2018 4th International Conference on Web Research: ICWR 2018, Tehran, Iran, 25-26 April 2018

Totally found 35 items. Search in result

  • [会议] Semantic code clone detection using abstract memory states and program dependency graphs
    摘要:The most important issue in software engineering is to produce efficient and high quality software with good services after sales. For this reason, software engineers introduced a special category called “Software Evolution”, in which the goal is to improving software after production. One of the basic subjects in software evolution is code clones or the duplicated code snippets in software. In fact, code clones detection may be introduced as the foundation of Software Evolution, in this way most topics in Software Evolution depends on the detection of code clones. In addition to other methods, two methods have been introduced yet, behavioral method (slower but more accurate) and memory state based method (faster with medium accuracy). In this research, the goal is to find more clones with a better accuracy than the Abstract Memory State (AMS) based method (Reduction of False Negative). To do this, the combinations of two methods of AMS and Program Dependency Graph (PDG) have been used. The method of executing code snippets is also used with random values. The method presented in this study is compared with the AMS based method and results show that it can detect more code clones than Memory State Based method, and reduced false negatives.
  • [会议] Semantic code clone detection using abstract memory states and program dependency graphs
    摘要:The most important issue in software engineering is to produce efficient and high quality software with good services after sales. For this reason, software engineers introduced a special category called “Software Evolution”, in which the goal is to improving software after production. One of the basic subjects in software evolution is code clones or the duplicated code snippets in software. In fact, code clones detection may be introduced as the foundation of Software Evolution, in this way most topics in Software Evolution depends on the detection of code clones. In addition to other methods, two methods have been introduced yet, behavioral method (slower but more accurate) and memory state based method (faster with medium accuracy). In this research, the goal is to find more clones with a better accuracy than the Abstract Memory State (AMS) based method (Reduction of False Negative). To do this, the combinations of two methods of AMS and Program Dependency Graph (PDG) have been used. The method of executing code snippets is also used with random values. The method presented in this study is compared with the AMS based method and results show that it can detect more code clones than Memory State Based method, and reduced false negatives.
  • [会议] Personalization of spinal cord injury mobility and transfer support application based on case-based reasoning
    摘要:In this decade with increasing technology development and wide usage of mobile devices, using applications in teaching patients specially the ones with particular restrictions such as Spinal Cord Injury (SCI) patients who are not able to move that easily, could be beneficial. On the other hand, due to their various body capabilities, the teaching material could not be the same for all the patients. Thus personalizing support systems seems to have a significant role on increasing user satisfaction, improving treatment trend and increasing the patient's ability to do daily activities. Taking the above point into account, in this paper we are going to describe the personalization process of “Spinal Cord Injury Mobility and Transfer Support Application” which is based on Case-Based Reasoning (CBR). The support application will provide personalized educational contents such as videos teaching mobility and transfer, text and audio guides, according to the patients' characteristics. In order to optimize the personalization process, performance of the support system was evaluated in two situations, changing the number of clusters and changing the similarity threshold. At the end, the results concluded that CBR can be used as an effective method in personalizing medical support systems.
  • [会议] Incorporating structural information in scientific document retrieval
    摘要:With the daily-increasing development of science, various methods have been designed to more and better retrieve the scientific documents based on the need and search of users. For some documents in the various scientific databases, no complete information exists and the users have to observe the inside of a document in order to catch up with its metadata inclusion the authors, their affiliations, the references cited and etc. Therefore, presence of a method based on extracting the information based on the available structural and geometrical properties in a document can assist the recovery of related and required documents. In addition, the available pitfall in the relational data based is the lack of direct and indirect relationships between the availabilities of each system for which a graph-oriented database can establish the relations between these availabilities. In this respect, after extracting metadata using the geometrical properties of document and using a graph-oriented model, the relations between various documents' availabilities such as authors, conferences, subjects and keywords and etc. are modeled in order to retrieve the information more effectively. The extracted data are refined and stored in the graph model and will be available for a user via a web-based user interface. To produce the results of each search, the related documents will be retrieved based on the graph relations and be weighed according to the rate of relatedness of each document and the number of references. In order to evaluate the proposed method, PubMed Database is used. The results of experiments show the proposed methods outperformed 60\% in contrast to the PubMed Database search engine in terms of the retrieved documents. Furthermore, based in the F-measure, and nDCG-measure of proposed method considerably outperformed the PubMed Database search engine in terms of the quality of retrieved documents.
  • [会议] A framework for comparing quantitative and qualitative criteria of IoT platforms
    摘要:With the daily increasing development of the Internet of Things, Internet of Things platform as a service, come into the world arena with different structures and characteristics. This leads to make the balance of their advantages and disadvantages, so that we can choose the appropriate platform to apply it for advancement of our aims. Data management, data monitoring, no loss data, speed, low latency and other criteria play very important role to select a good platform. Therefore, in this research, while investigating the quantitative and qualitative criteria of different platforms, we have tried to find a framework for evaluating them. In order to achieve this goal, some platforms such as Thingspeak, Xively and AWS IoT have been introduced and according to be mentioned criteria; we have implemented and tested different scenarios in the equal environment and conditions, and evaluated how the platforms behave according to the existing criteria.
  • [会议] Multi-emotion extraction from text based on linguistic analysis
    摘要:Emotions as one of the important elements of human nature are a part of everyday communications of people. We can distinguish person's emotions from some outcome behaviors such as speech, facial expression, body movements and gestures. Another outcome behavior that reflects the inner states of the person is his/her grammar and written method. Because nowadays, people are more likely to use textual tools to make connection, emotion extraction from the text has attracted a lot of attention. This paper provides a framework for the extraction of emotions in the text. By considering that a text may contain more than one emotion that only one of them is text dominant emotion, our proposed method, has modeled emotion extraction problem as a multi-label classification problem by removing the fixed boundaries of emotions, and recognizes all the existing emotions in the sentence and also dominant emotion. The proposed method extracts emotions by using structural and semantic information in the sentence, linguistic information and machine learning techniques. The experiments have been done on multi-label dataset contains 629 sentences with eight emotional categories. Based on the results, our proposed method, compared with used multi-label learning methods (BR, RAKEL, MLANN) have shown a better performance.
  • [会议] Ontology alignment using inductive logic programming
    摘要:Ontologies are one of the important and effective parts of semantic web which constitute the infrastructure and background knowledge of this realm of web science. Finding valid mappings as much as possible between the concepts or entities of ontologies, especially for the large ones, is a prominent task to align those concepts together and finally merge and integrate their ontologies to make a general and global ontology that is smaller and more flexible in many applications of semantic web. This paper describes a new learning-based ontology mapping method in which inductive logic programming (ILP) is used to learn ontology mapping using information gathered from instances of each entity in order to make some correct and valid alignments between concepts of different ontologies.
  • [会议] Fingerprint vulnerability: A survey
    摘要:With an increasing number of internet users utilizing smartphones and similar devices, fingerprint scanners are becoming increasingly important as a security feature. From a business point of view, a company put its reputation at risk if its biometric security system can be spoofed. From a user point of view, since the fingerprint last for a lifetime, once leaked they are leaked for the rest of user's life. This survey reviews the fingerprint system, classifies different parts of it including techniques for extracting features and matching procedures, different vulnerabilities and proposes three avenues for mitigating the vulnerabilities including software (big data and encryption/ decryption) and secure hardware techniques.
  • [会议] Popularity prediction of images and videos on Instagram
    摘要:We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77\% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.
  • [会议] Popularity prediction of images and videos on Instagram
    摘要:We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77\% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.
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