1,720,983 research outputs found
Analysis of a consensus protocol for extending consistent subchains on the bitcoin blockchain
Full text linkCurrently, an increasing number of third-party applications exploit the Bitcoin blockchain to store tamper-proof records of their executions, immutably. For this purpose, they leverage the few extra bytes available for encoding custom metadata in Bitcoin transactions. A sequence of records of the same application can thus be abstracted as a stand-alone subchain inside the Bitcoin blockchain. However, several existing approaches do not make any assumptions about the consistency of their subchains, either (i) neglecting the possibility that this sequence of messages can be altered, mainly due to unhandled concurrency, network malfunctions, application bugs, or malicious users, or (ii) giving weak guarantees about their security. To tackle this issue, in this paper, we propose an improved version of a consensus protocol formalized in our previous work, built on top of the Bitcoin protocol, to incentivize third-party nodes to consistently extend their subchains. Besides, we perform an extensive analysis of this protocol, both defining its properties and presenting some real-world attack scenarios, to show how its specific design choices and parameter configurations can be crucial to prevent malicious practices
An overview of blockchain-based systems and smart contracts for digital coupons
No full textAmong the accessory applications of the blockchain, the idea of using it as an immutable register for tracking and certifying documents is recently gaining interest in research and industry. The problems of traceability, non-counterfeiting and unique usage of digital coupons fall within this area; many couponing platforms are hence exploring the possibility of addressing the above limitations with blockchain technologies. In view of the foregoing, in this work we analyse and compare several blockchain-based couponing systems. To do so, we first propose a general schema of digital coupon and define the desirable properties of a couponing system. Then, we select a sample of these systems and we examine them, describing their design choices and summarizing their relevant properties. Finally, we inspect their code and study how the notion of couponing system is interpreted in their smart contracts. We also highlight their distinctive features and relevant implementation solutions. We conclude by discussing what emerged from our analysis and proposing some possible future investigations
Natural Interaction with Traffic Control Cameras Through Multimodal Interfaces
Full text linkHuman-Computer Interfaces have always played a fundamental role in usability and commands’ interpretability of the modern software systems. With the explosion of the Artificial Intelligence concept, such interfaces have begun to fill the gap between the user and the system itself, further evolving in Adaptive User Interfaces (AUI). Meta Interfaces are a further step towards the user, and they aim at supporting the human activities in an ambient interactive space; in such a way, the user can control the surrounding space and interact with it. This work aims at proposing a meta user interface that exploits the Put That There paradigm to enable the user to fast interaction by employing natural language and gestures. The application scenario is a video surveillance control room, in which the speed of actions and reactions is fundamental for urban safety and driver and pedestrian security. The interaction is oriented towards three environments: the first is the control room itself, in which the operator can organize the views of the monitors related to the cameras on site by vocal commands and gestures, as well as conveying the audio on the headset or in the speakers of the room. The second one is related to the control of the video, in order to go back and forth to a particular scene showing specific events, or zoom in/out a particular camera; the third allows the operator to send rescue vehicle in a particular street, in case of need. The gestures data are acquired through a Microsoft Kinect 2 which captures pointing and gestures allowing the user to interact multimodally thus increasing the naturalness of the interaction; the related module maps the movement information to a particular instruction, also supported by vocal commands which enable its execution. Vocal commands are mapped by means of the LUIS (Language Understanding) framework by Microsoft, which helps to yield a fast deploy of the application; furthermore, LUIS guarantees the possibility to extend the dominion related command list so as to constantly improve and update the model. A testbed procedure investigates both the system usability and multimodal recognition performances. Multimodal sentence error rate (intended as the number of incorrectly recognized utterances even for a single item) is around 15%, given by the combination of possible failures both in the ASR and gesture recognition model. However, intent classification performances present, on average across different users, accuracy ranging around 89–92% thus indicating that most of the errors in multimodal sentences lie on the slot filling task. Usability has been evaluated through task completion paradigm (including interaction duration and activity on affordances counts per task), learning curve measurements, a posteriori questionnaires
A local feature engineering strategy to improve network anomaly detection
Full text linkThe dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions
Decomposing Training Data to Improve Network Intrusion Detection Performance
No full textAnyone working in the field of network intrusion detection has been able to observe how it involves an everincreasing number of techniques and strategies aimed to overcome the issues that affect the state-of-the-art solutions. Data unbalance and heterogeneity are only some representative examples of them, and each misclassification made in this context could have enormous repercussions in different crucial areas such as, for instance, financial, privacy, and public reputation. This happens because the current scenario is characterized by a huge number of public and private network-based services. The idea behind the proposed work is decomposing the canonical classification process into several sub-processes, where the final classification depends on all the sub-processes results, plus the canonical one. The proposed Training Data Decomposition (TDD) strategy is applied on the training datasets, where it applies a decomposition into regions, according to a defined number of events and features. The reason that leads this process is related to the observation that the same network event could be evaluated in a different manner, when it is evaluated in different time periods and/or when it involves different features. According to this observation, the proposed approach adopts different classification models, each of them trained in a different data region characterized by different time periods and features, classifying the event both on the basis of all model results, and on the basis of the canonical strategy that involves all data
Analysis of a Consensus Protocol for Extending Consistent Subchains on the Bitcoin Blockchain
No full textCurrently, an increasing number of third-party applications exploit the Bitcoin blockchain to store tamper-proof records of their executions, immutably. For this purpose, they leverage the few extra bytes available for encoding custom metadata in Bitcoin transactions. A sequence of records of the same application can thus be abstracted as a stand-alone subchain inside the Bitcoin blockchain. However, several existing approaches do not make any assumptions about the consistency of their subchains, either (i) neglecting the possibility that this sequence of messages can be altered, mainly due to unhandled concurrency, network malfunctions, application bugs, or malicious users, or (ii) giving weak guarantees about their security. To tackle this issue, in this paper, we propose an improved version of a consensus protocol formalized in our previous work, built on top of the Bitcoin protocol, to incentivize third-party nodes to consistently extend their subchains. Besides, we perform an extensive analysis of this protocol, both defining its properties and presenting some real-world attack scenarios, to show how its specific design choices and parameter configurations can be crucial to prevent malicious practices
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Investigating the Effectiveness of 3D Monocular Object Detection Methods for Roadside Scenarios
No full textUrban environments are demanding effective and efficient detection in 3D of objects using monocular cameras, e.g., for intelligent monitoring or decision support. The limited availability of large-scale roadside camera datasets and the mere focus of existing 3D object detection methods on autonomous driving scenarios pose significant challenges for their practical adoption, unfortunately. In this paper, we conduct a systematic analysis of 3D object detection methods, originally applied to autonomous driving scenarios, on monocular roadside images. Under a common evaluation protocol, based on a synthetic dataset with images from monocular roadside cameras located at intersection areas, we analyzed the detection quality achieved by these methods in the roadside context and the influence of key operational parameters. Our study finally highlights open challenges and future directions in this field
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