Reviewerly/exHarmony huggingface dataset:
Enhanced hybrid multi-objective workflow scheduling approach based artificial bee colony in cloud computing

Abstract This paper presents a hybrid approach based Binary Artificial Bee Colony (BABC) and Pareto Dominance strategy for scheduling workflow applications considering different Quality of Services (QoS) requirements in cloud computing. The main purpose is to schedule a given application onto the available machines in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The proposed approach is called Enhanced Binary Artificial Bee Colony based Pareto Front (EBABC-PF). Our proposed approach starts by listing the tasks according to priority defined by Heterogeneous Earliest Finish Time (HEFT) algorithm, then gets an initial solution by applying Greedy Randomized Adaptive Search Procedure (GRASP) and finally schedules tasks onto machines by applying Enhanced Binary Artificial Bee Colony (BABC). Further, several modifications are considered with BABC to improve the local searching process by applying circular shift operator then mutation operator on the food sources of the population considering the improvement rate. The proposed approach is simulated and implemented in the WorkflowSim which extends the existing CloudSim tool. The performance of the proposed approach is compared with Heterogeneous Earliest Finish Time (HEFT) algorithm, Deadline Heterogeneous Earliest Finish Time (DHEFT), Non-dominated Sort Genetic Algorithm (NSGA-II) and standard Binary Artificial Bee Colony (BABC) algorithm using different sizes of tasks and various benchmark workflows. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.
Iterative quantum phase estimation with variationally prepared reference state

Abstract The iterative quantum phase estimation algorithm (IQPE) is theoretically appealing in its wide scope of being able to handle electronic correlation. However, the quality of the initial input state strongly enhances the probability of landing on the desired eigenstate. In this work, we systematically study two different parametrization schemes of the unitary coupled cluster (UCC) ansätz in the variational quantum eigensolver (VQE) framework toward the reference state preparation for IQPE. The efficacy of the UCC variants toward an appropriate state preparation is studied with prototypical H 4 molecule on a circle. While the conventional UCC ansätz can lead to high success probability across various degrees of electronic complexity, a resource efficient minimally parametrized UCC ansätz consisting of active space excitations is shown to incorporate the essential static correlation in the reference state description. We demonstrate that such a carefully prepared initial state can significantly reduce the effects of noise due to sampling in the estimation of the desired eigenphase.
Attack Dynamics: An Automatic Attack Graph Generation Framework Based on System Topology, CAPEC, CWE, and CVE Databases

Through a built-in security analysis feature based on metadata, this article provides a novel framework that starts with a scenario input and produces a collection of visualizations based on Common Attack Pattern Enumeration and Classification (CAPEC) and Common Weakness Enumeration (CWE) Standards. It immediately links enterprise mitigations from MITRE ATT&CK framework to the security flaws it discovered. It’s also integrated with a third-party optimization tool targeted at cutting security costs for businesses, which it can perform in real-time or later using JSON output in the preferred format, depending on the execution mode. All of these stages are conducted without human intervention. Adaptive metadata with a variety of rules for capturing different sorts of known or prospective attack types allows for the production of attack graphs. It can be used as a quick and practical what-if analysis tool to detect potential intrusions for a variety of network configuration setups and assigned access privileges. As a threat modeler, it is suitable for both novice and expert users. Due to the easy input scheme and human-readable outputs, it can also be utilized as an educational tool.
Behavioural Analysis in a 6-DoF VR System

This work presents an explorative behavioural analysis of users navigating in an immersive space aimed at enabling the next-generation multimedia systems. Our main goal is to understand how the user experience of immersive content with 6-Degrees-of-Freedom (DoF) is affected not only by the visual content and its quality but also by the disposition of the user. We based our investigations on traditional statistical metrics, on techniques that have been already used for 6-DoF, as well as adapted 3-DoF tools to be used in this new context. We show the limitation of each metric in giving a complete interpretation of user behaviour, and we draw insights on important factors to be considered when analysing and predicting navigation trajectories. Specifically, we have noticed in our behavioural investigations that the user disposition plays an important role in the way of interacting with the immersive content. This opens the gate to user profiles (i.e., a collection of key information that describes the behavioural features of a single or group of users) that would be beneficial for different purposes in future immersive applications such as enabling new modalities for live streaming services optimised per user profiles but also for user-based quality assessment methods.
An efficient QR-BiMGM model for probabilistic PV power forecasting

The inherent volatility and randomness of PV output has posed a severe danger to the safe and stable functioning of power systems in recent years, as grid-connected PV capacity has increased. Short-term probabilistic PV power predictions can provide power system decision-makers with more detailed information. A novel probabilistic PV power forecast model based on the fuzzy C-mean clustering algorithm and the QR-BiMGM model is proposed in this research. The MGM (Minimal Gated Memory) model reduces training time while enhancing prediction accuracy by simplifying the neuron structure. The model’s data mining capabilities is further enhanced by the bidirectional propagation structure. To provide probabilistic prediction results, the Quantile Regression (QR) model is employed to integrate the BiMGM model. Under diverse weather conditions, a comparative analysis using actual measurement data with four benchmark models shows that the proposed model has higher prediction performance and good generalization potential. The QR-BiMGM model’s mean integrated interval evaluation metric WC is 31.38 percent lower than QR-MGM, 50.21 percent lower than QR-LSTM, 44.85 percent lower than QR-BiLSTM, and 44.83 percent lower than QR-GRU, according to simulation results. The mean probabilistic prediction evaluation metric CRPS of the QR-BiMGM model is 3.5 percent lower than that of QR-MGM. 3.6 percent lower than QR-MGM, 20.91 percent lower than QR-LSTM, 17.56 percent lower than QR-BiLSTM, and 20.36 percent lower than QR-GRU. In addition, the probability density function based on the conditional quantile of PV power is estimated using a modified kernel density estimation approach.
Debiasing MDI Feature Importance and SHAP Values in Tree Ensembles

We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the default variable-importance measure in random forests, Gini importance. In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly developed SHAP values and suggest a remedy.
Personalized Privacy-Preserving Medical Data Sharing for Blockchain-based Smart Healthcare Networks

With the growing proliferation of intelligent end devices and data analytics techniques, real momentum towards the development of smart healthcare networks (SHN) has already been evident. Multiple parties in SHNs continuously exchange medical data in order to achieve a precise diagnosis and process optimization. Privacy issue emerges since medical data are susceptible, while the combination of a series of medical data may lead to further privacy leakage. Adversaries launch unceasingly launch poisoning attacks, a dominant attack to maliciously manipulate data, severely impact the authenticity of the data transmitting over the SHNs, leading to misdiagnosing or even physical damage. In this paper, we propose a personalized differential privacy model built upon blockchain, in which the community density is exploited to customize the degree of privacy protection and inject corresponding noise data. Besides using blockchain as the underlying network architecture to defeat poisoning attacks. The proposed model can guarantee the authentication of the differentially private data, traceability of data, and single-point failure avoidance in SHN. Evaluation and extensive results using real-world data sets demonstrate the superiority of the proposed model.
Software-in-the-loop simulation of the forerunner UAV system

The forerunner UAV means a camera equipped drone flying in front of the advancing first responder units to increase driver situational awareness with an aerial view of the traffic situation and notification about imminent dangers. This article presents the software-in-the-loop (SIL) simulation of the concept including UNREAL4-Carla as the virtual reality environment with a firetruck driven through a game controller, the Matlab simulation of the DJI M600 forerunner hexacopter with UDP communication between firetruck and M600 and the real-time AI processing of synthetic images to detect ground vehicles and pedestrians. The target of SIL development is threefold. First, to test M600 autopilot and AI-based object detection in close to realistic conditions before the real flights. Second, to make an exhaustive feasibility study of the whole forerunner concept with several simulated situations. Third, to generate the required large amount of image data for AI object detection tuning. After introducing all parts of the SIL simulation the article presents an illustrative example evaluating the tracking of the ground vehicle with the M600 and the inference system results.
Fraud Detection Techniques for Credit Card Transactions

Credit Card Management Organizations are important to select fraudulent transactions for your credit rating so that customers are charged for items that are not currently purchased. These issues can be solved with the help of data and technical knowledge of the data with the machine. This beginner demonstrates the knowledge obtained by the use of the gadgets, and the identification of the credit card fraud. The problem of detecting a credit score includes modeling of past transactions for credit cards with the facts of those who have been revealed to fraud. The version is then used to delay whether new transactions are fraudulent or are now not new transactions. Our goal here is to detect 100% of fraudulent transactions and minimize fraudulent misclassification. Credit score card fraud detection is a common class model. In this method, we focused on the analysis and preprocessing of several anomaly detection algorithms and record sets, such as "neighbor outliers" and "forest zone isolation" algorithms, in PCA-converted credit card transaction statistics.
Digital Brick: Enhancing the Student Experience Using Blockchain, Open Badges and Recommendations

The main purpose of this work is to describe the process of design and implementation of a novel e-Learning platform, named Digital Brick, intended to enhance the students’ experience in obtaining formal certifications of their competencies. The research method we followed starts form a deep study of the state of the art that showed us the need to invest more research effort on delivering open and flexible online environments to enable students in finding and passing courses with final formal certifications of learning. To reach this goal, we (i) designed a complete system architecture around a standard (SCORM compliant) learning management system in order the approach should be reusable as much as possible. We (ii) introduced specific modules to separate responsibilities on the definition and issuing of formal certifications using digital badges according to the IMS Open Badges standard. We (iii) exploited blockchain technology to make the sharing of badges among actors more secure, transparent and open. Finally, we (iv) introduced a new recommendation algorithm based on machine learning techniques to give advice to students about study materials and learning paths. We spent a significant part of our effort carrying out both a functional and quantitative validation of our proposal. The obtained results are presented through a laboratory case study that involves all the components of the architecture, and the outcomes are discussed providing numerical performance indicators. In conclusion, the resulting platform introduces digital badges and blockchain as tools to share recognized achievements among earners and issuers, and machine learning algorithms to provide students with recommendations on the learning material, learning objects, courses and learning paths more suitable for their learning styles.
Constacyclic and Negacyclic Codes over F_2+uF_2+vF_2 and Equivalents of Codes in F_2

In this work, we consider the finite ring $\mathbb{F}_{2}+u\mathbb{F}_{2}+v\mathbb{F}_{2}$, $u^{2}=1, v^{2}=0$, $u\cdot v=v\cdot u=0$ which is not Frobenius and chain ring. We studied constacyclic and negacyclic codes in $\mathbb{F}_{2}+u\mathbb{F}_{2}+v\mathbb{F}_{2}$ with odd length. These codes are compared with codes that had priorly been obtained on the finite field $\mathbb{F}_{2}$. Moreover, we indicate that the Gray image of a constacyclic and negacyclic code over $\mathbb{F}_{2}+u\mathbb{F}_{2}+v\mathbb{F}_{2}$ with odd length $n$ is a quasicyclic code of index $4$ with length $4n$ in $\mathbb{F}_{2}$. In particular, the Gray images are applied to two different rings $S_{1}=\mathbb{F}_{2}+v\mathbb{F}_{2}$, $v^{2}=0$ and $S_{2}=\mathbb{F}_{2}+u\mathbb{F}_{2}$, $u^{2}=1$ and negacyclic and constacyclic images of these rings are also discussed.
Less Is More: Robust and Novel Features for Malicious Domain Detection

Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C&amp;C, phishing, and spear-phishing). Despite the continuous progress in detecting cyber attacks, there are still critical weak spots in the structure of defense mechanisms. Since machine learning has become one of the most prominent malware detection methods, a robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant to evasion attacks. This mechanism exhibits a high performance based on empirical data. This paper makes two main contributions: First, it provides an analysis of robust feature selection based on widely used features in the literature. Note that even though the feature set dimensional space is cut by half, the performance of the classifier is still improved (an increase in the model’s F1-score from 92.92% to 95.81%). Second, it introduces novel features that are robust with regard to the adversary’s manipulation. Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models based on robust features are resistant to malicious perturbations and concurrently are helpful in classifying non-manipulated data.
Test and Evaluation of Reinforcement Learning via Robustness Testing and Explainable AI for High-Speed Aerospace Vehicles

Reinforcement Learning (RL) provides an ability to train an artificial intelligent agent in dynamic and uncertain environments. RL has demonstrated an impressive performance capability to learn nearly optimal policies in various application domains including aerospace. Despite the demonstrated performance outcomes of RL, characterizing performance boundaries, explaining the logic behind RL decisions, and quantifying resulting uncertainties in RL outputs are major challenges that slow down the adoption of RL in real-time systems. This is particularly true for aerospace systems where the risk of failure is high and performance envelopes of systems of interest may be small. To facilitate adoption of learning agents in real-time systems, this paper presents a three-part Test and Evaluation (T&#x0026;E) framework for RL built from Systems engineering for artificial intelligence (SE4AI) perspective. This T&#x0026;E framework introduces robustness testing approaches to characterize performance bounds on RL, employs Explainable AI techniques, namely Shapley Additive Explanations (SHAP) to examine RL decision-making, and incorporates validation of RL outputs with known and accepted solutions. This framework is applied to a high-speed aerospace vehicle emergency descent problem where RL is trained to provide an angle of attack command and the framework is utilized to comprehensively examine the impact of uncertainties in the vehicle&#x0027;s altitude, velocity, and flight path angle. The robustness testing characterizes acceptable ranges of disturbances in flight parameters, while SHAP exposes the most significant features that impact RL selection of angle of attack-in this case the vehicle altitude. Finally, RL outputs are compared to trajectory generated by indirect optimal control methods for validation.
Data Augmentation Techniques For Expanding The Dataset In The Task Of Image Processing

In this paper we’ve analyzed and elaborated the main problem within the field of Machine Learning (ML) which is the lack of data used for model training also referred to as data augmentation and how your neural network model generalizes data and makes sure it provides high-quality results. Throughout the paper it is mentioned and analyzed the various methods used during data augmentation in the field of image classification ranging from zooming, rotating, cropping and then presenting an approach that generates prime quality images as a combination of basic image and other image style transfer. Initially, a graph is presented that describes signs of overfitting and desired convergence, showing a model with the desired relationship between training and testing error. Then, images that show the different possibilities of stylizing a single image. and these images that are created are used for the training of the given neural network in order to improve the training process. The example of a drone is taken and the images transferred from the original one are presented. Then, it is shown why one of the techniques is more suitable than the others, how it works and what are its features, all the while representing the benefits and downsides of these methods being analyzed.
BDA-SketRet: Bi-level domain adaptation for zero-shot SBIR

The efficacy of zero-shot sketch-based image retrieval (ZS-SBIR) models is governed by two challenges. The immense distributions-gap between the sketches and the images requires a proper domain alignment. Moreover, the fine-grained nature of the task and the high intra-class variance of many categories necessitates a class-wise discriminative mapping among the sketch, image, and the semantic spaces. Under this premise, we propose BDA-SketRet, a novel ZS-SBIR framework performing a bi-level domain adaptation for aligning the spatial and semantic features of the visual data pairs progressively. In order to highlight the shared features and reduce the effects of any sketch or image-specific artifacts, we propose a novel symmetric loss function based on the notion of information bottleneck for aligning the semantic features while a cross-entropy-based adversarial loss is introduced to align the spatial feature maps. Finally, our CNN-based model confirms the discriminativeness of the shared latent space through a novel topology-preserving semantic projection network. Experimental results on the extended Sketchy, TU-Berlin, and QuickDraw datasets exhibit sharp improvements over the literature.
Smart lighting control system based on fusion of monocular depth estimation and multi-object detection

Smart lighting systems can reduce energy consumption by controlling lights according to the distribution of personnel. Most of the current smart lighting systems using vision-based indoor personnel positioning method suffer from difficulties in calibration and deployment. A smart lighting control system based on fusion of monocular depth estimation and multi-object detection is proposed in this paper. The system automatically recognizes the relative positions of people and lights from the video and controls the lights, thereby reducing the complexity of calibration and deployment. In order detect the relative positions, a multi-object relative position detection algorithm (Deep-YOLO) is proposed. Deep-YOLO fuses and optimizes Scaled-YOLOv4 and AdaBins, and it detects the relative positions of people and lights by performing simultaneous multi-object detection and depth estimation on videos. A dedicated image dataset (PL) and deep transfer learning are utilized to improve the object detection accuracy of Deep-YOLO. In order to control lights according to the relative positions, a K-Means-based lighting control algorithm is proposed, and a smart lighting control system verification platform is developed. The experimental results show that the mAP of Deep-YOLO is 98.16% and the control average error of the system is less than 1 m.
Breast Cancer Identification Using Machine Learning

Breast cancer is a cancer disease that seriously threatens women’s health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model.
Long distance measurement-device-independent three-party quantum key agreement

Key agreement is a basic cryptographic primitive, which plays an important role in private communications. In this work, we propose a measurement-device-independent three-party quantum key agreement protocol and then analyze its security, which shows that it can guarantee both the security and fairness for key agreement. Furthermore, this protocol is based on Greenberger–Horne–Zeilinger states while only Bell state measurements and single-particle state measurements are required, and therefore it is experimentally feasible with current technology. More importantly, this protocol can double the space distance between participants for quantum transmission by placing untrusted relays in the middle compared with conventional protocols.