Reviewerly/exHarmony huggingface dataset:
Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images

Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method.
Retracted: Intelligent distribution system of university student dormitory based on data fusion optimisation algorithm

Abstract Retraction: [Tingting Zheng, Intelligent distribution system of university student dormitory based on data fusion optimisation algorithm, IET Software 2023 ( https://doi.org/10.1049/sfw2.12100 )]. The above article from IET Software , published online on 1 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
TRANSPORT SERVICE SYSTEM DESIGN USING MODIFIED APRIORI ALGORITHM

This research is carried-out to evaluate the effectiveness of data mining in determining viability of routes, efficient scheduling and assigning of vehicles to commuters. The study was guided by the following objectives: modification of Apriori algorithm, implementation using C programming language, analyses and deductions from the results to determine if a given route is feasible. Data Mining (association rule) technique has been used to identify geographical locations where accidents have occurred and their characteristics, in road management to develop effective accident preventive measures, to determine estimated travel time and in market basket analysis applied in grocery stores. Data was collected from three transport companies for each route. The data was inputted into the program implemented using modified Apriori algorithm. The study findings revealed the volume of commuters per route and how vehicles can be assigned and scheduled. Using the above findings, effective transport service system is designed using routes viability
Training generative adversarial networks by auxiliary adversarial example regulator

Many variant Generative Adversarial Networks (GANs) have been proposed to address the problem that models are difficult to be trained, such as a network-based model, loss-based method, and training-based technique. However, these models rarely improve training stability by reducing the instability of the generator and discriminator simultaneously. For this purpose, inspired by the idea of network regulation, we design an auxiliary adversarial example regulator and propose a new training framework of GANs. In this method, to reduce the instability of the generator and discriminator simultaneously, we design a penalty to constrain directly and guide the generator to generate images, and gradually adjust the training of the discriminator by the auxiliary adversarial example regulator. With the designed constraint and discriminator, the generated image gets closer to the real image. Finally, experimental results demonstrate that the proposed method outperforms the baseline models. The code is available at https://github.com/AdleyGan/GAN-AE-P.
Digital Technologies to Enhance Infectious Disease Surveillance in Tanzania: A Scoping Review

Mobile phones and computer-based applications can speed up disease outbreak detection and control. Hence, it is not surprising that stakeholders in the health sector are becoming more interested in funding these technologies in Tanzania, Africa, where outbreaks occur frequently. The objective of this situational review is, therefore, to summarize available literature on the application of mobile phones and computer-based technologies for infectious disease surveillance in Tanzania and to inform on existing gaps. Four databases were searched—Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PubMed, and Scopus—yielding a total of 145 publications. In addition, 26 publications were obtained from the Google search engine. Inclusion and exclusion criteria were met by 35 papers: they described mobile phone-based and computer-based systems designed for infectious disease surveillance in Tanzania, were published in English between 2012 and 2022, and had full texts that could be read online. The publications discussed 13 technologies, of which 8 were for community-based surveillance, 2 were for facility-based surveillance, and 3 combined both forms of surveillance. Most of them were designed for reporting purposes and lacked interoperability features. While undoubtedly useful, the stand-alone character limits their impact on public health surveillance.
Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future.
EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT

The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs. For such a complex situation, this paper analyses adversarial attackers’ ability to deceive IDSs used in the IIoT and proposes the evaluation of an IDS with function-discarding adversarial attacks in the IIoT (EIFDAA), a framework that can evaluate the defence performance of machine learning-based IDSs against various adversarial attack algorithms. This framework is composed of two main processes: adversarial evaluation and adversarial training. Adversarial evaluation can diagnose IDS that is unfitting in adversarial environments. Then, adversarial training is used to treat the weak IDS. In this framework, five well-known adversarial attacks, the fast-gradient sign method (FGSM), basic iterative method (BIM), projected gradient descent (PGD), DeepFool and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) are used to convert attack samples into adversarial samples to simulate the adversarial environment. This study evaluates the capability of mainstream machine learning techniques as intrusion detection models to defend against adversarial attacks, and retrains these detectors to improve the robustness of IDSs through adversarial training. In addition, the framework includes an adversarial attack model that discards the attack function of the attack samples in the IIoT. Through the experimental results on the X-IIoTID dataset, the dropped adversarial detection rate of these detectors to nearly zero demonstrates that an adversarial attack has black-box attack capabilities for these IDSs. Additionally, the improved IDSs retrained with adversarial samples can effectively defend against adversarial attackers while maintaining the original detection rate for the attack samples. EIFDAA is expected to be a solution that can be applied to IDS for improving the robustness in the IIoT.
Quantum Computers’ threat on Current Cryptographic Measures and Possible Solutions

Cryptography is a requirement for confidentiality and authentic communication, and it is an indispensable technology used to protect data security.Quantum computing is a hypothetical model, still in tentative analysis but is rapidly gaining traction among scientific communities.Quantum computers have the potential to become a pre-eminent threat to all secure communication because their performance exceeds that of conventional computers.Consequently, quantum computers are capable of iterating through a large number of keys to search for secret keys or quickly calculate cryptographic keys, thereby endangering cloud security measures.This paper's main target is to summarize the vulnerability of current cryptographic measures in front of a quantum computer.The paper also aims to cover the fundamental concept of potential quantum-resilient cryptographic techniques and explain how they can be a solution to complete secure key distribution in a post-quantum future.
A three layer neural network can represent any multivariate function

In 1987, Hecht-Nielsen showed that any continuous multivariate function can be implemented by a certain type three-layer neural network. This result was very much discussed in neural network literature. In this paper we prove that not only continuous functions but also all discontinuous functions can be implemented by such neural networks.
Stochastic Resource Allocation in Quantum Key Distribution for Secure Federated Learning

Federated learning (FL) is a distributed machine learning paradigm with a promising future, which can preserve data privacy while training the global model collaboratively. However, FL is still facing model confidentiality issues. Therefore, in this paper, we propose a quantum key distribution (QKD) based secure FL scheme to facilitate FL model encryption against network eavesdropping attacks. Specifically, we introduce a stochastic resource allocation scheme for QKD to support FL networks. In the network, remote FL workers are connected to the server to train an aggregated global model in a distributed manner. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is not uniform. The proposed scheme can allocate QKD resources (i.e., wavelengths) in a way that minimizes the total cost given the stochastic demand. We formulate the optimization problem for the proposed scheme as a stochastic programming model. Numerical results demonstrate that the proposed scheme can successfully achieve the cost-minimizing objective while satisfying all uncertain demands and other security constraints.
The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.
Table Tennis Skill Learning in VR with Step by Step Guides using Forehand Drive as a Case Study

Recently sports training in Virtual Reality (VR) like table tennis is getting more popular. In our paper, we want to investigate and develop an intuitive, immersive training environment for table tennis beginners to learn forehand drive at home due to an increase in the popularity of home training systems. Therefore in our paper, we conducted a pilot study and discovered that it is important to have a stable posture, provide immediate feedback for inaccurate posture, visualize the overall posture and feedback on the most incorrect parts to the user and have a clear step-by-step learning guide. Based on the study, we created a website where coaches can create a step-by-step guide for trainees to train in VR with an RGB camera to predict 3D human pose. We use table tennis forehand drive as a case study to demonstrate its effectiveness. Even though the number of successful returns is insignificant, our qualitative results show that users find our system helpful.
Few-shot symbol classification via self-supervised learning and nearest neighbor

The recognition of symbols within document images is one of the most relevant steps involved in the Document Analysis field. While current state-of-the-art methods based on Deep Learning are capable of adequately performing this task, they generally require a vast amount of data that has to be manually labeled. In this paper, we propose a self-supervised learning-based method that addresses this task by training a neural-based feature extractor with a set of unlabeled documents and performs the recognition task considering just a few reference samples. Experiments on different corpora comprising music, text, and symbol documents report that the proposal is capable of adequately tackling the task with high accuracy rates of up to 95% in few-shot settings. Moreover, results show that the presented strategy outperforms the base supervised learning approaches trained with the same amount of data that, in some cases, even fail to converge. This approach, hence, stands as a lightweight alternative to deal with symbol classification with few annotated data.
Interval incremental learning of interval data streams and application to vehicle tracking

This paper presents a method called Interval Incremental Learning (IIL) to capture spatial and temporal patterns in uncertain data streams. The patterns are represented by information granules and a granular rule base with the purpose of developing explainable human-centered computational models of virtual and physical systems. Fundamentally, interval data are either included into wider and more meaningful information granules recursively, or used for structural adaptation of the rule base. An Uncertainty-Weighted Recursive-Least-Squares (UW-RLS) method is proposed to update affine local functions associated with the rules. Online recursive procedures that build interval-based models from scratch and guarantee balanced information granularity are described. The procedures assure stable and understandable rule-based modeling. In general, the model can play the role of a predictor, a controller, or a classifier, with online sample-per-sample structural adaptation and parameter estimation done concurrently. The IIL method is aligned with issues and needs of the Internet of Things, Big Data processing, and eXplainable Artificial Intelligence. An application example concerning real-time land-vehicle localization and tracking in an uncertain environment illustrates the usefulness of the method. We also provide the Driving Through Manhattan interval dataset to foster future investigation.
The roles of Kerr nonlinearity in a bosonic quantum neural network

Abstract The emerging technology of quantum neural networks (QNNs) offers a quantum advantage over classical artificial neural networks (ANNs) in terms of speed or efficiency of information processing tasks. It is well established that nonlinear mapping between input and output is an indispensable feature of classical ANNs, while in a QNN the roles of nonlinearity are not yet fully understood. As one tends to think of QNNs as physical systems, it is natural to think of nonlinear mapping originating from a physical nonlinearity of the system, such as Kerr nonlinearity. Here we investigate the effect of Kerr nonlinearity on a bosonic QNN in the context of both classical (simulating an XOR gate) and quantum (generating Schrödinger cat states) tasks. Aside offering a mechanism of nonlinear input-output mapping, Kerr nonlinearity reduces the effect of noise or losses, which are particularly important to consider in the quantum setting. We note that nonlinear mapping may also be introduced through a nonlinear input-output encoding rather than a physical nonlinearity: for example, an output intensity is already a nonlinear function of input amplitude. While in such cases Kerr nonlinearity is not strictly necessary, it still increases the performance in the face of noise or losses.
A proposal of a mixed diagnostic system based on decision trees and probabilistic experts rules

Decision trees and rule-based expert systems (RBES) are standard diagnostic tools. We propose a mixed technique that starts with a probabilistic decision tree where information is obtained from a real world data base. The decision tree is automatically translated into a set of probabilistic rules. Meanwhile a panel of experts proposes their own set of probabilistic rules, according with their experience on the subject. Both sets of rules are combined, generating a mixed RBES with probabilistic rules. The expected probabilities of the rules translating the knowledge in the decision tree are discretized by considering a mapping from intervals of expected probabilities into a set of five values. This way, knowledge coming from real data is completed with the experience of the panel of experts in order to provide a more accurate prediction of suffering from type 2 diabetes mellitus (T2DM) before seven and a half years in the future. The proposed technique is illustrated with a real case using a diabetes diagnosis probabilistic decision tree built using 1350 out of 1800 real cases and the rules provided by a panel of experts in diabetes. The final result takes into account both the probabilities of the rules and the number of times that each possible consequent is reached, giving a probabilistic result among seven possibilities. For modeling the decision tree, 75% of the individuals in the database (randomly selected) have been used and the rest (25%) have been used to test the results. The results of the Mixed RBES have been compared with the results of the Tree RBES (the RBES built using only the rules from the decision tree) and the results of the Experts’ RBES (the RBES built using only the rules from the panel of experts). The accuracy of the predictions of the Mixed RBES is much better.
Origami and Kirigami Structure for Impact Energy Absorption: Its Application to Drone Guards

As the use of drones grows, so too does the demand for physical protection against drone damage resulting from collisions and falls. In addition, as the flight environment becomes more complicated, a shock absorption system is required, in which the protective structure can be deformed based on the circumstances. Here, we present an origami- and kirigami-based structure that provides protection from various directions. This research adds a deformation capacity to existing fixed-shape guards; by using shape memory alloys, the diameter and height of the protective structure are controlled. We present three protective modes (1: large diameter/low height; 2: small diameter/large height; and 3: lotus shaped) that mitigate drone falls and side collisions. From the result of the drop impact test, mode 2 showed a 78.2% reduction in the maximum impact force at side impact. We incorporated kirigami patterns into the origami structures in order to investigate the aerodynamic effects of the hollow patterns. Airflow experiments yielded a macro understanding of flow-through behaviors on each kirigami pattern. In the wind speed experiment, the change in airflow velocity induced by the penetration of the kirigami pattern was measured, and in the force measurement experiment, the air force applied to the structure was determined.
Real-time 3D human pose estimation without skeletal a priori structures

This study is about real-time 2D-3D human pose estimation without using the a priori structure of the skeleton and with a low number of parameters for regression tasks. Current graph convolution-based 3D human pose tasks require structural knowledge of the skeleton, which limits the exploration of pose estimation for unknown structures. Inspired by tyre rotation and circular convolution, weights rotation is used to fully learn the potential connections between the human joints. We refer to this process as the weight cyclic sharing mechanism, a novel method for updating features. It does not require knowledge of the structure of the human skeleton and learns different constraints between joints with a low number of parameters. We propose an end-to-end weight circular sharing network (WCirSNet) based on the weight circular sharing mechanism. We propose a simple and efficient weighted residual block in this WCirSNet. The superiorities of the weight circular sharing mechanism and weighted residual block were verified by abundant ablation studies. Extensive evaluations on two challenging benchmark datasets (Human 3.6 M, MPI-INF-3DHP) show that the performance and generalization capabilities of our framework are superior to the results of many previously advanced methods.