Reviewerly/exHarmony huggingface dataset:
A novel automated guided vehicle for guidance applications

Abstract In recent years, as a result of the significant development of information and communications technology, people have been paying much attention to automated guided vehicles (AGVs). In the ongoing global coronavirus disease 2019 (COVID-19) pandemic, hospital services have been seriously impacted. In the severe medical situation in hospitals, there is a serious shortage of human resources. This paper presents a novel automated guided vehicle (AGV) for guidance and service. The AGV is comprised of a microcontroller unit (MCU), a power unit, a human sensing unit, a collision warning unit, and a path sensing unit. The motion speed of the AGV and the distance between the AGV and the person being guided are determined by the MCU, the power unit, sensors, and an algorithm. The collision warning unit comprises three ultrasonic sensors and an infrared sensor in the front of the AGV in order to avoid obstacles. The trajectory results of the planned and actual paths are in good agreement. The AGV provides safe and effective guidance for people moving towards their destination. The AGV achieves excellent guidance results and shows great potential for guidance applications in hospitals.
Sequence Contained Heterogeneous Graph Neural Network

Heterogeneous Information Network (HIN) has been proved to be effective when expressing rich information for realworld data recently. However, the previous HIN embedding methods can't handle well the sequence contained HIN, which consists of both non-sequence and sequence entities simultaneously. Previous methods simply represent sequential entities as non-sequential feature vectors by pre-process, leading to the reduction in sequence-dependency information (e.g., context-dependency, time-dependency) within entities. Due to the widespread of sequence-dependency information, the non-sequential methods limit the application of HIN in real-word. Therefore, we first propose a novel heterogeneous graph neural network, named as Sequence Contained Heterogeneous Graph Neural Network (SC-HGNN), to deal with sequential features in this paper. Specifically, we utilize Transformer Projection to preserve the relation within the sequence. Then we consider sequential features for node embedding by using Intra-type Sequence Attention and Inter-type Sequence Attention to capture the relation between homogeneous and heterogeneous sequence respectively. Finally, another transformer is used to encode the updated features of the center node. Extensive experimental results on a real-world dataset demonstrate that our SC-HGNN method outperforms state-of-the-art baselines in sequence contained HIN on node classification and clustering tasks.
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning

Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexity associated with the underlying deep neural networks (DNNs) leads to power-hungry implementations. This makes deep RL systems unsuitable for deployment on resource-constrained edge devices. To address this challenge, we propose a reconfigurable architecture with preemptive exits for effi-cient deep RL (RAPID-RL). RAPID-RL enables conditional activation of DNN layers based on the difficulty level of inputs. This allows to dynamically adjust the compute effort during inference while maintaining competitive performance. We achieve this by augmenting a deep Q-network (DQN) with side-branches capable of generating intermediate predictions along with an associated confidence score. We also propose a novel training methodology for learning the actions and branch confidence scores in a dynamic RL setting. Our experiments evaluate the proposed framework for Atari 2600 gaming tasks and a realistic Drone navigation task on an open-source drone simulator (PEDRA). We show that RAPID-RL incurs 0.34 × (0.25 ×) number of operations (OPS) while maintaining performance above 0.88 × (0.91 ×) on Atari (Drone navigation) tasks, compared to a baseline-DQN without any side-branches. The reduction in OPS leads to fast and efficient inference, proving to be highly beneficial for the resource-constrained edge where making quick decisions with minimal compute is essential.
Multi-Kernel Deformable 3D Convolution for Video Super-Resolution

Video super-resolution (VSR) methods align and fuse consecutive low-resolution frames to generate high-resolution frames. One of the main difficulties for VSR process is that video contains various motions, and the accuracy of motion estimation greatly affects the quality of video restoration. Diverse receptive fields in temporal and spatial dimensions have the potential to adapt to various motions, which is rarely paid attention in most known VSR methods. In this paper, we propose to provide adaptive receptive fields for VSR model. Firstly, we design a multi-kernel 3D convolution network and integrate it with a multi-kernel deformable convolution network for motion estimation and multiple frames alignment. Secondly, we propose a 2D multi-kernel convolution framework to improve texture restoration quality. Our experimental results show that the proposed framework outperforms the state-of-the-art VSR methods.
Integration of mHealth into Primary Health Care for Child Cancer Patients – An approach to reduce health inequities in Bangladesh

Globally, an estimated 160,000 children develop cancer each year, 80% of whom live in developing countries like Bangladesh. Without proper diagnosis, treatment, or care, children with cancer in Bangladesh die prematurely. Out of an estimated 9000 cases per year, less than 5.5% of children receive hospital treatment, which is concentrated at cancer referral centers in the capital or large cities. This creates a major disparity in access to services between rural and urban areas. There are no diagnosis and treatment facilities available at the primary health care (grass roots) level in rural areas where most of the population live. Over 160 million people in Bangladesh use mobile phones. This study proposes a Mobile-based Childhood Cancer Care System to establish a mechanism to connect patients and link primary health care and tertiary care facilities. This study aims to assess the impact of using modern technology (mHealth) to bridge this access gap and improve the services provided for children with cancer. This study also aims to determine the effectiveness of integration of mHealth into primary health care for management of childhood cancer in Bangladesh. Ultimately, the web and mobile phone-based system will allow creation of a childhood cancer patient registry in Bangladesh.
Highly Secured Steganography Method for Image Communication using Random Byte Hiding and Confused &amp; Diffused Encryption

This paper propounds a new notion for a secured method for image communication using random byte hiding (RBH) technique with confused and diffused data embedding technique. This entire method is based on dual keys for embedding as well as for retrieving. Security through obscurity followed by Kerckhoff’s principle is the main ideology of this method. Because of two keys, a large keyspace is needed, which can be effigiated only by brute-force attack. The cover image can be retrieved by using one key, and the secret image can be retrieved by using another key. Among the other dual-key encryption techniques, this method upholds its advantage in the form of security since the dual-key concept is used for two encryptions, which are very difficult to predict and break. The main motivation of the proposed technique is to reduce the time consumption along with increasing the security. The RBH algorithm reduces the encryption time and decryption time by 87.9% and 67.04%, respectively, compared to the conventional LSB steganography. The data hiding rate can also be improved to an extent of 98.33% compared to the conventional LSB technique.
Hierarchical Aitchison–Silvey models for incomplete binary sample spaces

Multivariate sample spaces may be incomplete Cartesian products, when certain combinations of the categories of the variables are not possible. Traditional log-linear models, which generalize independence and conditional independence, do not apply in such cases, as they may associate positive probabilities with the non-existing cells. To describe the association structure in incomplete sample spaces, this paper develops a class of hierarchical multiplicative models which are defined by setting certain non-homogeneous generalized odds ratios equal to one and are named after Aitchison and Silvey who were among the first to consider such ratios. These models are curved exponential families that do not contain an overall effect and, from an algebraic perspective, are non-homogeneous toric ideals. The relationship of this model class with log-linear models and quasi log-linear models is studied in detail in terms of both statistics and algebraic geometry. The existence of maximum likelihood estimates and their properties, as well as the relevant algorithms are also discussed.
Design and Simulation of Multi-modal Signal Generator

This paper proposes a frequency-adjustable signal generator based on Xilinx FPGA and uses Verilog language to realize long-range waveform output and adjustment through UART serial communication interface. The design realizes sine wave, triangle wave, square wave and other waveforms. The output design analyzes the principle and advantages of using FPGA to implement Direct Digital Synthesis (DDS). The main indicators reach 14-bit data width and 1024-bit depth ROM meter output, and the frequency adjustable output range is 1Hz-100MHz. And through the oscilloscope display and Vivado simulation tools for simulation waveform verification, the system meets the design requirements.
User Experience Analysis Based on a Virtual Mark-up Approach

The paper presents some results of interactive user interfaces implementation in practice. Virtual mark-up approach is used to classify the professional status of the users and respectively adapt the user interface. New software components are introduced as a part of Augmented Reality system that capture the user’s behavior, compare it to the typical patterns and generate virtual elements when necessary. The resulting solution is capable of providing alerts and notifications for novice users and hiding the redundant information for experts and thus personalizing the user interface. Artificial neural network provides classification based on the results of user performance in script execution according to pre-defined scenarios. The proposed approach is illustrated by an example of electrical meters surveying mobile application. Research results illustrate the possibility to improve and personalize the augmented reality user interfaces based on the analysis of user activity.
Platform-Enabled Crimes

Online intermediaries are omnipresent. Each day, across the globe, the corporations that run these platforms execute policies and practices that serve their profit model, typically by sustaining user engagement. Sometimes, these seemingly banal business activities enable principal perpetrators to commit crimes; yet online intermediaries are almost never held to account for their complicity in the resulting harms.This Article introduces the term and concept of platform-enabled crimes into the legal literature to draw attention to way that the ordinary business activities of online intermediaries can enable the commission of crime. It then singles out a subset of platform-enabled crimes—those where a social media company has facilitated international crimes—for the purpose of understanding and addressing the accountability gap associated with them.Adopting a survivor-centered methodology, and using Facebook’s complicity in the Rohingya genocide in Myanmar as a case study, this Article begins the work of addressing the accountability deficit for platform-enabled crimes. It advances a menu of options to be pursued in parallel, including amending domestic legislation, strengthening transnational cooperation between international and domestic prosecutors for criminal and civil corporate liability cases, and pursuing de-monopolizing regulatory action. I conclude by acknowledging that the advent of platform-enabled crimes is not something that any single body of law is equipped to respond to. However by pursuing a plurality of options to address this previously overlooked form of criminal facilitation, we can make a vast improvement on the status quo.
How to provide fast Mobile Internet (5G) to make the connection stable for all stakeholders of an educational process in Aktobe?

The study seeks to understand whether to expect 5G in Aktobe soon. To do this, the author studies how important this technology is for residents using the example of students. Therefore, the author conducted interviews among schoolchildren and people associated with telecommunications equipment. As a result, the results show that the transition to 5G is urgently needed and that the technology can only be expected by the end of this decade. The author concluded that the problem with the Internet in the city is the reluctance to invest unnecessarily.
Coarse-fine Surrogate Model Driven Preference-based Multi-objective Evolutionary Fuzzy Clustering Algorithm for Color Image Segmentation

When applied to image segmentation, most existing multi-objective evolutionary clustering algorithms usually consider the information at the pixel level. Sometimes ignoring the region information of the image may lead to unideal segmentation performance. Moreover, these multi-objective evolutionary clustering algorithms require expensive computation for evaluating fitness functions. In order to improve the segmentation performance and time efficiency for image segmentation, a coarse-fine surrogate model driven preference-based multi-objective evolutionary fuzzy clustering (CFS-PMEFC) algorithm is proposed in this paper. First of all, the coarse-fine surrogate model and the preference information derived from the user are introduced to design multi-objective evolutionary clustering framework, which can improve the evolutionary efficiency and quality of clustering centers. Then, Mahalanobis distance-based fitness functions fusing the region information of the image are constructed to further promote the segmentation performance. In addition, a clustering index with the region information of the image is designed to select the optimal solution from the final solution set of CFS-PMEFC. Experiments on Berkeley images show that CFS-PMEFC greatly improves the accuracy of image segmentation and meanwhile reduces the computational cost.
A calibrated 3D dual-barrel otoendoscope based on fringe-projection profilometry

An otoendoscopic structured-light system based on fringe-projection profilometry is presented. Two standard rigid otoendoscopes are mounted onto each other and combined with a digital light projector and high-speed camera with respective frame rates of 120 and 210 Hz. Four phase-shifted sinusoidal fringe patterns are projected onto the object's surface through the imaging channel of one endoscope with a 0° viewing angle, and the fringe patterns reflected by the object are recorded by the camera via the imaging channel of the other endoscope with a 30° viewing angle, resulting in a 3D shape measurement speed of 30 Hz in the current setup. Due to the angle between the projection and imaging axis, the projected patterns appear deformed by the object's shape to the camera, enabling the reconstruction of its full-field surface height map. Geometric lens distortions in the optics of the projection and imaging channels were calibrated, resulting in a depth measurement precision between 10 and 30 µm and a lateral resolution of 10 to 25 µm. Otoendoscopic profilometry adds an extra dimension of depth to the standard 2D otoendoscopic image and can be used as a noninvasive technique to expand the qualitative depth perception of the physician with quantitative 3D data of the tympanic-membrane surface. Hence, the system holds the potential to provide objective diagnosis of tympanic-membrane and middle-ear disease. In future, the system will be optimized to allow real-time surface-shape visualization and handheld operation for use in a clinical setting.
Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis

To solve the limitations of the current entrepreneurial ecosystem, the research on the digital entrepreneurial ecosystem is more meaningful. This article aims to study the dynamic evolution mechanism of the digital entrepreneurship ecosystem based on text sentiment computing analysis. It proposes an improved Bi-directional long short-term memory (Bi-LSTM) model, which uses a multilayer neural network to deal with classification problems. It has a higher accuracy rate, recall rate, and F1 value than the traditional LSTM model and can better perform sentiment analysis on text. The algorithm uses the optimized Naive Bayes algorithm, which is based on Euclidean distance weighting and can assign different weights to the final classification results according to different attributes. Compared with the general Bayes algorithm, it improves the calculation efficiency and can better match the digital entrepreneurial ecosystem, which is evolving dynamically, predicting and analyzing its future development. The experimental results in this article show that the improved Bi-LSTM is better than the traditional Bi-LSTM model in terms of accuracy and F1 value. The accuracy rate is increased by 1.1%, the F1 value is increased by 0.6%, and the recall rate is only &amp;lt;0.2%. Running on the Spark platform, although 3% accuracy is sacrificed, the running time is increased by 320%. Compared with the traditional cellular neural network (CNN) algorithm, the accuracy rate is increased by 4%, the recall rate is increased by 14%, and the F1 value is increased by 9%, which proves that it has a strong non-linear fitting ability. The performance improvement brought by the huge data set is very huge, which fully proves the feasibility of the digital entrepreneurship ecosystem.
Prostate biomedical images segmentation and classification by using U-NET CNN model

Prostate cancer is one of the most widespread types of cancer among men. The successful treatment of prostate cancer is based on accurate diagnosis. Gleason grading patterns system is one of the most efficient methods in diagnosing histological biopsies of prostate images by pathologists. Automatic detection and segmentation of the prostate on histological Gleason grading system is still the most powerful prognostic tool. In this paper, we propose a powerful deep convolutional neural network (CNN) technique called U-Net module to predict the prostate Gleason score based on tissue microarray (TMA) images. We developed a U-Net model for object semantic segmentation, where the goal is to precisely label each pixel in an image as being part of a given object (foreground) or not (background). Our proposed U-Net model of prostate segmentation achieved a mean test accuracy of 96%. The model achieved a mean Dice index coefficient (DI) of 0.56 and a mean IOU of 0.95 that show how close the output segments are to the corresponding lesions in the ground truth maps.
Towards configuring Hyperledger Fabric 2.0 Blockchain Platform for Industry 4.0 applications

The Industry 4.0 is offering novel opportunities for large enterprises or even small and medium enterprises (SME) to digitize and also simplify their existing production workflows. With the uprise of the Distributed Ledger Technology (DLT), the Industry 4.0 also receives new possibilities to set up a more secure, redundant and decentralized data infrastructure. Existing database systems can be exchanged with Blockchain systems, thus making use of its inherent immutability. Popular open-source Blockchain frameworks such as Hyperledger Fabric offer large customizability, which makes them attractive for dynamic Industry 4.0 applications. Though, the configuration of the required networks is not a trivial task. Due to its high flexibility and numerous configuration parameters, an interested developer needs to have a deep understanding or even some experience with the Hyperledger Fabric framework. Since most SMEs do not have a dedicated IT department, this paper aims to provide a design guideline for interested developers to optimally set up a Hyperledger Fabric v2.0 business network, which is suited for the desired use-case. With the special demands of Industry 4.0 in mind, this paper also provides three example use-cases, which can benefit from the use of DLT to enhance data security. The results are generalizable and comparable to previous works but differ in some version-specific aspects of Fabric.
GazeFlow: Gaze Redirection with Normalizing Flows

Gaze estimation often requires a large scale datasets with well annotated gaze information to train the estimator. However, such a dataset requires costive annotation and is usually very difficult to collect. Therefore, a number of gaze redirection approaches have been proposed to address such a problem. However, existing methods lack the ability to precisely synthesize images with target gaze and head pose in complex lighting scenes. As a powerful technique to model the distribution of given data, normalizing flows have the ability to generate photo-realistic images and provide flexible latent space manipulation. In this work, we present a novel flow-based generative model, GazeFlow <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code will be made available at https://github.com/CVI-SZU/GazeFlow, for gaze redirection. The visual results of gaze redirection show that the quality of eye images synthesized by GazeFlow is significantly higher than that of other approaches like Deep Warp and PRGAN. Our approach has also been applied to augment the training data to improve the accuracy of gaze estimators and significant improvement has been achieved for both within dataset and cross dataset experiments.
Layer-Wise Activation Cluster Analysis of CNNs to Detect Out-of-Distribution Samples

Convolutional neural network (CNN) models are widely used for image classification. However, CNN models are vulnerable to out-of-distribution (OoD) samples. This vulnerability makes it difficult to use CNN models in safety-critical applications (e.g., autonomous driving, medical diagnostics). OoD samples occur either naturally or in an adversarial setting. Detecting OoD samples is an active area of research. Papernot and McDaniel [] have proposed a detection method based on applying a nearest neighbor (NN) search on the layer activations of the CNN. The result of the NN search is used to identify if a sample is in-distribution or OoD. However, a NN search is slow and memory-intensive at inference. We examine a more efficient alternative detection approach based on clustering. We have conducted experiments for CNN models trained on MNIST, SVHN, and CIFAR-10. In the experiments, we have tested our approach on naturally occurring OoD samples, and several kinds of adversarial examples. We have also compared different clustering strategies. Our results show that a clustering-based approach is suitable for detecting OoD samples. This approach is faster and more memory-efficient than a NN approach.