Isabel Ermidal1, Idalete Dias2 and Filipa Pereira2, 1Department of English, Minho University, Braga, Portugal, 2Department of German, Minho University, Braga, Portugal, 3Department of Informatics, Minho University, Braga, Portugal
The complexities of Natural Language Processing have become more challenging in recent years, given the rapid spread of online comment forums where abusive, violent and hate-laden behaviour often smears otherwise democratic and free conversations. This paper aims to make a contribution to the detection of hate speech in social media. Given the polarity-centeredness of sentiment classification methods and the difficulties facing automatic emotion detection due to the linguistic and paralinguistic properties of usergenerated content, not to mention the hardships of context-dependency, we propose a mixed-method approach that combines opinion mining and emotion detection with linguistic input. We applied our model on a subset of the NetLang Corpus, namely texts classified under the prejudice type “Racism” and sociolinguistic variable “Ethnicity”. We departed from the hypothesis that adversative conjunctions are markers indicative of opinion conflict and emotional discord, two phenomena characteristic of hate speech. Firstly, we narrowed down the search results containing the conjunction “but” using regular expressions and further restricted the search to instances of “but” co-occurring with “not”. Secondly, sentiment polarity followed by emotion classification were carried out using SentiWordNet and NRC Lexicon respectively. Finally, the resulting comments underwent a qualitative categorization according to their illocutionary force.
Opinion Mining, Sentiment Analysis, NLP, Hate Speech Detection, Social Media, Online Discourse, Corpus, Adversative Constructions, Illocutionary Acts.
Sarthak Agrawal, Saksham Sharma and Surjeet Balhara, Department of Electronics and Communication Engineering, Bharti Vidyapeeth College of Engineering, New Delhi, India
With time demand of IoT devices is increasing day by day. Usage and production of the IoT devices has increased in recent years. With increase in the number of user base, security related issues are also increased. While there are many proposed approaches to deal with different security related aspects in IoT, one of the potential solutions to such issues is Blockchain. Blockchain is a rapidly emerging technology and is used in various fields. Blockchain technology has features like decentralisation and immutability which guarantees security. A blockchain based security model has been proposed in this paper for securing IoT devices from various security threats. Finally, proposed approach and its implementation using blockchain to secure IoT Ecosystem is discussed to make IoT ecosystem more secure.
Authentication, Blockchain, Data Protection, IoT, Security.
Geetika Tiwari1, Ruchi Jain2 and Dr Tryambak Hiwarkar3, 1Department of Computer Science, Sardar Patel University, Madhya Pradesh, India, 2Department of Computer Science, LNCT, Madhya Pradesh, India, 3Department of Computer Science, Sardar Patel University, Madhya Pradesh, India
Cloud computing has been promoted as one of the most effective methods of hosting and delivering services via the internet. Despite its broad range of applications, cloud security remains a serious worry for cloud computing. Many secure solutions have been developed to safeguard communication in such environments, the majority of which are based on attack signatures. These systems are often ineffective in detecting all forms of threats. A machine learning approach was recently presented. This implies that if the training set lacks sufficient instances in a specific class, the judgment may be incorrect. In this research, we present a novel firewall mechanism for safe cloud computing environments called machine learning system. Proposed Methods identifies and classifies incoming traffic packets using a novel combination methodology named most frequent decision, in which the nodes one previous decisions are coupled with the machine learning algorithms current decision to estimate the final attack category classification. This method improves learning performance as well as system correctness. UNSW-NB-15, a publicly accessible dataset, is utilised to derive our findings. Our data demonstrate that it enhances anomaly detection by 97.68 percent.
Cloud computing, Intrusion Detection System, Machine Learning, UNSW-NB-15.
Fredrik Kamphuis1, Bernardo Magri2, Ricky Lamberty1 and Sebastian Faust3, 1Corporate Research, Robert Bosch GmbH, 2The University of Manchester, 3Technical University of Darmstadt
In public transaction ledgers such as Bitcoin and Ethereum, it is generally assumed that miners do not have any preference on the contents of the transactions they include, such that miners eventually include all transactions they receive. However, Daian et al. S&P20 showed that in practice this is not the case, and the so called miner extractable value can dramatically increase miners prot by re-ordering, delaying or even suppressing transactions. Consequently an \unpopular" transaction might never be included in the ledger if miners decide to suppress it, making, e.g., the standard liveness property of transaction ledgers (Garay et al. Eurocrypt15) impossible to be guaranteed in this setting. In this work, we formally de ne the setting where miners of a transaction ledger are dictatorial, i.e., their transaction selection and ordering process is driven by their individual preferences on the transactions contents. To this end, we integrate dictatorial miners into the transaction ledger model of Garay et al. by replacing honest miners with dictatorial ones. Next, we introduce a new property for a transaction ledger protocol that we call content preference robustness (CPR). This property ensures rational liveness, which guarantees inclusion of transactions even when miners are dictatorial, and it provides rational transaction order preservation which ensures that no dictatorial miner can improve its utility by altering the order of received candidate transactions. We show that a transaction ledger protocol can achieve CPR if miners cannot obtain a-priori knowledge of the content of the transactions. Finally, we provide a generic compiler based on time-lock puzzles that transforms any robust transaction ledger protocol into a CPR ledger.
blockchain, liveness, censorship, rational adversary, miner extractable value.
Ashitosh Joshi1 and Surendra Bhosale2, 1M.Tech student of Department of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India, 2Faculty and Head of Department of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India
This paper proposes a very efficient method for magnetic signature reduction of underwater vessels commonly known as degaussing. Degaussing helps to protect the ferromagnetic vessels from magnetic anomaly detectors and mines and hence ensures stealth mode of operation. We propose a reinforcement learning (RL) based approach for degaussing of the vessel. The proposed algorithm is efficient in terms of computational efforts, speed, and accuracy. The proposed method is validated for a simulated model of prototype submarine as a ferromagnetic vessel. The main advantage of the proposed method is its ability to automatically find the optimal values of currents to be applied for signature reduction.
Degaussing, Magnetic Signatures, Reinforcement Learning, Q Learning.
Lei Miao1, Hongbo Zhang2, and Dingde Jiang3, 1Dept. of Engineering Technology, Middle Tennessee State University, Murfreesboro, TN 37132, USA, 2Dept. of Engineering Technology, Middle Tennessee State University, Murfreesboro, TN 37132, USA, 3School of Astronautics & Aeronautic, University of Electronic Science and Technology of China, Sichuan, China
Wireless secret sharing is crucial to information security in the era of Internet of Things. One method is to utilize the effect of the randomness of the wireless channel in the data link layer to generate the common secret between two legitimate users Alice and Bob. This paper studies this secret sharing mechanism from the perspective of game theory. In particular, we formulate a non-cooperative zero-sum game between the legitimate users and an eavesdropper Eve. In a symmetrical game where Eve has the same probability of successfully receiving a packet from Alice and Bob when the transmission distance is the same, we show that both pure and mixed strategy Nash equilibria exist. In an asymmetric game where Eve has different probabilities of successfully receiving a packet from Alice and Bob, a pure strategy may not exist; in this case, we show how a mixed strategy Nash equilibrium can be found.
secret sharing, wireless communications, game theory, Nash equilibrium.
Aleksander Berezowski, Department of Software Engineering, University of Calgary, Calgary, Canada
This paper will cover how I designed a reward function algorithm for a miniature race car’s reinforcement machine learning model. The research presented focuses on how to develop a reward function for AWS’s DeepRacer competition. Highlights include how different mathematical methods can be used to weigh different reward parameters, how reward function parameters are chosen, a complete breakdown of the code my research led me to make, the performance of my research, and how I would improve results going forward. This paper is titled as a research paper as it is the culmination of research, testing, and analysis done on one approach to this problem. The reason for this research is when I started to compete in DeepRacer there were no papers that broke down the rationale behind top performing code. This paper presents the process of building an experimental program, testing it, and figuring out how to improve it.
AWS DeepRacer, Reinforcement Learning, Competitive Programming.
Feihong Liu1 and Yu Sun2, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620
Athletes in technical sports often find it difficult to analyze their own technique while they’re playing . Often, athletes look at the technique of professional players to identify problems they may have. Unfortunately, many types of techniques, such as forehand and backhand swings in tennis, are relatively similar between a beginner and a professional, making it more difficult for comparison. On the other hand, techniques that appear different between professionals and casual can also present different challenges. This is especially true for serves in tennis, where the speed of the swing, the motion of the player, and the angle of the camera recording the player all pose a challenge in analyzing differences between professional and learning tennis players . In this paper, we used two machine learning approaches to compare the serves of two players. In addition, we also developed a website that utilizes these approaches to allow for convenient access and a better experience. We found that our results adjusted for different speeds between the two players and made comparison much simpler.
Pose-estimation, Machine Learning, Scikit-learn.