nils-herrmann/clean_scirepeval huggingface dataset:
IoT and agriculture data analysis for smart farm
Venue: Comput. Electron. Agric.
Year: 2019

In this paper, we propose developing a system optimally watering agricultural crops based on a wireless sensor network. This work aimed to design and develop a control system using node sensors in the crop field with data management via smartphone and a web application. The three components are hardware, web application, and mobile application. The first component was designed and implemented in control box hardware connected to collect data on the crops. Soil moisture sensors are used to monitor the field, connecting to the control box. The second component is a web based application that was designed and implemented to manipulate the details of crop data and field information. This component applied data mining to analyze the data for predicting suitable temperature, humidity, and soil moisture for optimal future management of crops growth. The final component is mainly used to control crop watering through a mobile application in a smartphone. This allows either automatic or manual control by the user. The automatic control uses data from soil moisture sensors for watering. However, the user can opt for manual control of watering the crops in the functional control mode. The system can send notifications through LINE API for the LINE application. The system was implemented and tested in Makhamtia District, Suratthani Province, Thailand. The results showed the implementation to be useful in agriculture. The moisture content of the soil was maintained appropriately for vegetable growth, reducing costs and increasing agricultural productivity. Moreover, this work represents driving agriculture through digital innovation.
Game Data Mining Competition on Churn Prediction and Survival Analysis Using Commercial Game Log Data
Venue: IEEE Transactions on Games
Year: 2019

Game companies avoid sharing their game data with external researchers. Only a few research groups have been granted limited access to game data so far. The reluctance of these companies to make data publicly available limits the wide use and development of data mining techniques and artificial intelligence research specific to the game industry. In this paper, we developed and implemented an international competition on game data mining using commercial game log data from one of the major game companies in South Korea: NCSOFT. Our approach enabled researchers to develop and apply state of the art data mining techniques to game log data by making the data open. For the competition, data were collected from Blade Soul, an action role playing game, from NCSOFT. The data comprised approximately 100 GB of game logs from 10 000 players. The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free to play model from a monthly subscription. The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression.
Longing for tomorrow: phenomenology, cognitive psychology, and the methodological bases of exploring time experience in depression
Year: 2020

The subjective experience of time in depression has been described to be altered in complex ways, with sensations of particular slowness, delay or stillness being the most often named articulations. However, the attempts to provide empirical evidence to the phenomenon of "time slowing down in depression" have resulted in inconsistent findings. In consequence, the overall claim that depressive time somehow differs from ordinary time has often been discarded as unfounded. The article argues against such conclusion, contending that the described ambiguity might be caused by the methods employed to assess the phenomenon under observation. In the first part of the article, a reconceptualization of the experience of time in depression is proposed on the grounds of classic and contemporary phenomenological psychiatry. This leads to identify the essential features of depressive time as described both in clinical and philosophical contexts. In the second part, a critique of the existing methods of time perception assessment is conducted, with a specific focus on duration estimation and time passage perception tasks. The above mentioned core features serve as guidelines in discussing to what degree such methods fit the phenomenon at stake. Finally, an alternative and innovative method is put forward, that might not only help to explore the scope of existing methods but might itself present an alternative to such: the micro phenomenological interview.
Catalytic cracking of model compounds of bio oil over HZSM 5 and the catalyst deactivation.
Venue: The Science of the total environment
Year: 2018

The catalytic cracking upgrading reactions over HZSM 5 of different model compounds of bio oil have been studied with a self designed fluid catalytic cracking (FCC) equipment. Typical bio oil model compounds, such as acetic acid, guaiacol, n heptane, acetol and ethyl acetate, were chosen to study the products distribution, reaction pathway and deactivation of catalysts. The results showed: C6 C8 aromatic hydrocarbons, C2 C4 olefins, C1 C5 alkanes, CO and CO2 were the main products, and the selectivity of olefins was: ethylene>propylene>butylene. Catalyst characterization methods, such as FI IR, TG TPO and Raman, were used to study the deactivation mechanism of catalysts. According to the catalyst characterization results, a catalyst deactivation mechanism was proposed as follows: Firstly, the precursor which consisted of a large number of long chain saturated aliphatic hydrocarbons and a small amount CC of aromatics formed on the catalyst surface. Then the active sites of catalysts had been covered, the coke type changed from thermal coke to catalytic coke and gradually blocked the channels of the molecular sieve, which accelerated the deactivation of catalyst.
Facile Microwave Assisted Synthesis of Functionalized Carbon Nitride Quantum Dots as Fluorescence Probe for Fast and Highly Selective Detection of 2,4,6 Trinitrophenol.
Venue: Journal of fluorescence
Year: 2020

Functionalized carbon nitride quantum dots (CNQDs) are fabricated by moderate carbonization of L tartaric acid and urea in oil acid media by a facile microwave assisted solvothermal method. The obtained CNQDs are monodispersed with a narrow size distribution (average size of 3.5 nm) and exhibit excellent selectivity and sensitivity of fluorescence quenching for 2,4,6 trinitrophenol (TNP) with a quenching efficiency coefficient Ksv of 4.75 x 104 M 1. This sensing system exhibits a fast response time within 1 min and a wide linear response range from 0.1 to 15 mM. The limit of detection is as low as 87 nM, which is comparable or lower than the other probes. The application of the developed probe to the detection of TNP in spiked water samples yields satisfactory results. The mechanism of fluorescence quenching is also discussed.
Synthesis of Crystallized Barium Molybdate Film via Chemical Solution Processing
Year: 2007

Stoichiometric BaMoO 4 thin films were prepared by chemical solution processing. The characterization of the crystal structure and surface morphology of the film was carried out. The influences of the solvent and the anneal temperature on film fabrication were studied. The photoluminescence properties of the BaMoO 4 films were measured. The results reveal that the BaMoO 4 thin films are well crystallized films even near room temperature, the films derived from glycerine solution are characteristic of oriented growth when they are annealed at above 550degC, and the films given a green emission peaking at 518 nm with the excitation of 20 eV photons.
GAN Poser: an improvised bidirectional GAN model for human motion prediction
Venue: Neural Computing and Applications
Year: 2020

A novel method called GAN Poser has been explored to predict human motion in less time given an input 3D human skeleton sequence based on a generator discriminator framework. Specifically, rather than using the conventional Euclidean loss, a frame wise geodesic loss is used for geometrically meaningful and more precise distance measurement. In this paper, we have used a bidirectional GAN framework along with a recursive prediction strategy to avoid mode collapse and to further regularize the training. To be able to generate multiple probable human pose sequences conditioned on a given starting sequence, a random extrinsic factor \varTheta$ Th has also been introduced. The discriminator is trained in order to regress the extrinsic factor \varTheta$ Th which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. In spite of being in a probabilistic framework, the modified discriminator architecture allows predictions of an intermediate part of pose sequence to be used as conditioning for prediction of the latter part of the sequence. This adversarial learning based model takes into consideration of the stochasticity, and the bidirectional setup provides a new direction to evaluate the prediction quality against a given test sequence. Our resulting novel method, GAN Poser, achieves superior performance over the state of the art deep learning approaches when evaluated on the standard NTU RGB D and Human3.6 M dataset.
Efektivitas Kepemimpinan Kepala Sekolah dalam Pembinaan Profesionalisme Guru
Year: 2011

Di sekolah, eksistensi dan kinerja Kepala Sekolah sangat menentukan dinamika jalannya roda pendidikan di lembaga yang dipimpinnya. Seorang Kepala Sekolah juga memiliki peran sentral dan bertanggung jawab penuh terhadap profesionalitas guru yang dipimpinnya. Karena itu, sebagai seorang pemimpin, Kepala Sekolah dituntut memiliki integritas dan kinerja yang optimal agar menjadi panutan bagi para guru, agar senantiasa tercipta hubungan yang harmonis antara semua jajarannya. Sebagai seorang administrator, ia juga dituntut memiliki skill managerial yang mantap untuk mengelola dan mengoptimalkan hal hal seputar perencanaan, pengorganisasian, pengawasan, kepegawaian dan pembiayaan yang ada di sekolah sebagai sebuah organisasi yang kompleks dan unik. Sebagai supervisor, seorang kepala sekolah diharapkan mampu mengidentifikasi berbagai permasalahan yang ada di lingkungan sekolahnya, selanjutnya mengadakan evaluasi dan follow up berupa pembinaan yang terencana, terarah dan berkelanjutan terhadap para tenaga pendidik dalam rangka mengembangkan potensi dan kreatifitasya sehingga dapat mencetak guru guru yang profesional. Kata Kunci Kepemimpinan, Kepala Sekolah, Profesionalisme Guru.
A New Approach to Integer Valued Time Series Modeling: The Neyman Type A INGARCH Model*
Year: 2015

The aim of this paper is to develop a probabilistic study of a wide class of conditionally heteroscedastic models recently introduced in the literature, the compound Poisson INGARCH processes [7] This class includes, in particular, some well known models like the Poisson INGARCH of Ferland, Latour, and Oraichi [4] or the negative binomial and generalized Poisson INGARCH introduced by Zhu in 2011 and 2012, respectively.Within this class, we analyze the existence and ergodicity of a strictly and weakly stationary solution. For a new particular model of that class, the Neyman type A INGARCH model, we derive the autocorrelation function, analyze the existence of higher order moments, and obtain an explicit form of their first four cumulants, from which we deduce the corresponding skewness and kurtosis.
Heterogeneous Item Recommendation for the Air Travel Industry
Venue: PAKDD
Year: 2019

Analyzing the travel behaviors and patterns of air passengers have always been of great significance to the air travel industry. Understanding the demands and interests of passengers behind their behaviors is a crucial and fundamental task for many applications. However, this task is challenging due to the lack of customer information, data sparsity, and the long tail distribution. In this paper, we investigate the problem of heterogeneous item recommendation by learning representations of items and passengers in a shared latent space. Specifically, we first establish a heterogeneous information network (HIN) through statistical analysis, where the edges represent the interactions between different nodes. Each node also contains some auxiliary attribute information that describes its travel behavior or that of its passenger groups. Then we devise a joint matrix factorization model to learn node representations based on the HIN, where both the heterogeneous edges and the node attributes are incorporated into the learning process. Moreover, a weighting strategy is further utilized to deal with the long tail distribution of passenger behaviors based on the implicit feedback information. Experimental results conducted on a real world passenger name record (PNR) dataset demonstrate the effectiveness of the proposed method.
Improving The Ease of Digital Marketing Learning Using Mobile Learning
Venue: Journal of Physics: Conference Series
Year: 2019

Digital marketing is a new way of advertisement that has been in high demand by the public lately. Slowly, people are beginning to abandon conventional marketing methods and switch to digital marketing. But in general, they are self taught. People who sell online really need digital marketing concepts to increase their understanding. If knowledge increases, marketing success increases. The main obstacle for people to get digital marketing courses is limited time and cost. Many digital marketing courses are offered but at high prices. In this study, we develop the digital marketing learning model that uses mobile technology and feedback learning method. By using this technology, people are expected to be able to increase their knowledge of digital marketing principles at any time at a very affordable cost. In this module, there are four main themes presented interactively, namely think, create, engage, and optimize. We design the mobile content using revised Bloom's concept that divided into four knowledge domains: factual, conceptual, procedure, and metacognitive. Along the learning process, the students are under monitoring, evaluating, and supervising their teacher. The results of the study show that mobile learning shows very attractive and exciting. The interesting finding in this research that the feedback part of the teaching is the most significant influence on student performance.
Current Overview on Hypercoagulability in COVID 19
Venue: American Journal of Cardiovascular Drugs
Year: 2020

The coronavirus disease 2019 (COVID 19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) has brought many unique pathologies, such as coagulopathy, prompting a desperate need for effective management. COVID 19 associated coagulopathy (CAC) can cause various thromboembolic complications, especially in critically ill patients. The pathogenesis is likely due to endothelial injury, immobilization, and an increase in circulating prothrombotic factors. Data on treatment are limited, although prophylactic anticoagulation is advised in all hospitalized patients. Herein, we have comprehensively reviewed the current literature available on CAC and highlight the pathogenesis, clinical features, and management of CAC.
RangeNet Fast and Accurate LiDAR Semantic Segmentation
Venue: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019

Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high quality deep learning algorithms for image based recognition, high level semantic perception tasks are pre dominantly solved using high resolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDAR only semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel post processing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms state of the art approaches, while still running online on a single embedded GPU. The code can be accessed at https:/github.com/PRBonn/lidar bonnetal.
Draft Genome Sequences of 14 Staphylococcus aureus Sequence Type 5 Isolates from California, USA
Venue: Genome Announcements
Year: 2017

Staphylococcus aureus is part of the human epithelial microbiota; however, it is also a pathogen. The acquisition of mobile genetic elements plays a role in the virulence of S. aureus isolates and contributes to treatment failures. This report details the draft genome sequences of 14 clinical S. aureus isolates.
Review and prospect of processing wound infection with honey after the operation of general surgery
Year: 2016

To prove that honey can promote healing of infectious wound after operation general surgery.A total of 102 patients with infectious wound who received abdominal and perineum operations from January 2010 to October 2012 in our hospital were selected and divided into experimental group and control group.Fourty eight cases experienced regular dressing without any addictive component in control group while wounds were treated with honey after regular dressing in 54 cases of experimental group.Velocity and appearance of wound healing,clearing of wounds,and times of dressing were better in experimental group than that in control group.There was significant difference between the two groups.As a adjuvant dressing,honey can effectively promote healing of infectious wound after many general surgeries.
A Swept Sine Pulse Compression Procedure for an Effective Measurement of Intermodulation Distortion
Venue: IEEE Transactions on Instrumentation and Measurement
Year: 2020

The ability to produce accurate models of nonlinear systems has grown enormously, thanks to the increase in the available computing power, thus making possible to use such models in a wide range of applications. Adopting these techniques not only enables to design much more efficient systems but also increases the need for accurate and straightforward methods to monitor distortion levels, even out of the testing laboratories. In this context, efficient estimators of total harmonic distortion (HD) HD of order $n$ and intermodulation distortion (IMD) are needed, which are among the most widely adopted indicators of nonlinearity. In this paper, we focus on the IMD, which is a robust feature indicator within the whole systems operating bandwidth, whose measurement is based on the effects produced by the nonlinear system when a composite signal feeds its input. We propose an efficient procedure to measure the IMD allowing the simultaneous evaluation of HD of order $n$ and of total HD. The proposed technique relies and extends the synchronized exponential swept sine technique for the characterization of nonlinear systems by introducing a suitable double exponential chirp input signal. The method estimates all the distortion level indicators by processing the response to a single, short duration signal, showing a moderate computational cost and low sensitivity to ambient noise. These advantages make the application of the proposed method extremely rapid and convenient, even in the measurement of operating field devices. Tests have been carried out both in simulated and real world scenarios, confirming the proposed approach validity.
Local multiple patterns based multiresolution gray scale and rotation invariant texture classification
Venue: Inf. Sci.
Year: 2012

The local binary pattern (LBP) is a robust but computationally simple approach in texture analysis. However, LBP does not have enough information to discriminate among multiple patterns due to its binary patterns only comprising of 0s and 1s. Thus, a multi resolution gray scale and rotation invariant texture classification based on local multiple patterns (LMP) is proposed in the paper. The LMP extends binary patterns to multiple patterns, which can preserve more structural information, and be more suitable for image analysis, including the analysis of flat image areas, in which case LBP code is often a random value and thus unsuitable. In addition, several extensions to the LMP, including multi resolution analysis with the 'uniform' LMP with rotation invariance, are presented. The 'uniform' LMP can be regarded as a gray scale and rotation invariant description in various applications and its discrete occurrence histogram over a region of image is proved to be a powerful invariant texture feature. Experimental results of multi resolution texture classification with support vector machine show that the proposed method obtains overall better performance than other common methods in several aspects of space resolution and gray scale variation, rotation, and noise.
Penanaman Nilai Tradisi Kepesantrenan Dalam Membendung Radikalisme Agama
Year: 2020

Kitab Kuning tradition conserved by boarding schools recently is identical as a place of terrorism, and of course the stigma is a very erroneous reading. Boarding School is viewed with suspicion, stigmatic, so the boarding school institution as the "last fort" of moral guardians face delegitimation. An Nahdlah boarding school in Makassar as an educational institution teaching Kitab Kuning without teaching violence. It is seen from the teaching methods and the curriculum taught. Application of the religious curriculum at all does not lead to the issue of radicalism but focuses on aspects of faith, morals, interpretation, fiqhi, muamalat and tasawuf, so it is avoided from the radical understanding and builds religious understanding according to the Islamic teaching that teaches peace
Distance learning during the early stages of the COVID 19 pandemic: Examining K 12 students' and parents' experiences and perspectives
Venue: IxD&A
Year: 2020

This study examines K 12 students and parents in the United States' experiences and challenges during the sudden shift to distance learning during the spring of 2020 The study also aims to analyze what can be done to better educate students in the case of continued distance learning during the COVID 19 crisis and during future emergencies The study was conducted with an online survey of K 12 students and parents in the United States, and both qualitative and quantitative data was collected However, the results should be interpreted with caution, as it was a smaller study that is not a representative sample of the population The study results show that a variety of strategies were utilized to teach students online, and that from the perspective of the participants both successful and unsuccessful methods were utilized