5 and IL 13. Lipid mediators (LTC4, LTD4, PGD2) and neuropeptide NMU promote production of IL 4 through the NFAT pathway. IL 2 and IL 7 family cytokines (IL 2, IL 7, IL 9, TSLP) activate the STAT5 pathway that induces survival of ILC2s and enhances cytokine production. The activation of STAT5 is necessary to potently induce cytokine and lipid mediator mediated production of type 2 cytokines. Inhibitory pathways for ILC2s have also become clearer. Type I and II interferons and IL 27 inhibit ILC2 functions through the activation of STAT1. Suppression mediated via b2 adrenergic receptor agonists, PGE2, and PGI2 occurs through cAMP and PKA. Glucocorticoid, testosterone, IL 10, and TGF b are also able to inhibit ILC2 mediated production of type 2 cytokines. Blockage of ILC2 activators, activation of inhibitory pathways of ILC2s, and suppression of ILC2 mediated pathways including type 2 cytokines (IL 5, IL 13, IL 4Ra) may become therapeutic strategies for airway type 2 inflammatory diseases.
The Determinants of Bank Profitability: How Malaysian Islamic Banks Response to the Financing Risk
Year: 2019

The purpose of this article is to investigate the relationship between financing risk and profitability of Malaysian Islamic banks to find the impact of financing risk on bank profitabily in achieving high level of stability. A regression analysis is built on an unbalanced panel data set comprising 175 observations of 17 top Malaysian Islamic banks over the period 1994 2015. To this end, the empirical data are collected from individual Islamic banks' financial statements as submitted to the Central Bank of Malaysia for supervisory purposes. Complementary sources include the Global Market Information Database, the World Bank's Annual Report through the International Monetary Fund (IMF) Statistical, Economic and Social Research and Training Center for Islamic Countries (SESRTCIC) and Asian Development Bank (ADB) All the determinant variables included in the model have statistically significant impacts on Malaysian Islamic banks' profitability. However, the effects are not uniform across profitability measures. Regression findings reveal that capital ratio, financing loss provision and liquidity ratio significantly affect the profitability of Islamic banks. Some variables of monetary policy variables such as interest rate and interbank rate have a significant role on the Islamic banks'profitability. Interestingly, there is a difference of the two variables where interest rate shown negative sign while positive sign from interbank rate. Findings also suggest the economic conditions factors such as GDP growth and unemployment also affects the profitability of Islamic banks despite the sign is different.
Massive vector field on curved background: Nonminimal coupling, quantization, and divergences
Year: 2017

We study the effective action for the massive vector field theory nonminimally coupled to external gravitational field. Such a theory is an interesting model both from the theoretical side and also due to the various phenomenological applications to cosmology and astrophysics. The present work pretends to initiate a systematic study of its properties at the quantum level, by exploring free massive vector coupled to an external symmetric second rank tensor. Stueckelberg scalar field is used to restore the gauge invariance. After that, by using a special gauge fixing and nonlocal in external fields change of variables, we diagonalize the bilinear form of the action and develop a consistent procedure to study the effective action. As a result we derive a complete nonlinear structure of divergences of the effective action and discuss its properties.
Combining attention based bidirectional gated recurrent neural network and two dimensional convolutional neural network for document level sentiment classification
Venue: Neurocomputing
Year: 2020

Neural networks lately have achieved a great success on sentiment classification due to their ability of feature extraction. However, it remains as an enormous challenge to model long texts in document level sentiment classification as well as to explore semantics between sentences and dependencies between features. Moreover, most existing methods can hardly differentiate the importance of different contents when constructing a document representation. To tackle these problems, we propose a novel neural network model: AttDR 2DCNN, which mainly consists of two parts. The first part utilizes a two layer compositional bidirectional Gated Recurrent Unit (GRU) to obtain the compositional semantics of the document, where the first layer learns the feature vector of the sentence, and the second layer learns the document matrix representation consisting of two dimensions of the time step dimension and feature dimension, from the sentence vectors. The second part applies a two dimensional convolution operation and two dimensional max pooling to capture more dependencies between sentences features. We further utilize different types of attention mechanism in these two parts to distinguish the importance of words, sentences and features in the document. Experiments are conducted on four publicly available document level review datasets and the result shows that the proposed model outperforms some existing models.