Reviewerly/exHarmony huggingface dataset:
BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells

Background and Objective: The classification of human peripheral blood cells yields significance in the detection of inflammation, infections and blood cell disorders such as leukemia. Limitations in traditional algorithms for blood cell classification and increased computational processing power have allowed machine learning methods to be utilized for this clinically prevalent task. Methods: In the current work, we present BloodCaps, a capsule based model designed for the accurate multiclassification of a diverse and broad spectrum of blood cells. Results: Implemented on a large-scale dataset of 8 categories of human peripheral blood cells, the proposed architecture achieved an overall accuracy of 99.3%, outperforming convolutional neural networks such as AlexNet(81.5%), VGG16(97.8%), ResNet-18(95.9%) and InceptionV3(98.4%). Furthermore, we devised three new datasets(low-resolution dataset, small dataset, and low-resolution small dataset) from the original dataset, and tested BloodCaps in comparison with AlexNet, VGG16, ResNet-18, and InceptionV3. To further validate the applicability of our proposed model, we tested BloodCaps on additional public datasets such as the All IDB2, BCCD, and Cell Vision datasets. Compared with the reported results, BloodCaps showed the best performance in all three scenarios. Conclusions: The proposed method proved superior in octal classification among all three datasets. We believe the proposed method represents a promising tool to improve the diagnostic performance of clinical blood examinations.
When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market

A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is $8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.
Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning

Cancer is a kind of non-communicable disease, progresses with uncontrolled cell growth in the body. The cancerous cell forms a tumor that impairs the immune system, causes other biological changes to malfunction. The most common kinds of cancer are breast, prostate, leukemia, lung, and colon cancer. The presence of the disease is identified with the proper diagnosis. Many screening procedures are suggested to find the presence of the condition under different stages. Medical practitioners further analyze these electronic health records to diagnose and treat the individual. In some cases, misdiagnosis can happen due to manual error or misinterpretation of the data. To avoid these issues, this paper presents an effective computer-aided diagnosis system supported by intelligence learning models. A machine learning-based feature modeling is proposed to improve predictive performance. From the University of California, Irvine repository, breast, cervical, and lung cancer datasets are accessed to conduct this experimental study. Supervised learning algorithms are employed to train and validate the optimal features reduced by the proposed system. Using the 10-Fold cross-validation method, the trained and performance model is evaluated with validation metrics such as accuracy, f-score, precision, and recall. The study's outcome attained 99.62%, 96.88%, and 98.21% accuracy on breast, cervical, and lung cancer datasets, respectively, which exhibits the proposed system's efficacy. Moreover, this system acts as a miscellaneous tool for capturing the pattern from many clinical trials for multiple types of cancer disease.
Optimization-Based Framework for Excavation Trajectory Generation

In thisletter, we present a novel optimization-based framework for autonomous excavator trajectory generation under task-specific constraints. Traditional excavation trajectory generators over-simplify the geometric trajectory parameterization thereby limiting the space for optimization. To expand the search space, we formulate a generic task specification for excavation by constraining the instantaneous motion of the bucket and adding a target-oriented constraint to control the amount of excavated soil. The trajectory is represented with a waypoint interpolating spline. Time intervals between waypoints are relaxed as variables to facilitate generating the time-optimal trajectory in one stage. Experiments on a real robot platform demonstrate that our method is adaptive to different terrain shapes and outperforms other optimal path planners in terms of the minimum joint length and minimum travel time.
SMOTE-LOF for noise identification in imbalanced data classification

Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset. These underrepresented classes are referred to as a minority, while the overrepresented ones are called the majority. The unequal distribution of data leads to the machine's inability to carry out predictive accuracy in determining the minority classes, thereby causing various costs of classification errors. Currently, the standard framework used to solve the unequal distribution of imbalanced data learning is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE can produce synthetic minority data samples considered as noise, which is also part of the majority classes. Therefore, this study aims to improve SMOTE to identify the noise from synthetic minority data produced in handling imbalanced data by adding the Local Outlier Factor (LOF). The proposed method is called SMOTE-LOF, and the experiment was carried out using imbalanced datasets with the results compared with the performance of the SMOTE. The results showed that SMOTE-LOF produces better accuracy and f-measure than the SMOTE. In a dataset with a large number of data examples and a smaller imbalance ratio, the SMOTE-LOF approach also produced a better AUC than the SMOTE. However, for a dataset with a smaller number of data samples, the SMOTE's AUC result is arguably better at handling imbalanced data. Therefore, future research needs to be carried out using different datasets with combinations varying from the number of data samples and the imbalanced ratio.
Adversarial Training Against Location-Optimized Adversarial Patches

Deep neural networks have been shown to be susceptible to adversarial examples -- small, imperceptible changes constructed to cause mis-classification in otherwise highly accurate image classifiers. As a practical alternative, recent work proposed so-called adversarial patches: clearly visible, but adversarially crafted rectangular patches in images. These patches can easily be printed and applied in the physical world. While defenses against imperceptible adversarial examples have been studied extensively, robustness against adversarial patches is poorly understood. In this work, we first devise a practical approach to obtain adversarial patches while actively optimizing their location within the image. Then, we apply adversarial training on these location-optimized adversarial patches and demonstrate significantly improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to adversarial training on imperceptible adversarial examples, our adversarial patch training does not reduce accuracy.
A survey of accelerator architectures for 3D convolution neural networks

3D convolution neural networks (CNNs) have shown excellent predictive performance on tasks such as action recognition from videos. Since 3D CNNs have unique characteristics and extremely high compute/memory-overheads, executing them on accelerators designed for 2D CNNs provides sub-optimal performance. To overcome these challenges, researchers have recently proposed architectures for 3D CNNs. In this paper, we present a survey of hardware accelerators and hardware-aware algorithmic optimizations for 3D CNNs. We include only those CNNs that perform 3D convolution and not those that perform only 2D convolution on 2D or 3D data. We highlight their key ideas and underscore their similarities and differences. We believe that this survey will spark a great deal of research towards the design of ultra-efficient 3D CNN accelerators of tomorrow.
An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.
Geometric distinguishability measures limit quantum channel estimation and discrimination

Quantum channel estimation and discrimination are fundamentally related information processing tasks of interest in quantum information science. In this paper, we analyze these tasks by employing the right logarithmic derivative Fisher information and the geometric R\'enyi relative entropy, respectively, and we also identify connections between these distinguishability measures. A key result of our paper is that a chain-rule property holds for the right logarithmic derivative Fisher information and the geometric R\'enyi relative entropy for the interval $\alpha\in(0,1) $ of the R\'enyi parameter $\alpha$. In channel estimation, these results imply a condition for the unattainability of Heisenberg scaling, while in channel discrimination, they lead to improved bounds on error rates in the Chernoff and Hoeffding error exponent settings. More generally, we introduce the amortized quantum Fisher information as a conceptual framework for analyzing general sequential protocols that estimate a parameter encoded in a quantum channel, and we use this framework, beyond the aforementioned application, to show that Heisenberg scaling is not possible when a parameter is encoded in a classical-quantum channel. We then identify a number of other conceptual and technical connections between the tasks of estimation and discrimination and the distinguishability measures involved in analyzing each. As part of this work, we present a detailed overview of the geometric R\'enyi relative entropy of quantum states and channels, as well as its properties, which may be of independent interest.
“Super-Heisenberg” and Heisenberg Scalings Achieved Simultaneously in the Estimation of a Rotating Field

The Heisenberg scaling, which scales as ${N}^{\ensuremath{-}1}$ in terms of the number of particles or ${T}^{\ensuremath{-}1}$ in terms of the evolution time, serves as a fundamental limit in quantum metrology. Better scalings, dubbed as ``super-Heisenberg scaling,'' however, can also arise when the generator of the parameter involves many-body interactions or when it is time dependent. All these different scalings can actually be seen as manifestations of the Heisenberg uncertainty relations. While there is only one best scaling in the single-parameter quantum metrology, different scalings can coexist for the estimation of multiple parameters, which can be characterized by multiple Heisenberg uncertainty relations. We demonstrate the coexistence of two different scalings via the simultaneous estimation of the magnitude and frequency of a field where the best precisions, characterized by two Heisenberg uncertainty relations, scale as ${T}^{\ensuremath{-}1}$ and ${T}^{\ensuremath{-}2}$, respectively (in terms of the standard deviation). We show that the simultaneous saturation of two Heisenberg uncertainty relations can be achieved by the optimal protocol, which prepares the optimal probe state, implements the optimal control, and performs the optimal measurement. The optimal protocol is experimentally implemented on an optical platform that demonstrates the saturation of the two Heisenberg uncertainty relations simultaneously, with up to five controls. As the first demonstration of simultaneously achieving two different Heisenberg scalings, our study deepens the understanding on the connection between the precision limit and the uncertainty relations, which has wide implications in practical applications of multiparameter quantum estimation.
A method of cross-layer fusion multi-object detection and recognition based on improved faster R-CNN model in complex traffic environment

Improving the detection accuracy and speed is the prerequisite of multi-object recognition in the complex traffic environment. Despite object detection has made significant advances based on deep neural networks, it remains a challenge to focus on small and occlusion objects. We address this challenge by allowing multiscale fusion. We introduce a cross-layer fusion multi-object detection and recognition algorithm based on Faster R-CNN, an approach that the five-layer structure of VGG16 (Visual Geometry Group) is used to obtain more characteristic information. We implement this idea with lateral embedding the 1×1 convolution kernel, max pooling and deconvolution, in conjunction with weighted balanced multi-class cross entropy loss function and Soft-NMS to control the imbalance between difficult and easy samples. Considering the actual situation in a complex traffic environment, we manually label mixed dataset. On Cityscapes and KITTI datasets, experimental results show that the proposed model achieves better effects than the current mainstream object detection models.
Forecasting Bitcoin Price Fluctuation by Twitter Sentiment Analysis

Bitcoin is one of the main phenomena in recent times together with other cryptocurrencies due to the redefinition of the money term and its price fluctuations. Moreover, scientists are increasingly recognizing Twitter's predictive power for a wide range of events, and particularly for financial markets. This article examines to what degree Bitcoin returns can be estimated using public opinion on Twitter. Using a sentiment analyzer on Bitcoin-related tweets and financial data, the Twitter sentiment was found to have predictive power for Bitcoin's results. Once again, our findings confirm the presence of a correlation between them. We observed 62.48% accuracy when making predictions based on bitcoin-related tweet sentiment and historical bitcoin price.
Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing

Due to the environmental differences, many face anti-spoofing methods fail to generalize to unseen scenarios. In light of this, we propose a unified unsupervised and semi-supervised domain adaptation network (USDAN) for cross-scenario face anti-spoofing, aiming at minimizing the distribution discrepancy between the source and the target domains. Specifically, two modules, i.e., marginal distribution alignment module (MDA) and conditional distribution alignment module (CDA), are designed to seek a domain-invariant feature space via adversarial learning and make the features of the same class compact, respectively. By adding/removing the CDA module, the network can be easily switched for semi-supervised/unsupervised setting, in which sense our method is named with “unified”. Moreover, the adaptive cross-entropy loss and normalization techniques are further incorporated to improve the generalization. Extensive experimental results show that the proposed USDAN outperforms state-of-the-art methods on several public datasets.
Foreign language acquisition via artificial intelligence and extended reality: design and evaluation

Artificial Intelligence (AI) and Extended Reality (XR) have been employed in several foreign language education applications to increase the availability of experiential learning methods akin to international immersion programs. However, research in multi-modal spoken dialogue in L2 combined with immersive technologies and collaborative learning is thin, limiting students’ experiences to solo interactions focused mostly on vocabulary and grammar in such settings. We intend to fill this gap as we present the Cognitive Immersive Language Learning Environment (CILLE). The AI in CILLE can hear, see, and understand its users and can engage with them in non-dyadic multimodal conversations. The XR offers students a feeling of being somewhere else without the use of intrusive devices and supports multi-party, multi-modal interactions. Together, AI and XR create naturalistic conversational interactions targeted towards comprehensive foreign language acquisition. We evaluate CILLE as a Chinese-as-a-foreign-language (CFL) education tool through a seven-week, mixed-methods study with university students (N = 10). Results display statistical significance and retained improvement in CFL vocabulary, comprehension, and conversation skills. Coupled with an analysis of student feedback and researcher observations, we show how CILLE is designed and experienced by students to learn CFL.
DFFNet: An IoT-perceptive dual feature fusion network for general real-time semantic segmentation

Semantic segmentation is a valuable yet challenging research in the Internet of Things (IoT), especially for some real-time and resource-constrained applications. Recently we witness a strong tendency of fusing multi-level features or multi-scale context information to achieve promising segmentation performance. However, we find that existing literature has at least one of the following issues: 1) relying on more resource-consuming feature extraction operations, e.g., standard convolution with large kernels, for multiple information fusion; 2) seldom considering that how to narrow the semantic gap between multi-layer features, leading to sub-optimal performance. To tackle these issues, we propose a novel IoT-perceptive Dual Feature Fusion Network (DFFNet) for semantic segmentation, which aims to leverage multi-level features and multi-scale context information in an efficient yet effective manner. Specifically, the multi-level feature fusion module (MFFM), which enhances the semantic consistency between multi-level features by two attention refinement blocks, is proposed to exploit multi-layer features for jointly learning spatial and semantic information with small overheads. Moreover, a multi-scale component termed as lightweight semantic pyramid module (LSPM) is presented to improve the efficiency of context encoding by depthwise factorized convolution operations. Extensive experimental results on benchmarks have demonstrated that DFFNet achieves better performance than existing advanced methods.
Next-Generation Internet of Things in Fintech Ecosystem

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. The study finds that the concept of communication between the devices and financial technology is not new yet only a considerable number of studies are done on IoT in fintech. The study highlights that both the IoT and fintech show an upward trend in the marketspace. Then, the study identifies that the increase in the demand for blockchain, Internet, mobile network, cloud storage, and IoT devices among the organization are the key drivers of IoT in fintech. The study also shows that the issues related to sustainable energy, digital payments, and security are the current area of applications and challenges for IoT in Fintech. Thereafter, the study concludes that the growth of neurotech will influence the next-generation IoT in fintech as the catalytic agent. Neurotech-enabled IoT (NIoT) in Fintech will enhance the man, machine, and memory (3Ms) relationship for business process innovation.
Grand Challenges for Augmented Reality

SPECIALTY GRAND CHALLENGE article Front. Virtual Real., 05 March 2021Sec. Augmented Reality https://doi.org/10.3389/frvir.2021.578080
Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction

A novel Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) is presented for the modeling of renewable energy systems. Not only does this paper present a new architecture of the type-2 fuzzy system (T2FS) for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also, it introduces an exquisite technique to acquire an optimal number of membership functions (MFs) and their corresponding rules. Using nonlinear functions in the “Then” part of fuzzy rules, introducing a new mechanism in structure learning, using an adaptive learning rate and performing convergence analysis of the learning algorithm are the innovations of this paper. Another novel innovation is using optimization techniques (including pruning fuzzy rules, initial adjustment of MFs). Next, a solar photovoltaic cell and a wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge as convincing. The root-mean-square-error (RMSE) is less than 0.006 and the number of fuzzy rules is equal to or less than four rules, which indicates the very good performance of the presented fuzzy neural network. Finally, the obtained model is used for the first time for a geographical area to examine the feasibility of renewable energies.
Dynamic Hand Gesture Recognition Based on 3D Hand Pose Estimation for Human–Robot Interaction

Dynamic hand gesture recognition is a challenging problem in the area of hand-based human–robot interaction (HRI), such as issues of a complex environment and dynamic perception. In the context of this problem, we learn from the principle of the data-glove-based hand gesture recognition method and propose a dynamic hand gesture recognition method based on 3D hand pose estimation. This method uses 3D hand pose estimation, data fusion and deep neural network to improve the recognition accuracy of dynamic hand gestures. First, a 2D hand pose estimation method based on OpenPose is improved to obtain a fast 3D hand pose estimation method. Second, the weighted sum fusion method is utilized to combine the RGB, depth and 3D skeleton data of hand gestures. Finally, a 3DCNN + ConvLSTM framework is used to identify and classify the combined dynamic hand gesture data. In the experiment, the proposed method is verified on a developed dynamic hand gesture database for HRI and gets 92.4% accuracy. Comparative experiment results verify the reliability and efficiency of the proposed method.