Reviewerly/exHarmony huggingface dataset:
Verifying Classic McEliece: Examining the Role of Formal Methods in Post-Quantum Cryptography Standardisation

Developers of computer-aided cryptographic tools are optimistic that formal methods will become a vital part of developing new cryptographic systems. We study the use of such tools to specify and verify the implementation of Classic McEliece, one of the code-based cryptography candidates in the fourth round of the NIST Post-Quantum standardisation Process. From our case study we draw conclusions about the practical applicability of these methods to the development of novel cryptography.
Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems

The reptile search algorithm (RSA) is a bionic algorithm proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, the encircling stage includes high walking and belly walking, and the hunting stage includes hunting coordination and cooperation. However, in the middle and later stages of the iteration, most search agents will move towards the optimal solution. However, if the optimal solution falls into local optimum, the population will fall into stagnation. Therefore, RSA cannot converge when solving complex problems. To enable RSA to solve more problems, this paper proposes a multi-hunting coordination strategy by combining Lagrange interpolation and teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation strategy will make multiple search agents coordinate with each other. Compared with the hunting cooperation strategy in the original RSA, the multi-hunting cooperation strategy has been greatly improved RSA's global capability. Moreover, considering RSA's weak ability to jump out of the local optimum in the middle and later stages, this paper adds the Lens pposition-based learning (LOBL) and restart strategy. Based on the above strategy, a modified reptile search algorithm with a multi-hunting coordination strategy (MRSA) is proposed. To verify the above strategies' effectiveness for RSA, 23 benchmark and CEC2020 functions were used to test MRSA's performance. In addition, MRSA's solutions to six engineering problems reflected MRSA's engineering applicability. It can be seen from the experiment that MRSA has better performance in solving test functions and engineering problems.
Effective Text Processing utilizing NLP

Summarizing is the practice of condensing a body of material into a more manageable size while retaining all of the key data elements and the intended meaning. Automatic text summarizing systems can now quickly retrieve summary phrases from input documents. However, it has a number of shortcomings, such as duplication, insufficient coverage, incorrect extraction of key lines, and poor sentence coherence. In this study, a new concept of summarizer technique is proposed using the Python spacy package. It extracts the most significant information from the text. The scoring system is also used to compute the score for the words in order to determine the word frequency. The findings show that the proposed method completes the summary process faster than the current algorithm. An online tool called the text to summary converter aids in material summarizing. This programmer will give us a summary of the data that we upload. The primary goal is to accurately summaries the data entered. The most crucial sentences will be removed before the unnecessary ones.
Peran Orang Tua dalam Meningkatkan Kemampuan Membaca Permulaan di Sekolah Dasar

As a language skill, reading is a complex thing to master. A person's reading ability is passed through the stages of initial reading and advanced reading. Beginning reading is given to students I and II while continuing reading is given to students in grades III, IV, V, and VI. Beginning reading ability is the competency possessed by students in reciting symbols in a clear and precise voice. Therefore, beginning reading becomes something that is important for students to master before acquiring/learning advanced reading. Beginning reading mastery is strongly influenced by how the teacher is able to develop various approaches, methods and strategies used in teaching beginning reading.&#x0D; Keywords; Role, Teacher, Parents, Beginning Reading Ability, Elementary School
Distributed Maximum Correntropy Cubature Information Filtering for Tracking Unmanned Aerial Vehicle

Aiming at improving the performance of tracking an unmanned aerial vehicle (UAV) on the battlefield, this article focuses on the algorithm involving tracking the maneuvering target with a distributed sensor network under non-Gaussian measurement noise. A novel distributed maximum correntropy cubature information filtering (DMCCIF) based on interactive multiple model (IMM) is proposed. Taking advantage of correntropy, we design maximum correntropy cubature information filtering (MCCIF) for each node to estimate the target state under non-Gaussian measurement noise. Then, distributed information fusion based on weighted average consensus is conducted to improve the stability of the sensor network. After that, the information pair is changed so that a distributed state estimation (DSE) algorithm based on IMM is developed to increase the reliability of the maneuvering target tracking. Simulation results and comparison with other algorithms in three typical non-Gaussian measurement noise scenarios are given to evaluate the effectiveness of the proposed algorithm.
Memristor-Based Hyperchaotic System and DNA Encoding Based Image Encryption Application on LabVIEW

With the advancement of technology, the growth of multimedia and communication tools has sped up data transfer, and guaranteeing image security has become a crucial concern, particularly during the transmission and storage of images. So, when images are sent over a public network, they should be encrypted before being sent to the receiving part. In this study, a memristor-based encryption system with Deoxyribonucleic acid (DNA) coding is proposed on the LabVIEW platform to ensure information security. Firstly, memrsitor based hyperchaotic system is used for chaotic sequence. The images are encrypted using the DNA and XOR arithmetic process on the LabVIEW platform. A memristor-based hyperchaotic system and the combination of techniques used are aimed at encrypting the image securely. In addition, security tests; histogram analysis, correlation analysis, differential attack, and entropy analysis, are performed on the proposed system and the results are presented. The aforementioned methods are thoroughly examined and tested to determine their efficacy. It has been determined that the proposed encryption schemes are effective and can therefore be used in real-time applications.
Evolution of Quantum Machine Learning and an Attempt of Its Application for SDN Intrusion Detection

Even though Quantum Computers are still in their developmental phases, their technological implementation regarding their integration with classical computations is showing fascinating results around the world. Quantum Computers when compared with Classical Computational devices can be interpreted as a ‘candle and light bulb’, two different things accomplishing the same motive. Quantum Computers can provide us with tremendous processing power, which was unforeseen and has led us to question the ways in which they can transform existing technologies even raising concerns for current cryptic methods. Quantum systems have shown great potential in several fields one such is Quantum Machine Learning (QML) which even though a considerably novel field has benefitted from the integration of Quantum algorithms and Machine Learning algorithms, presenting exceptional results from various results. Scientists at Google were able to proclaim Quantum supremacy, this presented the Quantum computer's ability to perform extensively large computations in an extremely short time compared to a Super Computer, this could be beneficial for Machine learning algorithms to process huge amounts of data. Recent usage of a VPN for teleconferencing and a “work from home” scheme during the pandemic has caused a huge surge in network traffic forcing IT infrastructure providers to switch toward Software-Defined Network (SDN), Software Defined Network (SDN) is a prominent technology to provide betterment to the traditional network architecture. Machine Learning and Artificial Intelligence have been used extensively for various aspects of SDN, hence we attempted to explore QML and SDN interactions to assess their ability and the benefits we can achieve from this integration. This manuscript attempts to provide insights on the developments of QML over time and experimenting with SDN to provide a robust and efficient SDN system.
Quantum-Enhanced DC Optimal Power Flow

Harrow-Hassidim-Lloyd (HHL) is a quantum computation algorithm that can solve a system of linear equations in a logarithmic time scale; hence, an exponential speed-up over best-known classical computers, which have polynomial time complexity, is accessible. This paper studies the integration of HHL into the DC optimal power flow problem. Optimality conditions are formulated as a system of equations in the form of $Ax=b$ that is solved by HHL. The performance of HHL for A-matrices appearing in DCOPF problems is discussed. The initialization of HHL for encoding data from classical data to a quantum state in an efficient manner is also investigated. The impact of HHL calculation error on DCOPF convergence and solution accuracy is analyzed. Qiskit module is used to test the HHL-based DCOPF problem on a 3-bus system. The impact of quantum computers and HHL errors on DCOPF is simulated for the IEEE 14- bus system.
Attention Based BiGRU-2DCNN with Hunger Game Search Technique for Low-Resource Document-Level Sentiment Classification

Extracting features with deep learning models recorded considerable performance in classifying various sentiments. However, modeling lengthy sentences from low-resource document-level datasets, and examining the semantic relationships between these sentences is challenging. Also, recent document representation modules deployed rarely distinguish between the significance of various sentiment features from general features. To solve these challenges, we exploit a neural network-based feature extraction technique. The technique exploits an attention-based two-layer bidirectional gated recurrent unit with a two-dimension convolutional neural network (AttnBiGRU-2DCNN). Specifically, the Bi-GRU layers extract compositional semantics of the low resource document. These layers generate the semantic feature vector of the sentence and present the documents in a matrix form with a time-step dimension and a feature dimension. Due to the high dimensional multiple features generated, we propose a Hunger Games Search Algorithm to select essential features and subdue unnecessary features to increase classification accuracy. Extensive experiments on two low-resourced datasets indicate that the proposed method captures low-resource sentimental relations and also outperformed known state-of-the-art approaches.
An Exploratory Analysis of GSDMM and BERTopic on Short Text Topic Modelling

Topic models may be a useful tool for locating latent subjects in collections of documents. Short text clustering has become a more important task as social networking sites like Twitter have gained popularity. Short text is characterised by high sparsity, high-dimensionality, and large-volume. These characteristics are challenging to overcome. Two of the most well-known short text modelling algorithms are BERTopic and the Gibbs Sampling Dirichlet Multinomial Mixture Model (GSDMM). GSDMM is a topic model which can infer the count of topic clusters automatically with a good compromise between the fullness and uniformity of the clustering results, and is fast to converge. BERTopic is a neural topic model that extracts coherent topic representations based on the semantic similarity of words and phrases in the and clustering with the help of a class-based form of TF-IDF. We compare these two algorithms in this paper to determine which model is more effective in short text topic modelling.
Shadow detection and removal of road images with maximum extraction of road marker using multi-colour-space

Road markers provide vital information ensuring safe vehicle manoeuvring on the road. Illumination condition, especially shadow, is the major contributor to the false detection of road markers. The shadow that falls on the road marker should be removed to reduce the error in abstracting road marker information. To tackle the adverse effect caused by shadows on the road marker, this paper attempts to detect shadows with image averaging from three different colour spaces, Saturation (S), Blue-difference (Cb) and Blue/Yellow Value (B), respectively, from HSV, YCbCr and LAB colour spaces. The combined data increases the accuracy of detecting shadow pixels for removal. The accuracy of the proposed algorithm reached 98.69%, with good feasibility and adaptability to perform in various road environments.
CFedPer: Clustered Federated Learning with Two-Stages Optimization for Personalization

Federated learning(FL) is a privacy-preserving dis-tributed learning paradigm in which clients cooperate with each other to train a global model. It is becoming progressively prevalent with the rapid development of edge devices. A critical challenge in federated learning is the data heterogeneity among clients, resulting in the global model generated by standard federated learning being unable to be adapted to all clients. To tackle this problem, we propose the CFedPer for personalized FL, which generates a personalized model for each cluster after clustering to address the deficiency of standard federated learning. Our algorithm is organized into two optimization phases. The pre-start phase clusters clients by our proposed similarity-based clustering model using distribution vector and similarity matrix. In the in-training phase, we represent the neural network as the base layer and personalization layer and propose a novel optimization objective with a regularization term for the personalization layer to achieve a balance between per-sonalization and generalization, preventing over-personalization. Extensive experiments on various datasets and data distributions indicate that the performance of our algorithm is superior to the existing algorithms in terms of average local accuracy and variance among clients.
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints

Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their privacy preserved. However, federated learning systems are vulnerable to Byzantine attacks from malicious participants, who can upload carefully crafted local model updates to degrade the quality of the global model and even leave a backdoor. While this problem has received significant attention recently, current defensive schemes heavily rely on various assumptions, such as a fixed Byzantine model, availability of participants' local data, minority attackers, IID data distribution, etc. To relax those constraints, this paper presents Robust-FL, the first prediction-based Byzantine-robust federated learning scheme where none of the assumptions is leveraged. The core idea of the Robust-FL is exploiting historical global model to construct an estimator based on which the local models will be filtered through similarity detection. We then cluster local models to adaptively adjust the acceptable differences between the local models and the estimator such that Byzantine users can be identified. Extensive experiments over different datasets show that our approach achieves the following advantages simultaneously: (i) independence of participants' local data, (ii) tolerance of majority attackers, (iii) generalization to variable Byzantine model.
Anomaly Detection Through Unsupervised Federated Learning

Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine learning model while keeping the data decentralized. The explosive growth of interest in the topic has led to rapid advancements in several core aspects like communication efficiency, handling non-IID data, privacy, and security capabilities. However, the majority of FL works only deal with supervised tasks, assuming that clients' training sets are labeled. To leverage the enormous unlabeled data on distributed edge devices, in this paper, we aim to extend the FL paradigm to unsupervised tasks by addressing the problem of anomaly detection (AD) in decentralized settings. In particular, we propose a novel method in which, through a preprocessing phase, clients are grouped into communities, each having similar majority (i.e., inlier) patterns. Subsequently, each community of clients trains the same anomaly detection model (i.e., autoencoders) in a federated fashion. The resulting model is then shared and used to detect anomalies within the clients of the same community that joined the corresponding federated process. Experiments show that our method is robust, and it can detect communities consistent with the ideal partitioning in which groups of clients having the same inlier patterns are known. Furthermore, the performance is significantly better than those in which clients train models exclusively on local data and comparable with federated models of ideal communities' partition.
Node Selection Strategy Design Based on Reputation Mechanism for Hierarchical Federated Learning

With the rapid development of Internet of Things (IoT) and 5G wireless communication technology, a large amount of data is generated at the edge of the network. The combination of mobile edge computing (MEC) and federated learning has become a key technology to improve performance and protect users' privacy data in mobile networks. The selection of nodes for Hierarchical Federated Learning (HFL) affects the quality of model training. In this paper, we investigate the optimization problem of node selection accuracy in HFL. In order to improve the quality of model training, we design an algorithm of node selection based on reputation (NSRA). In NSRA, the edge server selects the node with high reputation prediction value to participate in the model training, and the node selects the neighbor node with high transmission capacity to cooperate. D2D communication is adopted for node cooperation. Through extensive simulations, it is verified the performance of NSRA. The mutual trust between nodes is enhanced, so the ideal prediction effect is achieved. We also observe that compared with RSA, the accuracy is improved by 11.48% and 19.38% in MNIST and CIFAR-10, respectively.
An Efficient Face Recognition Method Based on CNN

As a prevailing research in artificial intelligence, the application of computer vision is widely used in many fields which are closely related to people's livelihood, such as industrial automation, new retail industry, smart transportation and security monitoring. And the proposed face recognition method is a branch in the field of computer vision, it integrates neural networks, biology, image signal processing, machine learning and other fields, which promote research and cross-development among different disciplines. Hence, this paper focuses on face recognition method by using convolutional neural network(CNN), and CNN has the property of "weight sharing", which has been widely popularized in image recognition, it can greatly simplify the work of large-scale network training. The experiments demonstrate that the proposed face recognition method is successful, and the accuracy of the proposed method can be as high as 98%.
Research on the Innovation System of Computer Artificial Intelligence Technology in Museum Financial Management System

This paper designs a museum financial management system based on computer artificial intelligence technology. The system introduces RPA technology to automatically collect, identify and classify image data of image recognition, speech recognition and original documents to help improve the efficiency of voucher entry and audit. This paper focuses on the general ledger management certificate of museum financial sharing service center to optimize the design of financial process automation. The system realizes the concentration and integration of business and financial activities through the process reengineering of financial fund settlement, accounting, budget preparation and financial analysis of the museum, so as to improve the quality of financial information disclosure, budget management and internal control efficiency of the museum.
Semantic Role Labeling for Amharic Text Using Multiple Embeddings and Deep Neural Network

Amharic is morphologically complex and under-resourced language, posing difficulties in the development of natural language processing applications.This paper presents the development of semantic role labeler for Amharic text using end-to-end deep neural network architecture.The system implicitly captures morphological, semantic and contextual features of a word at different levels of the architecture, and incorporates the syntactic structure of an input sentence.The proposed neural network architecture has four core layers from bottom to top, namely non-contextual word embedding, contextual word embedding, fully connected and sequence decoding layers.The non-contextual word embedding layer is formed from the concatenation of character-based, word-based and sentence-based word embeddings.This layer captures the morphological and semantic features of a given word by making use of BiLSTM recurrent neural network.At the contextual word embedding layer, a context sensitive embedding of a word is generated by applying a new LSTM layer on the top of the non-contextual concatenated word embedding layer.A fully connected network layer is added on top of contextual word embedding layer to supplement it by extracting dependencies among training samples in the corpus.At the sequence decoding layer, a sequence of semantic role labels is predicted using a linear-chain conditional random field algorithm by capturing the dependency among semantic role labels.In addition to the four core layers, the architecture has dropout layers to prevent overfitting problem.The proposed system achieves 94.96% accuracy and 81.2% F1 score when it is tested using test data.
Semi-Supervised Learning Based on Reference Model for Low-resource TTS

Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparable performance under low-resource conditions. To ad-dress this issue, we propose a semi-supervised learning method for neural TTS in which labeled target data is limited, which can also resolve the problem of exposure bias in the previous auto-regressive models. Specifically, we pre-train the reference model based on Fastspeech2 with much source data, fine-tuned on a limited target dataset. Meanwhile, pseudo labels generated by the original reference model are used to guide the fine-tuned model's training further, achieve a regularization effect, and reduce the overfitting of the fine-tuned model during training on the limited target data. Experimental results show that our proposed semi-supervised learning scheme with limited target data significantly improves the voice quality for test data to achieve naturalness and robustness in speech synthesis.