Reviewerly/exHarmony huggingface dataset:
Blockchain mechanism with Byzantine fault tolerance consensus for Internet of Drones services

Abstract The increasing number of drones brings various issues like security risks, increasing air traffic, misuse or crash of drones, and so on. Blockchain can provide the solution to mitigate these problems of drones. Hence, in this article, we have presented the Blockchain of Drones (BCoD), a conceptual approach of collaboration between blockchain and IoD to make drones advantageous through blockchain properties. But the main challenge for adopting blockchain is scalability as consensus in blockchain relies on the message communications by replicated nodes to attain flexibility against faulty nodes. Considering this, we present an advanced byzantine fault tolerance (ABFT) consensus for drone‐based applications. This protocol provides scalability with minimum cost and resources for achieving general agreement. It uses only f + 1 resources in normal case and 2 f + 1 resources in faulty case with O ( n ) communication complexity. The systematic analysis and evaluation show that the ABFT improves the performance of the system for a wide range of networks compared with existing BFT systems.
Sparse Discrete Communication Learning for Multi-Agent Cooperation Through Backpropagation

Recent approaches to multi-agent reinforcement learning (MARL) with inter-agent communication have often overlooked important considerations of real-world communication networks, such as limits on bandwidth. In this paper, we propose an approach to learning sparse discrete communication through backpropagation in the context of MARL, in which agents are incentivized to communicate as little as possible while still achieving high reward. Building on top of our prior work on differentiable discrete communication learning, we develop a regularization-inspired message-length penalty term, that encourages agents to send shorter messages and avoid unnecessary communications. To this end, we introduce a variable-length message code that provides agents with a general means of modulating message length while keeping the overall learning objective differentiable. We present simulation results on a partially-observable robot navigation task, where we first show how our approach allows learning of sparse communication behavior while still solving the task. We finally demonstrate our approach can even learn an effective sparse communication behavior from demonstrations of an expert (potentially communication-free) policy.
Requirements on Delay of VRU Context Detection for Cooperative Collision Avoidance

Every year, approximately 350 000 vulnerable road users (VRUs) still lose their lives in road traffic accidents world-wide. To reduce this number, cooperative VRU collision avoidance systems aim at complementing current vehicle or infrastructure-based systems for VRU protection. The cooperative approach assumes user equipment (UE) like a smartphone or a smartwatch on the VRU side, which allows to obtain and exchange movement and contextual information with other road users for determining the current risk level for a collision. In recent publications, the usage of additional contextual information, such as the pedestrian's current activity (e.g., standing, walking or crossing a curb), has been shown to improve the collision detection accuracy. However, those approaches focused on the detection accuracy, but did not investigate how time delays for both detection and communication of VRU contexts affect the ability to detect collisions. We fill this gap by investigating the influence of both activity recognition and communication delays on the collision detection performance. As a baseline, we use the standardized Euro NCAP test protocol. For the evaluation, we exemplarily consider the usage of a curb detection module for position correction, which improves the collision detection accuracy at the moment the pedestrian crosses the curb.
C-LIENet: A Multi-Context Low-Light Image Enhancement Network

Enhancement of low-light images is a challenging task due to the impact of low brightness, low contrast, and high noise. The inability to collect natural labeled data intensifies this problem further. Many researchers have attempted to solve this problem using learning-based approaches; however, most models ignore the impact of noise in low-lit images. In this paper, an encoder-decoder architecture, made up of separable convolution layers that solve the issues encountered in low-light image enhancement, is proposed. The architecture is trained end-to-end on a custom low-light image dataset (LID), comprising both clean and noisy images. We introduce a unique multi-context feature extraction module (MC-FEM) where the input first passes through a feature pyramid of dilated separable convolutions for hierarchical-context feature extraction followed by separable convolutions for feature compression. The model is optimized using a novel three-part loss function that focuses on high-level contextual features, structural similarity, and patch-wise local information. We conducted several ablation studies to determine the optimal model for low-light image enhancement under noisy and noiseless conditions. We have used performance metrics like peak-signal-to-noise ratio, structural similarity index matrix, visual information fidelity, and average brightness to demonstrate the superiority of the proposed work against the state-of-the-art algorithms. Qualitative results presented in this paper prove the strength and suitability of our model for real-time applications.
Selective Deletion in a Blockchain

The constantly growing size of blockchains becomes a challenge with the increasing usage. Especially the storage of unwanted data in a blockchain is an issue, because it cannot be removed naturally. In order to counteract this problem, we present the first concept for the selective deletion of single entries in a blockchain. For this purpose, the general consensus algorithm is extended by the functionality of regularly creating summary blocks. Previous data of the chain are summarized and stored again in a new block, leaving out unwanted information. With a shifting marker of the Genesis Block, data can be deleted from the beginning of a blockchain. In this way, the technology of the blockchain becomes fully transactional. The concept is independent of a specific block structure, network structure, or consensus algorithm. Moreover, this functionality can be adapted to current blockchains to solve multiple problems related to scalability. This approach enables the transfer of blockchain technology to further fields of application, among others in the area of Industry 4.0 and Product Life-cycle Management.
ARC-Net: Activity Recognition Through Capsules

Human Activity Recognition (HAR) is a crucial factor in assisted living systems and elderly care solutions where the activity of the subject can be used to ensure the safety of the elderly and provide more efficient services. HAR is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. Then, gradient based interpretations are provided and further discussed. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data.
African governments and the influence of corruption on the proliferation of cybercrime in Africa: wherein lies the rule of law?

(2021). African governments and the influence of corruption on the proliferation of cybercrime in Africa: wherein lies the rule of law? International Review of Law, Computers & Technology: Vol. 35, BILETA Special Edition, pp. 131-161.
An Automatic Vulnerability Scanner for Web Applications

With the progressive development of web applications and the urgent requirement of web security, vulnerability scanner has been particularly emphasized, which is regarded as a fundamental component for web security assurance. Various scanners are developed with the intention of that discovering the possible vulnerabilities in advance to avoid malicious attacks. However, most of them only focus on the vulnerability detection with single target, which fail in satisfying the efficiency demand of users. In this paper, an effective web vulnerability scanner that integrates the information collection with the vulnerability detection is proposed to verify whether the target web application is vulnerable or not. The experimental results show that, by guiding the detection process with the useful collected information, our tool achieves great web vulnerability detection capability with a large scanning scope.
Learning constraint-based planning models from demonstrations

How can we learn representations for planning that are both efficient and flexible? Task and motion planning models are a good candidate, having been very successful in long-horizon planning tasks-however, they've proved challenging for learning, relying mostly on hand-coded representations. We present a framework for learning constraint-based task and motion planning models using gradient descent. Our model observes expert demonstrations of a task and decomposes them into modes-segments which specify a set of constraints on a trajectory optimization problem. We show that our model learns these modes from few demonstrations, that modes can be used to plan flexibly in different environments and to achieve different types of goals, and that the model can recombine these modes in novel ways.
Digital Manufacturing for Smart Small Satellites Systems

The term Industry 4.0 – manufacturing with exploitation of digital technologies – comprises several challenging topics, among others intuitive machine programming, advanced maintenance-enabling technologies as well as flexible logistics. This paper gives an overview on recent research and development of our institute (Zentrum für Telematik, ZfT) in this field, the results of which are combined in an Industry 4.0 demonstration factory for the assembly of small satellites systems. We present a total of six tools to be used in such advanced manufacturing systems and their individual advantages. Based on our experience with industrial project partners, customers and visitors of our demonstration factory as well as on the evaluations of the jurors of several awards, we give a qualitative estimate of the effort required to port the individual tools to new production environments. Finally, utilizing our in-house expertise in the New-Space and Industry-4.0 sectors, we give an insight into the benefits achievable using digital manufacturing for small satellite assembly.
Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on [Formula: see text]-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.
Feature Extraction in Reference Signal Received Power Prediction Based on Convolution Neural Networks

In this letter, an environmental features (EFs) extraction model is proposed for estimating reference signal received power (RSRP) accurately. Firstly, 18-D measured data is transformed into 15-D physical features (PFs). Then 15-D PFs is reduced to 14-D by performing correlation analysis. Secondly, EFs are extracted from the environmental maps (EMs) by applying Convolution Neural Networks (CNNs). Finally, several Machine Learning Regressors (MLRs) are trained to predict RSRP combining PFs and EFs as inputs. The results, in test dataset, show that prediction performance of MLRs is improved through 14-D PFs, and is further improved in nonlinear MLRs combining PFs and EFs.
Learning to Take Good Pictures of People with a Robot Photographer

We present a robotic system capable of navigating autonomously by following a line and taking good quality pictures of people. When a group of people is detected, the robot rotates towards them and then back to line while continuously taking pictures from different angles. Each picture is processed in the cloud where its quality is estimated in a two-stage algorithm. First, features such as the face orientation and likelihood of facial emotions are input to a fully connected neural network to assign a quality score to each face. Second, a representation is extracted by abstracting faces from the image and it is input to a Convolutional Neural Network (CNN) to classify the quality of the overall picture. We collected a dataset in which a picture was labeled as good quality if subjects are well-positioned in the image and oriented towards the camera with a pleasant expression. Our approach detected the quality of pictures with 78.4% accuracy in this dataset and received a better mean user rating (3.71/5) than a heuristic method that uses photographic composition procedures in a study where 97 human judges rated each picture. Statistical analysis against the state-of-the-art verified the quality of the resulting pictures.
RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

While the widespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data training without exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users’ privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach.
Wireless Energy-Efficient System Based on the Directed Diffusion Routing Method for the High Density Seismic Array Survey

Abstract High-density seismic array survey has attracted high R&amp;D interest in geophysical methods in recent years, and it is an advanced seismic observation technology which is of great significance in oil and gas resource detection and underground space exploration. However, designing a portable sensor node that prolongs the lifetime and equips wireless monitoring system to improve construction efficiency has been still a challenge. In this paper, a portable energy-efficient wireless sensor node and a wireless communication system that content them with complex terrain, large-scale and reliable transmission is proposed to tackle the restrictions of high-density seismic array survey. This paper proposes a directed diffusion routing method which performs the energy-efficient design by maintaining the link gradient and the ability of automatic power adjustment. In addition, when the sink node initiates an instruction, it quickly covers all nodes and receives the response from the nodes. Then, a series of simulations and experiments are carried out for verification. The experiments show that the equivalent noise of the sensor node is 0.8μV@500Hz, which suggest good performance among the existing sensor nodes. Simulation results and performance analysis validate that the effectiveness of the proposed routing method can maintain the network with only 20mW at autonomous working mode. The field measurement results indicate that the wireless multi-hop energy-efficient system based on the directed diffusion routing method is suitable for the high-density seismic survey.
High-Speed Catching by Multi-Vision Robot Hand

In this paper, we propose the "multi-vision hand", in which a number of small high-speed cameras are mounted on the robot hand of a common 7 degrees-of-freedom robot. Also, we propose visual-servoing control by using a multi-vision system that combines the multi-vision hand and external fixed high-speed cameras. The target task was ball catching motion, which requires high-speed operation. In the proposed catching control, the catch position of the ball, which is estimated by the external fixed high-speed cameras, is corrected by the multivision hand in real-time. In experiments, the catch operation was successfully implemented by correcting the catch position, thus confirming the effectiveness of the multi-vision hand system.
Exploring the application of synthetic audio in training keyword spotters

The study of keyword spotting, a subfield within the broader field of speech recognition that centers around identifying individual keywords in speech audio, has gained particular importance in recent years with the rise of personal voice assistants such as Alexa. As voice assistants aim to rapidly expand to support new languages, keywords, and use cases, stakeholders face the issue of limited training data for these unseen scenarios. This paper details some initial exploration into the application of Text-To-Speech (TTS) audio as a "helper" tool for training keyword spotters in these low-resource scenarios. In the experiments studied in this paper, the careful mixing of TTS audio with human speech audio during training led to a reduction of over 11% in the detection-error-tradeoff (DET) area under the curve (AUC) metric.
Efficient Implementation of AES and CTR_DRBG on 8-Bit AVR-Based Sensor Nodes

Since Rijndael algorithm was selected as the Advanced Encryption Standard (AES) by NIST, optimization research for the AES has been actively conducted on various IoT-based processors. In an 8-bit AVR environment, LIGHT version of Fast AES CTR-mode Encryption (FACE-LIGHT) was proposed at ICISC'2019 conference. However, in a Wireless Sensor Network environment, where sessions are frequently changed, FACE-LIGHT seems not efficient in terms of available memory and generating a pre-computation table. In this article, we present a new column-wise fashion implementation. Unlike previous best AES implementations, our proposed implementation in an 8-bit AVR microcontroller combines SubBytes, ShiftRows, and MixColums operations and optimizes the operation speed through efficient register scheduling. Our constant-time implementation uses a significantly less table than FACE-LIGHT in an 8-bit AVR microcontroller, achieving 2,251, 2,706, and 3,160 clock cycles when encrypting 128-bit data for each of three security levels. In particular, our 256-bit security level AES implementation is the fastest AES implementation as far as we know in 8-bit AVR microcontroller. Finally, we apply our implementation in CounTeR-mode_Deterministic Random Bit Generator (CTR_DRBG), one of the upper algorithms of a symmetric-key algorithm, to prove the generality of our optimization technology in various operating modes of AES.
Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation

In the field of recommender systems, diversity as the measure of recommendation quality has gained much attention recently. Unfortunately, many researchers have shown that it has a trade-off relation with accuracy. Meanwhile, tensor factorization has been used as a useful technique that considers multi-correlations between user-item-other factors directly. However, it generally suffers from the model sparsity caused by high dimensionality and requirement of high computational costs. To improve diversity and response time while preserving accuracy in multi-criteria recommender systems (MCRS), we propose a decrease and conquer-based parallel tensor factorization (DnCPTF). In the DnCPTF, sentiment analysis alleviates the sparsity problem, and a two-phase clustering groups similar user reviews into sub-models. Furthermore, a controllable subdivision guarantees high diversity and short response time. The sub-models are then factorized in parallel to predict ratings, and top-N items are recommended via ratings consolidated from the sub-models. On a real-world dataset gathered from TripAdvisor, experimental results demonstrated that the DnCPTF significantly improve recommendation diversity (55× of a conventional tensor factorization (CTF)) and response time (182× of the CTF) with preserving high precision and recall. Furthermore, it outperformed recent techniques in precision, diversity and required response time within 1 s on average.