Reviewerly/exHarmony huggingface dataset:
Automatic Data Clustering Using Hybrid Chaos Game Optimization with Particle Swarm Optimization Algorithm.

In cluster analysis, classical approaches suffer from the problem of identifying the number of clusters, known as the automatic clustering problem. Therefore, automatic clustering has become a popular research area and offers opportunities in various data analysis applications such as bioinformatics, medicine, image processing and consumer segmentation. It is considered as NP- complete problem where it is preferable to use approximate approaches. In this study, we propose an hybrid approach between chaos game optimization and particle swarm optimization (CGOPSO). The Davies-Bouldin index (DBI) is used as a main objective of the proposed approach with the purpose to find the most accurate number of cluster centroids and their positions. To assess its performance, we compared CGOPSO with different other existing algorithms in the literature over 12 classical datasets using two different validity indexes: Davies Bouldin index (DBI) and Compact-Seperated index (CSI). The experimental results have demonstrated that CGOPSO shows better performance than other algorithms.
An Unified Iterative Hand-Eye Calibration Method for Eye-on-Base and Eye-in-Hand Setups

This paper presents an accurate and precise hand-eye calibration technique based on minimization of the reprojection error. Unlike traditional hand-eye calibration, the proposed method does not require an explicit estimate of the camera pose for each input image because it does not rely on mathematical description and problem formulation commonly used in standard hand-eye calibration algorithms. The proposed method is based on a nonlinear optimization problem, so that the estimation problem can be solved efficiently and robustly, and can be easily extended to different camera-robot setups (e.g., eye-on-base or eye-in-hand). An extensive evaluation based on simulated and real experiments has been performed, proving its good estimation accuracy in terms of reprojection error. The experimental results with real robots show that the proposed method is applicable to relevant industrial contexts and improves the quality and precision of the camera-robot transformation estimation with respect to state-of-the-art approaches.
Replacing Labeled Real-image Datasets with Auto-generated Contours

In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k shows 81.8% top-1 accuracy when fine-tuned on ImageNet-1k and FDSL shows 82.7% top-1 accuracy when pre-trained under the same conditions (number of images, hyperparameters, and number of epochs). Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from, and thus have tremendous potential for pre-training general models. To understand the performance of the synthetic images, we tested two hypotheses, namely (i) object contours are what matter in FDSL datasets and (ii) increased number of parameters to create labels affects performance improvement in FDSL pre-training. To test the former hypothesis, we constructed a dataset that consisted of simple object contour combinations. We found that this dataset can match the performance of fractals. For the latter hypothesis, we found that increasing the difficulty of the pre-training task generally leads to better fine-tuning accuracy.
MLST-Former: Multi-Level Spatial-Temporal Transformer for Group Activity Recognition

Group activity recognition, which aims to simultaneously understand individual action and group activity in video clips, plays a fundamental role in computer vision and video analysis. In this paper, we propose a novel relational inference framework, termed MLST-Former, for individual action and group activity recognition, capturing the various degrees of spatial-temporal dynamic interactions adaptively and jointly among actors and generating reasonable group representations. Specifically, we first design a multi-level spatial-temporal Transformer to capture the miscellaneous actors’ spatial-temporal contextual information to deal with unbalanced interactions between actors. Furthermore, our proposed network is capable of fully mining long-range spatial-temporal dependencies with the virtue of the merge function and cross attention mechanism. We then propose an inter-frame gating fusion mechanism (IGFM) to selectively aggregate the temporal and structural features of the interacting actors. A new multi-task learning strategy, consisting of the classification cost of individual actions and group activities, is also developed. Moreover, we adopt the motion trajectory branch to provide complementary dynamic features for improving recognition performance. A series of ablation studies demonstrate the effectiveness and respective contributions of the different components within the proposed method. Extensive experiments on four public GAR datasets clearly show that our approach can achieve very competitive performance by comparing them with state-of-the-art methods.
Maximum correntropy cubature Kalman filter and smoother for continuous-discrete nonlinear systems with non-Gaussian noises

To estimate the continuous-discrete nonlinear dynamic systems with non-Gaussian non-zero mean noises, a square-root maximum correntropy cubature Kalman filter with adaptive kernel width and its corresponding smoother are proposed in this paper. Based on the statistical linear regression (SLR) technique, the maximum correntropy criterion (MCC) instead of minimum mean square error (MMSE) is applied to our previously derived continuous-discrete Gaussian estimation method. Compared with MMSE, MCC can not only capture the second-order statistical information of non-Gaussian error, but also utilize its higher-order information. In addition, in order to ensure that MCC can work with an appropriate kernel width, an adaptive kernel width adjustment strategy is given by using the sliding window methodology. Further, for a reliable implementation of the proposed continuous-discrete MCC-based algorithms, they are structurally modified into the square root form. The newly proposed approaches are tested and compared with conventional estimation methods in three commonly used numerical applications. Experimental results show that the proposed algorithms are not only accurate and robust, but also have low computational complexities.
Recursive reasoning-based training-time adversarial machine learning

The training process of a machine learning (ML) model may be subject to adversarial attacks from an attacker who attempts to undermine the test performance of the ML model by perturbing the training minibatches, and thus needs to be protected by a defender. Such a problem setting is referred to as training-time adversarial ML. We formulate it as a two-player game and propose a principled Recursive Reasoning-based Training-Time adversarial ML (R2T2) framework to model this game. R2T2 models the reasoning process between the attacker and the defender and captures their bounded reasoning capabilities (due to bounded computational resources) through the recursive reasoning formalism. In particular, we associate a deeper level of recursive reasoning with the use of a higher-order gradient to derive the attack (defense) strategy, which naturally improves its performance while requiring greater computational resources. Interestingly, our R2T2 framework encompasses a variety of existing adversarial ML methods which correspond to attackers (defenders) with different recursive reasoning capabilities. We show how an R2T2 attacker (defender) can utilize our proposed nested projected gradient descent-based method to approximate the optimal attack (defense) strategy at an arbitrary level of reasoning. R2T2 can empirically achieve state-of-the-art attack and defense performances on benchmark image datasets.
A Proposed System for Understanding the Consumer Opinion of a Product Using Sentiment Analysis

Sentiment analysis is essentially a process used to analyse or understand the underlying sentiment or feeling of the text in hand. This paper describes a software that leverages machine learning approaches such as support vector machine (SVM), random forest and logistic regression to perform opinion mining on Amazon consumer reviews. With an F1-score of 0.84, logistic regression and SVM prove to be better choices than random forest for a binary classifier. With online shopping becoming the new trend, this application is a one stop solution for businesses to track the popularity and sentiment of their products.
A high-precision correction method in non-rigid 3D motion poses reconstruction

Occlusion, rotation and other factors affect human motion structure because of the incomplete acquired image sequence, resulting in poor performance of non-rigid three-dimensional (3D) motion pose reconstruction. A non-rigid 3D reconstruction and high-precision correction method for motion pose are studied in this paper. A non-rigid imaging model is designed to obtain 3D moving images. According to the frame difference and morphological processing, the background of image is separated and denoised. Combined with motion analysis, 3D motion pose features are extracted as identification of non-rigid 3D motion error actions in a hybrid Convolution Neural Network-Hidden Markov Model to train the correction coefficients, which are used to adjust the pose in 3D motion reconstruction and realise correction. Experimental results show that this method has high precision reconstruction and correction of non-rigid 3D motion pose.
PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.
Neural computing with coherent laser networks

Abstract We show that coherent laser networks (CLNs) exhibit emergent neural computing capabilities. The proposed scheme is built on harnessing the collective behavior of laser networks for storing a number of phase patterns as stable fixed points of the governing dynamical equations and retrieving such patterns through proper excitation conditions, thus exhibiting an associative memory property. It is discussed that despite the large storage capacity of the network, the large overlap between fixed-point patterns effectively limits pattern retrieval to only two images. Next, we show that this restriction can be uplifted by using nonreciprocal coupling between lasers and this allows for utilizing a large storage capacity. This work opens new possibilities for neural computation with coherent laser networks as novel analog processors. In addition, the underlying dynamical model discussed here suggests a novel energy-based recurrent neural network that handles continuous data as opposed to Hopfield networks and Boltzmann machines that are intrinsically binary systems.
The Environmental Data Initiative: Connecting the past to the future through data reuse

Abstract The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom‐up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.
Robust Saliency-Driven Quality Adaptation for Mobile 360-Degree Video Streaming

Mobile 360-degree video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient and variable wireless network bandwidth. Recently, saliency-driven 360-degree streaming overcomes the buffer size limitation of head movement trajectory (HMT)-driven solutions and thus strikes a better balance between video quality and rebuffering. However, inaccurate network estimations and intrinsic saliency bias still challenge saliency-based streaming approaches, limiting further QoE improvement. To address these challenges, we design a robust saliency-driven quality adaptation algorithm for 360-degree video streaming, RoSal360. Specifically, we present a practical, tile-size-aware deep neural network (DNN) model with a decoupled self-attention architecture to accurately and efficiently predict the transmission time of video tiles. Moreover, we design a reinforcement learning (RL)-driven online correction algorithm to robustly compensate the improper quality allocations due to saliency bias. Through extensive prototype evaluations over real wireless network environments including commodity WiFi, 4G/LTE, and 5G links in the wild, RoSal360 significantly enhances the video quality and reduces the rebuffering ratio, thereby improving the viewer QoE, compared to the state-of-the-art algorithms.
TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning

A social tagging system improves recommendation performance by introducing tags as auxiliary information. These tags are text descriptions of target items provided by individual users, which can be arbitrary words or phrases, so they can provide more abundant information about user interests and item characteristics. However, there are many problems to be solved in tag information, such as data sparsity, ambiguity, and redundancy. In addition, it is difficult to capture multi-aspect user interests and item characteristics from these tags, which is essential to the recommendation performance. In the view of these situations, we propose a tag-aware recommendation model based on attention learning, which can capture diverse tag-based potential features for users and items. The proposed model adopts the embedding method to produce dense tag-based feature vectors for each user and each item. To compress these vectors into a fixed-length feature vector, we construct an attention pooling layer that can automatically allocate different weights to different features according to their importance. We concatenate the feature vectors of users and items as the input of a multi-layer fully connected network to learn non-linear high-level interaction features. In addition, a generalized linear model is also conducted to extract low-level interaction features. By integrating these features of different types, the proposed model can provide more accurate recommendations. We establish extensive experiments on two real-world datasets to validate the effect of the proposed model. Comparable results show that our model perform better than several state-of-the-art tag-aware recommendation methods in terms of HR and NDCG metrics. Further ablation studies also demonstrate the effectiveness of attention learning.
Time-Varying Sequence Model

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, the neuron units and weights are shared at each time step to reduce computational costs, limiting their ability to learn time-varying relationships between model inputs and outputs. In this context, this paper proposes two methods to characterize the dynamic relationships in real-world sequential data, namely, the internal time-varying sequence model (ITV model) and the external time-varying sequence model (ETV model). Our methods were designed with an automated basis expansion module to adapt internal or external parameters at each time step without requiring high computational complexity. Extensive experiments performed on synthetic and real-world data demonstrated superior prediction and classification results to conventional sequence models. Our proposed ETV model is particularly effective at handling long sequence data.
The development of virtual healing environment in VR platform

The purpose of this research is to build a healing environment that can be experienced through virtual reality (VR) to reduce stress levels of the user because of the lengthy period of COVID-19 pandemic that forced people to stay at home. The healing environment is created based on several theories on the principles of Healing Environments related to Architectural design. And as for the development of the simulation software, it is using the method of Extreme Programming that is based on Agile principle that combined with Architectural design flow. The VR is targeted to run optimally in low-end devices with cardboard and common Bluetooth controller so that it can be accessed inclusively. The evaluation is conducted using a user acceptance test with expert judgment and survey to 32 respondents. The findings are that they enjoyed the simulation because of the guidance is clear and the virtual environment looks more real compared to others that looks cartoonish.
Mathematical model estimation and prediction application of Covid-19 infection in Indonesia using Levenberg-Marquardt Algorithm based on Python

COVID-19 was originally diagnosed in Wuhan, China, in late December 2019; it subsequently expanded internationally, affecting around 7 million people and caused 300,000 deaths by May 2020. An Application that could surveillance the Covid-19 in Indonesia is needed. Therefore, we proposed a prediction application for the COVID-19 pandemic situation in Indonesia by referring to public compliance with surveillance policies using the SPCIRD model. The Levenberg Marquardt curve fitting method was chosen because it is simple and produces a reasonably good model. According the result of questionnaire and black box testing our application performed really well. The result of our SPCIRD model with Levenberg-Marquardt optimization achieved R2 with the score -1.248 in active cases, -0.235 in recovered and -3.982 in death for 281 number of iterations. We are also achieved 93.3% that agree and very agree with the application usability to understand and to predict pattern from COVID-19. It was also had 83.3\% respondent that satisfy with the prototype.
An AI-Aided Diagnostic Framework for Hematologic Neoplasms Based on Morphologic Features and Medical Expertise

A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and laborious. Herein, we attempt to establish an artificial intelligence (AI)-aided diagnostic framework integrating medical expertise. This framework acts as a virtual hematological morphologist (VHM) for diagnosing hematological neoplasms. Two datasets were established as follows: An image dataset was used to train the Faster Region-based Convolutional Neural Network to develop an image-based morphologic feature extraction model. A case dataset containing retrospective morphologic diagnostic data was used to train a support vector machine algorithm to develop a feature-based case identification model based on diagnostic criteria. Integrating these 2 models established a whole-process AI-aided diagnostic framework, namely, VHM, and a 2-stage strategy was applied to practice case diagnosis. The recall and precision of VHM in bone marrow cell classification were 94.65% and 93.95%, respectively. The balanced accuracy, sensitivity, and specificity of VHM were 97.16%, 99.09%, and 92%, respectively, in the differential diagnosis of normal and abnormal cases, and 99.23%, 97.96%, and 100%, respectively, in the precise diagnosis of chronic myelogenous leukemia in chronic phase. This work represents the first attempt, to our knowledge, to extract multimodal morphologic features and to integrate a feature-based case diagnosis model for designing a comprehensive AI-aided morphologic diagnostic framework. The performance of our knowledge-based framework was superior to that of the widely used end-to-end AI-based diagnostic framework in terms of testing accuracy (96.88% vs 68.75%) or generalization ability (97.11% vs 68.75%) in differentiating normal and abnormal cases. The remarkable advantage of VHM is that it follows the logic of clinical diagnostic procedures, making it a reliable and interpretable hematological diagnostic tool.
Hierarchical multimodal fusion framework based on noisy label learning and attention mechanism for cancer classification with pathology and genomic features

Classification of subtype and grade is imperative in the clinical diagnosis and prognosis of cancer. Many deep learning-based studies related to cancer classification are based on pathology and genomics. However, most of them are late fusion-based and require full supervision in pathology image analysis. To address these problems, we present an integrated framework for cancer classification with pathology and genomics data. This framework consists of two major parts, a weakly supervised model for extracting patch features from whole slide images (WSIs), and a hierarchical multimodal fusion model. The weakly supervised model can make full use of WSI labels, and mitigate the effects of label noises by the self-training strategy. The generic multimodal fusion model is capable of capturing deep interaction information through multi-level attention mechanisms and controlling the expressiveness of each modal representation. We validate our approach on glioma and lung cancer datasets from The Cancer Genome Atlas (TCGA). The results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods, with the competitive AUC of 0.872 and 0.977 on these two datasets respectively. This paper establishes insight on how to build deep networks on multimodal biomedical data and proposes a more general framework for pathology image analysis without pixel-level annotation.