Reviewerly/exHarmony huggingface dataset:
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills. A high diversity of skills increases the chances of a robot to succeed at overcoming new situations since there are more potential alternatives to solve a new task. However, finding and storing a large behavioural diversity of multiple skills often leads to an increase in computational complexity. Furthermore, robot planning in a large skill space is an additional challenge that arises with an increased number of skills. Hierarchical structures can help to reduce this search and storage complexity by breaking down skills into primitive skills. In this article, we extend the analysis of the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills and leverages them to make the robot adapt quickly in the physical world. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable. Experiments with a hexapod robot both in simulation and the physical world show that our method solves a maze navigation task with up to, respectively, 20% and 43% less actions than the best baselines while having 78% less complete failures.
Photonic integrated spiking neuron chip based on a self-pulsating DFB laser with a saturable absorber

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback (DFB) laser chip with an intracavity saturable absorber (SA). The DFB laser with an intracavity SA (DFB-SA) contains a gain region and an SA region. The gain region is designed and fabricated by the asymmetric equivalent π -phase shift based on the reconstruction-equivalent-chirp technique. Under properly injected current in the gain region and reversely biased voltage in the SA region, periodic self-pulsation was experimentally observed due to the Q -switching effect. The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz. When the bias current is below the self-pulsation threshold, neuronlike spiking responses appear when external optical stimulus pulses are injected. Experimental results show that the spike threshold, temporal integration, and refractory period can all be observed in the fabricated DFB-SA chip. To numerically verify the experimental findings, a time-dependent coupled-wave equation model was developed, which described the physics processes inside the gain and SA regions. The numerical results agree well with the experimental measurements. We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron. We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network. By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function, we can achieve a recognition accuracy of 92.2%, which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks. To the best of our knowledge, this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA, which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chips.
Knowledge development in non-fungible tokens (NFT): a scoping review

Purpose The purpose of this paper is to investigate the potential of NFTs in revolutionizing innovation management and information systems. Innovations done by firms are blatantly used by other firms to develop cheap knock-off. This leads to huge economic loses to the firm investing in research and development activities. Firms are in need of trusted, immutable and verifiable means of storing information which cannot be used by others, even if publically available without their consent. Non-fungible tokens (NFTs) appear to be one such solution to this problem that has recently attracted a lot of investor interest. Using NFTs the information is tokenized and is stored in a secure manner. Design/methodology/approach Through this scoping review, the authors investigate the influence of NFTs towards the innovation management from the dual aspects of management and information systems. This scoping review is underpinned by the five-stage framework by Arksey and O’Malley. The five stages of Arksey and O’Malley’s framework were used in this analysis to classify the literature through five stages of identifying the initial research questions; locating relevant studies; study selection; charting the data; and compiling, summarizing and reporting the results. Findings This study suggests that NFTs on the blockchain have significant potential to revolutionize innovation management and information systems. Theoretical frameworks used in investigating the role of digital tokens in blockchain management are mainly based on contracts, diversity theory, portfolio theory and faking likelihood theory. The study reveals gaps in the literature, particularly in the under-researched areas of behavioural psychology and social psychology theories. The appropriate regulation and regulation authority for different types of digital tokens are required. The study also presents archetypes that represent patterns in the current landscape of blockchain tokens, which have significant potential for future research and practical applications. Originality/value This study is unique in its approach to assessing the future of NFTs in the field of innovation and information management. While many existing reviews have focused on describing the progress and development of NFTs in the past, this study takes a forward-looking perspective and projects the future potential of NFTs. This innovative approach allows for a deeper understanding of the potential impact of NFTs in various fields such as entrepreneurship, innovation management and tokenomics. Therefore, this study contributes to the literature on NFTs by providing insights and recommendations for future research and practical applications.
AdaMoW: Multimodal Sentiment Analysis Based on Adaptive Modality-Specific Weight Fusion Network

Multimodal sentiment analysis (MSA) is a crucial task in the field of natural language processing (NLP), with a wide range of applications. This paper proposes an adaptive modality-specific weight fusion network (AdaMoW) to address issues in the process of multimodal data fusion. Specifically, we use different weight calculation methods at various stages of the model. In the model training stage, diverse weights are assigned to different modalities by calculating the correlation between the single-modal sentiment prediction value and the real multimodal sentiment labels, and a weight-mapping network is designed to learn this “data-weight” mapping relationship. In the testing and verification phase of the model, the trained weight-mapping network is used to obtain the weights of different modalities. In addition, in order to optimize the multimodal fusion data, we designed a generator, which reversely generates the unimodal feature vector through the multimodal fusion vector, and compares it with the original unimodal feature extraction obtained after unimodal feature extraction. The modal feature vectors are compared and optimized, so that the fusion results can maintain the uniqueness of the modality while obtaining the multimodal data interaction information. AdaMoW is verified on two benchmark MSA datasets CMU-MOSI and CMU-MOSEI. The experimental results show that the effectiveness of AdaMoW surpasses the previous baseline model and achieves state-of-the-art results.
Two-Unitary Decomposition Algorithm and Open Quantum System Simulation

Simulating general quantum processes that describe realistic interactions of quantum systems following a non-unitary evolution is challenging for conventional quantum computers that directly implement unitary gates. We analyze complexities for promising methods such as the Sz.-Nagy dilation and linear combination of unitaries that can simulate open systems by the probabilistic realization of non-unitary operators, requiring multiple calls to both the encoding and state preparation oracles. We propose a quantum two-unitary decomposition (TUD) algorithm to decompose a $d$-dimensional operator $A$ with non-zero singular values as $A=(U_1+U_2)/2$ using the quantum singular value transformation algorithm, avoiding classically expensive singular value decomposition (SVD) with an $O(d^3)$ overhead in time. The two unitaries can be deterministically implemented, thus requiring only a single call to the state preparation oracle for each. The calls to the encoding oracle can also be reduced significantly at the expense of an acceptable error in measurements. Since the TUD method can be used to implement non-unitary operators as only two unitaries, it also has potential applications in linear algebra and quantum machine learning.
Ai robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors

Building on anthropomorphism and regulatory focus theories, this research examines the effect of humanizing artificial intelligence (AI)-powered robo-advisors on investment behaviors. Across three experimental studies, the authors find that when financial service marketers design robo-advisors to resemble humans rather than machines, prevention-focused consumers are motivated to invest more money. However, this effect disappears among promotion-focused consumers. Perceived certainty of investment advice is shown to mediate the interactive effect of robo-advisor anthropomorphism and regulatory focus. Theoretical insights and practical implications for using robo-advisors in financial service marketing strategies are discussed.
Exploring Gaze-assisted and Hand-based Region Selection in Augmented Reality

Region selection is a fundamental task in interactive systems. In 2D user interfaces, users typically use a rectangle selection tool to formulate a region using a mouse or touchpad. Region selection in 3D spaces, especially in Augmented Reality (AR) Head-Mounted Displays (HMDs) is different and challenging because users need to select an intended region via freehand mid-air gestures or eye-based actions that are touchless interactions. In this work, we aim to fill in the gap in the design of region selection techniques in AR HMDs. We first analyzed and discretized the interaction procedure of region selection and explored design possibilities for each step. We then developed four techniques for region selection in AR HMDs, which leveraged users' hand and gaze for unimodal or multimodal interaction. The techniques were evaluated via a user study with a controlled region selection task. The findings led to three design recommendations and two proof-of-concept application examples.
A novel graph-based missing values imputation method for industrial lubricant data

Missing values are unavoidable in lubricant formulation data in the chemical industry owing to the complexity of lubricant manufacturing. Therefore, imputing missing values using statistical analysis and data mining is essential to obtain meaningful information such as correlations and patterns. Traditional methods, such as random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), and deep neural networks (DNNs), have been employed for imputing missing values. However, these traditional methods neglect the latent structure because they only consider the feature information of the data. To this end, this study proposed a novel graph-based imputation method (GBIM) considering the feature information and the relations between data points to improve model performance. The proposed GBIM expresses the relation between each data point via a graph by consulting with dependency modeling and imputes missing values using a graph convolutional network (GCN). Experiments were performed for four physical properties in a lubricant formulation dataset. The results using GBIM were compared with those of traditional imputation methods (RF, k-NN, SVM, and DNN) by considering missing rates at 5% intervals from 5% to 50%. GBIM achieved 4–7% higher imputation accuracy than the other methods. The proposed GBIM can be applied in various industries as a powerful method for imputing missing values.
Design of compact off-axis freeform imaging systems based on optical-digital joint optimization

Using a freeform optical surface can effectively reduce the imaging system weight and volume while maintaining good performance and advanced system specifications. But it is still very difficult for traditional freeform surface design when ultra-small system volume or ultra-few elements are required. Considering the images generated by the system can be recovered by digital image processing, in this paper, we proposed a design method of compact and simplified off-axis freeform imaging systems using optical-digital joint design process, which fully integrates the design of a geometric freeform system and the image recovery neural network. This design method works for off-axis nonsymmetric system structure and multiple freeform surfaces with complicated surface expression. The overall design framework, ray tracing, image simulation and recovery, and loss function establishment are demonstrated. We use two design examples to show the feasibility and effect of the framework. One is a freeform three-mirror system with a much smaller volume than a traditional freeform three-mirror reference design. The other is a freeform two-mirror system whose element number is reduced compared with the three-mirror system. Ultra-compact and/or simplified freeform system structure as well as good output recovered images can be realized.
A systematic literature review of the acceptability of the use of Metaverse in education over 16 years

The use of Metaverse became popular to overcome the limitations of 2D e-learning. Metaverse has undergone significant changes in terms of application types, software, platform, and devices. Therefore, the emergence of the Metaverse influences users, especially students and teachers of different educational fields, in accepting or rejecting the use of the Metaverse. The PRISMA study aims to map the trends of changes in the use of Metaverse in education and examine its perceived usefulness and perceived ease of use from 2008 to 2022. The research findings are concluded as follows: (1) the trend of change from a single platform or software of the Metaverse to a more diverse combination of software and devices among the types of Metaverse in education; (2) the importance of perceived usefulness and perceived ease of use in the acceptance and rejection of the use of Metaverse in education. Future research suggests exploring the perceived usefulness and ease of use for a broader range of education fields, and considering different types in relation to the design of Metaverse platforms and devices.
Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

Breast cancer is the second most common type of cancer among women, and it can threaten women's lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient's abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.
A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission

Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system’s operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician’s fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches.
Digital literacies, social media, and undergraduate learning: what do students think they need to know?

This research addresses an identified need to further understand digital literacies (DL) and whether undergraduate students view DL as being important in their lives and in their learning. Using a cross-sectional survey sent to a stratified random sample of 2500 undergraduates representative of the overall student population at a medium-sized Canadian undergraduate university (survey response rate of 19.8%, N = 496), we explored the relationships between social media and digital literacies, particularly in different disciplinary contexts. We also explored the ways in which students report using social media in their university learning, showing that students value social media for collaboration, discussion, information finding and sharing, and practise activities related to their learning. Additionally, we examined the importance students place on DL, and how they perceive and rate their own abilities with digital literacies across three domains: procedural and technical, cognitive, and sociocultural. Findings illustrate an observable gap between the high importance that students place on digital literacies (including DL for social media) in their learning and their lives and the lack of coverage students reported receiving about these topics in their undergraduate education. Based on the study's findings, we discuss the specific ways that those in the higher education community can address this gap by engaging with and fostering development of digital literacies within specific disciplinary and professional contexts, and in interdisciplinary or transdisciplinary learning settings across the curriculum.
Diag-IoU Loss for Object Detection

Existing IoU-based loss functions have achieved promising performance for bounding box regression in object detection. However, they cannot fully reflect the relation between the predicted and target boxes in the case of box inclusions, and might thus deteriorate detection accuracy and efficiency. In this paper, we design a novel similarity measurement based on the box diagonal called Diag-IoU to well represent the divergence between the predicted and target boxes even in the case of box inclusions, and thus achieve superior localization accuracy and fast convergence. In particular, we equivalently represent a rectangular box with its box diagonal, which contains exclusive and informative geometrical factors, and define the Diag-IoU based on the similarities of a set of sampled point pairs from the predicted and target box diagonals. Based on the Diag-IoU, we design a general Diag-IoU loss, which can provide holistic information in measuring two boxes and thus differentiate the two boxes in the case of box inclusions. To validate the effectiveness of the proposed method, we apply the Diag-IoU loss to several representative object detectors, including YOLO v5s, Faster R-CNN, and FCOS. Extensive experiments on the synthetic data and two challenging object detection benchmark datasets, i.e., MS COCO and PASCAL VOC, demonstrate the superior performance of the proposed Diag-IoU loss compared to previous IoU-based losses as well as other metrics.
A secure payment channel rebalancing model for layer-2 blockchain

The concept of cryptocurrencies is prevalently used in the financial world. Some of the currencies like Bitcoin and Ethereum have shown promising outputs. The scalability problem of these cryptocurrencies is an ongoing challenge, solved through many facets. Bitcoin Lightning Network, a Layer-2 solution to Bitcoin Network, is an off-chain crypto transaction platform working on payment channels to transact bi-directionally between two parties. When the maximum payments flow is unidirectional, the individual balances of the parties in the direction of payment get exhausted. It results in the transaction failing for future payments in that direction and lowers the feasibility of network usage. In this paper, we propose a heuristic-based distributed solution by preserving the privacy and anonymity of the parties involved in the rebalancing procedure and improving the fund transfer capability of the channels. The rebalancing mechanism has a global solution that can rebalance to the maximum extent and a local solution using a significantly lower fee. Again the indirect and direct rebalancing schemes balance the imbalanced channel and prevent further transaction failure and closure of channels due to insufficient balance. The resultant method also has an updation in the HTLC message exchange protocol to avoid the Flood and Loot attack that may arise to maintain the safe state of every honest participant in the network.