Reviewerly/exHarmony huggingface dataset:
Hiding of Personal Information Areas Through a Dynamic Selection Strategy

Video content with exposed personal information is freely distributed to the general public through the Internet without user approval, which is a problem. In this paper, we propose a method of detecting areas representing personal information from continuous color images through an artificial neural network and hiding the detected target area appropriately for the surrounding environment. The proposed method first applies a color model and deep learning to robustly detect a target area representing personal information exposed from an image. The detected area is then tracked quickly based on position prediction and simultaneously blocked. In this paper, the hiding of the target object is performed by adaptively selecting one of the mosaic processing, image blurring, and virtual object insertion techniques in consideration of surrounding conditions. Experimental results show that the proposed method accurately extracts the personal information area, efficiently tracks the extracted area, and dynamically selects the hiding technique. The method proposed in this paper is expected to be useful in related fields such as object detection, big data analysis, and biometric recognition.
Context Detection and Identification in Multi-Agent Reinforcement Learning on Non-Stationary Environments

Although the real world is non-stationary, most reinforcement learning algorithms are based on the assumption that the environment is stationary. Non-stationary environments are composed of several different stationary components (i.e., sub-environments or contexts). Therefore, algorithms that accept this assumption are incapable of learning within non-stationary environments. Reinforcement Learning - Context Detection (RL-CD) da Silva et al. (2016) method allows agents to learn the environment, detect context change points, and learn the partial model of the context without previous knowledge of the environment. RL-CD is a single-agent algorithm and it has shortcomings in learning within a multi-agent environment. In this work, we introduce a new method called Multi-Agent Reinforcement Learning - Context Detection (MARL-CD), which can both detect context change points and enable agents to learn within non-stationary environments with multi-agent settings. This approach is based on RL-CD and works more effectively in terms of detecting context changes caused by agents and changes caused by the environment’s dynamics. It detects not only the change points originating from the environment, but also the change points created by the agents. Experimentally, agents spend 16% less energy compared to RL-CD and can detect change points more accurately and earlier.
Blockchain Enable Smart Contract Based Telehealth and Medicines System

Healthcare system is one of the most prime concerns for mankind. The pandemic compelled to search new alternative solutions for present healthcare system and results in telehealth. The paper proposed a model for the “Telehealth and Medicine System” based on one of the most powerful transparent, decentralized, and secured technology i.e., “Blockchain”. The basic aim of the proposed system is to provides the best remote healthcare facilities to people speedily and securely. The model also supports for managing the healthcare resources information for controlling the huge load of patients in hospitals. Present centralized system fails to maintain security, confidentiality, functional transparency, health-records immutableness, and traceability of medical apparatus and medicines. The present centralized system is also unable to detect scams correlated to patient’s transaction prerogatives and doctor authorized prescription data. In this paper we explore the possible techniques and opportunities of health care system using blockchain technology in telehealth and medicine area. This paper provides the illustration of blockchain technology which gives essential data safety and confidentiality to this model. Moreover, the paper deals with secure functioning-transparency, health-records immutableness, and traceability of medical apparatus and medicines information. Smart Contract is one of the most popular and secure application of ‘Blockchain’. It is based on public-blockchain framework. Blockchain technology can improve telehealth and medicine system by offering distant healthcare facilities on a decentralized platform along with tamper-proof transactions, transparency of data, flexible traceability of records, reliability to patients, and high security. This model identifies whole scenario of medicine and apparatus production from initial to final stage of delivery to the patient. This model also leads health specialists and doctors for treating patients correctly and share the prescriptions securely over the network. At the last, paper discuss numerous opportunities and techniques needs to be resolute the present system by acceptance of power decentralized technology ‘Blockchain’ in telehealth and health-care systems.
Neural-Network Based Adaptation of Variation Operators’ Parameters for Metaheuristics

The paper presents an idea of training an artificial neural network a relation between different parameters observed for a population in a metaheuristic algorithm. Then such trained network may be used for controlling other algorithms (if the network is trained in such way, that the knowledge gathered by it becomes agnostic regarding the problem). The paper focuses on showing the idea and also provides selected experimental results obtained after applying the proposed algorithm for solving popular benchmark problems in different dimensions.
Disease Classification Using Particle Swarm Optimization with Fuzzy Neural Network

Data mining in health care include analysis, early detection, prevention of diseases, prevention of hospital errors, and for cost savings. The importance of machine learning has drastically increased due to predicting and diagnosing disease at an early stage. Due to slow convergence level and it requires a large amount of calculation for fuzzy neural network algorithm, a particle swarm optimization-based fuzzy neural network is used to solve classification of medical dataset. For this work, three medical datasets such as heart, lymph, and hepatitis are used for prediction of diseases. It is proven that there is a better classification rate while using PSO-based FNN.
No-Reference Stereo Image Quality Assessment Based on Transfer Learning

In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the input image is preprocessed by phase congruency transformation, which further improves the performance of the algorithm. The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers. The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.
Machine-Assisted Proofs for Institutions in Coq

The theory of institutions provides an abstract mathematical framework for specifying logical systems and their semantic relationships. Institutions are based on category theory and have deep roots in a well-developed branch of algebraic specification. However, there are no machine-assisted proofs of correctness for institution-theoretic constructions, making them difficult to incorporate into mainstream formal methods. This paper provides an overview of our approach to formalizing the theory of institutions in the Coq proof assistant. We instantiate this framework with the institutions $$ FOPEQ $$ for first-order predicate logic and $$ EVT $$ for the Event-B specification language.
Analysis of Complexity Reduction in Kalman Filters Through Decoupling Control With Chattered Inputs in PMSM

The purpose of this paper is to investigate the idea that by implementing decoupling control algorithms in Permanent Magnet Synchronous Machine (PMSM) controllers, observers like extended Kalman filters (EKFs) can achieve an advantage in computational load. In particular, the KF is designed as a combined state and parameter estimator. The approach is based on the possibility to consider individual electrical current dynamics, together with key system parameters influencing their dynamics, i.e. their inductances $$L_d$$ , $$L_q$$ , which is enabled by a decoupling control. Even though the decoupled dynamics are linear, the resulting augmented subsystems, including the inductances as estimation variables, are nonlinear. Furthermore, the paper shows that high-frequency inputs, caused, for example, by control chattering, can enable the EKF-based estimation of the inductances by providing for a never-ending transient phase in the case of the decoupled variant. The computational advantage is ultimately achieved by reducing the complexity of the Kalman filters by around 75%. The results are validated using computer simulations of coupled as well as decoupled control systems, also demonstrating the benefits of chattery input voltages.
An Eye Tracking Analysis of Conversational Violations in Dyadic and Collaborative Interaction

Linguistic principles are crucial in maintaining reliable and transparent communication for dyadic interactions. However, violating these principles might result in unwieldy and problematic communications. We use gaze as a medium to explore how visual attention and task performance changes when conversational violations occur. We conducted an eye-tracking study (N = 17) measuring changes in visual patterns in response to social communication errors, specifically Grice’s Maxims violations. Our study investigates how social-communicative errors influence task performance and gaze during dyadic and collaborative social interactions. The results suggest participants’ visual exploration patterns shift towards the violator when the maxim of Relation is violated in a task instruction. Gaze stays mainly within the task area after receiving instructions with Quantity, Quality, and Manner violations. Moreover, it takes longer to respond to task instructions that include Quantity and Quality violations, than the Manner and Relation violations. Finally, our qualitative analysis revealed participants’ adaptive and non-adaptive strategies in response to the Quality violation. Our findings contribute to the design space of human performance in dyadic and collaborative interactions, with future work implications exploring human performance in joint system-human interactions.
Checkerboard Dropout: A Structured Dropout With Checkerboard Pattern for Convolutional Neural Networks

Dropout is adopted in many state-of-the-art Deep Neural Networks (DNNs) to ease the overfitting problem by randomly removing features from feature maps. However, previous studies show the limitations of dropout application to Convolutional Neural Networks (CNNs) due to an increase in spatial correlation of the zeroed-out values in the output feature maps, which limits the generalization and performance of the network. Recently, DropBlock has been successfully applied as an efficient structured dropout to mitigate the spatial correlation problem by dropping a continuous region and to allay the randomness of the conventional dropout. However, DropBlock does not completely remove the randomness. The reason is because DropBlock still randomly chooses the center points to generate regions to drops. This paper proposes a novel method, Checkerboard Dropout, to handle the randomness of the conventional dropout as well as DropBlock and to further reduce the spatial correlation between the zeroed-out values in output feature maps. The proposed method is evaluated with large-scale image classification and object detection tasks. Experimental results show that the proposed Checkerboard Dropout improves the top-1 accuracy by 2.17% for a baseline of ResNet-50 on ImageNet dataset, 1.44% AP for a baseline of RetinaNet with ResNet-50 + FPN backbone and outperformed the previous approach - DropBlock - 0.3% top-1 accuracy for ImageNet dataset and 0.64% AP for MS COCO dataset. Furthermore, the proposed Checkerboard Dropout also improves the model generalization, localization and segmentation capability toward complex objects through Grad-CAM and instance segmentation visualizations.
Effective Detection and Localization of the Text in Natural Scene Images Using Adaptive Kuwahara Filter

The text matter present in the natural scene images imbibes facts and statistics about the concerned image. The text detected and located in the scene images finds applications in many domains like content retrieval, tourist navigation, etc. The real challenges in this domain are the surrounding elements and other noisy backgrounds present in the images. This paper attempts to detect and localize the text with the help of novel method that involves preprocessing the images using the adaptive Kuwahara filter and MSERs. Classifying the components in text and non-text is achieved with the assistance of classifiers using the MATLAB. Further, the texts are grouped into words with the help of the K-means clustering algorithm. The bounding box constructed for the localization of the text is evaluated with the DetEval tool. The experiments are performed using the training dataset of ICDAR 2013 and the testing dataset of the ICDAR 2011. The results obtained are better than the other state of the arts, concerning the F-measure, recall, and precision.
Image imputation in cardiac MRI and quality assessment

Missing data is common in medical image research. For instance, corrupted or unusable slices owing to the presence of artifacts such as respiratory or motion ghosting, aliasing, and signal loss in images significantly reduce image quality and diagnostic accuracy. Also, medical image acquisition time is often limited by cost and physical or patient care constraints, resulting in highly under-sampled images, which can be formulated as missing in-between slices. Such clinically acquired scans violate underlying assumptions of many downstream algorithms. Another important application lies in multi-modal/multi-contrast imaging, where different medical images contain complementary information for improving the diagnosis. However, a complete set of different images is often difficult to obtain. All of these can be considered as missing image data, which can lead to a reduced statistical power and potentially biased results, if not handled appropriately. Thanks to the recent advances in deep neural networks and generative adversarial networks (GANs), the problem of missing image imputation can be viewed as an image synthesis problem, and its performance has been remarkably improved. In this chapter, we present cardiac MR imaging as a use case and investigate a robust approach, namely Image Imputation Generative Adversarial Network (I2-GAN), and compare it with several traditional and state-of-the-art image imputation techniques in context of missing slices.
Next Word Prediction Using Hindi Language

Natural language generation is a process that concerns on generating human understandable language. This study provides method to guess next word from previous sequence of words that are in Hindi language. This process reduces the keystrokes of a user by predicting next word. In this two machine learning technology, BERT (Bidirectional Encoder Representations from Transformers) Model and ML (Masked Language) model is used to predict next word from previous words. Next word prediction is a technology that takes input and simplify typing process as of suggest user the next word according to the understanding of previous words. It makes typing less time consuming and error free. In current times, everyone is writing data digitally and to make typing efficient in Hindi language we require such systems. There are various systems related to English Language. Similar systems are required for Hindi language as Hindi is a more frequent language in India.
Supporting Product Development by a Trend Analysis Tool Applying Aspect-Based Sentiment Detection

Incorporating product trends into innovation processes is imperative for companies to meet customers’ expectations and to stay competitive in fiercely opposing markets. Currently, aspect-based sentiment analysis has proven an effective approach for investigating and tracking towards products and corresponding features from social media. However, existing trend analysis tools on the market that offer aspect-based sentiment analysis capabilities, do not meet the requirements regarding the use case Product Development. Therefore, based on these requirements, we implemented an artifact by following the design science research. We applied our tool to real-world social media data (37,638 Yelp reviews) from one major fast-food restaurant in the US, and thereby demonstrated that our tool is capable of identifying remarkable and fine-grained product trends.
Status of Political Cartoons in Indian Society: A Human Commination Perspective

India and Indian society and people change here lots during last 10–15 years. As the influence of digitization People changes lot, there habits also change, print media is decreasing value day by day. Unknowingly we are surrounded by technology. This study aims to unfold the status of political cartoon based on available evidences from online and printed resources such as, articles, review articles, reports, books, news etc. This research work has successfully covered the characteristics of political cartoons in print and electronic media. Where Importance of political cartoon has changed cartoonist developed technical skills. Authors of this paper analyses the human factors issues related to political cartoons to assist scholars to discover novel concepts in evolving fields of Indian political cartoon.
Training Thinner and Deeper Neural Networks: Jumpstart Regularization

Neural networks are more expressive when they have multiple layers. In turn, conventional training methods are only successful if the depth does not lead to numerical issues such as exploding or vanishing gradients, which occur less frequently when the layers are sufficiently wide. However, increasing width to attain greater depth entails the use of heavier computational resources and leads to overparameterized models. These subsequent issues have been partially addressed by model compression methods such as quantization and pruning, some of which relying on normalization-based regularization of the loss function to make the effect of most parameters negligible. In this work, we propose instead to use regularization for preventing neurons from dying or becoming linear, a technique which we denote as jumpstart regularization. In comparison to conventional training, we obtain neural networks that are thinner, deeper, and—most importantly—more parameter-efficient.
Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms

Feature extraction and selection are very important stages in pattern recognition and computer vision solutions with far-reaching effects on their performance. In computer-aided diagnosis (CADx) systems, efficiency is affected by its subjectivity to the accuracy of the region of interest (ROI) extraction technique, which is largely dependent on the features extracted. Optimization algorithms are often used to improve the selection of discriminative features which thereby leads to improve accuracy of the CADx systems. This work considers the effects of optimizing selected features in the performance of breast tissue characterization in mammograms. It uses Whale Optimization Algorithm (WOA) to optimize Otsu fitness function of Gray Level Co-occurrence Matrix (GLCM) in extracting the region of interest (ROI). The extracted features were classified into BIRADS scales 1, 2 and 5 using Multiclass Support Vector Machine (MSVM). The performance of the developed algorithm was evaluated using specificity, sensitivity as well as accuracy and compared with other techniques namely Texture Signature (TS), Pixel-Based Morphological (PBM), Natural Language Processing (NLP), and Interactive Data Language (IDL). The result of the developed WOA-Otsu-GLCM-MSVM CADx algorithm for specificity, sensitivity, and accuracy are 96%, 92% and 94%, respectively. The developed algorithm gave an accuracy of 94.4% as against 81.0%, 85.7%, 93.0% and 82.5% for TS, PBM, NLP and IDL methods, respectively. The characterization of the breast tumour using the developed CADx algorithm performed better compared with the conventional methods.
Technical Communicators’ Use of and Requirements for Special Language Reference Tools

Background: Technical communicators use special language information to describe technology products. Researching such information is part and parcel of their job and thus occupies a relevant share of their working time. Literature review: Numerous studies examine information needs and search techniques of various professionals, such as engineers or translators. However, very little is known about technical communicators’ use of and requirements for information sources containing special language information. This article contributes to filling this research gap by discussing results of an empirical study. Research questions: 1. What types of nonhuman information sources do technical communicators use when researching special language information? 2. What properties do technical communicators expect from special language reference tools? Research methodology: We conducted a written online survey among technical communicators. In this article, we analyze and interpret survey data related to the two research questions. Results: Respondents use 14 major types of information sources for researching special language information. Half can be categorized as reference tools, while the other half are document-like. Respondents would like to have special language reference tools that are available electronically, can be adapted to their personal needs, and offer up-to-date information with good usability. Conclusions: Half of the information source types are document-like and can be used as text corpora. Thus, text corpus-management methods and tools should be promoted in technical communication practice and teaching. Technical communicators’ requirements and wishes described in this article lay the groundwork for developing tailor-made special language reference tools.