Reviewerly/exHarmony huggingface dataset:
A Hierarchical Classification Head Based Convolutional Gated Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public benchmark.To help other researchers, the source code will be uploaded to the github as long as the paper is published.
Path Planning of Multi-Robot Systems With Boolean Specifications Based on Simulated Annealing

In this letter, we address the path planning of multi-robot systems (i.e., a team of identical mobile robots) with a global high-level specification that is given as a Boolean formula over some regions of the environment. The task is composed of logical requirements on the trajectories and the final states of the robots for some interest regions. First, a preprocessing algorithm based on Dijkstra algorithm is developed to calculate the shortest path between each interest region according to the global map information and the Boolean specification. Then, a simulated annealing based approach is developed to obtain a path trajectory of each robot such that the Boolean specification is satisfied on the final state, while the total travel distance is minimized. Finally, several numerical studies are investigated to show that the developed approach is superior to the existing methods in terms of both the computational cost and the quality of the obtained solution.
Nonautoregressive Encoder–Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction

Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major challenges, such as the overlapping issue and long-distance dependency, have not been addressed effectively by the existing ASTE methods, which limits the performance of the task. In this article, we present an innovative encoder-decoder framework for end-to-end ASTE. Specifically, the ASTE task is first modeled as an unordered triplet set prediction problem, which is satisfied with a nonautoregressive decoding paradigm with a pointer network. Second, a novel high-order aggregation mechanism is proposed for fully integrating the underlying interactions between the overlapping structure of aspect and opinion terms. Third, a bipartite matching loss is introduced for facilitating the training of our nonautoregressive system. Experimental results on benchmark datasets show that our proposed framework significantly outperforms the state-of-the-art methods. Further analysis demonstrates the advantages of the proposed framework in handling the overlapping issue, relieving long-distance dependency and decoding efficiency.
Assessing the connectedness between Proof of Work and Proof of Stake/Other digital coins

Major cryptocurrencies such as bitcoin and etherium rely on the computationally expensive and energy inefficient Proof of Work (PoW) consensus mechanism to validate transactions and secure their networks. In response to such concerns digital coins that implement more energy efficient algorithms, e.g. Proof of Stake (PoS), have started to grow in popularity and some PoW based coins are planning to switch to PoS. We investigate linkages and transmission of price shocks across fourteen PoW and PoS/Other powered digital assets. PoW cryptocurrencies appear to be more strongly connected within the network of digital coins than are PoS/Other digital currencies. On average PoW coins export more uncertainty to other cryptocurrencies, while assets in both groups import similar levels of risk. PoS/Other cryptocurrency stakeholders need to be aware of the impact that PoW cryptocurrencies can exert on the riskiness of their assets.
Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning

We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that the detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or even eliminate the requirement on 3D hand annotation. Accordingly, in this work, we propose S2HAND, a self-supervised 3D hand reconstruction model, that can jointly estimate pose, shape, texture, and the camera viewpoint from a single RGB input through the supervision of easily accessible 2D detected keypoints. We leverage the continuous hand motion information contained in the unlabeled video data and explore S2HAND(V), which uses a set of weights shared S2HAND to process each frame and exploits additional motion, texture, and shape consistency constrains to obtain more accurate hand poses, and more consistent shapes and textures. Experiments on benchmark datasets demonstrate that our self-supervised method produces comparable hand reconstruction performance compared with the recent full-supervised methods in single-frame as input setup, and notably improves the reconstruction accuracy and consistency when using the video training data.
Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin is an individual cryptocurrency, but it is derived from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> . The change in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> prices affects the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> coin through the past days’ prices of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dash</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Litecoin</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bitcoin</i> as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.
A Methods-Time-Measurement based Approach to enable Action Recognition for Multi-Variant Assembly in Human-Robot Collaboration

Action Recognition (AR) has become a popular approach to ensure efficient and safe Human-Robot Collaboration. Current research approaches are mostly optimized for specific assembly processes and settings. This paper introduces a novel approach to extend the field of AR to multi-variant assembly processes. The approach is based on generalized action primitives derived from Methods-Time-Measurement (MTM) analysis that are detected by an AR system using skeletal data. Subsequently a search algorithm combines the information from AR and MTM to provide an estimate of the assembly progress. One possible implementation is shown in a proof of concept and results as well as future work are discussed.
A method for machine learning generation of realistic synthetic datasets for validating healthcare applications

Digital health applications can improve quality and effectiveness of healthcare, by offering a number of new tools to users, which are often considered a medical device. Assuring their safe operation requires, amongst others, clinical validation, needing large datasets to test them in realistic clinical scenarios. Access to datasets is challenging, due to patient privacy concerns. Development of synthetic datasets is seen as a potential alternative. The objective of the paper is the development of a method for the generation of realistic synthetic datasets, statistically equivalent to real clinical datasets, and demonstrate that the Generative Adversarial Network (GAN) based approach is fit for purpose. A generative adversarial network was implemented and trained, in a series of six experiments, using numerical and categorical variables, including ICD-9 and laboratory codes, from three clinically relevant datasets. A number of contextual steps provided the success criteria for the synthetic dataset. A synthetic dataset that exhibits very similar statistical characteristics with the real dataset was generated. Pairwise association of variables is very similar. A high degree of Jaccard similarity and a successful K-S test further support this. The proof of concept of generating realistic synthetic datasets was successful, with the approach showing promise for further work.
A Variable Granularity Search-Based Multiobjective Feature Selection Algorithm for High-Dimensional Data Classification

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection (FS). However, in most of the existing EA-based FS methods, one bit in the individual only represents one feature, which means with the number of features increasing, the search space of these methods increases exponentially and makes them not suitable for the data classification with high dimensions. To tackle the issue, in this article, a variable granularity search-based multiobjective EA, termed as VGS-MOEA, is proposed for high-dimensional FS, where one bit in the individual representation denotes a group of features and results in the search space reducing greatly. To be specific, at the beginning, the search granularity of VGS-MOEA is coarse (a bit denotes a great number of features), which helps the proposed algorithm detect the potentially good feature subsets quickly. As the evolution continues, the search granularity is refined gradually, where a bit denotes a smaller number of features until it only represents one feature. With this decomposition of granularity, a more refined search is performed and leads to the VGS-MOEA obtaining feature subsets with higher quality. Experimental results on 12 high-dimensional data sets with different characteristics have shown that in comparison with the state of the arts, the proposed VGS-MOEA has demonstrated its superiority in terms of the classification accuracy, the number of selected features, and the running time.
What Drives Users to Adopt a Digital Museum? A Case of Virtual Exhibition Hall of National Costume Museum

The onset of technological innovations (mobile and handhelds, virtual reality, multi-touch screens, and interactive 3D) have provided creative ideas and perspectives for online communication, dissemination, and protection of cultural heritage for costume museums. Digital costume museums (DCM) digitized clothing collections for the Internet, conducive to enhancing visitors’ understanding, enjoyment, and positive attitudes and stimulating further learning, experience, and exploration. However, little attention has been paid to the influence and effects of these technologies on visitors’ experience toward digital costume museums. Improving users’ behavior intention and expanding the influence of digital costume museums are issues that need further discussion. In this study, we expand the technology acceptance model (TAM) by adding information quality and information richness as the system characteristics, constructing the research model, and 11 hypotheses of users’ behavior intention toward digital costume museums. Analysis of data collected from 265 costume-related respondents reveal that information quality (IQ) positively influences perceived convenience (PC) and perceived ease of use (PEOU), while information richness (IR) has a positive impact on perceived usefulness (PU) and perceived playfulness (PP). The finding also reveals that perceived usefulness (PU) and perceived playfulness (PP) are significant predictors of users’ behavior intention (BI) toward using digital costume museums. The research conclusion enriches academic theories and brings practical inspiration for managers, curators, and practitioners to construct and innovate digital costume museums.
Deformable Convolutional Networks for Multimodal Human Activity Recognition Using Wearable Sensors

Recent years have witnessed significant success of convolutional neural networks (CNNs) in human activity recognition (HAR) using wearable sensors. Nevertheless, prior works have an obvious drawback. An activity sample may contain heterogeneous sensor modalities from different body parts. Moreover, the significance of each modality will change over time. Because a normal convolution filter usually samples activity data at a fixed regular grid, it is hard to capture salient features of activities along different sensor modalities or time intervals. What is the best filter form for activity recognition still remains a challenging task. In this article, to resolve this issue, we present a new deformable convolutional network for recognizing human activities from intricate sensory data. Specifically, the learned offsets and the feature amplitudes are added into standard convolution, which can be modulated to allow more free-form deformation over the sampling grid for sensory data. Comparing previous results, we achieve state-of-the-art recognition accuracies, e.g., 82.91&#x0025;, 80.02&#x0025;, 97.35&#x0025;, and 99.21&#x0025;, respectively, on several benchmark HAR datasets, including OPPORTUNITY, UNIMIB-SHAR, USC-HAD, and WISDM, hence indicating the advantage of the proposed method. The visual analysis is provided, which shows that the deformation could be conditioned on different input activity samples. The receptive field and the sampling locations can be adjusted in an adaptive manner, which leads to better interpretability for deep model behaviors. Installing PyTorch on a Raspberry Pi 3 B plus system, we evaluate actual run time of the deformable model. The results show that the deformable filter is able to still maintain almost the same inference time, which is very beneficial for activity recognition tasks. Our work can promote further research by leveraging intermodulating information to connect the deformable convolution and attention modules.
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models

The advent of transformer-based models such as BERT has led to the rise of neural ranking models. These models have improved the effectiveness of retrieval systems well beyond that of lexical term matching models such as BM25. While monolingual retrieval tasks have benefited from large-scale training collections such as MS MARCO and advances in neural architectures, cross-language retrieval tasks have fallen behind these advancements. This paper introduces ColBERT-X, a generalization of the ColBERT multi-representation dense retrieval model that uses the XLM-RoBERTa (XLM-R) encoder to support cross-language information retrieval (CLIR). ColBERT-X can be trained in two ways. In zero-shot training, the system is trained on the English MS MARCO collection, relying on the XLM-R encoder for cross-language mappings. In translate-train, the system is trained on the MS MARCO English queries coupled with machine translations of the associated MS MARCO passages. Results on ad hoc document ranking tasks in several languages demonstrate substantial and statistically significant improvements of these trained dense retrieval models over traditional lexical CLIR baselines.
Federated Learning Over Multihop Wireless Networks With In-Network Aggregation

Communication limitation at the edge is widely recognized as a major bottleneck for federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to enhance service coverage and spectrum efficiency at the edge, which could facilitate large-scale and efficient machine learning (ML) model aggregation. However, FL over multi-hop wireless networks has rarely been investigated. In this paper, we optimize FL over wireless mesh networks by taking into account the heterogeneity in communication and computing resources at mesh routers and clients. We present a framework that each intermediate router performs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-network</i> model aggregation before sending the data to the next hop, so as to reduce the outgoing data traffic and hence aggregate more models under limited communication resources. To accelerate model training, we formulate our optimization problem by jointly considering model aggregation, routing, and spectrum allocation. Although the problem is a non-convex mixed-integer nonlinear programming, we transform it into a mixed-integer linear programming (MILP), and develop a coarse-grained fixing procedure to solve it efficiently. Simulation results demonstrate the effectiveness of the solution approach, and the superiority of the in-network aggregation scheme over the counterpart without in-network aggregation.
Improving the usefulness of research data with better paradata

Abstract Considerable investments have been made in Europe and worldwide for developing research data infrastructures. Instead of a general lack of data about data, it has become apparent that a pivotal factor that drastically constrains data use is the absence of contextual knowledge about how data was created and how it has been curated and used. This applies especially to many branches of social science and humanities research, where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation, curation and use). The aim of this position paper is to draw attention 1) to the need for a better and more systematic understanding and documentation of the contexts of creation, curation and use of research data to make it useful and usable for researchers and other potential users in the future, and 2) to specific obstacles that make the capturing of this particular type of metadata, known as paradata , especially difficult. Failing to understand what information about the creation, curation and use of research data is needed and how to capture enough of that information risks that the currently collected vast amounts of research data become useless in the future.
Robust Matrix Factorization With Spectral Embedding

Nonnegative matrix factorization (NMF) and spectral clustering are two of the most widely used clustering techniques. However, NMF cannot deal with the nonlinear data, and spectral clustering relies on the postprocessing. In this article, we propose a Robust Matrix factorization with Spectral embedding (RMS) approach for data clustering, which inherits the advantages of NMF and spectral clustering, while avoiding their shortcomings. In addition, to cluster the data represented by multiple views, we present the multiview version of RMS (M-RMS), and the weights of different views are self-tuned. The main contributions of this research are threefold: 1) by integrating spectral clustering and matrix factorization, the proposed methods are able to capture the nonlinear data structure and obtain the cluster indicator directly; 2) instead of using the squared Frobenius-norm, the objectives are developed with the l2,1 -norm, such that the effects of the outliers are alleviated; and 3) the proposed methods are totally parameter-free, which increases the applicability for various real-world problems. Extensive experiments on several single-view/multiview data sets demonstrate the effectiveness of our methods and verify their superior clustering performance over the state of the arts.