Reviewerly/exHarmony huggingface dataset:
An Adaptive Fusion Algorithm for Depth Completion

Dense depth perception is critical for many applications. However, LiDAR sensors can only provide sparse depth measurements. Therefore, completing the sparse LiDAR data becomes an important task. Due to the rich textural information of RGB images, researchers commonly use synchronized RGB images to guide this depth completion. However, most existing depth completion methods simply fuse LiDAR information with RGB image information through feature concatenation or element-wise addition. In view of this, this paper proposes a method to adaptively fuse the information from these two sensors by generating different convolutional kernels according to the content and positions of the feature vectors. Specifically, we divided the features into different blocks and utilized an attention network to generate a different kernel weight for each block. These kernels were then applied to fuse the multi-modal features. Using the KITTI depth completion dataset, our method outperformed the state-of-the-art FCFR-Net method by 0.01 for the inverse mean absolute error (iMAE) metric. Furthermore, our method achieved a good balance of runtime and accuracy, which would make our method more suitable for some real-time applications.
An Automatic and Efficient BERT Pruning for Edge AI Systems

With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained devices for low-latency and high accuracy. Existing BERT pruning methods require domain experts to heuristically handcraft hyperparameters to strike a balance among model size, latency, and accuracy. In this work, we propose AE-BERT, an automatic and efficient BERT pruning framework with efficient evaluation to select a "good" sub-network candidate (with high accuracy) given the overall pruning ratio constraints. Our proposed method requires no human experts experience and achieves a better accuracy performance on many NLP tasks. Our experimental results on General Language Understanding Evaluation (GLUE) benchmark show that AE-BERT outperforms the state-of-the-art (SOTA) hand-crafted pruning methods on BERT. On QNLI and RTE, we obtain 75% and 42.8% more overall pruning ratio while achieving higher accuracy. On MRPC, we obtain a 4.6 higher score than the SOTA at the same overall pruning ratio of 0.5. On STS-B, we can achieve a 40% higher pruning ratio with a very small loss in Spearman correlation compared to SOTA hand-crafted pruning methods. Experimental results also show that after model compression, the inference time of a single BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> encoder on Xilinx Alveo U200 FPGA board has a 1.83× speedup compared to Intel(R) Xeon(R) Gold 5218 (2.30GHz) CPU, which shows the reasonableness of deploying the proposed method generated sub-networks of BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> model on computation restricted devices.
Spatio-temporal aggregation of skeletal motion features for human motion prediction

This study proposes a human body motion prediction model that can adapt to the disorders in various human motion patterns and represent the kinematic constraints. In human motion prediction, the acquisition of features that capture inter-motion and inter-joint linkages is considered effective. To generate links that are adaptive to the reference time of dominant features and their crossing-over joints, we construct an attention-based network that aggregates motion sequences temporally and spatially. We evaluated the motion prediction results using the Human3.6M dataset with the indices of mean angle error and mean per joint position error and showed that our method outperforms other state-of-the-art methods.
Personal Memories and Bodily-Cues Influence Our Sense of Self

How do our bodies influence who we are? Recent research in cognitive neuroscience has examined consciousness associated with the self and related multisensory processing of bodily signals, the so-called bodily self-consciousness. A parallel line of research has highlighted the concept of the autobiographical self and the associated autonoetic consciousness, which enables us to mentally travel in time. The subjective re-experiencing of past episodes is described as re-living them from within or outside one’s body. In this brief perspective, I aim to explore the underlying characteristics of self-consciousness and its relation to bodily signals and episodic memory. I will outline some recent behavioral and neuroimaging evidence indicating that bodily cues play a fundamental role in autobiographical memory. Finally, I will discuss these emerging concepts regarding the current understanding of bodily-self, autobiographical-self, their links to self-consciousness, and suggest directions for future research.
Unravelling the Deformation of Paleoproterozoic Marbles and Zn-Pb Ore Bodies by Combining 3D-Photogeology and Hyperspectral Data (Black Angel Mine, Central West Greenland)

The Black Angel Zn-Pb ore deposit is hosted in folded Paleoproterozoic marbles of the Mârmorilik Formation. It is exposed in the southern part of the steep and inaccessible alpine terrain of the Rinkian Orogen, in central West Greenland. Drill-core data integrated with 3D-photogeology and hyperspectral imagery of the rock face allow us to identify stratigraphic units and extract structural information that contains the geological setting of this important deposit. The integrated stratigraphy distinguishes chemical/mineralogical contrast within lithologies dominated by minerals that are difficult to distinguish with the naked eye, with a similar color of dolomitic and scapolite-rich marbles and calcitic, graphite-rich marbles. These results strengthen our understanding of the deformation style in the marbles and allow a subdivision between evaporite-carbonate platform facies and carbonate slope facies. Ore formation appears to have been mainly controlled by stratigraphy, with mineralizing fluids accumulating within permeable carbonate platform facies underneath carbonate slope facies and shales as cap rock. Later, folding and shearing were responsible for the remobilization and improvement of ore grades along the axial planes of shear folds. The contact between dolomitic scapolite-rich and calcitic graphite-rich marbles probably represents a direct stratigraphic marker, recognizable in the drill-cores, to be addressed for further 3D-modeling and exploration in this area.
Robust Segmentation Based on Salient Region Detection Coupled Gaussian Mixture Model

The impressive progress on image segmentation has been witnessed recently. In this paper, an improved model introducing frequency-tuned salient region detection into Gaussian mixture model (GMM) is proposed, which is named FTGMM. Frequency-tuned salient region detection is added to achieve the saliency map of the original image, which is combined with the original image, and the value of the saliency map is added into the Gaussian mixture model in the form of spatial information weight. The proposed method (FTGMM) calculates the model parameters by the expectation maximization (EM) algorithm with low computational complexity. In the qualitative and quantitative analysis of the experiment, the subjective visual effect and the value of the evaluation index are found to be better than other methods. Therefore, the proposed method (FTGMM) is proven to have high precision and better robustness.
Wearable Devices in Healthcare Services. Bibliometrix Analysis by using R Package

Purpose: The current study aims at exploring the theme – wearable devices in healthcare services from 2008 to 2021. It intends to identify the most prominent sources, authors, affiliations, countries, documents, words and trend topics.&#x0D; Methodology: Total 204 records have been extracted from Scopus after applying inclusion and exclusion criteria, and analysed by using biblioshiny software of R-package.&#x0D; Findings: Results of bibliometrix analysis show the prominent sources in ‘wearable devices &amp; healthcare’ search are IEEE Access and Sensors (Switzerland). Moreover, Lee S. and Shen J. are found be the most productive and prolific authors, King Saud University is leading institutions in producing the articles, China, South Korea, India and USA are identified as the most productive countries and Network Security, Cryptography, Deep Learning, Healthcare Application and Healthcare System are found to be the trending topics and themes in the year 2021.&#x0D; Originality: This study presents the deep analytics regarding wearable devices in healthcare services and it also suggests useful future research avenues and insights for researchers and practitioners.
The Effect of Technological and Task Considerations on Auditors' Acceptance of Blockchain Technology

ABSTRACT The increasing automation of accounting information makes it imperative for auditors to help organizations design and implement blockchain-based solutions to facilitate transactions. A precursor of this initiative is their acceptance of blockchain. From the task-technology fit and fit-as-mediation perspective, this paper proposes that the technological features of blockchain must fit the task needs of auditors to boost acceptance. A survey of 112 auditors confirms that auditors' task needs fully mediate the effect of blockchain features on acceptance. Not all blockchain features are equally important: While timestamping is most important for a shared database, the prevention of double-spending is most important for other task needs. It also finds that perceptions of blockchain features and task needs are influenced by auditors' accounting knowledge and accounting software knowledge, as well as their technology awareness. This shows the importance of strengthening auditors' knowledge and awareness to boost the acceptance of blockchain.
Distributed Blockchain Price Oracle

Blockchain oracles are systems that connect blockchains with the outside world by interfacing with external data providers. They provide decentralized applications with the external information needed for smart contract execution. In this paper, we focus on decentralized price oracles, which are distributed systems that provide exchange rates of digital assets to smart contracts. They are the cornerstone of the safety of some decentralized finance applications such as stable coins or lending protocols. They consist of a network of nodes called oracles that gather information from off-chain sources such as an exchange market’s API and feed it to smart contracts. Among the desired properties of a price oracle system are low latency, availability, and low operating cost. Moreover, they should overcome constraints such as having diverse data sources which is known as the freeloading problem or Byzantine failures. In this paper, we define the distributed price oracle problem and present PoWacle, the first asynchronous decentralized oracle protocol that copes with Byzantine behavior.
Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT

Recently, several research studies have used standard metaheuristic optimization algorithms rather than traditional algorithms and the Ziegler-Nichols (Z-N) method for tuning PID controller parameters.However, these studies have directly implemented these algorithms in order to configure the cascade control system one time.This paper presents a novel realtime monitoring and optimization architecture based on the Enhanced Harris Hawk Algorithm (EHHOA) and the Industrial Internet of Things (IIoT) for tuning the PID controller parameters for an Automatic Voltage Regulator (AVR) system.The EHHOA is based on a Chaotic map and an opposition-based learning technique that is linked to the IIoT layers.The proposed algorithm was implemented through Simulink in the MATLAB environment and it was compared with the Z-N method, the classical HHO/PID algorithm and the PSO/PID algorithm.The simulation results show that the proposed algorithm managed to enhance tuning with an insignificant difference in comparison with the other employed algorithms and EHHOA gave satisfactory results in adjusting the parameters of the PID controller, especially in IIoT real-time scenarios.
Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freestyle character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a–z) and digits (0–9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.
Active Learning on Pre-trained Language Model with Task-Independent Triplet Loss

Active learning attempts to maximize a task model’s performance gain by obtaining a set of informative samples from an unlabeled data pool. Previous active learning methods usually rely on specific network architectures or task-dependent sample acquisition algorithms. Moreover, when selecting a batch sample, previous works suffer from insufficient diversity of batch samples because they only consider the informativeness of each sample. This paper proposes a task-independent batch acquisition method using triplet loss to distinguish hard samples in an unlabeled data pool with similar features but difficult to identify labels. To assess the effectiveness of the proposed method, we compare the proposed method with state-of-the-art active learning methods on two tasks, relation extraction and sentence classification. Experimental results show that our method outperforms baselines on the benchmark datasets.
What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator

We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation. Such an extension enables PeVFA to preserve values of multiple policies at the same time and brings an appealing characteristic, i.e., value generalization among policies. We formally analyze the value generalization under Generalized Policy Iteration (GPI). From theoretical and empirical lens, we show that generalized value estimates offered by PeVFA may have lower initial approximation error to true values of successive policies, which is expected to improve consecutive value approximation during GPI. Based on above clues, we introduce a new form of GPI with PeVFA which leverages the value generalization along policy improvement path. Moreover, we propose a representation learning framework for RL policy, providing several approaches to learn effective policy embeddings from policy network parameters or state-action pairs. In our experiments, we evaluate the efficacy of value generalization offered by PeVFA and policy representation learning in several OpenAI Gym continuous control tasks. For a representative instance of algorithm implementation, Proximal Policy Optimization (PPO) re-implemented under the paradigm of GPI with PeVFA achieves about 40% performance improvement on its vanilla counterpart in most environments.
Use of Blockchain Technology in the Domain of Physical Exercise, Physical Activity, Sport, and Active Ageing: A Systematic Review

Blockchain technology provides a distributed support for information storage and traceability. Recently, it has been booming in a wide variety of domains: finance, food, energy, and health. In the field of physical activity, physical exercise, sport, and active ageing, this technology could also originate some interesting services introducing support for reliable repository of results, for gamification, or for secure data interchange. This systematic review explores the use of blockchain in this context. The objective is to determine to which extent this technology has fulfilled the potential of blockchain to bring these new added-value services. The authors explored 5 repositories in search of papers describing solutions applied to the above-mentioned frame. 17 papers were selected for full-text analysis, and they displayed diverse applications of blockchain, such as Fitness and healthcare, Sport, and Active ageing. A detailed analysis shows that the solutions found do not leverage all the possibilities of blockchain technology. Most of the solutions analyzed use blockchain for managing, sharing, and controlling access to data and do not exploit the possibilities of Smart Contracts or oracles. Additionally, the advantages of the blockchain model have not been fully exploited to engage users using approaches such as gamification.
Lifelong Person Re-identification by Pseudo Task Knowledge Preservation

In real world, training data for person re-identification (Re-ID) is collected discretely with spatial and temporal variations, which requires a model to incrementally learn new knowledge without forgetting old knowledge. This problem is called lifelong person re-identification (LReID). Variations of illumination and background for images of each task exhibit task-specific image style and lead to task-wise domain gap. In addition to missing data from the old tasks, task-wise domain gap is a key factor for catastrophic forgetting in LReID, which is ignored in existing approaches for LReID. The model tends to learn task-specific knowledge with task-wise domain gap, which results in stability and plasticity dilemma. To overcome this problem, we cast LReID as a domain adaptation problem and propose a pseudo task knowledge preservation framework to alleviate the domain gap. Our framework is based on a pseudo task transformation module which maps the features of the new task into the feature space of the old tasks to complement the limited saved exemplars of the old tasks. With extra transformed features in the task-specific feature space, we propose a task-specific domain consistency loss to implicitly alleviate the task-wise domain gap for learning task-shared knowledge instead of task-specific one. Furthermore, to guide knowledge preservation with the feature distributions of the old tasks, we propose to preserve knowledge on extra pseudo tasks which jointly distills knowledge and discriminates identity, in order to achieve a better trade-off between stability and plasticity for lifelong learning with task-wise domain gap. Extensive experiments demonstrate the superiority of our method as compared with the state-of-the-art lifelong learning and LReID methods.
Multi-Scale Distillation from Multiple Graph Neural Networks

Knowledge Distillation (KD), which is an effective model compression and acceleration technique, has been successfully applied to graph neural networks (GNNs) recently. Existing approaches utilize a single GNN model as the teacher to distill knowledge. However, we notice that GNN models with different number of layers demonstrate different classification abilities on nodes with different degrees. On the one hand, for nodes with high degrees, their local structures are dense and complex, hence more message passing is needed. Therefore, GNN models with more layers perform better. On the other hand, for nodes with low degrees, whose local structures are relatively sparse and simple, the repeated message passing can easily lead to over-smoothing. Thus, GNN models with less layers are more suitable. However, existing single-teacher GNN knowledge distillation approaches which are based on a single GNN model, are sub-optimal. To this end, we propose a novel approach to distill multi-scale knowledge, which learns from multiple GNN teacher models with different number of layers to capture the topological semantic at different scales. Instead of learning from the teacher models equally, the proposed method automatically assigns proper weights for each teacher model via an attention mechanism which enables the student to select teachers for different local structures. Extensive experiments are conducted to evaluate the proposed method on four public datasets. The experimental results demonstrate the superiority of our proposed method over state-of-the-art methods. Our code is publicly available at https://github.com/NKU-IIPLab/MSKD.
Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings

To deal with ambiguities in partial multi-label learning (PML), existing popular PML research attempts to perform disambiguation by direct ground-truth label identification. However, these approaches can be easily misled by noisy false-positive labels in the iteration of updating the model parameter and the latent ground-truth label variables. When labeling information is ambiguous, we should depend more on underlying structure of data, such as label and feature correlations, to perform disambiguation for partially labeled data. Moreover, large margin nearest neighbour (LMNN) is a popular strategy that considers data structure in classification. However, due to the ambiguity of labeling information in PML, traditional LMNN cannot be used to solve the PML problem directly. In addition, embedding is an effective technology to decrease the noise information of data. Inspried by LMNN and embedding technology, we propose a novel PML paradigm called Partial Multi-label Learning via Large Margin Nearest Neighbour Embeddings (PML-LMNNE), which aims to conduct disambiguation by projecting labels and features into a lower-dimension embedding space and reorganize the underlying structure by LMNN in the embedding space simultaneously. An efficient algorithm is designed to implement the proposed method and the convergence rate of the algorithm is analyzed. Moreover, we present a theoretical analysis of the generalization error bound for the proposed PML-LMNNE, which shows that the generalization error converges to the sum of two times the Bayes error over the labels when the number of instances goes to infinity. Comprehensive experiments on artificial and real-world datasets demonstrate the superiorities of the proposed PML-LMNNE.