Reviewerly/exHarmony huggingface dataset:
Frequency-Domain Decomposition and Deep Learning Based Solar PV Power Ultra-Short-Term Forecasting Model

Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of the power grid. However, PV power has great fluctuations due to various meteorological factors, which increase energy prices and cause difficulties in managing the grid. This article proposes an ultra-short-term PV power forecasting model based on the optimal frequency-domain decomposition and deep learning. First, the optimal frequency demarcation points for decomposition components are obtained through frequency-domain analysis. Then, the PV power is decomposed into the low-frequency and high-frequency components, which supports the rationality of decomposition results and solves the problem that the current decomposition model only uses the direct decomposition method and the decomposition components are not physical. Then, a convolutional neural network (CNN) is used to forecast the low-frequency and high-frequency components, and the final forecasting result is obtained by addition reconstruction. Based on the actual PV data in heavy rain days, the mean absolute percentage error (MAPE) of the proposed forecasting model is decreased by 52.97%, 64.07%, and 31.21%, compared with discrete wavelet transform, variational mode decomposition, and direct prediction models. In addition, compared with recurrent neural network and long-short-term memory model, the MAPE of the CNN forecasting model is decreased by 23.64% and 46.22%, and the training efficiency of the CNN forecasting model is improved by 85.63% and 87.68%. The results fully show that the proposed model in this article can improve both forecasting accuracy and time efficiency significantly.
A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.
Support vector machines in R

Support vector machine (SVM) algorithms are among the most useful and robust machine learning algorithms used for both classification and regression problems. These algorithms function by allowing certain data points to determine the decision boundaries or the regression fit. In this chapter, we will overview the principles of SVM algorithms and create SVM and SVR models for classification and regression problems, respectively.
Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making

The current healthcare sector is facing difficulty in satisfying the growing issues, expenses, and heavy regulation of quality treatment. Surely, electronic medical records (EMRs) and protected health information (PHI) are highly sensitive, personally identifiable information (PII). However, the sharing of EMRs, enhances overall treatment quality. A distributed ledger (blockchain) technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Privacy, security, and lack of confidence among stakeholders are the main downsides of extensive medical collaboration. This study, therefore, utilizes the transparency, security, and efficiency of blockchain technology to establish a collaborative medical decision-making scheme. This study considers the experience, skill, and collaborative success rate of four key stakeholders (patient, cured patient, doctor, and insurance company) in the healthcare domain to propose a local reference-based consortium blockchain scheme, and an associated consensus gathering algorithm, proof-of-familiarity (PoF). Stakeholders create a transparent and tenable medical decision to increase the interoperability among collaborators through PoF. A prototype of PoF is tested with multichain 2.0, a blockchain implementing framework. Moreover, the privacy of identities, EMRs, and decisions are preserved by two-layer storage, encryption, and a timestamp storing mechanism. Finally, superiority over existing schemes is identified to improve personal data (PII) privacy and patient-centric outcomes research (PCOR).
A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). Nevertheless, BPSO has limitations of premature convergence and the setting of inertia weight. Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive performance in feature selection, which is appropriate for application in engineering, rehabilitation and clinical areas.
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation

We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task. Within our framework, the task of generating a story given a sequence of images is divided across a two-level hierarchical decoder. The high-level decoder constructs a plan by generating a semantic concept (i.e., topic) for each image in sequence. The low-level decoder generates a sentence for each image using a semantic compositional network, which effectively grounds the sentence generation conditioned on the topic. The two decoders are jointly trained end-to-end using reinforcement learning. We evaluate our model on the visual storytelling (VIST) dataset. Empirical results from both automatic and human evaluations demonstrate that the proposed hierarchically structured reinforced training achieves significantly better performance compared to a strong flat deep reinforcement learning baseline.
Adaptive Period Embedding for Representing Oriented Objects in Aerial Images

We propose a novel method for representing oriented objects in aerial images named Adaptive Period Embedding (APE). While traditional object detection methods represent object with horizontal bounding boxes, the objects in aerial images are oritented. Calculating the angle of object is an yet challenging task. While almost all previous object detectors for aerial images directly regress the angle of objects, they use complex rules to calculate the angle, and their performance is limited by the rule design. In contrast, our method is based on the angular periodicity of oriented objects. The angle is represented by two two-dimensional periodic vectors whose periods are different, the vector is continuous as shape changes. The label generation rule is more simple and reasonable compared with previous methods. The proposed method is general and can be applied to other oriented detector. Besides, we propose a novel IoU calculation method for long objects named length independent IoU (LIIoU). We intercept part of the long side of the target box to get the maximum IoU between the proposed box and the intercepted target box. Thereby, some long boxes will have corresponding positive samples. Our method reaches the 1st place of DOAI2019 competition task1 (oriented object) held in workshop on Detecting Objects in Aerial Images in conjunction with IEEE CVPR 2019.
Spammer Detection and Fake User Identification on Social Networks

Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.
"Do you trust me?"

While the research area of artificial intelligence benefited from increasingly sophisticated machine learning techniques in recent years, the resulting systems suffer from a loss of transparency and comprehensibility. This development led to an on-going resurgence of the research area of explainable artificial intelligence (XAI) which aims to reduce the opaqueness of those black-box-models. However, much of the current XAI-Research is focused on machine learning practitioners and engineers while omitting the specific needs of end-users. In this paper, we examine the impact of virtual agents within the field of XAI on the perceived trustworthiness of autonomous intelligent systems. To assess the practicality of this concept, we conducted a user study based on a simple speech recognition task. As a result of this experiment, we found significant evidence suggesting that the integration of virtual agents into XAI interaction design leads to an increase of trust in the autonomous intelligent system.
An Energy Trade Framework Using Smart Contracts: Overview and Challenges

The increasing demand for clean, sustainable and reliable energy sources that are secure and stable requires the integration of renewable and edge energy products with the existing power grid. With the introduction of technological advancements and distributed resources, energy users (aka prosumers) can now generate, store and manage their energy requirements, and share their resources with others. BC is a promising technology that can provide secure and verifiable transactions for P2P energy trading, and promote energy conservation. This article recognizes the best practices for sustainable energy, and highlights the benefits of BC and smart contracts in the energy sector. A distributed trading framework and smart contracts are proposed for future versions of BC and integration with other energy products, and potential solutions are suggested.
Blockchain based integrated security measure for reliable service delegation in 6G communication environment

Sixth generation (6G) communication environment is unfolded in the recent years in order to provide high throughput less latency services for the mobile users. This environment encloses a variety of heterogeneous resources and communication standards to ensure seamless availability of services. In this open environment, security is a concerning issue due to heterogeneous standard integration and access delegations. This paper introduces blockchain-based integrated security measure (BISM) for providing secure access control and privacy preserving for the resources and the users. The access control process relies on the states of the virtualized resources at different time instances and the privacy preserving relies on longevity of service responses. The access delegation and denial is decided by the changeover of the states of the resources as aided by the Q-learning procedure for improving the service related performance. This performance for access control and privacy is verified using the metrics true positives, access denial and success ratio, access time and modification, memory utilization and time complexity.
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning

A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learning models. In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. We materialize the supervisory signals through determining whether a pair of samples is from one frame or from one video, and whether a triplet of samples is in the correct temporal order. We uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo). SeCo shows superior results under the linear protocol on action recognition (Kinetics), untrimmed activity recognition (ActivityNet) and object tracking (OTB-100). More remarkably, SeCo demonstrates considerable improvements over recent unsupervised pre-training techniques, and leads the accuracy by 2.96% and 6.47% against fully-supervised ImageNet pre-training in action recognition task on UCF101 and HMDB51, respectively. Source code is available at https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning.
Hierarchical Kinematic Human Mesh Recovery

We consider the problem of estimating a parametric model of 3D human mesh from a single image. While there has been substantial recent progress in this area with direct regression of model parameters, these methods only implicitly exploit the human body kinematic structure, leading to sub-optimal use of the model prior. In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model. This results in a strong prior-informed design of the regressor architecture and an associated hierarchical optimization that is flexible to be used in conjunction with the current standard frameworks for 3D human mesh recovery. We demonstrate these aspects by means of extensive experiments on standard benchmark datasets, showing how our proposed new design outperforms several existing and popular methods, establishing new state-of-the-art results. By considering joint interdependencies, our method is equipped to infer joints even under data corruptions, which we demonstrate by conducting experiments under varying degrees of occlusion.
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction

While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation. Initially designed for natural language processing tasks, Transformers have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies. In this paper, we propose TransDepth, an architecture that benefits from both convolutional neural networks and transformers. To avoid the network losing its ability to capture locallevel details due to the adoption of transformers, we propose a novel decoder that employs attention mechanisms based on gates. Notably, this is the first paper that applies transformers to pixel-wise prediction problems involving continuous labels (i.e., monocular depth prediction and surface normal estimation). Extensive experiments demonstrate that the proposed TransDepth achieves state-of-theart performance on three challenging datasets. Our code is available at: https://github.com/ygjwd12345/TransDepth.
Boosting Crowd Counting via Multifaceted Attention

This paper focuses on the challenging crowd counting task. As large-scale variations often exist within crowd images, neither fixed-size convolution kernel of CNN nor fixed-size attention of recent vision transformers can well handle this kind of variations. To address this problem, we propose a Multifaceted Attention Network (MAN) to improve transformer models in local spatial relation encoding. MAN incorporates global attention from vanilla transformer, learnable local attention, and instance attention into a counting model. Firstly, the local Learnable Region Attention (LRA) is proposed to assign attention exclusive for each feature location dynamically. Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations. Finally, we provide an Instance Attention mechanism to focus on the most important instances dynamically during training. Extensive experiments on four challenging crowd counting datasets namely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed method. Code: https://github.com/LoraLinH/Boosting-Crowd-Counting-via-Multifaceted-Attention.
HEIF: Highly Efficient Stochastic Computing-Based Inference Framework for Deep Neural Networks

Deep convolutional neural networks (DCNNs) are one of the most promising deep learning techniques and have been recognized as the dominant approach for almost all recognition and detection tasks. The computation of DCNNs is memory intensive due to large feature maps and neuron connections, and the performance highly depends on the capability of hardware resources. With the recent trend of wearable devices and Internet of Things, it becomes desirable to integrate the DCNNs onto embedded and portable devices that require low power and energy consumptions and small hardware footprints. Recently stochastic computing (SC)-DCNN demonstrated that SC as a low-cost substitute to binary-based computing radically simplifies the hardware implementation of arithmetic units and has the potential to satisfy the stringent power requirements in embedded devices. In SC, many arithmetic operations that are resource-consuming in binary designs can be implemented with very simple hardware logic, alleviating the extensive computational complexity. It offers a colossal design space for integration and optimization due to its reduced area and soft error resiliency. In this paper, we present HEIF, a highly efficient SC-based inference framework of the large-scale DCNNs, with broad applications including (but not limited to) LeNet-5 and AlexNet, that achieves high energy efficiency and low area/hardware cost. Compared to SC-DCNN, HEIF features: 1) the first (to the best of our knowledge) SC-based rectified linear unit activation function to catch up with the recent advances in software models and mitigate degradation in application-level accuracy; 2) the redesigned approximate parallel counter and optimized stochastic multiplication using transmission gates and inverse mirror adders; and 3) the new optimization of weight storage using clustering. Most importantly, to achieve maximum energy efficiency while maintaining acceptable accuracy, HEIF considers holistic optimizations on cascade connection of function blocks in DCNN, pipelining technique, and bit-stream length reduction. Experimental results show that in large-scale applications HEIF outperforms previous SC-DCNN by the throughput of 4.1×, by area efficiency of up to 6.5×, and achieves up to 5.6× energy improvement.
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks

The traffic driving environment is a complex and dynamic changing scene in which drivers have to pay close attention to salient and important targets or regions for safe driving. Modeling drivers' eye movements and attention allocation in traffic driving can also help guiding unmanned intelligent vehicles. However, until now, few studies have modeled drivers' true fixations and allocations while driving. To this end, we collect an eye tracking dataset from a total of 28 experienced drivers viewing 16 traffic driving videos. Based on the multiple drivers' attention allocation dataset, we propose a convolutional-deconvolutional neural network (CDNN) to predict the drivers' eye fixations. The experimental results indicate that the proposed CDNN outperforms the state-of-the-art saliency models and predicts drivers' attentional locations more accurately. The proposed CDNN can predict the major fixation location and shows excellent detection of secondary important information or regions that cannot be ignored during driving if they exist. Compared with the present object detection models in autonomous and assisted driving systems, our human-like driving model does not detect all of the objects appearing in the driving scenes, but it provides the most relevant regions or targets, which can largely reduce the interference of irrelevant scene information.
What is cognition?

Eleven authors with disparate relevant backgrounds give their view on what is meant by the word "cognition".
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments.Most work in argument mining has focused on modeling arguments in monologues.We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macrolevel (argument as process) models of argumentation.Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread.Our approach for relation prediction uses contextual information in terms of fine-tuning a pretrained language model and leveraging discourse relations based on Rhetorical Structure Theory.We additionally propose a candidate selection method to automatically predict what parts of one's argument will be targeted by other participants in the discussion.Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.