2,265 research outputs found

    The Evolution of Embedding Metadata in Blockchain Transactions

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    The use of blockchains is growing every day, and their utility has greatly expanded from sending and receiving crypto-coins to smart-contracts and decentralized autonomous organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite connectivity. In our research we look at the ability of blockchains to store metadata in an increasing volume of transactions and with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding metadata. This paper identifies and classifies real-life blockchain transactions embedding metadata of a number of major protocols running essentially over the bitcoin blockchain. The empirical analysis here presents the evolution of metadata utilization in the recent years, and the discussion suggests steps towards preventing criminal use. Metadata are relevant to any blockchain, and our analysis considers primarily bitcoin as a case study. The paper concludes that simultaneously with both expanding legitimate utilization of embedded metadata and expanding blockchain functionality, the applied research on improving anonymity and security must also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on Neural Network

    Scalable and Sustainable Deep Learning via Randomized Hashing

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    Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically reduce the amount of computation needed to train and test deep networks. Our approach combines recent ideas from adaptive dropouts and randomized hashing for maximum inner product search to select the nodes with the highest activation efficiently. Our new algorithm for deep learning reduces the overall computational cost of forward and back-propagation by operating on significantly fewer (sparse) nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping on average within 1% of the accuracy of the original model. A unique property of the proposed hashing based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous and parallel training leading to near linear speedup with increasing number of cores. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several real datasets

    Implementation and Evaluation of Steganography based Online Voting

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    Though there are online voting systems available, the authors propose a new and secure steganography based E2E (end-to-end) verifiable online voting system, to tackle the problems in voting process. This research implements a novel approach to online voting by combining visual cryptography with image steganography to enhance system security without degrading system usability and performance. The voting system will also include password hashed-based scheme and threshold decryption scheme. The software is developed on web-based Java EE with the integration of MySQL database server and Glassfish as its application server. The authors assume that the election server used and the election authorities are trustworthy. A questionnaire survey of 30 representative participants was done to collect data to measure the user acceptance of the software developed through usability testing and user acceptance testing

    Adding run history to CLIPS

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    To debug a C Language Integrated Production System (CLIPS) program, certain 'historical' information about a run is needed. It would be convenient for system builders to have the capability to request such information. We will discuss how historical Rete networks can be used for answering questions that help a system builder detect the cause of an error in a CLIPS program. Moreover, the cost of maintaining a historical Rete network is compared with that for a classical Rete network. We will demonstrate that the cost for assertions is only slightly higher for a historical Rete network. The cost for handling retraction could be significantly higher; however, we will show that by using special data structures that rely on hashing, it is also possible to implement retractions efficiently

    Reinforcement Learning for Racecar Control

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    This thesis investigates the use of reinforcement learning to learn to drive a racecar in the simulated environment of the Robot Automobile Racing Simulator. Real-life race driving is known to be difficult for humans, and expert human drivers use complex sequences of actions. There are a large number of variables, some of which change stochastically and all of which may affect the outcome. This makes driving a promising domain for testing and developing Machine Learning techniques that have the potential to be robust enough to work in the real world. Therefore the principles of the algorithms from this work may be applicable to a range of problems. The investigation starts by finding a suitable data structure to represent the information learnt. This is tested using supervised learning. Reinforcement learning is added and roughly tuned, and the supervised learning is then removed. A simple tabular representation is found satisfactory, and this avoids difficulties with more complex methods and allows the investigation to concentrate on the essentials of learning. Various reward sources are tested and a combination of three are found to produce the best performance. Exploration of the problem space is investigated. Results show exploration is essential but controlling how much is done is also important. It turns out the learning episodes need to be very long and because of this the task needs to be treated as continuous by using discounting to limit the size of the variables stored. Eligibility traces are used with success to make the learning more efficient. The tabular representation is made more compact by hashing and more accurate by using smaller buckets. This slows the learning but produces better driving. The improvement given by a rough form of generalisation indicates the replacement of the tabular method by a function approximator is warranted. These results show reinforcement learning can work within the Robot Automobile Racing Simulator, and lay the foundations for building a more efficient and competitive agent

    Spartan Daily, November 20, 1951

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    Volume 40, Issue 39https://scholarworks.sjsu.edu/spartandaily/11626/thumbnail.jp

    The Unfolding of the Relational Operant: A Real-time Analysis Using Electroencephalography and Reaction Time Measures

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    The current study attempted to capture in real time the unfolding of the relational operant using electroencephalography (EEG) and reaction time measures. Participants were exposed to relational pretraining to establish the contextual cues of Same and Opposite for two arbitrary stimuli. These cues were then used to establish a series of contextually controlled discriminations in order to create a simple relational network among a series of arbitrary stimuli. During the test for derived relations of Same and Opposite, EEG and reaction time measures were recorded for each individual test task during the acquisition of a stable derived relational response pattern. Participants were then exposed to an identical set of relational training and testing tasks with the important difference that an entirely different set of stimuli was used. EEG and reaction time measures were again recorded during the relational test phase. Results showed that reaction times decreased for all subjects across successive test tasks and from the first to the second stimulus set. EEG data also suggested that there was increasingly less higher cognitive activity during the derivation of successive stimulus relations within and across stimulus sets. Taken together these findings provide support for the idea that derived relational responding can be viewed as an operant activity that both develops and generalizes

    Password Cracking and Countermeasures in Computer Security: A Survey

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    With the rapid development of internet technologies, social networks, and other related areas, user authentication becomes more and more important to protect the data of the users. Password authentication is one of the widely used methods to achieve authentication for legal users and defense against intruders. There have been many password cracking methods developed during the past years, and people have been designing the countermeasures against password cracking all the time. However, we find that the survey work on the password cracking research has not been done very much. This paper is mainly to give a brief review of the password cracking methods, import technologies of password cracking, and the countermeasures against password cracking that are usually designed at two stages including the password design stage (e.g. user education, dynamic password, use of tokens, computer generations) and after the design (e.g. reactive password checking, proactive password checking, password encryption, access control). The main objective of this work is offering the abecedarian IT security professionals and the common audiences with some knowledge about the computer security and password cracking, and promoting the development of this area.Comment: add copyright to the tables to the original authors, add acknowledgement to helpe

    Determining the conformal window: SU(2) gauge theory with N_f = 4, 6 and 10 fermion flavours

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    We study the evolution of the coupling in SU(2) gauge field theory with Nf=4N_f=4, 6 and 10 fundamental fermion flavours on the lattice. These values are chosen close to the expected edges of the conformal window, where the theory possesses an infrared fixed point. We use improved Wilson-clover action, and measure the coupling in the Schr\"odinger functional scheme. At four flavours we observe that the couping grows towards the infrared, implying QCD-like behaviour, whereas at ten flavours the results are compatible with a Banks-Zaks type infrared fixed point. The six flavour case remains inconclusive: the evolution of the coupling is seen to become slower at the infrared, but the accuracy of the results falls short from fully resolving the fate of the coupling. We also measure the mass anomalous dimension for the Nf=6N_f=6 case.Comment: 22 pages, 12 figures. Proof readin
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