Reviewerly/exHarmony huggingface dataset:
Augmented Reality Visualization of Modal Analysis Using the Finite Element Method

Modal analysis provides the dynamic behavior of an object or structure, and is often undertaken using the Finite Element Method (FEM) due to its ability to deal with arbitrary geometries. This article investigates the use of Augmented Reality (AR) to provide the in situ visualization of a modal analysis for an aluminum impeller. Finite Element Analysis (FEA) software packages regularly use heat maps and shape deformation to visualize the outcomes of a given simulation. AR allows the superimposition of digital information on a view of the real-world environment, and provides the opportunity to overlay such simulation results onto real-world objects and environments. The presented modal analysis undertaken herein provides natural frequencies and the corresponding deformation of an aluminum impeller. The results indicate the ability for the design part and finite element analysis results to be viewed on the physical part. A mobile AR-FEA-based system was developed for Modal Analysis result visualization. This study offers designers and engineers a new way to visualize such simulation results.
Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.
On Fast Jerk–, Acceleration– and Velocity–Restricted Motion Functions for Online Trajectory Generation

Finding fast motion functions to get from an initial state (distance, velocity, acceleration) to a final one has been of interest for decades. For a solution to be practically relevant, restrictions on jerk, acceleration and velocity have to be taken into account. Such solutions use optimization algorithms or try to directly construct a motion function allowing online trajectory generation. In this contribution, we follow the latter strategy and present an approach which first deals with the situation where initial and final accelerations are 0, and then relates the general case as much as possible to this situation. This leads to a classification with just four major cases. A continuity argument guarantees full coverage of all situations which is not the case or is not clear for other available algorithms. We present several examples that show the variety of different situations and, thus, the complexity of the task. We also describe an implementation in MATLAB® and results from a huge number of test runs regarding accuracy and efficiency, thus demonstrating that the algorithm is suitable for online trajectory generation.
Rethinking the Pruning Criteria for Convolutional Neural Network

Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), and various pruning criteria have been proposed to remove the redundant filters of CNNs. From our comprehensive experiments, we find some blind spots on pruning criteria: (1) Similarity: There are some strong similarities among several primary pruning criteria that are widely cited and compared. According to these criteria, the ranks of filters’ importance in a convolutional layer are almost the same, resulting in similar pruned structures. (2) Applicability: For a large network (each convolutional layer has a large number of filters), some criteria can not distinguish the network redundancy well from their measured filters' importance. In this paper, we theoretically validate these two findings with our assumption that the well-trained convolutional filters in each layer approximately follow a Gaussian-alike distribution. This assumption is verified through systematic and extensive statistical tests.
Double-Channel Guided Generative Adversarial Network for Image Colorization

Image colorization has a widespread application in video and image restoration in the past few years.Recently, automatic colorization methods based on deep learning have shown impressive performance.However, these methods map grayscale image input into multi-channel output directly.In the process, it usually loses detailed information during feature extraction, resulting in abnormal colors in local areas of the colorization image.To overcome abnormal colors and improve colorization quality, we propose a novel Double-Channel Guided Generative Adversarial Network (DCGGAN).It includes two modules: a reference component matching module and a double-channel guided colorization module.The reference component matching module is introduced to select suitable reference color components as auxiliary information of the input.The double-channel guided colorization module is designed to learn the mapping relationship from the grayscale to each color channel with the assistance of reference color components.Experimental results show that the proposed DCGGAN outperforms existing methods on different quality metrics and achieves state-of-the-art performance.
Analysis of Patterns and Similarities in Service Tickets using Natural Language Processing

In this paper we propose an approach for classifying documents, embedding documents into feature vectors and using these embeddings for finding similarities between them. Our chosen domain for applying this method is the IT-Service Support branch, where the documents we try to analyse are support tickets and the potential of classifying and finding patterns between tickets is huge for optimizing the service process. We aim to tackle the problem with multiple methods of text classification and recognition, and data analysis, followed by comparison and interpretation of the results. Following our previous work in this field, we propose further means of validating our models, so we can describe and visualize several methods of feature extraction and recognition for service tickets that help the business process for Service Support.
Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.
Empirical Measurements on Pricing Oracles and Decentralized Governance for Stablecoins

Stable-coins are designed to address the volatility of crypto assets by maintaining a peg to a non-volatile currency such as the US Dollar. Decentralized Stable-coins that maintain their collateral on-chain need a pricing oracle to determine the current market value of the collateral. They also employ a decentralized governance system to make policy changes. In this paper we analyze the inner-workings of the pricing oracle and the decentralized governance mechanism employed in the MakerDao stable-coin, one of the largest and fully developed on-chain stable-coins. We study the accuracy of the pricing oracle over time, as well as disagreements between pricing reports received by MakerDao. We also study the robustness of the decentralized governance system. This work sheds detailed light on the practical operation of a pricing oracle and a decentralized governance mechanism in a large deployed system. We make a number of recommendations for improvements based on our findings.
Systematic literature review of sentiment analysis in the Spanish language

Purpose Most studies on Sentiment Analysis are performed in English. However, as the third most spoken language on the Internet, Sentiment Analysis for Spanish presents its challenges from a semantic and syntactic point of view. This review presents a scope of the recent advances in this area. Design/methodology/approach A systematic literature review on Sentiment Analysis for the Spanish language was conducted on recognized databases by the research community. Findings Results show classification systems through three different approaches: Lexicon based, Machine Learning based and hybrid approaches. Additionally, different linguistic resources as Lexicon or corpus explicitly developed for the Spanish language were found. Originality/value This study provides academics and professionals, a review of advances in Sentiment Analysis for the Spanish language. Most reviews on Sentiment Analysis are for English, and other languages such as Chinese or Arabic, but no updated reviews were found for Spanish.
Survey on Traffic Management in Data Center Network: From Link Layer to Application Layer

Due to the explosive growth of all kinds of Internet services, data centers have become an irreplaceable and vital infrastructure to support this soaring trend.Compared with traditional networks, data center networks (DCNs) have unique features, such as high bandwidth, low latency, many-to-one communication mode, shallow buffered switches, and multi-root topology.These new characteristics pose a lot of challenges to previous network technics (e.g., Ethernet, Equal Cost Multi-path (ECMP), TCP), making them hard to adapt to DCNs and leading to severe performance degradation.In order to solve these challenges, DCNs have attracted a lot of attention from the industry and academia in recent years, and many new mechanisms in different layers are proposed to improve the transmission performance of data center networks.In the meantime, many surveys have emerged currently to introduce the current research of data center networks.However, previous surveys of DCNs mainly focus on only one specific network layer, making them difficult for readers to know about advanced researches on a holistic level.To help readers comprehend the current research progress of data center networks quickly, we employ a multi-layered top down taxonomy to classify the literature and propose several probable dimensions for future research in this area.
Quantum-accelerated constraint programming

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">alldifferent</mml:mtext></mml:mrow></mml:math>global constraint and discuss its applicability to a broader family of global constraints with similar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault-tolerant quantum computers and beyond.
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection

In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (GANs) have easily generated fake images, so detecting manipulated images has become an urgent issue. Most state-of-the-art forgery detection methods assume that images include checkerboard artifacts which are generated by using DNNs. Accordingly, we propose a novel CycleGAN without any checkerboard artifacts for counter-forensics of fake-mage detection methods for the first time, as an example of GANs without checkerboard artifacts.
Lilotane: A Lifted SAT-based Approach to Hierarchical Planning

One of the oldest and most popular approaches to automated planning is to encode the problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a solution. In all established SAT-based approaches for Hierarchical Task Network (HTN) planning, grounding the problem is necessary and oftentimes introduces a combinatorial blowup in terms of the number of actions and reductions to encode. Our contribution named Lilotane (Lifted Logic for Task Networks) eliminates this issue for Totally Ordered HTN planning by directly encoding the lifted representation of the problem at hand. We lazily instantiate the problem hierarchy layer by layer and use a novel SAT encoding which allows us to defer decisions regarding method arguments to the stage of SAT solving. We show the correctness of our encoding and compare it to the best performing prior SAT encoding in a worst-case analysis. Empirical evaluations confirm that Lilotane outperforms established SAT-based approaches, often by orders of magnitude, produces much smaller formulae on average, and compares favorably to other state-of-the-art HTN planners regarding robustness and plan quality. In the International Planning Competition (IPC) 2020, a preliminary version of Lilotane scored the second place. We expect these considerable improvements to SAT-based HTN planning to open up new perspectives for SAT-based approaches in related problem classes.
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection

Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception. Using these point cloud and image data, perception models based on deep neural nets (DNNs) have achieved state-of-the-art performance in 3D detection. The vulnerability of DNNs to adversarial attacks have been heavily investigated in the RGB image domain and more recently in the point cloud domain, but rarely in both domains simultaneously. Multi-modal perception systems used in AVs can be divided into two broad types: cascaded models which use each modality independently, and fusion models which learn from different modalities simultaneously. We propose a universal and physically realizable adversarial attack for each type, and study and contrast their respective vulnerabilities to attacks. We place a single adversarial object with specific shape and texture on top of a car with the objective of making this car evade detection. Evaluating on the popular KITTI benchmark, our adversarial object made the host vehicle escape detection by each model type more than 50% of the time. The dense RGB input contributed more to the success of the adversarial attacks on both cascaded and fusion models.
Grover/Zeta Correspondence based on the Konno–Sato theorem

Recently, the Ihara zeta function for the finite graph has been extended to infinite one by Clair and Chinta et al. In this paper, we obtain the same expressions by a different approach from their analytical method. Our new approach is to take a suitable limit of a sequence of finite graphs via the Konno–Sato theorem. This theorem is related to explicit formulas of characteristic polynomials for the evolution matrix of the Grover walk. The walk is one of the most well-investigated quantum walks which are quantum counterpart of classical random walks. We call the relation between the Grover walk and the zeta function based on the Konno–Sato theorem “Grover/Zeta Correspondence” here.
Attention-Based Multimodal Entity Linking with High-Quality Images

Multimodal entity linking (MEL) is an emerging research field which uses both textual and visual information to map an ambiguous mention to an entity in a knowledge base (KB). However, images do not always help, which may also backfire if they are irrelevant to the textual content at all. Besides, the existing efforts mainly focus on learning a representation of both mentions and entities from their textual and visual contexts, without considering the negative impact brought by noisy irrelevant images, which happens frequently with social media posts. In this paper, we propose a novel MEL model, which not only removes the negative impact of noisy images, but also uses multiple attention mechanism to better capture the connection between mention representation and its corresponding entity representation. Our empirical study on a large real data collection demonstrates the effectiveness of our approach.
Data Augmentation for Improved Brain Tumor Segmentation

Deep neural networks (DNN) oblige large preprocessed samples of training annotated images for successful training, which makes the approach costly particularly in the biomedical imaging domain. The data augmentation technique is regularly used by researchers to enlarge the volume of training data, creating, and producing augmented data capable to train the network about the essential properties of uniformity and stoutness. The use of conventional methods of data augmentation in most training system scenarios strictly restrict its capabilities and negatively impact the output accuracy. In this paper, we propose an automatic data augmentation technique for synthesizing high-quality brain tumor images using generative adversarial network architecture to facilitate deep learning-based methods to be trained with the limited preprocessed samples more competently. The tumor segmentation has been performed through geodesic active contour via a level set formulation. The proposed technique has been validated with different modalities of magnetic resonance imaging brain image obtained from BRATS13 datasets. Simulational results showed an enhanced performance yielding a dice similarity coefficient of 0.942.
Frequent Pattern Mining in Big Social Graphs

With the popularity of graph applications, frequent pattern mining (FPM) has been playing a significant role in many domains, such as social networks and bioinformatics. However, due to the exponential time complexity of FPM, it is a challenge for most existing techniques in big dense graphs, such as social graphs. In this paper, with the defined concept of social pattern, a corresponding linear time computable support calculation measurement, called Minimum Independent Individual based support, is proposed. Then, we adopt the concept of pathgraph to store the appearance of social patterns and propose a novel approach ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SocMi</small> ) to solve the problem of frequent social pattern mining. Additionally, in order to reduce the exponential time consumption to explore big graphs, an approximate approach ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASocMi</small> ) with a quick exploration strategy is proposed. Moreover, the proposed approaches have been further optimized by using cache during processing. Finally, an extensive empirical study in real-world social graphs has demonstrated the effectiveness and efficiency of the proposed approaches compared with the state-of-the-art approaches.
Perspectives in pathomics in head and neck cancer

Pathology is the cornerstone of cancer care. Pathomics, which represents the use of artificial intelligence in digital pathology, is an emerging and promising field that will revolutionize medical and surgical pathology in the coming years. This review provides an overview of pathomics, its current and future applications and its most relevant applications in Head and Neck cancer care.The number of studies investigating the use of artificial intelligence in pathology is rapidly growing, especially as the utilization of deep learning has shown great potential with Whole Slide Images. Even though numerous steps still remain before its clinical use, Pathomics has been used for varied applications comprising of computer-assisted diagnosis, molecular anomalies prediction, tumor microenvironment and biomarker identification as well as prognosis evaluation. The majority of studies were performed on the most frequent cancers, notably breast, prostate, and lung. Interesting results were also found in Head and Neck cancers.Even if its use in Head and Neck cancer care is still low, Pathomics is a powerful tool to improve diagnosis, identify prognostic factors and new biomarkers. Important challenges lie ahead before its use in a clinical practice, notably the lack of information on how AI makes its decisions, the slow deployment of digital pathology, and the need for extensively validated data in order to obtain authorities approval. Regardless, pathomics will most likely improve pathology in general, including Head and Neck cancer care in the coming years.