nils-herrmann/clean_scirepeval huggingface dataset:
PENGARUH KOMPENSASI DAN MOTIVASI TERHADAP KINERJA PEGAWAI NON KEPENDIDIKAN PADA SMKN 8 JAKARTA
Venue: Jurnal Ekonomi Efektif
Year: 2019

ABSTRAK Tujuan penelitian ini adalah untuk mengetahui pengaruh kompensasi dan motivasi terhadap kinerja pegawai non kependidikan pada SMKN 8 Jakarta.Metode yang digunakan adalah metode deskriptif kuantitatif, Penelitian dilaksanakan di SMKN 8 Jakarta, kompleks Kemendikbud, Jalan Raya Pejaten. Pasar Minggu.  Dalam hal ini populasi yang dijadikan obyek penelitian untuk memperoleh data adalah seluruh pegawai non kependidikan yang ada di SMKN 8 Jakarta yang berjumlah 40 pegawai. Untuk menentukan sampel dalam penelitian, terdapat berbagai teknik sampling yang digunakan. Secara sistematis. Jumlah sampel dalam penelitian ini adalah berjumlah 40 orang maka penetapan jumlah sampel disebut sampel jenuh karena jumlah sampel yang digunakan sebanyak 40 orang sama dengan banyaknya populasi yang dijadikan objek dalam penelitian ini. Metode Pengumpulan Data menggunkan data primer dan data sekunder, serta uji statistik meliputi, persamaan regresi linear berganda, uji koefisien korelasi, uji deteminsi dan uji hipotesisHasil penelitian menunjukan kompensasi (X1) dan motivasi (X2) berpengaruh positif dan signifikan terhadap Kinerja Pegawai Non Kependidikan (Y) dengan persamaan regresi Y 9,643 0,387X1 0,373X2. Nilai koefisien korelasi diperoleh sebesar 0,742 artinya variabel bebas dengan variabel terikat memiliki pengaruh yang kuat dengan koefisien determinasi atau pengaruh secara simultan sebesar 55,1% sedangkan sisanya sebesar 44,9% dipengaruhi faktor lain. Uji hipotesis diperoleh nilai F hitung F tabel atau (22,702 2,860) hal tersebut juga diperkuat dengan probability signifikansi 0,000 0,05. Dengan demikian H0 ditolak dan H3 diterima. Artinya terdapat pengaruh positif dan signifikan secara simultan antara kompensasi dan motivasi terhadap Kinerja Pegawai Non Kependidikan di SMKN 8 Jakarta.  Kompensasi, Motivasi, Kinerja
Expressing Personalities of Conversational Agents through Visual and Verbal Feedback
Venue: Electronics
Year: 2019

As the uses of conversational agents increase, the affective and social abilities of agents become important with their functional abilities. Agents that lack affective abilities could frustrate users during interaction. This study applied personality to implement the natural feedback of conversational agents referring to the concept of affective computing. Two types of feedback were used to express conversational agents' personality: (1) visual feedback and (2) verbal cues. For visual feedback, participants (N 45) watched visual feedback with different colors and motions. For verbal cues, participants (N 60) heard different conditions of agents' voices with different scripts. The results indicated that the motions of visual feedback were more significant than colors. Fast motions could express distinct and positive personalities. Different verbal cues were perceived as different personalities. The perceptions of personalities differed according to the vocal gender. This study provided design implications for personality expressions applicable to diverse interfaces.
Russian Diaspora: Culture, Identity, and Language Change
Year: 2011

The book presents a broad interdisciplinary perspective on the contemporary Russian immigration to three countries: the United States, Germany, and Israel. The changes and transformations in three domains, i.e. cultural perception, self identification, and attitudes to first language maintenance, are explored through the Acculturation Framework that allows bringing together these essential aspects of immigration. A separate look at Jewish and Russian ethnic groups within the so called "Russian" immigration as well as its interdisciplinary nature sets this book apart from other studies on recent immigration from the former USSR.
Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry
Venue: Neural Computing and Applications
Year: 2020

Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient's medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises "similar" concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.
Higiene oral en escolares de 6 anos de la Parroquia Rural Banos Ecuador
Year: 2020

To determine the degree of oral hygiene in 6 year old schoolchildren in the rural parish of Banos.  The study had a descriptive quantitative approach. Use the data from the digital collection in the Epi INFO program. The sample consisted of 198 6 year old schoolchildren, of which 94 are female students representing 47% of the sample and 104 male schoolchildren representing 53% of the sample.  The results obtained showed that the children of the parish Baths indicated 64% in a Simplified Oral Hygiene Index equal to 0.9, a bacterial plaque index equal to 0.8 and the calculation index of 0.1. The educational centers of the parish six of them visualize a "good" oral hygiene and 3 manifestations a "regular" oral hygiene.  The 6 year old schoolchildren of the Banos parish have good oral hygiene, with no difference between the two genders.
Statins and hip fracture risk in men: a population based case control study.
Venue: Annals of epidemiology
Year: 2015

To estimate the association between hydroxymethylglutaryl CoA inhibitor (statin) use and hip fracture.  We conducted a population based case control study. Cases were 6774 male enrollees in a large managed care organization, aged 45 or more years, with an incident hip fracture from 1997 to 2006. Controls without fracture (n 6774) were matched to cases on age, race, and medical center. Electronic information on pharmaceutical use was used to identify the dispensing of statins from 1991 forward.  Overall, 1884 (27.8% cases and 2150 controls (31.7% used a statin before index date (matched odds ratio [mOR] 0.81, 95% confidence interval [CI] 0.74 0.87) Adjustment for comorbidity burden strengthened the magnitude of the overall association (mOR 0.68, CI 0.62 0.74) The adjusted association was similar across age groups but was strongest among men aged 80 years or more (mOR 0.62, CI 0.54 0.71) and was most pronounced in African Americans (mOR 0.43, CI 0.28 0.64) Greater duration of statin use did not alter the odds ratios.  These data add to the growing evidence of a potential protective effect of statin use on bone health. However, these results need to be replicated in a prospective study that can account for confounding by indication which may explain these findings.
Ji Yu ASP+SQL SERVERJi Zhu De Gao Xiao Ke Cheng Shui Ping Ce Shi Xi Tong Yan Jiu
Year: 2014

Yi Ju Zhuan Ye Ren Cai Pei Yang Zhi Liang Biao Zhun He Ke Cheng Yan Ge Kao He De Xu Yao ,Cai Yong ASPHe SQL SERVERJi Zhu ,Fen Xi He She Ji Gao Xiao Tong Yong De Zhuan Ye Zhu Gan Ke Cheng Shui Ping Ce Shi Xi Tong ,Yan Jiu Zu Juan Da An Bao Cun IPFang Wen Kong Zhi Shi Juan Zi Dong Pan Fen Deng Suan Fa ,Shi Xian Ke Cheng Shui Ping Ce Shi Xi Tong De Guan Jian Ji Zhu
The Adversarial Stackelberg Value in Quantitative Games
Venue: ICALP
Year: 2020

In this paper, we study the notion of adversarial Stackelberg value for two player non zero sum games played on bi weighted graphs with the mean payoff and the discounted sum functions. The adversarial Stackelberg value of Player 0 is the largest value that Player 0 can obtain when announcing her strategy to Player 1 which in turn responds with any of his best response. For the mean payoff function, we show that the adversarial Stackelberg value is not always achievable but epsilon optimal strategies exist. We show how to compute this value and prove that the associated threshold problem is in NP. For the discounted sum payoff function, we draw a link with the target discounted sum problem which explains why the problem is difficult to solve for this payoff function. We also provide solutions to related gap problems.
Angry White Men: American Masculinity at the End of an Era by Michael Kimmel (review)
Year: 2014

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.
Evaluating code complexity triggers, use of complexity measures and the influence of code complexity on maintenance time
Venue: Empirical Software Engineering
Year: 2017

Code complexity has been studied intensively over the past decades because it is a quintessential characterizer of code's internal quality. Previously, much emphasis has been put on creating code complexity measures and applying these measures in practical contexts. To date, most measures are created based on theoretical frameworks, which determine the expected properties that a code complexity measure should fulfil. Fulfilling the necessary properties, however, does not guarantee that the measure characterizes the code complexity that is experienced by software engineers. Subsequently, code complexity measures often turn out to provide rather superficial insights into code complexity. This paper supports the discipline of code complexity measurement by providing empirical insights into the code characteristics that trigger complexity, the use of code complexity measures in industry, and the influence of code complexity on maintenance time. Results of an online survey, conducted in seven companies and two universities with a total of 100 respondents, show that among several code characteristics, two substantially increase code complexity, which subsequently have a major influence on the maintenance time of code. Notably, existing code complexity measures are poorly used in industry.
Object oriented quality prediction through artificial intelligence and machine learning: a survey
Year: 2020

Software quality estimation is an important aspect as it eliminates design and code defects. Objectoriented quality metrics prediction can help in the estimation of software quality of any defects and the chances of errors. In this paper a survey and the case analytics have been presented for the object oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies. This survey also elaborates different object oriented parameters which is useful for the same problem. It also elaborates the problem aspects as well the limitations for the future directions. Machine learning and artificial intelligence methods have been considered mostly for this survey. The parameters considered are inheritance, dynamic behavior, encapsulation, objects etc.
Study on Terpenoid Degradation of Pinus massoniana Sawdust for Mycelium Germination and Cultivation of Edible Fungi
Year: 2019

The material basis and degradation of the terpenoids were investigated in Pinus massoniana sawdust during the composting process. In order to better utilize P. massoniana sawdust for the culturation of edible fungus, three treatments were designed according to the amount of brown sugar that was added: 4% (A1) 5% (A2) and 6% (A3) The brown sugar was added based on the dry weight of the P. massoniana sawdust. The results showed that the brown sugar addition of 5% yielded the fastest heating rate of composting and the longest temperature duration above 60 degC. The gas chromatography mass (GC MS) analysis showed that the amount of brown sugar did not affect the degradation of the terpenoids. The relative terpenoid content decreased from 3.89% to 2.10% and 0.31% after 30 d and 60 d of composting, respectively. Fourier transform infrared (FTIR) analysis indicated that the terpenoids decomposed a lot throughout the composting process. The mycelium cultivation demonstrated that the P. massoniana sawdust treated with 30 d of composting promoted the growth of edible fungi.
GNSS/INS Equipped Buoys for Altimetry Validation: Lessons Learnt and New Directions from the Bass Strait Validation Facility
Venue: Remote. Sens.
Year: 2020

Global Navigation Satellite System (GNSS) equipped buoys have a fundamental role in the validation of satellite altimetry. Requirements to validate next generation altimeter missions are demanding and call for a greater understanding of the systematic errors associated with the buoy approach. In this paper, we assess the present day buoy precision using archived data from the Bass Strait validation facility. We explore potential improvements in buoy precision by addressing two previously ignored issues: changes to buoyancy as a function of external forcing, and biases induced by platform dynamics. Our results indicate the precision of our buoy against in situ mooring data is ~15 mm, with a ~8.5 mm systematic noise floor. Investigation into the tether tension effect on buoyancy showed strong correlation between currents, wind stress and buoy against mooring residuals. Our initial empirical correction achieved a reduction of 5 mm in the standard deviation of the residuals, with a 51% decrease in variance over low frequency bands. Corrections associated with platform orientation from an Inertial Navigation System (INS) unit showed centimetre level magnitude and are expected to be higher under rougher sea states. Finally, we conclude with further possible improvements to meet validation requirements for the future Surface Water Ocean Topography (SWOT) mission.
Comparative Structure and Function Analysis of the RIG I Like Receptors: RIG I and MDA5
Venue: Front. Immunol.
Year: 2019

RIG I (Retinoic acid inducible gene I) and MDA5 (Melanoma Differentiation Associated protein 5) collectively known as the RIG I like receptors (RLRs) are key protein sensors of the pathogen associated molecular patterns (PAMPs) in the form of viral double stranded RNA (dsRNA) motifs to induce expression of type 1 interferons (IFN1) (IFNa and IFNb) and other pro inflammatory cytokines during the early stage of viral infection. While RIG I and MDA5 share many genetic, structural and functional similarities, there is increasing evidence that they can have significantly different strategies to recognize different pathogens, PAMPs, and in different host species. This review article discusses the similarities and differences between RIG I and MDA5 from multiple perspectives, including their structures, evolution and functional relationships with other cellular proteins, their differential mechanisms of distinguishing between host and viral dsRNAs and interactions with host and viral protein factors, and their immunogenic signaling. A comprehensive comparative analysis can help inform future studies of RIG I and MDA5 in order to fully understand their functions in order to optimize potential therapeutic approaches targeting them.
3,4 Seco Dammarane Triterpenoid Saponins with Anti inflammatory Activity Isolated from the Leaves of Cyclocarya paliurus.
Venue: Journal of agricultural and food chemistry
Year: 2020

Cyclocarya paliurus is commonly used for the prevention and treatment of hypertension, diabetes, and inflammation in South China. Although research on the anti inflammatory effects of C. paliurus leaves has been reported, no active anti inflammatory compounds have been identified. In the present study, RAW 264.7 cells were used to establish a bioactivity guided identification model to verify the inhibitory effects of C. paliurus leaves on inflammation and identify the anti inflammatory constituents. The anti inflammatory bioactivity guided isolation of an extract from the leaves of C. paliurus led to the isolation and characterization of eighteen dammarane triterpenoid saponins, including eleven new 3,4 seco dammarane triterpenoid saponins (1 11) the structures of which were identified by nuclear magnetic resonance (NMR) mass spectrometry (MS) and comparison with literature data. Compounds 7, 8, 10, and 11 strongly inhibited the release of nitric oxide (NO) with IC50 values ranging from 8.23 mM to 11.23 mM. These four compounds significantly decreased the secretion of prostaglandin E2 (PGE2) tumor necrosis factor alpha (TNF a) and interleukin 6 (IL 6) in lipopolysaccharide (LPS) induced RAW 264.7 cells. Furthermore, compound 7 markedly decreased the expression of the proteins inducible nitric oxide synthase (iNOS) nuclear factor kappa B (NF kB/p65) and cyclooxygenase 2 (COX 2) In addition, the structure activity relationships of the isolates were investigated. The results suggest that 3,4 seco dammarane triterpenoid saponins may be used as potential anti inflammatory drugs and warrant further investigation.
Small cell carcinoma of the urinary bladder.
Venue: Histology and histopathology
Year: 2010

Small cell carcinoma of the urinary bladder (SCCUB) is a rare and aggressive cancer of the bladder. SCCUB is part of neuroendocrine family of tumors that affect several organ systems including respiratory, gastrointestinal and male and female genitourinary tract. SCCUB affect males predominantly with common risk factors include smoking, bladder calculi, bladder manipulation, and chronic cystitis. Prognosis of SCCUB remains poor due to high metastatic potential and lack of symptoms in earlier stages of the disease. Pathogenesis of the disease is linked to loss of genetic material, hypermethylation of tumor suppressors and at times amplification of the chromosomal regions carrying oncogenes. Majority of cases are treated with local resection of the tumor with neoadjuvant or adjuvant platinum based chemotherapy regimen. Radiation therapy is used as alternative to radical cystectomy or as palliative measure. This article provides epidemiology, molecular pathogenesis, histochemistry, and current management options for SCCUB. Furthermore we reviewed all recent studies involving advancement in targeted molecular therapy for neuroendocrine tumors.
Self Attention Network for Session Based Recommendation With Streaming Data Input
Venue: IEEE Access
Year: 2019

In the current era of the rapid development of big data, it has become increasingly critical and practical to study recommender systems with streaming data input. However, the recommender system is often faced with the challenge that the history records of new users or anonymous users are not available. Specifically, session based recommendation, which aims to predict a user's next actions, is a typical task to overcome the challenge. To capture a user's long term preference in session based recommendations, recurrent neural networks (RNN) based models have been widely applied with impressive results, but the inherent sequential nature of RNNs prevents parallelism within training examples, which is critical in long sessions because memory constraints limit batching across instances. In this paper, we propose a novel method, i.e. self attention network for session based recommendation (SANSR) which is based on only attention mechanisms, dispensing with recurrence, and supports parallelism in the session. The proposed model attempts to find items that are relevant based on previous time steps in the ongoing session and to assign them different weights to predict the next item. The extensive experiments are conducted on two real world datasets, and the experimental results show that our proposed model is superior to the state of the art methods.
Gabor features for offline Arabic handwriting recognition
Venue: DAS '10
Year: 2010

Many feature extraction approaches for off line handwriting recognition (OHR) rely on accurate binarization of gray level images. However, high quality binarization of most real world documents is extremely difficult due to varying characteristics of noises artifacts common in such documents. Unlike most of these features, Gabor features do not require binarization of the document images, and thus are likely to be more robust to noises in document images. To demonstrate the efficacy of our proposed Gabor features, we perform subword recognition for off line Arabic handwritten images using Support Vector Machines (SVM) We also compare the recognition performance with other binarization based features which have been proven to be effective in capturing shape characteristics of handwritten Arabic subwords, such as GSC (a set of gradient, structure, and concavity features) and skeleton based Graph features. Our preliminary experimental results show that Gabor features outperform Graph features and are slightly better than GSC features for Arabic subword recognition. In addition, by combining Gabor and GSC features, we obtain a significant reduction in classification error rate over using GSC or Gabor features alone.
DCT Based blind audio watermarking scheme
Venue: 2010 International Conference on Signal Processing and Multimedia Applications (SIGMAP)
Year: 2010

During these recent years, different digital audio media are available due to internet and audio processing techniques. This is beneficial to our daily life but it brings the problem of ownership protection. As a solution, audio watermarking technique has been quickly developed. It consists of embedding information into digital audio data. This paper proposes an audio watermarking scheme operating in frequency domain using Discrete Cosine transforms (DCT) We take the advantage of hiding the mark in the frequency domain to guarantee robustness. To increase robustness of our schemes, the watermark is refined by the Hamming error correcting code. We study the band used to hide the watermark bits according essentially to the effects of the MP3 compression on the watermarked audio signal. To assure watermark embedding and extraction, we use neural network architecture. Experimental results show that the watermark is imperceptible and the algorithm is robust to many attacks like MP3 compression with several compression rates and various Stirmark attacks. Furthermore, the watermark can be blind extracted without needing the original audio.
Market awareness and profitability: case study of Mango production in Karnataka, India
Year: 2019

This article presents a case study of mango farmers in the Kolar district of Karnataka to understand the determinants of profitability and constraints faced by the horticulture industry in India in general. The mango value chain is analysed in depth to understand the market choices of farmers and the role of market intermediaries and is based on data collected from a primary survey of 131 farmers. The study uses an instrumental variable approach to model the profitability of farmers as a function of market awareness, distance from markets, farming practices, and control variables. It finds that in addition to age and education, distance to markets and farming practices are significant factors influencing the profitability of mango cultivators.