Reviewerly/exHarmony huggingface dataset:
An open-source Python library for self-organizing-maps

Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are the Machine Learning technique that is recently obtaining very good best results. This paper describes an open-source Python library called GEMA developed to work with a type of neural network model called Self-Organizing-Maps. GEMA is freely available under GNU General Public License at GitHub (https://github.com/ufvceiec/GEMA). The library has been evaluated in different a particular use case obtaining accurate results.
Self-Healing for Cyber-Security

Autonomic computing long since holds the promise to achieve properties including self-healing IT. The self-healing property provides a new perspective in the continuous battle between cyber attackers and defenders. Emerging containerization technology, and accompanying monitoring and deployment tools, can boost the state-of-the-art regarding self-healing IT. In this paper, we demonstrate how emerging technology can be integrated into a platform for regenerating and adaptive, self-healing containers. This Self-Healing for Cyber-Security (SH4CS) contribution paves the way towards a new type of autonomous cyber-security solutions.
A Traceable Capability-based Access Control for IoT

Delegation mechanism in Internet of Things (IoT) allows users to share some of their permissions with others.Cloud-based delegation solutions require that only the user who has registered in the cloud can be delegated permissions.It is not convenient when a permission is delegated to a large number of temporarily users.Therefore, some works like CapBAC delegate permissions locally in an offline way.However, this is difficult to revoke and modify the offline delegated permissions.In this work, we propose a traceable capability-based access control approach (TCAC) that can revoke and modify permissions by tracking the trajectories of permissions delegation.We define a time capability tree (TCT) that can automatically extract permissions trajectories, and we also design a new capability token to improve the permission verification, revocation and modification efficiency.The experiment results show that TCAC has less token verification and revocation/modification time than those of CapBAC and xDBAuth.TCAC can discover 73.3% unvisited users in the case of delegating and accessing randomly.This provides more information about the permissions delegation relationships, and opens up new possibilities to guarantee the global security in IoT delegation system.To the best of our knowledge, TCAC is the first work to capture the unvisited permissions.
AN INTEGRATION OF A MOBILE TRACKED ROBOT CONTROL SYSTEM DATA

Integration of information – the process of combining data to determine or predict the state of an object. The integration provides an increase in the robustness of the robot control and the accuracy of machine perception of information. The paper discusses one of the methods for integrating of the robot onboard data using the example of the mobile tracked robot data. One of the aim is showing how to obtain the Jacobi matrix of the process function and the Jacobi matrix of the observation function of the mobile robot system for subsequent data integration by the state observer, built on the basis of the Kalman filter, that can convert the robot onboard data into the elements of the state vector of the mobile robot system. Each nonzero element of the Jacobi matrix of the observation function of the system is a weight coefficient. It determines the contribution of an element of the output vector of the system corresponding to this weight coefficient to the result of information integration calculated by the Kalman filter – the state observer. The Kalman filter is a sequential recurrent algorithm for filtering information from discrete dynamical systems specified in the state space. A feature of the Kalman filter as an observer of the state of the system is the assumption that the observed system has the effect of white Gaussian noise (characterized by zero mathematical expectation) on its state.
CSGNN: Improving Graph Neural Networks with Contrastive Semi-supervised Learning

The Graph Neural Network (GNN) is a rising graph analysis model family that encodes node features into low-dimensional representation vectors by aggregating local neighbor information. Nevertheless, the performance of GNNs is limited since GNNs are trained only over predictions of the labeled data. Hence, effectively incorporating a great number of unlabeled nodes into GNNs will upgrade the performance of GNNs. To address this issue, we propose a Contrastive Semi-supervised learning based GNN (CSGNN) that improves the GNN from extra supervision predicted by contrastive learning. Firstly, CSGNN utilizes multi-loss contrast to learn node representations via maximizing the agreement between nodes, edges and labels of different views. Then, a semi-supervised fine-tuner learns from few labeled examples while making the best use of unlabeled nodes. Finally, we introduce the knowledge distillation based on label reliability, which further distills the node labels predicted by contrastive learning into the GNN. Experimentally, CSGNN effectively improves the classification performance of GNNs and outperforms other state-of-the-art methods in accuracy over a variety of real-world datasets.
Enhancing Continuous Variable Quantum Teleportation using Non-Gaussian Resources

Continuous Variable (CV) non-Gaussian resources are fundamental in the realization of quantum error correction for CV-based quantum communications and CV-based computing. In this work, we investigate the use of CV non-Gaussian states as quantum teleportation resource states in the context of the transmission of coherent and squeezed states through noisy channels. We consider an array of different non-Gaussian resource states, and compute the fidelity of state teleportation achieved for each resource. Our results show that the use of non-Gaussian states presents a significant advantage compared to the traditional resource adopted for CV teleportation; the Gaussian two-mode squeezed vacuum state. In fiber-based quantum communications, the range of quantum teleportation is increased by approximately 40% via the use of certain non-Gaussian states. In satellite-to-ground quantum communications, for aperture configurations consistent with the Micius satellite, the viable range of quantum teleportation is increased from 700 km to over 1200 km. These results represent a significant increase in the performance of pragmatic and realizable quantum communications in both terrestrial and space-based networks.
An Improved Genetic Algorithm for Rapid UAV Path Planning

Abstract UAV technologies have advanced rapidly and are widely used in military and civilian fields. For instance, the UAV swarms have been widely applied to oil and gas exploration, geometric mapping, and cargo transport. However, the UAV swarm system requires a more accurate plan before performing any mission. Path planning is one of the essential parts of mission planning because of the higher requirement of robustness and real-time communication. UAV path planning could generate the optimal path starting from the current position to the target in an environment with an obstacle. While the standard genetic algorithm has lacked efficiency in the iteration process and poor stability, a new genetic operator is proposed for the genetic algorithm and applied to the path planning simulation of UAV swarms in this study. A three-dimensional mapping and fitness function are already constructed for the simulation. The simulation result shows the algorithm with improved selection and mutation operator can efficiently and stably converge to the optimal solution.
Deep transfer learning based human activity recognition by transforming IMU data to image domain using novel activity image creation method

Human Activity Recognition (HAR) is the most popular research area in the pervasive computing field in recent years. Sensor data plays a vital role in identifying several human actions. Convolutional Neural Networks (CNNs) have now become the most recent technique in the computer vision phenomenon, but still, it is premature to use CNN for sensor data, particularly in ubiquitous and wearable computing.Deep CNN requires huge dataset and models which increases the computational complexity. Transfer learning that uses the pre trained CNNwith fine tuning is the better alternative to reduce the training cost.In this paper, we have proposed the idea of transforming the raw accelerometer and gyroscope sensor data to the visual domain by using our novel activity image creation method (NAICM). Pre-trained CNN (AlexNet) has been used on the converted image domain information. The proposed method is evaluated on several online available human activity recognition dataset. The results show that the proposed novel activity image creation method (NAICM) has successfully created the activity images with a classification accuracy of 98.36% using pre trained CNN.
LightGBM Model for Credit Card Fraud Discovery

The exponential growth of e-commerce and online-based payment options has created an empirical universe of financial fraud, with credit card fraud being the most prevalent. For several years, many researchers have developed a variety of data mining-based methods to address this issue. To detect credit card fraud, there has recently been a lot of interest in using machine learning algorithms instead of data mining techniques. In the digital space of financial transactions, on-going work is being conducted to put in a conceptual difference between fraud identification and predicting likely fraudulent opportunities. This paper extends the fraud detection technique and proposes a LightGBM-based detection algorithm. The dataset is a credit card dataset for credit card transactions in Europe. Our approach outperformed other traditional approaches such as random forest, AdaBoost, and XGBoost in this experiment. Furthermore, it demonstrates the value of feature engineering in terms of feature selection and performance tuning.
EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python

EvoCC framework is an open-source, free, and cross-platform framework implemented in Python which combines clustering, classification, and evolutionary computation methods. It optimizes the classification process by generating a classification model for each group generated by a clustering process where the clustering process is optimized by evolutionary optimization techniques. It includes the most well-known and recent nature-inspired metaheuristic optimization algorithms, well-known datasets, different fitness functions, and distance measures, and several well-known and highly-used classifiers. The aim is to provide the practitioners and researchers with a user-friendly and customizable implementation of classification-based nature-inspired optimization clustering algorithms that can be used by experienced and non-experienced users for the classification process in different domains. The current implementation of the framework includes eleven classification algorithms and five evaluation measures. It also utilizes the implementation of the EvoCluster framework which has ten metaheuristic optimizers, thirty datasets, five objective functions, more than twenty distance measures, and ten different ways for detecting the number of clusters (k value). The source code of EvoCC is publicly available at ( https://evo-ml.com/evocc/ )
Contrapositionally complemented Heyting algebras and intuitionistic logic with minimal negation

Abstract Two algebraic structures, the contrapositionally complemented Heyting algebra (ccHa) and the contrapositionally $\vee $ complemented Heyting algebra (c$\vee $cHa), are studied. The salient feature of these algebras is that there are two negations, one intuitionistic and another minimal in nature, along with a condition connecting the two operators. Properties of these algebras are discussed, examples are given and comparisons are made with relevant algebras. Intuitionistic Logic with Minimal Negation (ILM) corresponding to ccHas and its extension ${\textrm {ILM}}$-${\vee }$ for c$\vee $cHas are then investigated. Besides its relations with intuitionistic and minimal logics, ILM is observed to be related to Peirce’s logic and Vakarelov’s logic MIN. With a focus on properties of the two negations, relational semantics for ILM and ${\textrm {ILM}}$-${\vee }$ are obtained with respect to four classes of frames, and inter-translations between the classes preserving truth and validity are provided. ILM and ${\textrm {ILM}}$-${\vee }$ are shown to have the finite model property with respect to these classes of frames and proved to be decidable. Extracting features of the two negations in the algebras, a further investigation is made, following logical studies of negations that define the operators independently of the binary operator of implication. Using Dunn’s logical framework for the purpose, two logics $K_{im}$ and $K_{im-{\vee }}$ are discussed, where the language does not include implication. The $K_{im}$-algebras are reducts of ccHas and are different from relevant algebraic structures having two negations. The negations in the $K_{im}$-algebras and $K_{im-{\vee }}$-algebras are shown to occupy distinct positions in an enhanced form of Dunn’s kite of negations. Relational semantics for $K_{im}$ and $K_{im-{\vee }}$ is provided by a class of frames that are based on Dunn’s compatibility frames. It is observed that this class coincides with one of the four classes giving the relational semantics for ILM and ${\textrm {ILM}}$-${\vee }$.
A Nonuniform Sampling Strategy for Path Planning Using Heuristic-based Certificate Set

Collision checking is considered one of the most time-consuming tasks in sampling-based path planning algorithms. Traditionally, sampling-based path planning methods draw samples uniformly from the state space, which samples a lot of useless states regardless of previous successful experience. Besides, the sampled states do not contribute to subsequent sampling. However, using the information recorded by sampled states can avoid numerous unnecessary collision checks. Extra information such as previous successful experiences and the distance between the random sample and obstacles should be considered to accelerate the planning process. Thus, it is desirable to devise a nonuniform sampling strategy to utilize the extra information in the planning process. This paper proposes a novel nonuniform sampling strategy maintaining a heuristic-based certificate set during the planning process. The heuristic-based certificate set consists of sampled states with collision status and the minimum distance to the nearest obstacle, while the neural network gives the heuristic. We introduce this nonuniform sampling strategy into RRT and RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and evaluate the algorithms on different kinds of maps. The simulation results demonstrate that the nonuniform sampling strategy significantly speeds up these algorithms and improves their stability.
Design of Detection and Measurement System for Petroleum Transportation Pipeline Based on WEB Technology

With the rapid development in speed and quality of the economy in China, petroleum energy, as one of the main energy sources in today’s human society, has increasingly demonstrated its status. At the same time, petroleum transportation as an important part of petroleum applications should invest more research efforts. With the development of the information age and the continuous update and iteration of intelligent technology, the traditional petroleum transportation management model can no longer meet the requirements of the current era. The technological innovation focusing on the measurement of petroleum transportation pipelines is poised for take-off which studies the application of Internet methods combining the advantages of the information age. Based on the analysis of previous research results, this paper takes the overall requirements of the petroleum transportation pipeline as the goal for demand analysis and updates the key technologies on this basis to establish a new WEB-based petroleum transportation pipeline detection and measurement system, which studies the frontier technology of big data processing and the automatic measurement and storage of petroleum pipeline data is realized by combining database system, Java technology and Web technology.
Neuroevolutionary Models Based on Quantum-Inspired Evolutionary Algorithms

Artificial neural networks are powerful and flexible models that have proved useful in solving a countless number of real-world problems. More recently, deep neural networks have gained the attention of the machine learning community for surpassing human-level performance in a variety of tasks. To reach these excellent results, an expert needs to spend a significant amount of time designing the neural configuration, such as the best architecture, input variables, and training parameters, with long trial-and-error sessions. Neuroevolution is a method that tries to automate this design process by applied evolutionary computation algorithms to optimise the neural network hyperparameters. However, the high computational cost associated with the use of traditional evolutionary algorithms for neural network configuration is still an issue. In this regard, quantum-inspired evolutionary algorithms emerged as an excellent optimisation method for neuroevolution, as they provide faster convergence, with a reduced computational cost. This chapter presents an overview of quantum-inspired evolutionary algorithms and their application to the configuration of different neural network models, such as Multi-Layer Perceptrons, Echo-State Networks and Convolutional Neural Networks. Applications in system identification, image classification and concept drift environments are also detailed, with promising results.
AS-Net: Class-Aware Assistance and Suppression Network for Few-Shot Learning

Few-shot learning targets to recognize objects while only limited data is provided for each class. Existing methods tend to solve this problem by mapping the raw inputs into a shared embedding space and averaging them at the class level to form the corresponding prototypes. However, the prototypes are vulnerable to outliers, as they can contribute significantly to the class description when there is little data. In this paper, a class-aware assistance and suppression framework (AS-Net) is proposed to identify the informative patches and outliers as well as to further facilitate the rectification of prototypes. Specifically, we firstly introduce local features commonly shared across the whole set while distinguishable to other classes. These additional features can enhance the image embeddings and narrow the distance between the outliers and the majority of the class. We then attenuate the effect of outliers on the prototype by assigning lower weights to them. During these two stages, each labeled sample assists the rest samples of the same class to recognize their commonality, which provides positive attention for the intra-class consistency, i.e., which area or sample to focus on. At the same time, it suppresses the samples of other classes with similar features, which provides negative attention for the inter-class distinction, i.e., which area or sample not to focus on. Extensive experiments on several benchmark datasets demonstrate the superiority of our proposed method over the state-of-the-art approaches in the few-shot learning task.
A Clustering Method of Encrypted Video Traffic Based on Levenshtein Distance

In order to detect the playback of illegal videos, it is necessary for supervisors to monitor the network by analyzing traffic from devices. However, many popular video sites, such as YouTube, have applied encryption to protect users’ privacy, which makes it difficult to analyze network traffic at the same time. Many researches suggest that DASH (Dynamic Adaptive Streaming over HTTP) will leak the information of video segmentation, which is related to the video content. Consequently, it is possible to analyze the content of encrypted video traffic without decryption. At present, most of the encrypted video traffic analysis adopts supervised learning methods, and there is little research on its unsupervised methods. Analysts are usually faced with unlabeled data, in reality, so the existing approaches will not work. The encrypted video traffic analysis methods based on unsupervised learning are required. In this paper, we proposed a clustering method based on Levenshtein distance for title analysis of encrypted video traffic. We also run a thorough set of experiments that verify the robustness and practicability of the method. As far as I am concerned, it is the first work to apply cluster analysis for encrypted video traffic analysis.
What does this Python code do? An exploratory analysis of novice students’ code explanations

Motivation. Code reading skills are important for comprehension. Explain-in-plain-English tasks (EiPE) are one type of reading exercises that show promising results on the ability of such exercises to differentiate between particular levels of code comprehension. Code reading/explaining skills also correlate with code writing skills. Objective. This paper aims to provide insight in what novice students express in their explanations after reading a piece of code, and what these insights can tell us about how the students comprehend code. Method. We performed an exploratory analysis on four reading assignments extracted from a university-level beginners course in Python programming. We paid specific attention to 1) the core focus of student answers, 2) elements of the code that are often included or omitted, and 3) errors and misconceptions students may present. Results. We found that students prioritize the output that is generated by print-statements in a program. This is indication that these statements may have the ability to aid students make sense of code. Furthermore, students appear to be selective about which elements they find important in their explanation. Assigning variables and asking input was less often included, whereas control-flow elements, print statements and function definitions were more often included. Finally, students were easily confused or distracted by lines of code that seemed to interfere with the newly learned programming constructs. Also domain knowledge (outside of programming) both positively and negatively interfered with reading and interpreting the code. Discussion. Our results pave the way towards a better understanding of how students understand code by reading and of how an exercise containing self-explanations after reading, as a teaching instrument, may be useful to both teachers and students in programming education.
Attitude Control Based on Reinforcement Learning for Quadrotor

To design a good control system for a 6 degree-of-freedom aircraft is not an easy work. The classic linear method such as PID can’t provide a good performance for complex nonlinear systems in some cases. In this paper, an end-to-end policy using Reinforcement Learning (RL) based on Proximal Policy Optimization (PPO) is proposed. This mothed is used to established a mapping from the observed states to the motor signals to control the attitude of quadrotor. In order to evaluate the performance of the end-to-end controller based on PPO, a RL-PID controller is designed to compare with the end-to-end controller in this paper. The result shows that the performance of the end-to-end controller based on PPO is better.