Reviewerly/exHarmony huggingface dataset:
How to improve the competiveness of distributed energy resources in China with blockchain technology

Distributed energy resources are located near users, which can realize energy cascade utilization and improve the energy utilization efficiency. The features of distributed ledgers and smart contract auto-execution possessed by blockchain technology are in line with the requirements of distributed energy resources in billing and settlement, which provides a direction for the application of blockchain technology in the distributed energy resources. The integration of distributed energy resources in China with blockchain technology may break the existing pattern where the production, transportation, distribution and sales of energy are centralized. This paper first summarizes the current status of distributed energy resources in China and blockchain technology, and then uses the Michael Porter five forces model to analyze the competitiveness of distributed energy resources. The rivalry, threat of new entrants, threat of alternatives, bargaining power of suppliers, and bargaining power of buyers in energy industry are presented and analyzed. Based on the competitiveness analysis, the SWOT model is used to analyze the joint development model of "blockchain technology + distributed energy resources", and the feasibility of blockchain technology applied to distributed energy resources systems is revealed. Finally, the paper puts forward the key conclusions that blockchain technology can enhance the competitiveness of distributed energy resources as well as relevant policy recommendations.
Perceptual hashing for image authentication: A survey

Perceptual hashing is used for multimedia content identification and authentication through perception digests based on the understanding of multimedia content. This paper presents a literature review of image hashing for image authentication in the last decade. The objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing state-of-the-art techniques. In this article, the general structure and classifications of image hashing based tamper detection techniques with their properties are exploited. Furthermore, the evaluation datasets and different performance metrics are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.
Adaptive learning path recommender approach using auxiliary learning objects

In e-learning, one of the main difficulties is recommending learning materials that users can complete on time. It becomes more challenging when users cannot devote enough time to learn the entire course. In this paper, we describe two approaches to maximize users’ scores for a course while satisfying their time constraints. These approaches recommend successful paths based on the available time and knowledge background of users. We first briefly explain a method that has a similar goal to our method, and highlight its drawbacks. We then describe our proposal, which works based on a two-layered course graph (lesson and Learning Object (LO) layers; a lesson includes a few LO). Initially, our method uses the Depth First Search algorithm (DFS) to find all lesson sequences in the graph that start by a lesson (opted by a user). It then assigns LO for lessons of paths and estimates their score and time. Finally, a path that satisfies the user’s limited time while maximizing his/her score is recommended lesson by lesson. During a path recommendation, if the user could not get the estimated score from a lesson, our method recommends auxiliary LO for that lesson. To evaluate our method, we first assessed the quality of our estimation methods and then evaluate our recommender in a live environment. Results show that our estimation methods outperformed the ones in the literature. Results also present within the same amount of time, the users of our recommender proceeded more on the course than the users of another e-learning system.
Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.
Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network

Automatic segmentation of breast lesion from ultrasound images is a crucial module for the computer aided diagnostic systems in clinical practice. Large-scale breast ultrasound (BUS) images remain unannotated and need to be effectively explored to improve the segmentation quality. To address this, a semi-supervised segmentation network is proposed based on generative adversarial networks (GAN). In this paper, a semi-supervised learning model, denoted as BUS-GAN, consisting of a segmentation base network—BUS-S and an evaluation base network—BUS-E, is proposed. The BUS-S network can densely extract multi-scale features in order to accommodate the individual variance of breast lesion, thereby enhancing the robustness of segmentation. Besides, the BUS-E network adopts a dual-attentive-fusion block having two independent spatial attention paths on the predicted segmentation map and leverages the corresponding original image to distill geometrical-level and intensity-level information, respectively, so that to enlarge the difference between lesion region and background, thus improving the discriminative ability of the BUS-E network. Then, through adversarial training, the BUS-GAN model can achieve higher segmentation quality because the BUS-E network guides the BUS-S network to generate more accurate segmentation maps with more similar distribution as ground truth. The counterpart semi-supervised segmentation methods and the proposed BUS-GAN model were trained with 2000 in-house images, including 100 annotated images and 1900 unannotated images, and tested on two different sites, including 800 in-house images and 163 public images. The results validate that the proposed BUS-GAN model can achieve higher segmentation accuracy on both the in-house testing dataset and the public dataset than state-of-the-art semi-supervised segmentation methods. The developed BUS-GAN model can effectively utilize the unannotated breast ultrasound images to improve the segmentation quality. In the future, the proposed segmentation method can be a potential module for the automatic breast ultrasound diagnose system, thus relieving the burden of a tedious image annotation process and alleviating the subjective influence of physicians’ experiences in clinical practice. Our code will be made available on https://github.com/fiy2W/BUS-GAN.
An application of virtual reality in education: Can this technology enhance the quality of students’ learning experience?

Virtual reality (VR) technology is making its mark across market sectors (e.g., gaming, healthcare, tourism). This paper examines the use of VR in education, specifically in business classes, to better understand how this technology can help students improve their communication skills associated in delivering effective presentations and participating in public speaking events. The VR application allowed students to assess their presentation skills, to then practice in upgrading their skills, and gain more confidence in delivering effective presentations. Overall, this research demonstrates that the adoption of VR can be extremely beneficial to business educators in helping students enhance their presentation skills.
Cognition Digital Twins for Personalized Information Systems of Smart Cities: Proof of Concept

Amid the rapid development of information communication technologies (ICTs), residents of future smart cities are expected to be exposed to unprecedented amounts of real-time information on a daily basis. The cognitive overload driven by an excess of complex information has become a potential issue. Nonetheless, standardized information systems are still widely used, despite individual differences in information intake. To set a foundation for the intelligent information systems of smart cities, this paper introduces methods and tools for a cognition-driven, personalized information system, which acknowledges individual differences in information preference and helps reduce the cognitive load in daily lives and at work. The proposed method includes the use of virtual reality (VR) to simulate complex tasks paired with the digital twin modeling of workers’ cognitive reactions to different information formats and contents in VR simulation. Collected data are then used to build a personal digital twins model of information-driven cognition, or Cog-DT. A human subject experiment was performed with a simulated industrial facility shutdown maintenance task as a proof of concept of Cog-DT. The latest neuroimaging technology and analysis methods were applied to model unique cognitive processes pertaining to information processing. Results indicate that cognitive activities driven by different information stimuli in the work context are distinguishable and modelable with Cog-DT methods and tools. This study is expected to contribute to digital twin literature by testing a human-centered, individual-level digital twin modeling method of cognitive activities. It also sets a preliminary foundation for developing personalized information systems for the smart cities of the future.
DeepReS: A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenarios

The exponential growth in the production of video contents in different industries causes an urgent need for effective video summarization (VS) techniques, in order to get an optimal storage and preservation of key information in the video. Compared to other domains, industrial videos are more challenging to process, as they usually contain diverse and complex events, which make their online processing a difficult task. In this article, we introduce an online system for intelligent video capturing, coarse and fine redundancy removal, and summary generation. First, we capture video data through resource-constrained devices in an industrial Internet of Things network, equipped with vision sensors and apply coarse redundancy removal through the comparison of low-level features. Second, we transmit the resulting frames to the cloud for detailed analysis, where sequential features are extracted for the selection of candidate keyframes. Finally, we refine the candidate keyframes in order to discriminate those with maximum information as part of the summary. The key contributions of this article include the coarse and fine refining of video data implemented over resource-restricted devices and the presentation of important data in the form of a summary. Experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> [Online]. Available: https://github.com/tanveer-hussain/DeepRes-Video-Summarization. over publicly available datasets evince a 0.3-unit increase in the F1 score when compared to state-of-the-art and with reduced time complexity. Furthermore, we provide convincing results on our newly created dataset in an industrial environment, which is made publicly available for the research community along with its labeled ground truth.
Deep Learning Based Channel Estimation Algorithm for Fast Time-Varying MIMO-OFDM Systems

Channel estimation is very challenging for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with non-stationarity channel characteristics. In order to handle this problem, we propose a deep learning (DL)-based MIMO-OFDM channel estimation algorithm. By performing offline training to the learning network, the channel state information (CSI) generated by the training samples can be effectively utilized to adapt the characteristics of fast time-varying channels in the high mobility scenarios. The simulation results show that the proposed DL-based algorithm is more robust for the scenarios of high mobility in MIMO-OFDM systems, compared to the conventional algorithms.
Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difficult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefficient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classification model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85.3, 83.9, and 78.3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively.
An improved SVD-based blind color image watermarking algorithm with mixed modulation incorporated

This study systematically investigated the use of singular value decomposition (SVD) for the blind watermarking of color images. The proposed algorithm overcomes most of the problems typically encountered when using existing SVD-based schemes, while concurrently enhancing performance in terms of imperceptibility and robustness. After applying SVD to non-overlapping 4 × 4 image blocks, level shifting is used to control the embedding strength in accordance with the intensity of pixels in each block. The proposed watermark embedding process helps to preserve orthonormality in the unitary matrix and compensate for the resulting distortion. Iterative regulation ensures the accurate retrieval of the embedded watermark, while mixed modulation helps to improve robustness without compromising image quality. Experiment results demonstrate that the proposed watermarking algorithm is highly resistant to a variety of image processing attacks and error-free in the absence of attack. The proposed method outperforms existing SVD-based schemes in terms of imperceptibility and robustness at a payload capacity of 1/16 bit per pixel.
Blockchain-Enabled Cross-Domain Object Detection for Autonomous Driving: A Model Sharing Approach

Object detection for autonomous driving is a huge challenge in the cross-domain adaptation scenario, especially for the time- and resource-consuming task. Distributed deep learning (DDL) has demonstrated a considerably good balance between efficiency and computation complexity. However, the reliability of DDL is low. Moreover, the cost of training data and model is not priced well. In this article, a novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems. Based on the blockchain and mobile-edge computing (MEC) technology, a domain-adaptive you-only-look-once (YOLOv2) model is trained across nodes, which can reduce significantly the domain discrepancy for different object categories. Furthermore, smart contracts are developed to perform data storage and model sharing tasks efficiently. The reliability of model sharing is ensured with blockchain consensus. We evaluate the proposed method under public data sets. The simulation results demonstrate that the efficiency and reliability of the proposed approach are better than the reference model.
Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN.
Point Cloud Denoising via Feature Graph Laplacian Regularization

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are often corrupted with noise. Building on recent advances in graph signal processing, we design local algorithms for 3D point cloud denoising. Specifically, we design a signal-dependent feature graph Laplacian regularizer (SDFGLR) that assumes surface normals computed from point coordinates are piecewise smooth with respect to a signal-dependent graph Laplacian matrix. Using SDFGLR as a signal prior, we formulate an optimization problem with a general 'p-norm fidelity term that can explicitly remove only two types of additive noise: small but non-sparse noise like Gaussian (using '2 fidelity term) and large but sparser noise like Laplacian (using '1 fidelity term). To establish a linear relationship between normals and 3D point coordinates, we first perform bipartite graph approximation to divide the point cloud into two disjoint node sets (red and blue). We then optimize the red and blue nodes' coordinates alternately. For '2-norm fidelity term, we iteratively solve an unconstrained quadratic programming (QP) problem, efficiently computed using conjugate gradient with a bounded condition number to ensure numerical stability. For '1-norm fidelity term, we iteratively minimize an '1-'2 cost function using accelerated proximal gradient (APG), where a good step size is chosen via Lipschitz continuity analysis. Finally, we propose simple mean and median filters for flat patches of a given point cloud to estimate the noise variance given the noise type, which in turn is used to compute a weight parameter trading off the fidelity term and signal prior in the problem formulation. Extensive experiments show state-of-the-art denoising performance among local methods using our proposed algorithms.
Unsupervised Depth Completion From Visual Inertial Odometry

We describe a method to infer dense depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we have few hundreds to few thousand points, insufficient to inform the topology of the scene. Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points. We use a predictive cross-modal criterion, akin to “self-supervision,” measuring photometric consistency across time, forward-backward pose consistency, and geometric compatibility with the sparse point cloud. We also present the first visual-inertial + depth dataset, which we hope will foster additional exploration into combining the complementary strengths of visual and inertial sensors. To compare our method to prior work, we adopt the unsupervised KITTI depth completion benchmark, where we achieve state-of-the-art performance.
Deep Splitting and Merging for Table Structure Decomposition

Given the large variety and complexity of tables, table structure extraction is a challenging task in automated document analysis systems. We present a pair of novel deep learning models (Split and Merge models) that given an input image, 1) predicts the basic table grid pattern and 2) predicts which grid elements should be merged to recover cells that span multiple rows or columns. We propose projection pooling as a novel component of the Split model and grid pooling as a novel part of the Merge model. While most Fully Convolutional Networks rely on local evidence, these unique pooling regions allow our models to take advantage of the global table structure. We achieve state-of-the-art performance on the public ICDAR 2013 Table Competition dataset of PDF documents. On a much larger private dataset which we used to train the models, we significantly outperform both a state-ofthe-art deep model and a major commercial software system.
Photonic neuromorphic information processing and reservoir computing

Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.
RAFT Based Wireless Blockchain Networks in the Presence of Malicious Jamming

Blockchain shows great potential to be applied in wireless IoT ecosystems for establishing the trust and consensus mechanisms without central authority's involvement. Based on RAFT consensus mechanism, this letter investigates the security performance of wireless blockchain networks in the presence of malicious jamming. We first map and model the blockchain transaction as a wireless network composed of uplink and downlink transmissions by assuming the follower nodes' position as a Poisson Point Process (PPP) with selected leader location. The probability of achieving successful blockchain transactions is derived and verified by extensive simulations. The results provide analytical guidance for the practical deployment of wireless blockchain networks.