Reviewerly/exHarmony huggingface dataset:
Improving Differential Evolution through Bayesian Hyperparameter Optimization

We propose a novel Evolutionary Algorithm (EA) based on the Differential Evolution algorithm for solving global numerical optimization problem in real-valued continuous parameter space. The proposed MadDE algorithm leverages the power of the multiple adaptation strategy with respect to the control parameters and search mechanisms, and is tested on the benchmark functions taken from the CEC 2021 special session & competition on single-objective bound-constrained optimization. Experimental results indicate that MadDE is able to achieve superior performance on global numerical optimization problems when compared against state-of-the-art real-parameter optimizers. We also provide a hyperparameter optimization algorithm SUBHO for improving the search performance of any EA by finding an optimal set of control parameters, and demonstrate its efficacy in enhancing MadDE's performance on the same benchmark. The source code of our implementation is publicly available at https://github.com/subhodipbiswas/MadDE.
The role of the carbon market in relation to the cryptocurrency market: Only diversification or more?

The role of the carbon market in relation to the cryptocurrency market is still unclear. Given the carbon-intensive nature of the cryptocurrency industry, whether the carbon market is able to capture the carbon footprint of the cryptocurrency market (i.e., diversification) or act as a safe haven or a hedge against it remain unexplored issues. To address this issue, this paper employs the generalized autoregressive score-dynamic conditional score-Copula (GAS–DCS–Copula) model, incorporating the asymmetric tail distribution. We identify the asymmetric tail properties of both the carbon and cryptocurrency markets with significant otherness. Further, to account the importance of China in mining the cryptocurrencies, we incorporate Chinese carbon market in our analysis to investigate the difference with the European carbon market. Finally, we provide evidence that the European carbon market provides a safe haven and a hedge against the cryptocurrency market while Chinese carbon market is not. Our findings have implications for both investors and policymakers.
Decentralized transactive energy community in edge grid with positive buildings and interactive electric vehicles

Decarbonization, Digitalization and Decentralization are the three key pillars to meet the significant rise of energy demand with the rapid development of urbanization, which enable the global low-carbon economy with transactive energy market. The total energy consumption of buildings and transport accounts for more than 70% in global final energy consumption but renewables only meet less than 20% in the demand of heating, cooling and transport. Therefore, buildings and electric vehicles have great potentials in allowing the optimization and balance of supply and demand with their cross-sector transactive behaviors for full-scale flexibility. This paper provides a systematic overview of the positive roles of buildings and interactive transaction behaviors of electric vehicles in establishing sustainable transactive energy community from energy physical space, data cyber space, and human social space. Low-carbon transactive energy solutions with key technologies and latest advances for net zero energy building with high electric vehicle density is discussed in a hierarchical way. Internet of things as the fundamental architecture enables digitalization and interoperability of transactive energy. Blockchain as the core element enables decentralization and transparency of transactive energy. Edge computing as the accelerator alleviates the issues of blockchain and speeds up blockchain-based transactive energy. A comprehensive survey of currently known projects and startups on blockchain-based transactive energy for cross-sector local community with buildings and electric vehicles is provided in the end with the discussion of the open challenges and future perspectives for this promising area.
Robots should be seen and not heard…sometimes: Anthropomorphism and AI service robot interactions

Abstract There is a growing need to understand how consumers will interact with artificially intelligent (AI) domestic service robots, which are currently entering consumer homes at increasing rates, yet without a theoretical understanding of the consumer preferences influencing interaction roles such robots may play within the home. Guided by anthropomorphism theory, this study explores how different levels of robot humanness and social interaction opportunities affect consumers' liking for service robots. A review of the extant literature is conducted, yielding three hypotheses that are tested via 953 responses to an online scenario‐based experiment. Findings indicate that while consumers prefer higher levels of humanness and moderate‐to‐high levels of social interaction opportunity, only some participants liked robots more when dialogue (high‐interaction opportunity) was offered. Resulting from this study is the proposed Humanized‐AI Social Interactivity Framework. The framework extends previous studies in marketing and consumer behavior literature by offering an increased understanding of how households will choose to interact with service robots in domestic environments based on humanness and social interaction. Guidelines for practitioners and two overarching themes for future research emerge from this study. This paper contributes to an increased understanding of potential interactions with service robots in domestic environments.
Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton’s laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent’s position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon’s rank-sum tests.
CaraNet: context axial reverse attention network for segmentation of small medical objects

Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects’ sizes, shapes, and scanning modalities. Recently, many convolutional neural networks (CNN) have been designed for segmentation tasks and achieved great success. Few studies, however, have fully considered the sizes of objects, and thus most demonstrate poor performance for small objects segmentation. This can have a significant impact on the early detection of diseases. This paper proposes a Context Axial Reserve Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models. We test our CaraNet on brain tumor (BraTS 2018) and polyp (Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB) segmentation datasets. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, and results show a distinct advantage of CaraNet in the segmentation of small medical objects. Codes available: https://github.com/AngeLouCN/CaraNet
Federated Learning With Non-IID Data in Wireless Networks

Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learning with non-IID data in wireless networks is studied in this paper. Firstly, based on the derived upper bound of expected weight divergence, a federated averaging scheme is proposed to reduce the distribution divergence of non-IID data. Secondly, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, the simulation results based on a common-used image data set are provided to evaluate the performance of our proposed schemes, which can achieve significant performance gains with a small price of latency and energy consumption.
Embodiment in Virtual Reality Intensifies Emotional Responses to Virtual Stimuli

Modulating emotional responses to virtual stimuli is a fundamental goal of many immersive interactive applications. In this study, we leverage the illusion of illusory embodiment and show that owning a virtual body provides means to modulate emotional responses. In a single-factor repeated-measures experiment, we manipulated the degree of illusory embodiment and assessed the emotional responses to virtual stimuli. We presented emotional stimuli in the same environment as the virtual body. Participants experienced higher arousal, dominance, and more intense valence in the high embodiment condition compared to the low embodiment condition. The illusion of embodiment thus intensifies the emotional processing of the virtual environment. This result suggests that artificial bodies can increase the effectiveness of immersive applications psychotherapy, entertainment, computer-mediated social interactions, or health applications.
A Comprehensive Study on Face Recognition Biases Beyond Demographics

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user’s demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyze FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group-based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many nondemographic attributes strongly affect recognition performance, such as accessories, hairstyles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making the FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, enhance the robustness of these networks, and develop more generalized bias-mitigating FR solutions.
Multi-contrast MRI Super-Resolution via a Multi-stage Integration Network

Super-resolution (SR) plays a crucial role in improving the image quality of magnetic resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear display of soft tissues. However, current super-resolution methods only employ a single contrast, or use a simple multi-contrast fusion mechanism, ignoring the rich relations among different contrasts, which are valuable for improving SR. In this work, we propose a multi-stage integration network (i.e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR. In particular, our MINet first learns a hierarchical feature representation from multiple convolutional stages for each of different-contrast image. Subsequently, we introduce a multi-stage integration module to mine the comprehensive relations between the representations of the multi-contrast images. Specifically, the module matches each representation with all other features, which are integrated in terms of their similarities to obtain an enriched representation. Extensive experiments on fastMRI and real-world clinical datasets demonstrate that 1) our MINet outperforms state-of-the-art multi-contrast SR methods in terms of various metrics and 2) our multi-stage integration module is able to excavate complex interactions among multi-contrast features at different stages, leading to improved target-image quality.
Deep Architectures for Image Compression: A Critical Review

Deep learning architectures are now pervasive and filled almost all applications under image processing, computer vision, and biometrics. The attractive property of feature extraction of CNN has solved a lot of conventional image processing problems with much-improved performance & efficiency. The paper aimed to review over a hundred recent state-of-the-art techniques exploiting mostly lossy image compression using deep learning architectures. These deep learning algorithms consists of various architectures like CNN, RNN, GAN, autoencoders and variational autoencoders. We have classified all the algorithms under certain categories for the better and deep understanding. The review is written keeping in mind the contributions of researchers & the challenges faced by them. Various findings for the researchers along with some future directions for a new researcher have been significantly highlighted. Most of the papers reviewed in the compression domain are from the last four years using different methodologies. The review has been summarized by dropping a new outlook for researchers in the realm of image compression.
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer

We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics. Contrary to previous approaches, we rely on a decoder capable of unifying a variety of problems involving natural language. The layout is represented as an attention bias and complemented with contextualized visual information, while the core of our model is a pretrained encoder-decoder Transformer. Our novel approach achieves state-of-the-art results in extracting information from documents and answering questions which demand layout understanding (DocVQA, CORD, SROIE). At the same time, we simplify the process by employing an end-to-end model.
Entanglement across separate silicon dies in a modular superconducting qubit device

Abstract Assembling future large-scale quantum computers out of smaller, specialized modules promises to simplify a number of formidable science and engineering challenges. One of the primary challenges in developing a modular architecture is in engineering high fidelity, low-latency quantum interconnects between modules. Here we demonstrate a modular solid state architecture with deterministic inter-module coupling between four physically separate, interchangeable superconducting qubit integrated circuits, achieving two-qubit gate fidelities as high as 99.1 ± 0.5% and 98.3 ± 0.3% for iSWAP and CZ entangling gates, respectively. The quality of the inter-module entanglement is further confirmed by a demonstration of Bell-inequality violation for disjoint pairs of entangled qubits across the four separate silicon dies. Having proven out the fundamental building blocks, this work provides the technological foundations for a modular quantum processor: technology which will accelerate near-term experimental efforts and open up new paths to the fault-tolerant era for solid state qubit architectures.
Semi-supervised Contrastive Learning for Label-Efficient Medical Image Segmentation

The success of deep learning methods in medical image segmentation tasks heavily depends on a large amount of labeled data to supervise the training. On the other hand, the annotation of biomedical images requires domain knowledge and can be laborious. Recently, contrastive learning has demonstrated great potential in learning latent representation of images even without any label. Existing works have explored its application to biomedical image segmentation where only a small portion of data is labeled, through a pre-training phase based on self-supervised contrastive learning without using any labels followed by a supervised fine-tuning phase on the labeled portion of data only. In this paper, we establish that by including the limited label information in the pre-training phase, it is possible to boost the performance of contrastive learning. We propose a supervised local contrastive loss that leverages limited pixel-wise annotation to force pixels with the same label to gather around in the embedding space. Such loss needs pixel-wise computation which can be expensive for large images, and we further propose two strategies, downsampling and block division, to address the issue. We evaluate our methods on two public biomedical image datasets of different modalities. With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
Towards Discriminative Representation Learning for Unsupervised Person Re-identification

In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods typically follow a two-stage optimization pipeline, where the network is first pre-trained on source and then fine-tuned on target with pseudo labels created by feature clustering. Such methods sustain two main limitations. (1) The label noise may hinder the learning of discriminative features for recognizing target classes. (2) The domain gap may hinder knowledge transferring from source to target. We propose three types of technical schemes to alleviate these issues. First, we propose a cluster-wise contrastive learning algorithm (CCL) by iterative optimization of feature learning and cluster refinery to learn noise-tolerant representations in the unsupervised manner. Second, we adopt a progressive domain adaptation (PDA) strategy to gradually mitigate the domain gap between source and target data. Third, we propose Fourier augmentation (FA) for further maximizing the class separability of re-ID models by imposing extra constraints in the Fourier space. We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations. Experiments demonstrate that our method consistently achieves notable improvements over the state-of-the-art unsupervised re-ID methods on multiple benchmarks, e.g., surpassing MMT largely by 8.1%, 9.9%, 11.4% and 11.1% mAP on the Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT tasks, respectively.
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment

Contrastive learning has been widely used to train transformer-based vision-language models for video-text alignment and multi-modal representation learning. This paper presents a new algorithm called Token-Aware Cascade contrastive learning (TACo) that improves contrastive learning using two novel techniques. The first is the token-aware contrastive loss which is computed by taking into account the syntactic classes of words. This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words. Second, a cascade sampling method is applied to generate a small set of hard negative examples for efficient loss estimation for multi-modal fusion layers. To validate the effectiveness of TACo, in our experiments we finetune pretrained models for a set of downstream tasks including text-video retrieval (YouCook2, MSR-VTT and ActivityNet), video action step localization (CrossTask), video action segmentation (COIN). The results show that our models attain consistent improvements across different experimental settings over previous methods, set-ting new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.
A systematic review of immersive virtual reality for industrial skills training

Virtual reality (VR) training offers the capability to industrial workers to acquire skills and address complex tasks by immersing them in a safe and controlled virtual environment. Immersive VR (IVR) training is adopted in many diverse settings, yet little systematic work currently exists on how researchers have applied it for industrial skills training and if it holds the potential to be applied remotely. In this review, 78 representative studies were analysed to answer three key questions: Is IVR an effective training method for industrial skills training? How is research in this field applied? And how can we make IVR training more effective and applicable for remote training? We can testify that IVR is a promising training method with high effectiveness scores. However, our analysis has uncovered several gaps in the application of IVR training, like the lack of learning theories in the design process and limited metrics beyond time and scores. Additionally, our review also exposed unexplored but intriguing avenues of research, like the utilisation of biosensors for users’ data collection, haptics that increases realism and applications with remote training potential.
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

Abstract Majority voting and averaging are common approaches used to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. Annotator disagreements may capture important nuances in such tasks that are often ignored while aggregating annotations to a single ground truth. In order to address this, we investigate the efficacy of multi-annotator models. In particular, our multi-task based approach treats predicting each annotators’ judgements as separate subtasks, while sharing a common learned representation of the task. We show that this approach yields same or better performance than aggregating labels in the data prior to training across seven different binary classification tasks. Our approach also provides a way to estimate uncertainty in predictions, which we demonstrate better correlate with annotation disagreements than traditional methods. Being able to model uncertainty is especially useful in deployment scenarios where knowing when not to make a prediction is important.