Reviewerly/exHarmony huggingface dataset:
Accelerometer-based human fall detection using sparrow search algorithm and back propagation neural network

To reduce the injury caused by the fall and solve the problems of low efficiency and low accuracy of traditional fall prediction methods, an optimized BP neural network fall prediction model based on the Sparrow Search Algorithm (SSA) is established. Taking sliding window to extract discrete features, selecting the maximum value, minimum value, mean value, and variance as the output indicator, the three-axis acceleration and resultant acceleration as the input of influencing factors, the fall prediction model is established for error prediction after data preprocessing. Experimental results show that the improved BP neural network could avoid falling into the locally optimal solution, and the convergence speed is faster, the fall detection accuracy of 98.3%, 92.0% and 96.1% based on DLR, Smart Fall and URFall datasets, respectively. This study may provide technical supports for wearable fall detection that can adapt to different environmental requirements, portable and low power consumption.
A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP's feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50-100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.
Low-cost solar tracker to maximize the capture of solar energy in tropical countries

The trend towards shift in electric powered systems is increasing the demand for greener sources of energy supply such as renewable energy. In comparison to wind, tidal and hydropower sources of renewable energy, solar derived energy provides an extra advantage for countries located in the tropical regions where solar resource is in abundance. The purpose of this study is to devise a low-cost and portable solar tracker to maximize the capture of solar energy per square meter of photovoltaic cells by considering an innovative ball-joint device for the tracking mechanism. The design specifications of the solar tracker was determined following thorough analysis of existing components on the market, namely the tracking device and solar panel. The methodology section involved key stages of setting up of the solar tracking device, namely the design, sizing, manufacturing, and assembly phase. From results of system-testing, the ball-joint operated solar tracker proved to be effective to precisely orient the solar panels in accordance with the earth’s rotation. The energy consumed by the solar tracking device as a percentage of energy produced was approximately 0.15%, hence demonstrating a system of high efficiency. In terms of performance, panels from the solar tracker outperformed those in the static ones as they generated significantly higher current by up to 37%. The proposed device is projected to have a larger impact in terms of cost saving in scaled-up system. The low-cost, solar-tracking device with innovative tracking mechanism, have shown the potential to maximize the capture of solar power in tropical countries by using small-to medium size solar panels.
Connect2NFT: A Web-Based, Blockchain Enabled NFT Application with the Aim of Reducing Fraud and Ensuring Authenticated Social, Non-Human Verified Digital Identity

As of 2022, non-fungible tokens, or NFTs, the smart contract powered tokens that represent ownership in a specific digital asset, have become a popular investment vehicle. In 2021, NFT trading reached USD 17.6 billion and entered mainstream media with several celebrities and major companies launching tokens within the space. The rapid rise in popularity of NFTs has brought with it a number of risks and concerns, two of which will be discussed and addressed in this technical paper. Data storage of the underlying digital asset connected to an NFT is held off-chain in most cases and is therefore out of the NFT holders’ control. This issue will be discussed and addressed using a theoretical workflow developed and presented for a system that converges NFTs and verifiable credentials with the aim of storing underlying NFT digital assets in a decentralized manner. The second issue focuses on the rise of NFT infringements and fraud within the overall NFT space. This will be discussed and addressed through the development of a practical application, named “Connect2NFT”. The main functionality of this practical application will enable users to connect their Twitter social media accounts to the NFTs they own, thus ensuring that potential buyers or viewers of the NFT can comprehensively conclude who is the authentic owner of a specific NFT. An individual performance analysis of the proposed solution will be conducted in addition to being compared and evaluated against similar applications. Thorough development, implementation, and testing has been performed in order to establish a practical solution that can be tested and applied to current NFT use cases. The theoretical NFT storage solution is a minor but equally important contribution in comparison.
A Novel Color Image Encryption Algorithm Using Coupled Map Lattice with Polymorphic Mapping

Some typical security algorithms such as SHA, MD4, MD5, etc. have been cracked in recent years. However, these algorithms have some shortcomings. Therefore, the traditional one-dimensional-mapping coupled lattice is improved by using the idea of polymorphism in this paper, and a polymorphic mapping–coupled map lattice with information entropy is developed for encrypting color images. Firstly, we extend a diffusion matrix with the original 4 × 4 matrix into an n × n matrix. Then, the Huffman idea is employed to propose a new pixel-level substitution method, which is applied to replace the grey degree value. We employ the idea of polymorphism and select f(x) in the spatiotemporal chaotic system. The pseudo-random sequence is more diversified and the sequence is homogenized. Finally, three plaintext color images of 256×256×3, “Lena”, “Peppers” and “Mandrill”, are selected in order to prove the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm has a large key space, better sensitivity to keys and plaintext images, and a better encryption effect.
Adversarial style discrepancy minimization for unsupervised domain adaptation

Mainstream unsupervised domain adaptation (UDA) methods align feature distributions across different domains via adversarial learning. However, most of them focus on global distribution alignment, ignoring the fine-grained domain discrepancy. Besides, they generally require auxiliary models, bringing extra computation costs. To tackle these issues, this study proposes an UDA method that differentiates individual samples without the help of extra models. To this end, we introduce a novel discrepancy metric, termed style discrepancy, to distinguish different target samples. We also propose a paradigm for adversarial style discrepancy minimization (ASDM). Specifically, we fix the parameters of the feature extractor and maximize style discrepancy to update the classifier, which helps detect more hard samples. Adversely, we fix the parameters of the classifier and minimize the style discrepancy to update the feature extractor, pushing those hard samples near the support of the source distribution. Such adversary helps to progressively detect and adapt more hard samples, leading to fine-grained domain adaptation. Experiments on different UDA tasks validate the effectiveness of ASDM. Overall, without any extra models, ASDM reaches a 46.9% mIoU in the GTA5 to Cityscapes benchmark and an 84.7% accuracy in the VisDA-2017 benchmark, outperforming many existing adversarial-learning-based methods.
Next-point-of-interest recommendation based on joint mining of regularity and randomness

Point-of-interest (POI) recommendation is important in location-based applications and has attracted considerable research interest. Despite the inspiring achievements of POI recommendation in recent years, POI recommendation based on the modeling of sparse spatiotemporal data remains challenging, suffering from heterogeneity, randomness, and complexity. In this paper, we propose a novel method for next-POI recommendation. It consists of long- and short-term modules, which learn users' complex behavior using heterogeneous check-in data. We assume that users have relatively stable regularities on different feature spaces in the long term and randomness can be observed in the users' decision in the short term, which is influenced by various contexts. Hence, in the long-term module, we design a parallel gated recurrent unit (GRU) network to capture the regularities in the time-space, date-space, geo-space, and activity-space separately from the long trajectory history. In the short-term module, we utilize an attention-based multi-context GRU network to capture the context-aware randomness from the recent trajectory. Furthermore, we integrate the long-term regularity and short-term randomness to model the complex mechanism of human mobility and use the hybrid preference to recommend the next POI. We verify the effectiveness of our model on three public check-in datasets, and experimental results indicate that our approach outperforms state-of-the-art methods for POI recommendation.
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm

• We propose a novel infinite mixture of Gaussian processes functional regressions model based on the Dirichlet process, which sidesteps the model selection problem in existing methods. • We design an effective variational EM algorithm for parameter learning and posterior inference. • Some possible variants of the proposed model are discussed, including adaptive hyper-parameter learning, Pitman-Yor process based mixture model, and variational tempering. • Experimental results show that the proposed method outperforms the competing methods. Gaussian Process Functional Regression (GPFR) is a powerful tool in functional data analysis. In practical applications, functional data may be generated from different signal sources, and a single GPFR is not flexible enough to accurately model the data. To tackle the heterogeneity problem, a finite mixture of Gaussian Process Functional Regressions (mix-GPFR) was suggested. However, the number of components in mix-GPFR needs to be specified a priori, which is difficult to determine in practice. In this paper, we propose a Dirichlet Process Mixture of Gaussian Process Functional Regressions (DPM-GPFR), in which there are potentially infinite many GPFR components dominated by a Dirichlet process. Thus, DPM-GPFR is far more flexible than a single GPFR, and sidestep the model selection problem in mix-GPFR. We further develop a fully Bayesian treatment for learning DPM-GPFR based on the Variational Expectation-Maximization (VEM) algorithm. Experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness of our proposed method.
GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization

Partial label learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label, and only one of them is the true label. Most of the existing PLL algorithms directly disambiguate the candidate labels according to the instance feature similarity, but fail to discover the latent semantic relationship over the entire dataset. In this paper, method GraphDPI, an innovative deep partial label disambiguation by graph representation via mutual information maximization, is proposed. This method can capture the semantic clusters with the most unique information in the latent space and automatically adapt to different feature distributions. Specifically, a new sampling method based on the graph is proposed to estimate mutual information, extending GCN to the field of weakly supervised learning. Therefore, the graph representation of the data can contain more distinguishing information to disambiguate candidate labels by maximizing the mutual information of the local graph representation and the global one. Furthermore, the triplet loss is introduced to fully exploit the relationship between instances and extract the latent embedding representation over the entire dataset. It thereby can make the model output as large as possible on the inter-class variation and as small as possible on the intra-class variation. Finally, the candidate labels can be disambiguated by the difference between semantic clusters. Experiments reveal the overwhelming performances of GraphDPI.
Top-k sparsification with secure aggregation for privacy-preserving federated learning

The proposal of federated learning solves problems of data silos and privacy protection in the field of artificial intelligence. However, privacy attacks can infer or reconstruct sensitive information from the submitted gradient, which causes users' privacy leakage in federated learning. Secure aggregation (SecAgg) protocol can protect users' privacy while completing federated learning tasks, but it incurs significant communication overhead and wall clock training time on large-scale model training task. Thus, it is difficult to apply SecAgg in bandwidth-limited federated applications. Recently, Rand-k sparsification with secure aggregation (Rand-k SparseSecAgg) was proposed to optimize SecAgg protocol, while its optimization of communication overhead and training time is limited. In this paper, we replace Rand-k sparsification with Top-k sparsification, and design a Top-k sparsification with secure aggregation (Top-k SparseSecAgg) protocol for privacy-preserving federated learning to further reduce communication overhead and wall clock training time. In addition, we optimize the proposed protocol by assigning clients to different groups in the logical layer, which reduces the upper limit of compression ratio and practical communication overhead in Top-k SparseSecAgg. Experiments demonstrate that Top-k SparseSecAgg can reduce communication overhead by 6.25× as compared to SecAgg, 3.78× as compared to Rand-k SparseSecAgg, and reduce wall clock training time 1.43× as compared to SecAgg and 1.13× as compared to Rand-k SparseSecAgg. Thus, our protocol is more suitable in bandwidth-limited federated applications to protect privacy and complete learning task.
Packet switching in quantum networks: A path to the quantum Internet

Large-scale quantum networks with thousands of nodes require scalable network protocols and physical hardware to realize. In this paper, we introduce packet switching as a paradigm for quantum data transmission in both future and near-term quantum networks. We propose a classical-quantum data frame structure and explore methods of frame generation and processing. Further, we present conceptual designs for a quantum reconfigurable optical add-drop multiplexer to realize the proposed transmission scheme. Packet switching allows for a universal design for a next-generation Internet where classical and quantum data share the same network protocols and infrastructure. In this quantum networking paradigm, entanglement distribution, as with quantum key distribution, is an application built on top of the quantum network rather than as a network designed especially for those purposes. For analysis of the network model, we simulate the feasibility of quantum packet switching for some preliminary models of quantum key and entanglement distribution. Finally, we discuss how our model can be integrated with other network models toward a realization of the quantum Internet.
interiqr: Unobtrusive Edible Tags using Food 3D Printing

We present interiqr, a method that utilizes the infill parameter in the 3D printing process to embed information inside the food that is difficult to recognize with the human eye. Our key idea is to utilize the air space or secondary materials to generate a specific pattern inside the food without changing the model geometry. As a result, our method exploits the patterns that appear as hidden edible tags to store the data and simultaneously adds them to a 3D printing pipeline. Our contribution also includes the framework that connects the user with a data-embedding interface through the food 3D printing process, and the decoding system allows the user to decode the information inside the 3D printed food through backlight illumination and a simple image processing technique. Finally, we evaluate the usability of our method under different settings and demonstrate our method through the example application scenarios.
Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation

Abstract Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
Facial Age Estimation Using Machine Learning Techniques: An Overview

Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers’ attention over the past several years. Numerous human–computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research.
Robust and fuzzy ensemble framework via spectral learning for random projection-based fuzzy-c-means clustering

The ensembles of random projection-based fuzzy-c-means (RP-FCM) can handle high-dimensional data efficiently. However, the performance of these ensemble frameworks is still hindered by some issues, such as misaligned membership matrices, information loss of co-similar matrices, large storage space, unstable ensemble results due to the additional re-clustering, etc. To address these issues, we propose a robust and fuzzy ensemble framework via spectral learning for RP-FCM clustering. After using random projection to generate different dimensional datasets and obtaining the membership matrices via fuzzy-c-means, we first convert these membership matrices into regularized graphs and approximates the affinity matrices of these graphs by spectral matrices. This step not only avoids the alignment problems of membership matrices but also excludes the storage of large-scale graphs. The spectral matrices of the same size are used as the features of membership matrices for the ensemble, avoiding the possible information loss by applying co-similar matrix transformations. More importantly, an optimization model is designed in our framework to learn the fusion of spectral features. In this model, the proportion of each base clustering is adjusted adaptively through a fuzzification exponent, and the effect of outliers is also suppressed by a robust norm. Finally, the Laplacian rank constraint in the model guarantees the ensemble can achieve the exact final partition. An efficient algorithm for this model is derived, and its time complexity and convergence are also analyzed. Competitive experimental results on benchmark data demonstrate the effectiveness of the proposed ensemble framework in comparison to state-of-the-art methods.
Deep Learning-based Multi-stage segmentation method using ultrasound images for breast cancer diagnosis

Globally, breast cancer occurs frequently in women and has the highest mortality rate. Owing to the increased need for a rapid and reliable initial diagnosis of breast cancer, several breast tumor segmentation methods based on ultrasound images have attracted research attention. Most conventional methods use a single network and demonstrate high performance by accurately classifying tumor-containing and normal image pixels. However, tests performed using normal images have revealed the occurrence of many false-positive errors. To address this limitation, this study proposes a multistage-based breast tumor segmentation technique based on the classification and segmentation of ultrasound images. In our method, a breast tumor ensemble classification network (BTEC-Net) is designed to classify whether an ultrasound image contains breast tumors or not. In the segmentation stage, a residual feature selection UNet (RFS-UNet) is used to exclusively segment images classified as abnormal by the BTEC-Net. The proposed multistage segmentation method can be adopted as a fully automated diagnosis system because it can classify images as tumor-containing or normal and effectively specify the breast tumor regions.