Reviewerly/exHarmony huggingface dataset:
SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images

Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers' potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model's performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.
Convolutional Neural Network Based Image Classification And New Class Detection

Image Classification is the task of assigning an input image to a label from a set of fixed labels. This is one of the main problems in computer vision that have many practical applications. For any classification problem, the main aim is to achieve better classification accuracy. If the classification accuracy is less, then misclassification happens and this will leads to different kinds of problems. Many of the classification models only consider the existing class instances. When a new class instance arrives the classification model not detect it properly. They actually misclassified the new class instance into an existing class instance. The proposed method therefore shows a better accurate classification and new class detection model for images. Also if needed, then the new class can be added with the model to classify correctly in the future. Recent studies show that Convolutional Neural Network(CNN) can be effectively used for image classification tasks. So here creating this better accurate classification and new class detection model based on CNN. The detection of a new class is done by looking into the trend of the softmax prediction score of class labels. In this work, the model is built for CIFAR10 image dataset. This dataset is actually a complex dataset, so creating a model for this dataset can consider as a base and extended for the classification and new class detection in other images in different applications.
Lessons Learned From Immersive and Desktop VR Training of Mines Rescuers

This paper discusses results from two successive rounds of virtual mines rescue training. The first round was conducted in a surround projection environment (360-VR), and the second round was conducted in desktop virtual reality (Desktop-VR). In the 360-VR condition, trainees participated as groups, making collective decisions. In the Desktop-VR condition, trainees could control their avatars individually. Overall, 372 participants took part in this study, including 284 mines rescuers who took part in 360-VR, and 243 in Desktop-VR. (155 rescuers experienced both.) Each rescuer who trained in 360-VR completed a battery of pre- and post-training questionnaires. Those who attended the Desktop-VR session only completed the post-training questionnaire. We performed principal components analysis on the questionnaire data, followed by a multiple regression analysis, the results of which suggest that the chief factor contributing to positive learning outcome was Learning Context, which extracted information about the quality of the learning content, the trainers, and their feedback. Subjective feedback from the Desktop-VR participants indicated that they preferred Desktop-VR to 360-VR for this training activity, which highlights the importance of choosing an appropriate platform for training applications, and links back to the importance of Learning Context. Overall, we conclude the following: 1) it is possible to train effectively using a variety of technologies but technology that is well-suited to the training task is more useful than technology that is “more advanced,” and 2) factors that have always been important in training, such as the quality of human trainers, remain critical for virtual reality training.
Review of Image encryption techniques using neural network for optical security in the healthcare sector – PNO System

Image encryption is used to encrypt patient images that contain diagnostic information about patients in healthcare. The healthcare sector uses electronic media to support the transmission of scanning results, such as X-rays, MRI scans and ultrasound images. The primary purpose of this paper is to investigate encryption of images through techniques utilising neural networks to maintain security and privacy of patient records. Patient image data, neural network-based encryption, and optical security (PNO) systems are examined in this research work. These components will provide some validation in the use of neural network-enabled image encryption in healthcare. The evaluation of the PNO system is based on different quality factors, which are compared in a classification of the 30 state-of-the-art solutions in image encryption. The effectiveness of the encryption process can be increased in terms of high accuracy, less noise and enhanced security. We conclude that using neural network-based encryption techniques can increase security in visual media in the healthcare sector.
PAC-FIT: An efficient privacy preserving access control scheme for fog-enabled IoT

With the proliferation of data produced by IoT devices, a new paradigm called fog computing has developed that allows processing and analysis of data at the edge. Together with fog, cloud computing co-exists for purposes such as enormous storage, processing resources, etc. However, storage and computation of data at various levels increase the risk of data privacy. Therefore, an access control mechanism is necessary for the fog-cloud framework in an IoT environment. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic mechanism that provides confidentiality and fine-grained access control. Unfortunately, the existing CP-ABE schemes are not well suited for the cloud-fog-IoT environment as they do not provide the following functionalities together: key-escrow resistance, attribute revocation, attribute addition, and outsourcing of expensive operations. Therefore, this paper proposes a CP-ABE scheme named PAC-FIT that supports key-escrow resistance, attribute revocation, and attribute addition features. Additionally, the expensive encryption and decryption operations are outsourced to fog nodes, which greatly reduces the computational overhead from resource-constrained IoT devices. Further, the tasks of attribute revocation and addition are also outsourced to third parties. The cost incurred during attribute revocation and addition are efficient as only those key components and ciphertexts are updated, which are associated with revoked or added attributes. Meanwhile, unlike existing CP-ABE schemes, the user holds a constant size secret key, which remains unchanged throughout. The security analysis proves that PAC-FIT is secure against Chosen-Plaintext Attack under Decisional Bilinear Diffie-Hellman assumption. The performance analysis shows that PAC-FIT is efficient and suitable for IoT devices.
Randomness for quantum channels: Genericity of catalysis and quantum advantage of uniformness

Randomness can help one to implement quantum maps that cannot be realized in a deterministic fashion. Recently, it was discovered that explicitly treating a randomness source as a quantum system could double the efficiency as a catalyst for some tasks. In this work, we first show that every quantum channel that can be implemented with a randomness source without leaking information to it must be a catalysis. For that purpose, we prove a new no-go theorem that generalizes the no-hiding theorem, the no-secret theorem that states no quantum information can be shared with other system as a secret without leaking some information. Second, we show that non-degenerate catalysts should be used classically when no extra dimension is allowed, which leads to the fact that the quantum advantage of a catalytic process strictly comes from the uniformness of the randomness source. Finally, we discuss a method to circumvent the previous result that achieves quantum advantage with non-degenerate catalyst uniformized by employing extra work space.
Rare-class learning over Mg-doped ZnO nanoparticles

This interdisciplinary study is conducted to find answers to two important questions which researchers often face in Machine Learning (ML) and Material Science (MS) fields. In this work, we measure the performance of the most popular ML algorithms (more than a dozen) on rare-class learning problem and determine the best learning algorithm for atom type prediction over the Mg-doped ZnO nanoparticles data obtained from the density-functional tight-binding method. As a result, we observe that tree-based ML algorithms such as Extreme Gradient Boosting (XGB), Decision Trees (DT), Random Forest (RF), outperform other types of ML algorithms, e.g., cost-sensitive learning, prototype models, support vector machines, kernel methods, on both rare-class learning and atom type prediction.
ImbTreeEntropy and ImbTreeAUC: Novel R Packages for Decision Tree Learning on the Imbalanced Datasets

This paper presents two R packages ImbTreeEntropy and ImbTreeAUC to handle imbalanced data problems. ImbTreeEntropy functionality includes application of a generalized entropy functions, such as Rényi, Tsallis, Sharma–Mittal, Sharma–Taneja and Kapur, to measure impurity of a node. ImbTreeAUC provides non-standard measures to choose an optimal split point for an attribute (as well the optimal attribute for splitting) by employing local, semi-global and global AUC (Area Under the ROC curve) measures. Both packages are applicable for binary and multiclass problems and they support cost-sensitive learning, by defining a misclassification cost matrix, and weighted-sensitive learning. The packages accept all types of attributes, including continuous, ordered and nominal, where the latter type is simplified for multiclass problems to reduce the computational overheads. Both applications enable optimization of the thresholds where posterior probabilities determine final class labels in a way that misclassification costs are minimized. Model overfitting can be managed either during the growing phase or at the end using post-pruning. The packages are mainly implemented in R, however some computationally demanding functions are written in plain C++. In order to speed up learning time, parallel processing is supported as well.
Software Defect Prediction and Localization with Attention-Based Models and Ensemble Learning

Software defect prediction (SDP) utilizes a trained prediction model to predict the defect proneness of code modules in a software system by mining the inherent characteristics of historical defect data. An effective model can optimize the allocation of testing resources, thus improving the quality of software products. Most previous studies use handcrafted features to represent code snippets, but the main problem is that it is difficult to capture the semantic and structural information of the code context, which is often crucial for software defect prediction. Meanwhile, most of the existing software defect prediction models cannot make predictions at the code line level, which makes it extremely arduous to provide developers with more detailed reference information. To address these issues, in this paper, we propose a model based on ensemble learning techniques and attention mechanisms to offer more comprehensive prediction information to developers by locating suspect lines of code when making method-level defect predictions. This model leverages abstract syntax trees (ASTs) as the intermediate representation of code snippets. Since the historical defect data has a striking characteristic of class-imbalance, an approach based on Self-organizing Map (SOM) clustering is employed to handle noisy data. Experimental results show that, on average, the proposed model improves the F-measure by 17.7% and AUC by 37.8%, compared with the other four machine learning algorithms.
Parallel Optimization of Program Instructions Using Genetic Algorithms

This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written.We solve the problem of the optimal distribution of a set of instructions on available processors.We propose a genetic algorithm to parallelize computations, using evolution to search the solution space.The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with.In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem.This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem.The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized.The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method.Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.
Deep multiple non-negative matrix factorization for multi-view clustering

Multi-view clustering aims to group similar samples into the same clusters and dissimilar samples into different clusters by integrating heterogeneous information from multi-view data. Non-negative matrix factorization (NMF) has been widely applied to multi-view clustering owing to its interpretability. However, most NMF-based algorithms only factorize multi-view data based on the shallow structure, neglecting complex hierarchical and heterogeneous information in multi-view data. In this paper, we propose a deep multiple non-negative matrix factorization (DMNMF) framework based on AutoEncoder for multi-view clustering. DMNMF consists of multiple Encoder Components and Decoder Components with deep structures. Each pair of Encoder Component and Decoder Component are used to hierarchically factorize the input data from a view for capturing the hierarchical information, and all Encoder and Decoder Components are integrated into an abstract level to learn a common low-dimensional representation for combining the heterogeneous information across multi-view data. Furthermore, graph regularizers are also introduced to preserve the local geometric information of each view. To optimize the proposed framework, an iterative updating scheme is developed. Besides, the corresponding algorithm called MVC-DMNMF is also proposed and implemented. Extensive experiments on six benchmark datasets have been conducted, and the experimental results demonstrate the superior performance of our proposed MVC-DMNMF for multi-view clustering compared to other baseline algorithms.
Automated Recovery and Visualization of Test-to-Code Traceability (TCT) Links: An Evaluation

In the software development process, traceability links between unit tests and code are not explicitly maintained, and dependencies in most cases are manually identified. As a result, a large amount of effort and time is required during the comprehension process to establish the links between these artifacts. Although there are several methods that can infer such links based on different phenomenons, these methods usually produce different set of traceability links. This work expands upon previous traceability link recovery and visualization studies by implementing a combination of traceability recovery methods that automatically retrieve the links, and visualizing them to help developers to overview the links inferred by various recovery techniques, and also to select the right relations for analyses. The results of the usability study show that the visualization model presented herein can effectively support browsing, comprehension, and maintenance of Test-to Code Traceability (TCT) links in a system with enhanced efficiency, as well as visualization of TCT links from multiple sources is better than a visualization of single source of traceability links.
Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.
Three-Pass Cryptographic Protocol Based on Permutations

The paper proposes a new principle for the implementation of a three-pass cryptographic protocol. All operations implemented in it are performed on permutations. Such operations are the multiplication of permutations, raising to the power of their disjoint cycles, as well as the operation of finding the conjugate permutation. This avoids the problems associated with discrete logarithms, which can increase the cryptographic strength of the proposed protocol. The developed protocol can be used both for information transmission systems using factorial coding and for traditional systems.
Product Traceability Using Blockchain

In this modern era, as the demand for the products has substantially increased, it has been observed that compromises being made in the quality of the products. This has also led to many frauds due to the lack of transparency in the Supply Chain Management (SCM) systems. Due to the inconsistency and opaqueness of these systems, it is not possible to detect all the fraudulent steps that have been carried out with the product and other compromises that have been made to the quality of the product.Here we propose a blockchain-based product traceability system to overcome these inconsistencies in the current systems. With the help of enhanced security of blockchain technology [1], tampering of the data can be prevented and transparency of this system can help us develop and maintain trust among all the participating entities in the system. In this way, we can eradicate all the discrepancies present in the current system and ensure that the quality of the product is not being compromised.
Towards a Human-AI Hybrid System for Categorising Programming Problems

As programming skills are increasingly required world-wide and across disciplines, many students use online platforms that provide automatic feedback through a Programming Online Judge (POJ) mechanism. POJs are very popular e-learning tools, boasting large collections of programming problems. Despite their many benefits, students often struggle when solving problems not compatible with their prior knowledge. One important cause of this is that usually statements of problems are not classified according to programming topics (paradigms, data structures, etc.) and, hence, students waste time and effort in trying to solve exercises that are not tailored to their level and needs. Thus, to support students, we propose a new, "front-heavy" pipeline method to predict topics of POJ problems, using Bidirectional Encoder Representations from Transformers (BERT) for contextual text augmentation over the problem statements and further allowing for (lighter-weight) classical machine learning for classification. Our model outperformed all current state-of-the art, with an F1-score of 86% using stratified 10 fold cross-validation in a classically challenging multi-classification problem with seven categories. As a proof of concept, we conducted an experiment to show how our predictive model can be used as a human-AI hybrid complement for POJ, where learners would use AI-based recommendations to find the most appropriate problems.
Distributed interaction design

research-article Distributed interaction design: designing human-centered interactions in a time of social distancing Share on Authors: Abdullah X. Ali University of Washington University of WashingtonView Profile , Meredith Ringel Morris Microsoft Research Microsoft ResearchView Profile , Jacob O. Wobbrock University of Washington University of WashingtonView Profile Authors Info & Affiliations InteractionsVolume 28Issue 2March - April 2021 pp 82–87https://doi.org/10.1145/3447790Published:03 March 2021 0citation519DownloadsMetricsTotal Citations0Total Downloads519Last 12 Months519Last 6 weeks39 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Defeating Cache Timing Channels with Hardware Prefetchers

This article proposes a framework to mitigate cache timing attacks. The framework identifies attacks and targets by monitoring the cache access pattern and counteracts them by injecting noise using hardware prefetcher. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Università della Svizzera Italiana.