Reviewerly/exHarmony huggingface dataset:
Encouraging Human Interaction With Robot Teams: Legible and Fair Subtask Allocations

Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encourage nearby humans to join their teams in the first place? Inspired by economics, we recognize that humans care about more than just team efficiency — humans also have biases and expectations for team dynamics. Our hypothesis is that the way inclusive robots divide the task (i.e., how the robots split a larger task into subtask allocations) should be both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legible</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fair</i> to the human partner. In this paper we introduce a bilevel optimization approach that enables robot teams to identify high-level subtask allocations and low-level trajectories that optimize for legibility, fairness, or a combination of both objectives. We then test our resulting algorithm across studies where humans <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">watch</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">play</i> with robot teams. We find that our approach to generating legible teams makes the human’s role clear, and that humans typically prefer to join and collaborate with legible teams instead of teams that only optimize for efficiency. Incorporating fairness alongside legibility further encourages participation: when humans play with robots, we find that they prefer (potentially inefficient) teams where the subtasks or effort are evenly divided. See videos of our studies here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/cfN7O5na3mg</uri>
Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others.Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer (OC) is a kind of cancer that affects women's ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC.In this aspect, this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system (QBWO-MLDSS) for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study's novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.
Fully-Probabilistic Terrain Modelling and Localization With Stochastic Variational Gaussian Process Maps

Gaussian processes (GPs) are becoming a standard tool to build terrain representations thanks to their capacity to model map uncertainty. This effectively yields a reliability measure of the areas of the map, which can be directly utilized by Bayes filtering algorithms in robot localization problems. A key factor is that this map uncertainty can incorporate the noise intrinsic to the terrain surveying process through the GPs ability to train on uncertain inputs (UIs). However, existing techniques to build GP maps with UIs in a tractable manner are restricted in the form and degree of the input noise. In this letter, we propose a flexible and efficient framework to build large-scale GP maps with UIs based on Stochastic Variational GPs and Monte Carlo sampling of the UIs distributions. We validate our mapping approach on a large bathymetric survey collected with an autonomous underwater vehicle (AUV) and analyze its performance against the use of deterministic inputs (DI). Finally, we show how using UI SVGP maps yields more accurate particle filter localization results than DI SVGP on a real AUV mission over an entirely predicted area.
Deep Learning Framework Based on Audio–Visual Features for Video Summarization

The techniques of video summarization (VS) has garnered immense interests in current generation leading to enormous applications in different computer vision domains, such as video extraction, image captioning, indexing, and browsing. By the addition of high-quality features and clusters to pick representative visual elements, conventional VS studies often aim at the success of the VS algorithms. Many of the existing VS mechanisms only take into consideration the visual aspect of the video input, thereby ignoring the influence of audio features in the generated summary. To cope with such issues, we propose an efficient video summarization technique that processes both visual and audio content while extracting key frames from the raw video input. Structural similarity index is used to check similarity between the frames, while mel-frequency cepstral coefficient (MFCC) helps in extracting features from the corresponding audio signals. By combining the previous two features, the redundant frames of the video are removed. The resultant key frames are refined using a deep convolution neural network (CNN) model to retrieve a list of candidate key frames which finally constitute the summarization of the data. The proposed system is experimented on video datasets from YouTube that contain events within them which helps in better understanding the video summary. Experimental observations indicate that with the inclusion of audio features and an efficient refinement technique, followed by an optimization function, provides better summary results as compared to standard VS techniques.
NEWTS: A Corpus for News Topic-Focused Summarization

Text summarization models are approaching human levels of fidelity.Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or, professional content.To date, all summarization datasets operate under a one-size-fits-all paradigm that may not reflect the full range of organic summarization needs.Several recently proposed models (e.g., plug and play language models) have the capacity to condition the generated summaries on a desired range of themes.These capacities remain largely unused and unevaluated as there is no dedicated dataset that would support the task of topic-focused summarization.This paper introduces the first topical summarization corpus NEWTS, based on the wellknown CNN/Dailymail dataset, and annotated via online crowd-sourcing.Each source article is paired with two reference summaries, each focusing on a different theme of the source document.We evaluate a representative range of existing techniques and analyze the effectiveness of different prompting methods.
Landmark-Guided Conditional GANs for Face Aging

Face aging, which alters a person's facial photo to the appearance at a different age, is a popular topic in multimedia applications. Recently, conditional Generative Adversarial Networks (cGANs) have achieved visually impressive progress in this area. However, generating a convincing aging appearance while preserving the person's identity is still a challenging task. In this paper, we propose a novel Landmark-Guided cGAN (LGcGAN), which not only synthesizes texture changes related to aging, but also alters facial structures accordingly. We adapt a built-in attention mechanism to emphasize the most discriminative regions relevant to aging and minimize changes that affect personal identity and background. Conditioned with age vectors, the primal cGAN in our symmetric network converts input faces to target ages, and the dual cGAN inverts the previous task, which feeds synthesized target faces back to the original input age scope for enhancing age consistency. Both qualitative and quantitative experiments show that our method can generate appealing results in terms of image quality, personal identity, and age accuracy.
Improving Question Answering Quality Through Language Feature-Based SPARQL Query Candidate Validation

Question Answering systems are on the rise and on their way to become one of the standard user interfaces. However, in conversational user interfaces, the information quantity needs to be kept low as users expect a limited number of precise answers (often it is 1) – similar to human-human communication. The acceptable number of answers in a result list is a key differentiator from search engines where showing more answers (10–100) to the user is widely accepted. Hence, the quality of Question Answering is crucial for the wide acceptance of such systems. The adaptation of natural-language user interfaces for satisfying the information needs of humans requires high-quality and not-redundant answers. However, providing compact and correct answers to the users' questions is a challenging task. In this paper, we consider a certain class of Question Answering systems that work over Knowledge Graphs. We developed a system-agnostic approach for optimizing the ranked lists of SPARQL query candidates produced by the Knowledge Graph Question Answering system that are used to retrieve an answer to a given question. We call this a SPARQL query validation process. For the evaluation of our approach, we used two well-known Knowledge Graph Question Answering benchmarks. Our results show a significant improvement in the Question Answering quality. As the approach is system-agnostic, it can be applied to any Knowledge Graph Question Answering system that produces query candidates.
Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

The vulnerability of solar power producers to sunshine fluctuations exposes them to the volumetric risk that future electricity generation may deviate from predicted generation. Weather derivatives have recently emerged as a tool for hedging the volumetric risks of these power producers. However, the state-of-the-art instruments have several shortcomings, contributing to their limited application in the industry. Therefore, novel solar radiation-based weather derivative smart contract arrangements on a blockchain marketplace are proposed to address some of the main limitations of traditional instruments. In this regard, the cash flow of solar generators is modelled to assess the weather elements causing its stochasticity. Using this information, novel smart contract arrangements on a blockchain marketplace with solar radiation days as the underlying weather index are developed and analytically valued. Thereafter, a suite of novel smart contract autonomous mechanisms compelling contracting parties to behave rationally and maintain an enduring arrangement is presented. Finally, a trading strategy based on the developed smart contract arrangements is proposed to minimize the power producers’ volatility risk. Results emanating from notional simulations indicate that the proposed approach could be more suitable for hedging the volumetric risks of solar power producers than traditional instruments.
Pragmatic Approach for Online Document Verification Using Block-Chain Technology

As we all know, India has a plethora of universities, and many people graduate from them. Because it is possible for someone to falsify a degree, a secure based verification mechanism is required. It will be achieved in an existing system through the exchange of e-mails or postal mail; however, this is a time-consuming process that is insecure owing to human involvement. And, in order to solve these drawbacks, we implemented block chain technology into our system. When there is a security risk, block chain comes to mind. When it comes to data breaches, education is also not far away. For attackers, student data with little financial information has become a valuable commodity. At the same time, student verification is becoming a serious worry at educational institutions, which are being breached to generate phony identities and records. As a result, the greater the digitization of student information, the greater the need to protect student privacy.
Deep Learning Based Channel Estimation for OFDM Systems With Doubly Selective Channel

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with high mobility, channel estimation becomes challenging due to the double selectivity of channels. To solve this problem, a deep learning based method which fully considers the channel features and noise effects is proposed in this letter. In the pilot-based channel estimation scheme, a compression-and-reconstruction channel estimation network (CRCENet) is designed to improve the channel estimation performance, which fuses not only the low-level features of the rapid changes between adjacent subcarriers in the short term and the high-level features of the overall channel changes under large time spans in the backbone, but also the features using the attention mechanism in the up-sampling branches. Meanwhile, considering that the samples with different signal-to-noise ratios (SNRs) have different credibility, loss weights based on credibility are assigned to data with different SNRs during network training, to further improve the estimation performance of the network. The experimental results revealed that CRCENet performed better than the traditional least squares estimation algorithm and state-of-the-art learning networks.
Missing-Data Tolerant Hybrid Learning Method for Solar Power Forecasting

Solar power forecasting is a key task in modern power grid operation, which can be achieved by machine learning-based methods. Due to multiple practical issues, the data may be incomplete, making the existing machine learning models inaccurate or even ineffective. To counteract the missing data problem, this paper proposes a hybrid learning method. Firstly, a super-resolution perception convolutional neural network (SRPCNN) is designed to reconstruct the flawed data with missing data in both random missing and block missing patterns. With the recovered data, an incremental broad learning system (IBLS) is developed as the prediction model. Due to its strong approximation ability, low computational burden, and flexible structure, the IBLS model can be easily and rapidly updated by broadening the network structure to maintain the forecasting accuracy without the need for an entire retraining progress. Therefore, the proposed method is not only missing-data tolerant but also online updatable for accuracy maintenance/enhancement. On an open dataset <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in Australia,</b> the proposed method is tested and compared with existing methods. The simulation results <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verify the effectiveness of the proposed method.</b>
Noisy Zeroth-Order Optimization for Non-smooth Saddle Point Problems

This paper investigates zeroth-order methods for non-smooth convex-concave saddle point problems (with r-growth condition for duality gap). We assume that a black-box gradient-free oracle returns an inexact function value corrupted by an adversarial noise. In this work we prove that the standard zeroth-order version of the mirror descent method is optimal in terms of the oracle calls complexity and the maximum admissible noise.
Deep Residual Weight-Sharing Attention Network With Low-Rank Attention for Visual Question Answering

The attention-based networks have become prevailing recently in visual question answering (VQA) due to their high performances. However, the extensive memory consumption of attention-based models poses excessive-high demand for the implementation equipment, raising concerns about their future application scenarios. Therefore, designing an efficient and lightweight VQA model is central to expanding possible application areas. Our work presents a novel lightweight attention-based VQA model, namely residual weight-sharing attention network (RWSAN), consisting of residual weight-sharing attention (RWSA) layers cascaded in depth. Each RWSA layer models the textual representation with self residual weight-sharing attention (SRWSA) and captures question features and question-image interactions with self-guided residual weight-sharing attention (SGRWSA). Inside each RWSA layer, the proposed low-rank attention units perform residual learning with learned connection patterns and shared parameters, and every stacked RWSA layer also uses the same parameters. Extensive ablation experiments with quantitative and qualitative analysis are conducted to illustrate the effectiveness and generality of RWSA. Experiments on VQA-v2, GQA, and CLEVR datasets show that the RWSAN achieves competitive performance with much fewer parameters over the state-of-the-art methods.
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments

This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions or traces. We create SSC baselines from state-of-the-art open source networks and construct a benchmark real-time dense local semantic mapping algorithm, MotionSC, by leveraging recent 3D deep learning architectures to enhance SSC with temporal information. Our network shows that the proposed data set can quantify and supervise accurate scene completion in the presence of dynamic objects, which can lead to the development of improved dynamic mapping algorithms.
Quantum Fuzzy K-Means Algorithm Based on Fuzzy Theory

Cluster analysis is used to classification according to their different charac-teristics, affinity, and similarity. Because the boundary of the relationship between things is often unclear, it is inevitable to use the fuzzy method to perform cluster analysis. In this paper, according to the cross fusion of "fuzzy theory + K-means algorithm + quantum computing", a quantum fuzzy k-means algorithm based on fuzzy theory is proposed for the first time, which can classify samples with lower time complexity and higher ac-curacy. Firstly, the training data sets and the classified sample points can be encoded into quantum states, and swap test is used to calculate the similarity between the classified sample points and k cluster centers with high parallel computing abilities. Secondly, the similarity is stored with the form of quan-tum bits by using the phase estimation algorithm. The Grover algorithm is used to search the cluster points with the highest membership degree and de-termine the category of the test samples. Finally, by introducing quantum computing theory, the computation complexity of the proposed algorithm is improved, and the space complexity of the proposed algorithm is reduced. By introducing fuzzy theory, the proposed algorithm can deal with uncertain problems efficiently, the scope of application of the algorithm is expanded, and the accuracy is improved.
Blockchain-based e-voting protocols

The concept of voting has been in use for many purposes in the past decades. The conventional ballot-based voting (paper and ballet box) approach has many inherent use cases that could lead to severe malpractices and manipulations during and after the voting process. In addition, it can also lead to many debatable situations where the physical voting-related practices could be questionable. To overcome the issues raised in the conventional voting system, an electronic voting (e-voting) system was first introduced in 1981 by Chuam. Over the years, e-voting systems have gained popularity, and many countries are using the e-voting approaches for their internal elections. E-voting approaches are efficient and address the drawbacks of conventional e-voting systems, raising new challenges for researchers, such as voter identity verification, voter privacy protection, vote verifiability and integrity, and so on. In the literature, several variants of cryptographic primitives like blind signature, ring signature, etc., have been incorporated in the e-voting protocols to provide the e-voting system with enhanced security and robustness. But, most of the centralized e-voting protocols in the literature have still failed to provide all the required security features like integrity and transparency of the cast votes. Blockchain is a distributed system with an immutable ledger technology. Since the blockchain guarantees the data integrity, it will protect the system from coercion attacks, and therefore provides better solutions for transparent and trusted e-voting systems. From the introduction of blockchain technology in 2008 by Satoshi Nakamoto, several e-voting approaches have been presented in the literature that use blockchain concepts to achieve trust and transparency. In this chapter, we explore various e-voting protocols and present a comparative study of performance, security features, and limitations. Further, we focus on blockchain-based e-voting protocols to discuss the major achievements and challenges in the blockchain-based e-voting systems.
Detection of Breast Cancer with Lightweight Deep Neural Networks for Histology Image Classification

Many researchers have developed computer-assisted diagnostic (CAD) methods to diagnose breast cancer using histopathology microscopic images. These techniques help to improve the accuracy of biopsy diagnosis with hematoxylin and eosin-stained images. On the other hand, most CAD systems usually rely on inefficient and time-consuming manual feature extraction methods. Using a deep learning (DL) model with convolutional layers, we present a method to extract the most useful pictorial information for breast cancer classification. Breast biopsy images stained with hematoxylin and eosin can be categorized into four groups namely benign lesions, normal tissue, carcinoma in situ, and invasive carcinoma. To correctly classify different types of breast cancer, it is important to classify histopathological images accurately. The MobileNet architecture model is used to obtain high accuracy with less resource utilization. The proposed model is fast, inexpensive, and safe due to which it is suitable for the detection of breast cancer at an early stage. This lightweight deep neural network can be accelerated using field-programmable gate arrays for the detection of breast cancer. DL has been implemented to successfully classify breast cancer. The model uses categorical cross-entropy to learn to give the correct class a high probability and other classes a low probability. It is used in the classification stage of the convolutional neural network (CNN) after the clustering stage, thereby improving the performance of the proposed system. To measure training and validation accuracy, the model was trained on Google Colab for 280 epochs with a powerful GPU with 2496 CUDA cores, 12 GB GDDR5 VRAM, and 12.6 GB RAM. Our results demonstrate that deep CNN with a chi-square test has improved the accuracy of histopathological image classification of breast cancer by greater than 11% compared with other state-of-the-art methods.