Reviewerly/exHarmony huggingface dataset:
A Joint Learning Model to Extract Entities and Relations for Chinese Literature Based on Self-Attention

Extracting structured information from massive and heterogeneous text is a hot research topic in the field of natural language processing. It includes two key technologies: named entity recognition (NER) and relation extraction (RE). However, previous NER models consider less about the influence of mutual attention between words in the text on the prediction of entity labels, and there is less research on how to more fully extract sentence information for relational classification. In addition, previous research treats NER and RE as a pipeline of two separated tasks, which neglects the connection between them, and is mainly focused on the English corpus. In this paper, based on the self-attention mechanism, bidirectional long short-term memory (BiLSTM) neural network and conditional random field (CRF) model, we put forth a Chinese NER method based on BiLSTM-Self-Attention-CRF and a RE method based on BiLSTM-Multilevel-Attention in the field of Chinese literature. In particular, considering the relationship between these two tasks in terms of word vector and context feature representation in the neural network model, we put forth a joint learning method for NER and RE tasks based on the same underlying module, which jointly updates the parameters of the shared module during the training of these two tasks. For performance evaluation, we make use of the largest Chinese data set containing these two tasks. Experimental results show that the proposed independently trained NER and RE models achieve better performance than all previous methods, and our joint NER-RE training model outperforms the independently-trained NER and RE model.
An Automatic Model Table Entity Alignment Framework for SG-CIM Model

SG-CIM model is a kind of unified data model for power grids designed based on the business scenarios and data requirements of the national grid. The model is mainly composed of three different types of model tables, which are mainly designed manually based on the business requirements of the grid. Therefore, to apply the SG-CIM model to the national grid business scenarios, it is necessary to achieve the unification of physical, logical and standard tables in the SG-CIM public data model. In order to reach the above goal, an effective entity alignment unification method needs to be found to achieve the task of unification of different knowledge maps. In this paper, we propose an automatic model table entity alignment framework for SG-CIM model entities. This entity alignment framework can effectively calculate the similarity and association degree between model table entities, thus helping to realize the comparison and alignment of entities between different knowledge maps. In addition, the entity alignment model base framework proposed in this paper also enriches the number of triplet entities used for alignment by using inference rules for triplet entities, which improves the learning effect of the framework proposed in this paper for triplet entities. In this paper, relevant experiments are designed to demonstrate that this model table entity alignment framework outperforms traditional entity alignment methods on actual graph data.
Building an Ontology for Cyber Defence Exercises

Over the course of recent years, the volume of cyber attacks has increased. The Cyber Defence Exercises (CDX) help organisations evaluate their security capacity by engaging in real-life cyber attack scenarios and improving the skills of the cybersecurity specialists and security measures to protect their critical systems from any potential cyber attacks. During such exercises, cybersecurity data is generated in structured and unstructured forms ranging from system-generated logs to situational reports. However, there is no united governance of all the generated data within the CDX context, which may lead to knowledge mismanagement. In this paper, we develop ontology-based structured knowledge management within the CDX environment to provide common understanding, integration, and sharing of data in this domain. The CDX ontology is developed with RDF and OWL languages assembling the data in an organised and machine-understandable format which will be very handy for computational linguistics. The validation of the ontology is conducted by ontology reasoning, competency questions, and, most importantly, expert evaluation. The CDX ontology passes the validation methods successfully and is thought to be a valuable asset for future research in its domain.
Hydrodynamic Performances of Aeronautical Propeller for Drones

Abstract Currently, several countries already invest on the application of drones for different scopes. These purposes are mainly civilian and military and some of these include the presence of water as search and rescue, inspection of pipelines, getting a bird’s eyes view over oil spills or structural integrity monitoring of bridges. For this reason, an interesting feature for drones is their ability to maneuver in fluids with different density as air and water. This research topic, shows strong scientific potentiality but it also represents a great challenge from scientific and technical view point: i) aeronautical propeller for drones in water must generate reverse thrust or braking force through an off-design rotation in off-design condition (different fluid’s density); ii) the propeller rotational velocity in water is generally one order of magnitude lower than the one for air application requiring the motors to be properly desing to meet such a wide operational range. In this paper, an experimental campaign aimed at the performance quantification of a three-bladed propeller for drone propulsion in water has been carried out in a towing tank. In particular, the generated forces are acquired through a load cell for different advance ratios J provided by varying both rotational regime and free stream velocity. The results show expected losses in the performance for the off-design rotation in terms of both thrust and efficiency. At low rotational speeds, higher values of efficiency are presented for small advance ratio. The maximum efficiency increase for higher RPS and it’s slightly influenced by small variation of propeller disk angle.
Generation of Decoy Signals Using Optical Amplifiers for a Plug-and-Play Quantum Key Distribution System

In most quantum key distribution (QKD) systems, a decoy-state protocol is implemented for preventing potential quantum attacks and higher mean photon rates. An optical intensity modulator attenuating the signal intensity is used to implement it in a QKD system adopting a one-way architecture. However, in the case of the plug-and-play (or two-way) architecture, there are technical issues, including random polarization of the input signal pulse and long-term stability. In this study, we propose a method for generating decoy pulses through amplification using an optical amplifier. The proposed scheme operates regardless of the input signal polarization. In addition, a circulator was added to adjust the signal intensity when the signal enters the input and exits the QKD transmitter by monitoring the intensity of the output signal pulse. It also helps to defend against Trojan horse attacks. A test setup for the proof-of-principle experiment was implemented and tested, and it was shown that the system operated stably with a quantum bit error rate (QBER) value of less than 5% over 26 h using a quantum channel (QC) of 25 km.
Modified Lightweight AES based on Replacement Table and Chaotic System

Cryptography is being utilized in a variety of applications to ensure data transmission and real-time communications are secure. Lightweight AES has long been accepted around the world and is used to secure sensitive data in a variety of businesses, but it has been plagued by slow processing and long data transfer times. This work proposes a way for changing the Lightweight Advance Encryption Standard (ML-AES) Algorithm by modifying two stages in the encryption section, the SubByte and ShiftRows, and the InvSubByte and InvShiftRows in the decryption part. The ML-AES achieved good results in achieving communication with high performance standards in terms of randomness, encryption time, storage space, and avalanche effects after testing the results. Based on NIST statistical testing against assaults, the ML-AES has good randomness to ciphertext. Also, (ML-AES) lowered the encryption time by 10% compared to the original AES, making it faster than the original AES. The suggested approach also performed well in terms of memory use, with a value of (54.36) for the ML-AES and a value of (54.36) for the original AES (66.23). Furthermore, the avalanche effects utilized to calculate diffusion property are (52.08%) for the ML-AES and (51.82%) for the original AES.
Using Web Quests in Professional Training Student-Managers

The article confirms the effectiveness of the use of gaming technologies in the preparation of students of economic specialties. The article presents the web quest game technology, which is positioned as a game form of educational activity that allows us to solve problematic problems of an applied nature. The advantages of using web quests as an effective form of learning have been substantiated. Their positive influence on the intensity of acquiring new knowledge, the compensatory (emotional) effect of information educational overload was confirmed. A comparative analysis of online services (vseosvita.ua, Learnis, Gennially) for creating web quests based on various criteria was carried out: functionality, design, the level of complexity of developing a quest for a teacher, the possibility of creative solutions, the availability of tools for reflection, the ability to use incentive tools. Recommendations for creating a web quest are described. The results of a pedagogical experiment on the use of a series of web quests in the study of the discipline "Electronic commerce" for college students are presented.
Anonymity test attacks and vulnerability indicators for the “Patient characteristics” disclosure in medical articles

In the field of Privacy-Preserving Data Publishing (PPDP), a privacy violation attack based on a bias in the ratio of sensitive attribute values of disclosed information is called a homogeneity attack, and l-diversity has been proposed as an indicator of this vulnerability. In medical articles, especially in clinical trial, the ratio of attribute values is disclosed as “patient characteristics” which include statistical information such as the number of hypertension patients and age distribution of the patient group subject to clinical research. The patient characteristics could also be vulnerable to homogeneity attack but have not been studied. In this paper, we propose three new attack methods similar to the homogeneity attack that violate the anonymity of patient characteristics. We also propose three new indicators similar to l-diversity to evaluate anonymity against such attacks. Experimental results show that our new attacks can point out that actual patient characteristics leaks patient information that should be kept confidential. And the results also show that the new proposed indicators can measure the vulnerability to such attacks.
Reviewing Estimates of Cybercrime Victimisation and Cyber Risk Likelihood

Across both the public and private sector, cyberse-curity decisions could be informed by estimates of the likelihood of different types of exploitation and the corresponding harms. Law enforcement should focus on investigating and disrupting those cybercrimes that are relatively more frequent, all else being equal. Similarly, firms should account for the likelihood of different forms of cyber incident when tailoring risk management policies. This paper reviews the quantitative evidence available for both cybercrime victimi-sation and cyber risk likelihood, providing a bridge between the academic fields of criminology and cybersecurity. We extract estimates from 48 studies conducted by a mix of academics, statistical institutes, and cybersecurity vendors using a range of data sources including victim surveys, case-control studies, and the insurance market. The victimisation estimates are categorised into: cyber attack; malware; ran-somware; fraudulent email; online banking fraud; online sales fraud; unauthorised access; Denial of Service; and identity theft. For each category, we display all estimates in the years 2017–2021. Our review shows: (i) firms face higher victimisation rates than individuals, which increases in the number of employees; (ii) global surveys reveal a consistent relative ranking of countries in ransomware victimisation; (iii) although trends could be identified within studies that collect longitudinal data, these trends tended to contradict each other when compared across studies; and (iv) broad categories with unclear consequences (e.g. malware and fraudulent emails) displayed higher variance and average values than categories associated with specific outcomes (e.g. identity theft or online banking fraud). We discuss the outlook for cybercrime and cyber risk research.
A Topology for Fingerprint Image Encryption based on HDWT-SVD and Hyperchaos

The topology of an encryption algorithm that offers a fast and robust security of fingerprint images as they transit in porous transmission channels is presented. Pixels of fingerprint images were mapped with sequences generated from a logistic chaotic map and shuffled bidirectionally, before they were diffused by structured sequences generated by a hyperchaotic system. The diffused pixels were subsequently decomposed using the singular value decomposition technique (SDV). The SVD- decomposed image was further decomposed using 2D Haar wavelet transforms. The wavelet-decomposed image and new sequences generated from the hyperchaotic system are processed via bitwise XOR operations, which resulted in the final encrypted image. Cryptographic analyses such as correlation analysis, key and plaintext sensitivity tests, and entropy test on fingerprint images showed that the algorithm is efficient and robust against cryptographic attacks.
Arduino based Quadcopter

UAVs (Unmanned Aerial Vehicles) are increasingly being used for all sorts of tasks, of which UAV video shooting is a famous example. In order to reduce the cost of buying an UAV, there is the possibility of making your own UAV. The aim of this work was to make an UAV operated by the Arduino as the main "brain" instead of a commercial flight controller. The successful and rather cheap construction of the Arduino UAV proved that everyone can have their own UAV with a little effort.
Capture Salient Historical Information: A Fast and Accurate Non-autoregressive Model for Multi-turn Spoken Language Understanding

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference facing the impatience of human users. Existing work increases inference speed by designing non-autoregressive models for single-turn SLU tasks but fails to apply to multi-turn SLU in confronting the dialogue history. The intuitive idea is to concatenate all historical utterances and utilize the non-autoregressive models directly. However, this approach seriously misses the salient historical information and suffers from the uncoordinated-slot problems. To overcome those shortcomings, we propose a novel model for multi-turn SLU named Salient History Attention with Layer-Refined Transformer (SHA-LRT), which comprises a SHA module, a Layer-Refined Mechanism (LRM), and a Slot Label Generation (SLG) task. SHA captures salient historical information for the current dialogue from both historical utterances and results via a well-designed history-attention mechanism. LRM predicts preliminary SLU results from Transformer’s middle states and utilizes them to guide the final prediction, and SLG obtains the sequential dependency information for the non-autoregressive encoder. Experiments on public datasets indicate that our model significantly improves multi-turn SLU performance (17.5% on Overall) with accelerating (nearly 15 times) the inference process over the state-of-the-art baseline as well as effective on the single-turn SLU tasks.
Information Retrieval System of Arabic Alphabetic Characters by Using Hidden Markov Model

In this paper, recognizing Arabic printed text is presented. A long time ago Recognizing the Arabic text has received great interest in information technology applications, as the Arabic language is among the different languages, and what distinguishes it from other languages is also shared by the Persian language and the ancient Turkish language (Ottoman) the difficulty of recognizing the letter because the letter has several forms in The beginning of the word and a shape in its middle and a shape at the end of the word. In languages other than the letters are written, the shape of the letter is one in the word, so it is easy to manipulate it through ready-made Optical Character Recognition (OCR) programs as letters A to Z. As a result, this study proposes a method for distinguishing Arabic printed text from other printed text. He's been looking forward to this moment for a long time. We demonstrate an off-line system that recognizes printed Arabic text by employing a Hidden Markov Model and an algorithm that divides the recognize reviews literature into sections and characters.
Teleoperation of Semi-autonomous Robots Through Uncertain Environments

Robot navigation tasks are usually decomposed into sequential sub-tasks, thus requiring a hierarchical design of sub-systems governed by multiple decision and control loops. In practice, people are the experts in making high-level decisions based on environmental knowledge. However, obtaining the local, real-time environment information is difficult by ourselves, especially in unreachable or dangerous environments. On the contrary, robots are good at achieving lower-level tasks such as building a cost map and detecting nearby obstacles. Therefore, teleoperation is used to coordinate strategic human brains and faithful robot sensors and actuators, providing an information-sharing protocol that helps to build a human-in-loop system. In this paper, we design a set of interactive Robot Operating System (ROS) based teleoperation windows for the operators to supervise the robot’s navigation through uncertain environments. Human operators will have the ability to modify the global cost map of the robot, which describes where obstacles are present in the robot’s environment. Currently, the cost map is constructed by a static map of the environment and image data from the onboard depth camera and is used to formulate the robot’s global path plan with Djikstra’s algorithm. With our enhancement, users can draw artificial walls around non-traversable areas and have the robot update its global path plan in real-time. The user can also switch to keyboard teleoperation mode to either stop the robot or adjust the robot’s orientation. Finally, the user can specify waypoints and perform waypoint-based navigation, where the robot is given a set of smaller destinations rather than one final destination. This teleoperation stack is verified by a navigation task in a manually set complex Gazebo simulation environment.
Face recognition with a hyperbolic metric classification model

Facial recognition systems are increasingly being used in smartphones as biometric security instead of passwords, or in airports as automated electronic passport control. It is also emerging in other forms of technology, for example in robotics. This creates large collections of photos that cannot be managed. Data mining tools and machine learning methods can be used to process these datasets and use them for prediction and classification. In such algorithms, using the most suitable distance metrics to define similarities among data has a crucial role. This paper investigates the usage of the Poincaré metric, which is used primarily in hyperbolic geometry, on the well-known k Nearest Neighbours classification algorithm. We applied this method to a database of face images. Our results indicate that the Poincaré metric is helpful with the Large Margin Nearest Neighbour learning (LMNN) method tested on an image dataset. We found that for small values of k, up to five, the algorithm using the Poincaré metric with the Edge filter gave the best results.
Disk Failure Prediction Based on SW-Disk Feature Engineering

With the rapid development of cloud storage, the number of disks has increased dramatically, and the absolute number of failed disks has also continued to increase. Since the impact of these failures on the quality of cloud services cannot be ignored, the demand for disk failure prediction is also increasing. In addition, the accuracy of the disk failure prediction algorithm largely depends on the input data set. Therefore, according to the large difference in the feature morphology in the disk tracking dataset, we analyze the change trend of features in different dimensions, and propose a new method. The SW-Disk feature engineering method for disk failure prediction constructs features from both horizontal and vertical dimensions. Including feature crossover between indicators and indicators to construct higher-order features. In the time dimension, the time series features under the multi-scale sliding window are constructed. We use the random forest prediction model to predict whether the disk will fail in the next 30 days, so as to reserve more time to take measures on the disk to achieve the purpose of improving the reliability of the cloud storage system. We validated our SW-Disk feature engineering method and prediction model on the Backblaze2019 disk dataset, and the experimental results demonstrate the effectiveness and superiority of our proposed method.
Comparative analysis of algorithms for estimating traffic parameters in corporate multiservice communication networks

Abstract Adequate management of modern corporate communication networks is possible if many control procedures function in near real time. In this case, the processed network monitoring data must have accurate characteristics sufficient for making objective management decisions. This fully applies to the data monitoring of network traffic parameters, which determines the relevance of the proposed work. The proposed in the paper algorithm for on-line estimation of traffic parameters in corporate multiservice communication networks is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. Its essence is that the estimation of traffic parameters is performed in two stages - in the first stage, we evaluate the forecast values of parameters, and in the second, with the next observations of random sequences, we make adjustments to their values. Traffic parameter values forecasts are constructed in a small-sized sliding window, and the adjustment is implemented on the basis of pseudo-gradient procedures whose parameters are adjusted using a fuzzy control algorithm based on the Takagi-Sugeno method. The proposed algorithm belongs to the class of adaptive algorithms with prior learning. The maximum value of the average relative error of estimation of traffic parameters was less than 8.2%, which is a sufficient value for the implementation of operational network management tasks. At the same time, the actual scientific and technical task is to analyze the comparison of the characteristics of the developed adaptive algorithm with the characteristics of the optimal algorithms, the characteristics of which are the maximum achievable. Translated with www.DeepL.com/Translator (free version). The results of a comparison of the proposed method with the optimal Coleman filtration (OKF) are presented.