Reviewerly/exHarmony huggingface dataset:
Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study

Asthma hospital visits, including emergency department visits and inpatient stays, are a significant burden on health care. To leverage preventive care more effectively in managing asthma, we previously employed machine learning and data from the University of Washington Medicine (UWM) to build the world's most accurate model to forecast which asthma patients will have asthma hospital visits during the following 12 months.Currently, two questions remain regarding our model's performance. First, for a patient who will have asthma hospital visits in the future, how far in advance can our model make an initial identification of risk? Second, if our model erroneously predicts a patient to have asthma hospital visits at the UWM during the following 12 months, how likely will the patient have ≥1 asthma hospital visit somewhere else or ≥1 surrogate indicator of a poor outcome? This work aims to answer these two questions.Our patient cohort included every adult asthma patient who received care at the UWM between 2011 and 2018. Using the UWM data, our model made predictions on the asthma patients in 2018. For every such patient with ≥1 asthma hospital visit at the UWM in 2019, we computed the number of days in advance that our model gave an initial warning. For every such patient erroneously predicted to have ≥1 asthma hospital visit at the UWM in 2019, we used PreManage and the UWM data to check whether the patient had ≥1 asthma hospital visit outside of the UWM in 2019 or any surrogate indicators of poor outcomes. Such surrogate indicators included a prescription for systemic corticosteroids during the following 12 months, any type of visit for asthma exacerbation during the following 12 months, and asthma hospital visits between 13 and 24 months later.Among the 218 asthma patients in 2018 with asthma hospital visits at the UWM in 2019, 61.9% (135/218) were given initial warnings of such visits ≥3 months ahead by our model and 84.4% (184/218) were given initial warnings ≥1 day ahead. Among the 1310 asthma patients in 2018 who were erroneously predicted to have asthma hospital visits at the UWM in 2019, 29.01% (380/1310) had asthma hospital visits outside of the UWM in 2019 or surrogate indicators of poor outcomes.Our model gave timely risk warnings for most asthma patients with poor outcomes. We found that 29.01% (380/1310) of asthma patients for whom our model gave false-positive predictions had asthma hospital visits somewhere else during the following 12 months or surrogate indicators of poor outcomes, and thus were reasonable candidates for preventive interventions. There is still significant room for improving our model to give more accurate and more timely risk warnings.RR2-10.2196/5039.
Ecological Village Planning and Green Building Design from the Perspective of Rural Environmental Aesthetics

The society is making continuous progress, and the results of urban planning and construction are obvious to all. However, there are still many problems in the planning and construction of villages and towns, resulting in relatively backward development. The purpose of this article is to study the planning of ecological villages and towns and the design of green buildings from the perspective of rural environmental aesthetics. This article briefly introduces the principles and problems of ecological village planning through literature research and investigation and puts forward some suggestions on ecological village planning. Through investigation and experiment, the energy problem, structural material problem, and planning problem in green building design are analyzed. The results show that due to various limitations, the use and promotion of solar energy are blocked. Only by helping farmers solve their difficulties can clean energy be used in green buildings and the burning of straw be reduced. Nationwide, the proportion of brick-concrete structures, brick-wood structures, and reinforced concrete structures in rural housing reached 57.2%, 26%, and 12.5%, respectively. Many rural buildings pursue reinforced concrete structures, but brick and wood structures may be more suitable for most rural areas.
Bit-policy based chosen-plaintext collision attack on reused-mask AES

Side-channel collision attacks have shown good efficiency on breaking unprotected or reused-mask implementations of block ciphers. Depending on the relationship between “Hamming distance of values” and “average distance of traces”, this work presents a novel bit-policy based chosen-plaintext collision attack to break a software implementation of reused-mask AES. Some factors affecting the success rates of the proposed scheme and their reasons are analyzed, such as how to measure the distance of traces, signal-to-noise ratio (SNR), how to select points of interest (POI) and the number of traces (n). In this paper, two measurements, the least square method (LSM) and the central moment product method (CMP), are adopted to calculate the distance of traces. Then their probability distribution and efficiency are theoretically analyzed and simulated. As a result, CMP outperforms LSM, and all the other three factors, i.e. SNR, POI, n, are relevant to the distribution of LSM and CMP. When LSM and CMP are combined with the proposed scheme and the compared ones, both theoretical and practical analysis are given to illustrate how the number of traces impacts on the success rates at different levels of SNR; as a result the developed attack has the best performance among all the compared schemes at both low and high level of SNR, especially at high level of SNR. To gain a success rate of 95%, at SNR of 0 dB and −8 dB, our optimal scheme and the compared optimal scheme have 800 and 4500 less energy traces, respectively. Furthermore, our attack requires no detailed knowledge about the design of SBoxes, nor does it require templates for profiling.
Entropy of Dynamical Systems on Interval-Valued Intuitionistic Fuzzy Sets

In this work, we introduce the concepts of Shannon entropy and conditional entropy of experiments in the interval-valued intuitionistic fuzzy case, and study the basic properties of the information measures. Subsequently, by means of the suggested notion of entropy of partitions, we define the entropy of a dynamical system on interval-valued intuitionistic fuzzy sets (IVIF). A version of the Kolmogorov–Sinai theorem on generators for dynamical systems on the IVIF is proved. It is shown that this entropy is an invariant under isomorphisms of interval-valued intuitionistic fuzzy dynamical systems; thus, we obtain a tool for distinguishing some non-isomorphic interval-valued intuitionistic fuzzy dynamical systems. The proposed measure can be used as a measure of information of experiment whose outcomes are interval-valued intuitionistic fuzzy events.
Splitting proximate algorithm for deformable image registration based on functions of bounded deformation

Abstract Background Deformable image registration is a crucial task in the field of medical image analysis. Functions of bounded deformation (BD) have been proved effective for modeling the displacement fields between medical images since they can capture the discontinuity of displacement fields along edges of organs and tissues. Purpose However, we find that at the same time, BD functions‐based models tend to obtain discontinuous displacement fields inside the regions of organs and tissues due to image noises in some cases and the presented gradient descent algorithm is time‐consuming. To alleviate these problems, we propose a faster algorithm named SPA: splitting proximate algorithm. Methods In the framework of variable‐splitting scheme, we incorporate a proximal term in the deformable registration energy based on functions of BD. Results The proposed algorithm can efficiently solve the original model and obtain displacement fields, which look more natural and plausible. Numerical experiments show the effectiveness and stability of the proposed algorithm. Conclusions The proposed SPA is able to drastically register the images with a plausible deformation field and not sensitive to noise.
Thoreau’s Dialectic of Dissent and Contemporary Activism

Recent scholarship on Thoreau’s thought has pushed in two opposing directions: some have maintained that Thoreau’s withdrawals from political engagement were actually intended to serve democratic ends, whereas others have argued that Thoreau’s political engagement was a lapse in his better judgment. In this essay, I contend that neither interpretation of Thoreau’s thought fully captures the roles that political engagement and disengagement played in his life as a dissident. Instead, via an examination of Thoreau’s “Walking” and his reform papers, I argue that Thoreau modeled a dialectical approach to dissent, where the “antithesis” of withdrawal served as a specific antidote to the personal toll of the “thesis” of political action. As I show, Thoreau’s attention to the potential costs of radical dissent makes his dialectical model especially relevant for those for whom the costs are highest, including contemporary women activists of color. For these women, normalizing a practice of continual disengagement from activism might benefit them in ways that collaborative solidarity cannot.
Study about the Ability to Secure Websites using Blockchain

Over the years, the web has evolved from simple text content from one server to a complex ecosystem with different types of content from servers spread across several administrative domains. There is anecdotal evidence of users being frustrated with attempts to hack websites and steal accounts, money, ideas, or data from photos or documents. wherefore, websites need new techniques to increase security, reduce fraud, and reduce the risk of single point of failure. All these features can be found in Blockchain. Therefore, there is a need to propose a (refined) method as a countermeasure that keeps track of existing attacks. A framework is proposed for using blockchain as a technology to secure websites based on blockchain platform using steps: preparation, extraction, blockchain building, blockchain integration, and framework validation the results came in the form of a comparison table that helped us identify the main problem and the research path and we can get results by avoiding more than 80% of these attacks on websites.
Mobile Robot Model Predictive Control Model

The paper describes the approach to build a model predictive control system for a mobile robot with redundant architecture. In the paper we present a structure of such control system. This method is based on nonlinear models of kinematics and dynamics of the robot. A separate task is selection and adjustment of the optimal regulator. The proposed implementation combines the ideas of local planning and control of the actuators.
VCSNet: V‐fused color saliency net

Summary Image salient region detection methods, which can detect and extract interesting regions in pictures, have been a hot research direction in recent years. Most of current salient region detection algorithms and corresponding training datasets originated from general visual attention processes and primarily reflected attention to object shape in pictures. While color vision provided more useful information in vision systems, the color effect of visual attention should be and must be considered. First, we collected cue data of painting pictures from several observers using eye‐track recording technology when observers were asked to pay attention to color information of various paintings. Second, we constructed a color attention dataset, color saliency dataset (CSD), from the cue data and pictures. Thirdly, we designed a V‐fused color saliency net (VCSNet) model which included three modules: a color information fusion module, a prediction module, and an optimization module and trained the model using the CSD. Finally, we compared our method with previous algorithms on the CSD, and results showed that our method outperformed the previous algorithms in color saliency detection with MAE of 0.057 and Fmax of 0.265. We open source part of the self‐created dataset: https://github.com/InfiniteEM/ColorSaliencyDataSet .
Efficient Image and Sentence Matching

Recently, the accuracy of image and sentence matching has been continuously improved by larger and larger models. However, such large models not only need huge storage space but also slow down inference speed, which are not very suitable for low-cost devices in real-world applications. To our knowledge, this work makes the first attempt to improve the model efficiency in the context of image and sentence matching, and accordingly proposes a simple yet effective Whitened Similarity Distillation (WSD) method, which can distill cross-modal knowledge from a large teacher model to a small student model of both high efficiency and accuracy. The high efficiency is achieved by performing: 1) feature representation based on efficient backbone networks; and 2) similarity measurement in a fast N-to-N manner. However, the accuracy of such a student model is much worse than that of teacher model, because there exists very large variation inconsistency between two cross-modal similarity matrices of teacher and student models, which is hard to reduce during the similarity distillation. By performing two whitening-like transformations in the orthogonal space, the proposed WSD can reduce the large variation inconsistency more isotropically and is able to improve the accuracy of student model. We perform extensive experiments on two benchmark datasets and demonstrate the effectiveness of the proposed WSD. Compared with the teacher model, our distilled student model is 7× smaller (in model size) and 9× faster (in testing speed), only at the cost of 2% accuracy decrease.
Optimal identifying codes of two families of Cayley graphs

An identifying code of a graph Γ is a subset C of the vertex set V such that for each x∈V, the intersection of its closed neighbourhood with C is nonempty and unique. If Γ is a finite graph, the density of an identifying code C is defined as |C||V|, which naturally extends to a definition of density in certain infinite graphs which are locally finite. Denote by d∗(Γ) the infimum of the density of an identifying code of a finite or infinite graph Γ. In this paper, we study identifying codes of an infinite Cayley graph Γn, which is the Cartesian product of an infinite path and a complete graph on n vertices. We prove that d∗(Γ3)=512,d∗(Γ4)=922 and d∗(Γn)=n−12n for n≥5. As an application, we obtain d∗(Γ) if Γ is a connected quintic Cayley graph over a generalized quaternion group.
Peer Me Maybe?: A Data-Centric Approach to ISP Peer Selection

The Internet landscape is progressively transitioning towards a flat hierarchical model to prune multiple Internet Service Provider (ISP) tiers. At the core of this transition is settlement free peering, which plays a critical role in mediating traffic exchange among ISPs. It is pertinent to take a closer look at peering and accurately emulate their operative model into a computation model that enables a concrete characterization without losing generality. In this paper, we utilize publicly available data-sets to identify the importance of several factors that play role in the peering process. We conduct a detailed analysis on the relationship of ISPs and their motivation behind selecting a peer ISP and use these findings to develop a Machine Learning (ML) based model that identifies feasible peering relationships. Preliminary results show a high correlation to the ground truth.
Learning Rate Estimation Model in Restricted Boltizmann Machine Neural Network for Building Recommender Systems

This paper finds the mechanism to obtain learning rate value for training restricted Boltzmann artificial neural network that used for build recommender systems. One of the important problem in training the artificial neural network model is finding the appropriate learning rate values for making designed model reach the optimal result for learning process. The proposed model analyzes the behavior of the recommender system in context of mean squared error (MSE), mean absolute error (MAE), precision and recall as well as the free energy function. Proposed model
A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

In the process of crowdsensing, tasks allocation is an important part of the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may be aware of the location of each other, and then threaten their privacy. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the process of task matching by applicants and publishers is also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publishers can be preserved. Finally, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. In the last part of this paper, four types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of our proposed scheme.
Defending against Poisoning Attack in Federated Learning Using Isolated Forest

Federated learning is an emerging, distributed machine learning methodology in which participants submit parameters after using their own local data for training and the server aggregates model parameters submitted by users to form a federated learning model. By sending the local learning results rather than sending out the raw data to the server, the user cannot see how the global model is generated. This opacity protects user privacy. However, since the raw data is never sent to the server, its quality and integrity cannot be guaranteed. Malicious attackers can inject poisoning attacks into their own data or the uploading parameters. Thus, federated learning is very vulnerable to distributed poisoning attacks, and this causes the model performances to be reduced significantly. Designing a robust federated learning is a challenging task. In this paper, we propose an isolated forest-based federated learning defense model as IFFed to eliminate the poisoned model parameters of attackers. In each iteration of federated learning, we use isolated forests to partition the data space of model parameters and calculate the probability of anomaly for each participant. Further, we use auxiliary data to design dynamic thresholds to exclude attackers and minimize the impact of the defense model on benign users. Experiments show that IFFed can automatically defend against poisoning attacks, and the performance of the defending model can be significantly improved by adjusting the pre-trained auxiliary model to ensure proper training of the global model.
Monitoring Smart Home Traffic under Differential Privacy

Recent years have witnessed the proliferation of smart home ecosystems. Well-characterized traffic generated by smart home devices has promoted the development of security enhancing techniques for smart homes but exposes users to the privacy disclosure risk at the same time. Malicious eavesdroppers can infer working states of smart home devices and user activities based on spatial-temporal traffic characteristics. Existing countermeasures towards this kind of side channel attack ignore the utility of smart home traffic profiles and signatures for network management and attempt to completely eliminate them. In this paper, we give a comprehensive study on the trade offs between the usability of smart home traffic for security monitoring and its privacy threat. We propose to monitor the smart homes under differential privacy. Based on our solution, decoy traffic can be generated in a controlled manner so as to confound the attackers without disturbing the running monitoring systems. We prototyped our proposal and demonstrate its effectiveness empirically. An interview study is also conducted to learn the user acceptance of the proposed privacy preserving mechanism.
MULTIVARIATE ANALYSIS OF COMMODITY AVAILABILITY OF STAPLE FOODS USING COMPLETE LINKAGE HIERARCHICAL CLUSTERING METHOD

The government directly supervises 11 basic food commodities. The system of interplay between the price of goods and the availability of staple food directly has an impact on the high price of food at certain times. It is necessary to classify the food that is most needed by the community on big holidays in Indonesia so that it can be a reference for the government in preparing market needs in the coming year. In this study, the grouping of staple food availability was based on hierarchical cluster analysis with complete linkage method. The availability of food commodities in the discussion of this research is sourced from production materials and daily prices for meat, eggs, cooking oil and rice commodities. Cluster interpretation results in cluster 1 indicating Fulfilled Availability of 88-89%, Cluster 2 showing Sufficient Commodity Availability of 90-93% and Cluster 3 showing Availability of Rare Commodities of 87%. The three clusters formed are depicted in the form of a dendogram as a visualization of the relationship between food availability groupings.
Causal Relationships in Explainable Artificial Intelligence

The problem of explainability of artificial intelligence models has been solved for a long time by classical methods of explanation, generated by even more classical methods from the field of feature space analysis. This approach shows which of the parameters of the observed objects in the initial data set have the greatest influence on the decision being made (for example, in the problems of classifying brain MRI images by the presence of a disease). However, in the answer to the question about the parameters that have the greatest influence on the decision being made, there is no answer to the question about the reasons for the decision being made (it often takes a doctor a lot of time to explain to the patient the need for a particular action, for example, surgery. The problem of determining the significance of parameters is known due to the rich the history of decisions in the field of feature space analysis and is not essentially new.