Reviewerly/exHarmony huggingface dataset:
Application of 3D digital image correlation for the measurement of the tensile mechanical properties of high-strength steel

Abstract High-strength steel plays an important role in engineering fields such as infrastructure. For this reason, an accurate determination of its mechanical properties is of critical importance. Considering the inconvenience of conventional mechanical extensometers for the deformation measurement of small-scale specimens, 3D digital image correlation (3D-DIC) was used to measure the deformation of Grade 8.8 bolts and Q690 high-strength steel specimens by means of a uniaxial tensile test, and in this way, stress–strain curves, elastic modulus, yield strength, tensile strength, percentage elongation after fracture, and percentage reduction of area were obtained. Experimental results show that Grade 8.8 bolts and Q690 steel result in higher yield strength and tensile strength than common steel. Moreover, owing to the phenomenon that stress remains constant with strain increase in the yielding stage, the evolution process from elastic deformation to plastic deformation of the specimens during the yielding stage could be studied. Experimental results show that the axial strain of Grade 8.8 bolts increases from 0.3 to 1 % during the yielding stage and for Q690 specimens the corresponding strain increases from 0.4 to 1.8 %.
Semi-supervised oblique predictive clustering trees

Semi-supervised learning combines supervised and unsupervised learning approaches to learn predictive models from both labeled and unlabeled data. It is most appropriate for problems where labeled examples are difficult to obtain but unlabeled examples are readily available (e.g., drug repurposing). Semi-supervised predictive clustering trees (SSL-PCTs) are a prominent method for semi-supervised learning that achieves good performance on various predictive modeling tasks, including structured output prediction tasks. The main issue, however, is that the learning time scales quadratically with the number of features. In contrast to axis-parallel trees, which only use individual features to split the data, oblique predictive clustering trees (SPYCTs) use linear combinations of features. This makes the splits more flexible and expressive and often leads to better predictive performance. With a carefully designed criterion function, we can use efficient optimization techniques to learn oblique splits. In this paper, we propose semi-supervised oblique predictive clustering trees (SSL-SPYCTs). We adjust the split learning to take unlabeled examples into account while remaining efficient. The main advantage over SSL-PCTs is that the proposed method scales linearly with the number of features. The experimental evaluation confirms the theoretical computational advantage and shows that SSL-SPYCTs often outperform SSL-PCTs and supervised PCTs both in single-tree setting and ensemble settings. We also show that SSL-SPYCTs are better at producing meaningful feature importance scores than supervised SPYCTs when the amount of labeled data is limited.
Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence—A Viewpoint

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.
Symmetry Protected Quantum Computation

We consider a model of quantum computation using qubits where it is possible to measure whether a given pair are in a singlet (total spin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>0</mml:mn></mml:math>) or triplet (total spin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn></mml:math>) state. The physical motivation is that we can do these measurements in a way that is protected against revealing other information so long as all terms in the Hamiltonian are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi><mml:mi>U</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math>-invariant. We conjecture that this model is equivalent to BQP. Towards this goal, we show: (1) this model is capable of universal quantum computation with polylogarithmic overhead if it is supplemented by single qubit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math> gates. (2) Without any additional gates, it is at least as powerful as the weak model of "permutational quantum computation" of Jordan [14, 18]. (3) With postselection, the model is equivalent to PostBQP.
Pandemic Innovation: The Private Sector and the Development of Contact-Tracing and Exposure Notification Apps

From the early days of the COVID-19 pandemic, technology companies have participated in the search for solutions to new challenges. One of these has been the need for public health authorities to engage in contact-tracing to help curb the spread of the disease. Manual contact-tracing for public health purposes is a well-established component of the management of epidemics and pandemics. Public health authorities identify infected individuals, question them about close contacts, and then reach out to those contacts with the appropriate public health advice. From the outset of the pandemic, many believed that technology could play a role in supplementing manual contact-tracing. How it could do so quickly became tied to issues of how it should do so, engendering a normative discussion affecting the partnership between business and government in the large-scale design and deployment of technology for public health surveillance.
Tomographic Witnessing and Holographic Quantifying of Coherence

The detection and quantification of quantum coherence play significant roles in quantum information processing. We present an efficient way of tomographic witnessing for both theoretical and experimental detection of coherence. We prove that a coherence witness is optimal if and only if all of its diagonal elements are zero. Naturally, we obtain a bona fide homographic measure of coherence given by the sum of the absolute values of the real and the imaginary parts of the non-diagonal entries of a density matrix, together with its interesting relations with other coherence measures like $l_1$ norm coherence and robust of coherence.
MULTIPLE RGB IMAGE ENCRYPTION ALGORITHM WITH MULTILAYERS BY AFFINE HILL CIPHER WITH FRDCT AND ARNOLD TRANSFORM

In this paper, we aim to investigate the security of multiple images based on affine hill cipher (AHC), fractional discrete cosine transform (FrDCT) and Arnold transform (AT). Three original [Formula: see text] images are encoded as a single [Formula: see text] image by first converting three [Formula: see text] images into three indexed images by extracting their color maps. These three indexed image are treated as [Formula: see text], [Formula: see text] and [Formula: see text] channel of a single [Formula: see text] image. Each channel is first encrypted using cryptographic encryption scheme AHC in time domain. The partially encoded image is transformed to frequency domain using FrDCT. In frequency domain, the partially encoded image is scrambled with the help of AT. The proposed scheme assures security in time domain, frequency domain as well as in coordinate domain. The original images can be recovered correctly by using proper arrangements of secret keys. The storage and transmission of the encrypted image is easy due to single encrypted image. The robustness of the proposed approach is proved by conducting computer simulations on standard examples.
Vicarious pain is an outcome of atypical body ownership: Evidence from the rubber hand illusion and enfacement illusion

Some people report localised pain on their body when seeing other people in pain (sensory-localised vicarious pain responders). In this study, we assess whether this is related to atypical computations of body ownership which, in paradigms such as the rubber hand illusion (RHI), can be conceptualised as a Bayesian inference as to whether multiple sources of sensory information (visual, somatosensory) belong together on a single body (one’s own) or are distributed across several bodies (vision = other, somatosensory = self). According to this model, computations of body ownership depend on the degree (and precision) of sensory evidence, rather than synchrony per se. Sensory-localised vicarious pain responders exhibit the RHI following synchronous stroking and—unusually—also after asynchronous stroking. Importantly, this occurs only in asynchronous conditions in which the stroking is predictable (alternating) rather than unpredictable (random). There was no evidence that their bottom-up proprioceptive signals are less precise, suggesting individual differences in the top-down weighting of sensory evidence. Finally, the enfacement illusion (EI) was also employed as a conceptually related bodily illusion paradigm that involves a completely different response judgement (based on vision rather than proprioception). Sensory-localised responders show a comparable pattern on this task after synchronous and asynchronous stroking. This is consistent with the idea that they have top-down (prior) differences in the way body ownership is inferred that transcends the exact judgement being made (visual or proprioceptive).
Digital Social Contracts with AI Robots: Some Implications for Amazon.Com

The use of AI robots on platforms in the digital world is ubiquitous. Amazon.com has AI features that significantly enhance users’ productivity. There is no question that Amazon is subject to competition. Their competitors have their own standards. If standards can differentiate platforms, they could coexist. This study’s evaluation of Amazon along public policy guidelines suggests that there is no reason for imposing additional regulations for Amazon based on its current trend of operations. This study proposes a new thinking, passing beyond the analogy between platform operations and city operations, notably along the dimension of private vs. public spaces, since people are familiar with thinking of issues in that paradigm as the pre-internet days.
Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets

In this paper, we present various pre-training strategies that aid in improving the accuracy of the sentiment classification task. At first, we pre-train language representation models using these strategies and then fine-tune them on the downstream task. Experimental results on a time-balanced tweet evaluation set show the improvement over the previous technique. We achieve 76% accuracy for sentiment analysis on Latvian tweets, which is a substantial improvement over previous work.
Heterogeneous value coding in orbitofrontal populations.

Value signals in the brain are important for learning, decision-making, and orienting behavior toward relevant goals. Although they can play different roles in behavior and cognition, value representations are often considered to be uniform and static signals. Nonetheless, contextual and mixed representations of value have been widely reported. Here, we review the evidence for heterogeneity in value coding and dynamics in the orbitofrontal cortex. We argue that this diversity plays a key role in the representation of value itself and allows neurons to integrate value with other behaviorally relevant information. We also discuss modeling approaches that can dissociate potential functions of heterogeneous value codes and provide further insight into its importance in behavior and cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Every Byte Matters

Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is exchanged with a companion app running on a smartphone over a Bluetooth connection. In this work, we investigate what can be inferred from the metadata (such as the packet timings and sizes) of encrypted Bluetooth communications between a wearable device and its connected smartphone. We show that a passive eavesdropper can use traffic-analysis attacks to accurately recognize (a) communicating devices, even without having access to the MAC address, (b) human actions (e.g., monitoring heart rate, exercising) performed on wearable devices ranging from fitness trackers to smartwatches, (c) the mere opening of specific applications on a Wear OS smartwatch (e.g., the opening of a medical app, which can immediately reveal a condition of the wearer), (d) fine-grained actions (e.g., recording an insulin injection) within a specific application that helps diabetic users to monitor their condition, and (e) the profile and habits of the wearer by continuously monitoring her traffic over an extended period. We run traffic-analysis attacks by collecting a dataset of Bluetooth traces of multiple wearable devices, by designing features based on packet sizes and timings, and by using machine learning to classify the encrypted traffic to actions performed by the wearer. Then, we explore standard defense strategies; we show that these defenses do not provide sufficient protection against our attacks and introduce significant costs. Our research highlights the need to rethink how applications exchange sensitive information over Bluetooth, to minimize unnecessary data exchanges, and to design new defenses against traffic-analysis tailored to the wearable setting.
On Disentangled Representations Learned from Correlated Data

Despite impressive progress in the last decade, it still remains an open challenge to build models that generalize well across multiple tasks and datasets. One path to achieve this is to learn meaningful and compact representations, in which different semantic aspects of data are structurally disentangled. The focus of disentanglement approaches has been on separating independent factors of variation despite the fact that real-world observations are often not structured into meaningful independent causal variables. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of most prominent methods and disentanglement scores on correlated data in a large scale empirical study (including 4260 models). We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations, while widely used disentanglement scores fall short of capturing these latent correlations. Finally, we demonstrate how to disentangle these latent correlations using weak supervision, even if we constrain this supervision to be causally plausible. Our results thus support the argument to learn independent mechanisms rather than independent factors of variations.
Sentiment Analysis Technologies in AliMe — An Intelligent Assistant for E-commerce

In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction prediction. Detailed implementation of each module is demonstrated and experiments show our framework not only performs well on each single task but also manifests its competitive business value as a whole.
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based Retrieval (EBR) systems for recommendation share the similar ideas with sequential recommenders. Among them, how to comprehensively capture sequential user interest is a fundamental problem. However, most existing sequential recommendation models take as input clicked or purchased behavior sequences from user-item interactions. This leads to incomprehensive user representation and sub-optimal model performance, since they ignore the complete user behavior exposure data, i.e., items impressed yet unclicked by users. In this work, we attempt to incorporate and model those unclicked item sequences using a new learning approach in order to explore better sequential recommendation technique. An efficient triplet metric learning algorithm is proposed to appropriately learn the representation of unclicked items. Our method can be simply integrated with existing sequential recommendation models by a confidence fusion network and further gain better user representation. The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation. Moreover we deploy our new model (named XDM) into EBR of recommender system at Taobao, outperforming the previous deployed generation SDM.
Next frontier in planetary geological reconnaissance: Low-latency telepresence

The most compelling questions about the possibility of life on other planetary bodies will likely be answered only once the human mind can fully engage with the explored alien surface. Current interplanetary science operation models are primarily based on the paradigm of using robotic off-Earth assets for exploration. Conducting field geology research on other planetary bodies requires experts to use data collected from advanced technologies to substitute for their on-site presence to overcome the time delay (e.g., latency) and bandwidth constraints in the transfer of data. To overcome these constraints, astronauts will need to be placed either directly on the surface (e.g., "boots on the ground") or robotic systems will need to be deployed and directed by humans from Earth with massive time delays. In the next major stage of planetary reconnaissance, as presented here, deployment of teleoperated robotic assets with humans sufficiently proximal to the exploration targets (referred to here as "Low-Latency Telepresence (LLT)") will greatly enhance scientific return. Humans in orbit can be present electronically/digitally at multiple sites on a planetary surface, and that presence can be sterile, alleviating planetary protection concerns. Crewed astronauts using LLT, in partnership with robotic agents on the surface, will provide scientists the means to explore, for example, the mountains and vast canyon systems on Mars and the submarine environment of Jupiter's moon Europa. Consequently, because LLT does not require humans to be physically present at the exploration site, it is potentially advantageous in terms of schedule and cost, reduces human and planetary risks, while increasing the quantity and quality of the science data that can be returned.
Combining caesar cipher and hill cipher in the generating encryption key on the vigenere cipher algorithm

Abstract There is a risk in the process of exchanging information, especially text information. To reduce that risk, one of cryptographic method can be applied. That is Vigenere Cipher algorithm. But the Vigenere Cipher algorithm have a weakness. The weakness is a repetitive encryption key. It causes the chipertext to be predictable with the Babbage-Kasiski method. By combining Caesar Cipher and Hill Cipher methods in the process of generating the encryption key, its expected to cover up the weakness of the Vigenere Cipher method. The combination has the capability to hide the character appearance’s frequencies.
Rational Shapley Values

Explaining the predictions of opaque machine learning algorithms is an important and challenging task, especially as complex models are increasingly used to assist in high-stakes decisions such as those arising in healthcare and finance. Most popular tools for post-hoc explainable artificial intelligence (XAI) are either insensitive to context (e.g., feature attributions) or difficult to summarize (e.g., counterfactuals). In this paper, I introduce rational Shapley values, a novel XAI method that synthesizes and extends these seemingly incompatible approaches in a rigorous, flexible manner. I leverage tools from decision theory and causal modeling to formalize and implement a pragmatic approach that resolves a number of known challenges in XAI. By pairing the distribution of random variables with the appropriate reference class for a given explanation task, I illustrate through theory and experiments how user goals and knowledge can inform and constrain the solution set in an iterative fashion. The method compares favorably to state of the art XAI tools in a range of quantitative and qualitative comparisons.
Bayesian Inference on Multivariate Medians and Quantiles

In this paper, we consider Bayesian inference on a class of multivariate median and the multivariate quantile functionals of a joint distribution using a Dirichlet process prior. Since, unlike univariate quantiles, the exact posterior distribution of multivariate median and multivariate quantiles are not obtainable explicitly, we study these distributions asymptotically. We derive a Bernstein-von Mises theorem for the multivariate $\ell_1$-median with respect to general $\ell_p$-norm, which in particular shows that its posterior concentrates around its true value at $n^{-1/2}$-rate and its credible sets have asymptotically correct frequentist coverage. In particular, asymptotic normality results for the empirical multivariate median with general $\ell_p$-norm is also derived in the course of the proof which extends the results from the case $p=2$ in the literature to a general $p$. The technique involves approximating the posterior Dirichlet process by a Bayesian bootstrap process and deriving a conditional Donsker theorem. We also obtain analogous results for an affine equivariant version of the multivariate $\ell_1$-median based on an adaptive transformation and re-transformation technique. The results are extended to a joint distribution of multivariate quantiles. The accuracy of the asymptotic result is confirmed by a simulation study. We also use the results to obtain Bayesian credible regions for multivariate medians for Fisher's iris data, which consists of four features measured for each of three plant species.
A Scalable Two Stage Approach to Computing Optimal Decision Sets

Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning behind its decisions, warranting the need for explainable AI. Rule-based models, such as decision trees, decision lists, and decision sets, are conventionally deemed to be the most interpretable. Recent work uses propositional satisfiability (SAT) solving (and its optimization variants) to generate minimum-size decision sets. Motivated by limited practical scalability of these earlier methods, this paper proposes a novel approach to learn minimum-size decision sets by enumerating individual rules of the target decision set independently of each other, and then solving a set cover problem to select a subset of rules. The approach makes use of modern maximum satisfiability and integer linear programming technologies. Experiments on a wide range of publicly available datasets demonstrate the advantage of the new approach over the state of the art in SAT-based decision set learning.