Reviewerly/exHarmony huggingface dataset:
An Aggregate Signature Scheme Based on a Trapdoor Hash Function for the Internet of Things

With the rapid development of the Internet of Things (IoT), it becomes challenging to ensure its security. Identity authentication and integrity verification can be achieved by secure hash functions and digital signature algorithms for IoT applications. In order to solve the issues of bandwidth limitation and computational efficiency of secure communication in IoT applications, an aggregate signature scheme based on multi- trapdoor hash function is proposed in this paper. Firstly, to prevent key exposition, based on the elliptic curve discrete logarithm problem (ECDLP), we constructed a double trapdoor hash function (DTH) and proved its reliability. Secondly, the multi-trapdoor hash function (MTH) based on DTH is presented. Finally, an MTH-based aggregate signature scheme (MTH-AS) with constant signature length is proposed. Based on the assumption of ECDLP, the proposed scheme is proven unforgeable against adaptive chosen message attacks with the Forking Lemma. Different from the most signature schemes with bilinear mapping, the proposed scheme has higher computational efficiency and shorter aggregate signature length. Moreover, it is independent of the number of signers. Security analysis and performance evaluation has revealed that the proposed scheme is an ideal solution for secure IoT applications with limited computing power, storage capacity, or limited bandwidth, such as wireless sensor networks, vehicular ad hoc networks, or healthcare sensor networks.
A Multidimensional TDOA Association Algorithm for Joint Multitarget Localization and Multisensor Synchronization

This article considers the problem of multitarget localization using time difference of arrival (TDOA) measurements at multiple sensors with misaligned clocks in the presence of data association uncertainty. Sensor synchronization and data association are two essential steps in target localization and tracking to align sensor clocks and to associate measurements from different sensors. In practice, sensor synchronization errors can adversely affect data association performance and vice versa. Although these two processes affect each other, they are usually addressed separately. We propose a novel joint multidimensional association algorithm for multisensor synchronization (JMDA4MS), which performs data association and yields sensor clock offset and target position estimates simultaneously using TDOA measurements. Considering the observability of the unknown parameters, the joint multiframe multidimensional association algorithm is developed as a multiframe extension of the JMDA4MS algorithm. A gating method and a multidimensional plus sequential two-dimensional association approach are utilized to improve the efficiency of the proposed algorithms. The Cramér-Rao lower bound for the proposed maximum likelihood estimator is derived as a performance benchmark. Computer simulations are carried out to evaluate the performance of the proposed algorithms on different scenarios.
Universal Learning of Individual Data

Universal supervised learning of individual data is considered from an information theoretic point of view in the standard supervised “batch” learning where prediction is done on a test sample once the entire training data is observed. In this individual setting the features and labels, both in the training and the test, are specific individual, deterministic quantities. Prediction loss is naturally measured by the log-loss. The presented results provide a minimax universal learning scheme, termed the Predictive Normalized Maximum Likelihood (pNML) that competes with a “genie” (or reference) that knows the true test label. In addition, a pointwise learnability measure associated with the pNML, for the specific training and test, is provided. This measure may also indicate the performance of the commonly used Empirical Risk Minimizer (ERM) learner.
Game-Based Mobile Learning with Augmented Reality: Are Teachers Ready to Adopt It?

Although only recently mobile augmented reality (AR) games have been adopted for educational purposes, research recognizes their potential and offers guidelines for their use in education, to create smart-learning ecosystems. It is time for teachers to adopt these technologies, but are they receptive to them? The aim of this study is to reveal teachers’ readiness to adopt mobile AR technologies in their practices, after training, bridging the gap between educational practices and research in educational sciences. This paper reports a case study based on a workshop designed to support teachers’ adoption of teaching strategies involving game-based mobile learning (mLearning) with AR. The workshop was conducted under the EduPARK project and offered trainees the opportunity to collaboratively experience the use, in loco, of the EduPARK app for outdoor learning with AR, as well as prompted them to plan educational resources appropriating these technologies. Data were collected through individual questionnaires and focus group interviews, and they were analysed through descriptive statistics, the computing of the educational value scale (EVS) and of the system usability scale (SUS), as well as content analysis. The results show that teachers consider they are ready to integrate mobile AR games in their practices, but they are foreseeing difficulties that need to be addressed. This paper contributes to the field of teacher training in game-based mLearning with AR as it unveils teachers’ readiness to adopt these technologies and why. Moreover, it characterizes a typology of teacher training in this area that, according to the trainee teachers, is successful.
Extreme semi-supervised learning for multiclass classification

Semi-Supervised Support Vector Machines (S3VMs) provide a powerful framework for Semi-Supervised Learning (SSL) tasks which leverage widely available unlabeled data to improve performance. However, there exist three issues in S3VMs: (i) S3VMs require concurrently training c one-against-all (OAA) classifiers (c is the number of classes) for multiclass classification, which is prohibitive for large c; (ii) S3VMs require huge computational time and large storage (because of the large kernel matrix) in large-scale training and testing; (iii) S3VMs require the balance constraint in the unlabeled data, which not only needs prior knowledge from the unlabeled data (the prior knowledge is unavailable in some applications), but also makes their nonconvex optimization problem more intractable. To address these issues, a novel method called Extreme Semi-Supervised Learning (ESSL) is proposed in this paper. First, the framework of Extreme Learning Machine (ELM) is adopted to handle both binary and multiclass classification problems in a unified model. Second, the hidden layer is encoded by an extremely small approximate empirical kernel map (AEKM) to greatly reduce the computational cost and the memory usage for training and testing. Third, the balance constraint (prior knowledge) in the unlabeled data is removed through the elaborative design of weighting function (which emphasizes the importance of labeled data and the minority pattern in the labeled data).By these three ways, ESSL can be solved effectively and efficiently based on alternating optimization (AO). More specifically, ESSL can be analytically and simply solved by generalized pseudoinverse and oneHotMap function (without any optimization solver and the OAA strategy) in the AO procedure, and consequently, better performance and much faster training speed are always achieved in ESSL. Our empirical study shows that ESSL significantly outperforms existing efficient SSL methods (e.g., meanS3VM and SS-ELM) in terms of accuracy, efficiency and memory, especially for large-scale multiclass problems. As an example, on the 20Newsgroups dataset, ESSL respectively runs 45 and 120 times faster than meanS3VM for training and testing with the improvement in accuracy of 3%, while the memory usage is reduced to 1/14. It is noteworthy that even though all the model parameters are with default values, ESSL already produces very excellent performance without fine-tuning parameters.
Privacy Against Brute-Force Inference Attacks

Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we derive the optimal utility-privacy trade-off via a linear program with any f-information adopted as the utility measure, and show that the optimal utility is a concave and piece-wise linear function of the privacy-leakage budget.
Two Human-Like Imitation-Learning Bots with Probabilistic Behaviors

We present details of two imitation-learning, game-playing agents - "SVMBot" and "PETBot" - that feature probabilistic modelling of some low-level combat behaviours. Both bots used a support-vector-machine approach for aiming and a novel, probabilistic model for jumping behaviours. PET-Bot also used a probability-estimation-tree (PET) technique for combat movement. The bots were developed for the FPS game Unreal Tournament 2004 and one of each was submitted to the qualification round (SVMBot) and the final round (PETBot) of the 2009 2K BotPrize competition where they obtained good results. They were then compared with each other as independent variables in a human-computer-interaction (HCI) between-subjects experiment.
Online Voting in Ontario Municipal Elections: A Conflict of Legal Principles and Technology?

This paper presents the first comprehensive study of the use of online voting technology in the province of Ontario, Canada. Despite having one of the largest concentrations of online voters globally, its use is not governed by any federal or provincial standards. This has left many municipalities to make decisions largely in isolation, relying on for-profit vendors to set their own bar for cybersecurity and public accountability. This study presents important observations about online voting use in the 2018 Ontario municipal election and questions whether the legal principles are being met by the technology deployed in practice.
Temporally Consistent Depth Prediction With Flow-Guided Memory Units

Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on convolutional neural networks (CNNs) discard temporal coherence in the video sequence and estimate depth independently for each frame, which often leads to undesired inconsistent results over time. To address this problem, we propose to memorize temporal consistency in the video sequence, and leverage it for the task of depth prediction. To this end, we introduce a two-stream CNN with a flow-guided memory module, where each stream encodes visual and temporal features, respectively. The memory module, implemented using convolutional gated recurrent units (ConvGRUs), inputs visual and temporal features sequentially together with optical flow tailored to our task. It memorizes trajectories of individual features selectively and propagates spatial information over time, enforcing a long-term temporal consistency to prediction results. We evaluate our method on the KITTI benchmark dataset in terms of depth prediction accuracy, temporal consistency and runtime, and achieve a new state of the art. We also provide an extensive experimental analysis, clearly demonstrating the effectiveness of our approach to memorizing temporal consistency for depth prediction.
Parallel hierarchical architectures for efficient consensus clustering on big multimedia cluster ensembles

Consensus clustering is a useful tool for robust or distributed clustering applications. However, given the fact that time complexities of the consensus functions scale linearly or quadratically with the number of combined clusterings, execution can be slow or even impossible when operating on big cluster ensembles, a situation encountered when we pursue robust multimedia data clustering. This work introduces hierarchical consensus architectures, an inherently parallel approach based on the divide-and-conquer strategy for computationally efficient consensus clustering, in a bid to make faster, more effective consensus clustering possible in big multimedia cluster ensemble scenarios. Moreover, we define a specific implementation of hierarchical architectures, including a theoretical analysis of its fully parallel implementation computational complexity. In experiments conducted on unimodal and multimedia data sets involving small and big cluster ensembles, we find parallel hierarchical consensus architectures variants perform faster than traditional flat consensus in 75% of the experiments on small cluster ensembles, a percentage that rises to 100% on unimodal and multimedia big cluster ensembles, achieving an average speedup ratio of 30.5. Moreover, depending on the consensus function employed, the quality of the obtained consensus partitions ensures robust clustering results.
Multichannel interpolation of nonuniform samples with application to image recovery

The multichannel trigonometric reconstruction from uniform samples was proposed recently. It not only makes use of multichannel information about the signal but is also capable to generate various kinds of interpolation formulas according to the types and amounts of the collected samples. The paper presents the theory of multichannel interpolation from nonuniform samples. Two distinct models of nonuniform sampling patterns are considered, namely recurrent and generic nonuniform sampling. Each model involves two types of samples: nonuniform samples of the observed signal and its derivatives. Numerical examples and quantitative error analysis are provided to demonstrate the effectiveness of the proposed algorithms. Additionally, the proposed algorithms for recovering highly corrupted images are also investigated. In comparison with the median filter and the Shepard interpolation, the proposed algorithms produce superior results with lower errors.
Caught by the fuzz: Using FCM to prevent coastal adaptation stakeholders from fleeing the scene

Adaptation to climate change is an increasingly urgent priority for coastal managers. At the highest levels of governance, European Union and Member State adaptation policies and strategies are now well established, but meaningful adaptation interventions illustrating tangible gains in resilience remain scarce. A clear framework for generic adaptation processes, along with barriers to their smooth progress, have now been identified in the literature, and equally generic scenario analysis tools have been put forward to strengthen adaptation delivery by assisting coastal managers to overcome them. Fuzzy Cognitive Mapping (FCM) is a relatively novel option in this respect, having yet to achieve the kind of widespread uptake and trial that more orthodox futures approaches such as intuitive logic scenarios have. FCM is however growing rapidly in its range of uses and breadth of uptake, and its utility in overcoming the barriers to adaptation among coastal managers is therefore worthy of analysis. This case study, in which FCM was employed in place of intuitive logic scenario analysis within an adaptation strategy development process, found FCM to carry a number of key strengths that intuitive logic scenario analysis has been found to lack. Through enabling a more detailed and granular level of participatory development of the 'engine' of the futures process than possible under an intuitive logic approach, the level of buy-in and commitment to the adaptation strategy development process achieved among coastal managers and stakeholders was significantly greater.
Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population

Advances in reinforcement learning research have recently produced agents that are competent, or sometimes exceed human performance, in complex tasks. Most interesting real world problems however, are not restricted to one agent, but instead deal with multiple agents acting in the same environment and have proven to be challenging tasks to solve. In this work we present a study on a homogeneous open population of agents modelled as a multi-agent reinforcement learning (MARL) system. We propose a centralised learning approach, with decentralised execution in which agents are given the same policy to execute individually. Using the SimuLane highway traffic simulator as a test-bed we show experimentally that using a single-agent learnt policy to initialise the multi-agent scenario, which we then fine-tune to the task, out-performs agents that learn in the multi-agent setting from scratch. Specifically we contribute an open population MARL configuration, how to transfer knowledge from single- to a multi-agent setting and a training procedure for a homogeneous open population of agents.
The Price Is (Not) Right: Reflections on Pricing for Transient Cloud Servers

Amazon introduced spot instances in December 2009, enabling "customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current Spot Price.'' Amazon's real-time computational spot market was novel in multiple respects. For example, it was the first (and to date only) large-scale public implementation of market-based resource allocation based on dynamic pricing after decades of research, and it provided users with useful information, control knobs, and options for optimizing the cost of running cloud applications. Spot instances also introduced the concept of transient cloud servers derived from variable idle capacity that cloud platforms could revoke at any time. Transient servers have since become central to efficient resource management of modern clusters and clouds. As a result, Amazon's spot market was the motivation for substantial research over the past decade. Yet, in November 2017, Amazon effectively ended its realtime spot market by announcing that users no longer needed to place bids and that spot prices will "...adjust more gradually, based on longer-term trends in supply and demand.'' The changes made spot instances more similar to the fixed-price transient servers offered by other cloud platforms. Unfortunately, while these changes made spot instances less complex, they eliminated many benefits to sophisticated users in optimizing their applications. This paper provides a retrospective on Amazon's real-time spot market, including its advantages and disadvantages for allocating transient servers compared to current fixed-price approaches. We also discuss some fundamental problems with Amazon's spot market, which we identified in prior work (from 2016), that predicted its eventual end. We then discuss potential options for allocating transient servers that combine the advantages of Amazon's real-time spot market, while also addressing the problems that likely led to its elimination.
Capacity of Quantum Private Information Retrieval with Multiple Servers

We study the capacity of quantum private information retrieval (QPIR) with multiple servers. In the QPIR problem with multiple servers, a user retrieves a classical file by downloading quantum systems from multiple servers each of which containing the whole classical file set, without revealing the identity of the retrieved file to any individual server. The QPIR capacity is defined as the maximum rate of the file size over the whole dimension of the downloaded quantum systems. Assuming the preexisting entanglement among servers, we prove that the QPIR capacity with multiple servers is 1 regardless of the number of servers and files. We propose a rate-one protocol which can be implemented by using only two servers. This capacity-achieving protocol outperforms its classical counterpart in the sense of the capacity, server secrecy, and upload cost. The strong converse bound is derived concisely without using the secrecy conditions.
Designing a fast image encryption scheme using fractal function and 3D Henon Map

The sensitivity of the chaos-based encryption scheme towards the initial constraints, ergodic, unpredictability characteristics has gained maximum popularity in recent decade. In this paper, a Fast Chaos-based Image Encryption Scheme using Fractal and 3D Henon Maps (FCIES-Fr-HP) is proposed for improving the degree of security in the image transmission. This is attained by integrating the merits of the predominant fractal function with the indispensable features of Henon Maps to generate effective key stream which is essential for a reliable cryptosystem. In the proposed FCIES-Fr-HP scheme, the fractal function-based image encryption enhances the probability of preventing the cryptosystem from the brute-force attack. This improvement of fractal function is facilitated by improving the key space to the maximum order in hundreds of bits. Further, the trajectory mapping characteristics of the utilized 3D Henon Maps improves the ergodic characteristic and enforces superior unpredictability during the process of encryption. The advantages of complex XOR and chaotic circular pixel shuffling process (CCPSP) are incorporated in the proposed FCIES-Fr-HP scheme for enhancing the quality of the encryption process to a maximum extent. The experimental results of the proposed FCIES-Fr-HP scheme is investigated using differential analysis, statistical analysis and key space analysis confirmed a superior security level in the protection of the image under network transmission.
An Overview of the Ludii General Game System

The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000 traditional strategy games using modern techniques.One of the key aspects of this project is the development of Ludii, a general game system that will be able to model and play the complete range of games required by this project.Such an undertaking will create a wide range of possibilities for new AI challenges.In this paper we describe many of the features of Ludii that can be used.This includes designing and modifying games using the Ludii game description language, creating agents capable of playing these games, and several advantages the system has over prior general game software.
Optimising Level Generators for General Video Game AI

Procedural Content Generation is an active area of research, with more interest being given recently to methods able to produce interesting content in a general context (without task-specific knowledge). To this extent, we focus on procedural level generators within the General Video Game AI framework (GVGAI). This paper proposes several topics of interest. First, a comparison baseline for GVGAI level generators, which is more flexible and robust than the existing alternatives. Second, a composite fitness evaluation function for levels based on AI play-testing. Third, a new parameterized generator, and a Meta Generator for performing parameter search on such generators are introduced. We compare the Meta Generator against random and constructive generator baselines, using the new fitness function, on 3 GVGAI games: Butterflies, Freeway and The Snowman. The Meta Generator is suggested to perform on par with or better than the baselines, depending on the game. Encouraged by these results, the Meta Generator will be submitted to the 2019 GVGAI Level Generation competition.
Some remarks on Boas transforms of wavelets

In this paper, we study Boas transforms of wavelets and obtain a sufficient condition under which the Boas transform of a wavelet is the derivative of another wavelet. Also, a characterization of the Boas transform of a wavelet ψ∈BR−[−1,1] is given. A sufficient condition is given to obtain higher order vanishing moments of Boas transforms of wavelets. Further, we study the Boas transform of wavelets in L2(R2). Finally, higher order vanishing moments of Boas transforms of wavelets have been used to approximate finite energy functions.