nils-herrmann/clean_scirepeval huggingface dataset:
Machine learning assisted prediction of the mechanical properties of Cu Al alloy
Venue: International Journal of Minerals, Metallurgy and Materials
Year: 2020

The machine learning approach was investigated to predict the mechanical properties of Cu Al alloys manufactured using the powder metallurgy technique to increase the rate of fabrication and characterization of new materials and provide physical insights into their properties. Six algorithms were used to construct the prediction models, with chemical composition and porosity of the compacts chosen as the descriptors. The results show that the sequential minimal optimization algorithm for support vector regression with a puk kernel (SMOreg/puk) model demonstrated the best prediction ability. Specifically, its predictions exhibited the highest correlation coefficient and lowest error among the predictions of the six models. The SMOreg/puk model was subsequently applied to predict the tensile strength and hardness of Cu Al alloys and provide guidance for composition design to achieve the expected values. With the guidance of the SMOreg/puk model, Cu 12Al 6Ni alloy with a tensile strength (390 MPa) and hardness (HB 139) that reached the expected values was developed.
Evaluation Effect of Silybum Marianum, Cynara Scolymus L. and Ziziphus jujube Mill. Combination Extract on Non alcoholic Fatty Liver in Rats
Year: 2018

Non alcoholic fatty liver disease includes a spectrum of clinical syndromes from early steatosis to liver cirrhosis. The aim of this study was to evaluate the effect of combined extract of silymarin, artichoke and jujube on non alcoholic fatty liver, induced by high fat diet in rats.  In this experimental study, 40 male rats were divided randomly into two groups, a sham group (n=8) and a high fat diet group (n=32) After 4 months of being fed the high fat diet, rats suffering from fatty liver were divided into 4 groups (n=8 each) including a high fat diet group and the three groups receiving a dose of the combined extract 100, 200 and 400 mg/kg for 2 months. Finally, lipid profiles, activity of serum liver enzymes and liver histology were investigated.  The high fat diet increased Chol, TG and LDL and decreased HDL levels (P<0.001) This diet also increased serum activity of ALT, AST and ALP to 84, 113 and 228, respectively. Receiving extract at doses of 200 and 400 mg/kg improved lipid profiles and hepatic enzyme activity (P<0.05) Histopathology confirmed changes in liver induced by the high fat diet and the protective effect of the extract.  High fat diet induced nonalcoholic fatty liver in rats and treatment with combined extracts of silymarin, artichoke and jujube improved the complications of the disease. A(c) 2018, Endocrine Research Center.
Spectral bounding: Strictly satisfying the 1 Lipschitz property for generative adversarial networks
Venue: Pattern Recognit.
Year: 2020

Imposing the 1 Lipschitz constraint is a problem of key importance in the training of Generative Adversarial Networks (GANs) which has been proved to productively improve stability of GAN training. Although some interesting alternative methods have been proposed to enforce the 1 Lipschitz property, these existing approaches (e.g. weight clipping, gradient penalty (GP) and spectral normalization (SN) are only partially successful. In this paper, we propose a novel method, which we refer to as spectral bounding (SB) to strictly enforce the 1 Lipschitz constraint. Our method adopts very cost effective terms of both 1 norm and norm, and yet allows us to efficiently approximate the upper bound of spectral norms. In this way, our method provide important insights to the relationship between an alternative of strictly satisfying the Lipschitz property and explainable training stability improvements of GAN. Our proposed method thus significantly enhances the stability of GAN training and the quality of generated images. Extensive experiments are conducted, showing that the proposed method outperforms GP and SN on both CIFAR 10 and ILSVRC2015 (ImagetNet) dataset in terms of the standard inception score.
OIDDE Learning Model: Improving Higher Order Thinking Skills of Biology Teacher Candidates.
Year: 2018

As the massive advancement in 21st century, the role of education is to prepare generations in mastering the skills they need to face the challenges arised in their era. OIDDE is the abbreviation for Orientation, Identify,  Decision, and Engage in behaviour. The learning model designed by Hudha et al. (2016) is expected to be able to guide the learners to achieve Higher Order Thinking Skills (HOTS) in term of producing proper and wise decisions through the certain steps which enable them to comprehend the facts faced and engage the decision resulted in their behaviour. This research provides the information of how OIDDE can stimulate the students' higher order thinking skills by assessing the three aspects thinking skills namely: self regulated thinking, critical thinking, and creative thinking. This research employed Classroom Action Research (CAR) method conducted in two cycles which comprise of four meetings with duration of 3 x 50 minutes for each cycle. The participants were 45 biology education students in fifth semester.
Deep Modeling of Social Relations for Recommendation
Venue: AAAI
Year: 2018

Social based recommender systems have been recently proposed by incorporating social relations of users to alleviate sparsity issue of user to item rating data and to improve recommendation performance. Many of these social based recommender systems linearly combine the multiplication of social features between users. However, these methods lack the ability to capture complex and intrinsic non linear features from social relations. In this paper, we present a deep neural network based model to learn non linear features of each user from social relations, and to integrate into probabilistic matrix factorization for rating prediction problem. Experiments demonstrate the advantages of the proposed method over stateof the art social based recommender systems.
Ji Yu Java WebDe HTMLZai Xian Wen Ben Bian Ji Qi Jie Jue Fang An Yi FckEditorZai Xian Bian Ji Qi Wei Li
Year: 2011

FckEditorZuo Wei Zhong Duo You Xiu HTMLZai Xian Wen Ben Bian Ji Qi Zhi Yi ,Yi Qi Zhi Chi Duo Yu Yan De You Shi Er Shou Dao Zhong Duo Guo Nei Wai Kai Fa Zhe De Qin Lai Zai Java WebKai Fa Zhong Shi Yong FCKeditorKe Yi Shi Xian Lei Si Wei Ruan WordZhuo Mian Wen Ben Bian Ji Qi De Xu Duo Qiang Da Gong Neng ,Yin Ci Zhang Wo FckEditorZai Java WebKai Fa Huan Jing Xia De Pei Zhi Guo Cheng Ye Shi Kai Fa Zhe Bi Bei De Ji Neng Zhi Yi
A review on the polymeric laminates reinforced with natural fibers
Year: 2017

The use of composites in industries has become inevitable due to the enhancement in properties, reduction in the manufacturing cost and suitability to several applications. Among the different classifications, polymeric composites are majorly focused for their use as structural components. In a polymeric composite, the selection and composition of reinforcement play a vital role in deciding the characteristics of the composite. Although composites are developed with man made reinforcement in the beginning stage, in the present scenario, natural reinforcements have proved consummate results in terms of properties. Hence, present day researches are mainly focused on the use of different natural fibers in different forms as reinforcements in polymeric composite. The present study gives a clear picture of various available natural fibers and how they are made in the form of reinforcement for composites. The study also explains the diverse pre treatment methods used by the researchers for the enhancement of fiber quality. In addition, this review also gives a detailed insight of how the natural fibers are used in the hybrid form for the improvement of composite property.
KBPearl: A Knowledge Base Population System Supported by Joint Entity and Relation Linking
Venue: Proc. VLDB Endow.
Year: 2020

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end to end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state of the art techniques, through extensive experiments on real world datasets.  Xueling Lin, Haoyang Li, Hao Xin, Zijian Li and Lei Chen.  A Knowledge Base Population System Supported by Joint Entity and Relation Linking. PVLDB, 13(7) 1035 1049, 2020.
PENENTUAN LOKASI TURBIN PEMBANGKIT LISTRIK TENAGA ARUS LAUT SKALA KECIL DI PERAIRAN SELAT LIRUNG, TALAUD, SULAWESI UTARA
Year: 2016

Selat Lirung adalah selat yang terletak antara Pulau Salibabu dan Pulau Karakelong, Kepulauan Talaud. Penelitian potensi energi arus laut telah dilakukan di lokasi ini, untuk mengkaji kemungkinan dikembangkannya Pembangkit Listrik Tenaga Arus Laut (PLTAL) skala kecil (2 kW) dengan model turbin sumbu vertikal. Salah satu tahapan dalam implementasi PLTAL skala kecil adalah penentuan lokasi penempatan turbin. Beberapa kriteria teknis yang dipersyaratkan harus dipenuhi pada tahapan ini. Untuk maksud tersebut dilakukan kajian awal dengan melakukan analisis data kedalaman laut, Sea Bottom Profiling (SBP) arus bergerak, arus insitu, pasang surut, dan meteorologi. Data tersebut direpresentasikan dalam data kecepatan arus laut, morfologi dasar laut, kedalaman laut, durasi waktu kecepatan arus kuat, jarak terhadap garis pantai, dan pengaruh gelombang laut. Berdasarkan hasil analisis data tersebut diperoleh 2 (dua) lokasi yang paling representatif. Lokasi pertama di alur antara P. Sara Besar dengan Sara Kecil pada kedalaman laut sekitar 25 m dengan kecepatan arus maksimum sebesar 1.0 m/det 1.75 m/det. Sedangkan lokasi kedua di alur antara P. Sara Besar dengan P. Salibabu pada kedalaman laut antara 25 m 35 m dengan kecepatan arus maksimum sebesar 1.0 m/det 1.3 m/det. Kata kunci Tenaga arus laut, turbin, kecepatan arus, morfologi dasar laut, kedalaman laut The Lirung Strait is a strait located between the Salibabu Island and Karakelong Island, Talaud. Energy potential of marine current research has been done in this location, to examine the possibility of the development of Marine Current Power Plant (MCPP) small scale (2 kW) One of the stages in the implementation of MCPP small scale is determining the location of the turbines placement. Some technical criteria required to be met at this stage. For the purpose of initial studies to perform data analysis depth of the sea, Sea Bottom Profiling (SBP) the current is moving, insitu currents, tides, and meteorology. That data are represented within the sea current speed, the morphology of the seabed, ocean depth, duration of strong current speed, distance to the coastline,and the influence of waves. Based on the results of the analysis of the data obtained two (2) most representative locations. The first location is in the groove between the Sara Besar Island with Sara Kecil Island at the depths between 25 m with the maximum current speed of 1.0 m/s 1.75 m/s. The second location is in the groove between the Sara Besar Island with Salibabu Island at the depths between 25 m 35 m with the maximum current speed of 1.0 m/s 1.3 m/s.  Power of marine currents, turbines, current speed, seabed morphology, ocean depths
Safety Helmet Detection Based on YOLOv5
Venue: 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)
Year: 2021

As the most basic protection for workers, safety helmets have great significance to workers' lives. However, due to a lack of safety awareness, safety helmets are often not worn. With the continuous development of object detection technology, the YOLO series of algorithms with very high precision and speed has been used in various scene detection tasks. To establish a digital safety helmet monitoring system, we propose a safety helmet detection method based on YOLOv5 and annotate the 6045 collected data sets. Finally, we used the YOLOv5 model with different parameters for training and testing. The four models are compared and analyzed. Experimental results show that the average detection speed of YOLOv5s reaches 110 FPS. Fully meet the requirements of real time detection. Using the trainable target detector's pre training weight, the mAP of YOLOv5x reaches 94.7% proving the effectiveness of helmet detection based YOLOv5.
Prototype Development And Testing of A Power Electronics Converter For Green Energy Using dSPACE
Year: 2019

Green energy like Solar, Wind etc. which includes green electricity, is clean energy. In this case we use solar cell. It is produced with little to no environmental impact and does not dispense greenhouse gases into the air that contribute to global warming, the way fossil fuels do. By observing this advantages this paper presents a simulation modeling for hardware development of single phase inverter using dSPACE DS1202 controller board for photovoltaic application. The controller acquired the advantage of linking the simulated model in MATLAB/SIMULINK environment to a prototype. It generates the pulse width modulation signals for the switching of the Mosfet switches. The controller managed to stabilize 50 Hz sinusoidal ac output voltage of the inverter. The result obtained is near to expected ones, it suggests that the developed control system exhibits a good performance. Thus, it justified that the develop control strategy can be translated into the inverter prototype by utilizing the dSPACE platform.
Effect of strong base buffer on crude palm oil yield
Year: 2019

Palm oil and its derivatives are widely used in food and oleo chemical industries, and require high yield of crude palm oil (CPO) to fulfill the demand. In palm oil mills, the yield refers to oil extraction rate (OER) that indicates efficiency to squeeze and collect the crude oil from its palm fruit using steam and separation processes. Breakdown of oil cell bearing of mesocarp by the steam may be enhanced by induction of NaOH while the fiber is being digested. The problem of NaOH corrosivity on the steel material of existing unit operations can be controlled by using sodium silicate to form strong base buffer. In this study, the buffer was sprayed on palm mesocarp fiber after 130degC sterilization for 45 min and digestion. The concentration of NaOH in 1 sodium silicate and dosage of the buffer were applied at 0.8, 1.0 and 3.0 mol/L, and 1, 3 and 5 L/tonne, respectively. Pressing extraction was subsequently followed to imitate the current methods in the mills. The extracted liquid was further clarified by using a centrifuge and produced three layers: oil, water and non oily solid (NOS) As an indicator to broken cells, concentration of glucose in a mixture of water and NOS was determined using UV Vis spectroscopy. Oil after centrifuge (OAC) was also recorded. The mesocarp sample treated with the buffer showed improvement of maximum 6.3 wt% of OER and 53.5 wt% of glucose, respectively, implying improvement of oil release using the NaOH buffer.
Uncertainty guided Curriculum Learning via Infinitesimal Jackknife
Year: 2020

A key challenge in curriculum learning (CL) is to design reasonable metrics for scoring sample difficulty. Current data driven CL algorithms need to learn extra parameters for the sample scoring task. In contrast, we propose a novel approach, called uncertainty guided curriculum learning (UGCL) where the sample difficulty is scored according to both the uncertainty in model's decision making (epistemic) and the uncertainty inherent in data (aleatoric) Besides, we show a novel scoring metric based on signal tonoise ratio (SNR) which combines the uncertainty and predicted probability of the target class (measuring the difficulty for the model to make a correct prediction) together to evaluate sample difficulty simultaneously. Moreover, in order to avoid burdensome learning on Bayesian neural network, we apply the infinitesimal jackknife (IJ) to quantify uncertainty in model weights through a pseudo ensemble approach based on Fisher information matrix. Experiments on various image and text datasets empirically verify the superiority of UGCL over the state of the art CL baselines. The source code of our algorithm is available in https:/github.com/RyanWangZf/ Uncertainty Curriculum Learning.
Tau reconstruction, energy calibration and identification at ATLAS
Year: 2012

Tau leptons play a central role in the LHC physics programme, in particular as an important signature in many Higgs boson and supersymmetry searches. They are further used in Standard Model electroweak measurements, as well as detector related studies like the determination of the missing transverse energy scale. Copious backgrounds from QCD processes call for both efficient identification of hadronically decaying tau leptons, as well as large suppression of fake candidates. A solid understanding of the combined performance of the calorimeter and tracking detectors is also required. We present the current status of the tau reconstruction, energy calibration and identification with the ATLAS detector at the LHC. Identification efficiencies are measured in W tn events in data and compared with predictions from Monte Carlo simulations, whereas the misidentification probabilities of QCD jets and electrons are determined from various jet enriched data samples and from Z ee events, respectively. The tau energy scale calibration is described and systematic uncertainties on both energy scale and identification efficiencies discussed.
Local Interpretable Model Agnostic Explanations for Music Content Analysis
Venue: ISMIR
Year: 2017

The interpretability of a machine learning model is essential for gaining insight into model behaviour. While some machine learning models (e.g. decision trees) are transparent, the majority of models used today are still black boxes. Recent work in machine learning aims to analyse these models by explaining the basis of their decisions. In this work, we extend one such technique, called local interpretable model agnostic explanations, to music content analysis. We propose three versions of explanations: one version is based on temporal segmentation, and the other two are based on frequency and time frequency segmentation. These explanations provide meaningful ways to understand the factors that influence the classification of specific input data. We apply our proposed methods to three singing voice detection systems: the first two are designed using decision tree and random forest classifiers, respectively; the third system is based on convolutional neural network. The explanations we generate provide insights into the model behaviour. We use these insights to demonstrate that despite achieving 71.4% classification accuracy, the decision tree model fails to generalise. We also demonstrate that the model agnostic explanations for the neural network model agree in many cases with the model dependent saliency maps. The experimental code and results are available online.
HRGE Net Hierarchical Relational Graph Embedding Network for Multiview 3 D Shape Recognition
Year: 2019

View based approach that recognizes 3D shape through its projected 2D images achieved state of the art performance for 3D shape recognition. One essential challenge for view based approach is how to aggregate the multi view features extracted from 2D images to be a global 3D shape descriptor. In this work, we propose a novel feature aggregation network by fully investigating the relations among views. We construct a relational graph with multi view images as nodes, and design relational graph embedding by modeling pairwise and neighboring relations among views. By gradually coarsening the graph, we build a hierarchical relational graph embedding network (HRGE Net) to aggregate the multi view features to be a global shape descriptor. Extensive experiments show that HRGE Net achieves stateof the art performance for 3D shape classification and retrieval on benchmark datasets.
Pile toe bending machine of steel reinforcement cage of pre cast pile
Year: 2013

The invention discloses a pile toe bending machine of a steel reinforcement cage of a pre cast pile. The pile toe bending machine of the steel reinforcement cage of the pre cast pile comprises a rack which is provided with a pile toe bending device, wherein the pile toe bending device comprises a power mechanism and a reinforcing steel bar guiding and positioning mechanism; the reinforcing steel bar guiding and positioning mechanism comprises four groups of crescent buckles which are installed at four top corners of a through hole of a first vertical support plate respectively; one end of each crescent buckle is hinged to the first vertical support plate and a second vertical support plate through an articulated shaft while the other end of each crescent buckle penetrates and is connected to the first and second vertical support plates through a pin shaft; a first reinforcing steel bar insertion groove is formed in each crescent buckle; the shaft end of each pin shaft is connected to a second driving cylinder; the shaft end of each articulated shaft is connected to a chain wheel which is driven by a motor; the rack is also provided with a pile toe limiting mechanism. The pile toe bending machine of the steel reinforcement cage of the pre cast pile realizes automatic control of insertion and bending of the steel reinforcement cage, saves labor force and lowers labor cost.
An SINR Aware Joint Mode Selection, Scheduling, and Resource Allocation Scheme for D2D Communications
Venue: IEEE Transactions on Vehicular Technology
Year: 2019

In this paper, a new mode selection (MS) scheme for device to device (D2D) enabled cellular networks is proposed. The MS is carried out using a predefined signal to interference plus noise ratio (SINR) threshold and it aims to guarantee a minimum level of quality of service. With the new scheme, interfering effects are taken into consideration, while realistic assumptions for the acquisition of the channel information are made. Assuming a single user case, an analytical framework is developed, which is based on the Markov chain theory, and is used to study the system's performance. The various performance results obtained reveal that with the proposed MS, the probability of mode switching can be reduced (as compared to other systems) and thus, an increase on the overhead and signal processing is avoided. Moreover, a new approach is applied in a multi user communication scenario, where a joint MS, resource allocation (RA) and scheduling optimization problem is formulated. The solution to this problem is based on a greedy heuristic algorithm that properly assigns the available resources among cellular users and D2D pairs. The joint MS, RA, and scheduling algorithm considers limited SINR awareness and it provides significant performance improvement in terms of sum rate.