Reviewerly/exHarmony huggingface dataset:
How Deep Learning Helps Compromising USIM

It is known that secret keys can be extracted from some USIM cards using Correlation Power Analysis (CPA). In this paper, we demonstrate a more advanced attack on USIMs, based on deep learning. We show that a Convolutional Neural Network (CNN) trained on one USIM can recover the key from another USIM using at most 20 traces (four traces on average). Previous CPA attacks on USIM cards required high-quality oscilloscopes for power trace acquisition, an order of magnitude more traces from the victim card, and expert-level skills from the attacker. Now the attack can be mounted with a $1000 budget and basic skills in side-channel analysis.
Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&amp;A Site

In recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.
Enhancing nonclassical bosonic correlations in a quantum walk network through experimental control of disorder

The presence of disorder and inhomogeneities in quantum networks has often been unexpectedly beneficial for both quantum and classical resources. Here we experimentally realize a controllable inhomogenous quantum walk (QW) dynamics, which can be exploited to investigate the effect of coherent disorder on the quantum correlations between two indistinguishable photons. Through the imposition of suitable disorder configurations, we observe two-photon states that exhibit an enhancement in the quantum correlations between two selected modes of the network, compared to the case of an ordered QW. Different configurations of disorder can steer the system toward different realizations of such an enhancement, thus allowing spatial and temporal manipulation of quantum correlations between remote modes of QW networks.
Penetration path planning of stealthy UAV based on improved sparse A-star algorithm

In some certain specific scenarios, path planning for UAVs is a hot topic of current research. This paper takes the low-altitude flight path planning of UAV in a single radar detection range as the research background. In the conventional SAS algorithm, the search area is greatly reduced. However, in the process of node expansion, it can only expand in a fixed direction, which is not flexible and has a certain chance to miss the best node. To solve this problem, this paper proposes an improved directional SAS algorithm. The algorithm first expands the search area by taking the line between two adjacent path points as the angular bisector, using the smallest step length as the search radius and adds the UAV's attitude limit to the expanded node to reduce the search area. The simulation is carried out and the results show that SAS algorithm proposed in this paper has the following advantages compared with the conventional SAS algorithm: 1) shorter path length 2) less calculation time 3) lower exposure ratio and lower mean high detection probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> .
CodaQback

Attenuation study of a province is considered as a basic quantity for seismic hazard assessment. It has already been established that the study of two physical processes, namely the seismic sources and propagation of the waves, is essential for seismic-hazard mapping. Additionally, attenuation plays an important role towards scaling seismic hazard. Accordingly, a computational tool entitled CodaQback is presented. Based on back scattering model, this versatile software is equipped with user-friendly graphical user interface. It also allows quick picking of phases for computing coda attenuation parameter. All outputs after each execution step in CodaQback are efficiently exported step-wise into a separate folder in Excel and text formats. To validate the computing tool, it is tested in real data analysis and there is found to be good matching of computed values with already established ones. It is envisioned that this package will enable user to derive quick and reliable estimation of coda attenuation parameter irrespective of geological and geo-morphological units.
Day Ahead Solar Irradiance Forecasting using Markov Chain Model

Solar power technologies have emerged as a strong participant in energy market growth. In the past few years, there has been a corresponding increase in its grid penetration. Accurate short-term solar forecasting is very important for scheduling of the solar parks. The paper investigates the use of the Markov Chain Model for solar irradiance forecasting for a short-term period (hourly day-ahead solar irradiance forecast for an individual solar park). The proposed model depends on only one variable, i.e., Solar Irradiance. The geographical station considered for this study is taken at Bhadla, Jodhpur, Rajasthan, India. The performance of the proposed method is evaluated by calculating different statistical error measures like Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
Implementation of Blood Cell Counting Algorithm using Digital Image Processing Techniques

Blood is a connective tissue with cellular components like RBC, WBC, and platelets. If the blood cells are below the standard range it leads to varies health diseases and hence proper blood cell counting technique plays importance in the pathology department. The most widely used method for counting blood cells is the microscopic technique which gives better results however, it takes more time to count the number of blood cells. Blood cell count using a digital image processing technique can be considered to automate the microscopic method and minimize the time taken by the conventional method. In this paper, a digital image processing technique is proposed to accurately count all the blood cells in a given slide with maximum accuracy. A method such as morphological operator, texture-based classification method and Circular Hough Transformation (CHT) used to count the RBC which gives accuracy around 90%. Similarly, Morphological Operator, Vegetation method, and Texture Object-Based classification are implemented to count the WBC and Platelet. However, Texture Object-Based classification method gave 100 % accuracy compared to Morphological Operator and Vegetation method. From the results discussed in this paper conclude that for RBC count using Circular Hough Transformation gives better result and WBC, platelets count using the Texture Object-Based classification method gives better results.
What Patrons Really Want (In Their Streaming Media): Using Focus Groups to Better Understand Emerging Collections Use

With the increasing use of streaming video in distance and in-person learning, e-resource librarians are working to support faculty in choosing, finding, and licensing appropriate content, and deciding how best to allocate budgets to build appropriate collections. To inform their work, e-resource librarians at the University of North Carolina at Greensboro held focus groups with faculty in 2019. They found that better discovery within the library catalog and stronger liaison librarian awareness of streaming collections could help faculty find content appropriate to their disciplines. They uncovered a preference for chapters or the ability to divide into short clips in order to share digestible segments in class, as well as an overreliance on YouTube that highlighted the need for copyright outreach with faculty.
Convolutional Network and Moving Object Analysis for Vehicle Detection in Highway Surveillance Videos

Moving object detection is an underlying task in so many applications, especially video surveillance applications. One of the applications is for detecting moving vehicles on the highway. The detection of moving vehicles on the highway can then be further analyzed for intelligent transportation systems, such as vehicle counting, tracking, and classification. In the proposed method, the improved background subtraction through bilateral filter, morphology, and connected component is employed to detect moving vehicles preliminarily. Then, the obtained region of moving vehicles is fed into convolutional neural network-based YOLOv3 to detect the location and type of the vehicle. Two highway surveillance videos, including CDNET 2014-“highway” and a public highway video (from YouTube) are used to verify the proposed method. Our approach obtains significant improvement in terms of quantitative and qualitative results. Specifically, the obtained f. measure and percentage of wrong classification (PWC) are 0.8831 and 3.5234, respectively.
Basic Structure on Artificial Intelligence: A Revolution in Risk Management and Compliance

This paper discusses how risk assessment is changed through artificial intelligence (AI) solutions. The key AI strategies of benefit to risk management are first given a non-technical description. A study of the application of these methods to the risk management fields of credit risk, market risk, organizational risk, and enforcement is then presented, utilizing existing experience and empirical data. Further, this research will be concluded with some reflections on existing constraints and views on how in the short- to medium-term the sector is expected to evolve. Overall, an ambitious image of AI's position will be provided for risk management, but notice some realistic restrictions about effective data management practices, accountability and lack of requisite skill sets inside organizations.
Improved Nature-Inspired Algorithms for Numeric Association Rule Mining

Nowadays, only a few papers exist dealing with Association Rule Mining with numerical attributes. When we are confronted with solving this problem using nature-inspired algorithms, two issues emerge: How to shrink the values of the upper and lower bounds of attributes properly, and How to define the evaluation function properly? This paper proposes shrinking the interval of attributes using the so-called shrinking coefficient, while the evaluation function is defined as a weighted sum of support, confidence, inclusion and shrink coefficient. The four nature-inspired algorithms were applied on sport datasets generated by a random generator from the web. The results of the experiments revealed that, although there are differences between selecting a specific algorithm, they could be applied to the problem in practice.
Efficient Data Interaction of Blockchain Smart Contract with Oracle Mechanism

Blcokchain smart contract enables the execution of on-chain contracts automatically and definitized. But sometimes it would be triggered by out-chain data. With the help of Oracle Mechanism, which is an independent third party, we can extend the basic smart contract to a systemic and holonomic architecture. The extended system consist of smart contract on-chain and oracle mechanism out-chain. After making data request to oracle mechanism, oracle mechanism collects data from nodes, and make aggregation for the data, then response back to blockchain. In oracle mechanism, an incentive strategy is necessary to keep the nodes' motivation to provide authentic and accurate data. The extended system could bring efficient data interaction between blockchain and internet, which may drive blockchain be used more widely in various areas.
Collaborative Learning Analysis Using Business Card-Type Sensors

Although most studies have been focused on the language data for investigating cultural practices of learning in the quantitative ethnography, human communication is fundamentally multimodal. The integration of non-language modality data with language data would provide more accurate interpretation of how learners engage in their collaboration. In this study, therefore, we propose Sensor-based Regulation Profiler. Sensor-based Regulation Profiler automatically extracts and visualizes the points that researchers in learning science should notice to support qualitative analysis in collaborative learning. The Sensor-based Regulation Profiler consists of a business card-type sensor that acquires sensor data from each learner as well as a data mining technique that analyzes the acquired sensor data. The proposed data mining technique automatically extracts and visualizes social graphs, learning phases, and speakers during collaborative learning to reduce the costs of qualitative analysis. Experimental evaluations using business card sensors in collaborative learning showed that the social graph, and the learning phase could be automatically extracted and visualized from the acquired sensor data and the speaker identification could be realized with an average accuracy of 77.8% by using the Sensor-based Regulation Profiler.
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent

We study the generalization properties of the popular stochastic optimization method known as stochastic gradient descent (SGD) for optimizing general non-convex loss functions. Our main contribution is providing upper bounds on the generalization error that depend on local statistics of the stochastic gradients evaluated along the path of iterates calculated by SGD. The key factors our bounds depend on are the variance of the gradients (with respect to the data distribution) and the local smoothness of the objective function along the SGD path, and the sensitivity of the loss function to perturbations to the final output. Our key technical tool is combining the information-theoretic generalization bounds previously used for analyzing randomized variants of SGD with a perturbation analysis of the iterates.
A Recommender System for Enhancing Coastal Tourism

Nowadays, the coastal and maritime tourism sector is playing an increasingly important role thanks to the extraordinary beauty, variety and cultural richness of the coastlines. These represent distinctive places, which allows planning unique pathways able to attract different types of users. In this scenario, the Amalfi coast contains cities with an important touristic-cultural value. However, at the same time, not having the same investments in the tourism sector as in big cities, it needs to be enhanced through innovative methodologies. This paper aims to propose a proper methodology with a high-degree Context-Awareness able offering tailored services through a Recommender System for enhancing coastal and maritime tourism. The main innovation features concern the information content available to end-users (itineraries, points of interest, etc.), which present three different aspects: context representation, data management, inferential engines.
The Impact of Innovative Technology on the Aviation Industry and on Customers Preference

This research has investigated the impact of innovative technology on customer preference. Innovative technologies used by other carriers in the world were explored and populations from the Kingdom of Bahrain were asked if these technologies have any impact on their selection. The study showed that there’s a significant relationship between customer preference and innovative technology. Our chapter also has shown that the adoption of these technologies will increase the number of customers, which subsequently will have an impact on the revenue of the company. Other technologies also have a slight impact on the customer’s decision such as Near Field communication. The researchers were able to provide recommendations to the carrier in order to focus on several areas in order to attract more customers and gain a competitive advantage over carriers in the region.
Adversarial Patch Defense for Optical Flow Networks in Video Action Recognition

Deep neural networks (DNN) are being integrated into production-level systems all over the world. Due to their susceptibility to adversarial attacks that degrade their performance, the robustness of DNN models has become a concern for researchers. Recent developments on physically printable adversarial patches showcase their practical effectiveness in fooling DNNs in real-time. Although research on adversarial robustness was majorly focused on image classifiers, recent works have extended the studies in video classification systems. We demonstrate that attacking optical flow estimators in an action recognition system with a patch of size less than 1% of the resolution is sufficient to significantly degrade the performance of the model. We implement an existing defense called Local Gradient Smoothing (LGS) for such localized patch-based attacks in video classification systems. We further address and tackle the shortcomings of LGS, to develop a method called Inpainting with Laplacian Prior (ILP). It was found that ILP can produce accuracy improvement ranging from 3.7% to 37% over the existing defense mechanism LGS while maintaining the same consistency over different patch sizes and models across multiple datasets.
Aspect-Based Unsupervised Negative Sentiment Analysis

Twitter is a social media platform where users post their opinions on various events, products, services and celebrities. Automated analysis of these public posts is useful for tapping into public opinion and sentiment. Identifying negative public sentiment assumes importance when national security issues are at stake or when critical analysis of a product or policy is required. In this paper, a method is introduced that classifies tweets based on their negative content, without any prior training. Specifically, an unsupervised negative sentiment analysis is presented using an aspect-based approach. Phrase and keyword selection criteria are devised after identifying fourteen valid combinations of part-of-speech tags listed in a prioritized order, that are defined as phrase patterns. A sliding text window is passed through each sentence of the tweet to detect the longest valid phrase pattern. The keyword indicating the aspect information is detected using a dependency parser. SentiWordNet lexicon is used for scoring the terms in the detected keyword and phrase combination. The scores are summed up for each sentence of the tweet and transformed nonlinearly by a modified sigmoid function whose output is in the range [−2, 2]; this value comes out to be negative for negative tweets. The utility of our method is proved by superior results as compared to the state of the art on the benchmark SemEval 2013 twitter dataset.
Image Segmentation of Acute Myeloid Leukemia Using Multi Otsu Thresholding

Abstract Acute Myeloid Leukemia (AML) can be identified by utilizing image processing. The stages used in the image processing process include preprocessing, segmentation, and feature extraction. The purpose of this segmentation is to separate objects in the image. One of the image segmentation methods is Multi Otsu Thresholding. This method has a way of working by looking for the threshold value dynamically. Previously there has been researched using the Static Otsu Thresholding method, but the results are still unsatisfactory because the threshold value obtained in the Static Thresholding method is that the threshold value obtained is static, meaning that the threshold value cannot adjust to the brightness level of each image that will be identified. With the background of the segmentation result dissatisfaction factor from the static threshold value, this study aims to carry out the segmentation process to obtain a dynamic threshold value, namely the Multi Otsu Thresholding Method. The Multi Otsu Thresholding method will be applied to segment AML images of M0 and M1 types, with the hope that the segmentation results obtained can be used for the feature extraction process better and are useful for the identification and classification process. Image processing methods used in this research are YCbCr color space, median filter, multi otsu thresholding, and morphological operations. The cell identification process utilizes the cell type classification process using the Naïve Bayes Classifier. The extracted characteristics were WBC (White Blood Cell) diameter, nucleus ratio, and nucleus roundness. Image data to test the process are 29 images of AML M0 and 30 images of AML M1. It was found that the segmentation results using the Multi Otsu Thresholding method can be used for the feature extraction process in AML M0 and AML M1 images and used for the identification process using the Naïve Bayes Classifier resulting in an accuracy of 83.81%, in this case, it has been compared with Static Otsu Thresholding which results with an accuracy of 75.35 %.
Single-Shot Panoptic Segmentation

We present a novel end-to-end single-shot method that segments countable object instances (things) as well as background regions (stuff) into a non-overlapping panoptic segmentation at almost video frame rate. Current state-of-the-art methods are far from reaching video frame rate and mostly rely on merging instance segmentation with semantic background segmentation, making them impractical to use in many applications such as robotics. Our approach relaxes this requirement by using an object detector but is still able to re-solve inter- and intra-class overlaps to achieve a non-overlapping segmentation. On top of a shared encoder-decoder backbone, we utilize multiple branches for semantic segmentation, object detection, and instance center prediction. Finally, our panoptic head combines all outputs into a panoptic segmentation and can even handle conflicting predictions between branches as well as certain false predictions. Our network achieves 32.6% PQ on MS-COCO at 23.5 FPS, opening up panoptic segmentation to a broader field of applications.