Abstract Concept Instantiation with Context Relevance Measurement

2020 ◽  
Author(s):  
Shengwei Gu ◽  
Xiangfeng Luo ◽  
Hao Wang ◽  
Jing Huang ◽  
Subin Huang
Keyword(s):  
Web Search ◽  
False Negative ◽  
Model Performance ◽  
Category Structure ◽  
Abstract Concept ◽  
Contextual Constraint ◽  
Proposed Model ◽  
The Right ◽  
Novel Model ◽  
Walk Algorithm

In different contexts, one abstract concept (e.g., fruit) may be mapped into different concrete instance sets, which is called abstract concept instantiation. It has been widely applied in many applications, such as web search, intelligent recommendation, etc. However, in most abstract concept instantiation models have the following problems: (1) the neglect of incorrect label and label incompleteness in the category structure on which instance selection relies; (2) the subjective design of instance profile for calculating the relevance between instance and contextual constraint. The above problems lead to false prediction in terms of abstract concept instantiation. To tackle these problems, we proposed a novel model to instantiate the abstract concept. Firstly, to alleviate the incorrect label and remedy label incompleteness in the category structure, an improved random-walk algorithm is proposed, called InstanceRank, which not only utilize the category information, but it also exploits the association information to infer the right instances of an abstract concept. Secondly, for better measuring the relevance between instances and contextual constraint, we learn the proper instance profile from different granularity ones. They are designed based on the surrounding text of the instance. Finally, noise reduction and instance filtering are introduced to further enhance the model performance. Experiments on Chinese food abstract concept set show that the proposed model can effectively reduce false positive and false negative of instantiation results.

scholarly journals HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13953-13954
Author(s):  
Xu Wang ◽  
Shuai Zhao ◽  
Bo Cheng ◽  
Jiale Han ◽  
Yingting Li ◽  
...  
Keyword(s):  
Question Answering ◽  
Binary Classification ◽  
Experimental Results ◽  
Ranking Method ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Proposed Model ◽  
The Right ◽  
Novel Model ◽  
Heterogeneous Knowledge

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.

SCOPAS — SEMANTIC COMPUTATION OF PAGE SCORE

2013 ◽  
Vol 12 (06) ◽  
pp. 1309-1331
Author(s):  
K. S. KUPPUSAMY ◽  
G. AGHILA
Keyword(s):  
Information Needs ◽  
Web Search ◽  
Web Pages ◽  
Dimensional Approach ◽  
User Interest ◽  
Proposed Model ◽  
Micro Level ◽  
Semantic Computation ◽  
Novel Model ◽  
The Web

This paper presents a novel model for scoring web pages, entitled SCOPAS (Semantic COmputation of PAge Score). With the prolific growth in the number of users of World Wide Web and the heterogeneity of their information needs, it becomes mandatory to evaluate the relevance of a web page in terms of user specific requirements. SCOPAS is aimed at modeling the web pages to facilitate efficient evaluation by harnessing the inherent features of the page in terms of its content and structure. The proposed model further enriches the scoring procedure by fine-graining the evaluation to a micro level through segmentation of the page. A variable magnitude, multi-dimensional approach is proposed for evaluating each of the segments by incorporating the relevance of intra-segment level components. The user-interest is captured with the help of FOAF (Friend Of A Friend) Ontology to achieve personalized page scoring. The generic SCOPAS model is extended to SCOPAS-Rank, which explores utilization of the model in improving the web search engine's result ordering. A prototype implementation of the proposed SCOPAS-Rank model is developed and experiments were conducted on it. The results of the experiments validate the effectiveness of the proposed model.

scholarly journals A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT

2021 ◽  
Author(s):  
Justin Liu
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Model Performance ◽  
Chest Ct ◽  
Health Crisis ◽  
Fine Grained ◽  
Novel Approach ◽  
Proposed Model ◽  
Ct Data ◽  
Computational Resources

Abstract Background: In a worldwide health crisis as severe as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reversetranscription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. Findings: This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Additionally, image preprocessing algorithms were developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 92.71% and a sensitivity of 95.79%. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls.Conclusions: Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement a radiologist’s decision or serve as an alternative screening tool. Future endeavors could provide insight into infection severity, patient risk stratification, and more precise visualizations

EFEKTIVITAS METODE DEMONSTRASI DALAM MENINGKATKAN HASIL BELAJAR SISWA SEKOLAH MENENGAH KEJURUAN PADA PENGAJARAN SISTEM KOLOID

2013 ◽  
Vol 19 (1) ◽  
pp. 28
Author(s):  
Hamda Situmorang ◽  
Manihar Situmorang
Keyword(s):  
High School ◽  
High School Students ◽  
Abstract Concept ◽  
Vocational High School ◽  
School Students ◽  
Lecture Method ◽  
Colloid System ◽  
Before And After ◽  
The Right ◽  
And Control

Abstract Implementation of demonstration method in the teaching of chemistry is assigned as the right strategy to improve students’ achievement as it is proved that the method can bring an abstract concept to reality in the class. The study is conducted to vocational high school students in SMKN1 Pargetteng getteng Sengkut Pakfak Barat at accademic year 2013. The teaching has been carried out three cycles on the teaching of chemistry topic of colloid system. In the study, the class is divided into two class, experiment class and control class. The demontration method is used to teach students in experimental class while the teaching in control class is conducted with lecture method. Both are evaluated by using multiple choise tests before and after the teaching procedures, and the ability of students to answer the problems are assigned as students’ achievements. The results showed that demonstration method improved students’ achievement in chemistry. The students in experimental class who are taughed with demonstration method (M=19.08±0.74) have higher achievements compare with control class (M=12.91±2.52), and both are significantly different (tcalculation 22.85 > ttable 1.66). The effectivity of demostration method in experimental class (97%) is found higer compare to conventional method in control class (91%).

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

scholarly journals Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors

2021 ◽  
Vol 11 (4) ◽  
pp. 1946
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Tham Thi Tran
Keyword(s):  
Supply Chain ◽  
Electronic Waste ◽  
Decision Makers ◽  
Reverse Supply Chain ◽  
Satisfactory Solution ◽  
Level Of Satisfaction ◽  
Proposed Model ◽  
Objective Value ◽  
The Right ◽  
The Impact

Minimizing the impact of electronic waste (e-waste) on the environment through designing an effective reverse supply chain (RSC) is attracting the attention of both industry and academia. To obtain this goal, this study strives to develop an e-waste RSC model where the input parameters are fuzzy and risk factors are considered. The problem is then solved through crisp transformation and decision-makers are given the right to choose solutions based on their satisfaction. The result shows that the proposed model provides a practical and satisfactory solution to compromise between the level of satisfaction of constraints and the objective value. This solution includes strategic and operational decisions such as the optimal locations of facilities (i.e., disassembly, repairing, recycling facilities) and the flow quantities in the RSC.

Performance measurement and resource sharing among business sub-units in the presence of non-discretionary factors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rita Shakouri ◽  
Maziar Salahi
Keyword(s):  
Resource Sharing ◽  
Content Type ◽  
Development Level ◽  
Proposed Model ◽  
Decision Making Unit ◽  
Efficiency Estimation ◽  
Overall Efficiency ◽  
And Performance ◽  
The Right ◽  
The Impact

Purpose This paper aims to apply a new approach for resource sharing and efficiency estimation of subunits in the presence of non-discretionary factors and partial impacts among inputs and outputs in the data envelopment analysis (DEA) framework. Design/methodology/approach First, inspired by the Imanirad et al.’s model (2013), the authors consider that each decision-making unit (DMU) may consist of several subunits, that each of which can be affected by non-discretionary inputs. After that, the Banker and Morey’s model (1996) is used for modeling non-discretionary factors. For measuring performance of several subunits, which can be considered as DMUs, the aggregate efficiency is suggested. At last, the overall efficiency is computed and compared with each other. Findings One of the important features of proposed model is that each output in this model applies discretionary input according to its need; therefore, the result of this study will make it easier for the managers to make better decisions. Also, it indicates that significant predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the influence of non-discretionary input. Therefore, the proposed model provides a more reasonable and encompassing measure of performance in participating non-discretionary and discretionary inputs to better efficiency. An application of the proposed model for gaining efficiency of 17 road patrols is provided. Research limitations/implications More non-discretionary and discretionary inputs can be taken into consideration for a better analysis. This study provides us with a framework for performance measures along with useful managerial insights. Focusing upon the right scope of operations may help out the management in improving their overall efficiency and performance. In the recent highway maintenance management systems, the environmental differences exist among patrols and other geotechnical services under the climate diverse. Further, in some cases, there might exist more than one non-discretionary factor that can have different effects on the subunits’ performance. Practical implications The purpose of this paper was to measure the performance of a set of the roadway maintenance crews and to analyze the impact of non-discretionary inputs on the efficiency of the roadway maintenance. The application of the proposed model, on the one hand, showed that each output in this model uses discretionary input according to its requirement, and on the other hand, the result showed that meaningful predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the impact of non-discretionary input. Originality/value Providing information on resource sharing by taking into account non-discretionary factors for each subunit can help managers to make better decisions to increase the efficiency.

scholarly journals The Vibration of the String with the Interstitial Massive Point

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Miroslav Pardy ◽  
Keyword(s):  
Gamma Radiation ◽  
Coordinate System ◽  
Nuclear Resonance ◽  
Resonance Fluorescence ◽  
Mossbauer Effect ◽  
Massive Point ◽  
Atomic Nuclei ◽  
Proposed Model ◽  
Nuclear Resonance Fluorescence ◽  
The Right

We will consider the string, the left end of which is fixed at the beginning of the coordinate system, the right end is fixed at point l and mass m is fixed between the ends of the string. We determine the vibration of such system. The proposed model can be also related in the modified form to the problem of the Mossbauer effect, or recoil less nuclear resonance fluorescence, which is the resonant and recoil-free emission and absorption of gamma radiation by atomic nuclei bound in a solid.(Mossbauer,1958)

scholarly journals A dynamic model for series and parallel resistance of photovoltaic cell using material properties extraction and energy tunnel

2017 ◽  
Vol 16 (1) ◽  
pp. 9-21
Author(s):  
Kotchapong Sumanonta ◽  
Pasist Suwanapingkarl ◽  
Pisit Liutanakul
Keyword(s):  
Series Resistance ◽  
Photovoltaic Cell ◽  
Photovoltaic Module ◽  
Practical Application ◽  
Pv Module ◽  
Proposed Model ◽  
Outdoor Test ◽  
The Relationship ◽  
Parallel Resistance ◽  
Novel Model

This article presents a novel model for the equivalent circuit of a photovoltaic module. This circuit consists of the following important parameters: a single diode, series resistance (Rs) and parallel resistance (Rp) that can be directly adjusted according to ambient temperature and the irradiance. The single diode in the circuit is directly related to the ideality factor (m), which represents the relationship between the materials and significant structures of PV module such as mono crystalline, multi crystalline and thin film technology.  Especially, the proposed model in this article is to present the simplified model that can calculate the results of I-V curves faster and more accurate than other methods of the previous models. This can show that the proposed models are more suitable for the practical application. In addition, the results of the proposed model are validated by the datasheet, the practical data in the laboratory (indoor test) and the onsite data (outdoor test). This ensures that the less than 0.1% absolute errors of the model can be accepted.

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