Reviewerly/exHarmony huggingface dataset:
Towards a Camera-Based Road Damage Assessment and Detection for Autonomous Vehicles: Applying Scaled-YOLO and CVAE-WGAN

Initiatives such as the 2020 IEEE Global Road Damage Detection Challenge prompted extensive research in camera-based road damage detection with Deep Learning, primarily focused on improving the efficiency of road management. However, road damage detection is also relevant for automated driving to optimize passenger comfort and safety. We use the state-of-the-art object detection framework Scaled-YOLOv4 and develop two small-sized models that cope with the limited computational resources in the vehicle. With average F1 scores of 0.54 and 0.586, respectively, the models keep pace with the state-of-the-art solutions of the challenge. Since the data consists only of smartphone images, we also train expert models for autonomous driving utilizing vehicle camera data. In addition to detection, severity assessment is critical. We propose a semi-supervised learning approach based on the encodings learned by combining a class-conditional Variational Autoencoder and a Wasserstein Generative Adversarial Network to classify detected damage into different severity levels.
Correlation measure, linear complexity and maximum order complexity for families of binary sequences

The correlation measure of order k is an important measure of pseudorandomness for binary sequences. This measure tries to look for dependence between several shifted versions of a sequence. We study the relation between the correlation measure of order k and two other pseudorandom measures: the Nth linear complexity and the Nth maximum order complexity. We simplify and improve several state-of-the-art lower bounds for these two measures using the Hamming bound as well as weaker bounds derived from it.
Real-Time Globally Consistent 3D Reconstruction With Semantic Priors

Maintaining global consistency continues to be critical for online 3D indoor scene reconstruction. However, it is still challenging to generate satisfactory 3D reconstruction in terms of global consistency for previous approaches using purely geometric analysis, even with bundle adjustment or loop closure techniques. In this article, we propose a novel real-time 3D reconstruction approach which effectively integrates both semantic and geometric cues. The key challenge is how to map this indicative information, i.e., semantic priors, into a metric space as measurable information, thus enabling more accurate semantic fusion leveraging both the geometric and semantic cues. To this end, we introduce a semantic space with a continuous metric function measuring the distance between discrete semantic observations. Within the semantic space, we present an accurate frame-to-model semantic tracker for camera pose estimation, and semantic pose graph equipped with semantic links between submaps for globally consistent 3D scene reconstruction. With extensive evaluation on public synthetic and real-world 3D indoor scene RGB-D datasets, we show that our approach outperforms the previous approaches for 3D scene reconstruction both quantitatively and qualitatively, especially in terms of global consistency.
Open-Set source camera identification based on envelope of data clustering optimization (EDCO)

The task of source camera identification is devoted to linking an image to the source camera model and plays a significant role in forensics. Nevertheless, with the ongoing development of new camera models, it is difficult to keep a model database up to date, giving rise to the open-set problem. To deal with this problem, we propose a novel approach based on the envelope of data clustering optimization (EDCO). The new EDCO scheme can identify the camera model regardless of whether or not it is included in the database. The experimental results prove that EDCO efficiently separates unknown source images from known source images and links the query image identified as known with the source camera model. When the dataset is expanded with the new camera model, EDCO only needs to train the new model instead of retraining with all models together, which greatly improves the scalability. Compared with the state of the art, our method can effectively distinguish between images from known and unknown camera models, even in extreme cases.
Comparative study of symmetric cryptographic algorithms

This article provides an unbiased comparison of the most popular and commonly used algorithms in the field of data encryption. The capacity to secure data from various attacks, as well as the elapsed time and efficiency of data encryption, are the main features that distinguish encryption algorithms. We compared the most prevalent symmetric encryption algorithms, including DES, 3DES, Blowfish, MARS, and AES, in this study. Each algorithm was compared by processing data blocks of various sizes to estimate encryption and decryption speeds and compare entropy. The given comparison takes into account the behavior and performance of the algorithms while utilizing varied data loads because the main objective is to execute these algorithms with various settings. We also looked at characteristics including flexibility, key extension possibilities, potential attacks, entropy, and security vulnerability of the algorithms, all of which affect the cryptosystem’s efficiency.
Cluster-Based Association Rule Mining for an Intersection Accident Dataset

Large amounts of annual costs are made for safety and compensations of accidents in urban intersections, even those with traffic lights. The main reason for accidents seems to be the convergence of different traffic flows in a particular area. The presented paper used 576 cases at intersections comprised of accident data, plus 45 fatal accident data, geometry and control status in Isfahan-Iran intersections to analyse and predict the cause of leading-to-death/injury accidents. This study used the k-modes clustering method as the main segmentation task on intersection accident data to decrease the association rule mining algorithm’s search space and remove heterogeneity of road accident data. Association rule mining helps identify the different circumstances associated with an accident in each group obtained by the k-modes algorithm. The research result shows that the extracted rules of the dataset display some valuable information that can be useful to prevent and overcome accidents.
Transformer based prediction method for solar power generation data

In this paper, we propose a technique to increase the precision of solar power generation data prediction by using a time-series-based transformer deep learning model. By partially modifying the transformer model, which is widely used for language translation, we use it by changing the input and output of the model in the form of predicting future data. Finally, through comparison with other prediction models, it was confirmed that the proposed model showed very good prediction performance.
Using CGAN to Deal with Class Imbalance and Small Sample Size in Cybersecurity Problems

Predictive modelling in cybersecurity domains usually involves dealing with complex settings. The class imbalance problem is a well-know challenge typically present in the cybersecurity domain. For instance, in a real-world intrusion detection scenario, the number of attacks is expected to be a a very small percentage of the normal cases. Moreover, in these applications, the number of available examples labelled is also small due to the complexity and cost of the labelling process: teams of domain experts need to be involved in the process which becomes expensive, time consuming and prone to errors. To address these problems is critical to the success of predictive modelling in cybersecurity applications. In this paper we tackle the class imbalance and small sample size through the use of a CGAN-based up-sampling procedure. We carry out an extensive set of experiments that show the positive impact of applying this solution to address the class imbalance and small sample size problems. A large data repository is built and freely provided to the research community containing 114 binary datasets based on real-world cybersecurity problems that are generated with diversified levels of imbalance and sample size. Our experiments show a clear advantage of using the CGAN-based up-sampling method specially for situations where the sample size is small and there is a large imbalance between the problem classes. In the most critical scenarios associated with extreme rarity and very small sample size, an impressive performance boost is achieved. We also explore the behaviour of this approach when the presence of these problems is less marked and we found that, while CGAN-based up-sampling is not able to further improve the minority class performance, it also has no negative impact. Thus, it is a safe to use solution, also in these scenarios.
Vision-encoder-based Payload State Estimation for Autonomous MAV With a Suspended Payload

Autonomous delivery of suspended payloads with MAVs has many applications in rescue and logistics transportation. Robust and online estimation of the payload status is important but challenging especially in outdoor environments. The paper develops a novel real-time system for estimating the payload position; the system consists of a monocular fisheye camera and a novel encoder-based device. A Gaussian fusion-based estimation algorithm is developed to obtain the payload state estimation. Based on the robust payload position estimation, a payload controller is presented to ensure the re-liable tracking performance on aggressive trajectories. Several experiments are performed to validate the high performance of the proposed method.
Traveling the Past: Raising Awareness of Cultural Heritage through Virtual Reality

Technology-supported applications have been found to improve cultural heritage tourism experiences. This paper raises the question of whether and how Virtual Reality technology is accepted as a means to communicate cultural heritage. The second iteration of the Unified Theory of Acceptance and Use of Technology (UTAUT 2) is employed to investigate factors driving the user intention to adopt the technology and the subsequent usage behavior. We find that technology acceptance is mainly driven by the level of enjoyment and entertainment the user experiences. Due to the novelty of the technology in the analyzed setting, technology usage intention has not yet converted into actual technology usage.
Methodology of ISMS Establishment Against Modern Cybersecurity Threats

The chapter addresses the Information Security Management System (ISMS) establishment approach, ensuring necessary controls to avoid widespread cybersecurity threats nowadays. The most common attack vectors and techniques of the last three years were analyzed in this chapter to define a set of information security practices, which can minimize risks related to modern cybersecurity threats. Conducted analysis of cybersecurity frameworks such as ISO 27001/2, CIS Top 18, NIST 800-53, and their differentiators. An ISMS establishment algorithm is proposed in this chapter with a detailed explanation of each phase and controls required for system implementation. The document defines cybersecurity technologies for management systems are determined based on the infrastructure type. The documentation management framework and risk management methodology are proposed and analyzed modern awareness strategies and defined education roadmap for ISMS roles.
AFGSL: Automatic Feature Generation based on Graph Structure Learning

Feature engineering relies on domain knowledge and human intervention. To automate the process of feature engineering, automated feature construction methods use deep neural networks to capture feature interactions and attention coefficients to quantify the relationship between features. However, these methods ignore the influence of insignificant features that introduce noise and degrade the performance of the model. In this paper, we study the problem of feature interactions from the perspective of graph and propose a novel Automatic Feature Generation model based on Graph Structure Learning, called AFGSL. In this model, the adjacency matrix reflects the relationships between features, so that it can be used to filter out insignificant features. Furthermore, Q-learning is used to train the policy of stacking interaction layers, which enables it to make full use of both local and global information in the process of feature generation. The results of experiments on four real-world datasets show that AFGSL outperforms the state-of-the-art methods.
AdaHC: Adaptive hedge horizontal cross-section center detection algorithm

Finding the center point of the hedge is the key to automated pruning. In a complex outdoor environment, since the horizontal cross-section of the hedge is not a regular circle, and has many approximate circular contours, the performance of mainstream circle detection algorithms is not ideal. To this end, we propose an adaptive hedge horizontal cross-section center detection algorithm, named AdaHC, which can obtain the horizontal cross-section center coordinates of the hedge in real time by inputting the top view image of the hedge. The experimental results show that our proposed algorithm is significantly better than other circle detection algorithms. Its recognition accuracy can reach 100%, the average accuracy is over 80% (corresponding to an accuracy of 1 cm), and the average time consumption is 11.6 ms, be robust to the variations in light, hedge color, and background, fully meets the industrial requirements, and lays a good foundation for automatic hedge trimming.
Case Study: The Implications of Knowledge Management Tools on the Process of Overcoming COVID-19

The main aim of this paper is to identify the most important issues and problems that we faced in the UAE in the case of coronavirus pandemic and try to come up with solutions that will enhance the overall response of organizations and individuals to this crisis. Moreover, we want to investigate the importance of knowledge management in spreading information regarding COVID-19 and how knowledge management has been used to manage this pandemic as it continues to manifest itself in several areas and different capacities. In addition, this paper will highlight the significance of effective management of knowledge and information with regards to COVID-19, and the extent to which the knowledge that has been shared widely through different platforms can help in resolving the pandemic. These findings will also enable identification of the efficacy of knowledge management and its achievements in dealing with previous pandemics in a manner that gauges the progressive stages of how it was implemented. The quantitative and qualitative data that was gathered through a survey helped us to identify the problems and uncover hidden issues faced by individuals and organizations. The recommended solution we thought the best in terms of cost and efficiency is deploying crisis management in a professional way using knowledge management tools in organizations in public society. We explained the full process of the solution in this paper and the plan of execution and implementation.
Software Vulnerability Prediction Using Grey Wolf-Optimized Random Forest on the Unbalanced Data Sets

Any vulnerability in the software creates a software security threat and helps hackers to gain unauthorized access to resources. Vulnerability prediction models help software engineers to effectively allocate their resources to find any vulnerable class in the software, before its delivery to customers. Vulnerable classes must be carefully reviewed by security experts and tested to identify potential threats that may arise in the future. In the present work, a novel technique based on Grey wolf algorithm and Random forest is proposed for software vulnerability prediction. Grey wolf technique is a metaheuristic technique and it is used to select the best subset of features. The proposed technique is compared with other machine learning techniques. Experiments were performed on three datasets available publicly. It was observed that our proposed technique (GW-RF) outperformed all other techniques for software vulnerability prediction.
Preserving Privacy in Clouds using Fully Homomorphic Encryption

Cloud computing designates a big architectural change on the way to thin clients and accessible central facility of computing resources. Clients are more interested to outsource their data on commercial public clouds for more flexibility and to reduce economic investments. In clouds, there is no direct control over resources by clients, so the security of data privacy is at the sake, provided by service providers. Traditional cryptography techniques cannot impose privacy necessitated by cloud computing services. Powerful recently apprehended, Fully Homomorphic Encryption (FHE), a fully functional construct that can enable search over encrypted text. The Paper discussed different types of FHE schemes, including schemes based on integers and polynomial over the ring of integers. The paper also includes the basic steps needed to construct any FHE scheme. The schemes are explained, along with their pros and cons. The paper may contribute to the development of privacy-preserving schemes of Cloud Computing.
PlannerFlows: Learning Motion Samplers with Normalising Flows

Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The majority of traditional planners are inefficient as they use uninformative sampling distributions instead of exploiting structures and patterns in the problem to guide better sampling strategies. Moreover, most current learning-based planners are susceptible to posterior collapse or mode collapse due to the sparsity and highly varying nature of C-Space and motion plan configurations. This work introduces a conditional normalising flow-based distribution learned through previous experiences, which improves existing methods’ sampling scheme. Our distribution can be conditioned on the current problem instance to provide informative prior to sample configurations within promising regions. When we train our sampler with an expert planner, the resulting distribution is often near-optimal, and the planner can find a solution faster, with less invalid samples and less initial cost. The normalising flow-based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based sampler. Finally, we provide a formulation and theoretical foundation to sample from the distribution efficiently. Experimentally we demonstrate utilising the flow-based distribution in a sampling-based motion planner allows a solution to be found faster, with fewer samples and better overall runtime performance.
Multiscale Visual-Attribute Co-Attention for Zero-Shot Image Recognition

Zero-shot image recognition aims to classify data from unseen classes, by exploring the association between visual features and the semantic representations of each class. Most existing approaches focus on learning a shared single-scale embedding space (often at the output layer of the network) for both visual and semantic features, ignoring a fact that different-scale visual features exhibit different semantics. In this article, we propose a multi-scale visual-attribute co-attention (mVACA) model, considering both visual-semantic alignment and visual discrimination at multiple scales. At each scale, a hybrid visual attention is realized by attribute-related attention and visual self-attention. The attribute-related attention is guided by a pseudo attribute vector inferred via a mutual information regularization (MIR). The visual self-attentive features further influence the attribute attention to emphasize visual-associated attributes. Leveraging multiscale visual discrimination, mVACA unifies standard zero-shot learning (ZSL) and generalized ZSL tasks in one framework, achieving state-of-the-art or competitive performance on several commonly used benchmarks of both setups. To better understand the interaction between images and attributes in mVACA, we also provide visualized analysis.
Comparing the Effectiveness of Text-based and Video-based Delivery in Motivating Users to Adopt a Password Manager

Despite security experts advising the use of password managers to securely manage online account passwords, the adoption rate of password managers among users is still low. To compare the effectiveness of using text-based and video-based delivery methods in encouraging users to start using a password manager (i.e., behavior change), we designed a study (n = 285) with three groups (control, text and video). The control group answered only the survey questions. The video group was presented with a video explaining the risks of poor password hygiene and how password managers can help to mitigate these risks. The text group was presented with a text version of the same content with only minor changes, preserving the essence of the content. After the initial survey, we conducted two follow-up surveys over a span of two weeks to see whether text/video interventions were effective in motivating participants to start using a password manager. We found significant differences between the video and text groups in terms of risk perceptions, and attitudes towards using a password manager. In turn, participants who watched the video were found to be significantly more likely to adopt a password manager than both the text and control groups. Finally, we present our qualitative data analysis to shed light on participants’ motivations behind their decisions.
BEvote: Bitcoin-Enabled E-Voting Scheme with Anonymity and Robustness

When building the large-scale distributed decision control system based on mobile terminal devices (MTDs), electronic voting (E-voting) is a necessary technique to settle the dispute among parties. Due to the inherent insecurity of Internet, it is difficult for E-voting to attain complete fairness and robustness. In this study, we argue that Bitcoin blockchain offers better options for a more practical E-voting. We first present a coin mixing-based E-voting system model, which can cut off the relationship between the voter’s real identity and its Bitcoin address to achieve strong anonymity. Moreover, we devise a secret sharing-based E-voting protocol, which can prevent voting number from being leaked ahead and further realize strong robustness. We establish the probable security theory to prove its security. In addition, we use the experimental evaluation to demonstrate its efficiency.
Proactive Live Migration for Virtual Network Functions using Machine Learning

VM (Virtual Machine) live migration is a server virtualization technique for deploying a running VM to another server node while minimizing downtime of service the VM provides. Currently, in cloud data centers, VM live migration is widely used to apply load balancing on CPU workload and network traffic, to reduce electricity consumption, and to provide uninterrupted service during the maintenance of hardware and software updates on servers. It is critical to use VM live migration as a prevention or mitigation measure for possible failure when its indications are detected or predicted. Especially in NFV (Network Function Virtualization) environment, timely use of VNF (Virtual Network Function) live migration can maintain system availability and reduce operator's loss due to service failure. In this paper, we propose a proactive live migration method for vEPC (Virtual Evolved Packet Core) based on failure prediction. A machine learning model learns periodic monitoring data of resource usage and logs from servers and VMs/VNFs to predict future vEPC paging failure probability. We implemented the proposed method in OpenStack-based NFV environment to evaluate the real service performance gains for open source vEPC implementations.