Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969070
Aditya Yadav, Ishan Garg, Dr. Pratistha Mathur
Programmers face situations where they have to rely on messy documentation, other developers and online search for basic programming commands and queries when they encounter any new programming environment. This leads to the waste of time of developers and decreases productivity. In this paper, we present, “PACT”, a chat bot which assists the programmers with basic programming queries that they face when they are new to a programming environment. We use Neural Machine Translation architecture to generate coherent, non-rule based responses to a programmer’s query. The data that is fed to the neural machine translation model is collected from websites like StackOverflow, technical sub-reddits and technical StackExchanges.
{"title":"PACT - Programming Assistant ChaTbot","authors":"Aditya Yadav, Ishan Garg, Dr. Pratistha Mathur","doi":"10.1109/ICCT46177.2019.8969070","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969070","url":null,"abstract":"Programmers face situations where they have to rely on messy documentation, other developers and online search for basic programming commands and queries when they encounter any new programming environment. This leads to the waste of time of developers and decreases productivity. In this paper, we present, “PACT”, a chat bot which assists the programmers with basic programming queries that they face when they are new to a programming environment. We use Neural Machine Translation architecture to generate coherent, non-rule based responses to a programmer’s query. The data that is fed to the neural machine translation model is collected from websites like StackOverflow, technical sub-reddits and technical StackExchanges.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121129549","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969071
J. Pavanija, G. Jyothi, B. Dhanraj, G. Kumar, A. Bose, Pratibha Verma
In this paper, an effort to reduce the position error obtained from GNSS receivers-using Iterative Least Square Method (ILSM) and Particle Swarm Optimization (PSO) based algorithms for IRNSS and GPS constellation is presented. RINEX data from GNSS receiver is used as input for algorithms presented in the work. First satellite selection algorithm to obtain best GDOP is implemented to select best satellite set to prevent unnecessary navigational signals reception from multiple satellite constellations. Then ILSM and PSO algorithms are applied individually to the receiver coordinates obtained. Results are compared those show that PSO algorithm has better efficiency than iterative algorithm to minimize the position error solution in terms of precision. GNSS receiver coordinates within ± 10m error range is obtained,
{"title":"Reduction of Position Error in GNSS receiver Coordinates using Iterative and PSO based Algorithms","authors":"J. Pavanija, G. Jyothi, B. Dhanraj, G. Kumar, A. Bose, Pratibha Verma","doi":"10.1109/ICCT46177.2019.8969071","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969071","url":null,"abstract":"In this paper, an effort to reduce the position error obtained from GNSS receivers-using Iterative Least Square Method (ILSM) and Particle Swarm Optimization (PSO) based algorithms for IRNSS and GPS constellation is presented. RINEX data from GNSS receiver is used as input for algorithms presented in the work. First satellite selection algorithm to obtain best GDOP is implemented to select best satellite set to prevent unnecessary navigational signals reception from multiple satellite constellations. Then ILSM and PSO algorithms are applied individually to the receiver coordinates obtained. Results are compared those show that PSO algorithm has better efficiency than iterative algorithm to minimize the position error solution in terms of precision. GNSS receiver coordinates within ± 10m error range is obtained,","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121139717","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969068
Crystal Dias, Astha Jagetiya, Sandeep Chaurasia
Automatic license plate recognition is being widely used for numerous applications since its inception. The ability to procure license plate numbers accurately has been beneficial in maintaining traffic rules, parking enforcement, and security. In this paper, we have discussed the results of using ALPR for recognition of anonymous vehicles entering our university campus. We used deep learning for license plate localization and Tesseract OCR for license plate recognition. By doing so we could read the license plates of vehicles entering a particular campus and verify if the vehicle is authorized by comparing it with a predefined list of authorized vehicles. To efficiently extract these number plates we have trained our model using Faster RCNN and tuned it to get the best output. The results of which have been discussed in this paper. Further, the image processing techniques used for preprocessing the identified number plate have been mentioned here. For character segmentation and character recognition, we have used tesseract. While training our model for number plate extraction the minimum loss obtained was 0.011 with RMSprop optimizer at initial learning rate 0.002.
{"title":"Anonymous Vehicle Detection for Secure Campuses: A Framework for License Plate Recognition using Deep Learning","authors":"Crystal Dias, Astha Jagetiya, Sandeep Chaurasia","doi":"10.1109/ICCT46177.2019.8969068","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969068","url":null,"abstract":"Automatic license plate recognition is being widely used for numerous applications since its inception. The ability to procure license plate numbers accurately has been beneficial in maintaining traffic rules, parking enforcement, and security. In this paper, we have discussed the results of using ALPR for recognition of anonymous vehicles entering our university campus. We used deep learning for license plate localization and Tesseract OCR for license plate recognition. By doing so we could read the license plates of vehicles entering a particular campus and verify if the vehicle is authorized by comparing it with a predefined list of authorized vehicles. To efficiently extract these number plates we have trained our model using Faster RCNN and tuned it to get the best output. The results of which have been discussed in this paper. Further, the image processing techniques used for preprocessing the identified number plate have been mentioned here. For character segmentation and character recognition, we have used tesseract. While training our model for number plate extraction the minimum loss obtained was 0.011 with RMSprop optimizer at initial learning rate 0.002.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125086911","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}
Pub Date : 2019-09-01DOI: 10.1109/icct46177.2019.8969056
{"title":"ICCT 2019 Keynote Speakers","authors":"","doi":"10.1109/icct46177.2019.8969056","DOIUrl":"https://doi.org/10.1109/icct46177.2019.8969056","url":null,"abstract":"","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121259065","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969033
N. Sharma, Kavita, G. Agarwal
Security of a network has got a major importance in a wide range of systems. These days every place is connected to a network or via a network e.g. hospitals, offices, universities, finance sector etc. and almost everyone whether young or old is connected to social networking and community media. Though many systems are there that can secure any network, this attacking phenomenon keeps on increasing day by day. This paper focusses on some fundamentals like what basically a network attack is, how to prevent it, its types, preventive measures and current procedures that are focusing on this paradigm. Basically this paper is an attempt to help people understand the concept of attacks so as to avoid them.
{"title":"Network Attacks and Intrusion Detection System: A Brief","authors":"N. Sharma, Kavita, G. Agarwal","doi":"10.1109/ICCT46177.2019.8969033","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969033","url":null,"abstract":"Security of a network has got a major importance in a wide range of systems. These days every place is connected to a network or via a network e.g. hospitals, offices, universities, finance sector etc. and almost everyone whether young or old is connected to social networking and community media. Though many systems are there that can secure any network, this attacking phenomenon keeps on increasing day by day. This paper focusses on some fundamentals like what basically a network attack is, how to prevent it, its types, preventive measures and current procedures that are focusing on this paradigm. Basically this paper is an attempt to help people understand the concept of attacks so as to avoid them.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133811099","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969046
Satabdi Nayak, San Kumar, Mahesh Jangid
With about 200 million global instances and over 400,000 fatalities a year, malaria continues an enormous strain on global health. Modern information technology plays a major part in many attempts to combat the disease, along with biomedical research and political efforts. In specific, insufficient malaria diagnosis was one of the obstacles to a promising mortality decrease. The paper offers an outline of these methods and explores present advancement in the field of microscopic malaria detection and we have ventured into utilization of deep learning for detection of Malaria Parasite. Deep Learning over the years has proven to be much faster and much more accurate as it automates feature extraction of the dataset. In this research paper, we investigated various models of Deep Learning and monitored which of these models provided a better accuracy and faster resolution than previously used deep learning models. Our results show that Resnet 50 model gave the highest accuracy of 0.975504.
{"title":"Malaria Detection Using Multiple Deep Learning Approaches","authors":"Satabdi Nayak, San Kumar, Mahesh Jangid","doi":"10.1109/ICCT46177.2019.8969046","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969046","url":null,"abstract":"With about 200 million global instances and over 400,000 fatalities a year, malaria continues an enormous strain on global health. Modern information technology plays a major part in many attempts to combat the disease, along with biomedical research and political efforts. In specific, insufficient malaria diagnosis was one of the obstacles to a promising mortality decrease. The paper offers an outline of these methods and explores present advancement in the field of microscopic malaria detection and we have ventured into utilization of deep learning for detection of Malaria Parasite. Deep Learning over the years has proven to be much faster and much more accurate as it automates feature extraction of the dataset. In this research paper, we investigated various models of Deep Learning and monitored which of these models provided a better accuracy and faster resolution than previously used deep learning models. Our results show that Resnet 50 model gave the highest accuracy of 0.975504.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133009308","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8968781
S. Padhi, Suvendu Rup, Sanjay Saxena, Figlu Mohanty
Being the prime reason, after skin cancer, of high mortality rate among women in present day, breast cancer requires correct diagnosis and precise treatment at its earliest stage. From the time of the advent of diagnosis tools, medical practitioners have left no stone unturned in their efforts of delivering timely medication to the patients; but often human error has resulted in either death due to dosage of medicines resulting from wrongly detected malignancies or due to negligence arising from not detecting the tumors at the right time. Hence, computer-aided diagnosis (CADx) has come into light as a key tool in statistically analyzing medical images obtained from various imaging machines and classifying the specimens into the categories of normal, benign, and malignant. A major step involved in it is the segmentation of the medical image into various regions and determining the required region-of-interest (ROI) from them. Automated image segmentation is quintessential today in order to extract the correct suspicious regions for diagnosis, instead of relying on erroneous human eye judgment. The following study aims to compare and analyze the effectiveness of some existing segmentation methods used to extract the ROIs for analysis of digital mammograms for breast cancer detection.
{"title":"Mammogram Segmentation Methods: A Brief Review","authors":"S. Padhi, Suvendu Rup, Sanjay Saxena, Figlu Mohanty","doi":"10.1109/ICCT46177.2019.8968781","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8968781","url":null,"abstract":"Being the prime reason, after skin cancer, of high mortality rate among women in present day, breast cancer requires correct diagnosis and precise treatment at its earliest stage. From the time of the advent of diagnosis tools, medical practitioners have left no stone unturned in their efforts of delivering timely medication to the patients; but often human error has resulted in either death due to dosage of medicines resulting from wrongly detected malignancies or due to negligence arising from not detecting the tumors at the right time. Hence, computer-aided diagnosis (CADx) has come into light as a key tool in statistically analyzing medical images obtained from various imaging machines and classifying the specimens into the categories of normal, benign, and malignant. A major step involved in it is the segmentation of the medical image into various regions and determining the required region-of-interest (ROI) from them. Automated image segmentation is quintessential today in order to extract the correct suspicious regions for diagnosis, instead of relying on erroneous human eye judgment. The following study aims to compare and analyze the effectiveness of some existing segmentation methods used to extract the ROIs for analysis of digital mammograms for breast cancer detection.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848801","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}
Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people’s sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.
{"title":"Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model","authors":"Msr Hitesh, Vedhosi Vaibhav, Y.J Abhishek Kalki, Suraj Harsha Kamtam, S. Kumari","doi":"10.1109/ICCT46177.2019.8969049","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969049","url":null,"abstract":"Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people’s sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128773100","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969029
Pai Manohara M. M., S. Kolekar, R. Pai
Foot complications are considered to be a serious consequence of Diabetes Mellitus (DM), posing a major medical and economical threat. This paper discusses about a device which generates the temperature profiling which is useful to detect the foot complications at early stage. Using the developed device, the temperature of the plantar area is measured periodically at twenty-three strategic points and based on the temperature difference between the two feet, the abnormality is reported. The device is easy to use and can used in home to capture real time data without going through medical follow-ups.
{"title":"Temperature Profiling for Early Detection of Foot Complications","authors":"Pai Manohara M. M., S. Kolekar, R. Pai","doi":"10.1109/ICCT46177.2019.8969029","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969029","url":null,"abstract":"Foot complications are considered to be a serious consequence of Diabetes Mellitus (DM), posing a major medical and economical threat. This paper discusses about a device which generates the temperature profiling which is useful to detect the foot complications at early stage. Using the developed device, the temperature of the plantar area is measured periodically at twenty-three strategic points and based on the temperature difference between the two feet, the abnormality is reported. The device is easy to use and can used in home to capture real time data without going through medical follow-ups.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634445","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}
Pub Date : 2019-09-01DOI: 10.1109/ICCT46177.2019.8969012
Vartika, C. Krishna, Ravin Kumar, Yogita
The services of Indian Railway are availed by many people in the country. It is an important mode of transportation. Most of the users of Indian Railway express their views about it on different social media sites like Twitter, Facebook etc. It leads to generation of large amount of data and sentimental analysis of that data can be very helpful in understanding public opinions towards Indian Railway and in decision making. In this paper, the lexicon based sentimental analysis technique has been applied to the twitter data collected corresponding to three train accidents namely Puri-Haridwar-Kalinga Utkal Express, Delhi-bound Kaifiyat Express and Mumbai-Nagpur Duranto Express which took place on 19/08/2017, 23/08/2017 and 29/08/2017 respectively. Further, tweets are classified into different categories and analyzed in terms of percentage frequency. The results present the pattern how the sentiments of the public fluctuate with time as when derailment happens the negative tweets has high frequency of occurrence but with passage of time frequency of occurrence of neutral tweets become high.
{"title":"Sentiment Analysis of Train Derailment in India: A Case Study from Twitter Data","authors":"Vartika, C. Krishna, Ravin Kumar, Yogita","doi":"10.1109/ICCT46177.2019.8969012","DOIUrl":"https://doi.org/10.1109/ICCT46177.2019.8969012","url":null,"abstract":"The services of Indian Railway are availed by many people in the country. It is an important mode of transportation. Most of the users of Indian Railway express their views about it on different social media sites like Twitter, Facebook etc. It leads to generation of large amount of data and sentimental analysis of that data can be very helpful in understanding public opinions towards Indian Railway and in decision making. In this paper, the lexicon based sentimental analysis technique has been applied to the twitter data collected corresponding to three train accidents namely Puri-Haridwar-Kalinga Utkal Express, Delhi-bound Kaifiyat Express and Mumbai-Nagpur Duranto Express which took place on 19/08/2017, 23/08/2017 and 29/08/2017 respectively. Further, tweets are classified into different categories and analyzed in terms of percentage frequency. The results present the pattern how the sentiments of the public fluctuate with time as when derailment happens the negative tweets has high frequency of occurrence but with passage of time frequency of occurrence of neutral tweets become high.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124315879","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：