Reviewerly/exHarmony huggingface dataset:
Offline and Online Learning Techniques for Personalized Blood Pressure Prediction and Health Behavior Recommendations

Blood pressure (BP) is one of the essential indicators of human health and highly correlated to health behavior (e.g., exercise and sleep).However, the degree of impact of each health behavior on BP is unknown and may vary significantly between individuals.In this paper, we investigate the relationship between BP and health behavior using data collected from off-the-shelf wearable devices and wireless home BP monitors.We propose a personalized BP model based on random forest (RF), which can predict individual's BP using health behavior and historical BP, and identify the most important factors in predicting an individual's BP.The latter can be used to provide personalized health behavior recommendations to improve and manage BP.We propose RF with Feature Selection (RFFS), which performs RF-based feature selection to enhance the prediction.Furthermore, since BP and health behavior data are collected and learned sequentially, the performance of prediction is prone to the existence of concept drifts and anomaly points.To solve this problem, we propose an Online Weighted-Resampling (OWR) technique to enhance RFFS in an online learning scenario.To show the effectiveness of RFFS and OWR, we use existing machine learning methods on the proposed dataset as a comparison.Our experimental results show that the proposed approach achieves the lowest prediction error.We also validate the effectiveness of personalized recommendations based on feature importance, which influences the user to change lifestyle to improve his/her BP.
A Comparative Study of Perceptions and Use of Google Scholar and Academic Library Discovery Systems

Google Scholar and academic library discovery systems are both popular resources among academic users for finding scholarly information. By conducting an online survey with 975 users from more than 20 public research universities across the United States, this study comparatively investigates how and why academic users use these two resources. Results show that the ways participants used both resources were similar, and both were perceived as highly accessible and useful . Academic library discovery systems’ perceived comprehensiveness , subjective norm , loyalty , and intended use were higher than Google Scholar, while Google Scholar’s perceived ease of use , system quality , and satisfaction were higher than that of academic library discovery systems.
Joint Inference of Reward Machines and Policies for Reinforcement Learning

Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where the high-level knowledge is in the form of reward machines, a type of Mealy machines that encode non-Markovian reward functions. We focus on a setting in which this knowledge is a priori not available to the learning agent. We develop an iterative algorithm that performs joint inference of reward machines and policies for RL (more specifically, q-learning). In each iteration, the algorithm maintains a hypothesis reward machine and a sample of RL episodes. It uses a separate q-function defined for each state of the current hypothesis reward machine to determine the policy and performs RL to update the q-functions. While performing RL, the algorithm updates the sample by adding RL episodes along which the obtained rewards are inconsistent with the rewards based on the current hypothesis reward machine. In the next iteration, the algorithm infers a new hypothesis reward machine from the updated sample. Based on an equivalence relation between states of reward machines, we transfer the q-functions between the hypothesis reward machines in consecutive iterations. We prove that the proposed algorithm converges almost surely to an optimal policy in the limit. The experiments show that learning high-level knowledge in the form of reward machines leads to fast convergence to optimal policies in RL, while the baseline RL methods fail to converge to optimal policies after a substantial number of training steps.
GEval: Tool for Debugging NLP Datasets and Models

This paper presents a simple but general and effective method to debug the output of machine learning (ML) supervised models, including neural networks. The algorithm looks for features that lower the evaluation metric in such a way that it cannot be ascribed to chance (as measured by their p-values). Using this method – implemented as MLEval tool – you can find: (1) anomalies in test sets, (2) issues in preprocessing, (3) problems in the ML model itself. It can give you an insight into what can be improved in the datasets and/or the model. The same method can be used to compare ML models or different versions of the same model. We present the tool, the theory behind it and use cases for text-based models of various types.
The Explanation Game: Explaining Machine Learning Models with Cooperative Game Theory

A number of techniques have been proposed to explain a machine learning (ML)model's prediction by attributing it to the corresponding input features.Popular among these are techniques that apply the Shapley value method fromcooperative game theory. While existing papers focus on the axiomaticmotivation of Shapley values, and efficient techniques for computing them, theyneither justify the game formulations used nor address the uncertainty implicitin their methods' outputs. For instance, the SHAP algorithm's formulation maygive substantial attributions to features that play no role in a model.Furthermore, without infinite data and computation, SHAP attributions areapproximations subject to hitherto uncharacterized uncertainty. In this work,we illustrate how subtle differences in the underlying game formulations ofexisting methods can cause large differences in attribution for a prediction.We then present a general game formulation that unifies existing methods. Usingthe primitive of single-reference games, we decompose the Shapley values of thegeneral game formulation into Shapley values of single-reference games. Thisdecomposition enables us to introduce confidence intervals to quantify theuncertainty in estimated attributions. Additionally, this decomposition enablescontrastive explanations of a prediction through comparisons with differentgroups of reference inputs. We tie this idea to classic work on Norm Theory incognitive psychology, and propose a general framework for generatingexplanations for ML models, called formulate, approximate, and explain (FAE).
Secure Authentication for ATM transactions using NFC technology

Automated Teller Machine (ATM) is a convenient way to meet the banking needs of the users. However, the use of debit card or other types of cards during ATM transactions has some problems like prone to ATM skimming, magnetic strips of card getting damaged, manufacturing and transportation cost of cards, longer time to authenticate users etc. The objective of this research is to consider smart phone in Near-Field Communication (NFC) Card Emulation mode as an alternative to ATM cards. In NFC the distance between the respective devices needs to be very small (typically less than 4 cm) which makes NFC ideal for making payments and for other transactions involving sensitive/private data. In the proposed system, in order to authenticate at the ATM kiosk, the user needs to swipe his/her smart phone in front of the NFC reader. An ATM card is not required for authentication and the system will still have a stronger security compared to the system in which ATM card was used. Security analysis and threat modelling shown in this paper highlights the security strength of the system during authentication.
Signed Cryptographic Program Verification with Typed CryptoLine

We develop an automated formal technique to specify and verify signed computation in cryptographic programs. In addition to new instructions, we introduce a type system to detect type errors in programs. A type inference algorithm is also provided to deduce types and instruction variants in cryptographic programs. In order to verify signed cryptographic C programs, we develop a translator from the GCC intermediate representation to our language. Using our technique, we have verified 82 C functions in cryptography libraries including NaCl, wolfSSL, bitcoin, OpenSSL, and BoringSSL.
Reconstruction Distortion Oriented Light Field Image Dataset for Visual Communication

As a representation of three dimensional scenes, light field has received increasing attention. In light field image processing, reconstruction method plays an important role, which can not only produce dense views to improve the spatial and angular resolution of the light field, but also effectively reduce the transmission data. However, the reconstruction methods inevitably reduce the quality of light field images, so the corresponding light field image quality assessment is necessary. In this paper, a reconstruction distortion oriented light field image dataset is firstly established, with several different reconstruction methods and the corresponding subjective evaluation scores. Secondly, the subjective scoring results of source sequences and their types of distorted versions are compared and analyzed. Finally, the dataset is evaluated with the existing objective quality assessment metrics. Experimental results show that different reconstruction methods have different preferences on the input light field resolution, and the performance of the state-of-the-art objective quality metrics can be improved.
An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.
Semi-Supervised Multi-Modal Learning with Balanced Spectral Decomposition

Cross-modal retrieval aims to retrieve the relevant samples across different modalities, of which the key problem is how to model the correlations among different modalities while narrowing the large heterogeneous gap. In this paper, we propose a Semi-supervised Multimodal Learning Network method (SMLN) which correlates different modalities by capturing the intrinsic structure and discriminative correlation of the multimedia data. To be specific, the labeled and unlabeled data are used to construct a similarity matrix which integrates the cross-modal correlation, discrimination, and intra-modal graph information existing in the multimedia data. What is more important is that we propose a novel optimization approach to optimize our loss within a neural network which involves a spectral decomposition problem derived from a ratio trace criterion. Our optimization enjoys two advantages given below. On the one hand, the proposed approach is not limited to our loss, which could be applied to any case that is a neural network with the ratio trace criterion. On the other hand, the proposed optimization is different from existing ones which alternatively maximize the minor eigenvalues, thus overemphasizing the minor eigenvalues and ignore the dominant ones. In contrast, our method will exactly balance all eigenvalues, thus being more competitive to existing methods. Thanks to our loss and optimization strategy, our method could well preserve the discriminative and instinct information into the common space and embrace the scalability in handling large-scale multimedia data. To verify the effectiveness of the proposed method, extensive experiments are carried out on three widely-used multimodal datasets comparing with 13 state-of-the-art approaches.
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling

As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation. In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. Specifically, we leverage pivot-private embedding, layer coordination, as well as parameter sharing to sufficiently model commonality and diversity among source and target, ranging from lexical, through syntactic, to semantic levels. In order to examine the effectiveness of the proposed models, we collect 20 million monolingual corpus for each of Mandarin and Cantonese, which are official language and the most widely used dialect in China. Experimental results reveal that our methods outperform rule-based simplified and traditional Chinese conversion and conventional unsupervised translation models over 12 BLEU scores.
A Comparitive Study of Blockchain Applications for Enhancing Internet of Things Security

IoT devices or smart devices have become a part and parcel of the human existence. Every day, the counts of devices, which are being connected to the internet, are becoming unquantifiable. One among the prominent applications in commercial IoT is medical IoT. Privacy maintenance and ensuring security of user data is a major concern in certain scenarios such as that of the medical IoT. Conventionally the data generated from a sensor should be highly confidential and private. The centralized data management adopted in majority of IoT systems are having high set up cost and are prone to single point of failure and does not provide any guarantee of data authenticity and security. The inherent nature of IoT systems such as limited storage and low processing capabilities make them an unsuitable candidate for the use of complex and highly computational cryptographic algorithms. Even though the use of modern server platforms such as the AWS and Google helped in mitigating the issues of high setup cost and less security, the problems of data authenticity and access control still persists. Blockchain is a decentralized mechanism for secure management and exchange of data in any IoT system, particularly healthcare. Hence blockchain with its inherent properties of immutability, consensus mechanism, provenance and encryption can solve the problems of authenticity and access control in IoT. There exists several research works related to IoT security with the inclusion of blockchain framework. However, a collation of various works related to blockchain IoT is absent in the literature and this paper aims to provide a detailed amalgam of the wide range of works in blockchain IoT. We also aim to highlight the necessity of securing an IoT system and present a comparison of blockchain and other security techniques in terms of robustness, setup cost, risk of failure etc. and also propose an ideal security technique that can be adopted for IoT applications.
Smoothed A-star Algorithm for Nonholonomic Mobile Robot Path Planning

There are various path planning methods for mobile robots and one of them is A-star algorithm. It serves fairly well for looking the path accurately. Unfortunately, the conventional A-star algorithm may have some sharp turns. It causes problems for nonholonomic mobile robots to track the path. In this paper, we proposed a scheme to make A-star algorithm smoother. The smoothed path is easier for nonholonomic mobile robots to be tracked. We conduct simulation in the virtual environment that covers the physical behavior of mobile robot. Simulation and experiment verify that the smoothed A-star algorithm can be implemented for nonholonomic mobile robots. Keywords—Nonholonomic Mobile Robot, Path Planning, Smoothed A-star Algorithm.
Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering

Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of data-driven persona development performed today is based on qualitative data from a limited set of interviewees and transformed into personas using labour-intensive manual techniques. In this study, we propose a method that employs the modelling of user stereotypes to automate part of the persona creation process and addresses the drawbacks of the existing semi-automated methods for persona development. The description of the method is accompanied by an empirical comparison with a manual technique and a semi-automated alternative (multiple correspondence analysis). The results of the comparison show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters, while lowering the labour associated with finding the clusters in the dataset. The output of the method also represents the largest variances in the dataset identified by the multiple correspondence analysis.
Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics

Effective connectivity (EC) among functional near-infrared spectroscopy (fNIRS) signals is a quantitative measure of the strength of influence between brain activity associated with different regions of the brain. Evidently, accurate deciphering of EC gives further insight into the understanding of the intricately complex nature of neuronal interactions in the human brain. This article presents a novel approach to estimate EC in the human brain signals using enhanced fuzzy cognitive maps (FCMs). The proposed method presents a regularized methodology of FCMs, called effective FCMs (E-FCMs), with improved accuracy for predicting EC between real and synthetic fNIRS signals. Essentially, the revisions made in the FCM methodology include a more powerful prediction formula for FCM combined with independent tuning of the transformation function parameter. A comparison of EC in fNIRS signals obtained from E-FCM with that obtained from standard FCM, general linear model (GLM) parameters that power dynamic causal modeling (DCM), and Granger causality (GC) manifests the greater prowess of the proposed E-FCM over the aforementioned methods. For real fNIRS data, an empirical investigation is also made to gain an insight into the role of oxyhemoglobin and deoxy-hemoglobin (oxy-Hb, deoxy-Hb) in representing the cognitive activity. We believe this article has profound implications for neuroergonomics research communities.
Lightweight Single Image Super-Resolution With Multi-Scale Spatial Attention Networks

Convolutional neural networks (CNNs) generally provide higher performance gain for single image super-resolution (SISR) as the depth and number of parameters are increasing.However, just increasing the layers of straightforward deep networks has a problem that it requires an impractically large number of parameters for obtaining state-of-the-art performance.Instead, some researchers proposed lightweight networks, which is designed with more sophisticated network structures for achieving better performance than the straightforward networks at the same parameter requirement.In this paper, we propose new lightweight Multi-scale Spatial Attention Networks (MSAN) for SISR, which attempt to bring out a better performance from the relatively small number of parameters.Specifically, we adopt a dense connection with feature fusion layers to broadcast abundant features to every level of layers, and propose a double residual structure that provides an additional skip-connection.We also design a Multi-scale Spatial Attention Block (MSAB) to exploit multi-scale spatial contextual information.Furthermore, we introduce a spatial attention module which adaptively focuses on the most informative feature scale in a given region of the image.In the experiments, we validate that the proposed MSAN achieves significant accuracy compared to recent lightweight models and comparable performance to the state-of-the-art methods.INDEX TERMS Convolutional neural network (CNN), lightweight,
Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows

Gas-liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas-liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas-liquid two-phase flow. We conduct the vertical upward gas-liquid two-phase flow experiments to measure the flow signals by using the four-sector distributed conductance sensor. Then, we design a novel multitask-based temporal-channelwise convolutional neural network (MTCCNN) to predict the gas void fraction. In MTCCNN, we first utilize the decomposed convolutional block to extract temporal dependence and channel connection from fluid data. After further fusion by the dense layer, we apply multitask learning to make full use of the extracted features through both classification branch and gas void fraction prediction branch. We compare our MTCCNN with its variations to demonstrate the proposed improvements. We also present other competitive methods for comparisons, which shows that our MTCCNN presents a better performance in gas void fraction prediction.