Reviewerly/exHarmony huggingface dataset:
A study on missing values imputation using K-Harmonic means algorithm: Mixed datasets

Data cleaning is one step in the preprocessing which in the process often found missing values in the dataset. Missing values is the condition of the absence of data items on a subject. A quick step that can be taken to handle missing values is to remove data containing missing values, but this can reducing information in the data. Another way to handle missing values is by using imputation with mean, median, or mode, and several methods of imputation such as regression, likelihood, and the clustering approach. Imputation with the clustering approach is the focus of this study, where we used the K-Harmonic Means which has been adjusted to handle mixed data. K-Harmonic Means is an extension of K-Means by reducing random centroid initialization sensitivity problems. Imputation of the missing values is carried out by distributing missing values observation to the cluster and replacing the missing values with the information on the same centroid cluster. The results of the simulation were evaluated using the root mean square error and the accuracy values of each imputation value for numerical and categorical data respectively.
PicSys: Energy-Efficient Fast Image Search on Distributed Mobile Networks

Mobile devices collect a large amount of visual data that are useful for many applications. Searching for an object of interest over a network of mobile devices can aid human analysts in a variety of situations. However, processing the information on these devices is a challenge owing to the high computational complexity of the state-of-the-art computer vision algorithms that primarily rely on Convolutional Neural Networks (CNNs). Thus, this paper builds PicSys, a system that enables answering visual search queries on a mobile network. The objective of the system is to minimize the maximum completion time over all devices while taking into account the energy consumption of mobile devices as well. First, PicSys carefully divides the computation into multiple filtering stages, such that only a small percentage of images need to run the entire CNN pipeline. Splitting such CNN computation into multiple stages requires understanding the intermediate CNN features and systematically trading off accuracy for the computation speed. Second, PicSys determines where to run each of the stages of the multi-stage pipeline to fully utilize the available resources. Finally, through extensive experimentation, system implementation, and simulation, we show that PicSys performance is close to optimal and significantly outperforms other standard algorithms.
Handling Imbalanced Data through Affinity Propagation and SMOTE

Imbalanced datasets occur in real-world scenarios and affect the performance of the classifiers in supervised learning. To combat the imbalance between majority and minority instances, several techniques including SMOTE which generates artificial data along the line between minority samples and its selected neighbors are used in preprocessing. However, SMOTE suffers from generating synthetic data in noise regions and imbalanced distribution of samples within the minority class. Using clustering techniques prior to the oversampling step to determine the target areas of the input space where the generation of synthetic samples is effective, is known to be an extension of SMOTE. The proposed method uses affinity propagation as a clustering technique to generate clusters and its cluster exemplar without requiring the specification of the number of clusters a priori. The cluster exemplar becomes the basis of which samples are to be oversampled in each cluster. Simulation results on some publicly available datasets show the effectiveness of the proposed method in terms of F-measure, G-mean and AUC with a mean value across datasets of .685, .756 and .765 respectively.
Vis-a-Vis: Visual Exploration of Visualization Source Code Evolution

Developing an algorithm for a visualization prototype often involves the direct comparison of different development stages and design decisions, and even minor modifications may dramatically affect the results. While existing development tools provide visualizations for gaining general insight into performance and structural aspects of the source code, they neglect the central importance of result images unique to graphical algorithms. In this article, we present a novel approach that enables visualization programmers to simultaneously explore the evolution of their algorithm during the development phase together with its corresponding visual outcomes by providing an automatically updating meta visualization. Our interactive system allows for the direct comparison of all development states on both the visual and the source code level, by providing easy to use navigation and comparison tools. The on-the-fly construction of difference images, source code differences, and a visual representation of the source code structure further enhance the user's insight into the states' interconnected changes over time. Our solution is accessible via a web-based interface that provides GPU-accelerated live execution of C++ and GLSL code, as well as supporting a domain-specific programming language for scientific visualization.
SSP: Self-Sovereign Privacy for Internet of Things Using Blockchain and MPC

With the proliferation of the Internet of Things (IoTs), their devices have been collecting our personal data to enhance our life styles and their purposes. Care must be taken when handling such sensitive data to protect the data owners' privacy while guaranteeing the integrity when the data is accessed by a third party. Furthermore, data should be available without much delay to support medical emergencies. Unfortunately, IoT devices may be limited with storage and computational resources to operate highly secure operations and respond to data requests. In this paper, we propose SSP, a novel mechanism that protects the privacy and integrity of the data collected by IoT devices without having a single point of failure, while minimizing the cryptographic operations that must be performed on IoT devices. SSP utilizes the blockchain data structure for availability and integrity, and Multi-Party-Computations (MPC) for privacy protection. We implemented SSP in C/C++ and according to our evaluation results, SSP is efficient in computation, communication, and storage operations on IoT devices.
Server-Aided Revocable Predicate Encryption: Formalization and Lattice-Based Instantiation

Abstract Efficient user revocation is a necessary but challenging problem in many multi-user cryptosystems. Among known approaches, server-aided revocation yields a promising solution, because it allows to outsource the major workloads of system users to a computationally powerful third party, called the server, whose only requirement is to carry out the computations correctly. Such a revocation mechanism was considered in the settings of identity-based encryption and attribute-based encryption by Qin et al. (2015, ESORICS) and Cui et al. (2016, ESORICS ), respectively. In this work, we consider the server-aided revocation mechanism in the more elaborate setting of predicate encryption (PE). The latter, introduced by Katz et al. (2008, EUROCRYPT), provides fine-grained and role-based access to encrypted data and can be viewed as a generalization of identity-based and attribute-based encryption. Our contribution is 2-fold. First, we formalize the model of server-aided revocable PE (SR-PE), with rigorous definitions and security notions. Our model can be seen as a non-trivial adaptation of Cui et al.’s work into the PE context. Second, we put forward a lattice-based instantiation of SR-PE. The scheme employs the PE scheme of Agrawal et al. (2011, ASIACRYPT) and the complete subtree method of Naor et al. (2001, CRYPTO) as the two main ingredients, which work smoothly together thanks to a few additional techniques. Our scheme is proven secure in the standard model (in a selective manner), based on the hardness of the learning with errors problem.
Low-Light Image Enhancement Based On Modified U-Net

Recent years, researches in low-light image enhancement has done quite a lot and shown great success in real life application. In this paper, a modified U-Net-based method is proposed by combining with Recurrent Residual Convolutional Units (RRCU) and Dilated Convolution. In our method, we achieve higher accuracy by three enhancements. Firstly, replace the basic 3×3 convolution blocks with RRCU. Secondly, replace the 3×3 convolution bottle neck block with multi-ways concatenation. Lastly, replace the max pooling operation with dilated convolution between upper and lower levels. In experiment, the performance of the proposed modified U-Net network is proved to obtain obviously better accuracy than other existing methods in low-light image enhancement.
Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis

There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra- and interobserver variability among pathologists. This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis.Fifty-nine patients who underwent radical prostatectomy were selected under the approval of the institutional review board of our university hospital. For estimation, we followed the grade group criteria provided by the International Society of Urological Pathology in 2014. One hundred eight specimen slides obtained from the patients were digitized to extract 110 regions of interest (ROI) from hematoxylin and eosin-stained histological images using a digital whole slide scanner at ×20 magnification with a pixel size of 0.4 μm. Each color pixel value in the ROI was decomposed into six intensities corresponding to the RGB (red, green, and blue) and HSV (hue, saturation, and value) color models. Image features were extracted by histological image analysis, obtaining 54 features from the ROI based on histogram and texture analyses in the six types of decomposed histological images. Then, 40 representative features were selected from the 324 histological image features based on statistically significant differences (P < .05) between the mean image feature values for high (≥3, Gleason score ≥4 + 3) and low (≤2, Gleason score ≤3 + 4) grade groups. The relationship between grade groups and the most representative image feature (ie, complexity) was approximated using regression to estimate real-number grade groups defined by continuous numerical grading. Finally, the grade groups were expressed as the conventional grade groups (ie, integers from 1 to 5) using a piecewise step function.The grade groups were correctly estimated by the proposed approach without errors on training (70 ROIs) and validation (40 ROIs) data.Our results suggest that the proposed approach may support pathologists during the evaluation of grade groups for prostate cancer, thus mitigating intra- and interobserver variability.
Understanding the Disharmony between Weight Normalization Family and Weight Decay

The merits of fast convergence and potentially better performance of the weight normalization family have drawn increasing attention in recent years. These methods use standardization or normalization that changes the weight W to W′, which makes W′ independent to the magnitude of W. Surprisingly, W must be decayed during gradient descent, otherwise we will observe a severe under-fitting problem, which is very counter-intuitive since weight decay is widely known to prevent deep networks from over-fitting. Moreover, if we substitute (e.g., weight normalization) W′ = W∥W∥ in the original loss function ∑i L(ƒ(xi; W′),yi) + ½λ∥W′∥2, it is observed that the regularization term ½λ∥W′∥2 will be canceled as a constant ½ λ in the optimization objective. Therefore, to decay W, we need to explicitly append: ½λ∥W∥2. In this paper, we theoretically prove that ½λ∥W∥2 improves optimization only by modulating the effective learning rate and fairly has no influence on generalization when the weight normalization family is compositely employed. Furthermore, we also expose several serious problems when introducing weight decay term to weight normalization family, including the missing of global minimum, training instability and sensitivity of initialization. To address these problems, we propose an Adaptive Weight Shrink (AWS) scheme, which gradually shrinks the weights during optimization by a dynamic coefficient proportional to the magnitude of the parameter. This simple yet effective method appropriately controls the effective learning rate, which significantly improves the training stability and makes optimization more robust to initialization.
Extracting Rich Semantic Information about Cybersecurity Events

Articles about cybersecurity events like data breaches and ransomware attacks are common, both in general news and technical sources. Automatically extracting structured information from these can provide valuable information to inform both human analysts and computer systems. In this paper we describe how cybersecurity events can be described via semantic schemas, examined through an initial set of five event types. Using a collection of 1,000 news articles annotated with these event types, including their semantic roles, arguments, realis, and coreference, we detail a modular, deep-learning based information extraction (IE) pipeline, which extracts useful event information with high accuracy. We argue that the event argument set considered here can support many other cybersecurity events, facilitating the extension to new cybersecurity event types, such as distributed denial of service and SQL injection attacks.
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning

Noise and inconsistency commonly exist in real-world information networks, due to the inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent graph convolutional networks (GCNs) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. Noisy node content, combined with sparse features, provides essential challenges for existing methods to be used in real-world noisy networks. In this article, we propose feature-based attention GCN (FA-GCN), a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each node feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes to learn and vary feature importance, with respect to their connections. By using a spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than the state-of-the-art methods in both noise-free and noisy network environments.
Comparison User Engagement of Gamified and Non-gamified Augmented Reality Assembly Training

Augmented Reality (AR) is expanding its application field through many areas, including marketing, education, and medicine. Furthermore, industrial training and instructional support, especially in the context of maintenance and assembly, are also among the key field of application. Also, evidence has shown that good user experience and engagement leads to better performance, an engaged employee delivered a better result than those who not. Gamification is one of the various methods to enhance user experience and engagement. In this work, we present a training approach to guide novice users through an assembly task of changing batter for a robot arm. The training is developed as an augmented reality training with and without a gamification design. Furthermore, we evaluated the designs with 22 objects to validate if user engagement and performance of one design is better than the other. The result indicates a better outcome on the gamified application, however, the difference is not statistically significant.
A Karhunen-Loeve Expansion for One-mode Open Quantum Harmonic Oscillators Using the Eigenbasis of the Two-point Commutator Kernel

This paper considers one-mode open quantum harmonic oscillators with a pair of conjugate position and momentum variables driven by vacuum bosonic fields according to a linear quantum stochastic differential equation. Such systems model cavity resonators in quantum optical experiments. Assuming that the quadratic Hamiltonian of the oscillator is specified by a positive definite energy matrix, we consider a modified version of the quantum Karhunen-Loeve expansion of the system variables proposed recently. The expansion employs eigenvalues and eigenfunctions of the two-point commutator kernel for linearly transformed system variables. We take advantage of the specific structure of this eigenbasis in the one-mode case (including its connection with the classical Ornstein-Uhlenbeck process). These results are applied to computing quadratic-exponential cost functionals which provide robust performance criteria for risk-sensitive control of open quantum systems.
Deep Convolutional Autoencoder for Estimation of Nonstationary Noise in Images

A precise estimation of noise parameters is very important in many image processing applications, such as denoising, deblurring, compression, etc. This problem is well studied for the case of stationary noise in images, and much less studied for the case of nonstationary noise. In this paper, we develop an efficient method of nonstationary noise variance estimation in image regions, based on specially designed deep convolutional autoencoder (DCAE) with a small dimensionality reduction. Training of the proposed DCAE is carried out for a large set of image blocks, including fragments of noise free textures, faces and texts. In the numerical analysis, we compare the proposed method and method of blind estimation of nonstationary noise, based on block matching (BM). Additionally, we analyze efficiency of the proposed DCAE in comparison with the conventional autoencoder (AE). We show that usage of the proposed DCAE provides an error of noise variance estimation about 2 times smaller, that the error when the standard AE is used, and 4 times smaller than the variance estimation error of the BM method.
Remote Environment Exploration with Drone Agent and Haptic Force Feedback

Camera drones allow exploring remote scenes that are inaccessible or inappropriate to visit in person. However, these exploration experiences are often limited due to insufficient scene information provided by front cameras, where only 2D images or videos are supplied. Combining a camera drone vision with haptic feedback would augment users' spatial understandings of the remote environment. But such designs are usually difficult for users to learn and apply, due to the complexity of the system and unfluent UAV control. In this paper, we present a new telepresence system for remote environment exploration, with a drone agent controlled by a VR mid-air panel. The drone is capable of generating real-time location and landmark details using integrated Simultaneous Location and Mapping (SLAM). The SLAMs' point cloud generations are produced using RGB input, and the results are passed to a Generative Adversarial Network (GAN) to reconstruct 3D remote scenes in real-time. The reconstructed objects are taken advantage of by haptic devices which could improve user experience through haptic rendering. Capable of providing both visual and haptic feedback, our system allows users to examine and exploit remote areas without having to be physically present. An experiment has been conducted to verify the usability of 3D reconstruction result in haptic feedback rendering.
An Improved Hill Cipher Algorithm using CBC and Hexadecimal S-Box

In order to ensure the security and confidentiality of data, cryptography is used during the exchange of information. The hill cipher is a polygraph cipher with a basic structure and easy calculations. Nonetheless, it is vulnerable to a known-plaintext attack. The instability of an invertible key matrix for decryption is another drawback. In this paper, the method of encrypting plaintext in 128-bit blocks using multiple rounds of encryption process, cipher block chaining, and hexadecimal substitution box was used to resolve these limitations of the Hill cipher. The modified approach offers better data protection and overcomes the drawbacks of the original hill cipher algorithm. The test results prove that the ciphertext protection is 55.34 percent avalanche effect score and goes through the randomness check method based on the actual experiment.
Fuzzy Deep Neural Network for Classification of Overlapped Data

Deep Learning is a popular and promising technique for classification problems. This paper proposes the use of fuzzy deep learning to improve the classification capability when dealing with overlapped data. Most of the research focuses on classification and uses traditional truth and false criteria. However, in reality, a data item may belong to different classes at different degrees. Therefore, the degree of belonging of each data item to a class needs to be considered for classification purposes in some cases. When a data item belongs to different classes with different degrees, then there exists an overlap between the classes. For this reason, this paper proposes a Fuzzy Deep Neural Network based on Fuzzy C-means clustering, fuzzy membership grades and Deep Neural Networks to address the over-lapping issue focused on binary classes and multi-classes. The proposed method converts the original attribute values to relevant cluster centres using the proposed Fuzzy Deep Neural Network. It then trains them with the original output class values. Thereafter, the test data is checked with the Fuzzy Deep Neural Network model for its performance. Using three popular datasets in overlapped and fuzzy data literature, the method presented in this paper outperforms the other methods compared in this study, which are Deep Neural Networks and Fuzzy classification.
On the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e22" altimg="si19.svg"><mml:mi>b</mml:mi></mml:math>-distance of repeated-root constacyclic codes of prime power lengths

Let p be a prime, s, m be positive integers, λ be a nonzero element of the finite field Fpm. The b-distance generalizes the Hamming distance (b=1), and the symbol-pair distance (b=2). While the Hamming and symbol-pair distances of all λ-constacyclic codes of length ps are completely determined, the general b-distance of such codes was left opened. In this paper, we provide a new technique to establish the b-distance of all λ-constacyclic codes of length ps, where 1≤b≤⌊p2⌋. As an application, all MDS b-symbol constacyclic codes of length ps over Fpm are obtained.
A Two-Step Approach to Providing a Desired Quality of Lossy Compressed Images

A problem of lossy image compression with providing a desired quality according to a given quality metric is considered. Several approaches to its solving are discussed. A two-step approach based on using the averaged rate-distortion curve is proposed and tested for a coder AGU based on discrete cosine transform. It is shown that it is often possible to use two iteration steps instead of sufficierntly larger number of iterations that makes compression with providing a desired quality faster. Meanwhile, there are ranges of visual quality where further modifications are needed to produce a desired accuracy of quality providing.