Reviewerly/exHarmony huggingface dataset:
Cloud Computing Task Scheduling Method Based on a Coral Reefs Optimization Algorithm

Task scheduling is a difficult non-deterministic polynomial problem. Optimization of the scheduling algorithm is the key to improve the efficiency of cloud computing. The traditional meta-heuristic algorithm has slow convergence rate and is easy to fall into local optimal value. This paper proposes a new scheduling method based on a coral reefs algorithm. Firstly, the task scheduling model is formally described. The objective function is proposed to calculate load balancing rate, resource utilization and load balancing stability. Then the representation method of coral reef and the coding scheme of polyps are designed. Matrix random mapping method is applied to improve the variation effect of polyps. Finally, Ant Colony Optimization(ACO), the Genetic Algorithm(GA) andRound Robin(RR) algorithms are compared in terms of completion time, convergence effect and resource load. The simulation results show that the coral reef algorithm has reduced the completion time by 6.4%, 25.1%, 51.3%, and increased resource utilization by 10.0%, 15.2% and 51.3% when it is compared with the other three algorithms. It shows that the coral reef algorithm is suitable for task scheduling in the cloud environment.
Modelling and outcome analysis of a competitive environment – A probabilistic approach

Competition is one of the underlining phenomena in the world that has been continuously deforming the life and history of human beings, animals, plants and even microorganisms. The results of different competitions are fascinating from explanation of evolution to the dramatic industrial revolution. The article proposes a mathematical model to estimate the outcome probability of different competitors in a competition system on account of the number of failure gates associated. The article includes special cases like the tournament model considering the number of failure gates which are likely at various levels of competition. The study additionally gives the idea of identical probability curves on account of eight member competitions which is highly relevant in strategy and decision making process and there by sustainability of an individual competitor gets ensured. The concept of identical probability utilised here will help to understand the different scenarios in a competitive environment, with similar chance for occurrence. The model developed will definitely be a more practically relevant substitute for conventional probabilistic tools, including Bayesian approaches and whilst the character of considering bulk of competitors all together, enabling the model itself to find further extensions in the dimension of modern game theory as well. The time-dependent behaviour of number of failure gate envisaged provides more practical scope for the mathematical model presented. The model is applicable to analyse the trust related issues in supply chain with huge number of suppliers participating and also in the areas of decision making in the field of industrial management and completion science as a whole.
Use of augmented reality to teach basic computing concepts

Being able to create a computer program is one of the main skill that the younger generations must be capable to have, beginning in their earliest stages of education. We believe that this will be a determinant factor in accessing to more rewarding professional careers and, hopefully, a better quality of life. However, it is noticeable how despite having ubiquitous access to computer equipment they often are unable to produce digital content. One of the main reasons is the lack of familiarity with the basic concepts required to structure a computer program. Our approach aims to curve that problem by offering our users with an introduction to computer programming that uses their own physical space as a testing ground, via their smartphones. We employ the Karel programming language and augmented reality to help students master the basic skills required for creating their own code. In this paper we present the earliest results of our basic prototyping tests and a scope of the following implementation stages.
A Novel Automated Blood Cell Counting Method Based on Deconvolution and Convolution and Its Application to Neural Networks

Blood cell counting and disease detection are very important in healthcare, biomedical research and pathology. Conventional cell counting techniques have problems of their own due to cost, complexity, skill requirement, and time consumption. Alternative image processing techniques are also challenging for huge computational load. Moreover, the modern image processing is quickly moving toward the neural network and machine learning-based smart systems rather than algorithms. They are, however, heavily resource-hungry and complex, essentially, games of humongous number crunching and monstrous computing workforce. The amount of data required to train the networks is also very large. Here, three differnt methods have been presented which provide relatively simple alternative to the above challgnes–A) blood cell counting using deconvolution–convolution algorithm, B) cell counting and disease detection using convolution and finally, C) blood cell counting using neural network aided by deconvolution–convolution method for clustering and classification. They are simpler, robust, faster and less resource-hungry as far as the requirement of the computational power is concerned.
Development of Threat Modeling and Risk Management Tool in Automated Process Control System for Gas Producing Enterprise

The paper describes the development of a software complex based on the method of assessing information security (IS) risks of automated process control systems (APCS) at enterprises of the gas industry, which make it possible to increase the efficiency and control of the technological process. These enterprises are hazardous production facilities; their operation may include emergencies caused by insufficient security of the industrial control system. The proposed method considers the specific features of typical gas production enterprises, allowing identifying current threats and vulnerabilities in the system. Considering the characteristic requirements of providing IS of automation systems for gas production enterprises, actual classes of solutions for providing IS were considered, the most appropriate class was selected, and modern representatives of this class were considered. None of the listed solutions fully satisfied the requirements of providing IS for APCS of gas-producing enterprises. Consequently, a software complex was developed for hazard modeling and IS risk assessment that meets the specified requirements; the interface of the software is described.
Pelican Crossing Adaptive Time Arrangement using Convolutional Neural Network

Pedestrian is one of the important entities for the urban city and various facilities are already provided by the government to make pedestrian walking more safe and comfortable, including the pelican crossing system. Pelican crossing is designed for urban area and it will stop the traffic (by changing the traffic light to red) if pedestrians press a specific button. The main problem of pelican crossing is that the crossing time is fixed and it not adjusted based on the condition of the pedestrian, e.g. number and walks speed of the pedestrian. In this paper, we propose an adaptive time arrangement system on pelican crossing using convolutional neural network (CNN) classifier. The system is built using two different cameras, with the first camera pointing to the pedestrian waiting area and other camera pointing to the pelican crossing. We utilize MobileNet-SSD (Single Shot Detector) CNN architecture that originally used for object detection problem. The MobileNet-SSD CNN classifier was trained using MS-COCO dataset for the first step and fine-tuned the weights on VOC dataset. We remove all VOC categories except for person class because the class will be used for pedestrian detection in the proposed system. The pedestrian crossing time is then calculated based on the detected pedestrian and some predefined pedestrian walk speed and start-up time. To test the proposed system, we have collected several videos that represented the real system environment and conducted experiments on those data. Experiments show that the system is feasible to use in the pelican crossing situation with some appropriate configuration recommendation.
Unstructured Terrain Navigation and Topographic Mapping with a Low-cost Mobile Cuboid Robot

Current robotic terrain mapping techniques require expensive sensor suites to construct an environmental representation. In this work, we present a cube-shaped robot that can roll through unstructured terrain and construct a detailed topographic map of the surface that it traverses in real time with low computational and monetary expense. Our approach devolves many of the complexities of locomotion and mapping to passive mechanical features. Namely, rolling movement is achieved by sequentially inflating latex bladders that are located on four sides of the robot to destabilize and tip it. Sensing is achieved via arrays of fine plastic pins that passively conform to the geometry of underlying terrain, retracting into the cube. We developed a topography by shade algorithm to process images of the displaced pins to reconstruct terrain contours and elevation. We experimentally validated the efficacy of the proposed robot through object mapping and terrain locomotion tasks.
Multi-level feature extraction model for high dimensional medical image features

Recent technology evolution has emerged many applications that consumed data in extremely highly dimensional. For medical images, outsourcing the computation of image feature extraction to the cloud has become common method in order to alleviate the heavy computation workload for local devices. However, unlike other images, the medical images content cannot be easily manipulated because they exist in visual presentation that cannot be explored with textual data in order to capture the visual structure of the image. Hence, appropriate features are required to classify these images. Feature extraction for medical images based on image shape, color and texture using machine learning can improve the performance to categorize image features into homogeneous group. Feature extraction automatically learn and recognize complex patterns and make intelligent decisions based on features attributes. Therefore, this proposed a multi-level feature extraction model for high dimensional medical image features. By applying the multi-level model, features from medical images are extracted from general image features into specific features category. Later, a specified features categories are assigned to the image so that the image presentation can become more meaningful and assist the performance of image classification. We expect the findings derived from our method provides new approaches for extracting medical image features from big data source. It also improve the relevance and quality of image classification, thus enhance performance of medical imaging in the radiology service.
Manifold Mixup Improves Text Recognition with CTC Loss

Modern handwritten text recognition techniques employ deep recurrent neural networks. The use of these techniques is especially efficient when a large amount of annotated data is available for parameter estimation. Data augmentation can be used to enhance the performance of the systems when data is scarce. Manifold Mixup is a modern method of data augmentation with a regularization effect that meld two images, or the feature maps corresponding to these images, and the targets are fused accordingly. We propose to apply the Manifold Mixup to text recognition while adapting it to work with a Connectionist Temporal Classification cost. We show that Manifold Mixup consistently improves text recognition results on various languages and datasets.
Building Corpora of Spoken Filipino Words Using Speech Segmentation with Automatic Labeling

There are hardly any open access speech corpora in Filipino that are structured and can be used to train speech recognition systems that utilize deep learning models. The existing data sets of audios in Filipino language are in forms that require pre-processing and cleansing. This paper proposed a method that would allow building up of Filipino words corpora which can be used as data set for deep learning speech recognition systems. The method utilized speech segmentation technique to extract words from a sound file composed of sentences, words, and syllables. Three trials with variances on the length of silence were conducted to increase accuracy of segmentation and create a more robust Filipino words corpus. Using several Python tools combined with Google Cloud Speech Recognition Application Program Interface, automatic speech segmentation with automatic labeling for Filipino language was achieved with 95% accuracy.
Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting?

Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language.
A Novel Approach for Multi-View 3D HDR Content Generation via Depth Adaptive Cross Trilateral Tone Mapping

In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3D display. We are interested in the case where different exposures are taken from different viewpoints. The scheme employed a new depth-adaptive cross-trilateral filter (DA-CTF) for recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images captured at different exposure levels. Explicitly leveraging additional depth information in the tone mapping operation correctly identify global contrast change and detail visibility change by preserving the edges and reducing halo artifacts in the synthesized 3D views by depth-image-based rendering (DIBR) procedure. The experiments show that the proposed DA-CTF and DIBR scheme outperforms state-of-the-art operators in the enhanced depiction of tone mapped HDR stereo images on LDR displays.
Taxonomy &amp; Analysis of Cloud Computing Vulnerabilities through Attack Vector, CVSS and Complexity Parameter

The world is witnessing an exceptional expansion in the cloud enabled services which is further growing day by day due to advancement & requirement of technology. However, the identification of vulnerabilities & its exploitation in the cloud computing will always be the major challenge and concern for any cloud computing system. To understand the challenges and its consequences and further provide mitigation techniques for the vulnerabilities, the identification of cloud specific vulnerabilities needs to be examined first and after identification of vulnerabilities a detailed taxonomy must be positioned. In this paper several cloud specific identified vulnerabilities have been studied which is listed by the NVD, ENISA CSA etc accordingly a unified taxonomy for security vulnerabilities has been prepared. In this paper we proposed a comprehensive taxonomy for cloud specific vulnerabilities on the basis of several parameters like attack vector, CVSS score, complexity etc which will be further act as input for the analysis and mitigation of cloud vulnerabilities. Scheming of Taxonomy of vulnerabilities is an effective way for cloud administrators, cloud mangers, cloud consumers and other stakeholders for identifying, understanding and addressing security risks.
Adaptive Loss Balancing for Multitask Learning of Object Instance Recognition and 3D Pose Estimation

Object instance recognition and 3D pose estimation are important elements in robot vision technology. State-of-the-art methods improve the accuracy of both instance recognition and pose estimation using multitask learning. These methods use unified balancing parameters to integrate the loss of each task, which means task difficulties are the same for all objects. However, the method we propose can adjust the balancing parameters for each object. This idea is based on the assumption that task difficulties are different for each object, since the distinctiveness of object instances and poses depends on their appearance and shape. Our method sequentially estimates task difficulties for CNN based on the amount of loss change and calculates balancing parameters for each object. Our experiments show that our method improves the accuracy of both object instance recognition and pose estimation compared with state-of-the-art methods using the common LineMOD dataset.
Component Based Life Cycle Models Comparative Survey

Component based development (CBD) is the process of developing software by integrating the reusable components. These components are independent of each other. Independent components mean that any change made in one component does not affect the functionality of other. This paper compares different component based life cycle models which have been proposed earlier and this model also proposes a new component based life cycle model which makes the selection of components easier.
Classification method for imbalanced data set based on EKCStacking algorithm

The processing of imbalanced data sets has always been a hot issue in machine learning. The traditional classification method is to pursue the overall classification accuracy of data sets, and often ignores the classification effect of minority samples. Stacking is a framework algorithm. Based on the Stacking framework, in this paper, we introduce a new oversampling algorithm EKSMOTE and cost-sensitive theory into Stacking, and propose the EKCStacking algorithm. The algorithm uses the EKSMOTE algorithm to reduce imbalanced ratio of data set before data training, and then the Level 1 layer uses a cost-sensitive classifier. The experimental results of the data set in the Keel database show that EKCStacking improves the classification accuracy of minority samples and makes the performance more stable compared with the traditional algorithm.
A Hybrid Approach to Wide Baseline View Synthesis with Convolutional Neural Networks

Convolutional Neural Networks (CNN) have been recently employed for implementing complete end-to-end view synthesis architectures, from reference view warping to target view blending while dealing with occlusions as well. However, the convolutional sizes filters must increase with the distance between reference views, making all-convolutional approaches prohibitively complex for wide baseline setups. In this work we propose a hybrid approach to view synthesis where we first warp the reference views resolving the occlusions, and then we train a simpler convolutional architecture for blending the preprocessed views. By warping the reference views, we reduce the equivalent distance between reference views, allowing the use of smaller convolutional filters and thus lower network complexity. We experimentally show that our method performs favorably against both traditional and convolutional synthesis methods while retaining lower complexity with respect to the latter.
IDNS: A High-Performance Model for Identification of DNS Infrastructures on Large-scale Traffic

Domain Name System (DNS) is indispensable in a large number of network applications. Identifying DNS infrastructures into different roles hierarchically is highly desired for a variety of purposes such as network management and threat evaluation. However, traditional measurements almost all depend on active scanning without considering dynamic packet-level features of different DNS infrastructures.In this paper, we propose a high-performance model IDNS (Identifying DNS) based on passive measurement. IDNS: (i) extracts single-packet field features (SFF) and multi-packet statistical features (MSF) from DNS traffic, (ii) utilizes an estimation algorithm to calculate MSF for satisfying online processing speed, and (iii) applies several classifiers in Ensemble Learning and Incremental Learning. We perform an extensive evaluation based on a large volume of DNS queries and responses collected from one ISP. The evaluation results demonstrate that the best classifier in Ensemble Learning can reach 90% accuracy rate while the classifier in Incremental Learning can reach 80% with the highest scalability.
The Procedure of Building a Project Team on the Basis of Cognitive Maps and Genetic Algorithms

The article under focus deals with the possible approaches toward modified assignment problem. The approach under discussion allows to take into account specific assignments of the project when building a team of performers. It also allows to calculate labour intensity and continuity of assignment. Cognitive maps are used to make a proper prognosis of the project fulfillment success. The meanings of the main concept map are used when working out an objective function, the search for extreme meaning of which is based on standard genetic algorithm.
Mining Text in Incident Repositories: Experiences and Perspectives on Adopting Machine Learning Solutions in Practice

Machine learning based data-driven solutions have potential to significantly improve quality of incident management process and make it cost effective. We present our experiences while addressing a spectrum of interrelated problems encountered in practice including identifying semantically related incidents, assignee recommendation, and mapping of incidents to business processes. We argue that despite long-standing research, it is not always straightforward to adopt recommendations from research into practice due to variability and complexity of business constraints and nature of data. We discuss need for meta-level analysis and suggest our own recommendations towards designing pragmatic solutions with low barriers to adoption and addressing right level of challenges.
Research and Application of Process Object Intelligent Learning Modeling

Industrial process objects are often multi-order inertial systems. Least squares identification algorithm needs prior knowledge to determine the order of the object model and then identify it. The identified model is prone to large deviations. In this paper, an intelligent learning modeling method for process objects is proposed. Two DNN deep learning networks are used to train and model the input and output data of the model, and an intelligent modeling system is designed based on the third-order inertial discrete structure. Firstly, the pseudo-random sequence excitation signal is added to the object open-loop system, and the first-order inertia plus delay object is used to fit the characteristics of the object, and the first-order inertia time constant T is obtained by genetic optimization algorithm. Secondly, a third-order inertia link and DNN deep learning network are embedded in the discrete structure of the third-order inertia model to build an intelligent model structure. To ensure that the third-order inertia link and the object inertia time are similar, the inertia time constant of each link is set to T/3, and the input and output data are sent into the intelligent model for training, and the DNN1 model of the object is obtained. Finally, the DNN1 model is embedded in the intelligent model structure again, and a more accurate identification model is obtained by training the input and output data for the second time. The validity of the intelligent model is verified by testing the simulation data. This method has great practical significance for the design and application of intelligent learning modeling.