Reviewerly/exHarmony huggingface dataset:
Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance

The capability of distinguishing between small objects when manipulated with hand is essential in many fields, especially in video surveillance. To date, the recognition of such objects in images using Convolutional Neural Networks (CNNs) remains a challenge. In this paper, we propose improving robustness, accuracy and reliability of the detection of small objects handled similarly using binarization techniques. We propose improving their detection in videos using a two level methodology based on deep learning, called Object Detection with Binary Classifiers. The first level selects the candidate regions from the input frame and the second level applies a binarization technique based on a CNN-classifier with One-Versus-All or One-Versus-One. In particular, we focus on the video surveillance problem of detecting weapons and objects that can be confused with a handgun or a knife when manipulated with hand. We create a database considering six objects: pistol, knife, smartphone, bill, purse and card. The experimental study shows that the proposed methodology reduces the number of false positives with respect to the baseline multi-class detection model.
Blockchain-Enabled Secure Energy Trading With Verifiable Fairness in Industrial Internet of Things

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to distribute energy to energy nodes with the help of energy brokers. Afterwards, they apply blockchain to provide transparency, immutability, and auditability of peer-to-peer (P2P) energy trading. However, their schemes are constructed on a weak security model and do not consider the cheating attack initiated by energy sellers. Such an attack refers to an energy seller refusing to transfer the negotiated energy to an energy purchaser who already paid money. In this article, we propose FeneChain, a blockchain-based energy trading scheme to supervise and manage the energy trading process toward building a secure energy trading system and improving energy quality for Industry 4.0. Specifically, we leverage anonymous authentication to protect user privacy, and we design a timed-commitments-based mechanism to guarantee the verifiable fairness during energy trading. Moreover, we utilize fine-grained access control for energy trading services. We also build a consortium blockchain among energy brokers to verify and record energy trading transactions. Finally, we formally analyze the security and privacy of FeneChain and evaluate its performance (i.e., computational costs and communication overhead) by implementing a prototype via a local Ethereum test network and Raspberry Pi.
A Blockchain-Based Load Balancing in Decentralized Hybrid P2P Energy Trading Market in Smart Grid

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce the energy consumption cost, emission of harmful gases (as renewable energy sources are used to generate energy at user's premises) and increase the smart grid resilience.However, local energy trading with peers can have trust and privacy issues.A centralized system can be used to manage this energy trading but it increases the overall cost of the system and also faces several issues.In this paper, to implement a hybrid P2P energy trading market, a blockchain-based system is proposed.It is fully decentralized and allows the market members to interact with each other and trade energy without involving a third party.Smart contracts play a very important role in the blockchain-based energy trading market.They contain all the necessary rules for energy trading.We have proposed three smart contracts to implement the hybrid electricity trading market.The market members interact with the main smart contract, which requests P2P and prosumer to grid smart contracts for further processing.The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio of electricity.
Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such as noises (random noise), blurs (Gaussian and uniform blur), and downsampled (both spectral and spatial downsample), each corresponding to the HSI denoising, deblurring, and super-resolution tasks, respectively. Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit the structurally spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this article, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which nonlocal similarity within spectral-spatial cubic and spectral correlation are simultaneously captured by third-order tensors. Furthermore, to improve the capability and flexibility, we formulate it as a weighted low-rank tensor recovery (WLRTR) model by treating the singular values differently. We demonstrate the reweighed strategy, which has been extensively studied in the matrix, also greatly benefits the tensor modeling. We also consider the stripe noise in HSI as the sparse error by extending WLRTR to robust principal component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed WLRTR models consistently outperform state-of-the-art methods in typical HSI low-level vision tasks, including denoising, destriping, deblurring, and super-resolution.
Accurate and fast URL phishing detector: A convolutional neural network approach

Along with the development of the Internet, methods of fraud and ways to obtain important data such as logins and passwords or personal sensitive data have evolved. One way of obtaining such information is to impersonate a page the user knows. Such a site usually does not provide any services other than collecting sensitive information from the user. In this paper, we present a way to detect such malicious URL addresses with almost 100% accuracy using convolutional neural networks. Contrary to the previous works, where URL or traffic statistics or web content are analysed, we analyse only the URL text. Thus, the method is faster and detects zero-day attacks. The network we present is appropriately optimised so that it can be used even on mobile devices without significantly affecting its performance.
A parallel compact cuckoo search algorithm for three-dimensional path planning

The three-dimensional (3D) path planning of unmanned robots focuses on avoiding collisions with obstacles and finding an optimized path to the target location in a complex three-dimensional environment. An improved cuckoo search algorithm based on compact and parallel techniques for three-dimensional path planning problems is proposed. This paper implements the compact cuckoo search algorithm, and then, a new parallel communication strategy is proposed. The compact scheme can effectively save the memory of the unmanned robot. The parallel scheme can increase the accuracy and achieve faster convergence. The proposed algorithm is tested on several selected functions and three-dimensional path planning. Results compared with other methods show that the proposed algorithm can provide more competitive results and achieve more efficient execution.
Actor-Transformers for Group Activity Recognition

This paper strives to recognize individual actions and group activities from videos. While existing solutions for this challenging problem explicitly model spatial and temporal relationships based on location of individual actors, we propose an actor-transformer model able to learn and selectively extract information relevant for group activity recognition. We feed the transformer with rich actor-specific static and dynamic representations expressed by features from a 2D pose network and 3D CNN, respectively. We empirically study different ways to combine these representations and show their complementary benefits. Experiments show what is important to transform and how it should be transformed. What is more, actor-transformers achieve state-of-the-art results on two publicly available benchmarks for group activity recognition, outperforming the previous best published results by a considerable margin.
Learning Dynamic Routing for Semantic Segmentation

Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined static architectures, such as FCN, U-Net, and DeepLab series. This paper studies a conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing. The proposed framework generates data-dependent routes, adapting to the scale distribution of each image. To this end, a differentiable gating function, called soft conditional gate, is proposed to select scale transform paths on the fly. In addition, the computational cost can be further reduced in an end-to-end manner by giving budget constraints to the gating function. We further relax the network level routing space to support multi-path propagations and skip-connections in each forward, bringing substantial network capacity. To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space. Extensive experiments are conducted on Cityscapes and PASCAL VOC 2012 to illustrate the effectiveness of the dynamic framework. Code is available at https://github.com/yanwei-li/DynamicRouting.
Cell-Free Massive MIMO for Wireless Federated Learning

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework. This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process. To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework. An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency. This mixed-timescale stochastic nonconvex problem captures the complex interactions among the training time, and transmission and computation of training updates of one FL process. By employing the online successive convex approximation approach, we develop a new algorithm to solve the formulated problem with proven convergence to the neighbourhood of its stationary points. Our numerical results confirm that the presented joint design reduces the training time by up to 55% over baseline approaches. They also show that CFmMIMO here requires the lowest training time for FL processes compared with cell-free time-division multiple access massive MIMO and collocated massive MIMO.
Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems

A new optimization method namely the Search and Rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The ε-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible minima in some complex constrained optimization problems. SAR is applied to solve 18 benchmark constraint functions presented in CEC 2010, 13 benchmark constraint functions, and 7 constrained engineering design problems reported in the specialized literature. The performance of SAR is compared with some state-of-the-art optimization algorithms. According to the statistical comparison results, the performance of SAR is better or highly competitive against the compared algorithms on most of the studied problems.
A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios

Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.
Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system

The unscented Kalman filter (UKF) has proved to be a promising methodology to integrate INS and GPS for vehicular navigation. Nevertheless, the disturbance suppression of system noise uncertainty on the UKF performance is still an open issue. In this paper, based on the maximum likelihood (ML) principle, a new adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty for vehicular INS/GPS integration. The proposed method extends the concept of ML estimation from the linear Kalman filter to the nonlinear UKF to estimate the process noise covariance. Meanwhile, an estimation window for fixed-length memory is introduced to emphasize the use of the new observations and gradually discard the old ones. Since it has the capability to estimate and update the process noise covariance online, the proposed method improves the standard UKF by restraining the disturbance of process noise uncertainty on the filtering solution. The effectiveness and superiority of the proposed method have been verified through Monte Carlo simulations and practical experiment in vehicular INS/GPS integration system.
Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs' sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a secure federated learning framework for UAV-assisted MCS. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs' privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs' high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.
The impact of digital technology usage on economic growth in Africa

This study analyses the impact of the use of digital technology on economic growth for 39 African countries from 2012 to 2016. This analysis applies a system GMM estimator to understand the extent to which the usage of digital technology facilitates growth using a measure of digitalisation from the Networked Readiness Index. Unlike previous research, we distinguish between the impact of individual, business, and government ICT usage on growth and show that only individual usage has a positive impact. Furthermore, a disaggregated analysis of the types of usage reveals that two indicators, social media and the importance of ICTs to government vision, are significant for growth.
Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization

Accurate prediction of photovoltaic (PV) power for an ultra-short term can improve the usage of grid-connected PV power. In this study, data preprocessing based on an ultra-short-term PV model is explored. A support vector machine (SVM) is constructed based on the processed data, and the parameters of the SVM are optimized using ant colony optimization (ACO). A series of improvements are introduced to optimize the ACO. The results indicate that the regression coefficient (R2) of the model can be increased by 6.8% through reasonable data preprocessing. However, smoothing is not suitable for the preprocessing of PV models with large datasets. The R2 of the hybrid model reaches up to 0.997. In particular, the forecasting accuracies for peak power and nighttime are significantly improved, thereby improving the model’s full-time grid-connected generation abilities.
Self-reported technological pedagogical content knowledge (TPACK) of pre-service teachers in relation to digital technology use in lesson plans

TPACK is a prominent model of teacher expertise for effectively teaching with digital technologies. While numerous studies have investigated teachers’ TPACK by means of self-report surveys, its relation to more objective outcomes like lesson planning has only recently come into focus. The aim of this study was to investigate whether differences in use of digital technologies in lesson plans are related to self-reported TPACK. Lesson plans of 173 pre-service teachers were coded for whether or not they included the use of digital technologies as well as for whether this use was intended for teachers or students. Independent t tests and ANOVAs were used to compare individual TPACK components among groups. Subsequently, unique profiles of all TPACK components were identified using cluster analyses and investigated for group differences via cross tabulation. Logistic and multinomial regressions were conducted to investigate the relations between TPACK profiles and technology use controlling for gender, age, and subject group. Overall results showed no significant group differences for either individual TPACK components or for the two- and five-cluster solutions of TPACK profiles. Subject group emerged as the only significant predictor and STEM pre-service teachers showed positive relations of TPACK components and technology use in lesson plans.
Measuring Domain Shift for Deep Learning in Histopathology

The high capacity of neural networks allows fitting models to data with high precision, but makes generalization to unseen data a challenge. If a domain shift exists, i.e. differences in image statistics between training and test data, care needs to be taken to ensure reliable deployment in real-world scenarios. In digital pathology, domain shift can be manifested in differences between whole-slide images, introduced by for example differences in acquisition pipeline - between medical centers or over time. In order to harness the great potential presented by deep learning in histopathology, and ensure consistent model behavior, we need a deeper understanding of domain shift and its consequences, such that a model's predictions on new data can be trusted. This work focuses on the internal representation learned by trained convolutional neural networks, and shows how this can be used to formulate a novel measure - the representation shift - for quantifying the magnitude of model-specific domain shift. We perform a study on domain shift in tumor classification of hematoxylin and eosin stained images, by considering different datasets, models, and techniques for preparing data in order to reduce the domain shift. The results show how the proposed measure has a high correlation with drop in performance when testing a model across a large number of different types of domain shifts, and how it improves on existing techniques for measuring data shift and uncertainty. The proposed measure can reveal how sensitive a model is to domain variations, and can be used to detect new data that a model will have problems generalizing to. We see techniques for measuring, understanding and overcoming the domain shift as a crucial step towards reliable use of deep learning in the future clinical pathology applications.
Reluplex made more practical: Leaky ReLU

In recent years, Deep Neural Networks (DNNs) have been experiencing rapid development and have been widely used in various fields. However, while DNNs have shown strong capabilities, their security problems have gradually been exposed. Therefore, the formal guarantee of neural network output is needed. Prior to the appearance of the Reluplex algorithm, the verification of DNNs was always a difficult problem. Reluplex algorithm is specially used to verify DNNs with ReLU activation function. This is an excellent and effective algorithm, but it cannot verify more activation functions. ReLU activation function will bring about "Dead Neuron" problem, and Leaky ReLU activation function can solve this problem, so it is necessary to verify DNNs based on Leaky ReLU activation function. Therefore, we propose the Leaky-Reluplex algorithm, which is based on the Reluplex algorithm. Leaky-Reluplex algorithm can verify DNNs based on Leaky ReLU activation function.