Reviewerly/exHarmony huggingface dataset:
Highly Accurate Spam Detection with the Help of Feature Selection and Data Transformation

The amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.
Smooth Spline-based Trajectory Planning for Semi-Rigid Multi-Robot Formations

This paper presents an approach for smooth trajectory planning in semi-rigid nonholonomic mobile robot formations using Bezier-splines. Unlike most existing approaches, the focus is on maintaining a semi-rigid formation, as required in many scenarios such as object transport, handling or assembly. We use a Relaxed A* planner to create an optimal collision-free global path and then smooth this path using splines. The smoothed global path serves to create target paths for every robot in the formation. From these paths, we then calculate the trajectories for each robot. In an iterative process, we match the velocities of the robots so that all trajectories are synchronized, and the dynamic limits of all robots are maintained. We provide experimental validation, which confirms no violation of the dynamic limits and shows an excellent control performance for a system of three robots moving at 0.3 m/s.
THE SYSTEM OF ADDITIONAL ECOLOGICAL EDUCATION WITH USE OF IMMERSIVE TECHNOLOGIES AS A FACTOR OF SUSTAINABLE DEVELOPMENT

The article scientifically describes the principles and stages of designing a system of additional environmental edu- cation using immersive learning technologies. The projected system is aimed at forming the individual activity of schoolchildren in solving environmental problems on the basis of social, scientific and aesthetic axiological systems that determine the content of environmental-biotechnical, environmental-research, environmental-aesthetic activi- ties. The main result of the implementation of this system is the ability of students to plan their activities to study independently, preserve and creatively transform the environment using traditional and immersive technologies. Immersive learning technologies are considered by the authors as a tool for the development of environmental and social responsibility of society. The results of an experimental test of the effectiveness of the culture-creative system of additional environmental education using traditional and immersive learning technologies are presented, which showed the high efficiency of the influence of innovative forms, means and methods of additional environmental education in the formation of environmental value orientations of schoolchildren.
Research on Cross-site Scripting Vulnerability of XSS Based on International Student Website

The international student website is the window of the school's external publicity. Through the website, international students can understand the school in a wider range and in all aspects. The website is often attacked by network hackers. How to ensure the security of the website? Cross site scripting attack (XSS) is the most common web application security vulnerability. Through the analysis of cross site scripting attack (XSS), this paper puts forward the preventive methods for input verification, output coding, clearly specifying the coding mode of output, paying attention to the limitations of blacklist verification mode, being alert to standardization errors, and gives examples of not inserting untrusted data in the allowed position, and HTML coding these data when inserting untrusted data between HTML tags, Before inserting untrusted data into common HTML attributes, encode HTML attributes; before inserting untrusted data into HTML JavaScript, encode JavaScript; before inserting untrusted data into HTML style attribute values, encode CSS; before inserting untrusted data into HTML URL attributes, encode URL; when using rich text, use XSS rule engine to encode and filter and other corresponding defense rules.
Explicit Image Caption Editing

Given an image and a reference caption, the image caption editing task aims to correct the misalignment errors and generate a refined caption. However, all existing caption editing works are implicit models, i.e., they directly produce the refined captions without explicit connections to the reference captions. In this paper, we introduce a new task: Explicit Caption Editing (ECE). ECE models explicitly generate a sequence of edit operations, and this edit operation sequence can translate the reference caption into a refined one. Compared to the implicit editing, ECE has multiple advantages: 1) Explainable: it can trace the whole editing path. 2) Editing Efficient: it only needs to modify a few words. 3) Human-like: it resembles the way that humans perform caption editing, and tries to keep original sentence structures. To solve this task, we propose the first ECE model: TIger. It is a non-autoregressive transformer-based model, consisting of three modules: Tagger $$_{\text {del}}$$ , Tagger $$_{\text {add}}$$ , and Inserter. Specifically, Tagger $$_{\text {del}}$$ decides whether each word should be preserved or not, Tagger $$_{\text {add}}$$ decides where to add new words, and Inserter predicts the specific word for adding. To further facilitate ECE research, we propose two ECE benchmarks by re-organizing two existing datasets, dubbed COCO-EE and Flickr30K-EE, respectively. Extensive ablations on both two benchmarks have demonstrated the effectiveness of TIger.
Optimal Search with Neural Networks: Challenges and Approaches

Work in machine learning has grown tremendously in the past years, but has had little to no impact on optimal search approaches. This paper looks at challenges in using deep learning as a part of optimal search, including what is feasible using current public frameworks, and what barriers exist for further adoption. The primary contribution of the paper is to show how to learn admissible heuristics through supervised learning from an existing heuristic. Several approaches are described, with the most successful approach being based on learning a heuristic as a classifier and then adjusting the quantile used with the classifier to ensure heuristic admissibility, which is required for optimal solutions. A secondary contribution is a description of the Batch A* algorithm, which can batch evaluations for more efficient use by the GPU. While ANNs can effectively learn heuristics that produce smaller search trees than alternate compression approaches, there still exists a time overhead when compared to efficient C++ implementations. This point of evaluation points out a challenge for future work.
Reconstructing a Spatial Field with an Autonomous Robot Under a Budget Constraint

In this paper we consider the information path-planning problem for a single robot in a stochastic environment with static obstacles subject to a preassigned constraint on the distance it can travel. Given a set of candidate sampling locations, the objective is to determine a path for the robot that allows to visit as many sampling locations as possible to accurately reconstruct an unknown underlying scalar field while not exceeding the assigned travel budget. Starting from the assumption that the phenomenon being measured can be modeled by a Gaussian Process, our algorithm balances exploration and exploitation to determine a sequence of locations ensuring that a preassigned final site is reached before the budget is consumed. Using mutual information as a reward criterion, as well as a generative model to predict consumed energy, the algorithm iteratively determines where to sample next, and when to end the mission. Our findings are validated in simulation in various scenarios and lead to a better reconstruction with less failures when compared with other methods.
Decentralized Access Control for Internet of Things Using Decentralized Identifiers and Multi-signature Smart Contracts

The number of connected devices is growing enormously every year, and with the rapid development of 5G technology, it is expected to grow even more. The Internet of Things (IoT) deals with different types of information in different applications, including a huge amount of sensitive and personal information, so IoT security and privacy is becoming a major concern. Most IoT devices interact not only with the owner, but also with various third parties and its heterogeneity makes authentication and access control a challenging task. A new concept such as Decentralized Identifier (DID), which is unified identity that does not require a centralized trust anchor, has been gaining a lot of interest in both academia and industry recently. In this paper, we propose decentralized access control framework for Internet of Things utilizing smart contracts and DIDs. In our framework access control is managed by decentralized oracles using multi-signature for agreements to overcome single point of failure problem.
A Community Detection-Based Blockchain Sharding Scheme

Sharding has been considered a promising approach to improving blockchain scalability. However, multiple shards result in a large number of cross-shard transactions, which require a long confirmation time across shards and thus restrain the scalability of sharded blockchains. In this paper, we convert the blockchain sharding challenge into a graph partitioning problem on undirected and weighted transaction graphs that capture transaction frequency between blockchain addresses. We propose a new sharding scheme using the community detection algorithm, where blockchain nodes in the same community frequently trade with each other. The detected communities are used as shards for node allocation. The proposed community detection-based sharding scheme is validated using public Ethereum transactions over one million blocks. The proposed community detection-based sharding scheme is able to reduce the ratio of cross-shard transactions from 80% to 20%, as compared to baseline random sharding schemes, and retain the ratio of around 20% over the examined one million blocks.
Multi-domain Cooperative Action Planning Strategy Based on Reinforcement Learning

With the continuous development of unmanned system, the multi-domain cooperation between multiple unmanned systems has become a hot spot in the current research due to its many advantages. Multiple action units with significant functional differences in different spatial domains such as land, sea, air and sky can achieve functional complementarity through information sharing and fusion, action coordination and cooperation, so as to enhance the ability to cope with complex environments and missions. In this paper, a multi-domain cooperative joint sea crossing and landing (island) scenario is designed, and a multi-agent deep deterministic policy gradient algorithm based on attention mechanism is proposed, which alleviates the problems of low exploration efficiency and slow convergence. The state space and reward function of the algorithm model are designed, and the simulation experiments are carried out to verify that the learned action unit has the ability of cooperative operation, which can make the combat process more intelligent.
The estimation of Poisson’s ratio by time-averaging and Cornu’s method for isotropic beams

Prior research suggests that direct static (e.g., uniaxial testing) and dynamic (e.g., ultrasonic wave propagation analysis) measurements differ in the estimation of Poisson’s ratio because anisotropies and heterogeneities in the sample material affect the two types of tests differently. Even assuming isotropic and homogeneous material properties, prior research further suggests that discrepancies between static/dynamic test results will exist because the error of the diagnostic techniques for the measurand are inherently different. Finally, thermodynamic effects are not present in static tests but can significantly affect dynamic test results. Given the potential for all these variables to produce discrepancies, it would be helpful to have the measurement of Poisson’s ratio obtainable from the same theory and experimental measurements by either static or dynamic testing methods. Our finite element calculations show that by combining time-averaged scanning digital holography with Cornu’s method, it is theoretically possible to estimate the effective Poisson’s ratio from the anticlastic contours at the antinode of the first out-of-plane bending mode shape. This is true regardless of frequency and therefore applicable for both static and dynamic measurements. Our results show that the estimate of Poisson’s ratio by Cornu’s method using data from simulations of mode shapes approaches the true value of Poisson’s ratio. In addition, our research suggests that beam geometry and boundary conditions are fundamental factors limiting the convergence of the estimate of Poisson’s ratio to the true value of Poisson’s ratio regardless of performing a static or dynamic test.
ABOUT THE ECONOMIC ESSENCE OF CRYPTOCURRENCY, OR IS BITCOIN MONEY

The article analyzes the correspondence of bitcoin, most common cryptocurrency, to the concept of "money". The analysis was carried out from the point of view of the possibility of explaining the origin of bitcoin from the standpoint of competing approaches (evolutionary, charter and debt theory) and the performance of bitcoin functions of money. It is concluded that despite the partial fulfillment of monetary functions, bitcoin is not a universal medium of exchange, i.e. money. Potentially it could reach such a state in the case if the state abandoned the monopoly on money and allowed free competition of all instruments of exchange, and bitcoin would win the competition from other possible applicants for the role of money.
High-temporal-resolution event-based vehicle detection and tracking

Event-based vision has been rapidly growing in recent years justified by the unique characteristics it presents such as its high temporal resolutions (~1us), high dynamic range (>120dB), and output latency of only a few microseconds. This work further explores a hybrid, multi-modal, approach for object detection and tracking that leverages state-of-the-art frame-based detectors complemented by hand-crafted event-based methods to improve the overall tracking performance with minimal computational overhead. The methods presented include event-based bounding box (BB) refinement that improves the precision of the resulting BBs, as well as a continuous event-based object detection method, to recover missed detections and generate inter-frame detections that enable a high-temporal-resolution tracking output. The advantages of these methods are quantitatively verified by an ablation study using the higher order tracking accuracy (HOTA) metric. Results show significant performance gains resembled by an improvement in the HOTA from 56.6%, using only frames, to 64.1% and 64.9%, for the event and edge-based mask configurations combined with the two methods proposed, at the baseline framerate of 24Hz. Likewise, incorporating these methods with the same configurations has improved HOTA from 52.5% to 63.1%, and from 51.3% to 60.2% at the high-temporal-resolution tracking rate of 384Hz. Finally, a validation experiment is conducted to analyze the real-world single-object tracking performance using high-speed LiDAR. Empirical evidence shows that our approaches provide significant advantages compared to using frame-based object detectors at the baseline framerate of 24Hz and higher tracking rates of up to 500Hz.
Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records‐based mortality prediction

The interpretability of machine learning models, even though with an excellent prediction performance, remains a challenge in practical applications. The model interpretability and variable importance for well-performed supervised machine learning models are investigated in this study. With the commonly accepted concept of odds ratio (OR), we propose a novel and computationally efficient Variable Importance evaluation framework based on the Personalized Odds Ratio (VIPOR). It is a model-agnostic interpretation method that can be used to evaluate variable importance both locally and globally. Locally, the variable importance is quantified by the personalized odds ratio (POR), which can account for subject heterogeneity in machine learning. Globally, we utilize a hierarchical tree to group the predictors into five groups: completely positive, completely negative, positive dominated, negative dominated, and neutral groups. The relative importance of predictors within each group is ranked based on different statistics of PORs across subjects for different application purposes. For illustration, we apply the proposed VIPOR method to interpreting a multilayer perceptron (MLP) model, which aims to predict the mortality of subarachnoid hemorrhage (SAH) patients using real-world electronic health records (EHR) data. We compare the important variables derived from MLP with other machine learning models, including tree-based models and the L1-regularized logistic regression model. The top importance variables are consistently identified by VIPOR across different prediction models. Comparisons with existing interpretation methods are also conducted and discussed based on publicly available data sets.
Weighted Multi-tree RRT Algorithm for Efficient Path-planning of Mobile Robots

Path-planning of autonomous mobile robots is very challenging for complex large-scene environments. Existing path-planning strategies often encounter problems such as excessive searching in local areas and producing a large number of useless nodes. To address this problem, this paper proposes a new iterative path planning algorithm for mobile robots via weighted multi-tree rapidly-exploring random trees (WMRRT) algorithm. Aiming at achieving progressive optimization and accelerating convergence, the node list of the first random tree is utilized to heuristically search for subsequent random trees. Combining with artificial potential field, the planning efficiency and collision-free success rate can be strengthened. The related path parameters can be flexibly configurated to meet different requirements. Several experiments are performed to verify the effectiveness under complex scenarios.
Fair detection of poisoning attacks in federated learning on non-i.i.d. data

Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentralized machine learning technique that aggregates partial models trained by clients on their own private data to obtain a global deep learning model. Even if FL provides stronger privacy guarantees to the participating clients than centralized learning collecting the clients’ data in a central server, FL is vulnerable to some attacks whereby malicious clients submit bad updates in order to prevent the model from converging or, more subtly, to introduce artificial bias in the classification (poisoning). Poisoning detection techniques compute statistics on the updates to identify malicious clients. A downside of anti-poisoning techniques is that they might lead to discriminate minority groups whose data are significantly and legitimately different from those of the majority of clients. This would not only be unfair, but would yield poorer models that would fail to capture the knowledge in the training data, especially when data are not independent and identically distributed (non-i.i.d.). In this work, we strive to strike a balance between fighting poisoning and accommodating diversity to help learning fairer and less discriminatory federated learning models. In this way, we forestall the exclusion of diverse clients while still ensuring detection of poisoning attacks. Empirical work on three data sets shows that employing our approach to tell legitimate from malicious updates produces models that are more accurate than those obtained with state-of-the-art poisoning detection techniques. Additionally, we explore the impact of our proposal on the performance of models on non-i.i.d local training data.
Countermeasure for Negative Impact of a Practical Source in Continuous-Variable Measurement-Device-Independent Quantum Key Distribution

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) can defend all attacks on the measurement devices fundamentally. Consequently, higher requirements are put forward for the source of CV-MDI QKD system. However, the imperfections of actual source brings practical security risks to the CV-MDI QKD system. Therefore, the characteristics of the realistic source must be controlled in real time to guarantee the practical security of the CV-MDI QKD system. Here we propose a countermeasure for negative impact introduced by the actual source in the CV-MDI QKD system based on one-time-calibration method, not only eliminating the loophole induced from the relative intensity noise (RIN) which is part of the source noise, but also modeling the source noise thus improving the performance. In particular, three cases in terms of whether the preparation noise of the practical sources are defined or not, where only one of the users or both two users operate monitoring on their respective source outputs, are investigated. The simulation results show that the estimated secret key rate without our proposed scheme are about 10.7 times higher than the realistic rate at 18 km transmission distance when the variance of RIN is only 0.4. What's worse, the difference becomes greater and greater with the increase of the variance of RIN. Thus, our proposed scheme makes sense in further completing the practical security of CV-MDI QKD system. In other words, our work enables CV-MDI QKD system not only to resist all attacks against detectors, but also to close the vulnerability caused by the actual source, thus making the scheme closer to practical security.