Malware (or malicious software) is any program or file that brings harm to a computer system. Malware includes computer viruses, worms, trojan horses, rootkit, adware, ransomware and spyware. Due to the explosive growth in number and variety of malware, the demand of improving automatic malware detection has increased. Machine learning approaches are a natural choice to deal with this problem since they can automatically discover hidden patterns in large-scale datasets to distinguish malware from benign. In this paper, we propose different deep neural network architectures from simple to advanced ones. We then fuse hand-crafted and deep features, and combine all models together to make an overall effective ensemble framework for malware detection. The experiment results demonstrate the efficiency of our proposed method, which is capable to detect malware with accuracy of 96.24% on our large real-life dataset.
{"title":"An Effective Ensemble Deep Learning Framework for Malware Detection","authors":"D. V. Sang, Dang Manh Cuong, Le Tran Bao Cuong","doi":"10.1145/3287921.3287971","DOIUrl":"https://doi.org/10.1145/3287921.3287971","url":null,"abstract":"Malware (or malicious software) is any program or file that brings harm to a computer system. Malware includes computer viruses, worms, trojan horses, rootkit, adware, ransomware and spyware. Due to the explosive growth in number and variety of malware, the demand of improving automatic malware detection has increased. Machine learning approaches are a natural choice to deal with this problem since they can automatically discover hidden patterns in large-scale datasets to distinguish malware from benign. In this paper, we propose different deep neural network architectures from simple to advanced ones. We then fuse hand-crafted and deep features, and combine all models together to make an overall effective ensemble framework for malware detection. The experiment results demonstrate the efficiency of our proposed method, which is capable to detect malware with accuracy of 96.24% on our large real-life dataset.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132942422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mami Aotani, Ryo Nishide, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa
Breeding cattle are known to be social animals that make groups as humans. Focusing on the sociality of the cattle, this paper aims to grasp and predict the conditions of breeding cattle by detecting the interactions between them. In order to detect such interactions, it is necessary to follow the behaviors of the breeding cattle to examine how they approach each other. In this study, the positions and movements of the breeding cattle are detected from bird's eye images. In the preceding study, breeding cattle were experimentally detected by the background subtraction method using multiple background images because of the poor distinctive features of breeding cattle. However, the method employed in that study used images that may not completely remove breeding cattle in a background image in order to cope with the changing brightness, which may cause errors in detection. Moreover, a huge amount of time may be consumed in selecting the optimal background image for the input image. Therefore, we propose a method in this paper by applying composite background images and reduction of search images using brightness to the method of the preceding study. The composite background image is an image obtained by overriding other images to the breeding cattle region, resultantly removing the cattle region. When creating the composite background, we consider that the image that does not contain cattle region can be used as a background image which may successfully improve the detection accuracy. When selecting an optimal background image, we also consider as that the processing time will be shortened by reducing the search images by brightness. In the experiment, the precision and the processing time are compared based on the cases with or without composite background image and by reduction of the search images by brightness. As a result, it was confirmed that the detection accuracy was improved by the proposed method and the processing time could be shortened.
{"title":"Refined Cattle Detection Using Composite Background Subtraction and Brightness Intensity from Bird's Eye Images","authors":"Mami Aotani, Ryo Nishide, Yumi Takaki, C. Ohta, K. Oyama, T. Ohkawa","doi":"10.1145/3287921.3287945","DOIUrl":"https://doi.org/10.1145/3287921.3287945","url":null,"abstract":"Breeding cattle are known to be social animals that make groups as humans. Focusing on the sociality of the cattle, this paper aims to grasp and predict the conditions of breeding cattle by detecting the interactions between them. In order to detect such interactions, it is necessary to follow the behaviors of the breeding cattle to examine how they approach each other. In this study, the positions and movements of the breeding cattle are detected from bird's eye images. In the preceding study, breeding cattle were experimentally detected by the background subtraction method using multiple background images because of the poor distinctive features of breeding cattle. However, the method employed in that study used images that may not completely remove breeding cattle in a background image in order to cope with the changing brightness, which may cause errors in detection. Moreover, a huge amount of time may be consumed in selecting the optimal background image for the input image. Therefore, we propose a method in this paper by applying composite background images and reduction of search images using brightness to the method of the preceding study. The composite background image is an image obtained by overriding other images to the breeding cattle region, resultantly removing the cattle region. When creating the composite background, we consider that the image that does not contain cattle region can be used as a background image which may successfully improve the detection accuracy. When selecting an optimal background image, we also consider as that the processing time will be shortened by reducing the search images by brightness. In the experiment, the precision and the processing time are compared based on the cases with or without composite background image and by reduction of the search images by brightness. As a result, it was confirmed that the detection accuracy was improved by the proposed method and the processing time could be shortened.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125779528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Van Long Tran, É. Renault, Xuan Huyen Do, Viet Hai Ha
CAPE (Checkpointing-Aided Parallel Execution) is a framework that automatically translates and executes OpenMP on distributed-memory architectures based on checkpoint technique. In some experiments, this approach shows high-performance on distributed-memory system. However, it has not been fully developed yet. This paper presents an implementation of OpenMP data-sharing on CAPE that improves the capability, reduces checkpoint size and makes CAPE even more performance.
{"title":"Implementation of OpenMP Data-Sharing on CAPE","authors":"Van Long Tran, É. Renault, Xuan Huyen Do, Viet Hai Ha","doi":"10.1145/3287921.3287950","DOIUrl":"https://doi.org/10.1145/3287921.3287950","url":null,"abstract":"CAPE (Checkpointing-Aided Parallel Execution) is a framework that automatically translates and executes OpenMP on distributed-memory architectures based on checkpoint technique. In some experiments, this approach shows high-performance on distributed-memory system. However, it has not been fully developed yet. This paper presents an implementation of OpenMP data-sharing on CAPE that improves the capability, reduces checkpoint size and makes CAPE even more performance.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130232319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For a finite undirected graph G = (V, E) and a positive integer k ≥ 1, an edge set M ⊆ E is a distance-k matching if the pairwise distance of edges in M is at least k in G. The special case k = 2 has been studied under the name maximum induced matching (MIM for short), i.e., a maximum matching which forms an induced subgraph in G. MIM arises in many applications, such as artificial intelligence, game theory, computer networks, VLSI design and marriage problems. In this paper, we design an O(n2) solution for finding MIM in permutation graphs based on a dynamic programming method on edges with the aid of the sweep line technique. Our result is better than the best known algorithm.
{"title":"A dynamic programming algorithm for the maximum induced matching problem in permutation graphs","authors":"V. Nguyen, B. Pham, Viet-Hung Tran, Phan-Thuan Do","doi":"10.1145/3287921.3287961","DOIUrl":"https://doi.org/10.1145/3287921.3287961","url":null,"abstract":"For a finite undirected graph G = (V, E) and a positive integer k ≥ 1, an edge set M ⊆ E is a distance-k matching if the pairwise distance of edges in M is at least k in G. The special case k = 2 has been studied under the name maximum induced matching (MIM for short), i.e., a maximum matching which forms an induced subgraph in G. MIM arises in many applications, such as artificial intelligence, game theory, computer networks, VLSI design and marriage problems. In this paper, we design an O(n2) solution for finding MIM in permutation graphs based on a dynamic programming method on edges with the aid of the sweep line technique. Our result is better than the best known algorithm.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"42 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129714219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For session control servers of carriers networks, the scale out type session control server architecture that could control system performance flexibly has been studied. Network anomaly detection technology using autoencoder has attracted attention. An autoencoder is one of the dimensionality reduction algorithm using neural network. We propose methods to prevent data loss when serious trouble occurred in network equipment, such as servers and routers, of a high availability distributed cluster using consistent hashing. The methods control data redundancy before serious failure of servers or networks occur using anomaly detection technology. We evaluated three anomalous server selection methods by calculation and computer simulation. We also verified the operation of the data redundancy dynamic control methods by software implementation and operation experiment.
{"title":"Data Redundancy Dynamic Control Method for High Availability Distributed Clusters","authors":"T. Ono, K. Ueda","doi":"10.1145/3287921.3287967","DOIUrl":"https://doi.org/10.1145/3287921.3287967","url":null,"abstract":"For session control servers of carriers networks, the scale out type session control server architecture that could control system performance flexibly has been studied. Network anomaly detection technology using autoencoder has attracted attention. An autoencoder is one of the dimensionality reduction algorithm using neural network. We propose methods to prevent data loss when serious trouble occurred in network equipment, such as servers and routers, of a high availability distributed cluster using consistent hashing. The methods control data redundancy before serious failure of servers or networks occur using anomaly detection technology. We evaluated three anomalous server selection methods by calculation and computer simulation. We also verified the operation of the data redundancy dynamic control methods by software implementation and operation experiment.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126262773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huu Tam Tran, Alexander Jahl, K. Geihs, Ramaprasad Kuppili, X. Nguyen, Thi Thanh Binh Huynh
In the heterogeneous and dynamic Internet of Things (IoT), applications and services are frequently subject to change for various reasons such as maintaining their functionality, reliability, availability, and performance. Detecting and communicating these changes are still performed manually by responsible developers and administrators. Such a mechanism will not be adequate anymore in the future of large-scale IoT environments. Therefore, we present a comprehensive framework named DECOM for automatic detection and communication of service changes. Here, we assume that capabilities and interfaces of IoT devices are described and provided through REST services. To be able to detect syntactic as well as semantic changes, we transform an extended version of the interface description into a logic program and apply a sequence of analysis steps to detect changes. The feasibility and applicability of the framework are demonstrated in an IoT application scenario.
{"title":"DECOM: A framework to support evolution of IoT services","authors":"Huu Tam Tran, Alexander Jahl, K. Geihs, Ramaprasad Kuppili, X. Nguyen, Thi Thanh Binh Huynh","doi":"10.1145/3287921.3287979","DOIUrl":"https://doi.org/10.1145/3287921.3287979","url":null,"abstract":"In the heterogeneous and dynamic Internet of Things (IoT), applications and services are frequently subject to change for various reasons such as maintaining their functionality, reliability, availability, and performance. Detecting and communicating these changes are still performed manually by responsible developers and administrators. Such a mechanism will not be adequate anymore in the future of large-scale IoT environments. Therefore, we present a comprehensive framework named DECOM for automatic detection and communication of service changes. Here, we assume that capabilities and interfaces of IoT devices are described and provided through REST services. To be able to detect syntactic as well as semantic changes, we transform an extended version of the interface description into a logic program and apply a sequence of analysis steps to detect changes. The feasibility and applicability of the framework are demonstrated in an IoT application scenario.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128983435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agile and Offshoring are emerging as 2 prominent trends of the software industry. Unfortunately, offshoring might impact the results of Agile projects, and Agile practices also often cause difficulties to offshore development centers. This paper outlines the problems of migrating the offshore development centers to the Agile process as analyzed from the perspectives of the largest IT outsourcing company in Vietnam. The paper also details an improved model based on the Scrum framework, the most popular Agile implementation according to State of Agile in Version One 2017. The purpose of this model is to minimize the negative impacts of Agile in offshore development centers and vice versa. Finally, this paper also provides the result of applying the model into a "focal" project under the author's management.
{"title":"Migrating Vietnam Offshore into Agile","authors":"Le Gia Cuong, P. D. Hung, B. T. Vinh","doi":"10.1145/3287921.3287924","DOIUrl":"https://doi.org/10.1145/3287921.3287924","url":null,"abstract":"Agile and Offshoring are emerging as 2 prominent trends of the software industry. Unfortunately, offshoring might impact the results of Agile projects, and Agile practices also often cause difficulties to offshore development centers. This paper outlines the problems of migrating the offshore development centers to the Agile process as analyzed from the perspectives of the largest IT outsourcing company in Vietnam. The paper also details an improved model based on the Scrum framework, the most popular Agile implementation according to State of Agile in Version One 2017. The purpose of this model is to minimize the negative impacts of Agile in offshore development centers and vice versa. Finally, this paper also provides the result of applying the model into a \"focal\" project under the author's management.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131078035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantitative trading is an automated trading system in which the trading strategies and decisions are conducted by a set of mathematical models. Quantitative trading applies a wide range of computational approaches such as statistics, physics, or machine learning to analyze, predict, and take advantage of big data in finance for investment. This work studies core components of a quantitative trading system. Machine learning offers a number of important advantages over traditional algorithmic trading. With machine learning, multiple trading strategies are implemented consistently and able to adapt to real-time market. To demonstrate how machine learning techniques can meet quantitative trading, linear regression and support vector regression models are used to predict stock movement. In addition, multiple optimization techniques are used to optimize the return and control risk in trading. One common characteristic for both prediction models is they effectively performed in short-term prediction with high accuracy and return. However, in short-term prediction, the linear regression model is outperform compared to the support vector regression model. The prediction accuracy is considerably improved by adding technical indicators to dataset rather than adjusted price and volume. Despite the gap between prediction modeling and actual trading, the proposed trading strategy achieved a higher return than the S&P 500 ETF-SPY.
{"title":"Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques","authors":"Van-Dai Ta, Chuan-Ming Liu, Direselign Addis","doi":"10.1145/3287921.3287963","DOIUrl":"https://doi.org/10.1145/3287921.3287963","url":null,"abstract":"Quantitative trading is an automated trading system in which the trading strategies and decisions are conducted by a set of mathematical models. Quantitative trading applies a wide range of computational approaches such as statistics, physics, or machine learning to analyze, predict, and take advantage of big data in finance for investment. This work studies core components of a quantitative trading system. Machine learning offers a number of important advantages over traditional algorithmic trading. With machine learning, multiple trading strategies are implemented consistently and able to adapt to real-time market. To demonstrate how machine learning techniques can meet quantitative trading, linear regression and support vector regression models are used to predict stock movement. In addition, multiple optimization techniques are used to optimize the return and control risk in trading. One common characteristic for both prediction models is they effectively performed in short-term prediction with high accuracy and return. However, in short-term prediction, the linear regression model is outperform compared to the support vector regression model. The prediction accuracy is considerably improved by adding technical indicators to dataset rather than adjusted price and volume. Despite the gap between prediction modeling and actual trading, the proposed trading strategy achieved a higher return than the S&P 500 ETF-SPY.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130694096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
TCP-targeted low-rate distributed denial-of-service (LDDoS) attacks have created an opportunity for attackers to reduce their total attaking rate (and hence, the detection probability of the attacks) while inflicting the same damage to TCP flows as traditional flooding-based DDoS attacks. CPR-based approach has been proposed by Zhang et al. to detect and filter this kind of DDoS attacks, but its performance in terms of TCP throughput under attack is shown to be limited by the way it calculates CPR for each flow. In this paper, we will propose some modifications to the CPR-based approach in order to increase its performance. Simulation results show that the modifications can increase performance significantly.
{"title":"Techniques for Improving Performance of the CPR-Based Approach","authors":"M. Kieu, D. Nguyen, Thanh Thuy Nguyen","doi":"10.1145/3287921.3287940","DOIUrl":"https://doi.org/10.1145/3287921.3287940","url":null,"abstract":"TCP-targeted low-rate distributed denial-of-service (LDDoS) attacks have created an opportunity for attackers to reduce their total attaking rate (and hence, the detection probability of the attacks) while inflicting the same damage to TCP flows as traditional flooding-based DDoS attacks. CPR-based approach has been proposed by Zhang et al. to detect and filter this kind of DDoS attacks, but its performance in terms of TCP throughput under attack is shown to be limited by the way it calculates CPR for each flow. In this paper, we will propose some modifications to the CPR-based approach in order to increase its performance. Simulation results show that the modifications can increase performance significantly.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114790644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object detection is a major problem in computer vision. Recently, deep neural architectures have shown a dramatic boost in performance, but they are often too slow and burdensome for embedded and real-time applications such as video surveillance. In this paper, we describe a new object detection architecture that is faster than state-of-the-art detectors while improving the performance of small mobile models. Moreover, we apply this new architecture into the problem of vehicle detection, which is central to traffic surveillance systems. In more detail, our architecture uses an efficient backbone network in MobileNetV2, whose building blocks consist of depthwise convolutional layers. On top of this network, we build a feature pyramid using separable layers so that the model can detect objects at many scales. We train this network with smooth localization loss and weighted softmax loss in tandem with hard negative mining. Both training and test sets are built from recorded videos of Ho Chi Minh and Da Nang traffic or selected from DETRAC dataset. The experimental results show that our proposed solution can still achieve an mAP of 75% on the test set while using only around 3.4 million parameters and running at 100ms per image on a cheap machine.
{"title":"Mobile multi-scale vehicle detector and its application in traffic surveillance","authors":"Trung D. Q. Dang, Hy V. G. Che, T. Dinh","doi":"10.1145/3287921.3287957","DOIUrl":"https://doi.org/10.1145/3287921.3287957","url":null,"abstract":"Object detection is a major problem in computer vision. Recently, deep neural architectures have shown a dramatic boost in performance, but they are often too slow and burdensome for embedded and real-time applications such as video surveillance. In this paper, we describe a new object detection architecture that is faster than state-of-the-art detectors while improving the performance of small mobile models. Moreover, we apply this new architecture into the problem of vehicle detection, which is central to traffic surveillance systems. In more detail, our architecture uses an efficient backbone network in MobileNetV2, whose building blocks consist of depthwise convolutional layers. On top of this network, we build a feature pyramid using separable layers so that the model can detect objects at many scales. We train this network with smooth localization loss and weighted softmax loss in tandem with hard negative mining. Both training and test sets are built from recorded videos of Ho Chi Minh and Da Nang traffic or selected from DETRAC dataset. The experimental results show that our proposed solution can still achieve an mAP of 75% on the test set while using only around 3.4 million parameters and running at 100ms per image on a cheap machine.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115051640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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