Reviewerly/exHarmony huggingface dataset:
Calibrating predictive model estimates in a distributed network of patient data

Protecting the privacy of patient data is an important issue. Patient data are typically protected in local health systems, but this makes integration of data from different healthcare systems difficult. To build high-performance predictive models, a large number of samples are needed, and performance measures such as calibration and discrimination are essential. While distributed algorithms for building models and measuring discrimination have been published, distributed algorithms to measure calibration and recalibrate models have not been proposed.Recalibration models have been shown to improve calibration, but they have not been proposed for data that are distributed in various health systems, or "sites". Our goal is to measure calibration performance and build a global recalibration model using data from multiple health systems, without sharing patient-level data.We developed a distributed smooth isotonic regression recalibration model and extended established calibration measures, such as Hosmer-Lemeshow Tests, Expected Calibration Error, and Maximum Calibration Error in a distributed manner.Experiments on both simulated and clinical data were conducted, and the recalibration results produced by a traditional (ie, centralized) versus a distributed smooth isotonic regression were compared. The results were exactly the same.Our algorithms demonstrated that calibration can be improved and measured in a distributed manner while protecting data privacy, albeit at some cost in terms of computational efficiency. It also gives researchers who may have too few instances in their own institutions a method to construct robust recalibration models.Preserving data privacy and improving model calibration are both important to advancing predictive analysis in clinical informatics. The algorithms alleviate the difficulties in model building across sites.
Entanglement-assisted Reed–Solomon codes over qudits: theory and architecture

We develop a systematic theory for the construction of quantum codes from classical Reed–Solomon over $${\mathbb {F}}_{p^k}$$ , where p is prime and $$k \in {\mathbb {Z}}^{+}$$ . Based on two classical $$[n,n-d_m+1,d_m]$$ Reed–Solomon codes over $${\mathbb {F}}_{p^k}$$ , we provide the construction of an $$[[n,n+n_e-2(d_m-1),d_m]]$$ entanglement-assisted Reed–Solomon code over qudits of dimension $$d=p^k$$ that saturates the quantum Singleton bound and needs $$n_e$$ entangled qudits, which involves obtaining the explicit form of the stabilizers for the code. Our contributions towards the entanglement-assisted Reed–Solomon code are multi-fold as follows: (a) Based on the parity check matrices of the classical Reed–Solomon codes, the explicit form of the stabilizers of the entanglement-assisted Reed–Solomon code are provided and the rate-optimal code is obtained. (b) Based on the entanglement-assisted Reed–Solomon code, a burst error correcting code for qudits of dimension p that corrects a burst of $$k(t_m-1)$$ qudits or less, where $$t_m = \lfloor (d_m-1)/2\rfloor $$ is provided. (c) Finally, we provide amenable circuits for encoding and error correction with quantum circuit complexity of $${\mathcal {O}}(n^2)$$ , useful for practical implementations.
Generating Cryptographically-Strong Random Lattice Bases and Recognizing Rotations of $$\mathbb {Z}^n$$

Lattice-based cryptography relies on generating random bases which are difficult to fully reduce. Given a lattice basis (such as the private basis for a cryptosystem), all other bases are related by multiplication by matrices in $$GL(n,\mathbb {Z})$$ . We compare the strengths of various methods to sample random elements of $$GL(n,\mathbb {Z})$$ , finding some are stronger than others with respect to the problem of recognizing rotations of the $$\mathbb {Z}^n$$ lattice. In particular, the standard algorithm of multiplying unipotent generators together (as implemented in Magma's RandomSLnZ command) generates instances of this last problem which can be efficiently broken, even in dimensions nearing 1,500. Likewise, we find that the random basis generation method in one of the NIST Post-Quantum Cryptography competition submissions (DRS) generates instances which can be efficiently broken, even at its 256-bit security settings. Other random basis generation algorithms (some older, some newer) are described which appear to be much stronger.
Learning specialized activation functions with the Piecewise Linear Unit

The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show promising performance in various tasks and models. Swish, the automatically discovered activation function, has been proposed and outperforms ReLU on many challenging datasets. However, it has two main drawbacks. First, the tree-based search space is highly discrete and restricted, which is difficult for searching. Second, the sample-based searching method is inefficient, making it infeasible to find specialized activation functions for each dataset or neural architecture. To tackle these drawbacks, we propose a new activation function called Piecewise Linear Unit(PWLU), which incorporates a carefully designed formulation and learning method. It can learn specialized activation functions and achieves SOTA performance on large-scale datasets like ImageNet and COCO. For example, on ImageNet classification dataset, PWLU improves 0.9%/0.53%/1.0%/1.7%/1.0% top-1 accuracy over Swish for ResNet-18/ResNet-50/MobileNet-V2/MobileNetV3/EfficientNet-B0. PWLU is also easy to implement and efficient at inference, which can be widely applied in real-world applications.
Sorry, I Can’t Understand You! – Influencing Factors and Challenges of Chatbots at Digital Workplaces

Chatbot research is currently on its rise since many researchers focus on this topic from different perspectives. Thereby, the focus mostly lies on application areas that originate from business contexts. However, application areas and potential outcomes are already subject to research. The business perspective on influencing factors for an application of chatbots at workplaces or their corresponding challenges is underrepresented as less to none research exists. Therefore, we targeting this research gap by an empirical cross-section interview study with 29 domain experts for the application of chatbots at the digital workplace. We categorize the findings with an extension of the TOE-Framework and show that in the core categories of technological, organizational, individual, and environmental 11 sub-influencing factors exist. Furthermore, we also identify 36 challenges, which are relevant in the particular influencing factors.
Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

Most software systems have different stakeholders with a variety of concerns. The process of collecting requirements from a large number of stakeholders is vital but challenging. We propose an efficient, automatic approach to collecting requirements from different stakeholders’ responses to a specific question. We use natural language processing techniques to get the stakeholder response that represents most other stakeholders’ responses. This study improves existing practices in three ways: Firstly, it reduces the human effort needed to collect the requirements; secondly, it reduces the time required to carry out this task with a large number of stakeholders; thirdly, it underlines the importance of using of data mining techniques in various software engineering steps. Our approach uses tokenization, stop word removal, and word lemmatization to create a list of frequently accruing words. It then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest score. Our experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.
FocusedDropout for Convolutional Neural Network

In a convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except for randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units. In this paper, we propose a non-random dropout method named FocusedDropout, aiming to make the network focus more on the target. In FocusedDropout, we use a simple but effective method to search for the target-related features, retain these features and discard others, which is contrary to the existing methods. We find that this novel method can improve network performance by making the network more target focused. Additionally, increasing the weight decay while using FocusedDropout can avoid overfitting and increase accuracy. Experimental results show that with a slight cost, 10% of batches employing FocusedDropout, can produce a nice performance boost over the baselines on multiple datasets of classification, including CIFAR10, CIFAR100 and Tiny ImageNet, and has a good versatility for different CNN models.
Direct Policy Optimization Using Deterministic Sampling and Collocation

We present an approach for approximately solving discrete-time stochastic optimal-control problems by combining direct trajectory optimization, deterministic sampling, and policy optimization. Our feedback motion-planning algorithm uses a quasi-Newton method to simultaneously optimize a reference trajectory, a set of deterministically chosen sample trajectories, and a parameterized policy. We demonstrate that this approach exactly recovers LQR policies in the case of linear dynamics, quadratic objective, and Gaussian disturbances. We also demonstrate the algorithm on several nonlinear, underactuated robotic systems to highlight its performance and ability to handle control limits, safely avoid obstacles, and generate robust plans in the presence of unmodeled dynamics.
Augmented reality and virtual reality revolutionize rusiness transformation in digital marketing tech industry analysts and visionaries during Coronavirus (COVID 19)

Abstract Virtual Reality (VR) and Augmented Reality (AR) are among the new innovations that provide digital marketing platforms with immersive services for Companies.. While several VR/AR applications have been built to reveal details on digital marketing pages, they still lack of comprehensive models which taking into consideration the elements of informal marketing experience in the development of such applications. This study contributes a conceptual model to serve as guidelines for developing a augmented reality that considers an foundations informal learning component in business. This paper provides a thorough overview of new augmented reality (Ar) and virtual reality (VR) technology in ten critical areas where the application of augmented reality and virtual reality is growing at present. The aim of this research is to highlight how the consumer experience in digital marketing is changed and enriched by augmented reality and virtual reality. The authors present the subject of augmented reality and virtual reality briefly. They further describe the technological and hardware technology needed to incorporate a framework of augmented reality and virtual reality and the various types of displays needed to enhance the user interface. Also briefly mentioned is the rise of AR in markets. The implementations of AR are discussed in the three parts of the article. The use of augmented reality and virtual reality as a component of television and gaming is illustrated in multiplayer gaming, electronic games, broadcasting, and immersive films. Manufacturing, beauty industry, healthcare, planning to business, transformation marketing and fight against COVID-19 crisis. This study contributes a conceptual model to serve as guidelines for developing a augmented reality that considers an foundations informal learning component in business.
Precise Recognition of Vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network

The precise recognition of multi-hand signs in real-time under dynamic backgrounds, illumination conditions is a time consuming process. In this paper, a time efficient single stage convolutional neural network (CNN) You Only Look Once (YOLO-V2) model is proposed for real-time multi-hand sign recognition. The model utilizes DarkNet-19 CNN architecture as a feature extractor. The model is trained and tested on three distinct datasets (NUSHP-II, SENZ-3D and MITI-HD). The range of IoU from 0.5 to 0.95, the model is validated using test dataset. For the MITI-HD, the YOLO-V2 CNN model obtained an average precision value of 99.10% for AP0.5; 93.00% for AP0.75 and 78.30% for AP0.5:0.95. The Adam Optimizer on YOLO-V2 CNN model supersedes the other optimization methods. The prediction time of YOLO-V2 CNN is obtained as 20 ms, much lower than other single-stage hand sign recognition systems.
An implementation methodology for Neural Network on a Low-end FPGA Board

Artificial Intelligence(AI) has achieved unprecedented success in various fields including image/speech recognition which is useful for edge computing. Most of AI systems are implemented on power-hungry devices like GPU, high-end FPGA, or even TPU to process data with high performance. However, these energy budgets are often not affordable to edge computing. Low-end FPGA taking advantage of high energy-efficiency is a desirable platform to meet the requirements of image recognition working on small autonomous vehicles. In this paper, we propose the design methodology and implementation to adapt a neural network system to a low-end FPGA board using HLS description.The whole design consists of algorithm-level downscaling and hardware optimization. The former emphasizes the model downscale by considering accuracy. The latter applies various HLS design techniques to speed-up the application running on the target board. In the case study of tiny YOLO (You Only Look Once) v3, the model running on PYNQ-Z1 presents up to 22× acceleration comparing with the PYNQ ARM CPU. Energy efficiency also achieves 3× better than Xeon E5-2667.
Online Examination Behavior Detection System for Preschool Education Professional Skills Competition Based on MTCNN

At present, online exams have become very popular, replacing the inconvenience of offline testing, but the security of online exams is still a big problem. Design an online examination system. The camera on the client computer is used to track the facial expressions of students during the examination, and can be used to monitor student behavior and prevent any malicious behavior. The system uses deep learning to detect, track, mark and recognize the faces of students. When students exhibit abnormal behavior, the system will warn candidates or terminate their exams.
Laparoscopic augmented reality registration for oncological resection site repair

Abstract Purpose Resection site repair during laparoscopic oncological surgery (e.g. laparoscopic partial nephrectomy) poses some unique challenges and opportunities for augmented reality (AR) navigation support. This work introduces an AR registration workflow that addresses the time pressure that is present during resection site repair. Methods We propose a two-step registration process: the AR content is registered as accurately as possible prior to the tumour resection (the primary registration). This accurate registration is used to apply artificial fiducials to the physical organ and the virtual model. After the resection, these fiducials can be used for rapid re-registration (the secondary registration). We tested this pipeline in a simulated-use study with $$N=18$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>18</mml:mn> </mml:mrow> </mml:math> participants. We compared the registration accuracy and speed for our method and for landmark-based registration as a reference. Results Acquisition of and, thereby, registration with the artificial fiducials were significantly faster than the initial use of anatomical landmarks. Our method also had a trend to be more accurate in cases in which the primary registration was successful. The accuracy loss between the elaborate primary registration and the rapid secondary registration could be quantified with a mean target registration error increase of 2.35 mm. Conclusion This work introduces a registration pipeline for AR navigation support during laparoscopic resection site repair and provides a successful proof-of-concept evaluation thereof. Our results indicate that the concept is better suited than landmark-based registration during this phase, but further work is required to demonstrate clinical suitability and applicability.
Distributed adaptive cubature information filtering for bounded noise system in wireless sensor networks

Abstract This article is concerned with the nonlinear state estimation for the multisenor networked system with uncertain bounded noise. Traditional distributed methods only pay attention to the information fusion of state estimations, but neglect the fusion of noise statistics. The difference of noise statistics among sensor nodes usually affects the precision of state estimation in wireless sensor networks, especially for the distributed state estimation. In this article, in order to improve the accuracy of noise statistic estimations, a distributed noise statistic estimator is derived based on covariance intersection criterion and modified Sage–Husa maximum posterior. Then, distributed adaptive cubature information filtering (DACIF) is founded based on weighted average consensus to obtain accurate state estimation. Two‐step information fusion, including the information fusion of state estimations and noise statistics, is derived to enhance the precision of state estimations. Meanwhile, a novel weighted rule is devised based on the state and measurement innovation vectors to improve the accuracy of distribution information fusion. Next, the estimation errors of DACIF are proved to be bounded in mean square. Simulations and semisimulation experiments are conducted to verify the effectiveness of the proposed algorithm.
Single annotated pixel based weakly supervised semantic segmentation under driving scenes

Semantic segmentation tasks based on weakly supervised conditions have been put forward to achieve a lightweight labeling process. For simple images that only include a few categories, research based on image-level annotations has achieved acceptable performance. However, when facing complex scenes, since image contains a large number of classes, it becomes challenging to learn visual appearance based on image tags. In this case, image-level annotations are not useful in providing information. Therefore, we set up a new task in which a single annotated pixel is provided for each category in a whole dataset. Based on the more lightweight and informative condition, a three step process is built for pseudo labels generation, which progressively implements each class’ optimal feature representation, image inference, and context-location based refinement. In particular, since high-level semantics and low-level imaging features have different discriminative abilities for each class under driving scenes, we divide categories into “object” or “scene” and then provide different operations for the two types separately. Further, an alternate iterative structure is established to gradually improve segmentation performance, which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process. Experiments on the Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation tasks under complex driving scenes.
Self-Supervised Learning of Monocular Depth Estimation Based on Progressive Strategy

Monocular depth estimation has been carried out with convolutional neural networks (CNNs). However, vast quantities of ground truth depth data are required as a supervising signal. Recently, self-supervised learning is explored for monocular depth estimation, which uses the minor loss of image reconstruction, rather than depth information, as the supervising signal in the training phase. However, the blurring problem often occurs on the surface of a near object when using the image reconstruction as a major loss function. Here, we propose a novel Progressive Strategy Network (PSNet) to overcome this problem, which optimizes the depth map from coarse to fine with several progressive modules. A progressive module estimates a coarse depth map with a color image by CNNs and outputs it into the next module for progressively accurate refinement. It can simplify the difficulty of estimating the depth map with a large size and reduce the blurring problem. Experiments demonstrate that the proposed approach consistently improves the quality of depth map on the surface of large objects and keeps edges clear. Our proposed method performs high-quality depth estimation on the KITTI dataset and achieves a strong generalization on the Make3D dataset. Besides, it can be flexibly used on the real scenes captured by mobile phones without extra training.
A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark. Lastly, a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT. Thus, the watermarked image is generated by enclosing both keys into DWT and DCT coefficients. The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods. In order to validate the proposed technique, a standard dataset of medical images is used. Simulation results show higher performance of the visual quality (i.e., 57.65) for the watermarked forms of all types of medical images. In addition, EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation (NC). Finally, extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.