Reviewerly/exHarmony huggingface dataset:
JPEG Artifact Reduction Based on Deformable Offset Gating Network Controlled by a Variational Autoencoder

For the reduction of JPEG compression artifacts, there have been many methods using deep neural networks. Most of them use the JPEG compression quality factor (QF) as prior knowledge in designing and training the networks. However, since the images we get from the Internet are often recompressed, the given QF is not so informative or misleading. Also, early works validated their methods on low QFs less than 50, while recent smartphones use high QFs larger than or equal to 90. In this paper, we propose a new JPEG artifacts reduction network considering the above-stated problems. Specifically, to extract quality information from the input image itself instead of the QF provided in the header of the JPEG file, we use a variational autoencoder (VAE) and regard its latent vector as quality information. In designing the artifact reduction network, we let the network change flexibly according to the input image quality by employing a deformable offset gating (DOG) network. The gating network and VAE are merged as our overall network, dubbed DOG-VAE, where the information from the VAE is used to adjust the DOG network according to the input quality. The DOG-VAE is trained end-to-end with the QFs in the range of [10,90]. Extensive experiments validate that our method achieves comparable results to the state-of-the-art method for monochrome images and better results for color images. Our codes are available at https://github.com/yunjh410/DOGNet.
A Hybrid Method for Implicit Intention Inference Based on Punished-Weighted Naïve Bayes

Gaze-based implicit intention inference provides a new human-robot interaction for people with disabilities to accomplish activities of daily living independently. Existing gaze-based intention inference is mainly implemented by the data-driven method without prior object information in intention expression, which yields low inference accuracy. Aiming to improve the inference accuracy, we propose a gaze-based hybrid method by integrating model-driven and data-driven intention inference tailored to disability applications. Specifically, intention is considered as the combination of verbs and nouns. The objects corresponding to the nouns are regarded as intention-interpreting objects and served as prior knowledge, i.e., punished factors. The punished factor considers the object information, i.e., the priority in object selection. Class-specific attribute weighted naïve Bayes model learned through training data is presented to represent the relationship among intentions and objects. An intention inference engine is developed by combining the human prior knowledge, and the data-driven class-specific attribute weighted naïve Bayes model. Computer simulations: (i) verify the contribution of each critical component of the proposed model, (ii) evaluate the inference accuracy of the proposed model, and (iii) show that the proposed method is superior to state-of-the-art intention inference methods in terms of accuracy.
Multi-feature Integration with Adaptive Learning Based Correlation Filter for Visual Object Tracking

Correlation Filter (CF) based tracking is the most imperative part of computer vision and offers many potential benefits. To get maximum benefits, object trackers need to provide better accuracy in presence of visually challenging scenarios with less computational burden. Therefore, this research aims to develop a robust object tracker to deal with target variations in a real-time environment. At first, the multi-feature descriptor is implemented using the feature fusion technique which combines the response of Histogram of gradient (HOG), saliency, gray level intensities, and Color Naming (CN) features. Afterward, an adaptive learning strategy is integrated by utilizing the Peak-to-Sidelobe Ratio (PSR) to evaluate correlation peaks. The quality of the proposed methodology is validated on challenging datasets. Tracking results reveal that the proposed scheme outperforms the other advanced CF trackers with Distant Precision (DP) scores of 88.2%, 85.9%, and 74.1 % over OTB2013, OTB2015, and TempleColor128 datasets respectively.
Comparative Analysis of Deep Learning Methods in the Realm of Sentiment Analysis

Recent advances in deep learning have suggested number of methods which can be employed in several domains. Text classification is one of the most common natural language processing tasks and have given relevant results at the level of text classification to perform sentiment analysis. This paper compares the efficacy of different algorithms used to perform sentiment analysis. The comparison offers a global vision to contribute to a relevant system that can evaluate the different types of sentiment analysis by a Corpus (restaurant reviews). In our study we have used word embedding techniques to compare the efficacy of the simple RNN., LSTM., and BERT neural networks in the context of sentiment analysis. This research indicates that the use of BERT and LSTM yields the better outcomes., although BERT requires a longer training period.
Explainable YouTube Video Identification Using Sufficient Input Subsets

Neural network models are black boxes in nature. The mechanics behind these black boxes are practically unexplainable. Having the insight into patterns identified by these algorithms can help unravel important properties of the subject in query. These artificial intelligence based algorithms are used in every domain for prediction. This research focuses on patterns formed in network traffic that can be leveraged to identify videos streaming over the network. The proposed work uses a sufficient input subset (SIS) model on two separate video identification techniques to understand and explain the patterns detected by the techniques. The first technique creates the fingerprints of videos on a period-based algorithm to handle variable bitrate inconsistencies. These fingerprints are passed to a convolutional Neural Network (CNN) for pattern recognition. The second technique is based on traffic pattern plot identification that creates a graph of packet size with respect to time for each stream before passing that to a CNN as an image. For model explainability, a sufficient input subset (SIS) model is used to identify features that are sufficient to reach the same prediction under a certain threshold of confidence by the model. The generated SIS of each input sample is clustered using DBSCAN, K-Means, and cosine-based Hierarchical clustering. The clustered SIS highlight the common patterns for each class. The SIS patterns learnt by each model of three individual videos are discussed. Furthermore, these patterns are used to investigate misclassification and provide a rationale behind it to justify the working of the classifier model.
Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model’s input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead. Code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Jess-jpg-txt/DAE_for_adv_attacks_in_MIMO</uri> .
Fuben-Eki: Japanese for “Benefit of Inconvenience” that Illustrates a New Value Orthogonal to Efficiency and Functionality

Fuben-eki is Japanese. Fuben means inconvenience and Eki means benefits. Fuben-eki means benefits that are gained only by inconvenience. Pursuing benefits employing not convenience but inconvenience sounds like one of the old Buddhist teachings, but in fact, it is relatively new. Providing Fuben-eki to users is one of the principles of system design, especially human–machine systems. This principle is effective in several design fields. They are not just confined to designing physical systems, e.g., product design but spread to service and business designs. As the general introduction of this book, this chapter introduced a definition of Fuben-eki (benefit of inconvenience: BoI) and discussed the common properties of design fields that are effective to introduce Fuben-eki. The property was elucidated as having a value axis orthogonal to efficiency and functionality. In the second half of this chapter, design fields were introduced. They contribute commentary and case studies to this book. The relationships between these fields and Fuben-eki were explained in short. They will help readers of this book to find an overlap between their interests and this book.
Hölder Divergence-Based Reward Function for Poisson RFSs and Application to Multitarget Sensor Management

In this study, we propose a novel information theoretic reward function based on the statistical Hölder divergence (HD). The Hölder divergence is the generalization of Cauchy–Schwarz divergence (CSD). We extended Hölder divergence to the finite set statistics (FISST) densities, thus making it possible to use in the multiobject applications based on the random finite set (RFS) theory. We derive the analytic expressions for the extended Hölder divergence (EHD) for the case when the multitarget densities have the form of Poisson RFSs and apply it to the probability hypothesis density (PHD) filter in a sequential Monte Carlo (SMC) implementation. We evaluated the performance of the proposed reward function in a multitarget sensor management problem where the next position of a moving observer is decided according to the value of the EHD-based reward function. The performance of the algorithm is compared against, in terms of the optimal subpattern assignment (OSPA) metric, and it is shown that the proposed reward function is superior to other similar reward functions in multitarget sensor management literature. We also show that it is possible to adapt the management algorithm to different situations, such as static and dynamic environments.
Ideation Support for Designing Systems with Benefits of Inconvenience

For creating new products and services, one of the traditional design policies is thinking of it enough to provide convenience to users. On the other hand, a set of popular designs has revealed that the policy is no longer valid. Alternative policies implicitly exist. Among them, this chapter focused on “providing users benefits of inconvenience (BoI).” For designing new systems under such policy, this chapter summarized four methods of thinking derived from many years of design practice. The four types of methods can correspond to four transitions of the quadrants on the 2D plane that are defined by two axes, i.e., the axis of convenience/inconvenience and the axis of benefit/harm. The four types are named according to their characteristics, i.e., Value Mining type, Problem-solving type, Emergence type, and Innovation of Meaning type. This chapter also introduced two tools that support these types of thinking. The one is BoI cards that show how to make things inconvenient and which kind of benefits will be derived from those inconveniences. The other is the necessary conditions for judging systems that provide users BoI.
Lossy Compression of Noisy Data for Private and Data-Efficient Learning

Storage-efficient privacy-preserving learning is crucial due to increasing amounts of sensitive user data required for modern learning tasks. We propose a framework for reducing the storage cost of user data while at the same time providing privacy guarantees, without essential loss in the utility of the data for learning. Our method comprises noise injection followed by lossy compression. We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data as the sample size of the training data (or the dimension of the training data) increases. In this sense, the utility of the data for learning is essentially maintained, while reducing storage and privacy leakage by quantifiable amounts. We present experimental results on the CelebA dataset for gender classification and find that our suggested pipeline delivers in practice on the promise of the theory: the individuals in the images are unrecognizable (or less recognizable, depending on the noise level), overall storage of the data is substantially reduced, with no essential loss (and in some cases a slight boost) to the classification accuracy. As an added bonus, our experiments suggest that our method yields a substantial boost to robustness in the face of adversarial test data.
A Real-time Sequence Based Human Activity Detection System

During the last decade, human activity detection is increasingly attracting the attention of researchers, due to its numerous applications, such as in smart and automated shopping malls, hospitals, etc. Particularly, detecting human activity has been a challenge for researchers because complex situations may arise such as background clutter or changing illumination. To solve this issue, video segment classification cannot be tackled just as object identification. Therefore, it becomes inevitable to employ sequence-based techniques for video classification. In this paper, a Convolution Neural Network (CNN) is used in conjunction with Long Short-Term Memory (LSTM) to accomplish real-time human activity detection. In the proposed method, CNN serves as a spatial information detection algorithm from video while LSTM helps in the sequential tracking of identified objects quickly and accurately. This CNN-LSTM approach reduces the complexity of the model while also enhancing its accuracy along with enabling its real-time execution. Finally, a Raspberry Pi that functions as a standalone system is utilized for the implementation of the proposed CNN-LSTM approach. The results are presented and analyzed to solidify that the proposed standalone system can detect and classify events for real-time surveillance.
Digital transformation of the Business Registers Agency in the function of the modern digital society

Digitalization strongly affects the economy and society as a whole in different dimensions. The process of digitalization of the Business Registers Agency, including the registration procedure of business entities, is one of the priority tasks aimed at increasing efficiency and economy as well as economic prosperity in general. Certainly, there is a prerequisite referring to the existence of digital literacy, which is not an isolated category, but rather a superstructure of earlier forms of literacy. It is undeniable that every innovation arises as a privilege of the higher social classes, which, by inertia, turns into a need of the others. In this paper, it has been analyzed the legislative framework of electronic registration of business entities. Starting from the point of view that economic growth, competitiveness on the market, and socio-economic development represent the nowadays necessity, the authors, through this paper, primarily try to define the achieved progress of the work of the Business Registers Agency (BRA) in registering business entities in the digitalization process. The quintessence of this work is the identification of the availability of digital tools for the registration of economic entities, as well as their quantification and qualification.
$$H^{\infty }$$ Control and Fault-Tolerant Control of Quantum Systems

This chapter presents several results on $$H^\infty $$ control and fault-tolerant control of quantum systems. We present some results on $$H^{\infty }$$ control for a class of linear quantum systems in Sect. 8.2 and further consider robust $$H^{\infty }$$ control for uncertain linear quantum systems in Sect. 8.3 $$^\text {a}$$ . In Sect. 8.4, a time-varying fault-tolerant coherent $$H^\infty $$ control problem is solved for a class of linear quantum systems subject to Markovian jump faults $$^\text {b}$$ . In Sect. 8.5, an estimator-based approach is presented for fault-tolerant control of measurement-based quantum feedback systems $$^\text {c}$$ .
Introduction to Quantum Mechanics and Quantum Control

This chapter presents a brief introduction to the quantum mechanics postulates and several classes of quantum systems, and outlines quantum control models and various quantum control design methods including quantum optimal control, quantum Lyapunov control, measurement-based feedback controlMeasurement-based feedback control, coherent feedback control, quantum learning control and quantum robust control.
Introduction

This chapter presents a brief introduction to quantum technologies and quantum control. The scope and structure of this monograph are introduced and the topics of each chapter are outlined.
Sliding Mode Control of Quantum Systems

Sliding mode control is a powerful variable structure control method for classical control systems. The basic idea of sliding mode control can also be extended to the quantum domain for robust control. This chapter presents sliding mode control results for quantum systems. In Sect. 6.1, the concept of sliding mode control is introduced to the quantum domain and sliding modes are defined for quantum systems. Section 6.2 presents results on sliding mode control for two-level quantum systems. $$^\text {a}$$ Sliding mode control methods are extended to multi-level quantum systems in Sect. 6.3 $$^\text {b}$$ and then to open quantum systems in Sect. 6.4. $$^\text {c}$$ A summary and further reading are given in Sect. 6.5.
Power Spectral Density Distribution Based GCN Construction for Through-the-wall Target Detection

With the difference of the target state, the power spectral density (PSD) distribution on the channel states information (CSI) time-frequency map is different. Therefore, this paper analyzes the time-frequency domain feature of CSI and proposes an indoor through-the-wall (TTW) passive object detection algorithm based on graph convolutional network (GCN). The experimental results show that the algorithm proposed in this paper can achieve a higher precision target detection than the existing algorithm.
Digital Test Application for Mathematics Subject Based on Superitem Using the Wondershare Platform

The purpose of this research was to show a Superitem-based digital format test application using the Wondershare platform for Mathematics subjects used as a tool for measuring students' cognitive abilities ranging from low to high-level abilities. This application was created using the Wondershare Quiz Creator platform. There were several valid test questions used in this application, ranging from the lowest to the highest difficulty level. The application development stage refers to the Borg and Gall model which focuses on product development. The initial trial of the product involved six experts, including three educational evaluation experts and three informatics experts. The tool used in the initial trial of the product was questionnaires. The data from the trial results were analyzed using a quantitative descriptive technique by comparing the percentage of the product trial results with the product effectiveness standard which refers to an eleven's scale. The results of this research showed the effectiveness of the test application was included in the good classification. The impact of research results on the progress of the field of educational evaluation is that it makes it easier for teachers to measure students' cognitive abilities.
Sound Characteristic of Drone Fan and Influence of Ground Effect

"Flying cars", which are being developed in Japan and overseas, are expected to be a new means of transportation for people and things. But noise reduction is essential to use them in residential areas because they may not be able to meet the noise regulations. In this study, we investigated the directivity of a drone propeller’s noise using multiple microphones in order to understand the noise characteristics of a fan for vertical take-off and landing aircraft(VTOL). In addition, a target plate simulating the ground effect was installed downstream of the propeller, and changes in the sound field due to the difference in distance between the target plate and the propeller were investigated. Furthermore, we also grasped the noise reduction effect when the propeller blade has a tip anhedral.
Entangled Quantum Detection Based on Constant False Alarm Rate Model

Aiming at the problem that the classical target detection method is not suitable for the quantum, this paper proposes a new entangled quantum detection method based on the Constant False Alarm Rate (CFAR) model. According to the principle of quantum detection, we adopt a quantum model to quantify the continuous laser energy into a cumulative number of photons to analyze signal and noise characteristics, and select the coincidence threshold in quantum detection. Experimental results show that the proposed method can realize the CFAR function of quantum detection model under different noise conditions.
Development of a search system for legal and technical documents using labeling of lexical network information

The contents of laws and technical documents are difficult, the relationships between them are complex, and the number of laws and technical documents is increasing, making it difficult for users to find the necessary laws and technical documents. We propose a method to assist users in identifying necessary parts of legal and technical documents in a short time by using information such as the relationship between legal and technical documents. We created a network of legal and technical documents. In addition to the sentences that make up the documents and the words that make up the sentences, we added labels that group similar words together. This enables ordinary users who do not have expert knowledge of legal and technical documents to understand the structure of the documents and to grasp them systematically.
Application development to learn the degree of roasting and the coffee producing countries with a coffee roaster

Mechanical engineering has often developed by fusing with information technology in recent years. We have been developing a coffee roasting machine for mechanical engineering education. We can study machine design, metalworking, and heat transfer engineering in the process of developing a coffee roasting machine. In this paper describes an application development to learn coffee roasting degree and coffee bean production area. In the application to learn the roasting degree, click on the image of the roasted beans according to the button of the eight roasting degrees and explain the roasting degree by computer voice. In the application to learn the production area indicates the position on the map with a marker by clicking on the major coffee producing countries in the world, and explains the characteristics of the coffee production area by computer voice.