Reviewerly/exHarmony huggingface dataset:
Image Captioning Using Capsule Neural Network and LSTM

Machine translation and extracting meaningful language descriptions has always been a challenging task in the computer vision field. This paper presents a very modern approach which uses deep learning techniques to generate image description. The concept of Capsule Neural Network is applied in Image Captioning. A capsule denotes a nested layer inside the capsule. One major difference between traditional networks and Capsule Neural Network is that the Capsule Neural Network handles better segmentation and recognition. Evaluation of the model on Flickr 8 k data-set qualitatively and quantitatively shows the proper learning and language efficiency. For model evaluation a very popular metrics BLEU score has been considered which shows significant improvement from 28.9 to 37.8 for the latest Flickr 8 k data set.
Distributed Dictionary Learning Over Heterogeneous Clients Using Local Adaptive Dictionaries

This work examines the use of dictionary learning among distributed clients with heterogeneous tasks. We propose a distributed dictionary learning algorithm that enables collaborative training of a shared global dictionary among clients while adaptively constructing local dictionary elements to address the heterogeneity of local tasks. The proposed distributed dictionary learning with local adaptive dictionaries (DDL-LAD) algorithm consists of two parts: a distributed optimization procedure that enables joint training of the dictionaries without sharing of the local datasets with the server, and a splitting and elimination procedure that is used to adaptively construct local dictionary elements. The splitting procedure identifies elements in the global dictionary that exhibit discriminative features for the local tasks. The elements are split and appended to the local dictionaries. Then, to avoid overgrowing of the local dictionaries, an elimination procedure is adopted to prune elements with less usage. Experiments on a distributed EMNIST dataset is provided to demonstrate the effectiveness of the proposed DDL-LAD algorithm compared to existing schemes that adopt only a global shared dictionary.
Project-Based Learning Using Remote-Controlled Plane for Students in Mechatronics Course

The Ministry of Education, Culture, Sports, Science and Technology, Japan recommended educational institutions in Japan to carry out educational reforms by building active-learning courses. This paper explains the practice of project-based learning for sophomores in the mechatronics course, Tokyo University of Technology. We designed a project to teach simple principles of aeronautics, plant modeling, and mechatronics. Students are required to assemble radio-controlled planes. It tries to gain the motivation in learning mechatronics and the ability of teamwork and communication. We explained how we designed the project and the practical results. Statistics show that this project stimulated students’ curiosity about mechatronics.
Improved Retinex Low-light Image Enhancement Algorithm Based on Muti-channel Fusion Strategy

The algorithm first extracts the brightness component Y in the YIQ color space, and enhances it with the Directed filter, and then superimpose them and convert them into grayscale images. finally, the wavelet transform is used to fuse the two images to obtain a new brightness component, which is fused with the I and Q components and then converted back to the RGB color space, so as to obtain an image With high color fidelity and clear details. Expermental results show that this method effectively improves the edge detail information of the image, avoid color distortion, and has a good visual effet.
A novel Image Security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

Abstract This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.
Apply Formal Methods in Certifying the SyberX High-Assurance Kernel

SyberX is an operating system microkernel used for safety and security-critical applications, such as avionics and unmanned vehicles. In this paper, we present an effective approach to apply formal methods in the development and security evaluation high-assurance level of the SyberX based on Common Criteria (CC) methodology. To achieve the evaluation under the CC at evaluation assurance level 5 augmented (EAL5+), where “+” is applying formal methods compliant to the highest evaluation assurance level, several partners from industry and academia have contributed to this effort. Our work provides a standardized formal specification, security analysis as well as formal verification proofs of the SyberX system. All results have been formalized in the Isabelle/HOL theorem prover. During the verification and code review, we find a total of 5 bugs, all confirmed and fixed by developers.
Animated infographic as information media for earthquake and tsunami mitigation plan on the south coast of Bantul Indonesia

Abstract Bantul is a district located in the southern part of Yogyakarta Indonesia. Its located in the lowlands with coastal area on its border, Bantul is a district with the longest coastline in Yogyakarta Province, about 15.65 km. This condition makes Bantul Regency prone to earthquakes and tsunamis from the south coast. In normal situations, disaster management training and simulations are often carried out. However, during the current pandemic, this is no longer possible. This study aims to design an informative and effective information media about tsunami mitigation plan for the community in the pandemic situation. The research applied was descriptive qualitative method. The data were collected through literature study as the basis for designing infographic media. The result of this research is an animated infographic that uses a flat design visual style and is delivered in Javanese language.
Electrical, Magnetic Radiation and Power Density Analysis of Electronic Gadgets During Yajna

All of us are aware that the modern mechanical and electrical systems in collaboration with AI, CPS, and IoT have affected our lives drastically. At one end, they have provided ease and comfort. On the other end, the electrical and magnetic radiations emitted by them have threatened the mental and physical fitness. Surprisingly, the Asian Agnihotra process effectively reduces the electrical, magnetic radiations emitting from electronic devices. The chapter deals with the computational analysis of electrical, magnetic radiation, and power density measurement of environmental effects and gadgets after the Homa therapy. In the gap of a specific time, readings were recorded, and it was observed that drastic reduction occurred at the place where Yajna was performed. This gap was curtailed gradually as time increased. The phenomenon can undoubtedly be used to address human health threats due to electronic gadgets in the 21st century. Also, it supports the concepts of smart cities where one can quickly identify the numerous effects of the Yajna process.
Road Sign Recognition using Lenet5 Network Model

This paper proposes a framework that will detect different types of traffic signs from images for Traffic sign recognition and detection in advanced driver support system. In this paper classical LeNet5 network model is used with GTSRB dataset for evaluation purpose. Shape and color are the two main factors that distinguish road signs from other objects. Firstly, preprocessing of an image to highlight important information. Secondly, the training data are scaled, normalized, expanded, and augmented. Finally, the validation and testing phase in which traffic signs are classified and accuracy is calculated. It is concluded from the results that our system has achieved greater accuracy due to the architectural optimization of the neural network for detecting traffic signs. Significantly better performance was achieved with partially blurred images. Traffic sign are classified in 43 classes. For training 22271 images are used in which 6960 images used for testing and 5568 images are used in the evaluation purpose. With new design model 98.75% accuracy is obtained.
Comparative Analysis of K-Means and Traversal Optimisation Algorithms

This research aims to present a technical analysis of the Traversal Optimisation Algorithm (TOA) for clustering and K-means clustering algorithm. The goal is to rigorously test this algorithm against different data specifications beyond what has previously been used with K-means without artificially and subjectively setting the initial number of clusters. The experimental evaluation involve the use of diverse cluster optimisation techniques for K-means while applying a wider range of internal validation methods such as Davies-Bouldin Index, Dunn Index and Silhouette Method, for appraising cluster quality of the Traversal Optimisation Algorithm, while at the same time not compromising the configuration of the default algorithm. The findings in this work shows that the optimisation algorithm’s clustering quality as calculated by multiple internal validity indices can be very poor when operating on datasets with varying characteristics. This is owing to the algorithm’s lack of any add-on mechanism for computing the optimal number of clusters that a dataset needs apriori. The results reveal that in a data processing contexts where the number of clusters are specified, the TOA yields a favourable cost-benefit in terms of run-time complexity and clustering quality.
A Novel Pre-processing Method for Classification Problems in Medical Intelligent Tasks

In the industry of medical intelligence, classification is one of the most common tasks. It appears in various medical jobs, such as triage, diagnosis, and pathologic analysis. Many classification algorithms studied in machine learning can be chosen to help solve these tasks. However, due to the special nature of the medical industry, its data sets show a character of imbalance. Namely, the data are skewed distributed in different classes. Unfortunately, the classification problem of imbalanced data has a reputation of classic and hard-to-solve in data mining and artificial intelligence research community. What's worse, most proposed classification methods are designed to deal with binary classification case, while the common scenario in medical intelligence applications is multi-classification. To deal with this, a pre-processing structure called Cost-Sensitive Variable Neighbour Search (CSVNS) is proposed in this paper. It combines the ideas of sampling and cost-sensitive, which are two most commonly used strategies for multi-class imbalanced data classification tasks. As for the sampling process, a double-stack Variable Neighbour Search (VNS) structure is introduced and 15 different neighborhood structures are designed to help optimizing the process. Also, the classes are allocated different weights to improve the classifier's classification capacity. In the experiment part, the proposed method is evaluated on 4 medical data sets. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> - mean and mAUC are selected to represent the method's performance in medical classification tasks. Experimental results show the proposed method outperforms the classic methods in most situations. In the end, 3 extra data sets are tested to demonstrate the algorithms' scalability.
Progress of neural network quantization algorithm

Deep neural networks have achieved great success in various fields of artificial intelligence nowadays. However, the inference process of deep learning models requires a huge number of parameters and calculations. Therefore, how to deploy on mobile devices has become a problem. Any bit in the embedded hardware design is very important to the neural network. Extremely low bits are used to represent neural networks to reduce the consumption of resources such as memory, but it usually leads to a serious loss of accuracy. So how to train an extremely low-level neural network with high accuracy is very important. Most of the existing neural network quantization methods are based on low-level weight quantization or low-level activation quantization. This article reviews various neural network quantization methods commonly used in recent years and compares their effects. Compared with previous reviews, the quantization techniques introduced in this article are more comprehensive, and the classification of various methods is clearer. After comparison, the greatest difficulty of the quantization method is the large loss of accuracy on large-scale data sets. Compared with the full-precision model, the quantization model of 8bit and above can basically achieve accuracy without any loss.
Dimension Estimation Using Autoencoders and Application

Bahadur, Nitish Lewandowski, Brian Paffenroth, RandyDimension Estimation (DE) and Dimension Reduction (DR) are two closely related topics, but with quite different goals. In DR, one attempts to project a random vector, either linearly or nonlinearly, to a lower-dimensional space that preserves the information contained in the original higher-dimensional space. However, in DE, one attempts to estimate the intrinsic dimensionality or number of latent variables in a set of measurements of a random vector. DE and DR are closely linked because reducing the dimension to a smaller value than suggested by DE will likely lead to information loss. In particular, when considering linear methods such as Principal Component Analysis (PCA), DE and DR are often accomplished simultaneously. However, in this paper, we will focus on a particular class of deep neural networks called autoencoders (AEs), which are used extensively for DR but are less well studied for DE. We show that several important questions arise when using AEs for DE, above and beyond those that arise for more classic DR/DE techniques such as PCA. We address AE architectural choices and regularization techniques that allow one to transform AE latent layer representations into estimates of intrinsic dimension. We demonstrate the effectiveness of our techniques on synthetic, image processing benchmark problems, and most importantly, diverse applications such as the analysis of financial markets and network security.
Secured data storage in the cloud using logical Pk-Anonymization with Map Reduce methods and key generation in cloud computing

The significant security assessment is rising because the vast amount of data can be renewed continuously in the cloud. Cloud information is clustered and refreshed productively using a progressive clustering procedure over the data. To verify the security of a user's connection to the cloud, the development of information to the servers could be used. Thus, the trustworthiness of the information plays a vital role in determining the authority of the information. Map Reduce is used to deal with enormous volumes of information, and informational indexes are circulated on the cloud. A parallel information preparation structure is then received, allowing for the collection of current information that joins over time. Information anonymization methods are then used to achieve security and high information utility when an update occurs, resulting in less data loss and refresh time over time.
FAIR Metadata: A Community-Driven Vocabulary Application

FAIR metadata is critical to supporting FAIR data overall. Transparency, community engagement, and flexibility are key aspects of FAIR that apply to metadata. This paper presents YAMZ (Yet Another Metadata Zoo), a community-driven vocabulary application that supports FAIR. The history of YAMZ and its original features are reviewed, followed by a presentation of recent innovations and a discussion of how YAMZ supports FAIR principles. The conclusion identifies next steps and key outputs.
Deep Convolutional Neural Networks for Handwritten Kannada Numerals Recognition

Kannada is an official language of India mostly spoken by people of Karnataka in Southwestern India. This paper describes the recognition/classification of Handwritten Kannada numerals using Deep Convolutional neural network(Deep-CNN). The occurrence of handwritten text is abundant. Convolutional neural networks (CNN) have been known for it’s computationally efficient way of extracting features. For the extraction of features a series of convolution and pooling operations is performed. We have used the new Kannada MNIST dataset consisting of 60,000 samples of isolated handwritten numerals and a Handwritten dataset created by non-native users of the language called Dig-MNIST. The proposed system achieves an accuracy of 98.24% on the Kannada MNIST dataset and 86.85% on the Dig-MNIST dataset.
Are we done yet? Our Journey to Fight against Memory-safety Bugs

Memory-safety issues have been a long-standing concern of the security practitioners. According to Microsoft and Google, memory-safety bugs still represent 70% of the exploited vulnerabilities in complex, real-world programs like OSes and Web browsers. However, it doesn't mean that academics and practitioners haven't tried hard to alleviate the problem. Advances in automatic techniques like fuzzing and sanitizers revolutionize the way we tame the memory safety bugs, but the increasing volume of new software simply outpaces the adoption rate of these promising new techniques, setting the legacy programs aside.
"Trolleyology" and autonomous vehicles - moral and legal questions on the application of the doctrine of double effect

The paper focuses on a classical problem of ethics and law: the doctrine of double effect (DDE).Nowadays the doctrine is more and more popular since AI-technology and superintelligent machines have been developing rapidly.Maybe autonomous vehicles' most difficult dilemma is the following scenario: an autonomous car gets into an extreme road accident (collision) and the software should "decide" which direction-alternative to choose, but all of those possibilities end with death of human(s).This is a problem which requires a morally and legally justified answer.The principle emphasizes how to achieve a moral justification, what can be the classical cases of DDE and how to solve the most famous classical case, the trolley problem -which can be analogous to autonomous cars' collisioncase.The paper also higlights whether the doctrine is relevant from the perspective of legal justification and legal solutions, too.
Petrophysical target characterization with lithogeochemical clustering: the Metsämonttu Zn–Pb–Cu deposit, southern Finland

ABSTRACT In mineral exploration, reliable and comprehensive petrophysical groundwork helps evaluate the success rate of applying geophysical methods to the target and thus improves the quality of decision‐making related to geophysical surveys. Petrophysical data for potential field exploration can be efficiently collected from drill cores together with portable XRF data that provide insight into rock compositions. We acquired and analysed a multi‐parameter portable‐instrument drill core data set (2036 samples) with portable XRF, magnetic susceptibility and density measurements from the Metsämonttu Zn–Pb–Cu deposit, one of the base metal deposits in the Orijärvi Belt, southern Finland, with little post‐mining exploration during the last 50 years. Our study applied a machine learning technique (Gaussian Mixture Model clustering) to the portable XRF data to provide a pseudo‐lithological reclassification of the drill cores and used this as a basis for characterization of the deposit in petrophysical terms. We also used thermomagnetic measurements to inspect the magnetic mineralogy of the drill cores. Finally, we estimated the success of using potential field methods (magnetic and gravity) for brownfield exploration of analogous deposits in the area. With the portable XRF data clustering we were able to distinguish between barren mafic and felsic volcanic rocks, altered rocks and three different ore mineral enriched rock types. The petrophysical measurement data and thermomagnetic results were combined with the clusters to define the petrophysical characteristics for future mineral exploration with potential field methods. At Metsämonttu, the highest densities are related to the ore and iron sulphide‐bearing rocks. High natural remanent magnetization intensities are related to the iron sulphide‐bearing ore, and remanent magnetization is a significant magnetic anomaly source. Based on synthetic modelling results, both magnetic and gravimetric data could be used in direct exploration in the Metsämonttu area.
Generating Black-Box Adversarial Examples in Sparse Domain

Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many applications, recent investigations show that such networks are highly vulnerable to adversarial attacks. The black-box adversarial attack is one type of attack that the attacker does not have any knowledge about the model or the training dataset, but it has some input data set and their labels. In this paper, we propose a novel approach to generate a black-box attack in sparse domain whereas the most important information of an image can be observed. Our investigation shows that large sparse (LaS) components play a critical role in the performance of image classifiers. Under this presumption, to generate adversarial example, we transfer an image into a sparse domain and put a threshold to choose only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components. In contrast to the very recent works that randomly perturb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> low frequency (LoF) components, we perturb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components either randomly (query-based) or in the direction of the most correlated sparse signal from a different class. We show that LaS components contain some middle or higher frequency components information which leads fooling image classifiers with a fewer number of queries. We demonstrate the effectiveness of this approach by fooling six state-of-the-art image classifiers, the TensorFlow Lite (TFLite) model of Google Cloud Vision platform, and YOLOv5 model as an object detection algorithm. Mean squared error (MSE) and peak signal to noise ratio (PSNR) are used as quality metrics. We also present a theoretical proof to connect these metrics to the level of perturbation in the sparse domain.