Reviewerly/exHarmony huggingface dataset:
Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting

Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach.
Secure and Scalable Decentralized Supply Chain Management Using Ethereum and IPFS Platform

There are various applications using Blockchain technology to make sure the accessibility, stability and reliability. Nevertheless these applications are cladding the warehouse issue unsettled to the information capacity extension of transaction. In the paper, an IPFS based Blockchain repository prototype to resolve the trouble of transaction in a block through the entry of a specific block’s transaction. Supply chain management is administering passing the merchandise from the producer to the client and incorporating all procedures that consist of unprocessed materials into finished products. The supply chain unwraps everything beginning with manufacturing to output advancement. Supply chain management performance face reproving challenges affiliated with scalability, data integrity, transparency, traceability, efficiency, and security to the parties’ transactions. Blockchain is a digital documentation of ledger that issues a cautious and secure interchanging of data among supply chain stakeholders. The paper put forward a collective structure using IPFS, Ethereum smart contract, and decentralized repository organization to robotize the procedure and data swapping. The code was evolved and evaluated in Remix IDE to publish the Smart contracts to IPFS and compile transactions, Metamask as a wallet for Ethereum transaction.
Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement

In recent years, object detection algorithm based on deep learning has made great progress, but the detection effect is not ideal for small objects detection. Some methods use high-resolution features or enhance shallow features to improve the detection accuracy of small objects. However, using high-resolution features for detection needs higher computational cost, and enhancing shallow features by propagating semantic information from high-level into low-level may bring information aliasing. To address this issue, we propose a novel object detection method based on shallow feature fusion and semantic information enhancement (FFSI). The high-level semantic information is injected into low-level features to guide the enhancement of specific detail information. In order to reduce the information aliasing in shallow features and enhance the receptive field of shallow features, we design two parallel modules: context information enhancement module (CIE) and receptive field enhancement module (RFE). CIE highlights the location of objects by establishing the relationship between local and global context information. RFE enhances the receptive field of shallow features by using dilated convolution to adapt to object detection of different scales, especially small objects. The proposed model is evaluated extensively on PASCAL VOC, and COCO datasets. The experimental results demonstrate that the proposed FFSI model has competitive performance. More importantly, this study reveals that FFSI outperforms the state-of-the-art methods in detecting small objects.
The entry and exit dynamics of the cryptocurrency market

• We analyse the entry and exit dynamics of the cryptocurrency market with the index cohesive force. • We focus on the growth of initial coin offerings during 2015–2020. • We observe an increase in the collective behaviour from 2017 to 2019. • Since 2019, the presence of a strong co-movement is replaced by the dominance of the largest cryptocurrencies. • The market is still able to introduce new influential cryptocurrencies such as Ethereum (2015), Neo (2016), Eos (2017) and Cardano (2017). This paper presents an analysis of the entry and exit dynamics of the cryptocurrency market that focuses on the growth of initial coin offerings during 2015–2020. We used two different datasets: one includes long-lived cryptocurrencies, while the other includes the whole cryptocurrency system at our disposal–that is, it considers the entering and exiting cryptocurrencies. Comparing the dynamics between both datasets with the index cohesive force approach, we assessed how the growth of the initial coin offerings and the exiting cryptocurrencies affected the connectedness of the market. Our results show that the expansion of the cryptocurrency system gave rise to a strong collective movement during 2018–2019. Afterwards, the group pressure, due to the bubble of the initial coin offerings, decreased in favour of the largest cryptocurrencies. Lastly, we observed changes in the hierarchical order of the most influential cryptocurrencies. In particular, Ethereum became the most influential cryptocurrency, at the detriment of Bitcoin.
A Text Based Sentiment Analysis Model using Bi-directional LSTM Networks

The Web generates enormous quantities of data in the form of user attitudes, feelings, thoughts, and opinions about various social occasions, brands, manufacturers, and democracy through social media platforms, communities, online reviews, and blog posts. Users' opinions expressed on the internet have a significant impact on users, product manufacturers and legislators. Sentiment analysis is stated as text association which categorizes the conveyed ethos or state of mind in variousbehaviours such as good, bad, satisfactory, hostile, like, dislike, etc. Mostly in domain of Speech Recognition, the lack of sufficient marked information is critical with sentiment classification. To overcome this problem, sentiment analysis is performed using RNN as deep NN are successful due to its machine learning abilities. This paper highlights a methodology for sentiment classification by means of a deep NN model called BiLSTM recurrent neural networks for a variety of sentiment analysis challenges, including text categorization, cross-lingual issues, verbal and contextual assessment, and industry news assessment, among others.
Blind Omnidirectional Image Quality Assessment Based on Structure and Natural Features

Omnidirectional image quality assessment (OIQA) is a hot research topic in image processing. Besides, IQA is also very important in the field of instrumentation and measurement. In this article, we propose a new no-reference OIQA model by analyzing the relationship between image features and perceived quality. A gradient weighted local phrase quantization map is built to complement the gradient map to model the structure degradation of omnidirectional images caused by various distortions. Considering the large amount of information contained in the omnidirectional image, we extract the luminance features, global entropy instead of local entropy, and color features, which are ignored in most previous works to estimate the naturalness quality of the omnidirectional image. Finally, support vector regression is utilized to train and test our model based on the image features and human subjective quality assessment scores. The experiments on two databases show that our model outperforms the state-of-the-art IQA models and correlates well with subjective scores in the two databases.
Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack

Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices using these fingerprints, as they achieve higher accuracies compared to traditional approaches. However, it has been shown in domains such as computer vision that adding carefully crafted perturbations to legitimate inputs can fool such classifiers. This can undermine the security provided by the authenticator. Unlike adversarial attacks applied in other domains, an adversary has no control over the propagation environment. Therefore, to investigate the severity of this type of attack in wireless communications, we consider an unauthorized transmitter attempting to have its signals classified as authorized by a deep learning-based authenticator. We demonstrate a reinforcement learning-based attack where the impersonator—using only the authenticator’s binary authentication decision—distorts its signals in order to penetrate the system. Extensive simulations and experiments on a software-defined radio testbed indicate that at appropriate channel conditions and bounded by a maximum distortion level, it is possible to fool the authenticator reliably at a success rate of more than 90%.
Unsupervised Feature Selection With Weighted and Projected Adaptive Neighbors

In the field of data mining, how to deal with high-dimensional data is a fundamental problem. If they are used directly, it is not only computationally expensive but also difficult to obtain satisfactory results. Unsupervised feature selection is designed to reduce the dimension of data by finding a subset of features in the absence of labels. Many unsupervised methods perform feature selection by exploring spectral analysis and manifold learning, such that the intrinsic structure of data can be preserved. However, most of these methods ignore a fact: due to the existence of noise features, the intrinsic structure directly built from original data may be unreliable. To solve this problem, a new unsupervised feature selection model is proposed. The graph structure, feature weights, and projection matrix are learned simultaneously, such that the intrinsic structure is constructed by the data that have been feature weighted and projected. For each data point, its nearest neighbors are acquired in the process of graph construction. Therefore, we call them adaptive neighbors. Besides, an additional constraint is added to the proposed model. It requires that a graph, corresponding to a similarity matrix, should contain exactly c connected components. Then, we present an optimization algorithm to solve the proposed model. Next, we discuss the method of determining the regularization parameter γ in our proposed method and analyze the computational complexity of the optimization algorithm. Finally, experiments are implemented on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed method.
Copyright Gossip: Exploring Copyright Opinions, Theories, and Strategies on YouTube

This study investigates copyright discourses on YouTube. Through a qualitative content analysis of 144 YouTube videos, we explore how YouTube creators understand copyright law, how they minimize risks posed by copyright infringement, and how they navigate a highly technical and dynamic copyright enforcement ecosystem. Our findings offer insights into how digitally situated cultural producers are impacted by and respond to automated content moderation. This is important because increasingly lawmakers around the world are asking digital platforms to implement efficient systems for content moderation, and yet there is a lack of good information about the stakeholders most directly impacted by these practices. In this study, we present a systematic analysis of copyright gossip, building on the concept of algorithmic gossip, which comprises the opinions, theories, and strategies of creators who are affected by YouTube’s copyright enforcement systems.
Constrained Motion Planning Networks X

Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between a given start and goal configuration. These planning problems come up surprisingly frequently, such as in robot manipulation for performing daily life assistive tasks. However, few solutions to constrained motion planning are available, and those that exist struggle with high computational time complexity in finding a path solution on the manifolds. To address this challenge, we present Constrained Motion Planning Networks X (CoMPNetX). It is a neural planning approach, comprising a conditional deep neural generator and discriminator with neural gradients-based fast projection operator. We also introduce neural task and scene representations conditioned on which the CoMPNetX generates implicit manifold configurations to turbo-charge any underlying classical planner such as sampling-based motion planning methods for quickly solving complex constrained planning tasks. We show that our method finds path solutions with high success rates and lower computation times than state-of-the-art traditional path-finding tools on various challenging scenarios.
Deep dynamic imputation of clinical time series for mortality prediction

Missing values in clinical time-series data are pervasive and inevitable; they not only increase the complexity and difficulty of analyzing the data but also lead to biased results. To tackle these two problems, researchers have been exploring recurrent neural network (RNN)-based methods for detecting how well missing values are addressed with the aim of achieving state-of-the-art performance. However, these methods have two practical drawbacks. 1) Handling time-series data with multiple, irregular, abnormal values is difficult. 2) The patterns that may be present in the missing clinical data are not thoroughly considered. Moreover, to the best of our knowledge, none of these methods have been explicitly designed to dynamically optimize the imputation quality for better performance in the realm of clinical time-series analytics. By considering the quality of imputed values, we propose a 2-step integrated imputation-prediction model based on gated recurrent units (GRUs) for medical prediction tasks. In the first step, the missing values are imputed using a sophisticated model based on a replenished GRU with a hidden state decay mechanism (RGRU-D), which is followed by evaluation through two additional layers. In the second step, the optimized imputed values are used to predict the risk of mortality in critical patients. Our model effectively supplies missing values for the masking, time interval, bursty, and cumulative missing rate variables within an integrated deep architecture. Extensive experiments on a real-world ICU dataset demonstrate that our model performs better than the compared methods in terms of the imputation quality and prediction accuracy.
Novel distributed load balancing algorithms in cloud storage

From last decade, there is a rapid expanded upon in the data in cyberspace. In order to manage them efficiently, distributed storage came into the world. Cloud storage is a type of distributed storage based on cloud computing technology. Cloud storage acts as a repository in which data stored, managed and made accessible to users. Largest generated application datasets can flexibly be stored or deleted in the cloud and from here end users access this data by using cloud storage services interface, without accessing any storage server in real. Cloud storage system is considered of hundreds of independent storage servers which are distributed geographically, and handle millions client requests concurrently. Some of the storage servers get huge clients requests and some servers remain under loaded. Due to this unequal distribution of load on storage servers degrades the performance of overall system and increases the response time. This work addresses issues regarding to efficient utilization of storage servers in cloud storage. Handling various challenges regarding to the load balancing in the cloud storage is the one of the main objectives of this research. Though analyzing the contribution of other authors in this area, in this work two distributed load balancing algorithm CDLB and DDLB are proposed by exploiting the different parameter of storage server. The first proposed algorithm considers the service rate and queue length as a main parameter of the server. The second proposed algorithm considers extra server parameter such as service time and deadline time of the client request. This work monitors various aspects which leverage the overall performance of cloud storage. Both the algorithms try to balance the load of storage servers as well as effectively utilize the server capabilities. From the simulation results, it can be concluded that proposed algorithms balance the load, efficiently utilize the server capabilities, reduce the response time and leverage the overall system performance.
Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.
Deep tree-ensembles for multi-output prediction

Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their optimal performance depends on massive amounts of training data and the tuning of an extended number of parameters. As a countermeasure, some deep-forest methods have been recently proposed, as efficient and low-scale solutions. Despite that, these approaches simply employ label classification probabilities as induced features and primarily focus on traditional classification and regression tasks, leaving multi-output prediction under-explored. Moreover, recent work has demonstrated that tree-embeddings are highly representative, especially in structured output prediction. In this direction, we propose a novel deep tree-ensemble (DTE) model, where every layer enriches the original feature set with a representation learning component based on tree-embeddings. In this paper, we specifically focus on two structured output prediction tasks, namely multi-label classification and multi-target regression. We conducted experiments using multiple benchmark datasets and the obtained results confirm that our method provides superior results to state-of-the-art methods in both tasks.
A Survey on Deep Learning Methodologies of Recent Applications

In recent years, deep neural networks and their variants have generated remarkable benchmarks in multiple domains spanning from natural language processing to computer vision. The study of intelligence is a broadly followed territory in academia and the construction of intelligent systems to aid humans in diverse tasks. This chapter elaborates upon the key advancements in methodologies pertaining to computer vision and pattern recognition, self driving cars, generation of sound, creation of art and computed predictions.
PESA-Net: Permutation-Equivariant Split Attention Network for correspondence learning

Establishing reliable correspondences by a deep neural network is an important task in computer vision, and it generally requires permutation-equivariant architecture and rich contextual information. In this paper, we design a Permutation-Equivariant Split Attention Network (called PESA-Net), to gather rich contextual information for the feature matching task. Specifically, we propose a novel “Split–Squeeze–Excitation–Union” (SSEU) module. The SSEU module not only generates multiple paths to exploit the geometrical context of putative correspondences from different aspects, but also adaptively captures channel-wise global information by explicitly modeling the interdependencies between the channels of features. In addition, we further construct a block by fusing the SSEU module, Multi-Layer Perceptron and some normalizations. The proposed PESA-Net is able to effectively infer the probabilities of correspondences being inliers or outliers and simultaneously recover the relative pose by essential matrix. Experimental results demonstrate that the proposed PESA-Net relative surpasses state-of-the-art approaches for pose estimation and outlier rejection on both outdoor scenes and indoor scenes (i.e., YFCC100M and SUN3D). Source codes: https://github.com/x-gb/PESA-Net.
Hybrid FWPS cooperation algorithm based unmanned aerial vehicle constrained path planning

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The α constrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles is established to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints.