Reviewerly/exHarmony huggingface dataset:
Axial Attention Inside a U-Net for Semantic Segmentation of 3D Sparse LiDAR Point Clouds

Semantic segmentation of LiDAR point clouds is essential for driving guidance in autonomous driving. The current approaches may use complex data representations for sparse LiDAR point clouds or sophisticated neural networks to process these data; however, such methods may not be suitable for real-time prediction on mobile devices due to the high computational requirements.In this research, we propose to enhance U-Nets with axial attention modules to process the point clouds on 2D planes after the spherical projection. U-Nets are simple and baseline encoder-decoder networks for semantic segmentation with convolution operations of local context. We improved baseline U-Nets by simply adding axial attention modules inside U-Nets to increase the feature extraction effectiveness by incorporating the capability of modeling long-range dependencies from axial attention.Experiments were performed on the benchmark KITTI and our lab LiDAR datasets. In terms of mIoU (mean intersection over union), performance was increased from 51.3% of U-Nets to 54.8% of the mixed axial-attention U-Nets in KITTI, and from 81.7% of U-Nets to 92.3% of the mixed axial-attention U-Nets in our LiDAR dataset. In testing, the latency of the mixed axial-attention U-Nets in our LiDAR dataset was 22.3 ms per scan, which was fast enough to be real-time.We propose to embed axial attention in the decoder of U-Nets for semantic segmentation of 3D LiDAR point clouds with better IoU performance and real-time prediction capability on mobile devices.
HD Lane Map Generation Based on Trail Map Aggregation

We present a procedure to create high definition maps of lanes based on detected and tracked vehicles from perception sensor data as well as the ego vehicle using multiple observations of the same location. The procedure consists of two parts. First, an aggregation part in which the detected and tracked road users as well as the driving path of the ego vehicle are aggregated into a map representation. Second, lanes are extracted from those maps as lane center lines in a structured data format. The final lane centers are represented in a directed graph representation including the driving direction. They are accurate up to a few centimeters. Our procedure is not restricted to any environment and does not rely on any prior map information. In our experiments with real world data and available ground truth, we study the performance of different map aggregations e.g., based on the ego vehicle only or based on other road users. Furthermore, we study the dependence on the number of data recording repetitions.
Deep-Learning-Based Anomaly Detection for Lane-Changing Decisions

Vehicles can utilize their sensors or receive messages from other vehicles to acquire information about the surrounding environments. However, the information may be inaccurate, faulty, or maliciously compromised due to sensor failures, communication faults, or security attacks. The goal of this work is to detect if a lane-changing decision and the sensed or received information are anomalous. We develop three anomaly detection approaches based on deep learning: a classifier approach, a predictor approach, and a hybrid approach combining the classifier and the predictor. All of them do not need anomalous data nor lateral features so that they can generally consider lane-changing decisions before the vehicles start moving along the lateral axis. They achieve at least 82% and up to 93% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores against anomaly on data from Simulation of Urban MObility (SUMO) [1] and HighD [2]. We also examine system properties and verify that the detected anomaly includes more dangerous scenarios.
Auditory and visual warning information generation of the risk object in driving scenes based on weakly supervised learning

In this research, a two-stage risk object warning method is proposed to generate the auditory and visual warning information simultaneously from the driving scene. The auditory warning module (AWM) is designed as a classification task by combining the rough location and type information as warning sentences and treating each sentence as one class. The visual warning module (VWM) is designed as a weakly supervised method to save the labor-intensive bounding box marking of risk objects. To confirm the effectiveness of the proposed method, we also create a linguistic risk notification (LRN) dataset by describing the driving scenario as several different sentences. The average accuracy of auditory warning is 96.4% for generating the warning sentences. The average accuracy of the weakly supervised visual warning algorithm is 81.3% for getting the risk vehicle localization without any supervisory information.
Fusion Attention Network for Autonomous Cars Semantic Segmentation

Semantic segmentation is vital for autonomous car scene understanding. It provides more precise subject information than raw RGB images and this, in turn, boosts the performance of autonomous driving. Recently, self-attention methods show great improvement in image semantic segmentation. Attention maps help scene parsing with abundant relationships of every pixel in an image. However, it is computationally demanding. Besides, existing works focus either on channel attention, ignoring the pixel position factors, or on spatial attention, disregarding the impacts of the channels on each other. To address these problems, we present Fusion Attention Network based on self-attention mechanism to harvest rich contextual dependencies. This model consists of two chains: pyramid fusion spatial attention and fusion channel attention. We apply pyramid sampling in the spatial attention module to reduce the computation for spatial attention maps. Channel attention has a similar structure to the spatial attention. We also introduce a fusion technique to calculate contextual dependencies using features from both attention chains. We concatenate the results from spatial and channel attention modules as the enhanced attention map, leading to better semantic segmentation results. We conduct extensive experiments on popular datasets with different settings in addition to an ablation study to prove the efficiency of our approach. Our model achieves better results, on Cityscapes [7], compared to state-of-the-art methods, and also show good generalization capability on PASCAL VOC 2012 [9].
Comparison of BERT and XLNet accuracy with classical methods and algorithms in text classification

The aim of this publication is to compare the accuracy of the Bidirectional Encoder Representations from Transformers (BERT) and Generalized Autoregressive Pretraining for Language Understanding (XLNet) models in text classification with the accuracy of classical machine learning methods and algorithms. Analyzed: Bidirectional Encoder Representations from Transformers (BERT), Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bernoulli Naive Bayes classifier, Gaussian Naive Bayes classifier, Multinomial Naive Bayes classifier, Support Vector Machines. The results show that when classifying 50,000 reviews in English, XLNet ranks with the highest accuracy - 96%, which is nearly 8% more than the best-performing classic classifier Support Vector Machines.
A New Secure Image Encryption Model Based on Symmetric Key

This paper present s a new image encryption model with focus on symmetric key schemes. It discus 5 schemes: Random Generation Key (8B); Random Key Generation part A(4B) and part B(4B); Input User Key (4B), Encrypted key and Random Key generation (4B); Random Key Generate (4B) and Input User Encrypted Key (4B); Input User Key (8B). Experimental results are based on image encryption by DES algorithm (5 instances) and by AES algorithm (5 instances). A table with image quality values and a table with complex arithmetic mean error are done.
Universal mean-field upper bound for the generalization gap of deep neural networks

Modern deep neural networks (DNNs) represent a formidable challenge for theorists: according to the commonly accepted probabilistic framework that describes their performance, these architectures should overfit due to the huge number of parameters to train, but in practice they do not. Here we employ results from replica mean field theory to compute the generalization gap of machine learning models with quenched features, in the teacher-student scenario and for regression problems with quadratic loss function. Notably, this framework includes the case of DNNs where the last layer is optimized given a specific realization of the remaining weights. We show how these results-combined with ideas from statistical learning theory-provide a stringent asymptotic upper bound on the generalization gap of fully trained DNN as a function of the size of the dataset P. In particular, in the limit of large P and N_{out} (where N_{out} is the size of the last layer) and N_{out}≪P, the generalization gap approaches zero faster than 2N_{out}/P, for any choice of both architecture and teacher function. Notably, this result greatly improves existing bounds from statistical learning theory. We test our predictions on a broad range of architectures, from toy fully connected neural networks with few hidden layers to state-of-the-art deep convolutional neural networks.
Blockchain based Transparent and Genuine Charity Application

Abstract: [21]Charity plays an essential role in our society, and often recognized as a type of social debt, leading to the circulation of a significant amount of money worldwide. We have witnessed increased growth of non-commercial organizations and charity funds through recent years, collecting donations for various philanthropic needs. Unfortunately, charity funds frequently gain much traction from the unscrupulous organization, leading to significant damage for industry's reputation, reducing trust level, affecting the power to boost donations. [22]We strongly believe that utilizing blockchain technology will boost trust, increase efficiency, and encourage more donations. The Charity Webapp project, a blockchain-based charity foundation platform that facilitates the trustful network's formation and is accountable for collecting donation funds. The blockchain network would be comprised of publicly known, trustful, and prestigious organizations. All organizations' operations within the platform will become fully transparent and visual , leveraging properties of immutability, provenance, and nonrepudiation.[23]Therefore the platform will alleviate the results of dishonest actions, revealing fraudulent organizations' activities. Keywords: Blockchain; ensuring trust; NGOs; encryption, Bitcoin, Beneficiary, Donars
Federated Learning: The Effect of Device Clustering for Multi-hop Networks

Federated Learning (FL) is a cooperative machine learning (ML) training method that involves several participants in the training phase. The learning phase of ML algorithms is done in a distributed manner where devices train a specific model based on their local data sets and send the updated model back to a centralized entity. Several benefits are gained by using FL compared to conventional centralized learning techniques, such as increased security and data privacy since participant devices only send back the updated model and not their data sets. Recently, this has become a hot research topic due to its advantages in terms of user privacy. Most of the works in this field consider only star networks where mobile devices communicate directly to the base station. Few mentioned multi-hop scenarios. In this paper, we present a performance evaluation of FL in multi-hop networks and study the effect of clustering devices on FL accuracy.
A Blockchain Approach for eHealth Situation-Aware Data Processing

Situational awareness platforms support the continuous observation of interest situations. In the eHealth context, SA can improve health services by assessing and interpreting patients' current situations. For instance, it allows the healthcare providers to recognize changes and abnormalities in the patient's condition and take the necessary actions. However, SA platforms still suffer from centralized management issues, such as availability, security, and privacy. This paper presents a blockchain-based approach for SA data processing to overcome these issues. Blockchain is a decentralized and distributed technology that also brings the advantage of flexible data custody. We evaluate our system through a case study and discuss the benefits and challenges of adopting Blockchain as a solution for eHealth.
How developers feel about tools

Virtual kanban board is a tool widely used by software teams. The board provides a rich source of information about the teams' work and the project work-in-progress. Software startups often work with small teams with few resources and under constant time pressure. In this fast-paced environment of startups, the virtual kanban boards are helpful to improve communication keeping the whole organization members informed about the workflow and the allocation of tasks. However, startup software professionals often have little experience, and consequently, they can have difficulties maximizing the benefits of the virtual kanban to manage their work. In this paper, we present an empirical study with software startup professionals in which we analyzed qualitative data using the developer experience framework. This framework allows us to find out how development-related professionals perceive the resources of the virtual kanban tools and feel and see the board's contribution to the activities of their daily work. Our results revealed that the participants agreed that the virtual kanban boards are essential to tracking ongoing software projects and performing their work. However, they reported difficulties handling the tools, which can cause the professionals frustration. Besides, our findings showed that the participants saw the kanban virtual boards as a stimulus for their autonomy in routine activities. As the main contribution, our work provides a set of startup-related recommendations that can help startup teams better use virtual kanban board resources and, consequently, impact the software professionals' experience.
So who is impacted anyway

Due to the proliferation of disruptive technologies such as AI into almost every aspect of modern society, software systems increasingly affect the lives of people who do not directly use these systems - with potentially serious and harmful consequences. However, current software development practices do not yet account for this trend sufficiently well and frequently overlook indirect stakeholders. This paper presents the results of a preliminary interview-based study of software professionals aimed at understanding the state-of-practice of indirect stakeholder identification in the software industry. Our initial findings confirm that indirect stakeholders are often overlooked due to customer expectations, project constraints, the prevailing technology-centric software engineering culture and a lack of practical methods and tools. Based on these findings, we outline a roadmap for the investigation of methods and tools for the effective and efficient identification of indirect stakeholders.
Leveraging code-test co-evolution patterns for automated test case recommendation

Context: Prior research revealed that code components with similar structures tend to require structurally similar test cases and they often co-evolve over time. Objective: Leveraging this pattern, we implemented a prototype tool, Test Suite Evolver (TSE), to support the generation of test cases for structurally similar methods. Our prototype tool is applicable to both, incomplete test suites with the purpose of test case augmentation, as well as evolving test suites when a newer version of a software becomes available.
Energy-Efficient Residue-to-Binary Conversion Based on a Modulo-Adder-Free Architecture

This paper proposes a novel residue-to-binary converter for residue number system (RNS) based on the moduli set $\{2^{n+\mathrm{k}},\ 2^{2_{n}+1}-1,2^{n}+1,2^{n}-1\}$. By adopting the new Chinese Remainder Theorem II (CRT II) and the properties of modulo 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -1 arithmetic, the research herein presented shows the possibility of designing RNS residue-to-binary converters free of modular adders. By using exclusively regular binary adders, one can leverage the design of efficient residue-to-binary converters based on the large range of optimizations proposed to those regular arithmetic units. Experimental results show over 37% energy-improvement in comparison to conventional designs, which make use of modulo adders.
Image denoising using convolutional neural network

Noise removal is one of the classic problems in low-level machine vision and image processing. After camera invention, image noise removal became necessary. Its purpose is to eliminate noise while preserving the edges and other precise and vital details as much as possible. Due to the outstanding results produced by deep learning in a variety of domains, this study introduces a novel model for image denoising that uses a mix of dilated and regular convolution to remove noise from degraded photos with a specified noise level. The proposed model is assessed on the BSD68 dataset, and the findings reveal that it outperforms models such as DnCNN and FFDNet at all noise levels.
Smart Contract Development for Peer-to-Peer Energy Trading

In this paper, the analysis of peer-to-peer energy trading with smart contracts, which are applications run on blockchains, is presented. The software architecture and algorithm of the smart contracts that run on the Ethereum Virtual Machine are mentioned. The smart contracts are applied transactions that guarantee the information of both parties in a peer-to-peer transmission and are verified by the logic structure that operates within itself and do not cause any vulnerabilities in the system. In addition, a software background with an instrument panel in the user interface and offering prices within the framework of the supply-demand relationship is presented. In addition to these, the paper aims to promote decentralisation by integrating renewable energy sources with the framework of peer-to-peer trading methods on a Blockchain.
Analysis of Closed-Loop Inertial Gradient Dynamics

In this paper, we analyse the performance of the closed-loop Whiplash gradient descent algorithm [1] for L-smooth convex cost functions. Using numerical experiments, we study the algorithm’s performance for convex cost functions, for different condition numbers. We analyse the convergence of the momentum sequence using symplectic integration and introduce the concept of relaxation sequences which analyses the non-classical character of the whiplash method. Under the additional assumption of invexity, we establish a momentum-driven adaptive convergence rate. Furthermore, we introduce an energy method for predicting the convergence rate with convex cost functions for closed-loop inertial gradient dynamics, using an integral anchored energy function and a novel lower bound asymptotic notation, by exploiting the bounded nature of the solutions. Using this, we establish a polynomial convergence rate for the whiplash inertial gradient system, for a family of scalar quadratic cost functions and an exponential rate for a quadratic scalar cost function.
Deep Learning-Based Unmanned Aerial Vehicle Control with Hand Gesture and Computer Vision

Human drone Interaction is the subfield of Human-robot interaction (HRI), which deals with human interaction with drones; Drones are conventionally controlled by joysticks, onboard computers, mobile applications, and remote controllers. Drones controlled by conventional methods are affected by electromagnetic wave interference due to an unreliable connection between the drone and the controller. Special care is needed when working with drones in close vicinity to humans. In this proposed work, we have developed an agentless and non-wearable communication system between the drone and the user. The proposed system consists of two modules, i.e., the hand recognition module and drone controlling module. The hand recognition model is further subdivided into two modules, i.e., hand detection and gesture recognition. After the landmarks (palm and hand detection) are detected, they are given to the TensorFlow-based Deep learning model for classification among different hand gestures. The classified hand movement is further converted into actionable drone movements such as take-off, hovering, and landing. Experimentation was done on Parrot Mambo Drone, and it was concluded that there should be no more than 121.92cm distance between user and webcam for working system effectively.
Modified E<sup>3</sup> exploration algorithm for unknown environments with obstacles

An efficient autonomous exploration of an unknown environment is an important task for mobile robots, which is required in many domains. This paper considers three existing exploration approaches: frontier exploration algorithm (FEA), greedy algorithm (GrA) and Ergodic Environmental Exploration algorithm (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). While original E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> deals only with empty environments, we propose a new modified version of E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (ME <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> A) that allows to explore environments with obstacles. FEA, GrA and ME <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> A were implemented in Robot Operating System and their performance was evaluated in Gazebo simulator within 18 different environments. We employed a total travelled distance and a percentage of explored environment as a comparison criteria.
Reduced Computation for Extreme Learning Machine Based on Cholesky Factorization

This paper focuses on the implementation of matrix decomposition methods based on Cholesky factorization to reduce the computation time of pseudoinverse matrix solutions in the training process of extreme learning machine (ELM). The direct solution of the pseudoinverse matrix in ELM may result in singularity, and the cost of ELM computation time increases significantly as the number of hidden neurons grows. We proposed an approach for computing the pseudoinverse matrix in ELM based on the Cholesky factorization matrix decomposition method and compared our model to singular value decomposition (SVD), a well-known method for matrix decomposition. The numerical results show that our approach outperforms the SVD in reducing the computation time of the ELM training process without losing its generalization performance.
UAV Swarming Flight Guidance for Multi-UAV Configuration

Swarming UAV flight offers several advantages over single UAV flight. However, it requires a particular guidance paradigm since the collective behavior emerges from the behavior of each individual. This paper proposes a swarming guidance algorithm developed from the artificial potential field concept. The guidance algorithm is capable of aggregation, swarm collision avoidance, waypoint tracking, obstacle avoidance, and rotational maneuvers. The method is simulated using numerical simulation with a quadcopter UAV model.