Reviewerly/exHarmony huggingface dataset:
Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

With the rapid improvement of social economy and the enhancement of people’s health awareness, it is necessary to make an in-depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K-clustering and Levenberg–Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy.
Efficient and Secure Pairing Protocol for Devices with Unbalanced Computational Capabilities

Wearable devices that collect data about human beings are widely used in healthcare applications. Once collected, the health data will be securely transmitted to smartphones in most scenarios. Authenticated Key Exchange (AKE) can protect wireless communications between wearables and smartphones, and a typical solution is the Bluetooth Secure Simple Pairing (SSP) protocol with numeric comparison. However, this protocol requires equivalent computation on both devices, even though their computational capabilities are significantly different. This paper proposes a lightweight numeric comparison protocol for communications in which two parties have unbalanced computational capabilities, e.g., a wearable sensor and a smartphone, named UnBalanced secure Pairing using numeric comparison (UB-Pairing for short). The security of UB-Pairing is analyzed using the modified Bellare–Rogaway model (mBR). The analysis results show that UB-Pairing achieves the security goals. We also carry out a number of experiments to evaluate the performance of UB-Pairing. The results show that UB-Pairing is friendly to wearable devices, and more efficient than standard protocols when the computation capabilities of the two communication parties are highly unbalanced.
Digital learning environment as a tool for personalization of distance education programs

Introduction. The relevance of the article is due to the realities of the digital transformation of educational process and the increasing requirements for the quality of digital education in the context of distance learning. The article discusses the problems associated with the specifics of distance education. At the same time, it is noted that this type of education has a high practical orientation, but is characterized by the predominance of self-study in the development of educational material. Purpose setting. The purpose of this article is to consider various aspects of designing an electronic educational environment, taking into account the possibility of personalizing the educational trajectory in the context of distance learning. Methodology of the study. This study includes a review of bibliographic sources on the personalization of the educational trajectory by means of an digital learning environment in the context of distance learning. It also includes building a model of the structure of an e-course taking into account the student’s model and a description of an example of implementing an e-course in the LMS Moodle of the Bashkir State Agrarian University. Results. The results of the work include a description and analysis of the problems associated with the organization of distance learning, as well as the results of modeling and testing an electronic course focused on a flexible educational trajectory within the framework of personalization of training. Conclusion. The final part of the work also discusses possible ways to improve the organization of training for part-time students. At the same time, it is concluded that distance learning students are more interested in using flexible individual educational trajectories that take into account their time resources and capabilities. Further direction of studying the problem of personalization of distance learning programs is associated with the study of new technologies for the design of digital educational resources and their practical testing.
Sparse Hierarchical Table Ensemble–A Deep Learning Alternative for Tabular Data

Deep learning for tabular data is drawing increasing attention, with recent work attempting to boost the accuracy of neuron-based networks. However, when computational capacity is low as in Internet of Things (IoT), drone, or Natural User Interface (NUI) application, such deep learning models are deserted. We offer to enable deep learning capabilities using ferns (oblivious decision trees) instead of neurons, by constructing a <i>Sparse Hierarchical Table Ensemble</i> (S-HTE). S-HTE inference is dense at the beginning of the training process and becomes gradually sparse using an annealing mechanism, leading to an efficient final predictor. Unlike previous work with ferns, S-HTE learns useful internal representations, and it earns from increasing depth. Using a standard classification and regression benchmark, we show its accuracy is comparable to alternatives, while having an order of magnitude lower computational complexity. Our PyTorch implementation is available at https://anonymous.4open.science/r/HTE_CTE-60EB/.
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling

Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift problem that the learned model from the known-class domain might be unsuitable for the unknown-class domain. Addressing this problem, inspired by the success of transductive learning for alleviating the domain shift problem in many other visual tasks, we propose an Iterative Transductive OSR framework, called IT-OSR, which implements three explored modules iteratively, including a reliability sampling module, a feature generation module, and a baseline update module. Specifically, at each iteration, a dual-space consistent sampling approach is presented in the explored reliability sampling module for selecting some relatively more reliable ones from the test samples according to their pseudo labels assigned by a baseline method, which could be an arbitrary inductive OSR method. Then, a conditional dual-adversarial generative network under an orthogonal coding condition is designed in the feature generation module to generate discriminative sample features of both known and unknown classes according to the selected test samples with their pseudo labels. Finally, the baseline method is updated for sample re-prediction in the baseline update module by jointly utilizing the generated features, the selected test samples with pseudo labels, and the training samples. Extensive experimental results on both the standard-dataset and the cross-dataset settings demonstrate that the derived transductive methods, by introducing two typical inductive OSR methods into the proposed IT-OSR framework, achieve better performances than 15 state-of-the-art methods in most cases.
<i>Retracted on March 14, 2023</i> : Cross-lingual transfer learning for statistical type inference

NOTE OF RETRACTION: The authors, Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, and Yang Liu, of the paper “Cross-lingual transfer learning for statistical type inference” have requested their paper be Retracted due to errors in the paper. The authors all agree the major conclusions are erroneous:
Optimal adjustment of the FAST active reflecting surface based on a working paraboloid adjustment model

FAST is the world's largest single-aperture radio telescope developed independently by China, which adopts innovatively the active reflecting surface technology to realize real-time adjustment of the reflecting surface and dynamic tracking of celestial objects. In this paper, the ideal paraboloid for observing celestial objects is investigated in conjunction with the adjustment factors of the reflecting panel, and the adjustment of the working paraboloid is optimally designed. To address problem one, this paper first cognizes the ideal paraboloid, establishes four criteria that the ideal paraboloid should satisfy, and simplifies the problem in two dimensions based on symmetry. For problem two, this paper firstly establishes a mathematical model for solving the equations of the ideal paraboloid in the general case based on the results of problem one, using the coordinate transformation method.
The Distribution of Energies

The application of calculus is of great importance in physics. It is used to calculate energy, acceleration, velocity, and much more. In this paper, to calculate the internal energy of an ideal gas (gas molecules) we will use Maxwell-Boltzmann statistical distribution based entirely on calculus. The technique represents the mathematical concept that depends on integration and substitution in calculus to solve a problem.
A Study on Criteria for Training Collaborator Selection in Federated Learning

Federated learning is an important aspect of enabling the deployment of deep learning. It addresses data privacy and security concerns by decentralizing data. In a federated learning setup, models are trained locally in the data center called collaborators, and the model weights are aggregated by a central server. We present our work towards the efficient aggregation of trained model weights from multiple institutions which is Task 1 in the federated learning challenge (FeTS 2021). We devised a scoring system for selecting appropriate collaborators every round and aggregating their weight by simple averaging. We calculate the score based on the sensitivity, dice coefficient, and Hausdorff distance of each segmentation class on validation data from every collaborator. The best collaborators are chosen serially based on the scores with higher scores indicating better performing networks. Our approach gave mean dice score of 0.58 ± 0.259 & 0.639 ± 0.176 & 0.55 ± 0.201 on Enhancing Tumor, Whole Tumor and Tumor Core respectively.
How to transfer cyber security skill?

Cyber defense is a challenge to be taught as it seeks from attendees to adopt real technical skills. Skill is a capacity to resolve some practical problem, but sometimes it's needed to cover such a capability with theoretical background in order to better understand that area of interest. Teachers transferring such a skill must be well-educated and in position to create engaging course and training which can give an opportunity to attendees to take the most from such a lesson. In this article, we will stress out ongoing methods in producing cyber security workforce and highlight education as a strategic factor of any community, so far.
Exploring the Teaching Mode of English Audiovisual Speaking in Multimedia Network Environment

Introducing multimedia network tools in English audiovisual teaching and building a new model of network-based multimedia teaching can make English audiovisual teaching more in line with students’ cognitive thinking characteristics and processes. This can improve the overall efficiency of English teaching in schools. Computers have been widely used in language evaluation and speech recognition for language learning, and speech recognition technology is an important reflection of the level of language learning. The large amount of language signal data, complex pronunciation changes, and high dimensionality of pronunciation feature parameters in the language learning process make it difficult to identify pronunciation features. The computational volume of pronunciation evaluation and recognition is too large, which requires high hardware resources and software resources to realize high-speed processing of massive pronunciation signals. To address the problem of low recognition rate of English pronunciation, this study proposes a sound recognition algorithm based on adaptive particle swarm optimization (PSO) matching pursuit (MP) sparse decomposition. The algorithm firstly improves the parameter adaptive setting of PSO based on the particle and population evolution rate, establishes parameter adaptive PSO, and realizes the optimization of adaptive PSO optimized MP sparse decomposition. The continuous Gabor super-complete atomic set is constructed based on the continuous space search property of PSO to improve the optimal atomic matching of the evolutionary process. Finally, the recognition of English pronunciation is realized by the support vector machine (SVM) algorithm. The test results show that the misjudgement rate for different mispronunciations is less than 1% when the system is used to evaluate the English pronunciation level. It proves that the method can effectively detect the mispronunciation and has high evaluation accuracy.
Analysis and Evaluation of Enterprise Performance Appraisal Index Based on Fuzzy AHP Model

The decisive and decision-making challenges of business investment are for the good development of enterprises. In order to have a better market advantage of chemical granules for enterprises, the analytic hierarchy process (AHP) is constructed to analyze the company's performance. Under the environment of state-owned enterprises and foreign enterprises competing for market resources together, building a performance evaluation system suitable for the development stage of the company can adjust the planning strategy and analyze its own disadvantages in time. When analyzing the effective combination of performance appraisal and enterprise development, as well as the effective communication between employees and enterprise managers, feedback information is obtained through the experimental results. Under this analysis condition, this paper describes the feasibility and soundness of the AHP model system for enterprises, and what favorable decision-making methods can be provided for later development. Based on the selection of performance evaluation indicators, AHP analysis can clearly get the weight of each indicator and the correlation between them, in order to improve and build a new system of evaluation indicators. This paper concludes the following: (1) based on the analysis of enterprise performance appraisal indicators, using the AHP model evaluation method to calculate the indicators, the optimized indicators for the performance appraisal of the comprehensive and efficient significantly improved. (2) Comparing the traditional AHP calculation with the optimized model calculation, the results show that under the optimized calculation, the index is more concise and the fluctuation is reduced, which reduces the burden on administrators. (3) In the membership degree of index evaluation grade, the membership degree with an excellent grade is 0.9, which shows that the index plays a very important role in evaluation. (4) According to the development indicators of enterprises in recent years, it is found that the growth rate of resources is growing slowly, the demand for the talent market is still in a stable state as always, and its financial resources have increased by 120% in 2016.
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning techniques allow uncertainty estimation, training them with large-scale datasets is an expensive process that does not always yield models competitive with non-Bayesian counterparts. Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates. To address these issues, we propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation. BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset, enhancing pretrained non-Bayesian computer vision models by providing calibrated uncertainty estimates for the predictions without (i) hampering the performance of the model and (ii) the need for expensive retraining the model from scratch. The proposed method is agnostic to various architectures and tasks. We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures, including image super-resolution, deblurring, inpainting, and crucial application such as medical image translation. Moreover, we apply the derived uncertainty estimates to detect out-of-distribution samples in critical scenarios like depth estimation in autonomous driving. Code is available at https://github.com/ExplainableML/BayesCap.
Randomized Machine Learning and Forecasting of Nonlinear Dynamic Models Applied to SIR Epidemiological Model

We propose an approach to estimation of the parameters of non-linear dynamic models using the concept of Randomized Machine Learning (RML), based on the transition from deterministic models to random ones (with random parameters), followed by estimation of the probability distributions of parameters and noises on real data. The main feature of this method is its efficiency in conditions of a small amount of real data. The paper considers models formulated in terms of ordinary differential equations, which are converted to a discrete form for setting and solving the problem of entropy optimization. The application of the proposed approach is demonstrated on the problem of predicting the total number of infected COVID-19 using adynamic SIR epidemiological model. To do this, we construct a randomized SIR model (R-SIR) with one parameter, the entropy-optimal estimate of which is realized by its probability density function, as well as the probability density functions of the measurement noise at the points where training is performed. Next, the technique of randomized prediction with noise filtering is applied, based on the generation of the corresponding distributions and the construction of an ensemble of predictive trajectories with the calculation of the trajectory averaged over the ensemble. The paper implements a computational experiment using real operational data on the infection cases in the form of a comparative study with a well-known method for estimating model parameters based on the least squares method. The results obtained in the experiment demonstrate a significant decrease in the mean absolute percentage error (MAPE) with respect to real observations in the forecast interval, which shows the efficiency of the proposed method and its effectiveness in problems of the type considered in the work.
Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programs (Extended Abstract)

Extending the popular Answer Set Programming (ASP) paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express conditions on literals for being known or possible, i.e., contained in all or some answer sets, respectively. ELPs thus deliver multiple collections of answer sets, known as world views. Employing ELPs for reasoning problems so far has mainly been restricted to standard deci- sion problems (complexity analysis) and enumeration (development of systems) of world views. In this paper, we first establish quantitative reasoning for ELPs, where the acceptance of a certain set of literals depends on the number (proportion) of world views that are compatible with the set. Second, we present a novel system capable of efficiently solving the underlying counting problems required for quantitative reasoning. Our system exploits the graph-based measure treewidth by iteratively finding (graph) abstractions of ELPs.
Learning to Assemble Geometric Shapes

Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style parts of textured shapes, which greatly mitigate combinatorial challenges of the problem. In this work, we introduce the more challenging problem of shape assembly, which involves textureless fragments of arbitrary shapes with indistinctive junctions, and then propose a learning-based approach to solving it. We demonstrate the effectiveness on shape assembly tasks with various scenarios, including the ones with abnormal fragments (e.g., missing and distorted), the different number of fragments, and different rotation discretization.
Improving Transferability of Adversarial Examples with Virtual Step and Auxiliary Gradients

Deep neural networks have been demonstrated to be vulnerable to adversarial examples, which fool networks by adding human-imperceptible perturbations to benign examples. At present, the practical transfer-based black-box attacks are attracting significant attention. However, most existing transfer-based attacks achieve only relatively limited success rates. We propose to improve the transferability of adversarial examples through the use of a virtual step and auxiliary gradients. Here, the “virtual step” refers to using an unusual step size and clipping adversarial perturbations only in the last iteration, while the “auxiliary gradients” refer to using not only gradients corresponding to the ground-truth label (for untargeted attacks), but also gradients corresponding to some other labels to generate adversarial perturbations. Our proposed virtual step and auxiliary gradients can be easily integrated into existing gradient-based attacks. Extensive experiments on ImageNet show that the adversarial examples crafted by our method can effectively transfer to different networks. For single-model attacks, our method outperforms the state-of-the-art baselines, improving the success rates by a large margin of 12%~28%. Our code is publicly available at https://github.com/mingcheung/Virtual-Step-and-Auxiliary-Gradients.
Fintech, the digital age of financial services

Coexistence with technology has become part of daily life, the advantages of its use have spread to different disciplines such as finance, this evolution is part of the emergence of companies called Fintech (Financial Technology), a term that unifies Finance with technology, and that promotes the development of services and products that includes technological innovation to provide financial services, in a practical way and with easy access to people, companies or government. Fintech companies follow a customer-focused methodology, developing technological solutions according to the needs of their users and the current trend of the digital market, their most common products are focused on: means of payment and transfer, infrastructure for financial services, credits digital, personal finance, business finance, crowdfunding, disruptive financial entities, cryptocurrencies, Blockchain, and InsurTech. The objective of this article is to analyze the products offered by Financial Technologies (Fintech) in the design of solutions for companies or independent users in their inclusion in the use of technology-based financial services, taking as a normative reference the antecedents of the Law for Regulate Financial Technology Institutions (LRITF) in Mexico.