Reviewerly/exHarmony huggingface dataset:
An Improved GWO Algorithm for Data Clustering

Grey wolf optimization (GWO) is one among the most promising swarm intelligence based nature inspired meta-heuristic algorithm that improves its search process by mimicking the search for prey and attacking strategy of grey wolfs. To further improve its performance, here we have hybridized with Jaya algorithm that improves the exploration capability and hence maintains a trade between exploitation and exploration. An extensive simulation work is carried out to make a comparative analysis of our proposed method with respect to original GWO algorithm and three other meta-heuristic based clustering algorithms such as JAYA, PSO and ALO considering Accuracy, Sensitivity, Specificity and F-score performance measures. The proposed method is used to cluster each dataset taken from UCI machine learning repositories and the experiment is conducted for total 12 datasets separately. The statistical test of the proposed model is conducted by performing Friedman and Nemenyi hypothesis test and Duncan’s multiple test. The obtained results from the statistical test show the superiority of our proposed method with respect to other meta-heuristic based clustering methods.
Synthetic Data Generation with Python

In this chapter, we will explore how to generate synthetic data for regression, classification, and clustering problems using Python. First, we will discuss how to generate synthetic data from a known distribution. Next, we will apply Gaussian noise to a regression model. Then, we will discuss how to generate synthetic data for classification and clustering problems using the Friedman functions and symbolic regression. Finally, we will generate synthetic data for tabular data using GANs.
Toward Secure VMs Allocation: Analysis of VMs Allocation Behaviours in the Cloud Computing Environments

Side-channel attacks (SCAs) is a potential threat in cloud computing environments (CCEs) as it allows the malicious VMs to capture private information from the target VMs when they share the same PM. This malicious co-residency of VMs is an outcome of the VMs allocation algorithm behaviour, which is responsible for allocating the VMs to a specific PM based on defined allocation objectives. Earlier studies tackled the SCAs, through specific solutions, by focusing on either formulating VMs allocation algorithms or modifying the architecture of the CCEs to mitigate the threats of SCAs. However, most of them are oriented to specific situations and assumptions, leading to malicious co-residency when applied to other scopes or situations. In this paper, we presented the solution from a different holistic perspective by examining the allocation behaviours of different algorithms and other properties that affect and lead to obtaining a secure VMs allocation. The examinations are performed under different scenarios and structures for each behaviour to understand the possible situations that lead to secure VMs allocation. In addition, we develop a deterministic security-aware VMs allocation algorithm that aims to allocate the VMs securely to reduce the potential threats from malicious co-residency in CCEs.
Decalcification of Bony and Hard Tissue for Histopathology Processing

This chapter discusses the method of decalcification of bony and hard tissue for histopathology processing. The Decalcification technique helps to remove calcium salt from the tissue without affecting the morphology of the tissue and staining. Removal of calcium salt can be done with the help of acid, chelating agents, ion-exchange resin and electrolysis method. The relative advantages and disadvantages of various decalcification methods have also been discussed in this chapter. The endpoint of the decalcification process is determined by radiographic, chemical, and physical methods. Any successful decalcification of tissue depends on the assessment of the consistency of the tissue, the volume of the tissue, proper washing and fixation of tissue. In addition, the correct selection of the decalcifying agent and exact determination of the endpoint of the decalcification process is also essential.
Design and FPGA Realization of an Energy Efficient Artificial Neural Modular Exponentiation Architecture

Modular arithmetic computations are used widely in various data security and reliability techniques. Information security systems certainly benefit from the design of energy-efficient modular exponentiation architectures. The use of low-power logic adders to realize modular exponentiation operations is very essential in prospective cryptography contexts. In this paper, various full adder circuit designs are presented which are used in developing an energy efficient modular exponentiation architecture. Here, the full adder is designed using Register Transfer Level (RTL), Standard Logic Cell (SLC), Reversible Logic Gate (RLG), and Artificial Neural Network (ANN) logic methods. All full adder designs are imposed on modular exponentiation circuit to analyze performance metrics in terms of dynamic power dissipation, Figure of Merit, and Energy Delay Product. The Modular Exponentiation architecture is designed based on the above full adders and is simulated and synthesized using Xilinx Vivado Zynq-7000 family configurable device. From the synthesis results, the dynamic power dissipation, Figure of Merit (FOM), and Energy Delay Product (EDP) of the ANN Modular Exponentiation circuit shows an improvement compared to other designs. The total power consumption, dynamic power dissipation, FOM, and EDP of ANN Full Adder and Modular Exponentiation circuit can achieve (8%, 16%), (23.5%, 20.7%), (14.7%, 14%), and (28.5%, 16%) compared to RLG Full adder and Modular Exponentiation circuit.
Granular Access Control of Smart Contract Using Hyperledger Framework

The blockchain is an emerging technology and is used in various applications for data security and trustworthiness. One fundamental technique of blockchain is that any changes incorporated by the authorized user in the system have been inserted into the transaction and would be traceable. The smart contract access control mechanisms have gained considerable attention since its applications. However, there are no systematic efforts to analyze existing empirical evidence. To this end, we aim to synthesize literature to understand the state-of-the-art of smart contract-driven access control mechanisms concerning underlying platforms, utilized blockchain properties, nature of the models, and associated test beds and tools. The attribute-based access control access rights are granted to users by evaluating suitable attributes. An essential aspect of access control is to preserve the user’s identity accessing a service. This paper’s objective is to propose an access control mechanism of smart contracts to ensure that only authorized users can access the authorized component of the object in the Hyperledger framework.
Multi-client Private Decision Tree Classification Using Threshold Fully Homomorphic Encryption

Decision tree is a widely-used machine-learning classifier known for its simplicity and effectiveness. Recent works mainly focus on how to privately evaluate a decision tree in a two-party setting where the sensitive data of the client or the decision tree model of the server is kept secret from another party. However, collaboration among mutually untrusted clients for machine learning tasks is needed more and more. In this paper, we consider a mutli-client private decision tree classification protocol, in which one server holds the decision tree and multiple clients provide secret data. At the end of the protocol, either the clients or the server could learn the classification result while protect privacy of each individual input. More importantly, we would also minimize the interaction with each client. To reach the goal, we make use of the threshold fully homomorphic encryption. Our protocols are proven secure against “honest-but-lazy” adversaries. Furthermore, we try to improve the computation overhead and ciphertext size, making our construction much more efficient.
Techniques to Reject Atypical Patterns

Supervised Classification algorithms are only trained to recognize and classify certain patterns, those contained in the training group. Therefore, these will by default, classify the unknown patterns incorrectly, causing unwanted results. This work proposes several solutions, to make the referred algorithms capable of detecting unknown patterns. The main approach for the development of models capable of recognizing these patterns, was the use of three different models of Autoencoders: Simple Autoencoder (SAE), Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE), that are a specific type of Neural Networks. After carrying out several tests on each of the three models of Autoencoders, it was possible to determine which one performed best the task of detecting/rejecting atypical patterns. Afterwards, the performance of the best Autoencoder was compared to the performance of a Convolutional Neural Network (CNN) in the execution of the referred task. The conclusion was that the VAE effectively detected atypical patterns better than the CNN. Some conventional Machine Learning techniques (Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR)) were also tested. The one that presented the best performance was the RF classifier, achieving an accuracy of 75% in the detection of atypical/typical patterns. Thus, regarding the classification balance between atypical and typical patterns, Machine Learning techniques were not enough to surpass the Deep Learning methods, where the best accuracy reached 88% for the VAE.
Towards Self-monitoring Enclaves: Side-Channel Detection Using Performance Counters

Trusted execution environments like Intel SGX allow developers to protect sensitive code in so-called enclaves. These enclaves protect their code and data even in the cases of a compromised OS. However, such enclaves have also been shown to be vulnerable to numerous side-channel attacks. In this paper we propose an idea of self-monitoring enclaves and investigate the viability of using performance counters to detect a side-channel attacks against Intel SGX, specifically the Load Value Injection (LVI) class of attacks. We characterize the footprint of three LVI attack variants and design a prototype detection mechanism. The results show that certain attack variants could be reliably detected using this approach without false positives for a range of benign applications. The results also demonstrate reasonable levels of speed and overhead for the detection mechanism. Finally, we list four requirements for making self-monitoring based on such a detection mechanism feasible and point out that three of them are not satisfied in Intel SGX.
Optimal Sample Size Calculation for Machine Learning Based Analysis for Suicide Ideation

In the field of Machine Learning and Artificial Intelligence the dataset size and handling plays a vital role in determining the final outcome and prediction accuracy. Since most of the data collection methodology are automated and processing these data are becoming more robust and efficient, the size of data collected for prediction accuracy has become a matter of less important. This has given rise to the field of big data analysis. However, in the field of medical studies and psychological evaluation the data has to be physical collected by a human being and collated in a form which is machine interpretable. This step of manual interaction for data collection cannot be obviated. When compared to automated data collection mechanism, the dataset size is not a significant factor. However, in the case of manual data collection, the optimal size of the data to yield the best machine learning assisted prediction becomes prime importance. All data collection mechanism for medical and psychological studies have been analysed using statistical tool and no study have shown or brought out the optimal dataset requirement to yield the best results when analysing this collected data via machine learning algorithms. In this paper, we look into the domain of optimising the data size so that we can minimise the data collection time without compromising the desired accuracy result. We will draw our inspiration from various medical data collection methodology and build upon the same to work out an optimum data size for analysis through various machine learning algorithm.
MACEDONIZER - The Macedonian Transformer Language Model

Contextualized language models are becoming omnipresent in the field of Natural Language Processing (NLP). Their learning representation capabilities show dominant results in almost all downstream NLP tasks. The main challenge that low-resource languages face is the lack of language-specific language models since their pre-training process requires high-computing capabilities and rich resources of textual data. This paper describes our efforts to pre-train the first contextual language model in the Macedonian language (MACEDONIZER), pre-trained on a 6.5 GB corpus of Macedonian texts crawled from public web domains and Wikipedia. Next, we evaluate the pre-trained version of the model on three different downstream tasks: Sentiment Analysis (SA), Natural Language Inference (NLI) and Named Entity Recognition (NER). The evaluation results are compared to the cross-lingual version of the RoBERTa model - XML-RoBERTa. The results show that MACEDONIZER achieves state-of-the-art results in all downstream tasks. Finally, the pre-trained version of the MACEDONIZER is made for free usage and further task-specific fine-tuning via HuggingFace.
A Property of a Quasigroup Based Code for Error Detection

Due to the importance of the accuracy of the transmitted data, codes for error-control play important role in the digital communications. While codes for error correction are more robust and slower, the codes for error detection work faster and therefore are more suitable in fast networks with small probability of errors. In the recent years, few quasigroup based codes for error detection have been developed. In this paper is consider one such quasigroup based code for error detection that is defined previously. The probability that there will be errors that the code will not detect and the number of surely detected incorrectly transmitted bits are already obtained. Since the code has good error-detecting capabilities, it deserves to be further analyzed. Now, in this paper, using simulations, it will be shown that when a quasigroup from a given set of order 4 quasigroups is used for coding, the code has the property of always detecting errors in transmission in the codewords in which the number of bits that are not correctly transmitted is odd number.
Super-Resolution Face Recognition: An Approach Using Generative Adversarial Networks and Joint-Learn

Face Recognition is a challenging task present in different applications and systems. An existing challenge is to recognize faces when imaging conditions are adverse, for example when images come from low-quality cameras or when the subject and the camera are far apart, thus impacting the accuracy of these recognizing systems. Super-Resolution techniques can be used to improve both image resolution and quality, hopefully improving the accuracy of the face recognition task. Among these techniques, the actual state-of-the-art uses Generative Adversarial Networks. One promising option is to train Super-Resolution and Face Recognition as one single network, conducting the network to learn super resolution features that will improve its capability when recognizing faces. In the present work, we trained a super resolution face recognition model using a jointly-learn approach, combining a generative network for super resolution and a ResNet50 for Face Recognition. The model was trained with a discriminator network, following the generative adversarial training. The images generated by the network were convincing, but we could not converge the face recognition model. We hope that our contributions could help future works on this topic. Code is publicly available at https://github.com/OliRafa/SRFR-GAN .
Integrated NooJ Environment for Arabic Linguistic Disambiguation Improvement Using MWEs

Language resources are a necessary component to language Development in NLP. They are useful for any empirical language study including linguistic analysis, language translation and language disambiguation. The linguistic development environment NooJ ( http://www.nooj4nlp.net/ ) allow formalizing complex linguistic phenomena such as compound words generation, processing as well as analysis. NooJ offers the possibility to use the dynamic library NoojEngine.dll or the command-line program: noojapply.exe. In this study, we will take advantage of the noojapply.exe program that is freely available in the Standard edition of NooJ. Noojapply.exe allows users to apply dictionaries and grammars automatically to texts from external environments. In this paper, we introduce a module for Arabic MWEs recognition that is based on rules grammar. MWEs module allows recognizing several types of morphosyntactic variations that can occur to a Multi Word Expression. Then, these linguistic resources are compiled to be used as parameters in the command-line noojapply.exe in order to be integrated within an Arabic language processing environment for linguistic disambiguation. Our work is divided into three sections. First, we deal with a literature review on disambiguation tasks in the Arabic language. Then, we give a detailed description of our Integrated NooJ environment for Arabic linguistic disambiguation and the associated grammars. Finally, a set of tests and experiments is carried out to measure the impact of multi- word expression recognition in Word disambiguation.
Spatio-Temporal United Memory for Video Anomaly Detection

Video anomaly detection aims to identify the anomalous that do not conform to normal behavior. The abnormal events tend to relate to appearance and motion, in which there are considerable difference in each other. From the perspective of philosophy, “act according to circumstances”, we propose a dual-flow network to dissociate appearance information and motion information, processing these information in two individual branches. In addition, we employ a spatio-temporal united memory module to bridge the relationship between appearance and motion, since there is another saying in philosophy that “The things are universal and interact with each other”, thus the hidden relationship in them also ought to be utilized as a useful clue for anomaly detection. To the best of our knowledge, this is the first work to detect anomalies with a spatio-temporal united memory. The model is able to achieve AUC 96.92%, 87.43%, and 75.42% on UCSD Ped2, Avenue, and ShanghaiTech, respectively. Extensive experiments on three publicly available datasets demonstrate the excellent generalization and high effectiveness of the proposed method.
Local Differential Privacy for Private Construction of Classification Algorithms

In recent years, Local differential privacy (LDP), as a strong privacy preserving methodology, has been widely deployed in real world applications. It allows the users to perturb their data locally on their own devices before being sent out for analysis. In particular, LDP serves as an effective solution for the construction of privacy-preserving classifiers. While several approaches in the literature have been proposed to build classifiers over distributed locally differential private data, an understanding of the difference in the performance of these LDP-based classifiers is currently missing. In this study, we investigate the impact of using LDP on four well-known classifiers, i.e., Naïve Bayes, Decision Tree, Random Forest, and Logistic Regression classifiers. We evaluate the impact of dataset’s properties, LDP mechanisms, privacy budget, and classifiers’ structure on LDP-based classifiers’ performance.
Towards Data Governance for Federated Machine Learning

The application of Data Governance (DG) to Federated Machine Learning (FML) could enable support for improving the quality of Machine Learning (ML) models produced in a federation of participants belonging to different organizations. For that, we propose the adaptation of DG to provide such a support. The paper comprehends respective hypotheses, as well as the goals to prove their veracity. Then, a methodology to achieve the aforementioned goals is introduced. The paper also proposes a first approach of the conception that is currently taken as reference for the implementation process. The proposal aims to enable easier collaboration among participants belonging to different organizations, by providing mechanisms for improving quality of the machine learning models and enabling accountability in the federation. Finally, conclusions are drawn and the next steps of this research are outlined.
Administration of Vaccine Mechanism for COVID-19 Using Blockchain

Information related to Covid-19 either it is vaccination status of the country or the active Covid-19 cases both are the confidential matters. The privacy is utmost important concern in pandemic situation to secure access of patient vaccine data. Blockchain technique is one of the good techniques that affirm the privacy and data security. The consensus mechanisms in blockchain confirm that data stored in it, is authentic and secured. Proof of Work is one of the consensus algorithms, where miners in the blockchain network solves the puzzle and receive the reward accordingly. The difficulty level of the puzzle decides the security of the data in the network. Hence, this paper proposes blockchain based framework to store the vaccination data of patient by enhancing security using proof of work consensus algorithm. The performance of the proposed framework is measured on different level of difficulties, corresponding to time. The result shows that higher the difficulty level, take more time to solve the puzzle, results in more secure data.
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures

Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the Heat Kernel Signature and the Wave Kernel Signature, to create node embeddings able to capture local and global structural information for a given graph. For each node, we concatenate its structural embedding with the one-hot encoding vector of the node feature (if available) and we define a kernel between two input graphs in terms of the Wasserstein distance between the respective node embeddings. Experiments on standard graph classification benchmarks show that our kernel performs favourably when compared to widely used alternative kernels as well as graph neural networks.
Refining AttnGAN Using Attention on Attention Network

AttnGAN finds the semantic relation between text and image using an attention network. However, some of the words in the text description remain unattended. We propose a solution called Refined AttnGAN, which contains enhanced attention using Attention on Attention architecture. We apply the mode-seeking function to the network to improve the model diversity. The proposed Architecture is evaluated on the Caltech Birds and Microsoft Coco Dataset. Experimental results demonstrate that our model works very well compared to some state-of-the-art methods.