Reviewerly/exHarmony huggingface dataset:
Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction

The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively.
Application of improved image restoration algorithm and depth generation in English intelligent translation teaching system

The difficulty of English book translation lies in the need to recognize English characters in a complex background and perform accurate classification and translation. Based on the deep generative model, this paper constructs an intelligent translation system for English books based on machine learning and proposes an improved image restoration algorithm based on LSGAN on the basis of the image restoration algorithm based on DCGAN. Moreover, this paper optimizes the structure of the LSGAN network to a certain extent, improves the stability of the network, combines context loss and perceptual loss to find the most suitable generated image for filling. In addition, based on the VAE and GAN network structure, this paper proposes an improved image restoration algorithm based on VAE-GAN to improve the integrity of English characters and perform intelligent recognition. Finally, this paper analyzes through the experimental form, and after setting the identification object, the model constructed in this paper is used for translation, the translation accuracy and classification accuracy rate are calculated, and the performance analysis is performed after the statistical data. From the research results, it can be seen that the artificial intelligence model constructed in this paper has certain practical effects.
Hypergraph-Regularized Lp Smooth Nonnegative Matrix Factorization for Data Representation

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph-regularized Lp smooth nonnegative matrix factorization (HGSNMF) by incorporating the hypergraph regularization term and the Lp smoothing constraint term into the standard NMF model. The hypergraph regularization term can capture the intrinsic geometry structure of high dimension space data more comprehensively than simple graphs, and the Lp smoothing constraint term may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of the proposed method is theoretically investigated. The experimental results on five different data sets show that the proposed method has a better clustering effect than the related state-of-the-art methods in the vast majority of cases.
Deep Learning for Natural Language Processing: A Survey

Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many different applications; in this paper, we survey the applications of deep learning to natural language processing (NLP) problems. We begin by briefly reviewing the basic notions and major architectures of deep learning, including some recent advances that are especially important for NLP. Then we survey distributed representations of words, showing both how word embeddings can be extended to sentences and paragraphs and how words can be broken down further in character-level models. Finally, the main part of the survey deals with various deep architectures that have either arisen specifically for NLP tasks or have become a method of choice for them; the tasks include sentiment analysis, dependency parsing, machine translation, dialog and conversational models, question answering, and other applications.
A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration

Feature matching is a core step in multi-source remote sensing image registration approaches based on feature. However, for existing methods, whether traditional classical SIFT algorithm or deep learning-based methods, they essentially rely on generating descriptors from local regions of feature points, which can lead to low matching success rates due to various challenges, including gray-scale changes, content changes, local similarity, and occlusions between images. Inspired by the human approach of finding rough corresponding regions globally and then carefully comparing local regions, and the excellent global attention property of transformers, the proposed feature matching network adopts a coarse-to-fine matching strategy that utilizes both global and local information between images to predict corresponding feature points. Importantly, the network has great flexibility of matching corresponding points for any feature points and can be effectively trained without strong supervised signals of corresponding feature points and only require the true geometric transformation between images. The qualitative experiment illustrate the effectiveness of the proposed network by matching feature points extracted by SIFT or sampled uniformly. In the quantitative experiments, we used feature points extracted by SIFT, SuperPoint, and LoFTR as the keypoints to be matched. We then calculated the mean match success ratio (MSR) and mean reprojection error (MRE) of each method at different thresholds in the test dataset. Additionally, boxplot graphs were plotted to visualize the distributions. By comparing the MSR and MRE values as well as their distributions with other methods, we can conclude that the proposed method consistently outperforms the comparison methods in terms of MSR at different thresholds. Moreover, the MSR of the proposed method remains within a reasonable range compared to the MRE of other methods.
An Ontology for Spatio-Temporal Media Management and an Interactive Application

In addition to traditional viewing media, metadata that record the physical space from multiple perspectives will become extremely important in realizing interactive applications such as Virtual Reality (VR) and Augmented Reality (AR). This paper proposes the Software Defined Media (SDM) Ontology designed to describe spatio-temporal media and the systems that handle them comprehensively. Spatio-temporal media refers to video, audio, and various sensor values recorded together with time and location information. The SDM Ontology can flexibly and precisely represent spatio-temporal media, equipment, and functions that record, process, edit, and play them, as well as related semantic information. In addition, we recorded classical and jazz concerts using many video cameras and audio microphones, and then processed and edited the video and audio data with related metadata. Then, we created a dataset using the SDM Ontology and published it as linked open data (LOD). Furthermore, we developed “Web3602”, an application that enables users to interactively view and experience 360∘ video and spatial acoustic sounds by referring to this dataset. We conducted a subjective evaluation by using a user questionnaire. Web3602 is a data-driven web application that obtains video and audio data and related metadata by querying the dataset.
IMPLEMENTATION OF FUZZY LOGIC USING SUGENO METHOD OF SCHOLARSHIP DETERMINATION

Scholarships are funding that does not come from own funding or parents, but is provided by the government, educational institutions, private companies, universities, and schools that can provide opportunities to increase human resource capacity through education. To increase student interest in learning in the school environment by providing scholarships or assistance to students who are economically disadvantaged. The process of selecting scholarship recipients includes three variables, namely report cards, attendance, and student attitudes. Based on the variables above, the data will be processed to produce student decisions that deserve scholarships with a success rate of 88.88%. In order to produce fast and precise decisions and to avoid mistakes in selecting students, we need an appropriate method that can be used to assist decision makers in carrying out their duties. By using the right method, the school will be easier to make decisions. The method to overcome this problem is through the application of fuzzy logic with the Sugeno method.
Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork

Abstract Ad hoc teamwork (AHT) refers to the problem of enabling an agent to collaborate with teammates without prior coordination. State of the art methods in AHT are data-driven , using a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent’s behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes. Our recent work introduced an architecture that determined an ad hoc agent’s behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and models learned from limited examples to predict the behavior of other agents. This paper describes KAT, a knowledge-driven architecture for AHT that substantially expands our prior architecture’s capabilities to support: (a) online selection, adaptation, and learning of the behavior prediction models; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate KAT’s capabilities in two simulated benchmark domains for multiagent collaboration: Fort Attack and Half Field Offense. We show that KAT’s performance is better than a purely knowledge-driven baseline, and comparable with or better than a state of the art data-driven baseline, particularly in the presence of limited training data, partial observability, and changes in team composition.
MECCANO: A multimodal egocentric dataset for humans behavior understanding in the industrial-like domain

Wearable cameras allow to acquire images and videos from the user's perspective. These data can be processed to understand humans behavior. Despite human behavior analysis has been thoroughly investigated in third person vision, it is still understudied in egocentric settings and in particular in industrial scenarios. To encourage research in this field, we present MECCANO, a multimodal dataset of egocentric videos to study humans behavior understanding in industrial-like settings. The multimodality is characterized by the presence of gaze signals, depth maps and RGB videos acquired simultaneously with a custom headset. The dataset has been explicitly labeled for fundamental tasks in the context of human behavior understanding from a first person view, such as recognizing and anticipating human-object interactions. With the MECCANO dataset, we explored six different tasks including (1) Action Recognition, (2) Active Objects Detection and Recognition, (3) Egocentric Human-Objects Interaction Detection, (4) Egocentric Gaze Estimation, (5) Action Anticipation and (6) Next-Active Objects Detection. We propose a benchmark aimed to study human behavior in the considered industrial-like scenario which demonstrates that the investigated tasks and the considered scenario are challenging for state-of-the-art algorithms. To support research in this field, we publicy release the dataset at https://iplab.dmi.unict.it/MECCANO/.
AOG-LSTM: An adaptive attention neural network for visual storytelling

Visual storytelling is the task of generating a related story for a given image sequence, which has received significant attention. However, using general RNNs (such as LSTM and GRU) as the decoder limit the performance of the models in this task. This is because they can not differentiate different types of information representations. In addition, optimizing the probabilities of subsequent words conditioned on the previous ground-truth sequences can cause error accumulation during inference. Moreover, the existing method of alleviating error accumulation based on replacing reference words does not take into account the different effects of each word. To address the above problems, we propose a modified neural network named AOG-LSTM and a modified training strategy named ARS, respectively. AOG-LSTM can adaptatively pay appropriate attention to different information representations within it when predicting different words. During training, ARS replaces some words in the reference sentences with model predictions similar to the existing method. However, we utilize the selection network and selection strategy to select more appropriate words for the replacement to better improve the model. Experiments on the VIST Dataset demonstrate that our model outperforms several strong baselines on the most commonly used metrics.
A Shallow Convolution Network Based Contextual Attention for Human Activity Recognition

Human activity recognition (HAR) is increasingly important in ubiquitous computing applications. Recently, attention mechanism are extensively used in sensor-based HAR tasks, which is capable of focusing the neural network on different parts of the time series data. Among attention-based methods, the self-attention mechanism performs well in the HAR field, which establish the correlation of key-query to fuse the local information with global information. But self-attention fails to model the local contextual information between the keys. In this paper, we propose a contextual attention (COA) based HAR method, which utilize the local contextual information between keys to guide learning the global weight matrix. In COA mechanism, we use $$k \times k$$ kernel to encode input signal to local contextual keys to extract more contextual information between keys. By fusing local key and query to generate global weight matrix, we can establish the correlation between local features and global features. The values are multiplied by the weight matrix to get a global contextual key, which include global contextual information. We combine the local key and global key to enhance feature’s expression ability. Extensive experiments on five public HAR datasets, namely UCI-HAR, PAMAP2, UNIMIB-SHAR, DSADS, and MHEALTH show that the COA-based model is superior to the state-of-the-art methods.
Consortium Blockchain-Based Security and Efficient Resource Trading in V2V-Assisted Intelligent Transport Systems

As an effective method to increase the computing capability of vehicles, vehicle-to-vehicle (V2V) assisted vehicular edge computing (VEC) has great potential to reduce infrastructure spending and better adapt to mobility. However, its feasibility depends heavily on the sharing willingness, how to incentive selfish vehicles in such decentralized architecture is a vital problem. In this paper, we jointly consider the pricing strategy and the users’ concerns about security and privacy to incentive more participants. We first construct a P2P resource trading system based on consortium blockchain to build trust between strange vehicles and guarantee transaction security and user privacy without an authority center. To overcome the resource constraint on blockchain deployment and enhance the scalability of blockchain, the proposed system is built on edge-terminal architecture and improves the consistency algorithm. In addition, the V2V trading process is formalized as a two-stage Stackelberg game model to maximize the utility of both requesters and providers. The optimal pricing and trading volume strategy are derived from efficient optimization algorithms. Finally, we conduct a security analysis to show the performance of the trading process in terms of security and privacy. Numerical simulations show the effectiveness of the proposed strategy to motivate more participants.
FS-Net: LiDAR-Camera Fusion With Matched Scale for 3D Object Detection in Autonomous Driving

As a key task in autonomous driving, 3D object detection based on LiDAR-camera fusion is expected to achieve more robust results by the complementarity of the two sensors. However, LiDAR-camera fusion is non-trivial. An existing problem for this type of detector is that the scale and receptive field of LiDAR point features and image features are not matched, leading to information deficiency or redundancy in fusion. This paper proposes a Point-based Pyramid Attention Fusion (PPAF) module for LiDAR-camera fusion to solve the problem. The PPAF module learns corresponding image features of LiDAR points with a matched scale based on the image feature pyramid and attention mechanism for a better effect of fusion. Furthermore, based on the PPAF module, a new LiDAR-camera fusion-based 3D object detector named FS-Net is proposed, a two-stage detector with LiDAR voxel-based RPN and refinement network based on enriched LiDAR-camera features. Experiments on two public datasets demonstrate the effectiveness of our approach.
Energy-Minimized Scheduling of Intermittent Real-Time Tasks in a CPU-GPU Cloud Computing Platform

Due to the flexibility, availability, and scalability of cloud computing services, more and more users seek solutions via cloud computing techniques. A cloud computing platform often consists of a large number of infrastructures, and its energy consumption is a big problem. In this article, we study how to minimize the energy consumption of a cloud computing platform when handling some intermittent real-time tasks. Unlike previous works that abstract users’ submitted tasks as single computation jobs and process them using CPU, this work proposes using CPU and GPU to process intermittent real-time tasks that occur at irregular intervals and their released computation jobs must be completed within required time limits. The energy consumption minimization problem is formulated as an integer nonlinear programming problem that needs to decide on a task assignment plan and a specific resource allocation plan. To effectively solve this problem, we define a state that represents the optimal solution for a given set of tasks with a given amount of resources, as well as a value function that represents the value of a state. In this way, we derive a state-transition equation and develop a dynamic programming method to solve the problem. This method is also extended to handle tasks whose arrival time is dynamic and unpredictable. Experiments show that the proposed algorithm can effectively reduce energy consumption, while its computation time is quite low compared to some other greedy methods.
Image-Based Visual Servoing for Position Alignment With Orthogonal Binocular Vision

Image-based visual servoing methods have been widely used in many areas including the position alignment for assembly. However, the parallel vision system suffers from low sensitivity to motion along the optical axis due to the camera’s perspective characteristics, which can lead to a larger alignment error along the optical axis of the camera and affect subsequent tasks. To address this issue, an innovative binocular Eye-to-Hand (ETH) vision system with two cameras whose optical axes are approximate orthogonal is designed. By deriving and analyzing the interaction matrix of point feature for the proposed system, its ability is demonstrated to improve sensitivity for motion along the optical axis. Furthermore, an image-based visual servoing control method with the proposed ETH vision system is developed for position alignment in a screw grasping experiment. A calibration method for the image Jacobian matrix is developed based on active motions of the robot’s end-effector. Finally, the experimental results verify the effectiveness of the proposed methods. The position alignment errors in the experiments with the proposed vision system and method are less than 0.5 mm. The results indicate that the proposed method holds great potential for industrial applications in the area of small components assembly.
Information-Driven Path Planning for Hybrid Aerial Underwater Vehicles

This article presents a novel rapidly-exploring adaptive sampling tree algorithm for adaptive sampling missions using a hybrid aerial underwater vehicle (HAUV) in an air–sea 3-D environment. This algorithm innovatively combines the tournament-based point selection sampling strategy, the information heuristic search process, and the framework of the rapidly-exploring random tree algorithm. Hence, the vehicle can be guided to a region of interest to scientists for sampling and generate a collision-free path for maximizing information collection by the HAUV under the constraints of environmental effects of currents or wind and a limited budget. The simulation results show that the fast search adaptive sampling tree algorithm has higher optimization performance, faster solution speed, and better stability than the rapidly-exploring information gathering tree algorithm and the particle swarm optimization algorithm.
Dedicated FPGA Implementation of the Gaussian TinyYOLOv3 Accelerator

This brief presents a dedicated FPGA implementation of the Gaussian TinyYOLOv3 accelerator using a streamline architecture for object detection in mobile and edge devices. The proposed accelerator employs a hardware-friendly shift-based floating-fixed MAC operator and shift-based quantization method that significantly reduces hardware resources and minimizes accuracy degradation. The pipelined streamline architecture maximizes hardware utilization and stores all parameters in on-chip memory to minimize external memory access. Moreover, the Gaussian modeling-based performance enhancement technique is effectively processed in the programmable system to address the low accuracy issue in lightweight models. The proposed IP implemented on Xilinx XCVU9P achieves a processing speed of 62.9 FPS and an accuracy of 34.01% on the COCO2014 dataset, which demonstrates the superiority of the proposed accelerator over prior research in terms of the trade-off between throughput, hardware resources, and model accuracy.
Identifiable Face Privacy Protection via Virtual Identity Transformation

Massive face images collected in smart surveillance and social networks are vulnerable to malicious access, thus compromising individual privacy. Existing schemes have been able to protect face privacy while preserving a certain level of identifiability, but have different limitations, e.g., the lack of strong transferability or the inability to retain irrelevant attributes. This letter proposes a novel face privacy protection scheme via virtual identity transformation, which guarantees strong privacy protection and high identifiability. We first solve a specific identity mask for the user, which ensures that the identity features extracted only from the user's faces can be approximated to the given virtual identity. Based on it, the identity transformation networks transform the original face into the protected form, which belongs to the virtual identity while retaining irrelevant attributes. Lastly, the virtual identity of the protected face is extracted for face recognition. Adequate experiments show that our scheme has satisfactory privacy protection, high identifiability, and strong transferability.