Reviewerly/exHarmony huggingface dataset:
Automated and precise recognition of human zygote cytoplasm: A robust image-segmentation system based on a convolutional neural network

In this study, we evaluated the precision and reproducibility of convolutional neural network (CNN) measurements of zygote cytoplasm segmentation relative to experienced embryologists. We developed an automated system for segmenting human zygote cytoplasm using images and a CNN trained on a set of 550 zygote images labeled by embryologists. Further, we compared the consistency in cytoplasmic area measurements using images segmented by the CNN and the trained human eye (embryologists), as well as the precision and reproducibility of the measurements and the effect of several common image attributes. Moreover, the use of CNN results for 8377 zygote images for regularity classification revealed inter-observer agreement between two embryologists (98 ± 1.02 %) and between embryologists and the CNN regarding the cytoplasmic area (97.75 ± 1.45 %), with intra-observer agreement for the CNN system at 100 %. Furthermore, neither luminance nor image noise significantly affected CNN measurement precision; however, highly irregular zygote shapes reduced precision to 95 % for both. For classification, automatic segmentation of images of 8377 zygotes (area measurement: 9741.34 ± 7951.83 μm2; shapes: regular, 65.37 %; slightly irregular, 15.10 %; moderately irregular, 9.91 %; and highly irregular, 9.62 %) revealed good performance by the CNN in classifying both regularly and irregularly shaped zygotes (area under curve: 0.874 ± 0.043). These results demonstrated the efficacy of the CNN system for precisely measuring the zygote cytoplasmic area, classifying the shape regularity, and assisting with embryo assessment.
Light field image encryption based on spatial-angular characteristic

The 4D light field (LF) can be sliced into the spatial domain and the angular domain. Different from traditional images, LF image can simultaneously capture the direction and position of light propagation. With the development of image encryption technology, the security protection of LF images need to be paid further attention. Existing LF image encryption methods only explore the spatial information of the LF image, but ignore the angular information of LF image. In this paper, we propose a novel scheme for LF image encryption based on the internal spatial-angular characteristic of LF image. Firstly, considering the characteristic representation of the LF, we use the discrete fractional Fourier transform (DFrFT) to process the spatial domain of the LF image. Afterwards, in the angular domain, we employ the difference in viewing angles extracted from multiple angles to compose an epipolar plane image (EPI). The ciphertext EPI obtained by Arnold transform is displayed as an angular domain ciphertext image after reverse reconstruction. Furthermore, weighted linear blending and Chen’s chaotic system are performed on the ciphertext images in the spatial and angular domains to obtain the final ciphertext image. Experimental results demonstrate the effectiveness and applicability of the proposed scheme for LF image encryption.
Novel device detection using RF fingerprints

Radio transmitters can be identified by their unique radio frequency (RF) fingerprints, which are imparted by their distinct hardware differences and manufacturing variations. However, traditional classification approaches require all classes to be known during training - a constraint that may not hold in many real-world scenarios, with new RF devices appearing during data collection. In this paper, we present an approach for novel device detection in RF data that concurrently performs RF fingerprinting and novel device detection. We introduce a combined clustering loss that explicitly encourages a clustered representation on a lower dimensional manifold, and demonstrate when and where it is most helpful. Finally, we show how to improve performance by including additional devices during the training procedure in a semi-supervised setting.
Feature selection for imbalanced data with deep sparse autoencoders ensemble

Abstract Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile minority class observations. This need can be addressed by feature selection (FS), that offers several further advantages, such as decreasing computational costs, aiding inference and interpretability. However, traditional FS techniques may become suboptimal in the presence of strongly imbalanced data. To achieve FS advantages in this setting, we propose a filtering FS algorithm ranking feature importance on the basis of the reconstruction error of a deep sparse autoencoders ensemble (DSAEE). We use each DSAE trained only on majority class to reconstruct both classes. From the analysis of the aggregated reconstruction error, we determine the features where the minority class presents a different distribution of values w.r.t. the overrepresented one, thus identifying the most relevant features to discriminate between the two. We empirically demonstrate the efficacy of our algorithm in several experiments, both simulated and on high‐dimensional datasets of varying sample size, showcasing its capability to select relevant and generalizable features to profile and classify minority class, outperforming other benchmark FS methods. We also briefly present a real application in radiogenomics, where the methodology was applied successfully.
Bilateral Privacy-Utility Tradeoff in Spectrum Sharing Systems: A Game-Theoretic Approach

In spectrum sharing systems based on spectrum trading, user locations are vital for the efficiency of dynamic channel reuse. However, both primary users (PUs) and secondary users (SUs) undertake the risk of location information leakage: a malicious PU may illegally collect SUs' location information to manipulate market decisions; a malicious SU would threat a PU's operational privacy by inferring the PU's location through seemingly inoffensive queries. To protect both PUs' and SUs' location information in spectrum trading, a bilateral privacy preservation framework is introduced in this paper. A game-theoretic approach based on the Stackelberg model is proposed to achieve the tradeoff between the privacy-preserving level and user utility. With the proposed approach, both PUs and SUs can maximize their utilities while maintaining their location privacy to desired levels. Simulation results demonstrate that the proposed approach can effectively enhance user utility gain and strengthen user privacy guarantee by flexibly adjusting their privacy levels in practice.
A secure video steganography scheme using DWT based on object tracking

Videos are nowadays the most frequent and easy mode of communication over the internet; the reason behind the growth is the accessibility to video processing software available on the internet. Video steganography is a field where the data is embedded in video keeping the visual quality of the video intact. This study presents a new video steganography scheme with a stable trade-off between robustness and imperceptibility using 2D-DWT (Discrete Wavelet Transform) based on object detection and tracking. The main contribution of this paper includes embedding of secret data in the moving objects after applying object detection for the video frames where the secret bits are embedded in middle frequency sub-bands after applying 2D-DWT. To highlight the effectiveness of the proposed scheme, experimental results are carried out both quantitatively and qualitatively where quantitative analysis is done using different metrics such as PSNR, SSIM, BER and qualitative analysis is done using visual results of the frames. The experimental results illustrated that the proposed approach outperforms existing techniques in terms of qualitative and quantitative evaluation with high imperceptibility and robustness against noise attack. Eventually, the scheme has also been tested against existing steganalysis techniques to ensure the security of the proposed scheme.
Bathroom activity monitoring for older adults via wearable device

Performance on activities of daily life (ADLs) is often used to evaluate older adults' capability of living independently. Current sensor technology has already accomplished classifying several types of ADLs automatically. However, thus far, limited studies were performed in bathroom activity monitoring. In this study, we proposed using one wearable accelerometer to monitor six types of bathroom activities: dressing, undressing, washing hands, washing face, brushing teeth and toilet using. One hidden-layer neural network model was applied for activity classification. The acceleration signal was separated into patterns, using 16s sliding window with 50% overlapping. The model was developed both with (a) 10-fold cross validation or (b) leave-one-person-out (LOPO) of the training set. The classification result was evaluated on precision, recall and F1-score. The F1-score was varied in these bathroom activities. In 10-fold cross validation, the highest average F1-score was of 91.19%, which was for toileting, and the lowest average F1-score was of 51.91%, which was for washing face. In LOPO, toileting still had the highest F1-score of 80.00%, but washing hands had the lowest F1-score of 9.62%. Additionally, we tested whether one participant's toileting event can be monitored in 3 consecutive days in the LOPO mode. The F1-score for classifying toileting events was 85.61%. In sum, the results show that it is possible to use one wearable accelerometer to monitor toileting activity. However, for the other bathroom activities, further study is needed and personal differences in these activities' performance should be considered.
Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning

Although machine learning algorithms are progressively used in an expansive range of domains, the effective machine learning classifiers are often black-boxed, non-comprehensive to the end users and beyond their abilities to develop models themselves. To overcome this challenge, data visualization combined with self-service or democratized machine learning is proposed in the form of the Iterative Logical Classifier (ILC) algorithm with an added advantage of outperforming the accuracies of black-box machine learning classifiers on benchmark datasets. The algorithm is based on the concept of Shifted Paired Coordinates that allow 2-D visualization of n-D data without loss of n-D information.
CNN‐based novelty detection for terrestrial and extra‐terrestrial autonomous exploration

Novelty detection is concerned with detecting features that do not belong to any known class or are not well represented by existing models. Ergo, in autonomous navigation novelty detection determines whether an input camera frame contains certain entities of high interest which do not correspond to a known category. One of the key requirements for the future space exploration missions is the reduction of the information to be transferred back to Earth. Thus, novelty detection techniques have been developed to select the subset of acquired images with significant measurements that justify utilisation of the limited bandwidth from the available information link. Such methods are based on the identification of salient regions, which are then evaluated against a set of trained classifiers. We explore a novelty detection approach, based on the reasoning properties of Neural Networks, which follow the same guidelines while also being trainable in an end-to-end manner. This characteristic allows for the intertwined optimisation of the individual components leading to a closer estimation of a global solution. Our experiments reveal that the proposed novelty detection system achieves better performance, as compared to hand-crafted techniques, when the learning and testing examples refer to similar environments.
The Mobility Open Blockchain Initiative

Technological advancements in the transportation/automotive industry are continually increasing due to competition and consumer demands. The mobile open blockchain initiative (MOBI) is one way organizations are coming together to share innovating ways to revolutionize the transportation/automotive industry. This chapter explains the events that lead to the innovation of an open consortium, MOBI, and its members and highlights some of the cutting-edge technologies and innovative methods where blockchain is being adopted by the transportation/automotive industry.
Supporting Quality Assurance with Automated Process-Centric Quality Constraints Checking

Regulations, standards, and guidelines for safety-critical systems stipulate stringent traceability but do not prescribe the corresponding, detailed software engineering process. Given the industrial practice of using only semi-formal notations to describe engineering processes, processes are rarely "executable" and developers have to spend significant manual effort in ensuring that they follow the steps mandated by quality assurance. The size and complexity of systems and regulations makes manual, timely feedback from Quality Assurance (QA) engineers infeasible. In this paper we propose a novel framework for tracking processes in the background, automatically checking QA constraints depending on process progress, and informing the developer of unfulfilled QA constraints. We evaluate our approach by applying it to two different case studies; one open source community system and a safety-critical system in the air-traffic control domain. Results from the analysis show that trace links are often corrected or completed after the fact and thus timely and automated constraint checking support has significant potential on reducing rework.
totalvis: A Principal Components Approach to Visualizing Total Effects in Black Box Models

While a wide variety of machine-learning techniques have been productively applied to diverse prediction tasks, characterizing the nature of patterns and rules learned by these techniques has remained a difficult problem. Often called ‘black-box’ models for this reason, visualization has become a prominent area of research in understanding their behavior. One powerful tool for summarizing complex models, partial dependence plots (PDPs), offers a low-dimensional graphical interpretation by evaluating the effect of modifying individual predictors on fitted/predicted values. Nevertheless, in high-dimensional settings, PDPs may not capture more complex associations between groups of related variables and the outcome of interest. We propose an extension of PDPs based on the idea of grouping covariates, and interpreting the total effects of the groups. The method utilizes principal components analysis to explore the structure of the covariates, and offers several plots for assessing the approximation function. In conjunction with our diagnostic plot, totalvis gives insight into the total effect a group of covariates has on the prediction and can be used in situations where PDPs may not be appropriate. These tools provide a useful approach for pattern exploration, as well as a natural mechanism to reason about potential causal effects embedded in black-box models.
A Framework for Recommendation Algorithms Using Knowledge Graph and Random Walk Methods

A number of knowledge graph (KG)-based recommendation algorithms have been introduced; KGs enable users and items and their attributes to be treated in an integrated way and structural information to be captured through graphs. There are many variations of the KG based recommendation algorithms. Among them, KG embedding is often used, but doing this does not take advantage of the meta-path-level proximity between users and items. This paper presents a flexible framework combining random walk and KG embedding methods. The random walk model is formulated on the basis of the similarity between nodes revealed by the KG embedding. This enables the metapath level proximity of users and items to be efficiently utilized. Comparison testing demonstrated that the proposed framework performs better than random- walk-only methods and KG-embedding-only methods, and slightly better than the existing method we have extended.
Intelligent Handwritten Character Recognition For Malayalam Scripts Using Deep Learning Approach

Abstract Machine Learning, especially Deep Learning has been incorporated into Pattern Recognition and Image Processing for the Handwritten Character Recognition (HCR) research which is always a hot area of research. Foreign scripts are enriched with Handwritten Character Recognition (HCR) studies. But very less research can be seen for HCR in Indian scenario, especially in Malayalam script. This paper provides an overview of different feature extraction and classification techniques used for Handwritten Character Recognition of Malayalam Scripts using Deep Learning Approaches. The accuracy measures of classification of the different algorithms have been given so that the researchers can select a particular algorithm with high performance.
On special pairs of primitive elements over a finite field

The present paper deals with the problem of finding elements α and β in a finite field Fq, such that both are primitive and β is a rational function of α. Recently Cohen, Sharma and Sharma found a sufficient condition for the existence of such elements. In the present paper we present other conditions that guarantee the existence of such pair of elements, or prove it doesn't exist. We also improve some bounds found by Cohen, Sharma and Sharma which guarantee the existence of this primitive pair.
PhotoApp

Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting, and do not allow camera viewpoint editing. Thus, they only capture and control a subset of the reflectance field. Recently, portrait editing has been demonstrated by operating in the generative model space of StyleGAN. While such approaches do not require direct supervision, there is a significant loss of quality when compared to the supervised approaches. In this paper, we present a method which learns from limited supervised training data. The training images only include people in a fixed neutral expression with eyes closed, without much hair or background variations. Each person is captured under 150 one-light-at-a-time conditions and under 8 camera poses. Instead of training directly in the image space, we design a supervised problem which learns transformations in the latent space of StyleGAN. This combines the best of supervised learning and generative adversarial modeling. We show that the StyleGAN prior allows for generalisation to different expressions, hairstyles and backgrounds. This produces high-quality photorealistic results for in-the-wild images and significantly outperforms existing methods. Our approach can edit the illumination and pose simultaneously, and runs at interactive rates.
Next-Generation Digital Forensic Readiness BYOD Framework

Intel’s “Bring Your Own Device” (BYOD) adoption quickly became popular as an alternative workplace solution strategy. It enables employees to use their personally owned devices to perform business activities, leading to increased productivity and employee satisfaction. However, BYOD also brought associated risks because of exponential growth in the number of cybersecurity incidents due to which business ecosystem gets disrupted and fragmented. Although several methods and mechanisms have been developed and adopted to mitigate the risk associated with BYOD, they still represent a challenge as corporate network gets exposed to inherent threats caused by the BYOD threat landscape. This work demonstrates especially two key aspects: The first focuses on how to detect and protect BYOD environment from an advanced level attack which cannot be detected by traditional tools and techniques even though available tools are quite effective. Before the attack and damage to the critical infrastructure due to BYOD threat, a strategy was indeed the key requirement for detecting attacks and protecting the environment. The second part of the research focuses on conducting forensic investigation model and developing a new approach by providing a reliable forensic investigation infrastructure to find digital evidence and detect the source of attack. This research work concluded with two different novel strategic ideas. The first part contributes to a new method of detecting and protecting against malicious activities which cannot be otherwise detected and protected by traditional security technology like IPS, IDS, AntiBot, or AntiVirus. The proposed technique compared to the existing methods led to a significant contribution to the identification of threats before an attack takes place. The second part of the research contributes to the defining of a new approach of the next-generation digital forensic readiness (NG-DFR) model in order to build a cyber forensic ecosystem so that cyber secured BYOD environment can be enabled safely.
Implementation of Deep Learning Using Convolutional Neural Network Algorithm for Classification Rose Flower

Abstract Flora in Indonesia has about 25% of the species of flowering plant species present in the world. Roses are one type of flowering plants and are usually used as an ornamental plant that has a thorny stem. Roses have more than 150 species. In Indonesia there are several flower gardens that is larger than the others. One of the famous flower garden in Indonesia is located on Malang city, East Java. The flower garden in Malang has several varieties of many roses and has a large production of roses. To help the sales system of roses there, the researchers want to create a program that can classify the type of roses in order to help simplify the system of automatic sales of roses without through manual sorting. So that will accelerate the sale of roses with an automated system. Ordinary people with limited botanical knowledge usually don’t know how to classify the flowers just by looking at them. To classify the flowers properly, it is important to provide enough information, and one of them is the name of it. Convolutional Neural Network (CNN) is one method of deep learning that can be used for image classification process. The CNN design is motivated by the discovery of the visual mechanism, the visual cortex present in the brain. CNN has been widely used in many real-world applications, such as Face Recognition, Image Classification and Recognition, and Object Detection, because this is one of the most efficient method for extracting important features. In this research, the classification accuracy value obtained from the test data is 96.33% using 2-dimension Red Green Blue (RGB) input image, and the size of each image is 32 × 32 pixels that are trained with CNN algorithm and the network structure of four convolution layers and four layers pooling supported by dropout technique.