Reviewerly/exHarmony huggingface dataset:
Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning

Within the Pattern Recognition field, two representations are generally considered for encoding the data: statistical codifications, which describe elements as feature vectors, and structural representations, which encode elements as high-level symbolic data structures such as strings, trees or graphs. While the vast majority of classifiers are capable of addressing statistical spaces, only some particular methods are suitable for structural representations. The kNN classifier constitutes one of the scarce examples of algorithms capable of tackling both statistical and structural spaces. This method is based on the computation of the dissimilarity between all the samples of the set, which is the main reason for its high versatility, but in turn, for its low efficiency as well. Prototype Generation is one of the possibilities for palliating this issue. These mechanisms generate a reduced version of the initial dataset by performing data transformation and aggregation processes on the initial collection. Nevertheless, these generation processes are quite dependent on the data representation considered, being not generally well defined for structural data. In this work we present the adaptation of the generation-based reduction algorithm Reduction through Homogeneous Clusters to the case of string data. This algorithm performs the reduction by partitioning the space into class-homogeneous clusters for then generating a representative prototype as the median value of each group. Thus, the main issue to tackle is the retrieval of the median element of a set of strings. Our comprehensive experimentation comparatively assesses the performance of this algorithm in both the statistical and the string-based spaces. Results prove the relevance of our approach by showing a competitive compromise between classification rate and data reduction.
The fractional difference form of sine chaotification model

To improve the chaos complexity of existing chaotic maps, the fractional difference form of sine chaotification model (FSCM) is proposed in this paper based on discrete fractional calculus. In order to show its effect, we apply it to three chaotic maps. And the bifurcation diagrams and Lyapunov exponent of the generated new map are studied numerically. The experimental results show that FSCM has more stable and efficient performance. In addition, the dynamic behavior of the generated new map varies with the fractional order, which can be observed through analysis. Finally, FSCM is used for image encryption to show its performance in practical applications. The analysis of the results shows that the chaotic behavior of the fractional map generated by FSCM is more complex and its encryption effect is better.
ShrinkyKit: 3D Printing Shrinkable Adaptations for Everyday Objects

With the development of 3D printing techniques, recreating household objects has become a trend. We present ShrinkyKit - a material-orientation method that allows novices to easily make adaptations to everyday objects with a desktop fused deposition modeling (FDM) 3D printer. Compared to existing methods, our method can benefit from the shrinking property of printed thermoplastic to fasten arbitrary shapes without high-fidelity manual requirements. By means of a material experiment, we construct a design tool and multiple trigger environments through a set of daily design cases, which can enable users to custom design and quickly fabricate their adaptations to reform old items or prototype assistive technologies.
From asymmetry to correlations

Symmetry, as well as the dual concept of asymmetry, are essential, powerful and ubiquitous in physics and nature. Motivated by the original Wigner-Yanase skew information and its extension by Dyson (Wigner E. P. and Yanase M. M., Proc. Natl. Acad. Sci. U.S.A., 49 (1963) 910), which quantifies the information content of a quantum state skew to a conserved observable and may be reinterpreted as asymmetry of a state with respect to an observable, we address the issue of quantifying asymmetry and symmetry of states with respect to Lie groups and Lie algebras. We elucidate that correlations arise naturally from the difference between asymmetries of global and local states. This provides a fundamental insight into correlations. The idea is further illustrated by several examples.
Automated Synthetic-to-Real Generalization

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Yet the role of ImageNet knowledge is seldom discussed despite common practices that leverage this knowledge to maintain the generalization ability. An example is the careful hand-tuning of early stopping and layer-wise learning rates, which is shown to improve synthetic-to-real generalization but is also laborious and heuristic. In this work, we explicitly encourage the synthetically trained model to maintain similar representations with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize (L2O)} strategy to automate the selection of layer-wise learning rates. We demonstrate that the proposed framework can significantly improve the synthetic-to-real generalization performance without seeing and training on real data, while also benefiting downstream tasks such as domain adaptation. Code is available at: this https URL https://github.com/NVlabs/ASG.
MetaFun: Meta-Learning with Iterative Functional Updates

We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.
Grid-Based Quaternion Filter for SO(3) Estimation

A novel discrete Bayesian filtering scheme is proposed on the manifold of unit quaternions for rotation estimation. Existing quaternion filters rely on specific distributions (typically the Bingham distribution) to model the uncertainty in a parametric form. The scheme proposed in this paper allows non-parametric modeling of the underlying uncertainty using Dirac mixtures located on a hyperspherical grid. The grid is generated by first discretizing the tangent space layer-wise w.r.t. spherical coordinates. Then, the on-tangent-space grid is mapped back to the manifold via the exponential map. The on-manifold grid of quaternions is deployed in a discrete Bayesian filtering scheme. We evaluate the proposed grid-based quaternion filter for recursive rotation estimation with simulations. Results show that the tracking performance of the proposed approach is superior to Bingham-based quaternion filters.
Cybersecurity Disclosure by the Companies on the S&amp;P/TSX 60 Index

ABSTRACT Cybersecurity has become a topic of great interest since 2010. Accounting issues surrounding cybersecurity governance, management, and disclosure have gained attention from accounting standard setters, large accounting firms, and professional associations, but only a limited number of studies have looked at cybersecurity disclosure. In this study, we examine whether the content of cybersecurity disclosures of Canadian firms comprising the S&amp;P/TSX 60 index is aligned with best practices—that is, financial regulators' guidelines in that matter. A content analysis was performed of documents issued between January 2017 and mid‐2018, consisting of recent annual information forms (AIFs), annual and quarterly management's discussion and analysis (MD&amp;As), proxy circulars, material change reports, and news releases. To assess the nature and extent of cybersecurity disclosure, we developed a scoring grid featuring 40 items based on financial regulators' guidelines. Results show that cybersecurity disclosure levels are low. Companies vary widely in the amount of detail they provide, and the information is often not company‐specific. The variations among industrial sectors involve the categories related to cybersecurity risk, cybersecurity risk mitigation, and other items. Most of the companies provided cybersecurity disclosures in the annual MD&amp;A, and several reiterated some disclosure items in the AIF and proxy circular. The results of this study highlight some areas where cybersecurity disclosures have evolved and others where they could be improved. They suggest that some firms strive to avoid boilerplate language and be more company‐specific. The findings also suggest that financial regulators could issue more stringent requirements.
How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech

Translationese is a phenomenon present in human translations, simultaneous interpreting, and even machine translations.Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear.This study analyzes translationese patterns in translation, interpreting, and machine translation outputs in order to explore possible reasons.In our analysis we (i) detail two non-invasive ways of detecting translationese and (ii) compare translationese across human and machine translations from text and speech.We find that machine translation shows traces of translationese, but does not reproduce the patterns found in human translation, offering support to the hypothesis that such patterns are due to the model (human vs. machine) rather than to the data (written vs. spoken).
Lifted Successor Generation Using Query Optimization Techniques

The standard PDDL language for classical planning uses several first-order features, such as schematic actions. Yet, most classical planners ground this first-order representation into a propositional one as a preprocessing step. While this simplifies the design of other parts of the planner, in several benchmarks the grounding process causes an exponential blowup that puts otherwise solvable tasks out of reach of the planners. In this work, we take a step towards planning with lifted representations. We tackle the successor generation task, a key operation in forward-search planning, directly on the lifted representation using well-known techniques from database theory. We show how computing the variable substitutions that make an action schema applicable in a given state is essentially a query evaluation problem. Interestingly, a large number of the action schemas in the standard benchmarks result in acyclic conjunctive queries, for which query evaluation is tractable. Our empirical results show that our approach is competitive with the standard (grounded) successor generation techniques in a few domains and outperforms them on benchmarks where grounding is challenging or infeasible.
Studying gas exceptions in blockchain-based cloud applications

Abstract Blockchain-based cloud application (BCP) is an emerging cloud application architecture. By moving trust-critical functions onto blockchain, BCP offers unprecedented function transparency and data integrity. Ethereum is by far the most popular blockchain platform chosen for BCP. In Ethereum, special programs named smart contracts are often used to implement key components for BCP. By design, users can send transactions to smart contracts, which will automatically lead to code execution and state modification. However, unlike regular programs, smart contracts are restricted in execution by gas limit, i.e., a form of runtime resource. If a transaction uses up all available gas, an out of gas exception () will trigger, reverting state until right before that transaction.In this work, we study the out of gas exceptions (or gas exceptions in short) on Ethereum empirically for the very first time. In particular, we collect exception transactions using an instrumented Ethereum client. By investigation, we found gas exceptions stand out in terms of both occurrences and losses. Moreover, we focused on individual contracts and transactions, aiming at discovering and identifying common causing factors triggering these exceptions. At last, we also investigate existing tools in preventing gas exceptions. Our results suggest further research and study in this direction.
Measurement of Activities of Daily Living: a simulation tool for the optimisation of a Passive Infrared sensor network in a Smart Home environment

In this paper, a tool for the simulation of home environment capable of representing human moving patterns, related to Activities of Daily Living (ADL), and modelling Passive Infrared (PIR) sensor networks is presented. PIR sensors have been chosen because they are non-intrusive, contactless and low-cost. The tool has been programmed in MATLAB and provides a graphic interface from which the developer can change key simulation parameters. It makes it possible to load and visualise the 2D map of the home environment used for simulation as well as add, customise and place ceiling or wall mounted PIR sensors, and regulate users’ trajectory parameters, such as walking speed, step length or path efficiency. The simulator is useful for quickly generating synthetic data to train machine learning (ML) algorithms able to recognize user behavior, without the necessity to perform long acquisition periods. In order to demonstrate its applicability, the tool has been used to create normal and wandering trajectories and their related sensor activations. These data were employed to develop a ML algorithm able to detect overnight wandering, a common behaviour in patients with dementia. The results show that a Decision Tree (DT) algorithm is reliable for the purpose of distinguishing normal trajectories from the wandering ones detected by PIR sensor activations, obtaining an accuracy level of over 95% using a cross-validation approach.
Towards a Scalable IOTA Tangle-Based Distributed Intelligence Approach for the Internet of Things

Distributed Ledger Technology (DLT) brings a set of opportunities for the Internet of Things (IoT), which leads to innovative solutions for existing components at all levels of existing architectures. IOTA Tangle has the potential to overcome current technical challenges identified for the IoT domain, such as data processing, infrastructure scalability, security, and privacy. Scaling is a serious challenge that influences the deployment of IoT applications. We propose a Scalable Distributed Intelligence Tangle-based approach (SDIT), which aims to address the scalability problem in IoT by adapting the IOTA Tangle architecture. It allows the seamless integration of new IoT devices across different applications. In addition, we describe an offloading mechanism to perform proof-of-work (PoW) computation in an energy-efficient way. A set of experiments has been conducted to prove the feasibility of the Tangle in achieving better scalability, while maintaining energy efficiency. The results indicate that our proposed solution provides highly-scalable and energy efficient transaction processing for IoT DLT applications, when compared with an existing DAG-based distributed ledger approach.
humanportal – A React.js case study

The evolution of Web technologies triggers the appearance of frameworks and libraries aiming to improve the work conducted by those who use them. To maximize customer satisfaction and create solutions under current market standards, companies must adapt their products, with focus on web-based solutions, to enhance the usability and the flow of their use. In this way, this paper presents a real case study, where the company humansoft, revised one of its main solutions, humanportal, using one of the most recent and used web technology, React.js. React.js is a framework specially designed to deal with the visualization layer of web applications and their use in a business context, namely in the human resources management field, which is the focus of the humanportal solution. With this process, there was a real customer satisfaction degree, having them highlighted the greater flexibility and ease of use of the product, thus allowing optimization and capability of the human resources management procedures.
Polarity Classification of Twitter Messages using Audio Processing

Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.
Towards Evolving Symbiotic Cognitive Education Based on Digital Twins

In the past, professional education lasted for a lifetime. Since then, the industrial revolutions have accelerated the pace of knowledge doubling from a lifetime to months, and have altered the working environment so that professionals will have to move between many jobs in their life. Are we capable of adjusting to that pace? How can we learn all that is needed in the old educational system? The time has come to revamp the educational system at the core. The new system must be personalized to match the diversity of individual abilities and styles of learning. The new system must also be based not only on the body of knowledge (BoK), but body of experience (BoX). We envisage that the new personalized system of education being sufficiently agile and interactive so that it would become evolving in its symbiosis with humans. For that to happen, we must coexist with symbiotic autonomous cognitive systems, specifically involving digital twins. This paper addresses some aspects of this view.
Fault Injection as an Oscilloscope: Fault Correlation Analysis

Fault Injection (FI) attacks have become a practical threat to modern cryptographic implementations. Such attacks have recently focused more on exploitation of implementation-centric and device-specific properties of the faults. In this paper, we consider the parallel between SCA attacks and FI attacks; specifically, that many FI attacks rely on the data-dependency of activation and propagation of a fault, and SCA attacks similarly rely on data-dependent power usage. In fact, these are so closely related that we show that existing SCA attacks can be directly applied in a purely FI setting, by translating power FI results to generate FI ‘probability traces’ as an analogue of power traces. We impose only the requirements of the equivalent SCA attack (e.g., knowledge of the input plaintext for CPA on the first round), along with a way to observe the status of the target (whether or not it has failed and been “muted” after a fault). We also analyse existing attacks such as Fault Template Analysis in the light of this parallel, and discuss the limitations of our methodology. To demonstrate that our attacks are practical, we first show that SPA can be used to recover RSA private exponents using FI attacks. Subsequently, we show the generic nature of our attacks by performing DPA on AES after applying FI attacks to several different targets (with AVR, 32-bit ARM and RISC-V CPUs), using different software on each target, and do so with a low-cost (i.e., less than $50) power fault injection setup. We call this technique Fault Correlation Analysis (FCA), since we perform CPA on fault probability traces. To show that this technique is not limited to software, we also present FCA results against the hardware AES engine supported by one of our targets. Our results show that even without access to the ciphertext (e.g., where an FI redundancy countermeasure is in place, or where ciphertext is simply not exposed to an attacker in any circumstance) and in the presence of light jitter, FCA attacks can successfully recover keys on each of these targets.
Physical synthesis of six-string guitar plucks using the Udwadia-Kalaba modal formulation

Extending the previous work done by the authors, this paper develops a time domain synthesis method for the classical guitar based on substructuring concepts and using the Udwadia-Kalaba modeling strategy. Adopting a modal description of the dynamics of the separate flexible subsystems in terms of their unconstrained modes and enforcing coupling constraint conditions for the assembly, the result is an explicit dynamical modal formulation for the coupled system that directly lends itself to time-stepping methods for simulation. The guitar model couples six strings through an experimentally based body model of an actual instrument, includes two string polarizations, and the string geometrical nonlinear effects, as well as the string/fret interaction as the instrument is played. Details are given for putting all the vibrating components together in a satisfying manner, and a specific strategy is explored to allow for a nonrigid fret using flexible-dissipative-inertial constraints. The reliability of the approach is demonstrated with simulation examples that confirm the features one would expect regarding the dynamical behavior of classical guitars. Finally, a pragmatic approach is made to calculate the radiated sound by convolution, combining the computed bridge force with a measured vibro-acoustic impulse response of the instrument, which proved to give satisfactory sounding results.
Cluster non‐Gaussian functional data

Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This paper focuses on clustering non-Gaussian functional data without the prior information of the number of clusters. We introduce a semiparametric mixed normal transformation model to accommodate non-Gaussian functional data, and propose a penalized approach to simultaneously estimate the parameters, transformation function, and the number of clusters. The estimators are shown to be consistent and asymptotically normal. The practical utility of the methods is confirmed via simulations as well as an application of the analysis of Alzheimer's disease study. The proposed method yields much less classification error than the existing methods. Data used in preparation of this paper were obtained from the Alzheimer's Disease Neuroimaging Initiative database.
Mathematical Morphology on the Triangular Grid: The Strict Approach

Mathematical morphology provides various tools for image analysis. The two basic operations, dilation and erosion, are based on translations with the help of a given structural element (another ima...
Blockchain-Based Systems in Land Registry, A Survey of Their Use and Economic Implications

In recent years it has been demonstrated that the use of the traditional property registry models involves the risk of corruption along with long waiting times. This paper points out the main problems of conventional models and makes a survey on new ones, based on blockchain technology, already being developed as proof of concepts by different countries. With the use of this technology in land registry systems, it is possible to improve the transparency of the processes as well as optimizing costs and time in the realization of these. To show the theoretic results of this study, it has been taken the Spanish land registry as a use case to compare them.