nils-herrmann/clean_scirepeval huggingface dataset:
Deep neural mapping support vector machines
Venue: Neural Networks
Year: 2017

The choice of kernel has an important effect on the performance of a support vector machine (SVM) The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM) from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub network layer wise pre trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e. SVM with the kernel of radial basis function) demonstrating its effectiveness.
An Overview of Developments and Prospects for E Commerce Agrifood Products in Malaysia
Year: 2016

Application of electronic communications and multimedia (C&M) is gaining popular. It opens new opportunities for businesses, market and supply chain actors as a whole to enhance their operations. Many terms are used in literature to describe activities of Internet Technology between organisations and their environment, including suppliers and customers. Generally, ebusiness, e commerce, e sourcing, e marketing and e fulfillment are some of the commercial terms, which have been quoted in the literature. The term is referred to "doing business electronically" (Van der Vorst et al. 2002; Huff et al. 2000) Internet marketing or on line marketing refers to advertising and marketing efforts that use the Web or email to drive direct sales via electronic commerce. In other words, sales lead from Websites or emails. Internet marketing and on line advertising efforts are typically used in conjunction with conventional approaches such as advertising on radio, television, newspapers, and magazines. Internet marketing can be further classified into more specific categories such as Web marketing, email marketing, and social media marketing. This paper aims to present the current development and policies of agri food e commerce in Malaysia.
The potential of endophytic fungi isolated from Vietnamese medicinal plants
Year: 2009

Since Vietnam is a tropical country, with the primarily health care is still based on the traditional medicinal plants. But, as every where in over the world, Vietnam has been facing to the problems of environmental degradation, loss of biodiversity, and spoilage of land and water. Endophytes, microorganisms that reside in the tissues of living plants, are relatively unstudied and potential sources of novel natural products for exploitation in medicine, agriculture, and industry. It is noteworthy that, of the nearly 20,000 plant species that exist in Vietnam, each individual plant is host to one or more endophytes. Almost all of these plants have never been completely studied relative to their endophytic biology. Consequently, the opportunity to find new and interesting of finding new drugs that may be effective candidates for treating newly developing diseases in humans, plants, and animals is great. Novel antibiotics, antimycotics, immunosuppressant, and anticancer compounds are only a few examples of what have been found after the isolation, culture, purification, and characterization of some selected endophytes in the recent past. The experimental biologically laboratory belong to the Institute of Natural Products Chemistry (INPC) which is the Vietnamese national institution responsible for fundamental and applied Corresponding author.  lehuong00@gmail.com research in the field of natural products. Established in 1990, it originally belonged to the National Centre for Natural Sciences and Technology (NCNST) which changed its name in 2004 to the Vietnamese Academy of Science and Technology (VAST; www.vast.ac.vn) INPC is the only institute in Vietnam responsible for graduate training (M.Sc and PhD courses) in the chemistry of natural products (Code 62.44.27.02) Since November 1991, INPC is National Point of Contact on the Network for Chemistry of Natural Products of the UNESCO Regional in Asia and the Pacific Ocean. In the course of our lab. research for the last 6 years up to now, about 300 strains of endophytic fungi were isolated from well known medicinal plants such as: Croton tonkinensis Gagnep. Mallotus apelta Lour, Artemisia annua, Fibraurea recisa, Taxus wallichiana, etc. All isolates were fermented, extracted then evaluated their biological activities in antimicrobial, antioxidant, anticancer (cytotoxicity) and enzymological assays. 50% among isolated strains exhibited at least one biological activity, 12 most active strains were Latin name identified. In detail, up to 34% of all extracts showed cytotoxic activity (against at least 2 cancer cell lines) 42.6% exhibited antimicrobial activity against at least 3/8 tested strains, 11% were able to scavenge free radical of DPPH in antioxidant assay, 20% were enzymological active, and 15 bioactive compounds were chemical structure identified. The results contributed to emphasize the potential of endophytic fungi isolated from medicinal plants as a promising source for biologically active secondary metabolites.
Evaluation of Digital Forensic Process Models with Respect to Digital Forensics as a Service
Venue: ArXiv
Year: 2017

Digital forensic science is very much still in its infancy, but is becoming increasingly invaluable to investigators. A popular area for research is seeking a standard methodology to make the digital forensic process accurate, robust, and efficient. The first digital forensic process model proposed contains four steps: Acquisition, Identification, Evaluation and Admission. Since then, numerous process models have been proposed to explain the steps of identifying, acquiring, analysing, storage, and reporting on the evidence obtained from various digital devices. In recent years, an increasing number of more sophisticated process models have been proposed. These models attempt to speed up the entire investigative process or solve various of problems commonly encountered in the forensic investigation. In the last decade, cloud computing has emerged as a disruptive technological concept, and most leading enterprises such as IBM, Amazon, Google, and Microsoft have set up their own cloud based services. In the field of digital forensic investigation, moving to a cloud based evidence processing model would be extremely beneficial and preliminary attempts have been made in its implementation. Moving towards a Digital Forensics as a Service model would not only expedite the investigative process, but can also result in significant cost savings freeing up digital forensic experts and law enforcement personnel to progress their caseload. This paper aims to evaluate the applicability of existing digital forensic process models and analyse how each of these might apply to a cloud based evidence processing paradigm.
Elementary Teachers' Readiness toward the Online Learning Policy in the New Normal Era during Covid 19
Venue: International Journal of Instruction
Year: 2021

Online based learning system is a recent policy established by the Indonesian government in all schools as a result of Covid 19 pandemic. Shifting face to face schools with online learning based system creates its own problems, mainly for elementary school teachers. This study aims to investigate the readiness of elementary school teachers towards the adoption of online based learning policies during Covid 19. There are six indicators to measure teacher readiness, they are (1) availability of learning content, (2) availability of technological devices, (3) proficiency to apply the technology, (4) ability to purchase internet data, (5) availability of internet signals, and (6) student conditions. This research was conducted using descriptive quantitative method. Data was collected through a survey of 250 elementary school teachers in Indonesia. The results indicated that 50% of teachers were less available with the learning content;24% of teachers had inadequate technology tools;67.6% were less adapted at implementing technology;20.4% were less able to buy internet data packages;40.4% of teachers stated their problem finding internet signals and 53.2% of the students were less prepared for online learning. The research concluded that the teacher is not ready for the implementation of online learning policies.
Assedio Moral de LGBT no ambito Organizacional
Year: 2018

O assedio moral frente aos sujeitos LGBT acontece pela orientacao sexual ou pela identidade de genero adotada pelo grupo, uma vez que e praticado por pessoas que nao sabem conviver com as diferencas. O objetivo geral do trabalho se propoe a identificar a existencia do assedio moral de LGBT em Organizacoes Empresariais na Regiao Metropolitana do Cariri. A pesquisa caracteriza se como basica, exploratoria, descritiva, qualitativa, sendo realizado dois estudos de caso no municipio de Juazeiro do Norte CE, nos dias 24 e 26 de outubro de 2017. Os sujeitos da pesquisa foram um homossexual gay, e uma empresaria heterossexual. O local e sujeito foi selecionado por conveniencia e acessibilidade. Foi utilizado a entrevista semiestruturada, e a analise dos dados foi feita adotando se a tecnica de analise de conteudo. A empresaria da organizacao pesquisada ja impediu e demitiu do trabalho alguem por causa da orientacao sexual, afirma a pratica da Homofobia ou Assedio no ambiente de trabalho frente ao grupo praticando de uma forma cruel. Sente se realizada com uma situacao de exclusao e desrespeito aos LGBT dizendo que eles vao contra os seus principios. Menciona que sua organizacao nao tem nenhum prejuizo em nao contratar um integrante do grupo, e sim, que pode apresentar prejuizo se houver o processo de contratacao. Atraves da investigacao se tornou possivel constatar que o assedio LGBT e uma constante recheada pelo silencio e pelo tabu. O estudo conclui enfatiza que quando nao se tem como trabalhar a pessoa nao tera condicoes minimas de viver com dignidade, tendo em vista que permite que se perceba que as empresas devem se preocupar com a responsabilidade social e com o impacto que a Lgbtfobia podera proporcionar na vida das pessoas, no ingresso destas ao mercado de trabalho ou a permanencia nas organizacoes.
Improved Construction of Irregular Progressive Edge Growth Tanner Graphs
Venue: IEEE Communications Letters
Year: 2010

The progressive edge growth algorithm is a well known procedure to construct regular and irregular low density parity check codes. In this paper, we propose a modification of the original algorithm that improves the performance of these codes in the waterfall region when constructing codes complying with both, check and symbol node degree distributions. The proposed algorithm is thus interesting if a family of irregular codes with a complex check node degree distribution is used.
Design of Trusted Market Platforms using Permissioned Blockchains and Game Theory
Venue: ArXiv
Year: 2020

The blockchain concept forms the backbone of a new wave technology that promises to be deployed extensively in a wide variety of industrial and societal applications. Governments, financial institutions, banks, industrial supply chains, service companies, and even educational institutions and hospitals are investing in a substantial manner in the hope of improving business efficiency and operational robustness through deployment of blockchain technology. This thesis work is concerned with designing trustworthy business to business (B2B) market platforms drawing upon blockchain technology and game theory. The proposed platform is built upon three key ideas. First, we use permissioned blockchains with smart contracts as a technically sound approach for building the B2B platform. The blockchain deploys smart contracts that govern the interactions of enterprise buyers and sellers. Second, the smart contracts are designed using a rigorous analysis of a repeated game model of the strategic interactions between buyers and sellers. We show that such smart contracts induce honest behavior from buyers and sellers. Third, we embed cryptographic regulation protocols into the permissioned blockchain to ensure that business sensitive information is not revealed to the competitors. We believe our work is an important step in the direction of building a powerful B2B platform that maximizes social welfare and enables trusted collaboration between strategic enterprise agents.
PENEGAKAN HUKUM PEMERINTAHAN KEKUASAAN WALIKOTA PEKANBARU MEMBERLAKUKAN BELEIDSREGELS GUNA MEMUTUS PENYEBARAN VIRUS COVID 19 DI KOTA PEKANBARU
Year: 2020

Wabah virus Covid 19 pada awal bulan maret 2020 tersebar ke di Indonesia, termasuk di Pekanbaru. Pada tanggal 18 maret 2020, 1 orang pasien positif terinfeksi Covid 19, terdeteksi di Pekanbaru. Kemudian jumlah masyarakat yang diduga terinfeksi semakin meningkat, sehingga tanggal 22 maret 2020 Walikota Pekanbaru, Firdaus merespon dalam konteks pemerintahan dengan menaikkan status tanggap darurat Covid 19 untuk Kota Pekanbaru. Selanjutnya tanggal 23 Maret 2020 Walikota Pekanbaru mengeluarkan Surat Edaran Walikota Pekanbaru Nomor 100/Setda Tapem/661/2020 tentang Tindaklanjut Pencegahan Penyebaran Corona Virus Disease 2019 (Covid 19) di Lingkungan Pemerintah Kota Pekanbaru. Surat edaran ini meminta masyarakat menjaga jarak (Physical Distancing) dan anjuran diam di rumah Stay at Home) Penelitian ini bertujuan menggambarkan faktor faktor yang mempengaruhi kemampuan Walikota memaksakan berlakunya surat edaran melalui penegakan hukum pemerintahan kepada para pihak guna memutus penyebaran Covid 19. Penelitian menggunakan pendekatan kualitatif dengan teknik pengambilan data dokumentasi. Temuan penelitian menunjukkan salah satu penyebab surat edaran ini sulit dipaksakan berlakunya disebabkan edaran walikota ini tidak memuat sanksi bagi pelanggarnya, rendahnya tingkat kesadaran hukum masyarakat dan politik pemerintah pusat. Kata Kunci Kekuasaan, Walikota, Beleidsregel
NEA RWMC Workshop on Developing Safety Cases for Various Radioactive Waste Disposal Facilities Needs and Challenges of RWMC Organisations
Year: 2020

A safety case is a collection of safety evidence and arguments that demonstrate the safety of a management process or facility throughout its lifetime. While the types of data, calculations, and analyses are different for demonstrating safety in various waste management processes, the approach and strategies used to structure safety cases for different back end processes have aspects in common. For example, the safety objectives for both geological and near surface disposal are to identify a suitable disposal site, in which to design, construct, operate and close a disposal facility so that protection before and after closure is optimised, with social and economic factors taken into account. Thus, the expertise, approach and strategies required for the development of assessment scenarios, the management of uncertainties and the associated risk evaluations in compiling and presenting safety cases for different disposal facilities share some commonalities.
Clinico demographic profile hospital outcomes of COVID 19 patients admitted at a tertiary care centre in north India
Venue: The Indian journal of medical research
Year: 2020

In December 2019, a novel coronavirus (SARS CoV 2) emerged in China and rapidly spread globally including India. The characteristic clinical observations and outcomes of this disease (COVID 19) have been reported from different countries. The present study was aimed to describe the clinico demographic characteristics and in hospital outcomes of a group of COVID 19 patients in north India.  This was a prospective, single centre collection of data regarding epidemiological, demographic, clinical and laboratory parameters, management and outcome of COVID 19 patients admitted in a tertiary care facility in north India. Patient outcomes were recorded as death, discharge and still admitted.  Data of 144 patients with COVID 19 were recorded and analyzed. The mean age of the patients was 40.1+ 13.1 yr, with 93.1 per cent males, and included 10 (6.9% foreign nationals. Domestic travel to or from affected States (77.1% and close contact with COVID 19 patients in congregations (82.6% constituted the most commonly documented exposure. Nine (6.3% patients were smokers, with a median smoking index of 200. Comorbidities were present in 23 (15.9% patients, of which diabetes mellitus (n=16; 11.1% was the most common. A significant proportion of patients had no symptoms (n=64; 44.4% among the symptomatic, cough (34.7% was the most common symptom followed by fever (17.4% and nasal symptoms (2.15% Majority of the patients were managed with supportive treatment with hydroxychloroquine and azithromycin given on a case to case basis. Only five (3.5% patients required oxygen supplementation, four (2.8% patients had severe disease requiring intensive care, one required mechanical ventilation and mortality occurred in two (1.4% patients. The time to reverse transcription polymerase chain reaction (RT PCR) negativity was 16 18 days.  In this single centre study of 144 hospitalized patients with confirmed COVID 19 in north India, the characteristic findings included younger age, high proportion of asymptomatic patients, long time to PCR negativity and low need for intensive care unit care.
First graders' Agency as a Factor of Intellectual Resource Use in Overcoming the Adaptation Crisis
Year: 2016

This study explores the general criteria of agency (according to K.A. Abulkhanova) readiness and capacity to use one's abilities, personal qualities, mental capabilities and skills to solve problems. We have focused on the issue of first graders' agency during the period of age crisis and crisis of adaptation to school learning. 81 pupils were involved in the research; the data were collected three times in the first grade and at the end of primary school in the fourth grade. It is proven that only first graders with a high level of agency, whose inner schoolchild position (ISP) was already formed before starting school (as a result of the age crisis) use their intellectual resources to solve adaptation crisis problems. These agency criteria remain in force until the end of primary school, the research shows.
A new data driven intelligent fault diagnosis by using convolutional neural network
Venue: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Year: 2017

Along with the boosting of big data in manufacturing, applying the data driven analysis method to support the intelligent fault diagnosis becomes a new trend. Recently, deep learning emerged as a potential artificial intelligence technique. It can obtain the features of raw data automatically, which provides a new way to reduce the expert's bias as more as possible and to mine the inherent relationships hidden in data. Convolutional neural network (CNN) is a promoting kind of deep learning. A new intelligent fault diagnosis method based on CNN is proposed in this paper. Firstly, a transformation from signals to images is investigated to deal with the raw signal data in a simple way. Then these images are trained by CNN. The proposed method is tested on the motor bearing dataset from Case Western Reserve University. The results show that the proposed method achieves as high as 99.51% of the prediction accuracy. The good performance of proposed method is also proved by comparing it with other deep learning methods and traditional methods.
New Results of Energy Based Swing up Control for a Rotational Pendulum
Year: 2011

In this paper, we revisit the energy based swing up control problem for a rotational pendulum. Different from the existing energy based control solution, first, we present a necessary and sufficient condition for avoiding singular points in the control law and carry out a global motion analysis of the pendulum. Next, we remove the previous required constraint on the initial state of the pendulum and the control parameters for preventing the pendulum getting stuck at the downward equilibrium point by revealing that the point is saddle (hyperbolic and unstable) Specifically, we show that the Jacobian matrix evaluated at the point has two and two eigenvalues in the open leftand right half planes, respectively. We prove that the pendulum will eventually be swung up into the basin of attraction of any locally stabilizing controller for all initial conditions with the exception of a set of Lebesgue measure zero. Finally, we validate the presented theoretical results via numerical simulation of two rotational pendulums. Our simulation results show that the swing up control can be achieved quickly under the improved conditions on the control parameters.
Circle Loss Based Metric Learning For Few Shot Classification
Venue: 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA)
Year: 2020

Few shot classification aims to identify new categories from only a handful of labeled examples and has drawn considerable attention in machine learning. An efficient and effective approach for solving few shot classification has featured episodic training based metric learning which attempts to learn a task generic embedding space on extremely large episodes, each with a small labeled support set and its corresponding query set. Distances between embeddings of examples from the query set and support set are then used as a notion of similarity to do classification. This paper first adds a margin between different classes to increase the inter class distance in the embedding space(we call this method ML FSC) Furthermore, considering that a similarity score that deviates far from the optimum should be emphasized, this paper introduces the circle loss into the episodic training based metric learning, which simply re weights each similarity to highlight the less optimized similarity scores, contributing to a more discriminative embedding learning(we call this method CL FSC) We conduct comprehensive experiments to validate our method and we set new state of the art performance on three popular few shot classification benchmarks, namely omniglot, miniImageNet and tieredImageNet.
Pre trained language model representations for language generation
Venue: NAACL
Year: 2019

Pre trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre trained representations are most effective when added to the encoder network which slows inference by only 14% Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail.