Reviewerly/exHarmony huggingface dataset:
A Novel Approach on Auto-Scaling for Resource Scheduling Using AWS

The mostly wide adoptions in cloud computing for businesses have the several reason; among that the elasticity in cloud computing is the virtual infrastructures which will be the dominating leader. The most important area is elasticity that allows to auto-scale the resources which are on-demand. On the other hand, Web applications typically comprise dynamic workload and are hard to predict. The cloud service providers and researchers are jointly working together to condense the cost at the same time as maintaining quality of service (QoS). AWS command-line interface (CLI): the command-line tools, which is Python written, introduce the efficient use cases for managing the AWS services through the sets precisely modest in command. Forecasting on historical data by way of inputting in order to make the up-to-date assessment which are analytical in modeling a path for forthcoming trends. Prophet which acts as a tool will be built to report such concerns, and it delivers real-world methodology in forecasting that is at scalable. From the experimental result, it is found that when predicted value is greater than 51% of CPU usage, then a new EC2 instance is created.
Benefits of Using Requirements Management Tools on a Digitized API 17O Document

Abstract Currently oil and gas companies are spending billions of dollars on digitalization efforts. One important aspect of a project that needs to be digitized are requirements. Most oil and gas companies receive project documentation and requirements as PDF files. Receiving PDF documents make it very difficult for companies to manipulate the content to identify which parts of the document are requirements and which parts are just background information. In addition, documents that contain requirements have poorly written requirements that are ambiguous, and can have many interpretations, making it difficult to show compliance. To solve the issues caused by poorly written requirements and receiving PDF files instead of receiving requirements in a more usable format, the solution is to rewrite the requirements and use a requirements management (RM) tool to put the requirements in a database. The American Petroleum Institute (API) 17O 2nd Edition document and a representative list of High Integrity Pressure Protection System (HIPPS) product requirements are used to show the benefits of using a requirements management tool. This paper will describe the prerequisites prior to selecting an RM tool, show how using a requirements quality analyzer tool can aid in preparing requirements to be imported into an RM tool, demonstrate the main benefits of using an RM tool in a project context, and discuss lessons learned from adding an RM tool to a document-based project.
drop

This paper describes an interactive art installation titled "drop." It is the first artwork using the Waterdrop Projection-Mapping (WPM) system, which animates levitating waterdrops. With this artwork, the anno lab team infuses physical characteristics into computer graphics and materializes them as tangible pixels. WPM consists of a waterdrop generator and an ultra high-speed projector. The team uses an ultra high-speed projector to cast stroboscopic spotlights mapping on waterdrops to create an optical illusion of animating each waterdrop individually. This is a new technique to show computer animation by animating levitating waterdrops. This technique explores a new horizon to create animations with tangible pixels that the viewer can touch physically.
Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model

Emotion recognition plays a crucial role in human-robot emotional interaction applications, and the brain emotional learning model is one of several emotion recognition methods, but the learning rules of original brain emotional learning model play poor adaptation and do not work very well. In fact, existing facial emotion recognition methods do not have high accuracy and are not sufficiently practical in real-time applications. In order to solve this problem, this paper introduces an optimal model, which merges interval type-2 recurrent wavelet fuzzy system and brain emotional learning network for emotion recognition. The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. There are no rules initially, and then the structure and parameters of model are tuning online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. The proposed model could reduce the uncertainty in terms of vagueness by using type-2 recurrent wavelet fuzzy theory and removing noise samples. Finally, the superior performance of the proposed method is demonstrated by its comparison with some emotion recognition methods on five emotion databases.
A Strategy for Increasing the Employment Rate of Graduates Using a Compact Module

The aim is to increase the higher education employment rate in the first six months after graduation at Istanbul Technical University. The paper involves different systems and techniques used, such as; ITU Career and Skill Management System (KAYS), which is an internship- and job-seeking platform for Istanbul Technical University students, annual Career Summit performance analysis, workshops, interview simulations, company network, and mentoring modules. Initially, the process of determining the contents of the Career and Skill Management System platform has been addressed with a Management Engineering approach. In this process, the platform at hand was observed, and the details of the inner workings of the system were noted. Secondly, user groups of the system were interviewed, performance questionnaires of different activities from different user groups were analyzed, and the methodology has been determined. New strategies have been set according to the results, and the student employment rate has been recalculated after six months, which showed an acceptable increase in the new graduate employment rate.
Superpixel-Based Stereoscopic Video Saliency Detection Using Support Vector Regression Learning

In this study, a superpixel-based stereoscopic video saliency detection approach is proposed. Based on the input stereoscopic video sequences containing left-view and right-view video sequences, a sequence of right-to-left disparity maps is obtained. First, the simple linear iterative clustering (SLIC) algorithm (Achanta, IEEE Trans Pattern Anal Mach Intell, 34(11):2274–2282, 2012) is used to perform superpixel segmentation on all video frames. Second, the spatial, temporal, depth, object, and spatiotemporal features are extracted from video frames to generate the corresponding feature maps. Third, all feature maps are concatenated and support vector regression (SVR) learning using LIBLINEAR tools is employed to generate the initial saliency maps of video frames. Finally, the initial saliency maps are refined by using the center bias map, the significant increased map, visual sensitivity, and Gaussian filtering. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.
DESIGN STUDENTS’ TAKE ON CO-DESIGN IN HEALTH: REFLECTIONS FROM LEARNING AND EDUCATION PERSPECTIVES

Abstract Using three projects as a showcase, the aim of this article is to report on design students’ take on co-design in health, in terms of both outcomes and processes. It builds on project work conducted in a master’s course called ‘Design for Society’, reserved for 4th-year industrial design students in the Department of Design at the Norwegian University of Science and Technology. The students were encouraged to explore how healthcare services could facilitate active, informed and involved patients. The overall theme was provided by a cluster funded by the Norwegian Research Council addressing co-creative service design and innovation in health. Based on initial research, the students, organised in groups, identified a chronic illness that has a lifelong impact on everyday life, such as cystic fibrosis, fibromyalgia, and Alzheimer’s disease. We give an account of three selected projects by showcasing developed concepts, central methods and tools, and we share key reflections from the students and the course educators on their experiences of working with co-design in health in an academic environment.
The key technology of blockchain and its research in the field of information security

with the rapid development of new technologies, such as the Internet of Things (IoT), big data and cloud computing and so on. The driving direction of science and technology is shifting to blockchain that is the underlying technology. China should take block chain as an important breakthrough of independent innovation of core technology. We should accelerate the innovation and development of block chain technology and industry. Combined with the key technology of block chain and frame model, this paper firstly analyzed the current information security situation. From two aspects of access control and data protection, author put forward to applications with block chain technology. And the framework model is form in the field of information security. It is helpful to develop direction of block chain.
Virtual Manager for Medical Practitioners

The rise of Artificial Intelligence(AI) and Natural Language Processing (NLP) gave a perception of the use of computers in the Healthcare industry. The introduction of voice-assisted chatbots and digital data storage aided health organizations as well as the dependents. Currently, the interactions between doctors and patients are undocumented, and the prescription, in some cases, creates chaos for understanding and long-term maintenance. The project introduces an interactive virtual assistant system for searching, analyzing, and recording clinical data and laboratory data, maintaining electronic health records. It uses a Named Entity Extraction (NER) model for classifying the doctor-patient conversation, i.e., symptoms, detected disorder, prescription of drugs, and stores them in a Structured database. The system poses an ability to understand and respond to limited user queries and imitate human discourse to stimulate a conversation that resembles conversing with a real person. The response includes the recorded history of patients, overcoming the issue of file management, supporting clinical decisions. Generates automated prescriptions eliminating the problems of handwritten prescriptions, and provides live news updates with the web scraper module. Moreover, it plays a vital role in reminding patients about their deadlines for vaccinations and follow-ups.
On quality control of user-generated-content (UGC) compression

Exponential increase in the demand for high-quality user-generated content (UGC) videos and limited bandwidth pose great challenges for hosting platforms in practice. How to optimize the compression of UGC videos efficiently becomes critical. As the ultimate receiver is human visual system, there is a growing consensus that the optimization of the video coding and processing shall be fully driven by the perceptual quality, so traditional rate control-based methods may not be optimal. In this paper, a novel perceptual model on compressed UGC video quality is proposed by exploiting characteristics extracted from only source video. In the proposed method, content-aware features and quality-aware features are explored to estimate quality curves against quantization parameter (QP) variations. Specifically, content revelant deep semantic features from pre-trained image classification neural networks and quality revelant handcrafted features from various objective video quality assessment (VQA) models are utilized. Finally, a machine-learning approach is proposed to predict the quality of compressed videos of different QP values. Hence, the quality curves can be driven, by estimating the QP for given target quality, a quality-centered compression paradigm can be built. Based on experimental results, the proposed method can accurately model quality curves for various UGC videos and control compression quality well.
Problems and Task of Cyber Protection and Cybercrime

Presently, Cybercrime is one of the major crimes done by computer expert. In this paper, need of cyber security impacts of the cybercrime is clearly mentioned. Cyber security refers providing prevention against the cybercrime. Cybercrime refers that group of activities made by the people by creating disturbance in network, stealing others private information, documents, hacking the bank details and accounts and transferring money. The paper includes types, needs and issues in cyber security, its advantages and disadvantages, history of cybercrime, types of cybercrime.
Using Quantitative Characteristics of Document Images for Identiﬁcation of Laser Printers

The article provides a brief overview of the current state of the theory and practice of identifying laser printers and the results of research work aimed at discovering individual features of the printing mechanism of a laser printer. The author analyses the scheme of a laser printer, describes the printing cycle, presents the main results of the analysis of a printer’s mechanism and the inﬂuence of its individual parts on the optical density of printing. The method of assessment of the optical density of the print by the digital image of the printed document is proposed, the complex of the necessary technical and program tools is described. A hypothesis on the correlation between ﬂuctuations in the optical density of printing of solid ﬁlls and ﬂuctuations in the area of printed elements was put forward and conﬁrmed; a visual representation of the study results in graphical form is presented, the relationship between the shape of the obtained graphs and defects of the printer’s components and parts is substantiated. The author proposes ways to detect the inhomogeneity of printing density on text arrays based on changes in the area of printed elements and processing of the results, which allows comparing distributions for texts printed in fonts of diﬀerent sizes and styles. Based on experimental material, the individuality of the form of the obtained distributions and the possibility of their use as identifying features of the printing device are substantiated.
Exploring the multi-touch simulation of learning performance, mental models and potential behavior clusters in science concepts learning

This study aims to explore whether the application of human-computer interaction gestures into the learning of scientific concepts will have different effects on cognitive concepts and mental models. We developed a digital simulation based on gesture simulation and took the basic knowledge of optics as a teaching unit. Then we selected 28 students in the sixth grade of an elementary school and used cluster analysis to explore the influence of multi-touch simulation on knowledge acquisition and mental models. The results showed that students with high behavioral performance are able to use touch gestures to understand and apply optical concepts more accurately than students with low behavioral performance, which indicates that students with high behavioral performance/high learning outcomes can achieve improved cognitive concepts and mental model construction, which has great research significance for the application of science education.
Multimodal Segmentation of Medical Images with Heavily Missing Data

An important aim of research in medical imaging is the development of computer aided diagnosis (CAD) systems. A fundamental step in these systems is the image segmentation and convolutional neural networks (CNNs) are becoming the most commonly used approach to solve this task. However, despite their great power, in this domain CNNs are limited in their potential performance by the usually small amount of data [1]. Computed tomography (CT) and magnetic resonance imaging (MRI) scans are often used to examine the internal structure of human body and have their own unique properties and limitations. As a common practice, the investigations are usually done on a single modality, nonetheless, the simultaneous analysis of multiple modalities can significantly boost the segmentation accuracy. However, obtaining multiple imaging modalities for the same subject is very unlikely. In this paper we investigate the possibility of generating a multimodal CT-MRI representation for a segmentation task starting from a single modality, either CT or MRI. We considered this as a missing data problem, hence, we designed a pipeline where a CycleGAN was used to generate the missing modality. The synthetic modality was then paired with the real one to perform the required segmentation taking advantage of the multimodal representation and the augmented training dataset. To test the system we used two unrelated labeled datasets, one with CT data and the other one with MRI data. Results show that data enrichment with synthetic modalities improves the segmentation performance.
Support educational resource accessibility requirements in a knowledge graph modelling framework

Knowledge modelling techniques based on knowledge graphs can be used in the context of building customized and flexible e-learning systems to provide learning resources that are accessible to anyone, including learners with special needs and accessibility preferences. The paper describes how a personalized educational resource system that considers the needs of learners, especially when it comes to learners with accessibility needs, can benefit from knowledge graph-based technologies used as resource representation formalism. Knowledge graphs can be used to provide learners with suitable learning objects, learning activities, and teaching methods based on their different preferences and needs and to perform domain knowledge reasoning through modelled domain knowledge.
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots

Understanding a controller's performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks.If we do not have a model of the dynamics of the world, which is often the case in complex domains, we may need to approximate a performance function of the robot based on its interaction with the environment.Such a performance function gives us insights into the behaviour of the robot, allowing us to fine-tune the controller with manual interventions.In high-dimensionality systems, where the actionstate space is large, fine-tuning a controller is non-trivial.To overcome this problem, we propose a performance function whose domain is defined by external features and parameters of the controller.Attainment regions are defined over such a domain defined by feature-parameter pairs, and serve the purpose of enabling prediction of successful execution of the task.The use of the feature-parameter space -in contrast to the action-state space-allows us to adapt, explain and finetune the controller over a simpler (i.e., lower dimensional) space.When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness.When the robot fails to execute the task, we use the regions to debug the controller and find adaptive and counterfactual changes to the solutions.Another advantage of this approach is that we can generalise through the use of Gaussian processes regression of the performance function in the high-dimensional space.To test our approach, we demonstrate learning an approximation to the performance function in simulation, with a mobile robot traversing different terrain conditions.Then, with a sample-efficient method, we propagate the attainment regions to a physical robot in a similar environment.
Kernel Attention Based Multi-scale Adaptive Graph Convolutional Neural Network for Skeleton-Based

Graph convolution network is widely used in skeleton-based action recognition tasks. To improve the adaptability of the graph, the model should learn the structure of the graph automatically. The existing adaptive strategies nested by methods of action recognition merely depend on the spatial features of skeleton without considering the temporal factors. Such a model is not sufficient to generate a graph structure better suited to model the spatio-temporal correlation of skeletons. Furthermore, the features are easily overlooked during training and there is no suitable feature enhancer for skeleton-based data. To improve these defects, the paper (1) proposed a novel adaptive graph module by aggregating spatio-temporal features of skeleton sequences and (2) designed a new action recognition model with a powerful feature enhancer (kernel attention) called KA-AGCN. Finally, on three large-scale action recognition datasets NTU-RGBD 60, NTU-RGBD 120 and Skeleton-Kinetics, our results outperform most of the advanced methods.
An online weighted sequential extreme learning machine for class imbalanced data streams

Abstract When general online classification algorithms deal with imbalanced data streams, there are always some problems, such as over fitting phenomenon caused by insufficient simple learning and instability of training model. In this paper, we introduce online sequential extreme learning machine (OSELM) as the basic theory model, and combine with the cost-sensitive strategy, then propose a cost-sensitive learning based online sequential extreme learning machine algorithm (C-OSELM). Firstly, in order to solve the problem that minority classes are easily misclassified due to class imbalance, use cost-sensitive strategy, by assigning different penalty parameters to various samples, a weighting matrix is constructed to improve the misclassification cost, thereby effectively alleviating the excessive deviation of decision surface. On this basis, in order to solve the problem that the penalty parameter is too single and the algorithm is not universal, the cost adjustment function is introduced to optimize the weight parameters to select the appropriate weight. Finally, 16 class II imbalanced datasets are used for comparison and verification. The experimental results show that the classification performances of the proposed C-OSELM algorithm are better than other comparative algorithms.
Pre‐training Dataset Generation Using Visual Explanation for Classifying Beam of Vehicle Headlights from Nighttime Camera Image

For Automatic High Beam (AHB), the authors are working on a study to classify beam of car headlights as ‘High’ and ‘Low’ by realizing the presence of vehicle on road from nighttime in‐vehicle camera image using deep learning. In general, weights of deep learning model are pre‐trained on a large dataset which are then used as initial weights for other tasks such as image recognition. However, it has been reported that even if the pre‐trained weights are used as the initial weights, it is not a factor of accuracy improvement when domain differs. It is important to pre‐train a model on a dataset that is suitable for target domain. Therefore, we propose a method to generate a pre‐training dataset that can be easily created by using visual explanation which represents where a deep learning model is looking at when performs a task. Then, we applied the proposed pre‐training dataset on the headlight beam classification in the nighttime in‐vehicle camera image and verified the effectiveness of our method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.