Reviewerly/exHarmony huggingface dataset:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More

Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness. Moreover, they do not account for sparsity in the input which, as our findings show, is often essential for obtaining non-trivial guarantees. We propose a model-agnostic certificate based on the randomized smoothing framework which subsumes earlier work and is tight, efficient, and sparsity-aware. Its computational complexity does not depend on the number of discrete categories or the dimension of the input (e.g. the graph size), making it highly scalable. We show the effectiveness of our approach on a wide variety of models, datasets, and tasks -- specifically highlighting its use for Graph Neural Networks. So far, obtaining provable guarantees for GNNs has been difficult due to the discrete and non-i.i.d. nature of graph data. Our method can certify any GNN and handles perturbations to both the graph structure and the node attributes.
Uso de las redes sociales entre los jóvenes y ciudadanía digital: análisis tras la COVID-19

This article is part of a project approved and funded by the Research Council of Social Sciences and Humanities of Canada, on social media, citizen participation and education. The objective is to explore and understand the impacts of the use, dissemination and production of social networks on citizen participation and democracy. Ours is an interpretive, descriptive study of the behavior of young people and corresponds to its initial phase. An online questionnaire has been answered by 127 students, of whom 95,05% are Spanish. This paper presents the most significant qualitative and quantitative results, as well as the critical issues based on the results obtained with the ATLAS-ti tool, to identify relevant guidelines and establish a framework that offers a meaning to the answers, and concludes that the problems to which young people surveyed make more references in relation to the use of social networks are the ethical problems for instance: the deception, cruelty, cyberbullying, the use of networks to disqualify or discredit people, competitiveness, disrespect for other people's opinions, unfounded judgements and the existence of "dark" content, which leads us to question how this fact positively or negatively affects their understanding of "citizen participation".
Improved Distorted Configuration Space Path Planning and Its Application to Robot Manipulators

Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length.
Robust Light Field Watermarking by 4D Wavelet Transform

Unlike common 2D images, the light field representation of a scene delivers spatial and angular description which is of paramount importance for 3D reconstruction. Despite the numerous methods proposed for 2D image watermarking, such methods do not address the angular information of the light field. Hence the exploitation of such methods may cause severe destruction of the angular information. In this paper, we propose a novel method for light field watermarking with extensive consideration of the spatial and angular information. Considering the 4D innate of the light field, the proposed method incorporates 4D wavelet for the purpose of watermarking and converts the heavily-correlated channels from RGB domain to YUV. The robustness of the proposed method has been evaluated against common image processing attacks.
A Deep 2D Convolutional Network for Waveform-Based Speech Recognition

Due to limited computational resources, acoustic models of early automatic speech recognition (ASR) systems were built in lowdimensional feature spaces that incur considerable information loss at the outset of the process.Several comparative studies of automatic and human speech recognition suggest that this information loss can adversely affect the robustness of ASR systems.To mitigate that and allow for learning of robust models, we propose a deep 2D convolutional network in the waveform domain.The first layer of the network decomposes waveforms into frequency sub-bands, thereby representing them in a structured high-dimensional space.This is achieved by means of a parametric convolutional block defined via cosine modulations of compactly supported windows.The next layer embeds the waveform in an even higher-dimensional space of high-resolution spectro-temporal patterns, implemented via a 2D convolutional block.This is followed by a gradual compression phase that selects most relevant spectro-temporal patterns using wide-pass 2D filtering.Our results show that the approach significantly outperforms alternative waveform-based models on both noisy and spontaneous conversational speech (24% and 11% relative error reduction, respectively).Moreover, this study provides empirical evidence that learning directly from the waveform domain could be more effective than learning using hand-crafted features.
A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus

Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes.
Chaotic Systems Based Real-Time Implementation of Visual Cryptography Using LabVIEW

In recent years, chaotic systems have begun to take a substantial place in literature due to increasing importance of secure communication. Chaotic synchronization which has emerged as a necessity for secure communication can now be performed with many different methods. This study purposes the Master-Slave synchronization via active controller and real time simulation of five different chaotic systems such as Lorenz, Sprott, Rucklidge, Moore-Spiegel, Rössler. Master slave synchronization was performed because of synchronization realized between the same type of chaotic systems with different initial parameters and also because of the systems were expected to behave similarly as a result of synchronization. Active control method was used to amplify the difference signal between master and slave systems which have different initial parameters and to return back synchronization information to the slave system. The real time simulation and synchronization of the master and slave systems performed successfully in LabVIEW environment. Furthermore, for the real time implementation, analogue outputs of NI-DAQ card used and real time results also were observed on an oscilloscope and secure communication application using sinusoidal signal and an image encryption application achieved successfully.
The Impact of COVID-19, Day-of-the-Week Effect, and Information Flows on Bitcoin’s Return and Volatility

Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate.The study uses the ARMA-GARCH model to capture Bitcoin's return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973).There were 3,064 samples collected during the period from 1 st of January 2012 to 20 th April, 2020.Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility.The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility.When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect.Under the impact of COVID-19 event, Bitcoin's condition volatility has increased significantly, indicating the risk of price changes.Finally, the Bitcoin's return has no impact on COVID-19 events and holidays (Saturday & Sunday).
The Delayed-Choice Quantum Eraser Leaves No Choice

A realizable delayed-choice quantum eraser, using a modified Mach-Zehnder (MZ) interferometer and polarization entangled photons, is theoretically analyzed here. The signal photon goes through a modified MZ interferometer, and the polarization of the idler photon provides path information for the signal photon. The setup is very similar to the delayed-choice quantum eraser experimentally studied by the Vienna group. In the class of quantum erasers with discrete output states, it is easy to see that the delayed mode leaves no choice for the experimenter. The which-way information is always erased, and every detected signal photon fixes the polarization state of the idler, and thus gives information on precisely how the signal photon traversed the two paths. The analysis shows that the Vienna delayed-choice quantum eraser is the first experimental demonstration of the fact that the delayed mode leaves no choice for the experimenter, and the which-way information is always erased. Additionally it is shown that this argument holds even in a conventional two-slit quantum eraser. Every photon registered anywhere on the screen, fixes the state of the two-state which-way detector in a unique mutually unbiased basis. In the delayed-choice quantum eraser experiments, the role of mutually unbiased basis sets for the which-way detector, has been overlooked till now.
Relationship between geomorphology of the ocean seafloor with the tectonic structure of the Earth's crust and anomalous geophysical fields: Makran Trench, Arabian Sea

This study examined the relationships between topographic structure and submarine geomorphology, sediment thickness, geophysical anomaly fields, geological settings and tectonic lineament stretching of the Arabian Sea region, Carlsberg Ridge morphology, Makran Trench depths by GMT. The study included spatial analysis of the high-resolution datasets (GEBCO, EGM96, GlobSed) and geomorphological modeling of the 300km-width cross-section profiles of the Makran Trench. The analysis shown correlation between complex geologic and tectonic structure, asymmetric geomorphology and geophysical anomaly fields. The Makran Trench is formed in the subduction zone of the Arabian and Eurasian plates at the basement of the continental margin of Pakistan. Submarine geomorphic structure of the Arabian Sea is complicated by the Carlsberg Ridge, Owen Fracture Zone, Aden-Owen- Carlsberg Triple junction, numerous faults and rifts. The geophysical fields of the marine free- air gravity correlate with distribution of these geomorphic structures. Bathymetric analysis of the trench revealed the most frequent depth (448 samples) at -3,250 to -3,500 m, following by intervals: -3,000 to -3,250 m (225 samples), -2,750 to -3,000 m (201 samples). Gently declining continental slope of the coastal elevations correlate with gradually decreasing depths, as equally distributed bins: 124 samples (-2,500 to -2,750 m), 96 (-2,250 to -2,500 m), 86 (-2,000 to -2,250 m). Makran Trench has asymmetric geomorphology with a high slope steepness on the continental slope of Pakistan and low steepness with flat valley on the oceanward side.
Digital Preservation and Reconstruction of Old Cultural Elements in Augmented Reality (AR)

This research aimed to preserve traditional elements and urban fabric with enabled interaction in augmented reality (AR). Cultural elements and fabrics are mutually influential in Lukang, Taiwan. Evolved routes for tourism and religious activities have created characteristic elements and activity-based fabrics and facilities. The sustainable promotion of digital cultural assets started from photogrammetry modeling of alley space and shops. The application of AR enabled situated learning of 68 objects, including decorated façades, jar walls, the Lukang Gate, beggar seats, and other creative cultural elements. The heritages were promoted under a new interactive measure of feasibility that facilitated cultural sustainability in a remote site. A mobile interface with a convenient smartphone configured certain settings that were sufficiently flexible and easy to apply. The study presented an effective and efficient remote and situated learning process that correlated the development or setting of both locations. Correlation was achieved with a high fidelity of appearance and utilizing a flexible transformation interface. An approach, which recreated the background and formerly reconstructed objects during AR simulation, was used to verify the outcome of the situated study with conflicting qualitative and quantitative findings.
Self-and-Mixed Attention Decoder with Deep Acoustic Structure for Transformer-Based LVCSR

The Transformer has shown impressive performance in automatic speech recognition. It uses the encoder-decoder structure with self-attention to learn the relationship between the high-level representation of the source inputs and embedding of the target outputs. In this paper, we propose a novel decoder structure that features a self-and-mixed attention decoder (SMAD) with a deep acoustic structure (DAS) to improve the acoustic representation of Transformer-based LVCSR. Specifically, we introduce a self-attention mechanism to learn a multi-layer deep acoustic structure for multiple levels of acoustic abstraction. We also design a mixed attention mechanism that learns the alignment between different levels of acoustic abstraction and its corresponding linguistic information simultaneously in a shared embedding space. The ASR experiments on Aishell-1 shown that the proposed structure achieves CERs of 4.8% on the dev set and 5.1% on the test set, which are the best results obtained on this task to the best of our knowledge.
New Representations of the AES Key Schedule

In this paper we present a new representation of the AES key schedule, with some implications to the security of AES-based schemes. In particular, we show that the AES-128 key schedule can be split into four independent parallel computations operating on 32 bits chunks, up to linear transformation. Surprisingly, this property has not been described in the literature after more than 20 years of analysis of AES. We show two consequences of our new representation, improving previous cryptanalysis results of AES-based schemes. First, we observe that iterating an odd number of key schedule rounds results in a function with short cycles. This explains an observation of Khairallah on mixFeed, a second-round candidate in the NIST lightweight competition. Our analysis actually shows that his forgery attack on mixFeed succeeds with probability 0.44 (with data complexity 220 GB), breaking the scheme in practice. The same observation also leads to a novel attack on ALE, another AES-based AEAD scheme. Our new representation also gives efficient ways to combine information from the first subkeys and information from the last subkeys, in order to reconstruct the corresponding master keys. In particular we improve previous impossible differential attacks against AES-128.
Selfish Mining in Proof-of-Work Blockchain with Multiple Miners: An Empirical Evaluation

Proof-of-Work blockchain, despite its numerous benefits, is still not an entirely secure technology due to the existence of Selfish Mining (SM) strategies that can disrupt the system and its mining economy. While the effect of SM has been studied mostly in a two-miners scenario, it has not been investigated in a more practical context where there are multiple malicious miners individually performing SM. To fill this gap, we carry out an empirical study that separately accounts for different numbers of SM miners (who always perform SM) and strategic miners (who choose either SM or Nakamoto’s mining protocol depending on which maximises their individual mining reward). Our result shows that SM is generally more effective as the number of SM miners increases, however its effectiveness does not vary in the presence of a large number of strategic miners. Under specific mining power distributions, we also demonstrate that multiple miners can perform SM and simultaneously gain higher mining rewards than they should. Surprisingly, we also show that the more strategic miners there are, the more robust the systems become. Since blockchain miners should naturally be seen as self-interested strategic miners, our findings encourage blockchain system developers and engineers to attract as many miners as possible to prevent SM and similar behaviour.
A General Approach to Domain Adaptation with Applications in Astronomy

The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications. The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario. An example is to build a predictive model on spectroscopic data to identify Supernovae IA, while subsequently trying to adapt such model on photometric data. In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions. Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples. Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation. Results using two real astronomical problems, Supernova Ia classification and identification of Mars landforms, show two main advantages with our approach: increased accuracy performance and substantial savings in computational cost.
Strong Noncontingency: On the Modal Logics of an Operator Expressively Weaker Than Necessity

Operators can be compared in at least two respects: expressive strength and deductive strength. Inspired by Hintikka’s treatment of question embedding verbs, the variations of noncontingency operator, and also the various combinations of modal operators and Boolean connectives, we propose a logic with (deductively) strong noncontingency operator as the only primitive modality. The novel operator is deductively but not expressively stronger than both noncontingency operator and essence operator, and expressively but not deductively weaker than the necessity operator. The frame-definability power of this new logic is in between standard modal logic and noncontingency logic. A notion of bisimulation is proposed to characterize this logic within standard modal logic and first-order logic. Axiomatizations over various frame classes are presented, among which the minimal logic is related to the treatment of an alternative semantics of the agreement operator proposed by Lloyd Humberstone.
Context-Aware Helpfulness Prediction for Online Product Reviews

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that “X out of Y” users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.