Reviewerly/exHarmony huggingface dataset:
Simon Chaotic: An enhancement of SIMON block cipher by using Arnold and Henon chaotic maps

The US National Security Agency (NSA) developed a Simon lightweight block cipher in June 2013. It supports internet of things (IoT) environments. However, recently Simon has suffered from several attacks. The Problem of SIMON does not contain perfect confusion and diffusion processes. Furthermore, it has multiple iterative calculations. These iterative increase the computational effort. This paper proposes the fast initial and final permutations with the Substitution box (S-box) to get SIMON's confusion and diffusion processes to get perfect security with a few iterations against attacks. Furthermore, this paper improves the security of the Simon block cipher with keeps on the speed and memory level simultaneously by using chaotic maps (Arnold and Henon maps) to overcome their weaknesses by attacking. Analysis results of the proposed SimonChaotic algorithm based on NIST randomness tests show that this proposed algorithm is secure with keep on the speed and memory level compared with the original algorithm.
Lightweight Encryption for IoT Using Dynamic Stream Key

IoT applications have covered many aspects of our daily lives, such as smart cities, smart buildings, smart homes, and healthcare monitoring. One of the problems with the Internet of Things is dealing with and securing the huge amount of data generated by heterogeneous IoT devices. These interconnected devices need to gather real-time information and connect them to other cloud resources to collect, store, and analyze different data streams. All of these processes’ effects on security of sensitive information. Therefore, it was necessary to secure the data resulting from these applications. Classical encryption algorithms were not suitable for Internet of Things constraint devices due to limited memory, power, and simple processing capabilities. In this paper a lightweight encryption algorithm was proposed to encrypt images for secure data transfer in the internet of things platforms and wireless networking with fog/edge computing. The proposed algorithm utilizes the power of One-Time Pad (OTP) key generated by a Dynamic Linear Feedback Shift Register (DLFSR). The good statistical test analysis for generated key and ciphertext and the algorithm high performance evaluation suggest the suitability of the proposed encryption for providing security in constrained devices. Experimental results show the efficiency of the proposed algorithm as compared to the previous works.
A Novel Network Model for Artificial Intelligence Algorithms

The field of Artificial Intelligence (AI) has noticeably developed in recent years. The literature includes a tremendous number of contributions proposed by worldwide researchers. These algorithms can be used for a variety of purposes such as classifications, clustering, and forecasting, to mention a few. The problem that researchers frequently face is the appropriateness of the algorithm they use for their data. Moreover, researchers usually benchmark their work with the literature, which also needs to select appropriate algorithms that fit their needs. Another problem that researchers face is that when developing a new algorithm or modify an existing they need to have extensive knowledge about algorithms. These issues may take a lot of time and effort that are consumed in exploring the literature. This research comes to provide a network model as a guide that gathers most of the AI available algorithms in the literature aiming to make it easier for researchers to understand AI algorithms. The proposed network model includes nodes and edges. Each node represents an AI algorithm and the edges are created among them when they have parameters in common. The findings show the efficiency of the proposed model in supporting researchers in selecting appropriate algorithms for their works. Finally, this work is considered novel since it collects most of the AI algorithms in a standalone model.
Image Encryption Algorithm based on A Novel 5D Chaotic System

The rapid advancement of computer and digital connections, as well as the increasing popularity of internet communication, led to an increase in the demand for secure data sharing. For a fast and efficient color image encryption technique a novel five-dimensional hyperchaotic system proposed in this research has traits like increased sensitivity to primary factors and conditions. As well as has two Lyapunov exponents that are positive demonstrate that the proposed system is hyperchaotic, add a new method was presented based on a hyperchaotic system to structure a color image cryptosystem to improve data security. In this proposed method, there are two stages. In the initial stages, the pixel locations of an image are confused in the permutation step by a chaotic sequence created, secondly, substitution stage, Using the XOR operation, between the pixel value of the image from the previous step with a pseudo-random number sequence (key2) created from the system proposed. The proposed algorithm was evaluated using Evaluation measures such as key space, key sensitivity, histogram, information entropy, unified average change intensity (UACI), and several pixel changes rates (NPCR). The proposed system’s efficiency and security it demonstrated by experimental results and performance evaluations, correlation coefficient values are acquired as -0.0003, 0.0075, and 0.0018 for H, V, and D directions, respectively. The entropy value is 7.9974 bits. The mean UACI and NPCR values are determined to be 33.4635% and 99.7061%, respectively. results showed that the suggested system’s properties are robust to various attacks.
Software effort estimation Based on long short term memory and stacked long short term memory

Software effort estimation (SEE) is an important process for predicting the effort required to develop or maintain software projects. It is considered one of the difficult challenges for software engineering because the more accurate the estimation, the greater the chances of success in developing software projects to complete and deliver them within the specified scheduling and budget. The diversity in software projects led to the use of many techniques to estimate the effort. The main objective of the research is to find the best technique to get the estimated effort as close to the real effort, using Machine learning Algorithms, artificial neural network Long Short-Term Memory(LSTM) and the artificial neural network Stacked Long Short-Term Memory (Stacked_LSTM), with six data sets are China, Kitchenham, Kemerer, Cocomo 81, Albrecht, Decharnais, and comparing the performance of the two machine learning algorithms performance, Neuralnet and Lasso from previous research, used the same data sets and using the same evaluation metrics, which are: the root mean square error (RMSE), mean absolute error (MAE) and R_Squared. The results demonstrated the superiority of the Stacked _LSTM algorithm by all metrics with the dataset of China, Kemerer and Albrecht, while in the data sets, Descharnais and Kitchenham, the LSTM algorithm outperformed the rest of the methods. While in the Cocomo 81 dataset, Neuralnet algorithm outperformed the rest of the techniques used, but the best rating was obtained. It is for Stacked _LSTM algorithm with China data.
Machine Learning Based Classification of Multi-Component Shape Descriptors

The concept of Multi-component shapes is motivated by researchers in image classification and computer vision applications. In this paper, we have introduced several descriptors that are modified versions of the Hu moment invariants and suitable for measuring certain properties of multi-component shapes. These descriptors are invariant in translation, rotation, and scaling-based transformation, and this is important because they can significantly develop systems for object identification, classification, matching, and analysis. The classification of multiple objects from the MPEG-7 dataset is applied using the descriptors introduced in this research. Multiple machine learning classifiers are used to test the new descriptors’ performance, where SVM has shown notable results i.e., 87.5%, 88.96, 87.5, and 88.23 for Accuracy, Precision, Recall, and F1-score, respectively, as proof of the worthiness of new descriptors.
Efficiency and Effectiveness in Utilizing Blended Learning in Context Aware Ubiquitous Learning Systems

Blended learning is widely spreading and uses many technologies as well as many serves learning objects. Various methods and models implemented and developed in this field. Blended learning definition, components, tools, and aspects are modified according to the rapid evolution of learning subjects, materials, and resources. Meantime the presence of context aware ubiquitous learning opens the way for enabling learners to learn about various subjects in learning systems that are free from the restrictions of time and place. Additionally, efficiency and effectiveness requirements of any learning framework have high consideration when applying any learning theory in particular subject. This paper presents a proposed framework with a summarization of the factors, criteria, and possible ways to achieve efficiency and effectiveness of utilizing blended learning models in context aware ubiquitous learning systems.
Improvement of Semantic-BERT Model with Bidirectional LSTM for Arabic Language

Large, pretrained transformer-based approaches like BERT have transformed the Natural Language Processing (NLP) field. BERT is a multilayer bidirectional Transformer trained on plaintext for masked word prediction and last sentence prediction. The pretrained BERT model can then be finetuned with task-specific training data for a subsequent task, The Improvement of BERT results are one of the main challenges that many studies focus on recently. In this work we proposed a Semantic-Bert model for Arabic Question/ Answer (Q/A) applications that incorporates explicit contextual semantics from pretrained semantic role labeling. It also introduces an enhanced language representation model, which is called Semantics aware BERT. The model is utilized bidirectional Long short-term memory LSTM (Bi-LSTM) to improve the correction in retrieving the answer when it selects the correct paragraph. The model is trained for 200 epoch and the results show that the proposed model achieves 0.75% and 80% in sensitivity. In addition, it achieves 61.5% and 90.4% in the sentence match. The selection of answering for most questions is highly accurate that most answers near from what need. Based on these results, it can be indicated that the proposed model is able to retrieve the answer correctly as it chooses the correct paragraph.
DeepCAT: A Cost-Efficient Online Configuration Auto-Tuning Approach for Big Data Frameworks

To support different application scenarios, big data frameworks usually provide a large number of performance-related configuration parameters. Online auto-tuning these parameters based on deep reinforcement learning to achieve a better performance has shown their advantages over search-based and machine learning-based approaches. Unfortunately, the time consumption during the online tuning phase of conventional DRL-based methods is still heavy, especially for big data applications. Therefore, in this paper, we propose DeepCAT, a cost-efficient deep reinforcement learning-based approach to achieve online configuration auto-tuning for big data frameworks. To reduce the total online tuning cost: 1) DeepCAT utilizes the TD3 algorithm instead of DDPG to alleviate value overestimation; 2) DeepCAT modifies the conventional experience replay to fully utilize the rare but valuable transitions via a novel reward-driven prioritized experience replay mechanism; 3) DeepCAT designs a Twin-Q Optimizer to estimate the execution time of each action without the costly configuration evaluation and optimize the sub-optimal ones to achieve a low-cost exploration-exploitation trade off. Experimental results based on a local 3-node Spark cluster and HiBench benchmark applications show that DeepCAT is able to speed up the best execution time by a factor of 1.45 × and 1.65 × on average respectively over CDBTune and OtterTune, while consuming up to 50.08% and 53.39% less total tuning time.
Tensor-Accelerated Fourth-Order Epistasis Detection on GPUs

The improved accessibility of gene sequencing technologies has led to creation of huge datasets, i.e. patient records related to certain human diseases (phenotypes). Hence, deriving fast and accurate algorithms for efficiently processing these datasets is a paramount concern to enable some key healthcare scenarios, such as personalizing treatments, explaining the occurrence of and/or susceptibility to complex conditions and reducing the spread of infectious diseases. This is especially true for high-order epistasis detection, one of the most computationally challenging problems in bioinformatics, where associations between a given phenotype and single nucleotide polymorphisms (SNPs) of a population can often only be uncovered through evaluation of a large number of SNP combinations. To tackle this challenge, we propose a novel fourth-order epistasis detection algorithm that leverages tensor processing capabilities of two distinct accelerator architectures by efficiently mapping core computations related to processing quads of SNPs to binary tensor-accelerated matrix operations. Experimental results show that the proposed approach delivers very high performance even in single-GPU environments, e.g., 27.8 and 90.9 tera quads of SNPs per second, scaled to the sample size, were processed on Titan RTX (Turing) and A100 (Ampere) PCIe GPUs, respectively. Being the first approach that exploits tensor cores for accelerating searches with interaction order above three, the proposed method achieved a performance of up to 835.4 tera quads of SNPs per second on the 8-GPU HGX A100 server, which represents performance two or more orders of magnitude higher than that of related art.
HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks

To efficiently perform inference with neural networks, the underlying tensor programs require sufficient tuning efforts before being deployed into production environments. Usually, enormous tensor program candidates need to be sufficiently explored to find the one with the best performance. This is necessary to make the neural network products meet the high demand of real-world applications such as natural language processing, auto-driving, etc. Auto-schedulers are being developed to avoid the need for human intervention. However, due to the gigantic search space and lack of intelligent search guidance, current auto-schedulers require hours to days of tuning time to find the best-performing tensor program for the entire neural network.
Two-Stage Pre-processing for License Recognition

Various financial insurance and investment application websites require customers to upload identity documents, such as vehicle licenses, to verify their identities. Manual verification of these documents is costly. Hence, there is a clear demand for automatic document recognition. This study proposes a two-stage method to pre-process a vehicle license for a better text recognition. In the first stage, the distortion that often appears in photographed documents is repaired. In the second stage, each data field is carefully located. The subsequent captured fields are then processed by a commercial text recognition software. Due to the sensitivity of vehicle licenses, it is difficult to collect enough data for model training. Consequently, artificial vehicle licenses are synthesized for model training to mitigate overfitting. In addition, an encoder is applied to reduce the background noise, remove the border crossing over text, and make the blurred text clearer before text recognition. The proposed method on a real dataset shows that the accuracy is close to 90%.
SMEGA2: Distributed Asynchronous Deep Neural Network Training With a Single Momentum Buffer

As the field of deep learning progresses, and neural networks become larger, training them has become a demanding and time consuming task. To tackle this problem, distributed deep learning must be used to scale the training of deep neural networks to many workers. Synchronous algorithms, commonly used for distributing the training, are susceptible to faulty or straggling workers. Asynchronous algorithms do not suffer from the problems of synchronization, but introduce a new problem known as staleness. Staleness is caused by applying out-of-date gradients, and it can greatly hinder the convergence process. Furthermore, asynchronous algorithms that incorporate momentum often require keeping a separate momentum buffer for each worker, which cost additional memory proportional to the number of workers. We introduce a new asynchronous method, SMEGA2, which requires a single momentum buffer regardless of the number of workers. Our method works in a way that lets us estimate the future position of the parameters, thereby minimizing the staleness effect. We evaluate our method on the CIFAR and ImageNet datasets, and show that SMEGA2 outperforms existing methods in terms of final test accuracy while scaling up to as much as 64 asynchronous workers. Open-Source Code: https://github.com/rafi-cohen/SMEGA2
An Improved Principal Component Analysis for Side-Channel Attacks

Side-channel attacks (SCAs) often need to collect a lot of power consumption data, and it takes a great deal of time to crack the secret key through these data. Ordinary principal component analysis (PCA) compression techniques consume a lot of time when processing power traces containing a large number of sample points. In this paper, two improved algorithms, absolute value integration PCA (APCA) and maximum value extraction PCA (MPCA), are proposed to optimize PCA. Both can compress the original power traces to about 10%. Compared with PCA, their processing speed is significantly improved. The more sample points, the more obvious the improvement. And through experiments, it is proved that APCA has more advantages than MPCA in the convergence of partial guessing entropy (PGE).
Robust Phase Reconstruction Using Random Three Step Phase-Shift in Wavelength Tuning Interferometer

The standard stochastic two-step phase shift phase demodulation method analyzes the tangent function of the phase to be measured to extract the phase to be evaluated. It is necessary to eliminate the background light intensity of interference fringes, which has a significant impact on phase reconstruction accuracy. A random three-step phase shift algorithm (RTP) is suggested in this study. The phase shift between two adjacent interference fringes is extracted using the least squares approach in conjunction with a K -order twodimensional polynomial. Additionally, it employs the interference fringe graph’s difference operation to reduce the background light intensity and actualize the restoration of the measurement phase. The experimental and simulations results reveal that phase shift, luminous non-uniformity, and random noise have little effect on the random three-step phase shift method and have strong environmental adaptability.
Maintainability Challenges in ML: A Systematic Literature Review

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted by academics and businesses alike. However, ML has a number of different challenges in terms of maintenance not found in traditional software projects. Identifying what causes these maintainability challenges can help mitigate them early and continue delivering value in the long run without degrading ML performance. Aim: This study aims to identify and synthesise the maintainability challenges in different stages of the ML workflow and understand how these stages are interdependent and impact each other’s maintainability. Method: Using a systematic literature review, we screened more than 13000 papers, then selected and qualitatively analysed 56 of them. Results: (i) a catalogue of maintainability challenges in different stages of Data Engineering, Model Engineering workflows and the current challenges when building ML systems are discussed; (ii) a map of 13 maintainability challenges to different interdependent stages of ML that impact the overall workflow; (iii) Provided insights to developers of ML tools and researchers. Conclusions: In this study, practitioners and organisations will learn about maintainability challenges and their impact at different stages of ML workflow. This will enable them to avoid pitfalls and help to build a maintainable ML system. The implications and challenges will also serve as a basis for future research to strengthen our understanding of the ML system’s maintainability.
An Evaluation of Effort-Aware Fine-Grained Just-in-Time Defect Prediction Methods

CONTEXT: Software defect prediction (SDP) is an active research topic to support software quality assurance (SQA) activities. It was observed that unsupervised prediction models were often competitive with supervised ones at release-level and change-level defect prediction. Fine-grained just-in-time defect prediction focuses on defective files in a change, rather than the whole change. A recent study showed that the fine-grained just-in-time defect prediction was cost-effective in terms of effort-aware performance measures. Those studies did not explore the effectiveness of supervised and unsupervised models at that finer level in terms of effort-aware performance measures. OBJECTIVE: To examine the performance of supervised and unsupervised prediction models in the context of fine-grained defect prediction in terms of effort-aware performance measures. METHOD: Experiments with a time-sensitive approach were conducted to evaluate the predictive performance of supervised and unsupervised methods proposed in past studies. Datasets from OSS projects with manually validated defect links were employed from a past study. RESULTS: The use of manually validated links led to low-performance results. No clear difference among supervised and unsupervised methods was found while CBS+, a supervised method, was the best method in terms of F-measure. Even CBS+ did not achieve reasonable performance. A non-linear learning algorithm did not help the performance improvement. CONCLUSION: No clear preference among unsupervised and supervised methods. CBS+ was the best method on average. The predictive performance was still a challenge.
Adopting DevOps Paradigm in Technical Debt Prioritization and Mitigation

The constantly growing amount of software in use, accompanied by huge amount of technical debt, gradually raises concern in the industry. New technologies and software development processes become yet another degree of freedom boosting the complexity. As the software development and delivery techniques evolve, technical debt perspective should follow. Taking into account all software artefacts enabling value delivery to customers, embracing DevOps paradigm and its holistic focus on software development lifecycle, the strategy presented in this paper enabled stabilization of a large telecommunication software system after a set of consecutive complex merges. The research question of this paper looks for evidence whether prioritization of technical debt mitigation efforts bring a faster return on investment. A 2-year-long case study focused on technical debt prioritization and mitigation that was conducted on this software system resulted in improved quality and stabilization of feature development efforts (cost and time based). Therefore, the tangible gains from applying this approach comprise over 50% decrease in stability issues, improved screening by over 30%, and 6 times better predictability of delivery time (reducing allocation of stabilization effort and time).