Reviewerly/exHarmony huggingface dataset:
Superpixel segmentation with attention convolution neural network

A superpixel consists of a series of small regions composed of pixel points that are located next to each other and have similar features such as color, luminance, and texture. Most of these small areas retain the original information of the image, which facilitates faster follow-up processing. Most of the existing superpixel segmentation methods do not use deep learning network architectures. Some methods use deep learning, but they also have a very simple network architecture. And there are a few superpixel segmentation methods combined with deep learning. In this method, a more complex convolutional neural network with an attention module is applied to superpixel segmentation. The resulting superpixel segmentation network is more complex, and the addition of the attention module allows for more accurate chunking of the images, thus yielding more comprehensive and detailed segmentation results. By experimenting on a public dataset BSDS500, the method has higher accuracy in superpixel segmentation. Also, the segmentation speed of the method is similar to that of the existing simple segmentation networks.
Hybrid Cloud Resource Scheduling With Multi-dimensional Configuration Requirements

Task scheduling with multi-dimensional configuration requirements is widely used in cloud platforms such as OpenStack and Kubernetes. In this paper, we consider the problem of scheduling tasks with multi-dimensional configuration to hybrid resources. An energy-aware scheduling algorithm on tasks with multi-dimensional configuration requirements (ESMCR in short) is presented. ESMCR is combined with a decomposition-based multi-objective evolutionary algorithm to minimize energy consumption and provide sufficient capacity for the data center. An entropy-based performance index is modeled to measure the QoS. The experimental results indicate that the proposed algorithms outperform the compared algorithms significantly.
Automatic Manifesto Comparison using NLP Techniques and The Manifesto Project Domains - Case Study: 2021 Ecuadorian Presidential Elections

Democracies rely on the capability of a country to conduct fair elections. And, fair elections rely on an open participation of candidates and general public. In particular, we explore a way to compare campaign proposals aiding general public to make an informed decision while choosing candidates. This document explores a way to compare campaign proposals through each candidate manifest using natural language processing techniques (i.e. Doc2Vec algorithm). As a linguistic corpus we used all the articles written in Spanish from Wikipedia and we used two models of neural networks, Distributed Bag of Words (DBOW) and Distributed Memory Model (DM). We chose the 2021 Ecuadorian Presidential Elections in its second round and tagged each manifesto paragraph (from the runoff candidates) into the seven domains according to the Manifesto Project. Finally, we compute manifesto comparisons by topic and also as a whole for different vector configurations. Our results indicate that Doc2Vec produces reasonable results while comparing documents but the DBOW model provides better results while dealing with larger documents and the DM model while dealing with smaller ones.
Joint Coupled Transform Learning Framework for Multimodal Image Super-Resolution

Insights from multiple imaging modalities have recently been applied in solving many computer vision related applications. In this paper, we model the cross-modal dependencies between different modalities for Multimodal Image Super-Resolution (MISR), i.e., enhance the Low Resolution (LR) image of target modality with the guidance of a High Resolution (HR) image from another modality. We introduce a joint optimization based transform learning frame-work referred to as Joint Coupled Transform Learning (JCTL) to combine the information from multiple modalities to generate the HR image of the target modality. All the necessary intermediate steps and the corresponding closed form solution updates are pro-vided. The performance of the proposed JCTL is benchmarked against the state-of-the-art MISR approaches on different multi-modal datasets with different upscaling factors. The results show better performance with the proposed JCTL approach compared to other state-of-the-art techniques both in terms of PSNR and SSIM.
Defending deep neural networks from adversarial attacks on three-dimensional images by compressive sensing

We demonstrate the utility of compressive sensing to defend against adversarial attacks on deep learning classifiers and to encrypt the 3D image, thus, to avoid counterattacks.
Chapter 8. The discourse markers well and so and their equivalents in the Portuguese and Turkish subparts of the TED-MDB corpus

The present research describes how the PDTB style of discourse annotation has been applied to transcribed TED Talks in the TED-MDB corpus, which include multifunctional elements such as well and so . The occurrence of these two markers is analyzed in English in comparison to the way they are conveyed in Portuguese and Turkish translations of TED-MDB. Their presence in discourse relations, as well as in question-response pairs found in TED Talks, are investigated. The implicitation of well and so in translation is shown to be related to their function in the context. Results also point to differences in the target languages regarding implicitation, especially when the discourse marker doesn’t fulfil a connective function.
Semiotic Approach in Visual Communication Design

Visual analytics means are a set of tools used to obtain a visual representation of the data explored by the user, their interpretation and complete analysis.It is quite possible that such a set will be a developed specialized environment for continuous user interaction with a data stream, which has the capacity to control and replace the way of visual data representation.A wide range of existing visualization tools, as well as unlimited borrowing of technological solutions originally intended for a narrow application area, and the transfer of the capabilities they create to other directions make it urgent to form a sound approach to the comparison and selection of means of data visual representation of a given complexity [1].The purpose of this selection is to save the end user's resources, based on the absence or minimization of resource losses arising at the stage of selection and testing of visual analytics tools.The paper shows the possibility of using a semiotic visualization model for purposeful design or selection of visualization tools.
Integrity Protection for Kubernetes Resource Based on Digital Signature

Integrity of the cloud is the most important requirement for mission-critical enterprise workloads. NIST SP 800–53 states that information systems must prevent the installation of any components that have not been verified digitally with a signed certificate that is recognized and approved by the organization's information system. On a Kubernetes cluster, the admission controller can control requests for application installation, and it would be a powerful protection tool if it could control requests for Kubernetes resources based on signature verification. However, there are various technical challenges when it comes to verifying the signature for a Kubernetes resource at the admission controller because a signed resource is rewritten automatically by internal cluster work and many requests that include internal mutation without a signature are generated. In this work, we propose an approach to protect the integrity of a Kubernetes resource with signature verification at the admission controller. Our approach addresses the issue that the differences between the signed resource in the admission request and the signature message occur automatically in Kubernetes and conducts signature verification properly by using Dry Run. We also propose a profile framework to address the internal mutation request that cannot be attached to the signature. Our experimental results demonstrate that standard applications can be protected by our approach.
Application of Actor-Critic Deep Reinforcement Learning Method for Obstacle Avoidance of WMR

A state-of-the-art framework, i.e., deep deterministic policy gradient (DDPG), has obtained a certain effect in the robotic control field. When the wheeled mobile robot (WMR) executes operation in unstructured environment, it is critical to endow the WMR with the capacity to avoid the static and dynamic obstacles. Thus, a obstacle avoidance algorithm based on DDPG is proposed to realize the autonomous navigation in the unknown environment. The WMR in this study installs the requisite sensors to provide the fully observable environment information at any moment. The continuous state space description for WMR and obstacles is designed, together with the reward mechanism and action space. The learning agent. i.e., the studied mobile robot, utilizes the DDPG model, through the continuous interaction with the surrounding environment and the application of historical experience data, the WMR can learn the optimal action behavior. Simulation along with test works strongly verify the collision-free ability in static and dynamic scenarios with multiple observable obstacles.
A Comprehensive Tool Survey for Blockchain to IoT Applications

The system generally has lot of errors. The IoT was still a growing area. To deal with all the errors, the testing as well as validating plays a major role. The testing and validating are easy to use. The system is insufficient for the solution of testing. In this paper, we provide an overview on test methods, tools, and methodologies for both the blockchain as well as Internet of Things, its software and its devices. We all are still lagging behind the Software Engineering community in the past decades.
Subtle Attention Guidance for Real Walking in Virtual Environments

Virtual reality is today being applied to an increasing number of fields such as education, industry, medicine, or gaming. Attention guidance methods are used in virtual reality to help users navigate the virtual environment without being overwhelmed by the over-abundance of sensory stimuli. However, visual attention guidance methods can be overt, distracting and confusing as they often consist of artefacts placed in the center of the user’s field of view. This is the case for the arrow method, which consists of an arrow pointing towards a target object and which serves as a reference for our study. In this paper, we compare such an arrow to two methods that are less distracting and more subtle: haptic feedback and temporal luminance modulation. The haptic feedback method guides a user to a target using controller vibration. The temporal luminance modulation method makes use of flickering visual artefacts placed at the user’s peripheral field of view and thus do not cover regions of interest that are typically in the central field of view. This creates a subtle attention guidance since these flickering artefacts can be perceived by the user, but not recognized in terms of form and shape. To compare the different attention guidance methods, we designed a virtual environment that can be explored through real walking, wherein a user performs a search task. We then conducted a pilot study with seven participants to compare the haptic feedback and the temporal luminance modulation methods to the arrow method and to a baseline condition of navigation without any attention guidance. The preliminary results suggest that all three methods are more effective than the condition without guidance. Moreover, the temporal luminance modulation method appears to be comparable to the more effective, but non-subtle arrow method in terms of task completion time.
Self-Attending Task Generative Adversarial Network for Realistic Satellite Image Creation

We introduce a self-attending task generative adversarial network (SATGAN) and apply it to the problem of augmenting synthetic high contrast scientific imagery of resident space objects with realistic noise patterns and sensor characteristics learned from collected data. Augmenting these synthetic data is challenging due to the highly localized nature of semantic content in the data that must be preserved. Real collected images are used to train a network what a given class of sensor&#x0027;s images should look like. The trained network then acts as a filter on noiseless context images and outputs realistic-looking fakes with semantic content unaltered. The architecture is inspired by conditional GANs but is modified to include a task network that preserves semantic information through augmentation. Additionally, the architecture is shown to reduce instances of hallucinatory objects or obfuscation of semantic content in context images representing space observation scenes.
Analysis of Blood and Urine Findings Using the Fuzzy System

Laboratory analysis of medical findings is one of the basic methods for diagnosing the disease. Based on the findings, the existence of one or more diseases in the organism can be approximately determined. In this paper, an analysis of blood and urine findings was performed, where using fuzzy logic, an application was created that based on the input parameters of the system as an output gives feedback on the health status of the patient. The implemented application consists of several separate fuzzy systems created for analysis of blood findings, lipid status, urine and general liver function, where based on actual data the disease is diagnosed and a recommendation containing the optimal choice of medical treatment is given. Considering the input parameters of the fuzzy system differ by gender, during the initial startup of the application, the menu is offered and it has option of selecting the analysis of blood and urine findings separately for men and women.
Digital Twins in Contemporary Education: Virtual Workshop

The paper discusses the application of existing solutions based on digital technologies to the practical need of International Center for Neutron Research (PIK) in the field of education and staff training. It is proposed to use the digital twins of existing unique scientific facilities as a new tool in contemporary education. Main reasons for that approach are discussed. The basic requirements for the successful transfer of scientific methods to the educational field are presented. An example of practical course on synchrotron research using digital twins of megascience facilities is considered. Main objectives of an educational product based on the digital twin of unique scientific facility are listed. Proposed approach could ease world tensions, facilitate achieving sustainable development goals, and bridge the gap between science, technology, and society.
MSM: A Method of Multi-Neighborhood Sampling Matching for Entity Alignment

The heterogeneity of knowledge graphs brings great challenges to entity alignment. In particular, the attributes of network entities in the real world are complex and changeable. The key to solving this problem is to expand the neighborhoods in different ranges and extract the neighborhood information efficiently. Based on this idea, we propose Multi-neighborhood Sampling Matching Network (MSM), a new KG alignment network, aiming at the structural heterogeneity challenge. MSM constructs a multi-neighborhood network representation learning method to learn the KG structure embedding. It then adopts a unique sampling and cosine cross-matching method to solve different sizes of neighborhoods and distinct topological structures in two entities. To choose the right neighbors, we apply a down-sampling process to select the most informative entities towards the central target entity from its one-hop and two-hop neighbors. To verify the effectiveness of matching this neighborhood with any neighborhood in the corresponding node, we give a cosine cross-graph neighborhood matching method and conduct detailed research and analysis on three entity matching datasets, which proves the effectiveness of MSM.
A Research on Modulation in Digital Communication System

Abstract With the development of network and communication technology in modern society, WiFi technology has become an indispensable part of people’s lives. WiFi shows its future application prospects with its wireless portability and multi-user access. This paper briefly introduces the meaning and development of WiFi technology and predicts its future development trend. The communication modulation technology QPSK, 16QAM in WiFi is described in detail, and its characteristics are compared by systemview and MATLAB software. Finally, we summarize the common techniques in WiFi.
Research on Wearable Monitoring System for Freezing of Gait in Parkinson’s Disease

Freezing of gait (FOG) is a common symptom in the late stage of Parkinson’s disease, and it is an important cause of falls in patients. In this study, we have designed a FOG monitoring and management system that can be used in the home environment based on wearable and telemedicine technology. The system uses force-sensitive insoles and inertial sensors to obtain the motion signals of patients, and segments gait cycle and detects FOG in real time in the elastic computing server. The monitoring results can be fed back to the patient to help them restore normal walking ability. Doctors can also know the frequency and degree of FOG attacks in each patient through the system, so as to provide more accurate medical evaluation and drug treatment.
Classification of Colon Polyp on Endoscopic Image Using Support Vector Machine

Colon polyps are abnormal tissues belonging to tumors that grow on the walls of the human colon. Colon polyps can be visualized using the technology that is endoscopy. However, the results of endoscopy image examination often differ depending on the accuracy and experience of the doctor. To overcome these problems, a system was built by implementing digital image processing that can detect types of colon polyps based on endoscopy images. There are several stages of the process in this system. The first process is preprocessing using scaling, and bilateral filtering which is used to improve endoscopic image quality. Then, the the second process is segmentation to separate the area of polyp from other object using watershed, thresholding, and cropping. The third process is extraction used to get the area, length, width, and texture of the polyp. The last process is classifying colon polyps based on their type is a hyperplastic, adenoma, and sessile using the SVM (Support Vector Machine) method. The system in this study was tested on 20 endoscopic images, and this system has a high level of accuracy, sensitivity, precision, and specificity in classifying colon polyps. is 93.54%, 94.73%, 85.71%, and 93.02% respectively.
Automatic Drowsiness Detection for Preventing Road Accidents

Drowsiness or fatigue or drunk and drive are the main reasons for accidents and has maximum complications for the safety of the road. More number of accidents could be prevented when the drowsy drives are alerted in time. Drunk driving is the reason behind most of the deaths. The proposed system is going to detect whether the person who is driving a vehicle is drowsy /drunk or not, using facial features such as eye blinking, eye closure and yawning with the help of images and videos, without analyzing their physical structure with gadgets. A different face – detecting algorithm is introduced to analysis and improve the detecting accuracy. Infra-red camera is used to capture the images. Facial regions are detected to analyze the driver's state. Using these features of mouth and eyes, this system can warn the driver with the help of an alert message. Also, the system is connected to a network from where the person who is driving is being monitored and if any necessity of help will be provided by the caretaker who will get the alert on the monitoring webpage automatically.
Flatness Measurement of Platform Screen System Welding Assembly Using Stereo Vision and Grid Pattern Projector

The welding assemblies are the main components of the platform screen door system, which can offer the subway with safety, economy, and silence. The deformed welding assemblies can reduce the carrying capacity of the platform screen door system, so the flatness needs to be measured before installation. In this study, a flatness measuring robot based on stereo vision and grid pattern projector measuring system is developed to online measure the welding assembly flatness in workshop. The developed robot is capable of measuring the flatness by incorporating the following solutions: 1) measuring system consisting of stereo vision and grid pattern projector is designed; 2) grid intersection location method using grid intersection probability calculation, candidate preselection, grid intersection reexamination and sub-pixel refinement is proposed, which can consider both false positives and false negatives by using preselection-and-reexamination; 3) 3D coordinates of sampling points (grid intersection) are calculated based on stereo camera calibration and robot hand-eye calibration; 4) flatness measuring method based on the least squares plan is employed to calculate the flatness of welding assembly. The developed flatness measuring robot is robust, accurate, and efficient, as demonstrated by the following performances: an average measuring bias of 0.10 mm in actual working conditions, and an average measurement time of 4.1 s for single welding assembly, meeting the engineering requirements. It is foreseeable that the developed flatness measuring robot has great application market in the online measurement with a middling accuracy and high efficiency.
Planning for an Efficient Implementation of Hypothetical Bousi∼Prolog

Abstract This paper explores the integration of hypothetical reasoning into an efficient implementation of the fuzzy logic language Bousi ∼ Prolog. To this end, we first analyse what would be expected from a logic inference system, equipped with what is called embedded implication, to model solving goals with respect to assumptions. We start with a propositional system and incrementally build more complex systems and implementations to satisfy the requirements imposed by a system like Bousi ∼ Prolog. Finally, we propose an inference system, operational semantics and the translation function to generate efficient Prolog programmes from Bousi ∼ Prolog programmes.
A multi‐tenancy and robust workflow management system

Abstract Workflow management system (WfMS) in cloud always works as platform as a service to manage customized business processes for massive enterprises. In big data era, non‐functional guarantees of such systems are significant when facing a large number of users and concurrent requests. It is not trivial to support multi‐tenancy and hold high‐availability, because traditional architecture cannot simultaneously satisfy requirements about data isolation and runtime efficiency. In this paper, a modularized distributed workflow management system is proposed, which considers both multi‐tenancy and high‐availability in storage and engine parts of the system. A multiple‐worker‐with‐separate‐schema mechanism is defined to jointly manage the data for tenants, and a proactive strategy is presented to intelligently dispatch large concurrent requests from users to engine workers. After extensive case studies and experiments in practical scenes, our system deployed on modest machines is proved to support tens of thousands of tenants, second‐level response time for 10 K concurrency, and no‐human‐intervened failure recovery for a fail‐stop system node.