Reviewerly/exHarmony huggingface dataset:
A Hybrid Blockchain Consensus Algorithm Using Locational Marginal Pricing for Energy Applications

Blockchain technology has recently witnessed a rapid proliferation across multiple industries and has the potential to evolve as a popular energy transaction platform for grid operators and general users. The current proof-of-work consensus algorithm used by many blockchain protocols suffer from vulnerabilities such as the majority attacks problem and strip mining. The algorithm often requires specialized application-specific hardware and involves energy-intensive computation, making it an unlikely candidate for power grid applications. On the other hand, the proof-of-stake consensus algorithm is susceptible to the well-known nothing-at-stake vulnerability issue, making it equally unlikely to be used in large-scale secured energy transactions. An energy-efficient locational marginal pricing-based hybrid proof-of-work, proof-of-stake consensus algorithm is proposed to mitigate such risks. The hybrid consensus algorithm uses an optimized market-based energy pricing model to select the mining and forging nodes. A detailed framework of the hybrid consensus algorithm is provided, along with an overview of its potential application to record large volumes of energy transactions and execute obligatory smart-contract agreements.
Robust optical flow estimation via edge preserving filtering

It is known that optical flow estimation techniques suffer from the issues of ill-defined edges and boundaries of the moving objects. Traditional variational methods for optical flow estimation are not robust to handle these issues since the local filters in these methods do not hold the robustness near the edges. In this paper, we propose a non-local total variation NLTV-L1 optical flow estimation method based on robust weighted guided filtering. Specifically, first, the robust weighted guided filtering objective function is proposed to preserve motion edges. The proposed objective function is based on the linear model which is computationally efficient and edge-preserving in complex natural scenarios. Second, the proposed weighted guided filtering objective function is incorporated into the non-local total variation NLTV-L1 energy function. Finally, the novel NLTV-L1 optical flow method is performed using the coarse-to-fine process. Additionally, we modify some state-of-the-art variational optical flow estimation methods by the robust weighted guided filtering objective function to verify the performance on Middlebury, MPI-Sintel, and Foggy Zurich sequences. Experimental results show that the proposed method can preserve edges and improve the accuracy of optical flow estimation compared with several state-of-the-art methods.
Model Reconciliation in Logic Programs

Inspired by recent research in explainable planning, we investigate the model reconciliation problem between two logic programs \(\pi _a\) and \(\pi _h\), which represent the knowledge bases of an agent and a human, respectively. Given \(\pi _a\), \(\pi _h\), and a query q such that \(\pi _a\) entails q and \(\pi _h\) does not entail q (or \(\pi _a\) does not entail q and \(\pi _h\) entails q), the model reconciliation problem focuses on the question of how to modify \(\pi _h\), by adding \(\epsilon ^+ \subseteq \pi _a\) to \(\pi _h\) and removing \(\epsilon ^- \subseteq \pi _h\) from \(\pi _h\) such that the resulting program \(\hat{\pi }_h = (\pi _h{\setminus } \epsilon ^-) \cup \epsilon ^+\) has an answer set containing q (or has no answer set containing q). The pair \((\epsilon ^+,\epsilon ^-)\) is referred to as a solution for the model reconciliation problem \((\pi _a,\pi _h,q)\) (or \((\pi _a, \pi _h, \lnot q)\)). We prove that, for a reasonable selected set of rules \(\epsilon ^+ \subseteq \pi _a\) there exists a way to modify \(\pi _h\) such that \(\hat{\pi }_h\) is guaranteed to credulously entail q (or skeptically entail \(\lnot q\)). Given that there are potentially several solutions, we discuss different characterizations of solutions and algorithms for computing solutions for model reconciliation problems.
A new characterization of discrete decomposable graphical models

Decomposable graphical models, also known as perfect directed acyclic graph (DAG) models, play a fundamental role in standard approaches to probabilistic inference via graph representations in modern machine learning and statistics. However, such models are limited by the assumption that the data-generating distribution does not entail strictly context-specific conditional independence relations. The family of staged tree models generalizes DAG models so as to accommodate context-specific knowledge. We provide a new characterization of perfect discrete DAG models in terms of their staged tree representations. This characterization identifies the family of balanced staged trees as the natural generalization of discrete decomposable models to the context-specific setting.
Seagull-Cuckoo Search Algorithm for Function Optimization

This paper anticipates to frame a hybrid Seagull Optimization Algorithm - Cuckoo Search (SOA-CS) algorithm for objective function minimization. The migration and attacking strategies of Seagulls are complimented by the social breeding behaviour of cuckoo birds. In addition, the thought of Lévy flight walking methodology is also inherited. Twenty-three standard benchmark test functions are used for validation. To figure out the outcomes - the proposed algorithm converges faster and provides better results over both the SOA and CS algorithms.
Optimization Assisted Convolutional Neural Network for Sentiment Analysis with Weighted Holoentropy-based Features

Analyzing and gathering the people’s reactions on product trading, public services, etc. are crucial. Sentiment analysis (also termed as opinion mining) is a usual dialogue preparing act that plans on discovering the sentiments after opinions in texts on changing subjects. This research work adopts a novel sentiment analysis approach that comprises six phases like (i) Pre-processing, (ii) Keyword extraction and its sentiment categorization, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Accordingly, the Mongodb documented tweets initially underwent pre-processing with stop word removal, stemming, and blank space removal. Regarding the extracted keywords, the existing semantic words are derived after categorizing the sentiment of keywords. Additionally, the semantic similarity score is evaluated along with their keywords. The subsequent step is feature extraction, where the Holoentropy features such as cross Holoentropy and joint Holoentropy are formulated. Along with this, the extraction of weighted holoentropy features is the major work, where weight is multiplied with the holoentropy features. Moreover, in order to enhance the performance of classification results, the constant term utilized in evaluating the weight function is optimized. For this optimal tuning, a new, improved algorithm termed as Self Adaptive Moth Flame Optimization (SA-MFO) is introduced, which is the adaptive version of MFO algorithm. For classification, this paper aims to use the Deep Convolutional Neural network (DCNN), where the batch size is fine-tuned using the same SA-MFO algorithm. Finally, the performance of the proposed work is compared over other conventional models with respect to different performance measures.
Real Life Implementation of Object Detection and Classification Using Deep Learning and Robotic Arm

Deep learning is one of most favourable domain in today’s era of computer science. In this paper we discussed, the implementation of deep learning concepts by using Auduino uno with robotic application. For the purpose of object detection and classification, a robotic arm is used in the project which is controlled to automatically detect and classify of different object (fruits in our project). In this project, the camera will capture an image of fruit for further processing in the model based on convolutional neural network (CNN). When the trained model will detect the object in image, a particular signal will be sent to robotic arm using Arduino uno, which will place the detected object into a basket. In this way our project will recognize and classify two different fruits and will place it into different baskets. This project is a demonstration of combination of deep learning concept together with Arduino programming, which itself is a complete framework. This combination can be used to solve so many real life problems. After implementation, we found up to 99.22% of accuracy in object detection.
Fast Accurate and Automatic Brushstroke Extraction

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.
An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems

One of the key challenges in cyber-physical systems (CPS) is the dynamic fitting of data sources under multivariate or mixture distribution models to determine abnormalities. Equations of the models have been statistically characterized as nonlinear and non-Gaussian ones, where data have high variations between normal and suspicious data distributions. To address nonlinear equations of these distributions, a cuckoo search algorithm is employed. In this paper, the cuckoo search algorithm is effectively improved with a novel strategy, known as a convergence speed strategy, to accelerate the convergence speed in the direction of the optimal solution for achieving better outcomes in a small number of iterations when solving systems of nonlinear equations. The proposed algorithm is named an improved cuckoo search algorithm (ICSA), which accelerates the convergence speed by improving the fitness values of function evaluations compared to the existing algorithms. To assess the efficacy of ICSA, 34 common nonlinear equations that fit the nature of cybersecurity models are adopted to show if ICSA can reach better outcomes with high convergence speed or not. ICSA has been compared with several well-known, well-established optimization algorithms, such as the slime mould optimizer, salp swarm, cuckoo search, marine predators, bat, and flower pollination algorithms. Experimental outcomes have revealed that ICSA is superior to the other in terms of the convergence speed and final accuracy, and this makes a promising alternative to the existing algorithm.
A Multi-shot ASP Encoding for the Aircraft Routing and Maintenance Planning Problem

The Aircraft Routing and Maintenance Planning problems are integral parts of the airline scheduling process. We study these relevant combinatorial optimization problems from the perspective of Answer Set Programming (ASP) modeling and solving. In particular, we contrast traditional single-shot ASP solving methods to a novel multi-shot solving approach geared to discover near-optimal solutions to sub-problems of increasing granularity rapidly. As it turns out, our multi-shot solving techniques can heavily speed up the optimization process without deteriorating the solution quality compared to single-shot solving. We also provide a customizable instance generator and a solution viewer to facilitate intensive investigation of Aircraft Routing and Maintenance Planning as a benchmark problem. However, our multi-shot solving techniques are not limited to this benchmark alone, and the underlying ideas can be naturally applied to a variety of scheduling problems.
Unpaired Underwater Image Synthesis with a Disentangled Representation for Underwater Depth Map Prediction

As one of the key requirements for underwater exploration, underwater depth map estimation is of great importance in underwater vision research. Although significant progress has been achieved in the fields of image-to-image translation and depth map estimation, a gap between normal depth map estimation and underwater depth map estimation still remains. Additionally, it is a great challenge to build a mapping function that converts a single underwater image into an underwater depth map due to the lack of paired data. Moreover, the ever-changing underwater environment further intensifies the difficulty of finding an optimal mapping solution. To eliminate these bottlenecks, we developed a novel image-to-image framework for underwater image synthesis and depth map estimation in underwater conditions. For the problem of the lack of paired data, by translating hazy in-air images (with a depth map) into underwater images, we initially obtained a paired dataset of underwater images and corresponding depth maps. To enrich our synthesized underwater dataset, we further translated hazy in-air images into a series of continuously changing underwater images with a specified style. For the depth map estimation, we included a coarse-to-fine network to provide a precise depth map estimation result. We evaluated the efficiency of our framework for a real underwater RGB-D dataset. The experimental results show that our method can provide a diversity of underwater images and the best depth map estimation precision.
NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolution

A novel convolution neural network model, abbreviated NL-CNN is proposed, where nonlinear convolution is emulated in a cascade of convolution + nonlinearity layers. The code for its implementation and some trained models are made publicly available. Performance evaluation for several widely known datasets is provided, showing several relevant features: i) for small / medium input image sizes the proposed network gives very good testing accuracy, given a low implementation complexity and model size; ii) compares favorably with other widely known resources-constrained models, for instance in comparison to MobileNetv2 provides better accuracy with several times less training times and up to ten times less parameters (memory occupied by the model); iii) has a relevant set of hyper-parameters which can be easily and rapidly tuned due to the fast training specific to it. All these features make NL-CNN suitable for IoT, smart sensing, biomedical portable instrumentation and other applications where artificial intelligence must be deployed in energy-constrained environments.
DeepMerge: Learning to Merge Programs

In collaborative software development, program merging is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the</i> mechanism to integrate changes from multiple programmers. Merge algorithms in modern version control systems report a conflict when changes interfere textually. Merge conflicts require manual intervention and frequently stall modern continuous integration pipelines. Prior work found that, although costly, a large majority of resolutions involve re-arranging text without writing any new code. Inspired by this observation we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first data-driven approach</i> to resolve merge conflicts with a machine learning model. We realize our approach in a tool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepMerge</small> that uses a novel combination of (i) an edit-aware embedding of merge inputs and (ii) a variation of pointer networks, to construct resolutions from input segments. We also propose an algorithm to localize manual resolutions in a resolved file and employ it to curate a ground-truth dataset comprising 8,719 non-trivial resolutions in JavaScript programs. Our evaluation shows that, on a held out test set, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepMerge</small> can predict correct resolutions for 37% of non-trivial merges, compared to only 4% by a state-of-the-art semistructured merge technique. Furthermore, on the subset of merges with upto 3 lines (comprising 24% of the total dataset), <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepMerge</small> can predict correct resolutions with 78% accuracy.
Evaluating User Experiences in Mixed Reality

extended-abstract Share on Evaluating User Experiences in Mixed Reality Authors: Dmitry Alexandrovsky DMLab University of Bremen Bremen, Germany DMLab University of Bremen Bremen, GermanyView Profile , Susanne Putze DMLab University of Bremen Bremen, Germany DMLab University of Bremen Bremen, GermanyView Profile , Valentin Schwind Frankfurt University of Applied Sciences Frankfurt, Germany Frankfurt University of Applied Sciences Frankfurt, GermanyView Profile , Elisa D. Mekler Aalto University Helsinki, Finland Aalto University Helsinki, FinlandView Profile , Jan David Smeddinck Newcastle University, UK Newcastle University, UKView Profile , Denise Kahl DFKI Saarland Informatics Campus Saarbrücken, Germany DFKI Saarland Informatics Campus Saarbrücken, GermanyView Profile , Antonio Krüger DFKI Saarland Informatics Campus Saarbrücken, Germany DFKI Saarland Informatics Campus Saarbrücken, GermanyView Profile , Rainer Malaka DMLab University of Bremen, Germany DMLab University of Bremen, GermanyView Profile Authors Info & Claims CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing SystemsMay 2021 Article No.: 87Pages 1–5https://doi.org/10.1145/3411763.3441337Published:08 May 2021Publication History 1citation425DownloadsMetricsTotal Citations1Total Downloads425Last 12 Months239Last 6 weeks36 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Subspace Outlier Detection in High Dimensional Data using Ensemble of PCA-based Subspaces

Outlier detection in high dimensional data faces the challenge of curse of dimensionality where irrelevant features may prevent detection of outliers. The Principal Component Analysis (PCA) is widely used for dimensionality reduction in high dimensional outlier detection problem. While no single subspace can to thoroughly capture the outlier data points; we propose to combine the result of multiple subspaces to deal with this situation. In this research, we propose a subspace outlier detection algorithm in high dimensional data using an ensemble of PCA-based subspaces (SODEP) method. Three relevant subspaces are selected using PCA features to discover different types of outliers and subsequently, compute outlier scores in the projected subspaces. The experimental results show that our ensemble-based outlier selection is a promising method in high dimensional data and has better efficiency than other compared methods.
Attentive Part-aware Networks for Partial Person Re- identification

Partial person re-identification (re-ID) refers to re-identify a person through occluded images. It suffers from two major challenges, i.e., insufficient training data and incomplete probe image. In this paper, we introduce a part-aware learning method for partial person re-identification. On the one hand, we adopt data augmentation operation to enrich the training data and improve the robustness of the model. On the other hand, we intuitively find that the partial person images usually have fixed percentages of parts, therefore, in partial person re-ID task, the probe image could be cropped from the pictures and divided into several different partial types following fixed ratios. Based on the cropped images, we propose the Cropping Type Consistency (CTC) loss to classify the cropping types of partial images. Moreover, in order to help the network better fit the generated and cropped data, we incorporate the Block Attention Mechanism (BAM) into the framework for attentive learning. To enhance the retrieval performance in the inference stage, we implement cropping on gallery images according to the predicted types of probe partial images. Through calculating feature distances between the partial image and the cropped holistic gallery images, the model can recognize the right person from the gallery. To validate the effectiveness of our approach, we conduct extensive experiments on the partial re- ID benchmarks and achieve state-of-the-art performance.
I Feel More Engaged When I Move!: Deep Learning-based Backward Movement Detection and its Application

Movement is one of the key elements in virtual reality (VR) and significantly influences user experience. In particular, walking-in-place is a method of supporting movement in a limited space, and many studies are being conducted on its effective support. However, most studies have focused on forward movement despite many situations in which backward movement is needed. In this paper, we present the development of a prediction model for forward/backward movement while considering a user's orientation and the verification of the model's effectiveness. We built a deep learning-based model by collecting sensor data on the movement of the user's head, waist, and feet. We developed three realistic VR scenarios that involve backward movement, set three conditions (controller-based, treadmill-based, and model-based) for movement, and evaluated user experience in each condition through a study of 36 participants. As a result, the model-based condition showed the highest sensory sensitivity, effectiveness, and satisfaction and similar cognitive burden compared with the other two conditions. The results of our study demonstrated that movement support through modeling is possible, suggesting its potential for use in many VR applications.