Reviewerly/exHarmony huggingface dataset:
Blockchain in the energy sector. An analysis of the Brooklyn case.

In order to contribute to the balancing of the volatile renewable energy production and consumption characteristics, energy trading based on blockchain technology gained attention in both the academic and global business debate. This article looks at innovations in prosumership, discusses details regarding the digitalization of the energy infrastructure and explains opportunities and limitations of blockchain usage. Taking the Brooklyn Microgrid as a case study, the article then analyzes the application of blockchain to energy trading on the community level as well as its usefulness as a possible climate technology able to efficiently contribute to the decarbonizing of the energy sector.
Legal Considerations in the Use of Artificial Intelligence in the Investigation of Online Child Exploitation

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Communication-Efficient Semihierarchical Federated Analytics in IoT Networks

The convergence of the Internet of Things (IoT) and data analytics has great potential to accelerate knowledge discovery, while the traditional approach of centralized data collection then processing is becoming infeasible in many applications due to efficiency and privacy concerns. Federated learning (FL) has emerged as a new paradigm that enables model learning across distributed IoT devices without sharing raw data. However, previous works on FL are either relying on a single central server or fully decentralized. In this article, we propose a semihierarchical federated analytics framework combining the advantages of the above architectures. The proposed framework leverages multiple edge servers for aggregating updates from IoT devices and fusing learned model weights without the need of cloud or a central server. Besides, we develop a new local client update rule to further improve the communication efficiency by reducing the communication rounds between IoT devices and edge servers. We analyze the convergence properties of the presenting approach and investigate its characteristics considering the effects of varying parameters, unreliable links, and packet loss. The experimental results demonstrate the effectiveness of our proposed methodology in providing communication-efficient, robust, and fault-tolerant data analytics to IoT networks.
Localized Feature Aggregation Module for Semantic Segmentation

We propose a new information aggregation method which called "Localized Feature Aggregation Module" based on the similarity between the feature maps of an encoder and a decoder. The proposed method recovers positional information by emphasizing the similarity between decoder’s feature maps with superior semantic information and encoder’s feature maps with superior positional information. The proposed method can learn positional information more efficiently than conventional con-catenation in the U-net and attention U-net. Additionally, the proposed method also uses localized attention range to reduce the computational cost. Two innovations contributed to improve the segmentation accuracy with lower computational cost. By experiments on the Drosophila cell image dataset and COVID-19 image dataset, we confirmed that our method outperformed conventional methods.
Review of Autonomous Campus and Tour Guiding Robots with Navigation Techniques

Autonomous robots are designed to support humans in diverse ways including companionship and assistance, as technology evolves, the burden of physical tasks which were performed by humans in the past are getting reduced. Autonomous guidance and navigation are popular fields that use mobile robots to replace humans. These robots are widely used in space, defence, education, and industry and require navigating safely through different environments. A significant aspect of Autonomous Robot Navigation is an obstacle and pedestrian avoidance. Most of the outdoor guiding robots follow the concept of SLAM (Simultaneous Localisation and Mapping) for moving from the initial point to the goal point. Moreover, image processing plays a vital role in the detection and recognition between humans and objects. The existing methodology comprises implementing autonomous tour guide robots are based in embedded systems which has a high degree of recognition accuracy. This paper provides a comprehensive review of the existing autonomous campus and tour guiding robots. This paper is steered at providing a satellite view of these existing assemblies and the advances made with them.
Application of Clustering Analysis Based on Ant Colony Algorithm in Performance Evaluation

With the rapid development of domestic economy, the demand for talents is also increasing. Colleges and universities in the cultivation of students with the actual needs of the society does not match the phenomenon, based on this problem, educators began to use ant colony algorithm, based on it to establish college English practice teaching effect evaluation system, according to the ant colony algorithm for the current college English data optimization processing. This paper is to use the advantages of ant colony algorithm to process data, according to the problems in the current college English practice teaching to evaluate it effectively, and improve the practice teaching strategy according to the evaluation results, so as to cultivate more qualified English talents for the society.
Loop-checking and the uniform word problem for join-semilattices with an inflationary endomorphism

We solve in polynomial time two decision problems that occur in type checking when typings depend on universe level constraints.
Multi-Strategy Fusion for Medical Named Entity Recognition

This paper describes a method of recognizing Chinese clinically named entities based on multi-strategy fusion. In this clinical medical entity recognition task, we need to identify the boundaries of 18 types of entities from the medical dialogue. There are some problems in this task, such as insufficient data, uneven distribution of entity types, and inconsistent annotation. We alleviate these problems through the integration of multiple strategies. We first use the semi-supervised learning method to expand the training data, and then use the ensemble learning method to integrate the recognition results of multiple models, reducing the recognition bias caused by a single model. Finally, dictionary matching, prefix matching, and other post-processing methods for fusion results, to maximize the solution of label inconsistencies. The F1 value of our proposed method on the final test set is 85.035801, ranking first.
Placing Arrows in Directed Graph Layouts: Algorithms and Experiments

Abstract We study how to place arrow heads in directed graph drawings aiming at minimizing their overlaps and avoiding intersections between arrow heads and edges. The objective is to support users to correctly and quickly recognize edge orientations, i.e. to deduce unambiguously the edge orientations. Our contribution is two‐fold: (i) We present exact and heuristic algorithms for this arrow placement problem, along with an extensive experimental analysis of these techniques; and (ii) we report on a user study aimed to understand the impact of different arrow placement strategies on performing global and local analysis tasks on directed graph layouts.
Research on Collaborative Security Based on On-blockchain and Off-blockchain in the Scenario of Power Data Acquisition

This paper introduces the current status of power data acquisition system, and focuses on the credibility problem between suppliers and consumers, proposes a plan of data acquisition system of electricity consumption, constructed a new type of data acquisition system using the technology of blockchain, divided the data transmission into two processes, on-chain and off-chain. Off-chain is defined as the data transmission stage in the process of power data acquisition of users, whereas on-chain is the process of data transmitting in the blockchain system. This paper bases on two different types of security protection mechanism by symmetric encryption algorithms and asymmetric encryption algorithms. Comparing the advantages and disadvantages of two algorithms, this paper proposes the security protection mechanism in the conditions of on-chain and off-chain, which reduces the risk of data tamper and achieved the collaborative protection in the processes of on-chain and off-chain.
Improving kernel online learning with a snapshot memory

We propose in this paper the Stochastic Variance-reduced Gradient Descent for Kernel Online Learning (DualSVRG), which obtains the $$\varepsilon$$ -approximate linear convergence rate and is not vulnerable to the curse of kernelization. Our approach uses a variance reduction technique to reduce the variance when estimating full gradient, and further exploits recent work in dual space gradient descent for online learning to achieve model optimality. This is achieved by introducing the concept of an instant memory, which is a snapshot storing the most recent incoming data instances and proposing three transformer oracles, namely budget, coverage, and always-move oracles. We further develop rigorous theoretical analysis to demonstrate that our proposed approach can obtain the $$\varepsilon$$ -approximate linear convergence rate, while maintaining model sparsity, hence encourages fast training. We conduct extensive experiments on several benchmark datasets to compare our DualSVRG with state-of-the-art baselines in both batch and online settings. The experimental results show that our DualSVRG yields superior predictive performance, while spending comparable training time with baselines.
On exploring data lakes by finding compact, isolated clusters

Data engineers are very interested in data lake technologies due to the incredible abundance of datasets. They typically use clustering to understand the structure of the datasets before applying other methods to infer knowledge from them. This article presents the first proposal that explores how to use a meta-heuristic to address the problem of multi-way single-subspace automatic clustering, which is very appropriate in the context of data lakes. It was confronted with five strong competitors that combine the state-of-the-art attribute selection proposal with three classical single-way clustering proposals, a recent quantum-inspired one, and a recent deep-learning one. The evaluation focused on exploring their ability to find compact and isolated clusterings as well as the extent to which such clusterings can be considered good classifications. The statistical analyses conducted on the experimental results prove that it ranks the first regarding effectiveness using six standard coefficients and it is very efficient in terms of CPU time, not to mention that it did not result in any degraded clusterings or timeouts. Summing up: this proposal contributes to the array of techniques that data engineers can use to explore their data lakes.
Cosine Similarity through Control Flow Graphs For Secure Software Engineering

As the recommender systems need data to generate a recommendation, the dataset is intended as a standardized input for research on recommendation systems for software engineering but is also useful in many other areas that analyze source code. In this paper, we present a collection of 557-cryptography C# solutions, which includes 19590 classes. From these repositories of classes are generated two datasets with 19,590 lines of code, respectively 1,191,340 lines of code using control flow graph rules. For two datasets are calculated TF-IDF and Cosine Similarity.
Multiplicative Complexity of XOR Based Regular Functions

XOR-AND Graphs (XAGs) are an enrichment of the classical AND-Inverter Graphs (AIGs) with XOR nodes.In particular, XAGs are networks composed by ANDs, XORs, and inverters.Besides several emerging technologies applications, XAGs are often exploited in cryptography-related applications based on the multiplicative complexity of a Boolean function.The multiplicative complexity of a function is the minimum number of AND gates (i.e., multiplications) that are sufficient to represent the function over the basis {AND, XOR, NOT}.In fact, the minimization of the number of AND gates is important for high-level cryptography protocols such as secure multiparty computation, where processing AND gates is more expensive than processing XOR gates.Moreover, it is an indicator of the degree of vulnerability of the circuit, as a small number of AND gates corresponds to a high vulnerability to algebraic attacks.In this paper we study the multiplicative complexity of Boolean functions characterized by two particular regularities, called autosymmetry and D-reducibility.Moreover, we exploit these regularities for decreasing the number of AND nodes in XAGs.The experimental results validate the proposed approaches.
GAD: A Global-Aware Diversity-Based Template Matching Method

A novel similarity measurement metric, global-aware diversity (GAD), between a template and an image is proposed in this article, which can be efficiently utilized in a template matching method. Unlike the existing nearest neighbor field (NNF)-based methods, which use either the local or global diversity alone, GAD utilizes the global context to guide the local diversity. We verify that the global context is important for template matching task and can largely remove the background and outliers. The state-of-the-art NNF-based methods in general are not efficient enough as resolution grows. Thus, we also elaborately design a very efficient algorithm of GAD to reduce the complexity from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\vert \,\mathcal {I}\,\vert \cdot \vert \,\mathcal {T}\,\vert)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\vert \,\mathcal {I}\,\vert)$ </tex-math></inline-formula> . First, the GAD algorithm has been evaluated on two challenging benchmark datasets best-buddies similarity (BBS) and TinyTLP. Experiments show that the GAD algorithm achieves the state-of-the-art performance. As GAD does not impose any prior on unseen data, it is more insensitive to large rotation or deformation than the deformation-based algorithms. Besides, the GAD algorithm can run at 150 ms on average with high accuracy on the dataset of resolution 1280 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times780$ </tex-math></inline-formula> .
Detection boundary for a sparse gamma scale mixture model

Summary We derive the detection boundary for the one‐sided version of the gamma scale mixture model where the contaminating component has a larger mean than the known reference distribution. We also derive an adaptive test which is able to almost uniformly attain the best possible performance in terms of detection of local alternatives.
SAM-Net: LiDAR Depth Inpainting for 3D Static Map Generation

Various sensors can be attached and added to autonomous vehicles, included visual cameras, radar, LiDAR (Light Detection And Ranging), and GNSS (Global Navigation Satellite System). These sensors have been studied in many research areas, in particular studies on building precise 3D maps. It is essential for autonomous driving to create an accurate 3D map of the surrounding scene. However, creating an accurate static 3D map is difficult due to changes in moving objects or dynamic environments. Spurious objects on the 3D map can be handled by removing or ignoring them for 3D mapping. Following this idea, we propose an object segmentation and inpainting network. The proposed network called SAM-Net, addresses the object duplication issue by segmenting the objects and inpainting them with the segmentation results. Conventional inpainting research has dealt with RGB images. No matter how well such approaches reconstruct holes or corrupted images, they do not establish 3D points’ relationship with the point cloud frame. Therefore, we suggest a depth inpainting method for outdoor object segmentation and inpainting tasks that utilizes a high-precision depth range sensor (Velodyne HDL-64E), which is not suggested before. Unfortunately, no dataset exists for the outdoor depth inpainting task. Thus, to train our model, we generate a new dataset by locating objects on a clean static background. Moreover, our proposed method shows outstanding depth performance compared to the previous visual inpainting method. Our dataset will be available at: “ <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JunhyeopLee/lidar_inpainting</uri> ”.
Indian Sign Language Recognition Using Random Forest Classifier

Communication is the foundation of all human relationships, both personal and professional. It is one of the basic requirements for survival in a society. Verbal communication is impossible without a well-defined language that is understood by both parties. Around 26% of the disabled population in India use sign language for communication. As a result, there is a pressing need to bridge the communication gap between the general public and the speech impaired. The proposed idea is to develop a pair of sensor gloves which detects Indian Sign Language (ISL) gestures and converts them into audible speech. The gloves are mounted with various sensors and modules such as the Arduino Nano microcontrollers, flex sensors, touch sensors, Inertial Measurement Units (IMU), RF and Bluetooth modules. With these sensors, the state of both hands can be quantified in a series of numerical data to capture their motion. The sensor values are also sent to a Machine Learning classification algorithm which improves the accuracy of gesture recognition. The audible speech output is obtained by creating an app on a smart phone that can convert text to speech.
Dual-Population Social Group Optimization Algorithm Based on Human Social Group Behavior Law

Inspired by the behavior law of human social groups, a new swarm intelligence algorithm named the dual-population social group optimization (DPSGO) algorithm is proposed in this article. Based on the primitive social group optimization (SGO) algorithm, dual-population grouping technology, reverse learning technology, immigration migration technology, and Gaussian mutation are introduced to further simulate the behavior law of actual human social groups. Experimental results and performance comparison show that the DPSGO algorithm has a better searchability and convergence rate. In addition, aiming at the socially hot issue of aviation safety, the simulation and experimental results show that the temperature measurement error can be reduced to less than 7.5 °C by using the DPSGO algorithm combined with reflected radiation correction to process the aeroengine multispectral radiation temperature measurement data. This article is of great significance to the design and optimization of swarm intelligence algorithms by using the behavior law of human social groups and provides valuable guidance for enhancing the safety monitoring of aeroengines.
Traffic Control System for Smart City Using Image Processing

Due to the quickly changing population with the economic development of the country, traffic congestion has come out as a serious threat to society. Since the present traffic system is only predetermined time-based traffic control, it's high time to come out with a self-adaptive system for better traffic administration. This chapter represents a smart traffic control technique for smart cities that can be implemented by using image processing. A web camera is positioned on a traffic signal to seize the photo sequences. An empty road image is about a reference photograph and the real-time traffic images are matched with the usage of image matching. For this purpose, edge detection is used and a percentage matching happens between a reference image and a real-time traffic image, the traffic light time allocation can be done with percentage image matching. The results obtained in the above processes will help in reducing delay time at the traffic lane.
Continuous-Time Stochastic Differential Networks for Irregular Time Series Modeling

Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling irregular time series, whose observations are irregular and sparse in both time and dimension. For a given system whose latent states and observed data are multivariate, it is generally impossible to derive a precise continuous-time stochastic process to describe the system behaviors. To solve the above problem, we apply Variational Bayesian method and propose a flexible continuous-time stochastic recurrent neural network named Variational Stochastic Differential Networks (VSDN), which embeds the complicated dynamics of the irregular time series by neural Stochastic Differential Equations (SDE). VSDNs capture the stochastic dependency among latent states and observations by deep neural networks. We also incorporate two differential Evidence Lower Bounds to efficiently train the models. Through comprehensive experiments, we show that VSDNs outperform state-of-the-art continuous-time deep learning models and achieve remarkable performance on prediction and interpolation tasks for irregular time series.
Learning an Occlusion-Aware Network for Video Deblurring

Video deblurring is a challenging task since the blur is caused by camera shake, object motions, etc. The success of the state-of-the-art methods stems mainly from exploiting the temporal information of neighboring frames through alignment. When there exists occlusion among the sequence, these approaches become less effective for inaccurate alignment. In this paper, we propose an effective occlusion-aware network to handle the occlusion for video deblurring. The proposed module first generates a coarse pixel-wise alignment filter to explore the temporal information and then learns an adaptive affine transformation to deal with the occluded areas. In addition, a self-attention mechanism is developed to better model the occluded pixels. To further improve the performance, we progress a multi-scale strategy and train the network in an end-to-end manner. Both quantitative and qualitative experimental results show that the proposed method achieves favorable performance against state-of-the-art methods on the benchmark datasets. The code and trained models are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XQLuck/code.git</uri>