Reviewerly/exHarmony huggingface dataset:
Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

Institutions that possess certain collections of digital image libraries, such as museums, are progressively interested in making such collections accessible anytime and anywhere for any Image Retrieval (IR) activities namely browsing and searching. Many researchers have shown that IR methods, in filtering images based on their features such as colors, would provide better indexing and can be able to deliver/provide more accurate results. The color composition of an image, e.g. color histogram has proven to be a powerful feature that can be analyzed and used for image indexing because of its robust standardization of image transformation such as scaling and orientation. In this research, the efforts in narrowing the gap between low relevancy human text descriptions for Malaysian users and image scene color appearances have been brought into attention. The methods are first, to investigate the color concepts and color appearance descriptions of a scene and secondly, to identify a set of ground-truth images for each color appearance category. Psychophysical experiments are conducted to determine a collection of ground-truth images that effectively match five color appearance descriptions for image scenes in accordance with human judgement and perception. The results of the experiments are presented together with the inter-rater agreement analysis. These descriptions that are commonly queried by humans are the following keywords, Bright, Pastel, Dull, Pale, and Dark. The agreement analysis indicates that the Bright category is the most comprehensible by humans and subsequently followed by the Pastel and Dark categories. Dull and Pale categories, on the other hand are fairly understood by humans. All the images involved in this research are landscape painting collections from the internet and they are used for academic purposes only. The results show the top ten ground-truth images for each category that encapsulates a high level of agreeability between humans.
A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets

A new transfer learning strategy is proposed for image classification in this work, based on an 8-layer convolutional neural network. The transfer learning process consists in a training phase of the neural network on a source dataset of images. Then, the last two layers are retrained using a different small target dataset of images. A preliminary study was conducted to train and test the transfer learning proposal on Malaria cell images for a binary classification problem. The methodology proposed has provided a $$6.76\%$$ of improvement with respect to other three different strategies of training non-transfer learning models. The results achieved are quite promising and encourage to conduct further research in this field.
Resumption of virtual machines after adaptive deduplication of virtual machine images in live migration

In cloud computing, load balancing, energy utilization are the critical problems solved by virtual machine (VM) migration. Live migration is the live movement of VMs from an overloaded/underloaded physical machine to a suitable one. During this process, transferring large disk image files take more time, hence more migration and down time. In the proposed adaptive deduplication, based on the image file size, the file undergoes both fixed, variable length deduplication processes. The significance of this paper is resumption of VMs with reunited deduplicated disk image files. The performance measured by calculating the percentage reduction of VM image size after deduplication, the time taken to migrate the deduplicated file and the time taken for each VM to resume after the migration. The results show that 83%, 89.76% reduction overall image size and migration time respectively. For a deduplication ratio of 92%, it takes an overall time of 3.52 minutes, 7% reduction in resumption time, compared with the time taken for the total QCOW2 files with original size. For VMDK files the resumption time reduced by a maximum 17% (7.63 mins) compared with that of for original files.
Study on Operational case of non-face-to-face realtime online Korean classes and Improvement Direction: focused on the full-scale operation of Korean language education institutions

This paper would like to share the operational experience of non-face-to-face real-time online Korean language classes conducted entirely at the institutional level, and suggest future improvements for the first time in Korea. COVID 19, which occurred in 2019, forced education around the world to undergo rapid changes with non-face-to-face education. The institute where this research was also conducted ran classes using real-time video class applications for learners throughout the entire course. A survey of 335 learners and 93 teachers was conducted after a real-time Korean language class in a semester. The investigation found problems in the psychological aspects, such as difficulties in technical aspects and learners’ understanding of class, and the weakening of class atmosphere and motivation. To improve this, it is necessary to strengthen the pre-education and supplement the manpower that solves technical problems, provide diverse and rich teaching materials and expand the opportunity for students to ask questions and speak, create a free and active teaching atmosphere, and make efforts to motivate the learners by the teachers. Furthermore, It is expected that non-face-to-face real-time classes will be established as new courses for various learners, not as an alternative to offline ones.
Recognizing Magnification Levels in Microscopic Snapshots

Recent advances in digital imaging has transformed computer vision and machine learning to new tools for analyzing pathology images. This trend could automate some of the tasks in the diagnostic pathology and elevate the pathologist workload. The final step of any cancer diagnosis procedure is performed by the expert pathologist. These experts use microscopes with high level of optical magnification to observe minute characteristics of the tissue acquired through biopsy and fixed on glass slides. Switching between different magnifications, and finding the magnification level at which they identify the presence or absence of malignant tissues is important. As the majority of pathologists still use light microscopy, compared to digital scanners, in many instance a mounted camera on the microscope is used to capture snapshots from significant field- of-views. Repositories of such snapshots usually do not contain the magnification information. In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition. We compared the results with LBP, a well-known handcrafted feature extraction method. The proposed approach achieved a mean accuracy of 96% when a multi-layer perceptron was trained as a classifier.
Retrofitting Leakage Resilient Authenticated Encryption to Microcontrollers

The security of Internet of Things (IoT) devices relies on fundamental concepts such as cryptographically protected firmware updates. In this context attackers usually have physical access to a device and therefore side-channel attacks have to be considered. This makes the protection of required cryptographic keys and implementations challenging, especially for commercial off-the-shelf (COTS) microcontrollers that typically have no hardware countermeasures. In this work, we demonstrate how unprotected hardware AES engines of COTS microcontrollers can be efficiently protected against side-channel attacks by constructing a leakage resilient pseudo random function (LR-PRF). Using this side-channel protected building block, we implement a leakage resilient authenticated encryption with associated data (AEAD) scheme that enables secured firmware updates. We use concepts from leakage resilience to retrofit side-channel protection on unprotected hardware AES engines by means of software-only modifications. The LR-PRF construction leverages frequent key changes and low data complexity together with key dependent noise from parallel hardware to protect against side-channel attacks. Contrary to most other protection mechanisms such as time-based hiding, no additional true randomness is required. Our concept relies on parallel S-boxes in the AES hardware implementation, a feature that is fortunately present in many microcontrollers as a measure to increase performance. In a case study, we implement the protected AEAD scheme for two popular ARM Cortex-M microcontrollers with differing parallelism. We evaluate the protection capabilities in realistic IoT attack scenarios, where non-invasive EM probes or power consumption measurements are employed by the attacker. We show that the concept provides the side-channel hardening that is required for the long-term security of IoT devices.
A Privacy-By-Design Architecture for Indoor Localization Systems

The availability of mobile devices has led to an arising development of indoor location services collecting a large amount of sensitive information. However, without accurate and verified management, such information could become severe back-doors for security and privacy issues. We propose in this paper a novel Location-Based Service (LBS) architecture in line with the GDPR's provisions. For feasibility purposes and considering a representative use-case, a reference implementation, based on the popular Telegram app, is also presented.
Predicting Mortality in Critical Care Patients with Fungemia Using Structured and Unstructured Data

Fungemia is a life-threatening infection, but predictive models of in-patient mortality in this infection are few. In this study, we developed models predicting all-cause in-hospital mortality among 265 fungemic patients in the Medical Information Mart for Intensive Care (MIMIC-III) database using both structured and unstructured data. Structured data models included multivariable logistic regression, extreme gradient boosting, and stacked ensemble models. Unstructured data models were developed using Amazon Comprehend Medical and BioWordVec embeddings in logistic regression, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We evaluated models trained on all notes, notes from only the first three days of hospitalization, and models trained on only physician notes. The best-performing structured data model was a multivariable logistic regression model that achieved an accuracy of 0.74 and AUC of 0.76. Liver disease, acute renal failure, and intubation were some of the top features driving prediction in multiple models. CNNs using unstructured data achieved similar performance even when trained with notes from only the first three days of hospitalization. The best-performing unstructured data models used the Amazon Comprehend Medical document classifier and CNNs, achieving accuracy ranging from 0.99-1.00, and AUCs of 1.00. Therefore, unstructured data - particularly notes composed by physicians - offer added predictive value over models based on structured data alone.
Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances

The notion of learning from different problem instances, although an old and known one, has in recent years regained popularity within the optimization community. Notable endeavors have been drawing inspiration from machine learning methods as a means for algorithm selection and solution transfer. However, surprisingly approaches which are centered around internal sampling models have not been revisited. Even though notable algorithms have been established in the last decades. In this work, we progress along this direction by investigating a method that allows us to learn an evolutionary search strategy reflecting rough characteristics of a fitness landscape. This latter model of a search strategy is represented through a flexible mixture-based distribution, which can subsequently be transferred and adapted for similar problems of interest. We validate this approach in two series of experiments in which we first demonstrate the efficacy of the recovered distributions and subsequently investigate the transfer with a systematic from the literature to generate benchmarking scenarios.
Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens

Attackers use a variety of techniques to insert redirection JavaScript that leads a user to a malicious webpage, where a drive-by-download attack is executed. In particular, the redirection JavaScript in the landing site is obfuscated to avoid detection systems. In this paper, we propose a lightweight detection system based on static analysis to classify the obfuscation type and to promptly detect the obfuscated redirection JavaScript. The proposed model detects the obfuscated redirection JavaScript by converting the JavaScript into an abstract syntax tree (AST). Then, the structure and token information are extracted. Specifically, we propose a lightweight AST to identify the obfuscation type and the revised term frequency-inverse document frequency to efficiently detect the malicious redirection JavaScript. This approach enables rapid identification of the obfuscated redirection JavaScript and proactive blocking of the webpages that are used in drive-by-download attacks.
Niching Evolutionary Computation With a Priori Estimate for Solving Multi-Solution Traveling Salesman Problem

Multi-solution traveling salesman problem has diverse optimal routes. To obtain the different optimal solutions, current researches incorporate evolutionary algorithms with niching techniques. However, without knowing the problem characteristics in advance, the algorithms suffer from difficulties in setting the niching parameters. To address this issue, we utilize a graph neural network to predict a prior knowledge about the optimal tour length. Then, with the prior estimate, the niche radius can be adjusted for a specific problem. We develop a niching evolutionary algorithm that utilizes the calculated niche radius to identify diverse niches. Besides, a selective local search strategy is embedded into the algorithm to enhance the search capability. The experimental results show that the proposed algorithm has a competitive performance over the comparison algorithms on the benchmark suite.
3D car-detection based on a Mobile Deep Sensor Fusion Model and real-scene applications

Unmanned vehicles need to make a comprehensive perception of the surrounding environmental information during driving. Perception of automotive information is of significance. In the field of automotive perception, the sterevision of car-detection plays a vital role and sterevision can calculate the length, width, and height of a car, making the car more specific. However, under the existing technology, it is impossible to obtain accurate detection in a complex environment by relying on a single sensor. Therefore, it is particularly important to study the complex sensing technology based on multi-sensor fusion. Recently, with the development of deep learning in the field of vision, a mobile sensor-fusion method based on deep learning is proposed and applied in this paper——Mobile Deep Sensor Fusion Model (MDSFM). The content of this article is as follows. It does a data processing that projects 3D data to 2D data, which can form a dataset suitable for the model, thereby training data more efficiently. In the modules of LiDAR, it uses a revised squeezeNet structure to lighten the model and reduce parameters. In the modules of cameras, it uses the improved design of detecting module in R-CNN with a Mobile Spatial Attention Module (MSAM). In the fused part, it uses a dual-view deep fusing structure. And then it selects images from the KITTI's datasets for validation to test this model. Compared with other recognized methods, it shows that our model has a fairly good performance. Finally, it implements a ROS program on the experimental car and our model is in good condition. The result shows that it can improve performance of detecting easy cars significantly through MDSFM. It increases the quality of the detected data and improves the generalized ability of car-detection model. It improves contextual relevance and preserves background information. It remains stable in driverless environments. It is applied in the realistic scenario and proves that the model has a good practical value.
Genetic Programming-Based Feature Learning for Facial Expression Classification

Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.
A Region Adaptive Image Classification Approach Using Genetic Programming

Feature extraction, as one essential step of image classification, can potentially reduce image data dimensionality and capture effective information for improving performance. However, most existing image descriptors are designed to conduct specific tasks and might not be sufficient for different types of images. Genetic programming (GP) can automatically extract multiple important and discriminative features by incorporating diverse image descriptors into a GP program. Furthermore, different regions in an image have different structural characteristics. In this paper, we propose a region adaptive image classification approach based on GP, which can automatically extract informative image features by automatically applying different image descriptors in different regions of an image. A new flexible GP program structure with a new function set and a new terminal set is developed in this approach. The performance of the proposed method is evaluated on four various data sets and compared with other state-of-the-art classification methods. Experimental results illustrate that the proposed approach is capable of achieving better or competitive performance than these baseline methods. Further analysis of some good programs shows the high interpretability of the proposed method.
Randomized Neural Networks Based Decentralized Multi-Task Learning via Hybrid Multi-Block ADMM

In multi-task learning (MTL), related tasks learn jointly to improve generalization performance. To exploit the high learning speed of feed-forward neural networks (FNN), we apply the randomized single-hidden layer FNN (RSF) to the MTL problem, where the output weights of RSFs for all the tasks are learned collaboratively. We first present the RSF based MTL problem in the centralized setting, which is solved by the proposed MTL-RSF algorithm. Due to the fact that many data sets of different tasks are geo-distributed, decentralized machine learning is studied. We formulate the decentralized MTL problem based on RSF as majorized multi-block optimization with coupled bi-convex objective functions. To solve the problem, we propose the DMTL-RSF algorithm, which is a hybrid Jacobian and Gauss-Seidel Proximal multi-block alternating direction method of multipliers (ADMM). Further, to reduce the computation load of DMTL-RSF, DMTL-RSF with first-order approximation (FO-DMTL-RSF) is presented. Theoretical analysis shows that the convergence to the stationary point of proposed decentralized algorithms can be guaranteed conditionally. Through simulations, we demonstrate the convergence of presented algorithms, and also show that they can outperform existing MTL methods. Moreover, by adjusting the dimension of hidden feature space, there exists a trade-off between communication load and learning accuracy for DMTL-RSF.
A Threshold-free Classification Mechanism in Genetic Programming for High-dimensional Unbalanced Classification

Class imbalance is an unavoidable issue in many real-world applications. Learning from unbalanced data, classifiers are often biased toward the majority class, while the minority class is important as well (even more important in many cases). How the issue of class imbalance is addressed becomes more challenging if a classification task further encounters the high dimensionality issue. This paper proposes a new genetic programming (GP) approach to high-dimensional unbalanced classification. A new classification mechanism is proposed for GP to improve its classification performance. This new classification mechanism is independent of a classification threshold to separate the majority class and the minority class. The effectiveness of the proposed method is examined on seven high-dimensional unbalanced datasets. Experimental results indicate that the proposed GP method often performs better than other GP methods that use a fitness function to solve the issue of class imbalance, in terms of classification performance and training time.
An Economic Model of Blockchain: The Interplay between Transaction Fees and Security

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Data Science Methods and Machine Learning Algorithm Implementations for Customized Pratical Usage

Due to the unprecedented rise of data content over the last decade, an opportunity for databased personalization and analysis has become a norm in the modern world. By implementing Machine Learning algorithms and Data Science methods no industry remained unchanged. This paper applied these methods and algorithms in personal, practical examples in order to see their benefits in our day-today lives. For the purpose of this case study, we analyzed three cases: a personal movie recommender, messages analysis and real estate trends and predictions on the local market. For this research we used global and personal data, and applied a suitable machine learning model. The purpose of this paper is to establish how one individual, and in what measure, with the use of these new technological tools, can ease his decision making process and manage a more tailored lifestyle.