1,297 resources related to Bot
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ICC 2021 - IEEE International Conference on Communications
IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.
The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
The IEEE Global Engineering Education Conference (EDUCON) 2020 is the eleventh in a series of conferences that rotate among central locations in IEEE Region 8 (Europe, Middle East and North Africa). EDUCON is one of the flagship conferences of the IEEE Education Society. It seeks to foster the area of Engineering Education under the leadership of the IEEE Education Society.
All topics related to engineering and technology management, including applicable analytical methods and economical/social/human issues to be considered in making engineering decisions.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The IEEE Transactions on Advanced Packaging has its focus on the modeling, design, and analysis of advanced electronic, photonic, sensors, and MEMS packaging.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...
2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018
The more traditional approach towards launching a complaint is through written complaints that would be dropped into the complaint boxes available in some sectors, which has a lot of problems associated with it such as loss of complaints, security issues, etc. The proposed model that is structured towards replacement of this approach is an online complaint bot that would accept ...
2017 International Conference on Computer Science and Engineering (UBMK), 2017
One of the important problems in social media platforms like Twitter is the large number of social bots or sybil accounts which are controlled by automated agents, generally used for malicious activities. These include directing more visitors to certain websites which can be considered as spam, influence a community on a specific topic, spread misinformation, recruit people to illegal organizations, ...
2017 International Conference on Service Systems and Service Management, 2017
Accompany with the growth of Sina-Weibo users, mendacious Bot users also emerge, which lead to network environment pollution and lower management efficiency. This paper focuses on Sina-Weibo users, extracts the effective features of Bot user through behavior analysis and features study. Then based these features, Bot user identification model is trained by machine learning process and model performance evaluation. The ...
2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017
At Present, the most genuine exhibit of cutting edge malware is botnet. Botnet is across the board malware and it emerges usually in today's digital wrongdoing, which brings about genuine dangers to our system. It is cluster of compromised computer (bot), which is remotely controlled by Master commonly known as BotHerder; under a typical command and control (C&C) framework. C&C ...
2012 11th Mexican International Conference on Artificial Intelligence, 2012
Electronic Learning (e-Learning) is used to educate people in these days. Using e-Learning, a number of world ranking universities are starting different courses for high school level to degree level and even at post graduate level through distance learning. This paper describes the best-known different machine learning techniques to boost up the e-Learning education standard and model. Comprehensively supervised and ...
Lithography: When Top Down Meets Bottom Up - IEEE Rebooting Computing Industry Summit 2017
Merge Network for a Non-Von Neumann Accumulate Accelerator in a 3D Chip - Anirudh Jain - ICRC 2018
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Virtual World Symposium 2011 - CoLab and Mars
Engineering Meets Biology in Tech News
Device versus Circuit Engineer
Key Technology Trends in Wireless for the Aerospace Industry
Magnetic Nanowires: Revolutionizing Hard Drives, RAM, and Cancer Treatment
Robotics History: Narratives and Networks Oral Histories: Nils Nilsson
Kathleen Kramer: Lessons for Locals and Local Visibility - Studio Tech Talks: Sections Congress 2017
Part 2: Transforming the Electric Utility Industry with a Smart Grid: IEEE TAB Speakers Burea
Why Participate in Standards Development?
From Bits to Atoms - Neil Gershenfeld: 2016 International Conference on Rebooting Computing
Molecular Cellular Networks: A Non von Neumann Architecture for Molecular Electronics - Craig Lent: 2016 International Conference on Rebooting Computing
Q&A with Sorel Reisman & Sheikh Iqbal Ahamed, Part 1: IEEE Big Data Podcast, Episode 12
IEEE Region 5 Presidents Forum - 2018
Additive-generative Industrial Design for Robotic Automation
IEEE Low-Power Image Recognition Challenge (LPIRC)
The more traditional approach towards launching a complaint is through written complaints that would be dropped into the complaint boxes available in some sectors, which has a lot of problems associated with it such as loss of complaints, security issues, etc. The proposed model that is structured towards replacement of this approach is an online complaint bot that would accept complaints through the onion routing algorithm. This would indeed secure complainant's identity and the bot would also use NLP to prioritize the complaints based on keywords. It would also direct it towards concerned authorities with a timestamp associated with each complaint and this complaint would have an id being sent back to the complainant for tracking of the issue on an online portal. These traits would in turn result into a system where data flows faster than the traditional approach and effective results a shorter time interval.
One of the important problems in social media platforms like Twitter is the large number of social bots or sybil accounts which are controlled by automated agents, generally used for malicious activities. These include directing more visitors to certain websites which can be considered as spam, influence a community on a specific topic, spread misinformation, recruit people to illegal organizations, manipulating people for stock market actions, and blackmailing people to spread their private information by the power of these accounts. Consequently, social bot detection is of great importance to keep people safe from these harmful effects. In this study, we approach the social bot detection on Twitter as a supervised classification problem and use machine learning algorithms after extensive data preprocessing and feature extraction operations. Large number of features are extracted by analysis of Twitter user accounts for posted tweets, profile information and temporal behaviors. In order to obtain labeled data, we use accounts that are suspended by Twitter with the assumption that majority of these are social bot accounts. Our results demonstrate that our framework can distinguish between bot and normal accounts with reasonable accuracy.
Accompany with the growth of Sina-Weibo users, mendacious Bot users also emerge, which lead to network environment pollution and lower management efficiency. This paper focuses on Sina-Weibo users, extracts the effective features of Bot user through behavior analysis and features study. Then based these features, Bot user identification model is trained by machine learning process and model performance evaluation. The result shows that these extracted features have satisfactory discrimination and the identification model has good performance.
At Present, the most genuine exhibit of cutting edge malware is botnet. Botnet is across the board malware and it emerges usually in today's digital wrongdoing, which brings about genuine dangers to our system. It is cluster of compromised computer (bot), which is remotely controlled by Master commonly known as BotHerder; under a typical command and control (C&C) framework. C&C is utilized to disperse commands to bot for performing vindictive activity, for example; information capturing, form grabbing, sending spam mails and performing DDOS attack and so on. Subsequently it is required to distinguish the botnet keeping in mind the end goal to give secure system benefit. The projected work is aimed towards detecting and deactivating P2P Zeus bot by applying bound steps. Starting is identifying Bot by observation of network traffic behavior. Second step is employed to detect Bot by identifying most access port data in conjunction with its count. Last step is deactivating Bot activity from victim machine by using Port block and removing registry key entry through programming.
Electronic Learning (e-Learning) is used to educate people in these days. Using e-Learning, a number of world ranking universities are starting different courses for high school level to degree level and even at post graduate level through distance learning. This paper describes the best-known different machine learning techniques to boost up the e-Learning education standard and model. Comprehensively supervised and unsupervised techniques are described here for the e-Learning paradigm to auto reply of students' questions. Web bot is incorporated for the learning of students who are taking courses by remote mode. Due to the number of students enrolled in a particular course, student teacher interaction is a major challenge. Thus the solution is to train the Web based bot and make it available for the students' interaction 24/7. Main drawback of e-Learning environment is not frequent replies of student queries which we are going to cover by using web bot. The key demand is to deal with learning techniques but not fully automated. Training of the machine is performed on training data and validation is performed on test data set. Proposed idea of using the web bot in e-Learning is helpful to increase the learning curve of the students.
This paper analyzes all possible kinds of risks in the executive process of BOT projects bidding program and studies the problems of risk assessment of BOT projects bidding program. First of all, this paper constructs the indicator system of risk evaluation from the point of main sources of risk. Then, it makes the appraisal for the risk evaluation model of BOT project bidding program with the method of analytic hierarchy process and fuzzy comprehensive evaluation. This paper provides a new risk evaluation method of choosing the best bidding program of BOT projects. The risk evaluation of BOT project bidding program can be more rational and efficient and the results of the model have reference value for the relevant projects bidding program.
Intelligent tutoring systems are computer programs that aim at providing personalized instruction to students. In recent years, conversational robots, usually known as chatterbots, become very popular in the Internet, and ALICE (artificial linguistic internet computer entity) is probably the most popular one. ALICE brain is written in AIML (artificial intelligence markup language), an open XML language. We consider the combination of both approaches, i.e, the use of AIML-based bots for tutoring purposes in open e-learning platforms like Claroline or Moodle. With that aim in mind, we have developed two different bots for helping the students during the learning process and for supporting the teaching activities of the professor. One of them is a tutor bot (T-Bot), and is able to analyse the requests made by the learners in written natural language and to provide adequate and domain specific answers orienting the student to the right course contents. The other one is an evaluation bot (Q-Bot), and is oriented to track and supervise the student progress by means of personalized questionnaires. Both bots have been already developed and integrated as user-friendly modules in Claroline and Moodle.
As social networks are becoming popular; it raises concerns among data analyzers for the quality of content over social media platforms. For better and fair predictive analysis, the quality of data is important. Low quality content may result into prediction of improper cause of an event, misleading trending issues and more importantly the sensitive stock price may fluctuate. The content over social media may be flooded or corrupted by various bots such as Influence Bots, Spam Bots. We are targeting twitter for the identification of such bots, as it is mostly used by data scientists for applications related to scientific prediction and sentiment analysis. In this paper, we capitalize on earlier approaches and used a machine learning based approach for the classification between a bot profile and human profile. We have identified 10 attributes of user profile and tweet pattern for an account and calculated a score called botScore for each profile to model the behavior as bot or as human. We have extended the list of features in distinguishing between bot and human to more fine-grained label. The method proposed was found to be more accurate than traditional Baye's classification technique.
Cheating is one of the biggest and constant problems in MMOGs. Games with high frequency of cheating will surely lose its appeal to genuine players who want to play the game. This is the reason why game provider these days put cheating prevention as one of the top priorities. Bot is just one way of cheating, but very efficient one. There are various methods to prevent cheating using bot. In this paper, we examine the potential of Artificial Neural Network (ANN) to detect and recognize bot from human players. We start with the assumption that one bot always acts in the similar pattern in gameplay. Meanwhile, it is much more rarer to see 2 players with similar gameplay pattern. The result of our experiment supports our initial hypothesis with the potential for future research in order to get better results.
Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.
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