Reviewerly/exHarmony huggingface dataset:
Robust Object Detection and Localization Using Semantic Segmentation Network

Abstract The advancements in the area of object localization are in great progress for analyzing the spatial relations of different objects from the set of images. Several object localization techniques rely on classification, which decides, if the object exist or not, but does not provide the object information using pixel-wise segmentation. This work introduces an object detection and localization technique using semantic segmentation network (SSN) and deep convolutional neural network (Deep CNN). Here, the proposed technique consists of the following steps: Initially, the image is denoised using the filtering to eliminate the noise present in the image. Then, pre-processed image undergoes sparking process for making the image suitable for the segmentation using SSN for object segmentation. The obtained segments are subjected as the input to the proposed Stochastic-Cat Crow optimization (Stochastic-CCO)-based Deep CNN for the object classification. Here, the proposed Stochastic-CCO, obtained by integrating stochastic gradient descent and the CCO, is used for training the Deep CNN. The CCO is designed by the integration of cat swarm optimization (CSO) and crow search algorithm and takes advantages of both optimization algorithms. The experimentation proves that the proposed Stochastic-CCO-based Deep CNN-based technique acquired maximal accuracy of 98.7.
Analysis and simulation of handwritten recognition system

Handwriting recognition is the automatic transcription of handwriting, where only the image of the handwriting is available. Manual matching shall be used by banks for the authentication of checks and signatures. In forensics, handwriting corresponding algorithms can help handwriting analysts to predict the author with greater precision. This handwriting needs to be scanned to the computer for the handwriting recognition system to access it and analyse it consequently. A variety of handwriting applications, including transcription papers, mail routing, and processor forms, checks, and faxes, may be envisaged. Several applications are also possible. The extensive research effoncentrated on the field of character recognition (CR), due both to its possible applications and to the difficulties involved in simulating human reading. The new Offline Handwritten Recognition (OHR) is designed for both Urdu and English. It mainly focuses on the removal of noises in word, character segmentation methods with higher recognition rate. The images which are scanned may contain noises and image de-noising steps consist of binarization, noise elimination, and size normalization. Words and character segmentation are performed by using Particle Swarm Optimization (PSO) algorithm. Then those segmented samples are used for the next step which is feature extraction. Finally, word recognition is performed by using the deep neural network classifier.
Formal verification and complexity analysis of confidentiality aware textual clinical documents framework

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.
Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Abstract Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.
A novel auto-encoder induced chaos based image encryption framework aiding DNA computing sequence

The day-to-day progress in communication plays a vital role in transmitting millions and trillions of data through the unsecured network channels. It creates a way where the user’s data becomes the victim of various security threats. Among those users’ data, images act as primary data, and its encryption security methodologies are fascinating. The conventional encryption techniques don’t work well against the various other hidden security threats but require substantial computational time and cost with poor permutation performance. Hence to deal with this, an auto-encoder induced DNA (Deoxyribonucleic acid) sequence via chaotic image encryption framework is designed in our proposed work. It integrates the properties of DNA encoding and the chaotic maps to handle the data losses effectively and resist several attacks such as statistical attacks, chosen-plaintext attacks, etc. Moreover, an auto-encoder is used to control the data noises, thereby ensuring a better encryption performance. Here, the auto-encoder is activated to generate a permuted image with less time complexity and noise. A secret key is then initialized with the aid of SHA-256. Finally, image encryption and decryption are achieved, followed by the successful transmission of data over a digital network. The performance of the proposed work is analyzed with varied metrics to strengthen its efficiency over the prior techniques.
Assessing Function Names and Quantifying the Relationship Between Identifiers and Their Functionality to Improve Them

When students first learn to program, they often focus on functionality: does a program work? In an era where software volume and complexity increase exponentially, it is equally important that they learn to write code with style. Quality code starts with the building blocks for any program, its functions. A carefully chosen name is vital for program maintainability and manageability. The identifier is the most portable and concise way to summarize what the function does. What makes for the right choice? And can we automatically assess the quality of function names? Using natural language processing, we were able to create a probabilistic model to evaluate their clarity. Using functionality encodings, we attempt to learn the relationship between functions in different programs to improve their names. We analyzed a total of 3,900 programs tackling three novice programming tasks submitted by 1,300 students in CS1.
Research on Super-Resolution Reconstruction Algorithm of Image Based on Generative Adversarial Network

Abstract Image super-resolution is to use a series of algorithms to improve the original image resolution. The process of obtaining high-resolution image through some low-resolution images is image super-resolution reconstruction technology. There are two main research fields in super-resolution reconstruction, one is to restore the real details of the image, and the other is to not require too much detail, and only pay attention to the overall visual effect of the image. In this paper, an improved super-resolution reconstruction algorithm based on generative adversarial network is proposed. The network model and loss function are improved and optimized. The auxiliary VGG-19 network is used to extract the image features, and the extended convolution is used to expand the receptive field, which makes the image have a better reconstruction effect. Using DIV2k data set as training set and set5, set14, bsd100 data sets as test set, a series of experimental analysis is to prove the feasibility of the procedure method. Compared with the existing mainstream models, the perceptual effect of the image has been improved.
A bridge too far for artificial intelligence?: Automatic classification of stanzas in Spanish poetry

The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer-assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts.
Adaptive Weighted Exposure Algorithm Based on Region Luminance Detection

When the traditional global auto-exposure algorithm is applied to high contrast special scenes such as dark background and bright target, there is often over-exposure of the target, which leads to the loss of the key information of the subject concerned. To solve this problem, an adaptive weight exposure method based on regional brightness detection is proposed. Firstly, the whole imaging field of view is divided into several regions, and then the position of the target in the field of view is identified by independent judgment. Finally, the automatic exposure control of gray statistics is carried out according to the preset region weight. The experimental results show that the proposed method can effectively improve the image quality of the bright target or dark target in the dark background, avoid the phenomenon of the target too bright or too dark, and better capture the texture features of the target.
A Geometric Perspective on Information Plane Analysis

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.
A Corpus-based Study on the Construction of Online+Offline Mixed Mode in College English Teaching

English is the most widely used language in the world and one of the most important international languages at present. With the globalization of social economy, politics and culture, the role and status of English are becoming more and more important. At present, the mixed teaching reform of college English has attracted more and more attention from all walks of life. The mixed teaching mode should be more conducive to teaching in the new era, and it is necessary to combine information technology with education industry to make college English teaching more and more convenient. Data-driven learning is a learning method based on corpus context co-occurrence, in which learners actively explore the law of language use by inductive learning by contacting real corpus. This paper discusses the "online+offline" blended teaching mode, studies the construction method of online+offline blended teaching mode of college English based on corpus, and analyzes how to improve the teaching quality of college English through the advantages and characteristics of blended teaching.
Technical Debt in Hardware Systems and Elements

This paper explores the application of the technical debt metaphor to hardware-centric systems. We specifically evaluate how technical debt impacts hardware development and how it can best be handled. Technical debt management can help make better decisions by tracking the debt that is building up in hardware elements and objectively assessing when correcting technical debt items is more cost-effective than allowing them to continue to charge interest in the form of lower productivity and higher risk. Technical debt management has shown great value in the software development field, and recent applications show that the concept can be applied to other engineering disciplines, including systems engineering. There has been less research into technical debt in hardware systems, but many of the same concerns - dependencies, complexity, patterns and conventions, integration, and non-functional requirements - apply. In this paper, we will describe applications of known technical debt types, such as architectural debt, implementation debt, configuration debt, modeling and simulation debt, quality debt, integration debt and testing debt, to hardware centric environments. Then, we will analyze a set of hardware-centric programs for concrete examples of technical debt and their impact. Based on this information, we will expand the definition of System Technical Debt and recommend approaches for identifying, managing, and eliminating technical debt in hardware-centric systems.
Research and Application of Deep Belief Neural Network for Distribution Transformer Feeding Area Line Loss Reduction

The line loss rate is an important technical and economic indicator for power supply company, which is also an important content to measure company's comprehensive management level and operating efficiency. This paper combines cluster analysis and neural network algorithms to classify distribution transformer feeding area line loss of shanghai power grid. DBN obtains the initial parameters of the network by layer-by-layer unsupervised pre-training, which effectively solves a series of problems caused by the random initialization of traditional neural network parameters. In addition to having a good initial point, DBN can also effectively solve the over-fitting and under-fitting problems that often appear in neural networks through pre-training. This paper verifies the effectiveness and accuracy of DBN model, revealing different loss related electrical characteristics. By learning a large amount of historical data, training the line loss neural network model and obtaining the change rule of distribution transformer feeding area of Shanghai Power Grid, this paper presents a guiding solution for loss reduction measures. For missing operating data input scenarios, this neural network model can also provide loss rate confidence interval.
Multilingual Age of Exposure

The ability to objectively quantify the complexity of a text can be a useful indicator of how likely learners of a given level will comprehend it. Before creating more complex models of assessing text difficulty, the basic building block of a text consists of words and, inherently, its overall difficulty is greatly influenced by the complexity of underlying words. One approach is to measure a word’s Age of Acquisition (AoA), an estimate of the average age at which a speaker of a language understands the semantics of a specific word. Age of Exposure (AoE) statistically models the process of word learning, and in turn an estimate of a given word’s AoA. In this paper, we expand on the model proposed by AoE by training regression models that learn and generalize AoA word lists across multiple languages including English, German, French, and Spanish. Our approach allows for the estimation of AoA scores for words that are not found in the original lists, up to the majority of the target language’s vocabulary. Our method can be uniformly applied across multiple languages though the usage of parallel corpora and helps bridge the gap in the size of AoA word lists available for non-English languages. This effort is particularly important for efforts toward extending AI to languages with fewer resources and benchmarked corpora.
Optimization of Deep Reinforcement Learning with Hybrid Multi-Task Learning

As an outcome of the technological advancements occurred within artificial intelligence (AI) domain in recent times, deep learning (DL) has been established its position as a prominent representation learning method for all forms of machine learning (ML), including the reinforcement learning (RL). Subsequently, leading to the evolution of deep reinforcement learning (DRL) which combines deep learning's high representational learning capabilities with current reinforcement learning methods. Undoubtedly, this new direction has caused a pivotal role towards the performance optimization of intelligent RL systems designed by following model-free based methodology. optimization of the performance achieved with this methodology was majorly restricted to intelligent systems having reinforcement learning algorithms designed to learn single task at a time. Simultaneously, single task-based learning method was observed as quite less efficient in terms of data, especially when such intelligent systems required operate under too complex as well as data rich conditions. The prime reason for this was because of the restricted application of existing methods to wide range of scenarios, and associated tasks from those operating environments. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. Objective of this research paper is to present a parallel multi-task learning (PMTL) approach for the optimization of deep reinforcement learning agents operating within two different by semantically similar environments with related tasks. The proposed framework will be built with multiple individual actor-critic models functioning within each environment and transferring the knowledge among themselves through a global network to optimize the performance.
Extraction and Evaluation of Statistical Information from Social and Behavioral Science Papers

With substantial and continuing increases in the number of published papers across the scientific literature, development of reliable approaches for automated discovery and assessment of published findings is increasingly urgent. Tools which can extract critical information from scientific papers and metadata can support representation and reasoning over existing findings, and offer insights into replicability, robustness and generalizability of specific claims. In this work, we present a pipeline for the extraction of statistical information (p-values, sample size, number of hypotheses tested) from full-text scientific documents. We validate our approach on 300 papers selected from the social and behavioral science literatures, and suggest directions for next steps.
Implementation of Fuzzy Logic in Identification of Calcification in Mammogram Image

Mammogram is a specialized medical imaging tool used for finding the presence of Microcalcification in the women's breast. Microcalcification generally refers to an initial form of breast cancer. A mammogram image helps the doctor to understand the approximate number of calcifications identified in the breast. Many analytical methods have been proposed to identify the extent of MC in the mammogram image. Those analytical methods can help in Computer Aided Detection (CAD) of MC. To encircle the Microcalcification locations, many commercial software have been developed. The proposed system uses Fuzzy logic for segmentation and Gray Level Co-Occurrence Matrix to extract features based on texture for identifying MC from the mammogram images. Comparison of ground truth image with the extracted features is done to find the segmentation accuracy. The accuracy of the algorithm is analyzed by PSNR metric in identifying the calcification in mammogram.
Towards Automatic Content Analysis of Rhetorical Structure in Brazilian College Entrance Essays

Essay scorers manually look for the presence of required rhetorical categories to evaluate coherence, which is a time-consuming task. Several attempts in the literature have been reported to automate the identification of rhetorical categories in essays with machine learning. However, existing machine learning algorithms are mostly trained on content features which can lead to over-fitting and hindering model generalizability. Thus, this paper proposed a set of content-independent features to identify rhetorical categories. The best performing classifier, XGBoost, achieved performance comparable to human annotation and outperformed previous models.