Reviewerly/exHarmony huggingface dataset:
Methods for Flexible Management of Blockchain-Based Cryptocurrencies in Electricity Markets and Smart Grids

The growing trend in the use of blockchain-based cryptocurrencies in modern communities provides several advantages, but also imposes several challenges to energy markets and power systems, in general. This paper aims at providing recommendations for efficient use of digital cryptocurrencies in today's and future smart power systems, in order to face the challenging aspects of this new technology. In this paper, existing issues and challenges of smart grids in the presence of blockchain-based cryptocurrencies are presented and some innovative approaches for efficient integration and management of blockchain-based cryptocurrencies in smart grids are proposed. Also some recommendations are given for improving the smart grids performance in the presence of digital cryptocurrencies and some future research directions are highlighted.
Leveraging Domain Context for Question Answering Over Knowledge Graph

Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.
Biomedical-domain pre-trained language model for extractive summarization

In recent years, the performance of deep neural network in extractive summarization task has been improved significantly compared with traditional methods. However, in the field of biomedical extractive summarization, existing methods cannot make good use of the domain-aware external knowledge; furthermore, the document structural feature is omitted by existing deep neural network model. In this paper, we propose a novel model called BioBERTSum to better capture token-level and sentence-level contextual representation, which uses a domain-aware bidirectional language model pre-trained on large-scale biomedical corpora as encoder, and further fine-tunes the language model for extractive text summarization task on single biomedical document. Especially, we adopt a sentence position embedding mechanism, which enables the model to learn the position information of sentences and achieve the structural feature of document. To the best of our knowledge, this is the first work to use the pre-trained language model and fine-tuning strategy for extractive summarization task in the biomedical domain. Experiments on PubMed dataset show that our proposed model outperforms the recent SOTA (state-of-the-art) model by ROUGE-1/2/L.
A Comparative Analysis of 3D User Interaction: How to Move Virtual Objects in Mixed Reality

Using one’s hands can be a natural and intuitive method for interacting with 3D objects in a mixed reality environment. This study explores three hand-interaction techniques, including the gaze and pinch, touch and grab, and worlds-in-miniature interaction for selecting and moving virtual furniture in the 3D scene. Overall, a comparative analysis reveals that the worlds-in-miniature provided the best usability and task performance than other studied techniques. We also conducted in-depth interviews and analyzed participants’ hand gestures in order to identify desired attributes for 3D hand interaction design. Findings from interviews suggest that, when it comes to enjoyment and discoverability, users prefer directly manipulating the virtual furniture to interacting with objects remotely or using in-direct interactions such as gaze. Another insight this study provides is the critical roles of the virtual object’s visual appearance in designing natural hand interaction. Gesture analysis reveals that shapes of furniture, as well as its perceived features such as weight, largely determined the participant’s instinctive form of hand interaction (i.e., lift, grab, push). Based on these findings, we present design suggestions that can aid 3D interaction designers to develop a natural and intuitive hand interaction for mixed reality.
Cyber resilience in firms, organizations and societies

Cyber resilience involves most societal actors, i.e. organizations, individuals, threat actors, governments, insurers, etc., at most levels of organization. Actors are embedded within each other and choose strategies based on beliefs and preferences which impact and is impacted by cyber resilience. The article reviews the literature, attempting to capture the core ingredients of cyber resilience. Non-threat actors seeking to obtain cyber resilience are distinguished from threat actors. Actors have resources, competence, technology, and tools. They make choices that impact the cyber resilience for all actors, including themselves. Cyber resilience relates to cyber insurance through entry requirements or preconditions for cyber contracts, need for various services such as incident response, data gathering, and cover limitations. Cyber resilience is linked to the internet of things which in the future can be expected to simplify life through artificial intelligence and machine learning, while being vulnerable through a large attack surface, insufficient technology, challenging handling of data, possible high trust in computers and software, and ethics.
A novel data clustering algorithm based on gravity center methodology

The concept of clustering is to separate clusters based on the similarity which is greater within cluster than among clusters. The similarity consists of two principles, namely, connectivity and cohesion. However, in partitional clustering, while some algorithms such as K-means and K-medians divides the dataset points according to the first principle (connectivity) based on centroid clusters without any regard to the second principle (cohesion), some others like K-medoids partially consider cohesion in addition to connectivity. This prevents to discover clusters with convex shape and results are affected negatively by outliers. In this paper a new Gravity Center Clustering (GCC) algorithm is proposed which depends on critical distance (λ) to define threshold among clusters. The algorithm falls under partition clustering and is based on gravity center which is a point within cluster that verifies both the connectivity and cohesion in determining the similarity of each point in the dataset. Therefore, the proposed algorithm deals with any shape of data better than K-means, K-medians and K-medoids. Furthermore, GCC algorithm does not need any parameters beforehand to perform clustering but can help user improving the control over clustering results and deal with overlapping and outliers providing two coefficients and an indicator. In this study, 22 experiments are conducted using different types of synthetic, and real healthcare datasets. The results show that the proposed algorithm satisfies the concept of clustering and provides great flexibility to get the optimal solution especially since clustering is considered as an optimization problem.
Blockchain-Based Energy Trading in Electric-Vehicle-Enabled Microgrids

This article presents a blockchain-based scheme for energy trading between electric vehicles (prosumers) and critical load (consumer) in a logical network.Unlike traditional wholesale energy markets where retailers sell energy to consumers, our proposed model directly connects prosumers with consumers to meet temporary energy demands.We exploit blockchain technology to establish a trusted energy trading ecosystem and develop an application to remotely monitor energy trading activities between trading entities.Experimental results illustrate that the energy trading system is effective in finding, associating, and routing prosumers to consumers, while protecting privacy of entities.Numerical results show a favorable performance of our optimization model in comparison to traditional frameworks.
Self-Adaptive Neural Module Transformer for Visual Question Answering

Vision and language understanding is one of the most fundamental and difficult tasks in Multimedia Intelligence. Simultaneously Visual Question Answering (VQA) is even more challenging since it requires complex reasoning steps to the correct answer. To achieve this, Neural Module Network (NMN) and its variants rely on parsing the natural language question into a module layout (i.e., a problem-solving program). In particular, this process follows a feedforward encoder-decoder pipeline: the encoder embeds the question into a static vector and the decoder generates the layout. However, we argue that such conventional encoder-decoder neglects the dynamic nature of question comprehension (i.e., we should attend to different words from step to step) and per-module intermediate results (i.e., we should discard module performing badly) in the reasoning steps. In this paper, we present a novel NMN, called Self-Adaptive Neural Module Transformer (SANMT), which adaptively adjusts both of the question feature encoding and the layout decoding by considering intermediate Q&A results. Specifically, we encode the intermediate results with the given question features by a novel transformer module to generate dynamic question feature embedding which evolves over reasoning steps. Besides, the transformer utilizes the intermediate results from each reasoning step to guide subsequent layout arrangement. Extensive experimental evaluations demonstrate the superiority of the proposed SANMT over NMN and its variants on four challenging benchmarks, including CLEVR, CLEVR-CoGenT, VQAv1.0, and VQAv2.0 (on average the relative improvement over NMN are 1.5, 2.3, 0.7 and 0.5 points with respect to accuracy).
Radar Emitter Classification With Attention-Based Multi-RNNs

Analyzing and recognizing radar signals are important tasks for effective Electronic Support Measurement (ESM) system operation. The electromagnetic environment is highly complex nowadays, however, resulting in non-uniformed distributed pulse streams. The high-dimensional features of the radar emitters are also overly complicated. Isolating useful information of the pulse streams and removing noise can assist in the emitter classification process. This letter proposes an attention-based approach for radar emitter classification using recurrent neural networks (RNNs). Several RNNs assigned to individual features exploit the intrinsic patterns of the radar pulse streams via supervised learning; the learned patterns are then used to identify patterns of interest in the test pulse streams and place them into different categories. The attention mechanism demonstrates effective treatment of high missing and spurious pulse ratios, especially in cases of multiple consecutive missing pulses and multifunctional radar pulses. Simulation results also show that the proposed model outperforms other state-of-the-art neural network structures.
Application Driven Graph Partitioning

Graph partitioning is crucial to parallel computations on large graphs. The choice of partitioning strategies has strong impact on not only the performance of graph algorithms, but also the design of the algorithms. For an algorithm of our interest, what partitioning strategy fits it the best and improves its parallel execution? Is it possible to develop graph algorithms with partition transparency, such that the algorithms work under different partitions without changes? This paper aims to answer these questions. We propose an application-driven hybrid partitioning strategy that, given a graph algorithm A, learns a cost model for A as polynomial regression. We develop partitioners that given the learned cost model, refine an edge-cut or vertex-cut partition to a hybrid partition and reduce the parallel cost of A. Moreover, we identify a general condition under which graph-centric algorithms are partition transparent. We show that a number of graph algorithms can be made partition transparent. Using real-life and synthetic graphs, we experimentally verify that our partitioning strategy improves the performance of a variety of graph computations, up to 22.5 times.
How Indians responded to the Arogya Setu app?

The mHealth app Arogya Setu can substantially contribute to the containment and management of COVID-19. This study explores the experiences and expectations of Arogya Setu app users by conducting a combined content analysis of their reviews. Five hundred and three most relevant reviews were analyzed using the descriptive statistics and thematic analysis. The reviews are primarily posted in the areas of user acceptance (80%), app usefulness (72.8%), and app features (62.2%). The thematic analysis resulted in four themes: user acceptance, app usefulness, promptness of the Indian Government in bringing the app on time, and concerns and cautions raised by the users. These help in strengthening the app features enabling the real-time data capture and analytics and providing timely information to authorities for better decision-making.
What Are You Talking To?: Understanding Children's Perceptions of Conversational Agents

Conversational agents (CAs) available in smart phones or smart speakers play an increasingly important role in young children's technological landscapes and life worlds. While a handful of studies have documented children's natural interactions with CAs, little is known about children's perceptions of CAs. To fill this gap, we examined three- to six-year-olds' perceptions of CAs' animate/artifact domain membership and properties, as well as their justifications for these perceptions. We found that children sometimes take a more nuanced position and spontaneously attribute both artifact and animate properties to CAs or view them as neither artifacts nor animate objects. This study extends current research on children's perceptions of intelligent artifacts by adding CAs as a new genre of study and provides some underlying knowledge that may guide the development of CAs to support young children's cognitive and social development.
Salient Object Detection by Fusing Local and Global Contexts

Benefiting from the powerful discriminative feature learning capability of convolutional neural networks (CNNs), deep learning techniques have achieved remarkable performance improvement for the task of salient object detection (SOD) in recent years. However, most existing deep SOD models do not fully exploit informative contextual features, which often leads to suboptimal detection performance in the presence of a cluttered background. This paper presents a context-aware attention module that detects salient objects by simultaneously constructing connections between each image pixel and its local and global contextual pixels. Specifically, each pixel and its neighbors bidirectionally exchange semantic information by computing their correlation coefficients, and this process aggregates contextual attention features both locally and globally. In addition, an attention-guided hierarchical network architecture is designed to capture fine-grained spatial details by transmitting contextual information from deeper to shallower network layers in a top-down manner. Extensive experiments on six public SOD datasets show that our proposed model demonstrates superior SOD performance against most of the current state-of-the-art models under different evaluation metrics.
Dziban: Balancing Agency &amp; Automation in Visualization Design via Anchored Recommendations

Visualization recommender systems attempt to automate design decisions spanning choices of selected data, transformations, and visual encodings. However, across invocations such recommenders may lack the context of prior results, producing unstable outputs that override earlier design choices. To better balance automated suggestions with user intent, we contribute Dziban, a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context. Dziban uses the Draco knowledge base to automatically complete partial specifications and suggest appropriate visualizations. In addition, it extends Draco with chart similarity logic, enabling recommendations that also remain perceptually similar to a provided "anchor" chart. Existing APIs for exploratory visualization, such as ggplot2 and Vega-Lite, require fully specified chart definitions. In contrast, Dziban provides a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations.
Cognitive support for assembly operations by means of augmented reality: an exploratory study

This study aims to compare the effects of oral, paper and augmented reality (AR) instructions during assembly tasks. 44 assembly operators, the majority of which have cognitive or motor disabilities, participate in a within-subjects experiment in which they perform three different assembly tasks. In doing so, the different instructional media are alternated. The outcome variables under study are productivity, quality, stress, help-seeking behaviour, perceived complexity, physical effort and competence frustration. In addition, this study examines the effects of characteristics of operators by taking into account their cognitive skills and experience with each task type as predictor variables. Finally, as operators perform one of the assembly tasks four times, learning curves are studied as well. Results of a multilevel analysis show that operators using AR instructions outperform their colleagues using more traditional instructional media with regard to quality. In addition, AR instructions give rise to less help-seeking behavior as compared to oral instructions. Finally, there is some evidence for AR instructions to yield a lower perceived complexity than oral instructions. Furthermore, the results indicate that the operators’ cognition and experience play a significant role in terms of many outcome variables. Learning curves reveal that operators using AR instructions experience less stress from the first assembly attempt onwards, whereas for operators using oral or paper instructions, it takes several attempts before being equally relaxed.
A Pattern Recognition Algorithm for Quantum Annealers

Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.
Breeds Classification with Deep Convolutional Neural Network

In this work, we utilized a famous convolutional neural network structure with small convolutional filters and deep layers to distinguish different breeds of cats, and this network reached high accuracy. What is more important, this work explored what evidence neural networks depended on to identify only slightly different objects. To make our network more comprehensible, we did the visualization, including the images that each filter most wanted to see, the output images of convolutional layers, and the heat maps. By analyzing these results, we generalized the special case to ordinary cases, and explained the method convolutional neural networks use to identify features. Finally, we discussed the similarities of between how humans and convolutional neural networks see the world.
Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

We introduce an inverse procedural modeling approach that learns L-system representations of pixel images with branching structures. Our fully automatic model generates a compact set of textual rewriting rules that describe the input. We use deep learning to discover atomic structures such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures are combined into a tree. The initial representation is analyzed, and repeating parts are encoded into a small grammar by using greedy optimization while the user can control the size of the detected rules. The output is an L-system that represents the input image as a simple text and a set of terminal symbols. We apply our approach to a variety of examples, demonstrate its robustness against noise and blur, and we show that it can detect user sketches and complex input structures.
A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem

The subject of a class imbalance is a well-investigated topic which addresses performance degradation of standard learning models due to uneven distribution of classes in a dataspace. Cluster-based undersampling is a popular solution in the domain which offers to eliminate majority class instances from a definite number of clusters to balance the training data. However, distance-based elimination of instances often got affected by the underlying data distribution. Recently, ensemble learning techniques have emerged as effective solution due to its weighted learning principle of rare instances. In this article, a boosting aided adaptive cluster-based undersampling technique is proposed to facilitate elimination of learning- insignificant majority class instances from the clusters, detected through AdaBoost ensemble learning model. The proposed work is validated with seven existing cluster based undersampling techniques for six binary datasets and three classification models. The experimental results have established the effectives of the proposed technique than the existing methods.
Maximum Correntropy Rauch–Tung–Striebel Smoother for Nonlinear and Non-Gaussian Systems

We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-Gaussian process and measurement noises, i.e., the nominal Gaussian noise is polluted by large noise from unknown distributions. Taking advantage of correntropy in handling non-Gaussian noise, a robust Rauch-Tung-Striebel smoother is derived according to the maximum-correntropy-criterion-based cost functions with nonlinear functions linearized by their first-order Taylor series expansions, where two weights are utilized to adjust the estimation gains of forward filtering and backward smoothing, respectively. Simulation results demonstrate the effectiveness of the proposed smoother in the presence of various non-Gaussian process and measurement noises, especially the shot sequences and multimodal noise.