Reviewerly/exHarmony huggingface dataset:
A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms

Nowadays a lot of deep learning algorithm has been developing for data analytics on both structured and unstructured data. There is a big challenge for biomedical image such as CT image, MRI, X-ray for automatic detection of some deadly diseases. A field of advance deep learning that made available a plethora of architecture as increase the dimension and complexity of the mammogram images has been focused, in this study. Removal of noise is a crucial part for better visibility of noisy mammograms and hence a deep learning method for degrading mammogram restoration scheme has been suggested in this chapter. A deep convolutional denoising autoencoder method based on total variational multi-norm loss function minimization approach has been introduced for the restoration of mammograms. The proposed scheme is utilized to restore the perceptible structural details of mammograms as well as decrease the noise level. Moreover, processing speed for target noisy images is faster than other methods after fine tuning the network. The proposed method has been validated by several state-of-the-art methods.
Fine Grained Sentiment Analysis of Malayalam Tweets Using Lexicon Based and Machine Learning Based Approaches

Fine-Grained Sentiment Analysis (FGSA) of Malayalam Tweets have been implemented in this work. The tweets are classified into positive, strongly positive, negative, strongly negative, and neutral sentiments. Both lexicon-based and machine learning-based approaches are used for sentiment classification of Malayalam Tweets. Lexicon based approach uses both dictionary-based and corpus-based approach. The dictionary-based approach is used in this work. The machine learning algorithms such as Support Vector Machine (SVM) and Random Forest (RF) classifiers are used for sentiment classification of the dataset. Bag of Words (BoW), Term-Frequency vs. Inverse Document Frequency (TF-IDF), and Sentiwordnet feature matrices are used to vectorize the input dataset. Lexicon based approach got an accuracy of 84.8%. In machine learning algorithms, the SVM (kernel = linear), SVM (kernel = RBF) and RF with the Sentiwordnet feature vector got an accuracy of 92.6%, 92.9%, and 93.4%, respectively.
Natural Language to Python Source Code using Transformers

Writing code using natural language is a very exciting application of Neural Machine Translation. To achieve a small part of such an application, in this paper, we try to generate python source code snippets from natural English language descriptions using the Django dataset. We trained the self-attention based transformer architecture on the snippets from the dataset. We achieved a BLEU score of 64.29
Analysis The Opinion of School-from-Home during The COVID-19 Pandemic using LSTM Approach

The purpose of opinion analysis in this research is to perceive public responses concerning School-From-Home (SFH) policy during the pandemic in attempt to curb virus spread and worry about new cluster emergences. The policy entails diverse reactions from the societies, including the citizens in virtual world through their chirps in social media, such as Twitter. Analysis on the social media has proved that it has remarkable potentials to apprehend public opinions on various issues. The opinion analysis was performed to get insights about public perception towards SFH policy. As initially predicted, the result of our analysis would show that the public perceptions towards SFH would be mainly negative. The researcher adopted LSTM model as a deep learning approach. Moreover, implementing the N-Gram extraction technique was able to improve the model’s performance. Model performance accuracy reached 83.30%. It is concluded that the increasing of model accuracy is about 0.018%. While the running time efficiency of LSTM has improved 19.4%. The results of the analysis of SFH’s opinion were 77.90% negative and 22.10% positive.
An Effectual Optical Character Recognition Using Efficient Learning System

There is an emerging attention for the various automatic classification frameworks to distinguish characters in software structure when information is looked over piece of paper records as it is noticed that the amount of daily identifications and records which are in published manner recognized with various businesses. This is popularly known as the document image exploration. To utilize Optical Character Recognition efficiently for character categorization so as to achieve image analysis, the data is utilizing in Grid assemblies. For high processing of documents, the heavy industries require a product as the framework which is known as character recognition classification. Also, there is need to create character recognition programming agenda to achieve document analysis in the form of image processing which deals with the organization of the material to the electronic reading arrangement. So this paper deals with the efficient learning approach which deals with the automatic classification from using processing of the images and machine learning for the optical character recognition process. In this research we have worked on the feature extraction using Independent component analysis with the swarm intelligence approach with Firefly algorithm, because it helps us to reduce more error probabilities and reduces the false positive and negative rates and increase the high learning rate. The learning is achieved using neural network. It is noticed that the proposed approach is able to perform high in terms of high specificity, sensitivity and recognition rate through which it is noticed that the proposed approach is able to achieve high true positive and true negative rates.
Comparing SARS-CoV-2 Sequences using a Commercial Cloud with a Spot Instance Based Dynamic Scheduler

There has been an increasing interest in running High Performance Computing (HPC) applications in the cloud, mainly due to rapid resource provisioning and significant reduction of operational costs. Biological sequence comparison is an important HPC application that compares sequences in search of similarities. MASA-OpenMP is a highly optimized sequence comparison tool that obtains optimal results. Yet, it can take a long time, depending on the number of sequences compared and their lengths. The Covid-19 pandemic study is of particular interest nowadays, and the comparison of SARS-CoV-2 sequences is crucial to understanding this disease. In this paper, we compare SARS-CoV-2 sequences with MASA-OpenMP in the Amazon Elastic Compute Cloud (Amazon EC2), using both spot and on-demand instances. To efficiently execute a MASA-OpenMP application composed of more than 22,000 tasks on EC2 respecting a given deadline, we propose an execution modeling for MASA-OpenMP on top of the Burst-HADS framework. Burst-HADS is a spot instance-based dynamic scheduler for Bag-of-Tasks applications in the cloud, which minimizes both execution time and financial costs regarding a given deadline even in the presence of spot interruptions. Performance results reveal that, by using spots, our Burst-HADS strategy considerably reduces the monetary cost for executing 22,600 SARS-CoV-2 sequence comparisons with MASA-OpenMP when contrasted to the on-demand only approach. We also show that our strategy can meet the deadlines, even in scenarios with several spot interruptions.
A Deep Neural Approach Toward Staining and Tinting of Monochrome Images

The paper presents a system that colorizes monochrome images driven by deep neural networks without any additional supportive aid. Throughout the paper, we explore feature engineering, transfer learning, color spaces and color channels, and various architectural neural frameworks. Eventually, stained and tinted colorized images or image sequences (video) have been derived from monochrome images that are more aesthetically pleasing, information rich, and more vivid with higher traceability. The experimental results illustrae the viability of our methodology and divulge a favorable route for future work.
Curvature and Entropy Statistics-Based Blind Multi-Exposure Fusion Image Quality Assessment

The multi-exposure fusion (MEF) technique provides humans a new opportunity for natural scene representation, and the related quality assessment issues are urgent to be considered for validating the effectiveness of these techniques. In this paper, a curvature and entropy statistics-based blind MEF image quality assessment (CE-BMIQA) method is proposed to perceive the quality degradation objectively. The transformation process from multiple images with different exposure levels to the final MEF image leads to the loss of structure and detail information, so that the related curvature statistics features and entropy statistics features are utilized to portray the above distortion presentation. The former features are extracted from the histogram statistics of surface type map calculated by mean curvature and Gaussian curvature of MEF image. Moreover, contrast energy weighting is attached to consider the contrast variation of the MEF image. The latter features refer to spatial entropy and spectral entropy. All extracted features based on a multi-scale scheme are aggregated by training the quality regression model via random forest. Since the MEF image and its feature representation are spatially symmetric in physics, the final prediction quality is symmetric to and representative of the image distortion. Experimental results on a public MEF image database demonstrate that the proposed CE-BMIQA method achieves more outstanding performance than the state-of-the-art blind image quality assessment ones.
Decamouflage: A Framework to Detect Image-Scaling Attacks on CNN

Image-scaling is a typical operation that processes the input image before feeding it into convolutional neural network models. However, it is vulnerable to the newly revealed image-scaling attack. This work presents an image-scaling attack detection framework, Decamouflage, consisting of three independent detection methods: scaling, filtering, and steganalysis, to detect the attack through examining distinct image characteristics. Decamouflage has a pre-determined detection threshold that is generic. More precisely, as we have validated, the threshold determined from one dataset is also applicable to other different datasets. Extensive experiments show that Decamouflage achieves detection accuracy of 99.9% and 98.5% in the white-box and the black-box settings, respectively. We also measured its running time overhead on a PC with an Intel i5 CPU and 8GB RAM. The experimental results show that image-scaling attacks can be detected in milliseconds. Moreover, Decamouflage is highly robust against adaptive image-scaling attacks (e.g., attack image size variances).
Learning sufficient scene representation for unsupervised cross-modal retrieval

In this paper, a novel unsupervised Cross-Modal retrieval method via Sufficient Scene Representation (CMSSR) is proposed. Distinguished from the existing methods which mainly focus on simultaneously preserving the mutually-constrained intra- and inter-modal similarity relation, CMSSR considers data of different modalities as the descriptions of a scene from different views and accordingly integrates information of different modalities to learn a complete common representation containing sufficient information of the corresponding scene. To obtain such common representation, Gaussian Mixture Model (GMM) is firstly utilized to generate statistic representation of each uni-modal data, while the uni-modal spaces are accordingly abstracted as uni-modal statistical manifolds. In addition, the common space is assumed to be a high-dimensional statistical manifold with different uni-modal statistical manifolds as its sub-manifolds. In order to generate sufficient scene representation from uni-modal data, a representation completion strategy based on logistic regression is proposed to effectively complete the missing representation of another modality. Then, the similarity between different multi-modal data can be more accurately reflected by the distance metric in common statistical manifold. Based on the distance metric in common statistical manifold, Iterative Quantization is utilized to further generate binary code for fast cross-modal retrieval. Extensive experiments on three standard benchmark datasets fully demonstrate the superiority of CMSSR compared with several state-of-the-art methods.
A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Explainable Predictive Maintenance

The role of maintenance in modern manufacturing systems is becoming a more significant contributor to organizational benefit. World-class enterprises are pushing forward with “predict-and prevent” maintenance instead of embracing the drawbacks of reactive maintenance (or a “fail-and fix” approach). The advancement towards Artificial Intelligence (AI), Internet of Things (IoT) and cloud computing has led to a shift in maintenance paradigms with the rising interest in Machine Learning (ML) and in particular deep learning. However, opaque box AI models are complex and difficult to understand and explain to the lay user. This limits the use of these models in predictive maintenance where it is crucial to understand and analyze the model before deployment and it is imperative to understand the logic behind any given decision. This paper introduces a Type-2 Fuzzy Logic System (FLS) optimized by the Big-Bang Big-Crunch algorithm that allows maximizing the interpretability of a model as well as its prediction accuracy for the faults which may occur in future. We tested the proposed type-2 FLS model on water pumps where data was collected in real-time by our proprietary hardware deployed at Aquatronic Group Management Plc. The observations indicate that the proposed system provides a highly interpretable and accurate model for predicting the faults in equipment for building services, process and water industries. The system predictions are used to understand why a particular fault may occur, leading to improved and better-informed service visits for the customers thus reducing the disruptions faced due to equipment failures.
Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval

Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising solution in this context, by building links in the data without relying on human supervision. However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. Increasing generalization and robustness to variations remains an open challenge, poorly understood in the context of real-world applications. Introducing the alegoria benchmark, containing multi-date vertical and oblique aerial digitized photography mixed with more modern street-level pictures, we formulate the problem of low-data, heterogeneous image retrieval, and propose associated evaluation setups and measures. We propose a review of ideas and methods to tackle this problem, extensively compare state-of-the-art descriptors and propose a new multi-descriptor diffusion method to exploit their comparative strengths. Our experiments highlight the benefits of combining descriptors and the compromise between absolute and cross-domain performance.
Spin-boson model of quantum dissipation in graphene: Nonlinear electrical response

Dissipation in the quantum solid of graphene is couched in the methodology of nonequilibrium statistical mechanics of an open quantum system. Of specific interest is the absorption accompanied by relaxation of energy because of an external frequency-dependent oscillatory electric field. At optical frequencies and for sizable amplitude of the electric field, the response is nonlinear, the treatment of which is simplified by means of a ``rotating wave approximation'' in which rapidly changing, off-resonance terms are omitted. The characteristic resonant frequency is what quantifies the tunneling between the valence and the conduction bands across the so-called Dirac point in graphene. The valence and the conduction states are mapped into the eigenstates of pseudo Pauli spin operators and the corresponding Hamiltonian, when embedded in a dissipative heat bath comprising the surrounding phonons and other electrons, makes possible a comprehensive analysis in terms of the much-studied spin-boson model of dissipative quantum statistical mechanics. A master equation for the density operator associated with this Hamiltonian yields rate equations for the mean population of the valence and the conduction states as well as the transition (matrix elements) between them. Further approximation of these rate equations allows contact with phenomenological treatments of the nonlinear optical conductivity. The present paper then provides a microscopic framework for investigating the response characteristics of a material of great topical interest using contemporary methods of quantum dissipation.
Intelligent Communication: Application of Deep Learning at the Physical Layer of Communication

The application of deep learning in the communication physical layer is the mainstream direction of future communication development. Compared with traditional communication methods, intelligent communication has initially shown its unique advantages.This paper studies the application of deep learning in communication physical layer.Firstly, the research and development of deep neural network, cyclic neural network and convolutional neural network are introduced in combination with the latest research achievements in academic circles.Secondly, the advantages and disadvantages of intelligent communication methods in channel estimation and signal detection, channel coding and decoding, and end-to-end communication are compared and summarized.Finally, the development direction of deep learning in the communication physical layer is predicted.
Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. In this article, we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and coverage of environment assumptions and demonstrate the flexibility of our approach in prioritizing either of these criteria. We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.
Examining academic libraries’ use of instagram

The use of social networking sites has been significant among academic libraries. Although various studies have investigated the use of social media platforms, studies that analyze the use of Instagram by academic libraries are still limited. This study investigates and analyzes academic libraries’ Instagram posts and presents a categorization scheme to group these posts. The Cronbach α was also used as a measure to test the reliability of the categorization scheme proposed in this study. Results showed that posts of library news and information were the largest category at 29%.
The Economics of Blockchain-Based Supply Chain Traceability in Developing Countries

Shirking, cheating, and other misbehaviors are pervasive in many developing countries, increasing the costs of economic exchanges. Blockchain-based solutions may address these problems and help developing world-based economic actors to engage in exchange relationships.
A Method of Offline Reinforcement Learning Virtual Reality Satellite Attitude Control Based on Generative Adversarial Network

Virtual reality satellites give people an immersive experience of exploring space. The intelligent attitude control method using reinforcement learning to achieve multiaxis synchronous control is one of the important tasks of virtual reality satellites. In real-world systems, methods based on reinforcement learning face safety issues during exploration, unknown actuator delays, and noise in the raw sensor data. To improve the sample efficiency and avoid safety issues during exploration, this paper proposes a new offline reinforcement learning method to make full use of samples. This method learns a policy set with imitation learning and a policy selector using a generative adversarial network (GAN). The performance of the proposed method was verified in a real-world system (reaction-wheel-based inverted pendulum). The results showed that the agent trained with our method reached and maintained a stable goal state in 10,000 steps, whereas the behavior cloning method only remained stable for 500 steps.
Synthesising Facial Macro- and Micro-Expressions Using Reference Guided Style Transfer

Long video datasets of facial macro- and micro-expressions remains in strong demand with the current dominance of data-hungry deep learning methods. There are limited methods of generating long videos which contain micro-expressions. Moreover, there is a lack of performance metrics to quantify the generated data. To address the research gaps, we introduce a new approach to generate synthetic long videos and recommend assessment methods to inspect dataset quality. For synthetic long video generation, we use the state-of-the-art generative adversarial network style transfer method-StarGANv2. Using StarGANv2 pre-trained on the CelebA dataset, we transfer the style of a reference image from SAMM long videos (a facial micro- and macro-expression long video dataset) onto a source image of the FFHQ dataset to generate a synthetic dataset (SAMM-SYNTH). We evaluate SAMM-SYNTH by conducting an analysis based on the facial action units detected by OpenFace. For quantitative measurement, our findings show high correlation on two Action Units (AUs), i.e., AU12 and AU6, of the original and synthetic data with a Pearson's correlation of 0.74 and 0.72, respectively. This is further supported by evaluation method proposed by OpenFace on those AUs, which also have high scores of 0.85 and 0.59. Additionally, optical flow is used to visually compare the original facial movements and the transferred facial movements. With this article, we publish our dataset to enable future research and to increase the data pool of micro-expressions research, especially in the spotting task.